[med-svn] [r-cran-data.table] 01/02: Imported Upstream version 1.9.6
Andreas Tille
tille at debian.org
Wed Mar 23 09:15:27 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-data.table.
commit dfa8d9cba18fe9d42847940bb9b5c30a07c90a66
Author: Andreas Tille <tille at debian.org>
Date: Wed Mar 23 10:04:45 2016 +0100
Imported Upstream version 1.9.6
---
DESCRIPTION | 21 +
MD5 | 150 +
NAMESPACE | 137 +
R/AllS4.R | 23 +
R/IDateTime.R | 205 +
R/between.R | 9 +
R/bmerge.R | 115 +
R/c.factor.R | 40 +
R/cedta.R | 44 +
R/data.table.R | 2619 +
R/duplicated.R | 99 +
R/fcast.R | 228 +
R/fmelt.R | 39 +
R/foverlaps.R | 222 +
R/fread.R | 121 +
R/getdots.R | 10 +
R/last.R | 13 +
R/like.R | 16 +
R/merge.R | 80 +
R/onAttach.R | 8 +
R/onLoad.R | 104 +
R/setkey.R | 656 +
R/setops.R | 44 +
R/shift.R | 14 +
R/tables.R | 44 +
R/test.data.table.R | 211 +
R/timetaken.R | 21 +
R/transpose.R | 18 +
R/uniqlist.R | 24 +
R/utils.R | 49 +
R/xts.R | 18 +
README.md | 3060 +
build/vignette.rds | Bin 0 -> 360 bytes
inst/doc/datatable-faq.R | 224 +
inst/doc/datatable-faq.Rnw | 585 +
inst/doc/datatable-faq.pdf | Bin 0 -> 322575 bytes
inst/doc/datatable-intro-vignette.R | 210 +
inst/doc/datatable-intro-vignette.Rmd | 716 +
inst/doc/datatable-intro-vignette.html | 974 +
inst/doc/datatable-intro.R | 206 +
inst/doc/datatable-intro.Rnw | 293 +
inst/doc/datatable-intro.pdf | Bin 0 -> 191827 bytes
inst/doc/datatable-keys-fast-subset.R | 165 +
inst/doc/datatable-keys-fast-subset.Rmd | 534 +
inst/doc/datatable-keys-fast-subset.html | 735 +
inst/doc/datatable-reference-semantics.R | 128 +
inst/doc/datatable-reference-semantics.Rmd | 368 +
inst/doc/datatable-reference-semantics.html | 566 +
inst/doc/datatable-reshape.R | 66 +
inst/doc/datatable-reshape.Rmd | 254 +
inst/doc/datatable-reshape.html | 486 +
inst/tests/1206FUT.txt | 309 +
inst/tests/2008head.csv | 500 +
inst/tests/ch11b.dat | 100 +
inst/tests/doublequote_newline.csv | 40 +
inst/tests/fread_line_error.csv | 69 +
inst/tests/issue_1095_fread.txt | 100 +
inst/tests/issue_1113_fread.txt | 4 +
inst/tests/issue_1330_fread.txt | 8 +
inst/tests/issue_563_fread.txt | 5 +
inst/tests/issue_773_fread.txt | 28 +
inst/tests/issue_785_fread.txt | 5 +
inst/tests/quoted_multiline.csv | 126 +
inst/tests/russellCRCRLF.csv | 20 +
inst/tests/russellCRLF.csv | 20 +
inst/tests/test-S4.R | 56 +
inst/tests/test-data.frame-like.R | 160 +
inst/tests/tests.Rraw | 6994 +
man/IDateTime.Rd | 224 +
man/J.Rd | 57 +
man/address.Rd | 23 +
man/all.equal.data.table.Rd | 50 +
man/as.data.table.xts.Rd | 27 +
man/as.xts.data.table.Rd | 24 +
man/assign.Rd | 102 +
man/between.Rd | 29 +
man/chmatch.Rd | 87 +
man/copy.Rd | 41 +
man/data.table-class.Rd | 33 +
man/data.table.Rd | 307 +
man/dcast.data.table.Rd | 99 +
man/duplicated.Rd | 106 +
man/foverlaps.Rd | 89 +
man/frank.Rd | 71 +
man/fread.Rd | 261 +
man/last.Rd | 24 +
man/like.Rd | 28 +
man/melt.data.table.Rd | 104 +
man/merge.Rd | 156 +
man/na.omit.data.table.Rd | 52 +
man/patterns.Rd | 24 +
man/rbindlist.Rd | 65 +
man/rleid.Rd | 36 +
man/setDF.Rd | 42 +
man/setDT.Rd | 59 +
man/setNumericRounding.Rd | 59 +
man/setattr.Rd | 68 +
man/setcolorder.Rd | 35 +
man/setkey.Rd | 92 +
man/setorder.Rd | 61 +
man/shift.Rd | 77 +
man/subset.data.table.Rd | 55 +
man/tables.Rd | 30 +
man/test.data.table.Rd | 26 +
man/timetaken.Rd | 24 +
man/transform.data.table.Rd | 69 +
man/transpose.Rd | 42 +
man/truelength.Rd | 44 +
man/tstrsplit.Rd | 39 +
src/Makevars | 5 +
src/assign.c | 926 +
src/bmerge.c | 324 +
src/chmatch.c | 81 +
src/data.table.h | 78 +
src/dogroups.c | 585 +
src/fastmean.c | 141 +
src/fastradixdouble.c | 253 +
src/fastradixint.c | 100 +
src/fcast.c | 419 +
src/fmelt.c | 701 +
src/forder.c | 1512 +
src/frank.c | 193 +
src/fread.c | 1320 +
src/gsumm.c | 443 +
src/ijoin.c | 691 +
src/init.c | 176 +
src/rbindlist.c | 926 +
src/reorder.c | 143 +
src/shift.c | 206 +
src/transpose.c | 98 +
src/uniqlist.c | 135 +
src/vecseq.c | 42 +
src/wrappers.c | 98 +
tests/autoprint.R | 46 +
tests/autoprint.Rout.save | 124 +
tests/knitr.R | 4 +
tests/knitr.Rmd | 11 +
tests/knitr.Rout.save | 67 +
tests/test-all.R | 4 +
tests/tests.R | 10 +
vignettes/Makefile | 15 +
vignettes/css/bootstrap.css | 118 +
vignettes/datatable-faq.Rnw | 585 +
vignettes/datatable-intro-vignette.Rmd | 716 +
vignettes/datatable-intro.Rnw | 293 +
vignettes/datatable-keys-fast-subset.Rmd | 534 +
vignettes/datatable-reference-semantics.Rmd | 368 +
vignettes/datatable-reshape.Rmd | 254 +
vignettes/flights14.csv | 253317 +++++++++++++++++++++++++
vignettes/melt_default.csv | 6 +
vignettes/melt_enhanced.csv | 6 +
151 files changed, 292531 insertions(+)
diff --git a/DESCRIPTION b/DESCRIPTION
new file mode 100644
index 0000000..292915f
--- /dev/null
+++ b/DESCRIPTION
@@ -0,0 +1,21 @@
+Package: data.table
+Version: 1.9.6
+Title: Extension of Data.frame
+Author: M Dowle, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, E Antonyan
+Maintainer: Matt Dowle <mattjdowle at gmail.com>
+Depends: R (>= 2.14.1)
+Imports: methods, chron
+Suggests: ggplot2 (>= 0.9.0), plyr, reshape, reshape2, testthat (>=
+ 0.4), hexbin, fastmatch, nlme, xts, bit64, gdata,
+ GenomicRanges, caret, knitr, curl, zoo, plm
+Description: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.
+License: GPL (>= 2)
+URL: https://github.com/Rdatatable/data.table/wiki
+BugReports: https://github.com/Rdatatable/data.table/issues
+MailingList: datatable-help at lists.r-forge.r-project.org
+VignetteBuilder: knitr
+ByteCompile: TRUE
+NeedsCompilation: yes
+Packaged: 2015-09-19 04:47:43.628 UTC; mdowle
+Repository: CRAN
+Date/Publication: 2015-09-19 22:13:43
diff --git a/MD5 b/MD5
new file mode 100644
index 0000000..9105fff
--- /dev/null
+++ b/MD5
@@ -0,0 +1,150 @@
+8e843c4734d52481552fc5112dcdec70 *DESCRIPTION
+2a95b4283f27fb6f510d766d001e151a *NAMESPACE
+4377e4b917c44366f66862324c8fd32a *R/AllS4.R
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+b8be0229f2bc451f00a99973c2333466 *R/data.table.R
+081129adbf5a24e7ec8c4466017c90c0 *R/duplicated.R
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+e646383ee56798334d95549f835b5ad4 *R/fmelt.R
+5d423874e740e04bf204d90386413e2b *R/foverlaps.R
+ec044b964fdeaf668e3bcf288715b097 *R/fread.R
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+797a7cbea6b37489bb15fd46bd64b907 *R/onLoad.R
+db58675c3a18162334749ed51cddbba0 *R/setkey.R
+b270fb622c3463b4c425aaf7886e1d57 *R/setops.R
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+1432e4ed7a2a3d6be052527b9005cd8d *R/tables.R
+a67909fc208084de93587c7f522c4170 *R/test.data.table.R
+f84b342bd4a2df21900a7a596cc122d3 *R/timetaken.R
+9a22293f18bc61f6b9700af628f52805 *R/transpose.R
+085cbece46eb9e4689189ac6cda84457 *R/uniqlist.R
+fda476f057d8bc9cdd88f97265d4d162 *R/utils.R
+8442ff0ef5733397a269e55a8cc112b6 *R/xts.R
+bc4abbeed288ce6d57a44d6210423e37 *README.md
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+e09361ebcd90900aa2b1371e602ccb0d *man/truelength.Rd
+d8a91751bc39321203576a2b5ca5baf7 *man/tstrsplit.Rd
+15a12eef17e9186ef30d7acc88dac93b *src/Makevars
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+771f833144f4bcd530d36944c9ece29f *src/chmatch.c
+6adf9a9731b360a25362df22d69e0bdb *src/data.table.h
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+e3d5b289ef4b511e4f89c9838b86cbd0 *src/fastmean.c
+6c365b327989acaea94103075ec7bd2f *src/fastradixdouble.c
+5d0213fd2b7c2f916b558f631a19de71 *src/fastradixint.c
+5efb4470c5f95c51132964f6678da9bf *src/fcast.c
+c0f7d576b3914b54797878ec8f421522 *src/fmelt.c
+e7d0296adc515500c439f111d581150c *src/forder.c
+fcd7100afa206eea6fc64af21f7f47f1 *src/frank.c
+4aa4ba82f5207091091c6cb940a29c99 *src/fread.c
+8038e32e74b05c45a0f68ede4ef86e9e *src/gsumm.c
+47792eafb3cee1c03bbcb972d00c4aad *src/ijoin.c
+17a5ea8d81686098067123620f12aa67 *src/init.c
+a9f5e09254925500d8ee16bb56a6351d *src/rbindlist.c
+091c9c0e14a7d55debb0b15fac8b82e4 *src/reorder.c
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+fc58eacd0513bb60e72dc601d46157f7 *src/uniqlist.c
+75a359ce5e8c6927d46dd9b2aa169da1 *src/vecseq.c
+566dad18bf6dd9e6ffe38bc54d1ac867 *src/wrappers.c
+39d85ffda439359be47c1d38ba0a4eae *tests/autoprint.R
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+105cff3529e59e908e3e655e6ea1f6b3 *tests/test-all.R
+cf45f2117922e04a24da6add785480db *tests/tests.R
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+5dcf8be4b810d38fc5d4d0817167b079 *vignettes/css/bootstrap.css
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+b9c16a06bba12bf90f688afffd8810d2 *vignettes/datatable-keys-fast-subset.Rmd
+6082a466c5f34d47b545112a6e321c76 *vignettes/datatable-reference-semantics.Rmd
+056b4a2e8142bea0e9fddb0a602fabf1 *vignettes/datatable-reshape.Rmd
+47c3d35a8fa9fed8ed988bff69f70d3b *vignettes/flights14.csv
+f84166315a27e89bf88bb54f3b7e454b *vignettes/melt_default.csv
+30d44359d84026ba2b7140b0ff33abaa *vignettes/melt_enhanced.csv
diff --git a/NAMESPACE b/NAMESPACE
new file mode 100644
index 0000000..f8fd373
--- /dev/null
+++ b/NAMESPACE
@@ -0,0 +1,137 @@
+useDynLib(datatable, .registration=TRUE)
+
+## For S4-ization
+import(methods)
+exportClasses(data.table, IDate, ITime)
+##
+
+export(data.table, tables, setkey, setkeyv, key, "key<-", haskey, CJ, SJ, copy)
+export(set2key, set2keyv, key2)
+export(as.data.table,is.data.table,test.data.table,last,like,"%like%",between,"%between%")
+export(timetaken)
+export(truelength, alloc.col, ":=")
+export(setattr, setnames, setcolorder, set, setDT, setDF)
+export(setorder, setorderv)
+export(setNumericRounding, getNumericRounding)
+export(chmatch, "%chin%", chorder, chgroup)
+export(rbindlist)
+export(fread)
+export(foverlaps)
+export(shift)
+export(transpose)
+export(tstrsplit)
+export(frank)
+export(frankv)
+export(address)
+export(.SD,.N,.I,.GRP,.BY,.EACHI)
+export(rleid)
+export(rleidv)
+export(as.xts.data.table)
+export(uniqueN)
+
+S3method("[", data.table)
+S3method("[<-", data.table)
+# S3method("[[", data.table)
+# S3method("[[<-", data.table)
+S3method("$<-", data.table)
+S3method(print, data.table)
+S3method(as.data.table, data.table)
+S3method(as.data.table, data.frame)
+S3method(as.data.table, matrix)
+S3method(as.data.table, list)
+S3method(as.data.table, integer)
+S3method(as.data.table, numeric)
+S3method(as.data.table, character)
+S3method(as.data.table, logical)
+S3method(as.data.table, factor)
+S3method(as.data.table, ordered)
+S3method(as.data.table, Date)
+S3method(as.data.table, table)
+S3method(as.data.table, xts)
+S3method(as.data.table, default)
+S3method(as.data.frame, data.table)
+S3method(as.list, data.table)
+S3method(as.matrix, data.table)
+#S3method(cbind, data.table)
+#S3method(rbind, data.table)
+export(.rbind.data.table)
+S3method(dim, data.table)
+S3method(dimnames, data.table)
+S3method("dimnames<-", data.table)
+S3method("names<-", data.table)
+S3method(duplicated, data.table)
+S3method(unique, data.table)
+S3method(merge, data.table)
+S3method(subset, data.table)
+S3method(transform, data.table)
+S3method(within, data.table)
+S3method(is.na, data.table)
+S3method(format, data.table)
+S3method(Ops, data.table)
+S3method(all.equal, data.table)
+
+S3method(anyDuplicated, data.table)
+
+export(melt)
+export(melt.data.table)
+S3method(melt, data.table)
+export(dcast)
+export(dcast.data.table)
+S3method(dcast, data.table)
+
+import(utils)
+S3method(tail, data.table)
+S3method(head, data.table)
+import(stats)
+S3method(na.omit, data.table)
+
+# IDateTime support:
+export(as.IDate,as.ITime,IDateTime)
+export(hour,yday,wday,mday,week,month,quarter,year)
+
+importFrom(chron, chron, as.chron)
+export(as.chron.IDate,as.chron.ITime)
+
+S3method("[", ITime)
+S3method(as.character, ITime)
+S3method(as.data.frame, ITime)
+## S3method(as.chron, IDate)
+## S3method(as.chron, ITime)
+S3method(as.Date, IDate)
+S3method(as.IDate, Date)
+S3method(as.IDate, default)
+S3method(as.ITime, character)
+S3method(as.ITime, default)
+S3method(as.ITime, POSIXlt)
+S3method(as.ITime, times)
+S3method(as.list, IDate)
+S3method(as.POSIXct, IDate)
+S3method(as.POSIXct, ITime)
+S3method(as.POSIXlt, ITime)
+S3method(c, IDate)
+S3method(cut, IDate)
+S3method(format, ITime)
+S3method(IDateTime, default)
+S3method(mean, IDate)
+S3method(print, ITime)
+S3method(rep, IDate)
+S3method(rep, ITime)
+S3method(round, IDate)
+S3method(seq, IDate)
+S3method(split, IDate)
+S3method(unique, IDate)
+S3method(unique, ITime)
+
+
+
+# [.factor
+# c.factor
+# duplist
+# getdots
+# NCOL
+# NROW
+# take
+# trim
+# which.first
+# which.last
+
diff --git a/R/AllS4.R b/R/AllS4.R
new file mode 100644
index 0000000..0ba5e31
--- /dev/null
+++ b/R/AllS4.R
@@ -0,0 +1,23 @@
+## Functions to let data.table play nicer with S4
+if ("package:data.table" %in% search()) stop("data.table package loaded. When developing don't load package")
+
+## Allows data.table to be defined as an object of an S4 class,
+## or even have data.table be a super class of an S4 class.
+setOldClass(c('data.frame'))
+setOldClass(c('data.table', 'data.frame'))
+
+## as(some.data.frame, "data.table")
+setAs("data.frame", "data.table", function(from) {
+ as.data.table(from)
+})
+
+## as(some.data.table, "data.frame")
+setAs("data.table", "data.frame", function(from) {
+ as.data.frame(from)
+})
+
+setOldClass("IDate")
+setOldClass("ITime")
+
+setAs("character", "IDate", function(from) as.IDate(from))
+setAs("character", "ITime", function(from) as.ITime(from))
diff --git a/R/IDateTime.R b/R/IDateTime.R
new file mode 100644
index 0000000..6adba1d
--- /dev/null
+++ b/R/IDateTime.R
@@ -0,0 +1,205 @@
+
+###################################################################
+# IDate -- a simple wrapper class around Date using integer storage
+###################################################################
+
+as.IDate <- function(x, ...) UseMethod("as.IDate")
+
+as.IDate.default <-
+ function(x, ...) as.IDate(as.Date(x, ...))
+
+as.IDate.Date <- function(x, ...) {
+ structure(as.integer(x), class=c("IDate","Date"))
+}
+
+as.IDate.IDate <- function(x, ...) x
+
+as.Date.IDate <- function(x, ...) {
+ structure(as.numeric(x), class="Date")
+}
+
+mean.IDate <-
+cut.IDate <-
+seq.IDate <-
+c.IDate <-
+rep.IDate <-
+split.IDate <-
+unique.IDate <-
+ function(x, ...) {
+ as.IDate(NextMethod())
+ }
+
+# fix for #1315
+as.list.IDate <- function(x, ...) NextMethod()
+
+# rounding -- good for graphing / subsetting
+## round.IDate <- function (x, digits, units=digits, ...) {
+## if (missing(digits)) digits <- units # workaround to provide a units argument to match the round generic and round.POSIXt
+## units <- match.arg(digits, c("weeks", "months", "quarters", "years"))
+round.IDate <- function (x, digits=c("weeks", "months", "quarters", "years"), ...) {
+ units <- match.arg(digits)
+ as.IDate(switch(units,
+ weeks = round(x, "year") + 7 * (yday(x) %/% 7),
+ months = ISOdate(year(x), month(x), 1),
+ quarters = ISOdate(year(x), 3 * (quarter(x)-1) + 1, 1),
+ years = ISOdate(year(x), 1, 1)))
+}
+
+###################################################################
+# ITime -- Integer time-of-day class
+# Stored as seconds in the day
+###################################################################
+
+as.ITime <- function(x, ...) UseMethod("as.ITime")
+
+as.ITime.default <- function(x, ...) {
+ as.ITime(as.POSIXlt(x, ...))
+}
+
+as.ITime.character <- function(x, format, ...) {
+ x <- unclass(x)
+ if (!missing(format))
+ return(as.ITime(strptime(x, format = format, ...)))
+ y <- strptime(x, format = "%H:%M:%OS", ...)
+ y.nas <- is.na(y)
+ y[y.nas] <- strptime(x[y.nas], format = "%H:%M", ...)
+
+ return(as.ITime(y))
+}
+
+as.ITime.POSIXlt <- function(x, ...) {
+ structure(with(x, as.integer(sec) + min * 60L + hour * 3600L),
+ class = "ITime")
+}
+
+as.character.ITime <- format.ITime <- function(x, ...) {
+ # adapted from chron's format.times
+ # Fix for #811. Thanks to @StefanFritsch for the code snippet
+ neg <- x < 0L
+ x <- abs(unclass(x))
+ hh <- x %/% 3600L
+ mm <- (x - hh * 3600L) %/% 60L
+ ss <- trunc(x - hh * 3600L - 60L * mm)
+ res = paste(substring(paste("0", hh, sep = ""), nchar(paste(hh))),
+ substring(paste("0", mm, sep = ""), nchar(paste(mm))),
+ substring(paste("0", ss, sep = ""), nchar(paste(ss))), sep = ":")
+ if (any(neg)) res[neg] = paste("-", res[neg], sep="")
+ res
+}
+
+as.data.frame.ITime <- function(x, ...) {
+ # This method is just for ggplot2, #1713
+ # Avoids the error "cannot coerce class '"ITime"' into a data.frame", but for some reason
+ # ggplot2 doesn't seem to call the print method to get axis labels, so still prints integers.
+ # Tried converting to POSIXct but that gives the error below.
+ # If user converts to POSIXct themselves, then it works for some reason.
+ ans = list(x)
+ # ans = list(as.POSIXct(x,tzone="")) # ggplot2 gives "Error: Discrete value supplied to continuous scale"
+ setattr(ans,"class","data.frame")
+ setattr(ans,"row.names", .set_row_names(length(x)))
+ setattr(ans,"names",NULL)
+ ans
+}
+
+print.ITime <- function(x, ...) {
+ print(format(x))
+}
+
+rep.ITime <- function (x, ...)
+{
+ y <- rep(unclass(x), ...)
+ structure(y, class = "ITime")
+}
+
+"[.ITime" <- function(x, ..., drop = TRUE)
+{
+ cl <- oldClass(x)
+ class(x) <- NULL
+ val <- NextMethod("[")
+ class(val) <- cl
+ val
+}
+
+unique.ITime <- function(x, ...) {
+ ans = NextMethod()
+ setattr(ans,"class","ITime")
+ ans
+}
+
+# create a data.table with IDate and ITime columns
+# should work for most date/time formats like chron or POSIXct
+
+IDateTime <- function(x, ...) UseMethod("IDateTime")
+IDateTime.default <- function(x, ...) {
+ data.table(idate = as.IDate(x), itime = as.ITime(x))
+}
+
+# POSIXt support
+
+as.POSIXct.IDate <- function(x, tz = "UTC", time = 0, ...) {
+ if (missing(time) && inherits(tz, "ITime")) {
+ time <- tz # allows you to use time as the 2nd argument
+ tz <- "UTC"
+ }
+ if (tz == "") tz <- "UTC"
+ as.POSIXct(as.POSIXlt(x, ...), tz, ...) + time
+}
+
+as.POSIXct.ITime <- function(x, tz = "UTC", date = as.Date(Sys.time()), ...) {
+ if (missing(date) && any(class(tz) %in% c("Date", "IDate", "POSIXt", "dates"))) {
+ date <- tz # allows you to use date as the 2nd argument
+ tz <- "UTC"
+ }
+ as.POSIXct(as.POSIXlt(date), tz = tz) + x
+}
+
+as.POSIXlt.ITime <- function(x, ...) {
+ as.POSIXlt(as.POSIXct(x, ...))
+}
+
+# chron support
+
+as.chron.IDate <- function(x, time = NULL, ...) {
+ if (!is.null(time)) {
+ chron(dates. = as.chron(as.Date(x)), times. = as.chron(time))
+ } else {
+ chron(dates. = as.chron(as.Date(x)))
+ }
+}
+
+as.chron.ITime <- function(x, date = NULL, ...) {
+ if (!is.null(date)) {
+ chron(dates. = as.chron(as.Date(date)), times. = as.chron(x))
+ } else {
+ chron(times. = as.character(x))
+ }
+}
+
+as.ITime.times <- function(x, ...) {
+ x <- unclass(x)
+ daypart <- x - floor(x)
+ secs <- as.integer(round(daypart * 86400))
+ structure(secs,
+ class = "ITime")
+}
+
+###################################################################
+# Date - time extraction functions
+# Adapted from Hadley Wickham's routines cited below to ensure
+# integer results.
+# http://gist.github.com/10238
+# See also Hadley's more advanced and complex lubridate package:
+# http://github.com/hadley/lubridate
+# lubridate routines do not return integer values.
+###################################################################
+
+hour <- function(x) as.POSIXlt(x)$hour
+yday <- function(x) as.POSIXlt(x)$yday + 1L
+wday <- function(x) as.POSIXlt(x)$wday + 1L
+mday <- function(x) as.POSIXlt(x)$mday
+week <- function(x) yday(x) %/% 7L + 1L
+month <- function(x) as.POSIXlt(x)$mon + 1L
+quarter <- function(x) as.POSIXlt(x)$mon %/% 3L + 1L
+year <- function(x) as.POSIXlt(x)$year + 1900L
+
+
diff --git a/R/between.R b/R/between.R
new file mode 100644
index 0000000..a56b25d
--- /dev/null
+++ b/R/between.R
@@ -0,0 +1,9 @@
+
+between <- function(x,lower,upper,incbounds=TRUE)
+{
+ if(incbounds) x>=lower & x<=upper
+ else x>lower & x<upper
+}
+
+"%between%" <- function(x,y) between(x,y[1],y[2],incbounds=TRUE)
+# If we want non inclusive bounds with %between%, just +1 to the left, and -1 to the right (assuming integers)
diff --git a/R/bmerge.R b/R/bmerge.R
new file mode 100644
index 0000000..85f420f
--- /dev/null
+++ b/R/bmerge.R
@@ -0,0 +1,115 @@
+
+bmerge <- function(i, x, leftcols, rightcols, io, xo, roll, rollends, nomatch, verbose)
+{
+ # TO DO: rename leftcols to icols, rightcols to xcols
+ # NB: io is currently just TRUE or FALSE for whether i is keyed
+ # TO DO: io and xo could be moved inside Cbmerge
+ # bmerge moved to be separate function now that list() doesn't copy in R
+ # types of i join columns are promoted to match x's types (with warning or verbose)
+
+ # Important that i is already passed in as a shallow copy, due to these coercions for factors.
+ # i.e. bmerge(i<-shallow(i),...)
+ # The caller ([.data.table) then uses the coerced columns to build the output
+
+ # careful to only plonk syntax (full column) on i from now on (otherwise i would change)
+ # TO DO: enforce via .internal.shallow attribute and expose shallow() to users
+ # This is why shallow() is very importantly internal only, currently.
+
+ origi = shallow(i) # Needed for factor to factor/character joins, to recover the original levels
+ # Otherwise, types of i join columns are anyways promoted to match x's
+ # types (with warning or verbose)
+ resetifactor = NULL # Keep track of any factor to factor/character join cols (only time we keep orig)
+ for (a in seq_along(leftcols)) {
+ # This loop is simply to support joining factor columns
+ # Note that if i is keyed, if this coerces, i's key gets dropped and the key may not be retained
+ lc = leftcols[a] # i # TO DO: rename left and right to i and x
+ rc = rightcols[a] # x
+ icnam = names(i)[lc]
+ xcnam = names(x)[rc]
+ if (is.character(x[[rc]])) {
+ if (is.character(i[[lc]])) next
+ if (!is.factor(i[[lc]]))
+ stop("x.'",xcnam,"' is a character column being joined to i.'",icnam,"' which is type '",typeof(i[[lc]]),"'. Character columns must join to factor or character columns.")
+ if (verbose) cat("Coercing factor column i.'",icnam,"' to character to match type of x.'",xcnam,"'.\n",sep="")
+ set(i,j=lc,value=as.character(i[[lc]]))
+ # no longer copies all of i, thanks to shallow() and :=/set
+ next
+ }
+ if (is.factor(x[[rc]])) {
+ if (is.character(i[[lc]])) {
+ if (verbose) cat("Coercing character column i.'",icnam,"' to factor to match type of x.'",xcnam,"'. If possible please change x.'",xcnam,"' to character. Character columns are now preferred in joins.\n",sep="")
+ set(origi, j=lc, value=factor(origi[[lc]])) # note the use of 'origi' here - see #499 and #945
+ # TO DO: we need a way to avoid copying 'value' for internal purposes
+ # that would allow setting: set(i, j=lc, value=origi[[lc]]) without resulting in a copy.
+ # until then using 'val <- origi[[lc]]' below to avoid another copy.
+ } else {
+ if (!is.factor(i[[lc]]))
+ stop("x.'",xcnam,"' is a factor column being joined to i.'",icnam,"' which is type '",typeof(i[[lc]]),"'. Factor columns must join to factor or character columns.")
+ }
+ # Retain original levels of i's factor columns in factor to factor joins (important when NAs,
+ # see tests 687 and 688).
+ # Moved it outside of 'else' to fix #499 and #945.
+ resetifactor = c(resetifactor,lc)
+ if (roll!=0.0 && a==length(leftcols)) stop("Attempting roll join on factor column x.",names(x)[rc],". Only integer, double or character colums may be roll joined.") # because the chmatch on next line returns <strike>NA</strike> <new>0</new> for missing chars in x (rather than some integer greater than existing). Note roll!=0.0 is ok in this 0 special floating point case e.g. as.double(FALSE)==0.0 is ok, and "nearest"!=0.0 is also true.
+ val = origi[[lc]] # note: using 'origi' here because set(..., value = .) always copies '.', we need a way to avoid it in internal cases.
+ lx = levels(x[[rc]])
+ li = levels(val)
+ newfactor = chmatch(li, lx, nomatch=0L)[val] # fix for #945, a hacky solution for now.
+ levels(newfactor) = lx
+ class(newfactor) = "factor"
+ set(i, j=lc, value=newfactor)
+ # COMMENT BELOW IS NOT TRUE ANYMORE... had to change nomatch to 0L to take care of case where 'NA' occurs as a separate value... See #945.
+ # <OUTDATED> NAs can be produced by this level match, in which case the C code (it knows integer value NA)
+ # can skip over the lookup. It's therefore important we pass NA rather than 0 to the C code.
+ }
+ # Fix for #1108.
+ # TODO: clean this code up...
+ # NOTE: bit64::is.double(int64) returns FALSE.. but base::is.double returns TRUE
+ is.int64 <- function(x) inherits(x, 'integer64')
+ is.strictlydouble <- function(x) !is.int64(x) && is.double(x)
+ if (is.integer(x[[rc]]) && (base::is.double(i[[lc]]) || is.logical(i[[lc]]))) {
+ # TO DO: add warning if reallyreal about loss of precision
+ # or could coerce in binary search on the fly, at cost
+ if (verbose) cat("Coercing ", typeof(i[[lc]])," column i.'",icnam,"' to integer to match type of x.'",xcnam,"'. Please avoid coercion for efficiency.\n",sep="")
+ newval = i[[lc]]
+ if (is.int64(newval))
+ newval = as.integer(newval)
+ else mode(newval) = "integer" # retains column attributes (such as IDateTime class)
+ set(i, j=lc, value=newval)
+ } else if (is.int64(x[[rc]]) && (is.integer(i[[lc]]) || is.logical(i[[lc]]) || is.strictlydouble(i[[lc]]) )) {
+ if (verbose) cat("Coercing ",typeof(i[[lc]])," column i.'",icnam,"' to double to match type of x.'",xcnam,"'. Please avoid coercion for efficiency.\n",sep="")
+ newval = bit64::as.integer64(i[[lc]])
+ set(i, j=lc, value=newval)
+ } else if (is.strictlydouble(x[[rc]]) && (is.integer(i[[lc]]) || is.logical(i[[lc]]) || is.int64(i[[lc]]) )) {
+ if (verbose) cat("Coercing ",typeof(i[[lc]])," column i.'",icnam,"' to double to match type of x.'",xcnam,"'. Please avoid coercion for efficiency.\n",sep="")
+ newval = i[[lc]]
+ if (is.int64(newval))
+ newval = as.numeric(newval)
+ else mode(newval) = "double"
+ set(i, j=lc, value=newval)
+ }
+ }
+
+ # Now that R doesn't copy named inputs to list(), we can return these as a list()
+ # TO DO: could be allocated inside Cbmerge and returned as list from that
+ f__ = integer(nrow(i))
+ len__ = integer(nrow(i))
+ allLen1 = logical(1)
+
+ if (verbose) {last.started.at=proc.time()[3];cat("Starting bmerge ...");flush.console()}
+ .Call(Cbmerge, i, x, as.integer(leftcols), as.integer(rightcols), io<-haskey(i), xo, roll, rollends, nomatch, f__, len__, allLen1)
+ # NB: io<-haskey(i) necessary for test 579 where the := above change the factor to character and remove i's key
+ if (verbose) {cat("done in",round(proc.time()[3]-last.started.at,3),"secs\n");flush.console()}
+
+ # in the caller's shallow copy, see comment at the top of this function for usage
+ # We want to leave the coercions to i in place otherwise, since the caller depends on that to build the result
+ if (length(resetifactor)) {
+ for (ii in resetifactor)
+ set(i,j=ii,value=origi[[ii]])
+ if (haskey(origi))
+ setattr(i, 'sorted', key(origi))
+ }
+ return(list(starts=f__, lens=len__, allLen1=allLen1))
+}
+
+
diff --git a/R/c.factor.R b/R/c.factor.R
new file mode 100644
index 0000000..eadd4ef
--- /dev/null
+++ b/R/c.factor.R
@@ -0,0 +1,40 @@
+# nocov start
+# don't include functions not used for coverage
+
+# c.factor was intended to be deprecated but we need it for rbind. No longer used by 'by'.
+# In dogroups, we now drop factor levels in .SD, work with integers, and add levels back afterwards.
+
+c.factor <- function(...)
+{
+ args <- list(...)
+ for (i in seq_along(args)) if (!is.factor(args[[i]])) args[[i]] = as.factor(args[[i]])
+ # The first must be factor otherwise we wouldn't be inside c.factor, its checked anyway in the line above.
+ newlevels = unique(unlist(lapply(args,levels),recursive=TRUE,use.names=TRUE))
+ if (length(o <- forderv(newlevels)))
+ newlevels <- newlevels[o]
+ nm <- names(unlist(args,recursive=TRUE,use.names=TRUE))
+ ans = unlist(lapply(args, function(x) { # Its faster this way when there are many references to the same level, which is normally the case
+ m = match(levels(x), newlevels)
+ m[as.integer(x)]
+ }))
+ structure(ans, levels = newlevels, names = nm, class = "factor")
+}
+
+#"[.factor" <- function(x, ...)
+#{
+# # change default action of factors to drop unused levels. This saves memory space and copying. It also makes tapply() work as you expect since the levels contain the unique values only, otherwise you get many NAs for the unused factor levels.
+# # The base::"[.factor" first creates the integer subset, with a pointer to the original levels, then 'if(drop)' calls factor() to then remove the unused levels.
+# # Here we force drop=TRUE for efficiency always (not changeable), and do the operation more efficiently.
+# # If you really want R's default [.factor, then call the base version directly using base::"[.factor"()
+# # This [.factor is within the data.table NAMESPACE so users should not see it, other than e.g. subsetting factor columns
+# # R's default [.factor assings constrasts also. Not considered here.
+# y <- NextMethod("[")
+# u = unique(y)
+# su = u[fastorder(list(u), na.last = NA)]
+# attr(y, "levels") = attr(x, "levels")[su] # relying on the original factor levels being sorted
+# y[] = chmatch(y, su)
+# class(y) = oldClass(x)
+# y
+#}
+
+# nocov end
diff --git a/R/cedta.R b/R/cedta.R
new file mode 100644
index 0000000..f2d9d01
--- /dev/null
+++ b/R/cedta.R
@@ -0,0 +1,44 @@
+
+cedta.override = NULL # If no need arises, will deprecate.
+
+cedta.pkgEvalsUserCode = c("gWidgetsWWW","statET","FastRWeb","slidify","rmarkdown","knitr", "IRkernel")
+# These packages run user code in their own environment and thus do not
+# themselves Depend or Import data.table. knitr's eval is passed envir=globalenv() so doesn't
+# need to be listed here currently, but we include it in case it decides to change that.
+# The others create their own environment to eval in.
+# The packages might use data.frame syntax elsewhere in their package code so we just want
+# the eval (only) of user code to be data.table-aware.
+
+# If a new package needs to be added to this vector, a user may add to it using :
+# assignInNamespace("cedta.pkgEvalsUserCode", c(data.table:::cedta.pkgEvalsUserCode,"<nsname>"), "data.table")
+# But please let us know so we can add the package to this vector in the package upstream, so other
+# users don't have to tread the same path. Then you can remove your assignInNamepace() call.
+
+# Packages may also set a variable in their namespace :
+# .datatable.aware = TRUE
+# which makes them data.table-aware optionally and possibly variably.
+# http://stackoverflow.com/a/13131555/403310
+
+cedta <- function(n=2L) {
+ # Calling Environment Data Table Aware
+ te = topenv(parent.frame(n))
+ if (!isNamespace(te)) {
+ # e.g. DT queries at the prompt (.GlobalEnv) and knitr's eval(,envir=globalenv()) but not DF[...] inside knitr::kable v1.6
+ return(TRUE)
+ }
+ nsname = getNamespaceName(te)
+ ans = nsname == "data.table" ||
+ "data.table" %chin% names(getNamespaceImports(te)) ||
+ "data.table" %chin% tryCatch(get(".Depends",paste("package",nsname,sep=":"),inherits=FALSE),error=function(e)NULL) ||
+ (nsname == "utils" && exists("debugger.look",parent.frame(n+1L))) ||
+ (nsname == "base" && all(c("FUN", "X") %in% ls(parent.frame(n))) ) || # lapply
+ (nsname %chin% cedta.pkgEvalsUserCode && any(sapply(sys.calls(), "[[", 1L)=="eval")) ||
+ nsname %chin% cedta.override ||
+ identical(TRUE, tryCatch(get(".datatable.aware",asNamespace(nsname),inherits=FALSE),error=function(e)NULL))
+ if (!ans && getOption("datatable.verbose"))
+ cat("cedta decided '",nsname,"' wasn't data.table aware\n",sep="")
+ # so we can trace the namespace name that may need to be added (very unusually)
+ ans
+}
+
+
diff --git a/R/data.table.R b/R/data.table.R
new file mode 100644
index 0000000..2363882
--- /dev/null
+++ b/R/data.table.R
@@ -0,0 +1,2619 @@
+deconstruct_and_eval <- function(expr, envir = parent.frame(), enclos = parent.frame()) {
+ if (!mode(expr) %in% c("call", "expression", "(")) return(expr)
+ # Fix for #774.
+ # the only place where a call is of length 1, that I can think of is
+ # a function call with empty arguments: e.g., a[[1L]]() or a$b() or b()
+ # and in all these cases, we *don't* want to deconstruct and eval
+ # hence the "else if" statement is commented below. No existing tests
+ # are broken in doing so. If there are other reports, will revisit.
+ if (length(expr) == 1L) {
+ if (is.expression(expr)) return (deconstruct_and_eval(expr[[1L]]))
+ # else if (is.call(expr[[1L]])) return (list(deconstruct_and_eval(expr[[1L]])))
+ else return(expr)
+ }
+ # Fix for #2496. The `{` in `DT[, {var := bla}, by=x]` is caught and removed from `j`.
+ if (expr[[1L]] == "{" & is.call(expr[[2L]])) {
+ if (identical(expr[[2L]][[1L]], quote(`:=`))) {
+ warning('Caught and removed `{` wrapped around := in j. := and `:=`(...) are
+ defined for use in j, once only and in particular ways. See help(":=").')
+ return(deconstruct_and_eval(expr[[2L]], envir, enclos))
+ }
+ }
+ # don't evaluate eval's if the environment is specified
+ if (expr[[1L]] == quote(eval) && length(expr) < 3L) {
+ return(deconstruct_and_eval(eval(expr[[2L]], envir, enclos), envir, enclos))
+ }
+ ff <- function(m) {
+ if (is.call(m)) {
+ if (m[[1L]] == quote(eval))
+ # fix for #880. Hopefully this resolve the eval(parse(.)) issue for good.
+ if (is.call(m[[2L]]) && m[[2L]][[1L]] == quote(parse))
+ deconstruct_and_eval(m, envir, enclos)
+ else deconstruct_and_eval(eval(m[[2L]], envir, enclos), envir, enclos)
+ else deconstruct_and_eval(m, envir, enclos)
+ } else m
+ }
+ lapply(expr, ff)
+}
+
+construct <- function(l) {
+ if (length(l) == 0L) return(NULL)
+ if (is.name(l)) return(l) # fix for error in cases as reported in Bug #5007: DT[, (cols) := lapply(.SD, function(x) MyValueIsTen), by=ID]
+ # construct(l[[3L]] would give an error when l[[3L]] is MyValueIsTen if not for this line)
+ if (length(l) == 1L) {
+ if (length(l[[1L]]) == 1L & !is.call(l)) return(l[[1L]]) # so that DT[, test()] does not return just the function definition
+ else return(as.call(list(construct(l[[1L]]))))
+ }
+
+ if (identical(l[[1L]], quote(`function`))) return(as.call(list(l[[1L]], l[[2L]], construct(l[[3L]]))))
+
+ if (!is.list(l)) return(l)
+
+ as.call(setNames(lapply(l, function(m) {
+ if (length(m) == 1L) m
+ else construct(m)
+ }), names(l)))
+}
+
+replace_dot <- function(e) {
+ if (is.call(e)) {
+ if (e[[1L]] == ".")
+ e[[1L]] = quote(list)
+ le = as.list(e)
+ for (i in seq_along(le)) {
+ if (is.call(le[[i]])) {
+ if (le[[i]][[1L]] == ".")
+ le[[i]][[1L]] = quote(list)
+ le[[i]] = as.call(replace_dot(le[[i]]))
+ }
+ }
+ e = as.call(le)
+ }
+ e
+}
+
+dim.data.table <- function(x) {
+ if (length(x)) c(length(x[[1L]]), length(x))
+ else c(0L,0L)
+ # TO DO: consider placing "dim" as an attibute updated on inserts. Saves this 'if'.
+}
+
+.global <- new.env() # thanks to: http://stackoverflow.com/a/12605694/403310
+setPackageName("data.table",.global)
+.global$print = ""
+
+.SD = .N = .I = .GRP = .BY = .EACHI = NULL
+# These are exported to prevent NOTEs from R CMD check, and checkUsage via compiler.
+# But also exporting them makes it clear (to users and other packages) that data.table uses these as symbols.
+# And NULL makes it clear (to the R's mask check on loading) that they're variables not functions.
+# utils::globalVariables(c(".SD",".N")) was tried as well, but exporting seems better.
+# So even though .BY doesn't appear in this file, it should still be NULL here and exported because it's
+# defined in SDenv and can be used by users.
+
+print.data.table <- function(x,
+ topn=getOption("datatable.print.topn"), # (5) print the top topn and bottom topn rows with '---' inbetween
+ nrows=getOption("datatable.print.nrows"), # (100) under this the whole (small) table is printed, unless topn is provided
+ row.names = TRUE, quote = FALSE, ...)
+{
+ if (.global$print!="" && address(x)==.global$print) { # The !="" is to save address() calls and R's global cache of address strings
+ # := in [.data.table sets .global$print=address(x) to suppress the next print i.e., like <- does. See FAQ 2.22 and README item in v1.9.5
+ # The issue is distinguishing "> DT" (after a previous := in a function) from "> DT[,foo:=1]". To print.data.table(), there
+ # is no difference. Now from R 3.2.0 a side effect of the very welcome and requested change to avoid silent deep copy is that
+ # there is now no longer a difference between > DT and > print(DT). So decided that DT[] is now needed to guarantee print; simpler.
+ # This applies just at the prompt. Inside functions, print(DT) will of course print.
+ # Other options investigated (could revisit): Cstack_info(), .Last.value gets set first before autoprint, history(), sys.status(),
+ # topenv(), inspecting next statement in caller, using clock() at C level to timeout suppression after some number of cycles
+ SYS <- sys.calls()
+ if (length(SYS) <= 2 || # "> DT" auto-print or "> print(DT)" explicit print (cannot distinguish from R 3.2.0 but that's ok)
+ ( length(SYS) > 3L &&
+ SYS[[length(SYS)-3L]][[1L]] == "knit_print.default") ) { # knitr auto print to mimic the prompt
+ .global$print = ""
+ return(invisible())
+ }
+ }
+ .global$print = ""
+ if (!is.numeric(nrows)) nrows = 100L
+ if (!is.infinite(nrows)) nrows = as.integer(nrows)
+ if (nrows <= 0L) return(invisible()) # ability to turn off printing
+ if (!is.numeric(topn)) topn = 5L
+ topnmiss = missing(topn)
+ topn = max(as.integer(topn),1L)
+ if (nrow(x) == 0L) {
+ if (length(x)==0L)
+ cat("Null data.table (0 rows and 0 cols)\n") # See FAQ 2.5 and NEWS item in v1.8.9
+ else
+ cat("Empty data.table (0 rows) of ",length(x)," col",if(length(x)>1L)"s",": ",paste(head(names(x),6),collapse=","),if(ncol(x)>6)"...","\n",sep="")
+ return()
+ }
+ if (topn*2<nrow(x) && (nrow(x)>nrows || !topnmiss)) {
+ toprint = rbind(head(x, topn), tail(x, topn))
+ rn = c(seq_len(topn), seq.int(to=nrow(x), length.out=topn))
+ printdots = TRUE
+ } else {
+ toprint = x
+ rn = seq_len(nrow(x))
+ printdots = FALSE
+ }
+ toprint=format.data.table(toprint, ...)
+ # FR #5020 - add row.names = logical argument to print.data.table
+ if (isTRUE(row.names)) rownames(toprint)=paste(format(rn,right=TRUE,scientific=FALSE),":",sep="") else rownames(toprint)=rep.int("", nrow(toprint))
+ if (is.null(names(x))) colnames(toprint)=rep("NA", ncol(toprint)) # fixes bug #4934
+ if (printdots) {
+ toprint = rbind(head(toprint,topn),"---"="",tail(toprint,topn))
+ rownames(toprint) = format(rownames(toprint),justify="right")
+ print(toprint,right=TRUE,quote=quote)
+ return(invisible())
+ }
+ if (nrow(toprint)>20L)
+ # repeat colnames at the bottom if over 20 rows so you don't have to scroll up to see them
+ toprint=rbind(toprint,matrix(colnames(toprint),nrow=1)) # fixes bug #4934
+ print(toprint,right=TRUE,quote=quote)
+ invisible()
+}
+
+# FR #2591 - format.data.table issue with columns of class "formula"
+is.formula <- function(x) class(x) == "formula"
+
+format.data.table <- function (x, ..., justify="none") {
+ if (is.atomic(x) && !is.null(x)) {
+ stop("Internal structure doesn't seem to be a list. Possibly corrupt data.table.")
+ }
+ format.item <- function(x) {
+ if (is.atomic(x) || is.formula(x)) # FR #2591 - format.data.table issue with columns of class "formula"
+ paste(c(format(head(x,6), justify=justify, ...), if(length(x)>6)""),collapse=",") # fix for #5435 - format has to be added here...
+ else
+ paste("<",class(x)[1L],">",sep="")
+ }
+ # FR #1091 for pretty printing of character
+ # TODO: maybe instead of doing "this is...", we could do "this ... test"?
+ char.trunc <- function(x, trunc.char = getOption("datatable.prettyprint.char")) {
+ trunc.char = max(0L, suppressWarnings(as.integer(trunc.char[1L])), na.rm=TRUE)
+ if (!is.character(x) || trunc.char <= 0L) return(x)
+ idx = which(nchar(x) > trunc.char)
+ x[idx] = paste(substr(x[idx], 1L, as.integer(trunc.char)), "...", sep="")
+ x
+ }
+ do.call("cbind",lapply(x,function(col,...){
+ if (!is.null(dim(col))) stop("Invalid column: it has dimensions. Can't format it. If it's the result of data.table(table()), use as.data.table(table()) instead.")
+ if (is.list(col)) col = sapply(col, format.item)
+ else col = format(char.trunc(col), justify=justify, ...) # added an else here to fix #5435
+ col
+ },...))
+}
+
+is.data.table <- function(x) inherits(x, "data.table")
+is.ff <- function(x) inherits(x, "ff") # define this in data.table so that we don't have to require(ff), but if user is using ff we'd like it to work
+
+#NCOL <- function(x) {
+# # copied from base, but additionally covers data.table via is.list()
+# # because NCOL in base explicity tests using is.data.frame()
+# if (is.list(x) && !is.ff(x)) return(length(x))
+# if (is.array(x) && length(dim(x)) > 1L) ncol(x) else as.integer(1L)
+#}
+#NROW <- function(x) {
+# if (is.data.frame(x) || is.data.table(x)) return(nrow(x))
+# if (is.list(x) && !is.ff(x)) stop("List is not a data.frame or data.table. Convert first before using NROW") # list may have different length elements, which data.table and data.frame's resolve.
+# if (is.array(x)) nrow(x) else length(x)
+#}
+
+null.data.table <-function() {
+ ans = list()
+ setattr(ans,"class",c("data.table","data.frame"))
+ setattr(ans,"row.names",.set_row_names(0L))
+ alloc.col(ans)
+}
+
+data.table <-function(..., keep.rownames=FALSE, check.names=FALSE, key=NULL)
+{
+ # NOTE: It may be faster in some circumstances to create a data.table by creating a list l first, and then setattr(l,"class",c("data.table","data.frame")) at the expense of checking.
+ # TO DO: rewrite data.table(), one of the oldest functions here. Many people use data.table() to convert data.frame rather than
+ # as.data.table which is faster; speed could be better. Revisit how many copies are taken in for example data.table(DT1,DT2) which
+ # cbind directs to. And the nested loops for recycling lend themselves to being C level.
+
+ x <- list(...) # doesn't copy named inputs as from R >= 3.1.0 (a very welcome change)
+ if (!.R.listCopiesNamed) .Call(CcopyNamedInList,x) # to maintain the old behaviour going forwards, for now. See test 548.2.
+ # **TO DO** Something strange with NAMED on components of `...`. To investigate. Or just port data.table() to C. This is why
+ # it's switched, because extra copies would be introduced in R <= 3.1.0, iiuc.
+
+ # fix for #5377 - data.table(null list, data.frame and data.table) should return null data.table. Simple fix: check all scenarios here at the top.
+ if (identical(x, list(NULL)) || identical(x, list(list())) ||
+ identical(x, list(data.frame(NULL))) || identical(x, list(data.table(NULL)))) return( null.data.table() )
+ tt <- as.list(substitute(list(...)))[-1L] # Intention here is that data.table(X,Y) will automatically put X and Y as the column names. For longer expressions, name the arguments to data.table(). But in a call to [.data.table, wrap in list() e.g. DT[,list(a=mean(v),b=foobarzoo(zang))] will get the col names
+ vnames = names(tt)
+ if (is.null(vnames)) vnames = rep.int("",length(x))
+ vnames[is.na(vnames)] = ""
+ novname = vnames==""
+ if (any(!novname)) {
+ if (any(vnames[!novname] == ".SD")) stop("A column may not be called .SD. That has special meaning.")
+ }
+ for (i in which(novname)) {
+ # if (ncol(as.data.table(x[[i]])) <= 1) { # cbind call in test 230 fails if I write ncol(as.data.table(eval(tt[[i]], parent.frame()))) <= 1, no idea why... (keep this for later even though all tests pass with ncol(.).. because base uses as.data.frame(.))
+ if (is.null(ncol(x[[i]]))) {
+ if ((tmp <- deparse(tt[[i]])[1]) == make.names(tmp))
+ vnames[i] <- tmp
+ }
+ }
+ tt = vnames==""
+ if (any(tt)) vnames[tt] = paste("V", which(tt), sep = "")
+ # so now finally we have good column names. We also will use novname later to know which were explicitly supplied in the call.
+ n <- length(x)
+ if (n < 1L)
+ return( null.data.table() )
+ if (length(vnames) != n) stop("logical error in vnames")
+ vnames <- as.list.default(vnames)
+ nrows = integer(n) # vector of lengths of each column. may not be equal if silent repetition is required.
+ numcols = integer(n) # the ncols of each of the inputs (e.g. if inputs contain matrix or data.table)
+ for (i in seq_len(n)) {
+ xi = x[[i]]
+ if (is.null(xi)) stop("column or argument ",i," is NULL")
+ if ("POSIXlt" %chin% class(xi)) {
+ warning("POSIXlt column type detected and converted to POSIXct. We do not recommend use of POSIXlt at all because it uses 40 bytes to store one date.")
+ x[[i]] = as.POSIXct(xi)
+ } else if (is.matrix(xi) || is.data.frame(xi)) { # including data.table (a data.frame, too)
+ xi = as.data.table(xi, keep.rownames=keep.rownames) # TO DO: allow a matrix to be a column of a data.table. This could allow a key'd lookup to a matrix, not just by a single rowname vector, but by a combination of several columns. A matrix column could be stored either by row or by column contiguous in memory.
+ x[[i]] = xi
+ numcols[i] = length(xi)
+ } else if (is.table(xi)) {
+ x[[i]] = xi = as.data.table.table(xi, keep.rownames=keep.rownames)
+ numcols[i] = length(xi)
+ }
+ nrows[i] <- NROW(xi) # for a vector (including list() columns) returns the length
+ if (numcols[i]>0L) {
+ namesi <- names(xi) # works for both data.frame's, matrices and data.tables's
+ if (length(namesi)==0L) namesi = rep.int("",ncol(xi))
+ namesi[is.na(namesi)] = ""
+ tt = namesi==""
+ if (any(tt)) namesi[tt] = paste("V", which(tt), sep = "")
+ if (novname[i]) vnames[[i]] = namesi
+ else vnames[[i]] = paste(vnames[[i]], namesi, sep=".")
+ }
+ }
+ nr <- max(nrows)
+ ckey = NULL
+ recycledkey = FALSE
+ for (i in seq_len(n)) {
+ xi = x[[i]]
+ if (is.data.table(xi) && haskey(xi)) {
+ if (nrows[i]<nr) recycledkey = TRUE
+ else ckey = c(ckey, key(xi))
+ }
+ }
+ for (i in which(nrows < nr)) {
+ # TO DO ... recycle in C, but not high priority as large data already regular from database or file
+ xi <- x[[i]]
+ if (identical(xi,list())) {
+ x[[i]] = vector("list", nr)
+ next
+ }
+ if (nrows[i]==0L) stop("Item ",i," has no length. Provide at least one item (such as NA, NA_integer_ etc) to be repeated to match the ",nr," rows in the longest column. Or, all columns can be 0 length, for insert()ing rows into.")
+ # Implementing FR #4813 - recycle with warning when nr %% nrows[i] != 0L
+ if (nr%%nrows[i] != 0L) warning("Item ", i, " is of size ", nrows[i], " but maximum size is ", nr, " (recycled leaving remainder of ", nr%%nrows[i], " items)")
+ # if (nr%%nrows[i] == 0L) {
+ if (is.data.frame(xi)) { # including data.table
+ ..i = rep(seq_len(nrow(xi)), length.out = nr)
+ x[[i]] = xi[..i,,drop=FALSE]
+ next
+ }
+ if (is.atomic(xi) || is.list(xi)) {
+ # TO DO: surely use set() here, or avoid the coercion
+ x[[i]] = rep(xi, length.out = nr)
+ next
+ }
+ stop("problem recycling column ",i,", try a simpler type")
+ # }
+ stop("argument ",i," (nrow ",nrows[i],") cannot be recycled without remainder to match longest nrow (",nr,")")
+ }
+ if (any(numcols>0L)) {
+ value = vector("list",sum(pmax(numcols,1L)))
+ k = 1L
+ for(i in seq_len(n)) {
+ if (is.list(x[[i]]) && !is.ff(x[[i]])) {
+ for(j in seq_len(length(x[[i]]))) {
+ value[[k]] = x[[i]][[j]]
+ k=k+1L
+ }
+ } else {
+ value[[k]] = x[[i]]
+ k=k+1L
+ }
+ }
+ } else {
+ value = x
+ }
+ vnames <- unlist(vnames)
+ if (check.names) # default FALSE
+ vnames <- make.names(vnames, unique = TRUE)
+ setattr(value,"names",vnames)
+ setattr(value,"row.names",.set_row_names(nr))
+ setattr(value,"class",c("data.table","data.frame"))
+ if (!is.null(key)) {
+ if (!is.character(key)) stop("key argument of data.table() must be character")
+ if (length(key)==1L) {
+ key = strsplit(key,split=",")[[1L]]
+ # eg key="A,B"; a syntax only useful in key argument to data.table(), really.
+ }
+ setkeyv(value,key)
+ } else {
+ # retain key of cbind(DT1, DT2, DT3) where DT2 is keyed but not DT1. cbind calls data.table().
+ # If DT inputs with keys have been recycled then can't retain key
+ if (length(ckey)
+ && !recycledkey
+ && !any(duplicated(ckey))
+ && all(ckey %in% names(value))
+ && !any(duplicated(names(value)[names(value) %in% ckey])))
+ setattr(value, "sorted", ckey)
+ }
+ alloc.col(value) # returns a NAMED==0 object, unlike data.frame()
+}
+
+.massagei <- function(x) {
+ if (is.call(x) && as.character(x[[1L]]) %chin% c("J","."))
+ x[[1L]] = quote(list)
+ x
+}
+
+# A (relatively) fast (uses DT grouping) wrapper for matching two vectors, BUT:
+# it behaves like 'pmatch' but only the 'exact' matching part. That is, a value in
+# 'x' is matched to 'table' only once. No index will be present more than once.
+# This should make it even clearer:
+# chmatch2(c("a", "a"), c("a", "a")) # 1,2 - the second 'a' in 'x' has a 2nd match in 'table'
+# chmatch2(c("a", "a"), c("a", "b")) # 1,NA - the second one doesn't 'see' the first 'a'
+# chmatch2(c("a", "a"), c("a", "a.1")) # 1,NA - this is where it differs from pmatch - we don't need the partial match.
+chmatch2 <- function(x, table, nomatch=NA_integer_) {
+ .Call(Cchmatch2, x, table, as.integer(nomatch)) # this is in 'rbindlist.c' for now.
+}
+
+"[.data.table" <- function (x, i, j, by, keyby, with=TRUE, nomatch=getOption("datatable.nomatch"), mult="all", roll=FALSE, rollends=if (roll=="nearest") c(TRUE,TRUE) else if (roll>=0) c(FALSE,TRUE) else c(TRUE,FALSE), which=FALSE, .SDcols, verbose=getOption("datatable.verbose"), allow.cartesian=getOption("datatable.allow.cartesian"), drop=NULL, on=NULL)
+{
+ # ..selfcount <<- ..selfcount+1 # in dev, we check no self calls, each of which doubles overhead, or could
+ # test explicitly if the caller is [.data.table (even stronger test. TO DO.)
+ # the drop=NULL is to sink drop argument when dispatching to [.data.frame; using '...' stops test 147
+ if (!cedta()) {
+ # Fix for #5070 (to do)
+ Nargs = nargs() - (!missing(drop))
+ ans = if (Nargs<3L) `[.data.frame`(x,i) # drop ignored anyway by DF[i]
+ else if (missing(drop)) `[.data.frame`(x,i,j)
+ else `[.data.frame`(x,i,j,drop)
+ # added is.data.table(ans) check to fix bug #5069
+ if (!missing(i) & is.data.table(ans)) setkey(ans,NULL) # See test 304
+ return(ans)
+ }
+ if (!mult %chin% c("first","last","all")) stop("mult argument can only be 'first','last' or 'all'")
+ if (length(roll)!=1L || is.na(roll)) stop("roll must be a single TRUE, FALSE, positive/negative integer/double including +Inf and -Inf or 'nearest'")
+ if (is.character(roll)) {
+ if (roll!="nearest") stop("roll is '",roll,"' (type character). Only valid character value is 'nearest'.")
+ } else {
+ roll = if (isTRUE(roll)) +Inf else as.double(roll)
+ }
+ force(rollends)
+ if (!is.logical(rollends)) stop("rollends must be a logical vector")
+ if (length(rollends)>2) stop("rollends must be length 1 or 2")
+ if (length(rollends)==1) rollends=rep.int(rollends,2L)
+ # TO DO (document/faq/example). Removed for now ... if ((roll || rolltolast) && missing(mult)) mult="last" # for when there is exact match to mult. This does not control cases where the roll is mult, that is always the last one.
+ missingnomatch = missing(nomatch)
+ if (!is.na(nomatch) && nomatch!=0L) stop("nomatch must either be NA or 0, or (ideally) NA_integer_ or 0L")
+ nomatch = as.integer(nomatch)
+ if (!is.logical(which) || length(which)>1) stop("'which' must be a logical vector length 1. Either FALSE, TRUE or NA.")
+ if ((isTRUE(which)||is.na(which)) && !missing(j)) stop("'which' is ",which," (meaning return row numbers) but 'j' is also supplied. Either you need row numbers or the result of j, but only one type of result can be returned.")
+ if (!is.na(nomatch) && is.na(which)) stop("which=NA with nomatch=0 would always return an empty vector. Please change or remove either which or nomatch.")
+ .global$print=""
+ if (missing(i) && missing(j)) {
+ # ...[] == oops at console, forgot print(...)
+ # or some kind of dynamic construction that has edge case of no contents inside [...]
+ return(x)
+ }
+ if (!with && missing(j)) stop("j must be provided when with=FALSE")
+ if (!missing(j)) {
+ jsub = substitute(j) # Ignore allow.cartesian when `jsub` has `:=`. Search for #800 to see where we need this.
+ # deconstruct and eval everything with just one argument, then reconstruct back to a call
+ if (is.call(jsub)) jsub = construct(deconstruct_and_eval(replace_dot(jsub), parent.frame(), parent.frame()))
+ }
+ bysub=NULL
+ if (!missing(by)) bysub=substitute(by)
+ if (!missing(keyby)) {
+ if (!missing(by)) stop("Provide either 'by' or 'keyby' but not both")
+ by=bysub=substitute(keyby)
+ # Assign to 'by' so that by is no longer missing and we can proceed as if there were one by
+ }
+ byjoin = FALSE
+ if (!missing(by)) {
+ if (missing(j)) stop("'by' or 'keyby' is supplied but not j")
+ byjoin = is.symbol(bysub) && bysub==".EACHI"
+ }
+ irows = NULL # Meaning all rows. We avoid creating 1:nrow(x) for efficiency.
+ notjoin = FALSE
+ rightcols = leftcols = integer()
+
+ # To take care of duplicate column names properly (see chmatch2 function above `[data.table`) for description
+ dupmatch <- function(x, y, ...) {
+ if (anyDuplicated(x))
+ pmax(chmatch(x,y, ...), chmatch2(x,y,0L))
+ else chmatch(x,y)
+ }
+
+ # setdiff removes duplicate entries, which'll create issues with duplicated names. Use '%chin% instead.
+ dupdiff <- function(x, y) x[!x %chin% y]
+
+ if (!missing(i)) {
+ xo = NULL
+ isub = substitute(i)
+ # Fixes 4994: a case where quoted expression with a "!", ex: expr = quote(!dt1); dt[eval(expr)] requires
+ # the "eval" to be checked before `as.name("!")`. Therefore interchanged.
+ restore.N = remove.N = FALSE
+ if (exists(".N", envir=parent.frame(), inherits=FALSE)) {
+ old.N = get(".N", envir=parent.frame(), inherits=FALSE)
+ locked.N = bindingIsLocked(".N", parent.frame())
+ if (locked.N) eval(call("unlockBinding", ".N", parent.frame())) # eval call to pass R CMD check NOTE until we find cleaner way
+ assign(".N", nrow(x), envir=parent.frame(), inherits=FALSE)
+ restore.N = TRUE
+ # the comment below is invalid hereafter (due to fix for #1145)
+ # binding locked when .SD[.N] but that's ok as that's the .N we want anyway
+
+ # TO DO: change isub at C level s/.N/nrow(x); changing a symbol to a constant should be ok
+ } else {
+ assign(".N", nrow(x), envir=parent.frame(), inherits=FALSE)
+ remove.N = TRUE
+ }
+ if (is.call(isub) && isub[[1L]]=="eval") { # TO DO: or ..()
+ isub = eval(.massagei(isub[[2L]]), parent.frame(), parent.frame())
+ if (is.expression(isub)) isub=isub[[1L]]
+ }
+ if (is.call(isub) && isub[[1L]] == as.name("!")) {
+ notjoin = TRUE
+ if (!missingnomatch) stop("not-join '!' prefix is present on i but nomatch is provided. Please remove nomatch.");
+ nomatch = 0L
+ isub = isub[[2L]]
+ # #932 related so that !(v1 == 1) becomes v1 == 1 instead of (v1 == 1) after removing "!"
+ if (is.call(isub) && isub[[1L]] == "(" && !is.name(isub[[2L]]))
+ isub = isub[[2L]]
+ }
+ if (is.call(isub) && isub[[1L]] == as.name("order") && getOption("datatable.optimize") >= 1) { # optimize here so that we can switch it off if needed
+ if (verbose) cat("order optimisation is on, i changed from 'order(...)' to 'forder(DT, ...)'.\n")
+ isub = as.list(isub)
+ isub = as.call(c(list(quote(forder), quote(x)), isub[-1L]))
+ }
+ if (is.null(isub)) return( null.data.table() )
+ if (is.call(isub) && isub[[1L]] == quote(forder)) {
+ order_env = new.env(parent=parent.frame()) # until 'forder' is exported
+ assign("forder", forder, order_env)
+ assign("x", x, order_env)
+ i = eval(isub, order_env, parent.frame()) # for optimisation of 'order' to 'forder'
+ # that forder returns integer(0) is taken care of internally within forder
+ } else if (is.call(isub) && getOption("datatable.auto.index") &&
+ as.character(isub[[1L]]) %chin% c("==","%in%") &&
+ is.name(isub[[2L]]) &&
+ (isub2<-as.character(isub[[2L]])) %chin% names(x) &&
+ is.null(attr(x, '.data.table.locked'))) { # fix for #958, don't create auto index on '.SD'.
+ # LHS is a column name symbol
+ # simplest case for now (single ==). Later, top level may be &,|,< or >
+ # TO DO: print method could print physical and secondary keys at end.
+ # TO DO: move down to if (is.data.table) clause below, later ...
+ RHS = eval(isub[[3L]], x, parent.frame())
+ # fix for #961
+ if (is.list(RHS)) RHS = as.character(RHS)
+ if (isub[[1L]] == "==" && length(RHS)>1) {
+ if (length(RHS)!=nrow(x)) stop("RHS of == is length ",length(RHS)," which is not 1 or nrow (",nrow(x),"). For robustness, no recycling is allowed (other than of length 1 RHS). Consider %in% instead.")
+ i = x[[isub2]] == RHS # DT[colA == colB] regular element-wise vector scan
+ } else if ( (is.integer(x[[isub2]]) && is.double(RHS) && isReallyReal(RHS)) || (mode(x[[isub2]]) != mode(RHS) && !(class(x[[isub2]]) %in% c("character", "factor") &&
+ class(RHS) %in% c("character", "factor"))) ) {
+ # re-direct all non-matching mode cases to base R, as data.table's binary
+ # search based join is strict in types. #957 and #961.
+ i = if (isub[[1L]] == "==") x[[isub2]] == RHS else x[[isub2]] %in% RHS
+ } else {
+ # fix for #932 (notjoin) and also when RHS is NA (and notjoin is also TRUE)
+ if (isub[[1L]] == "==") {
+ # RHS is of length=1 or n
+ if (any_na(as_list(RHS))) {
+ notjoin = FALSE
+ RHS = RHS[0L]
+ } else if (notjoin) {
+ RHS = c(RHS, if (is.double(RHS)) c(NA, NaN) else NA)
+ }
+ }
+ if (haskey(x) && isub2 == key(x)[1L]) {
+ # join to key(x)[1L]
+ xo <- integer()
+ rightcols = chmatch(key(x)[1],names(x))
+ } else {
+ xo = get2key(x,isub2) # Can't be any index with that col as the first one because those indexes will reorder within each group
+ if (is.null(xo)) { # integer() would be valid and signifies o=1:.N
+ if (verbose) {cat("Creating new index '",isub2,"'\n",sep="");flush.console()}
+ set2keyv(x,isub2)
+ xo = get2key(x,isub2)
+ } else {
+ if (verbose) {cat("Using existing index '",isub2,"'\n",sep="");flush.console()}
+ }
+ rightcols = chmatch(isub2, names(x))
+ }
+ # convert RHS to list to join to key (either physical or secondary)
+ i = as.data.table( unique(RHS) )
+ # To do: wrap isub[[3L]] with as.data.table() first before eval to save copy
+ leftcols = 1L
+ ans = bmerge(i, x, leftcols, rightcols, io<-FALSE, xo, roll=0.0, rollends=c(FALSE,FALSE), nomatch=0L, verbose=verbose)
+ # No need to shallow copy i before passing to bmerge; we just created i above ourselves
+ i = if (ans$allLen1 && !identical(suppressWarnings(min(ans$starts)), 0L)) ans$starts else vecseq(ans$starts, ans$lens, NULL)
+ if (length(xo)) i = fsort(xo[i])
+ leftcols = rightcols = NULL # these are used later to know whether a join was done, affects column order of result. So reset.
+ }
+ } else if (!is.name(isub)) i = eval(.massagei(isub), x, parent.frame())
+ else i = eval(isub, parent.frame(), parent.frame())
+ if (restore.N) {
+ assign(".N", old.N, envir=parent.frame())
+ if (locked.N) lockBinding(".N", parent.frame())
+ }
+ if (remove.N) rm(list=".N", envir=parent.frame())
+ if (is.matrix(i)) {
+ if (is.numeric(i) && ncol(i)==1L) { # #826 - subset DT on single integer vector stored as matrix
+ i = as.integer(i)
+ } else {
+ stop("i is invalid type (matrix). Perhaps in future a 2 column matrix could return a list of elements of DT (in the spirit of A[B] in FAQ 2.14). Please let datatable-help know if you'd like this, or add your comments to FR #657.")
+ }
+ }
+ if (is.logical(i)) {
+ if (notjoin) {
+ notjoin = FALSE
+ i = !i
+ }
+ if (identical(i,NA)) i = NA_integer_ # see DT[NA] thread re recycling of NA logical
+ }
+ if (is.null(i)) return( null.data.table() )
+ if (is.character(i)) i = data.table(V1=i) # for user convenience; e.g. DT["foo"] without needing DT[.("foo")]
+ else if (identical(class(i),"list") && length(i)==1L && is.data.frame(i[[1L]])) i = as.data.table(i[[1L]])
+ else if (identical(class(i),"data.frame")) i = as.data.table(i) # TO DO: avoid these as.data.table() and use a flag instead
+ else if (identical(class(i),"list")) i = as.data.table(i)
+ if (is.data.table(i)) {
+ if (!haskey(x) && missing(on) && is.null(xo)) {
+ stop("When i is a data.table (or character vector), x must be keyed (i.e. sorted, and, marked as sorted) so data.table knows which columns to join to and take advantage of x being sorted. Call setkey(x,...) first, see ?setkey.")
+ }
+ if (!missing(on)) {
+ if (!is.character(on))
+ stop("'on' argument should be a named atomic vector oc column names indicating which columns in 'i' should be joined with which columns in 'x'.")
+ if (is.null(names(on))) {
+ if (verbose)
+ cat("names(on) = NULL. Assigning 'on' to names(on)' as well.\n")
+ names(on) = on
+ }
+ rightcols = chmatch(names(on), names(x))
+ leftcols = chmatch(unname(on), names(i))
+ xo = forderv(x, by = rightcols)
+ } else if (is.null(xo)) {
+ rightcols = chmatch(key(x),names(x)) # NAs here (i.e. invalid data.table) checked in bmerge()
+ leftcols = if (haskey(i))
+ chmatch(head(key(i),length(rightcols)),names(i))
+ else
+ seq_len(min(length(i),length(rightcols)))
+ rightcols = head(rightcols,length(leftcols))
+ xo = integer() ## signifies 1:.N
+ if (missing(by) && with && isTRUE(getOption("datatable.old.bywithoutby"))) {
+ # To revert to <=v1.9.2 behaviour. TO DO: remove option after Sep 2015
+ warning("The data.table option 'datatable.old.bywithoutby' for grouping on join without providing `by` will be deprecated in the next release, use `by=.EACHI`.", call. = FALSE)
+ by=bysub=as.symbol(".EACHI")
+ byjoin=TRUE
+ txtav = c(names(x)[-rightcols], names(i)[-leftcols])
+ if (missing(j)) j = jsub = as.call(parse(text=paste(".(",paste(txtav, collapse=","),")",sep="")))[[1]]
+ }
+ }
+ # Implementation for not-join along with by=.EACHI, #604
+ if (notjoin && byjoin) {
+ notjoin = FALSE
+ if (verbose) {last.started.at=proc.time()[3];cat("not-join called with 'by=.EACHI'; Replacing !i with i=setdiff(x,i) ...");flush.console()}
+ orignames = copy(names(i))
+ i = setdiff_(x, i, rightcols, leftcols) # part of #547
+ if (verbose) {cat("done in",round(proc.time()[3]-last.started.at,3),"secs\n");flush.console}
+ setnames(i, orignames[leftcols])
+ setattr(i, 'sorted', names(i)) # since 'x' has key set, this'll always be sorted
+ }
+ io = if (missing(on)) haskey(i) else identical(unname(on), head(key(i), length(on)))
+ i = .shallow(i, retain.key = io)
+ ans = bmerge(i, x, leftcols, rightcols, io, xo, roll, rollends, nomatch, verbose=verbose)
+ f__ = ans$starts
+ len__ = ans$lens
+ allLen1 = ans$allLen1
+ # length of input nomatch (single 0 or NA) is 1 in both cases.
+ # When no match, len__ is 0 for nomatch=0 and 1 for nomatch=NA, so len__ isn't .N
+ # If using secondary key of x, f__ will refer to xo
+ if (is.na(which)) {
+ w = if (notjoin) f__!=0L else is.na(f__)
+ return( if (length(xo)) fsort(xo[w]) else which(w) )
+ }
+ if (mult=="all") {
+ if (!byjoin) {
+ # Really, `anyDuplicated` in base is AWESOME!
+ # allow.cartesian shouldn't error if a) not-join, b) 'i' has no duplicates or c) jsub has `:=`.
+ irows = if (allLen1) f__ else vecseq(f__,len__,
+ if( allow.cartesian ||
+ notjoin || # #698 fix. When notjoin=TRUE, ignore allow.cartesian. Rows in answer will never be > nrow(x).
+ !anyDuplicated(f__, incomparables = c(0L, NA_integer_)) || # #742 fix. If 'i' has no duplicates, ignore as well.
+ (!missing(j) && all.vars(jsub, TRUE)[1L] == ":=")) # #800 fix. if jsub[1L] == ":=" ignore allow.cartesian.
+ # TODO: warn on `:=` when `i` has duplicates?
+ NULL
+ else as.double(nrow(x)+nrow(i))) # rows in i might not match to x so old max(nrow(x),nrow(i)) wasn't enough. But this limit now only applies when there are duplicates present so the reason now for nrow(x)+nrow(i) is just to nail it down and be bigger than max(nrow(x),nrow(i)).
+ # Fix for #1092 and #1074
+ # TODO: implement better version of "any"/"all"/"which" to avoid
+ # unnecessary construction of logical vectors
+ if (identical(nomatch, 0L) && allLen1) irows = irows[irows != 0L]
+ } else {
+ if (length(xo) && missing(on)) stop("Cannot by=.EACHI when joining to a secondary key, yet")
+ # since f__ refers to xo later in grouping, so xo needs to be passed through to dogroups too.
+ if (length(irows)) stop("Internal error. irows has length in by=.EACHI")
+ }
+ } else {
+ irows = if (mult=="first") f__ else f__+len__-1L
+ if (identical(nomatch,0L)) irows = irows[len__>0L] # 0s are len 0, so this removes -1 irows
+ if (length(len__)) len__ = pmin(len__,1L) # for test 456, and consistency generally
+ # the if() is for R < 2.15.1 when pmin was enhanced, see v1.8.6.
+ }
+ if (length(xo) && length(irows)) irows = xo[irows] # TO DO: fsort here?
+ } else {
+ if (!missing(on)) {
+ stop("logical error. i is not a data.table, but 'on' argument is provided.")
+ }
+ # TO DO: TODO: Incorporate which_ here on DT[!i] where i is logical. Should avoid i = !i (above) - inefficient.
+ # i is not a data.table
+ if (!is.logical(i) && !is.numeric(i)) stop("i has not evaluated to logical, integer or double")
+ if (is.logical(i)) {
+ if (isTRUE(i)) irows = i = NULL # fixes #1249
+ else if (length(i)==nrow(x)) irows = i = which(i) # e.g. DT[colA>3,which=TRUE]
+ # also replacing 'i' here - to save memory, #926.
+ else irows=seq_len(nrow(x))[i] # e.g. recycling DT[c(TRUE,FALSE),which=TRUE], for completeness
+ # it could also be DT[!TRUE, which=TRUE] (silly cases, yes).
+ # replaced the "else if (!isTRUE(i))" to just "else". Fixes bug report #4930
+ } else {
+ irows = as.integer(i) # e.g. DT[c(1,3)] and DT[c(-1,-3)] ok but not DT[c(1,-3)] (caught as error)
+ irows = .Call(CconvertNegativeIdx, irows, nrow(x)) # simplifies logic from here on (can assume positive subscripts)
+ # maintains Arun's fix for #2697 (test 1042)
+ # efficient in C with more detailed messages
+ # falls through quickly (no R level allocs) if no negatives
+ # minor TO DO: can we merge this with check_idx in fcast.c/subset ?
+ }
+ }
+ if (notjoin) {
+ if (byjoin || !is.integer(irows) || is.na(nomatch)) stop("Internal error: notjoin but byjoin or !integer or nomatch==NA")
+ irows = irows[irows!=0L]
+ i = irows = if (length(irows)) seq_len(nrow(x))[-irows] else NULL # NULL meaning all rows i.e. seq_len(nrow(x))
+ leftcols = integer() # proceed as if row subset from now on, length(leftcols) is switched on later
+ rightcols = integer()
+ # Doing this once here, helps speed later when repeatedly subsetting each column. R's [irows] would do this for each
+ # column when irows contains negatives.
+ }
+ if (which) return( if (is.null(irows)) seq_len(nrow(x)) else irows )
+ } else { # missing(i)
+ i = NULL
+ }
+
+ byval = NULL
+ xnrow = nrow(x)
+ xcols = xcolsAns = icols = icolsAns = integer()
+ if (missing(j)) {
+ # missing(by)==TRUE was already checked above before dealing with i
+ if (!length(x)) return(null.data.table())
+ if (!length(leftcols)) {
+ ansvars = names(x)
+ jisvars = character()
+ xcols = xcolsAns = seq_along(x)
+ } else {
+ jisvars = names(i)[-leftcols]
+ tt = jisvars %chin% names(x)
+ if (length(tt)) jisvars[tt] = paste("i.",jisvars[tt],sep="")
+ ansvars = make.unique(c(names(x), jisvars))
+ icols = c(leftcols, seq_along(i)[-leftcols])
+ icolsAns = c(rightcols, seq.int(ncol(x)+1L, length.out=ncol(i)-length(leftcols)))
+ xcols = xcolsAns = seq_along(x)[-rightcols]
+ }
+ ansvals = chmatch(ansvars, names(x))
+ } else {
+ # These commented lines are moved to the top for #800.
+ # jsub = substitute(j)
+ # # deconstruct and eval everything with just one argument, then reconstruct back to a call
+ # if (is.call(jsub))
+ # jsub = construct(deconstruct_and_eval(jsub, parent.frame(), parent.frame()))
+ if (is.null(jsub)) return(NULL)
+
+ if (is.call(jsub) && jsub[[1L]]==as.name(":=")) {
+ if (identical(irows, integer())) { # short circuit do-nothing, don't do further checks on .SDcols for example
+ .global$print = address(x)
+ return(invisible(x)) # irows=NULL means all rows at this stage
+ }
+ if (!with) {
+ if (is.null(names(jsub)) && is.name(jsub[[2L]])) {
+ # TO DO: uncomment these warnings in next release. Later, make both errors.
+ ## warning("with=FALSE is deprecated when using :=. Please wrap the LHS of := with parentheses; e.g., DT[,(myVar):=sum(b),by=a] to assign to column name(s) held in variable myVar. See ?':=' for other examples.")
+ jsub[[2L]] = eval(jsub[[2L]], parent.frame(), parent.frame())
+ } else {
+ ## warning("with=FALSE ignored, it isn't needed when using :=. See ?':=' for examples.")
+ }
+ with = TRUE
+ }
+ }
+ if (!with) {
+ # missing(by)==TRUE was already checked above before dealing with i
+ if (is.call(jsub) && deparse(jsub[[1]], 500L) %in% c("!", "-")) {
+ notj = TRUE
+ jsub = jsub[[2L]]
+ } else notj = FALSE
+ # fix for #1216, make sure the paranthesis are peeled from expr of the form (((1:4)))
+ while (is.call(jsub) && jsub[[1L]] == "(") jsub = as.list(jsub)[[-1L]]
+ if (is.call(jsub) && length(jsub) == 3L && jsub[[1L]] == ":") {
+ j = eval(jsub, setattr(as.list(seq_along(x)), 'names', names(x)), parent.frame()) # else j will be evaluated for the first time on next line
+ } else {
+ j = eval(jsub, parent.frame(), parent.frame())
+ }
+ if (is.logical(j)) j <- which(j)
+ if (!length(j)) return( null.data.table() )
+ if (is.factor(j)) j = as.character(j) # fix for FR: #4867
+ if (is.character(j)) {
+ if (notj) {
+ w = chmatch(j, names(x))
+ if (any(is.na(w))) {
+ warning("column(s) not removed because not found: ",paste(j[is.na(w)],collapse=","))
+ w = w[!is.na(w)]
+ }
+ # changed names(x)[-w] to use 'setdiff'. Here, all instances of the column must be removed.
+ # Ex: DT <- data.table(x=1, y=2, x=3); DT[, !"x", with=FALSE] should just output 'y'.
+ # But keep 'dup cols' beause it's basically DT[, !names(DT) %chin% "x", with=FALSE] which'll subset all cols not 'x'.
+ ansvars = if (length(w)) dupdiff(names(x), names(x)[w]) else names(x)
+ ansvals = dupmatch(ansvars, names(x))
+ } else {
+ # once again, use 'setdiff'. Basically, unless indices are specified in `j`, we shouldn't care about duplicated columns.
+ ansvars = j # x. and i. prefixes may be in here, and they'll be dealt with below
+ # dups = FALSE here.. even if DT[, c("x", "x"), with=FALSE], we subset only the first.. No way to tell which one the OP wants without index.
+ ansvals = chmatch(ansvars, names(x))
+ }
+ } else if (is.numeric(j)) {
+ if (all(j == 0L)) return (null.data.table())
+ if (any(abs(j) > ncol(x) | j==0L)) stop("j out of bounds")
+ if (any(j<0L) && any(j>0L)) stop("j mixes positive and negative")
+ if (any(j<0L)) j = seq_len(ncol(x))[j]
+ ansvars = names(x)[ if (notj) -j else j ] # DT[,!"columntoexclude",with=FALSE], if a copy is needed, rather than :=NULL
+ # DT[, c(1,3), with=FALSE] should clearly provide both 'x' columns
+ ansvals = if (notj) setdiff(seq_along(x), as.integer(j)) else as.integer(j)
+ }
+ if (!length(ansvals)) return(null.data.table())
+ } else { # with=TRUE and byjoin could be TRUE
+ bynames = NULL
+ allbyvars = NULL
+ if (byjoin) {
+ bynames = names(x)[rightcols]
+ } else if (!missing(by)) {
+ # deal with by before j because we need byvars when j contains .SD
+ # may evaluate to NULL | character() | "" | list(), likely a result of a user expression where no-grouping is one case being loop'd through
+ bysubl = as.list.default(bysub)
+ bysuborig = bysub
+ if (is.name(bysub) && !(as.character(bysub) %chin% names(x))) { # TO DO: names(x),names(i),and i. and x. prefixes
+ bysub = eval(bysub, parent.frame(), parent.frame())
+ # fix for # 5106 - http://stackoverflow.com/questions/19983423/why-by-on-a-vector-not-from-a-data-table-column-is-very-slow
+ # case where by=y where y is not a column name, and not a call/symbol/expression, but an atomic vector outside of DT.
+ # note that if y is a list, this'll return an error (not sure if it should).
+ if (is.atomic(bysub)) bysubl = list(bysuborig) else bysubl = as.list.default(bysub)
+ }
+ if (length(bysubl) && identical(bysubl[[1L]],quote(eval))) { # TO DO: or by=..()
+ bysub = eval(bysubl[[2]], parent.frame(), parent.frame())
+ bysub = replace_dot(bysub) # fix for #1298
+ if (is.expression(bysub)) bysub=bysub[[1L]]
+ bysubl = as.list.default(bysub)
+ } else if (is.call(bysub) && as.character(bysub[[1L]]) %chin% c("c","key","names", "intersect", "setdiff")) {
+ # catch common cases, so we don't have to copy x[irows] for all columns
+ # *** TO DO ***: try() this eval first (as long as not list() or .()) and see if it evaluates to column names
+ # to avoid the explicit c,key,names which already misses paste("V",1:10) for example
+ # tried before but since not wrapped in try() it failed on some tests
+ # or look for column names used in this by (since if none it wouldn't find column names anyway
+ # when evaled within full x[irows]). Trouble is that colA%%2L is a call and should be within frame.
+ tt = eval(bysub, parent.frame(), parent.frame())
+ if (!is.character(tt)) stop("by=c(...), key(...) or names(...) must evaluate to 'character'")
+ bysub=tt
+ } else if (is.call(bysub) && !as.character(bysub[[1L]]) %chin% c("list", "as.list", "{", ".")) {
+ # potential use of function, ex: by=month(date). catch it and wrap with "(", because we need to set "bysameorder" to FALSE as we don't know if the function will return ordered results just because "date" is ordered. Fixes #2670.
+ bysub = as.call(c(as.name('('), list(bysub)))
+ bysubl = as.list.default(bysub)
+ } else if (is.call(bysub) && bysub[[1L]] == ".") bysub[[1L]] = quote(list)
+
+ if (mode(bysub) == "character") {
+ if (length(grep(",",bysub))) {
+ if (length(bysub)>1L) stop("'by' is a character vector length ",length(bysub)," but one or more items include a comma. Either pass a vector of column names (which can contain spaces, but no commas), or pass a vector length 1 containing comma separated column names. See ?data.table for other possibilities.")
+ bysub = strsplit(bysub,split=",")[[1L]]
+ }
+ tt = grep("^[^`]+$",bysub)
+ if (length(tt)) bysub[tt] = paste("`",bysub[tt],"`",sep="")
+ bysub = parse(text=paste("list(",paste(bysub,collapse=","),")",sep=""))[[1L]]
+ bysubl = as.list.default(bysub)
+ }
+ allbyvars = intersect(all.vars(construct(bysubl), FALSE),names(x))
+
+ orderedirows = .Call(CisOrderedSubset, irows, nrow(x)) # TRUE when irows is NULL (i.e. no i clause)
+ # orderedirows = is.sorted(f__)
+ bysameorder = orderedirows && haskey(x) && all(sapply(bysubl,is.name)) && length(allbyvars) && identical(allbyvars,head(key(x),length(allbyvars)))
+ if (is.null(irows))
+ byval = eval(bysub, x, parent.frame())
+ else {
+ if (!is.integer(irows)) stop("Internal error: irows isn't integer") # length 0 when i returns no rows
+ # Passing irows as i to x[] below has been troublesome in a rare edge case.
+ # irows may contain NA, 0, negatives and >nrow(x) here. That's all ok.
+ # But we may need i join column values to be retained (where those rows have no match), hence we tried eval(isub)
+ # in 1.8.3, but this failed test 876.
+ # TO DO: Add a test like X[i,sum(v),by=i.x2], or where by includes a join column (both where some i don't match).
+ # TO DO: Make xss directly, rather than recursive call.
+ if (!is.na(nomatch)) irows = irows[irows!=0L] # TO DO: can be removed now we have CisSortedSubset
+ if (length(allbyvars)) { ############### TO DO TO DO TO DO ###############
+ if (verbose) cat("i clause present and columns used in by detected, only these subset:",paste(allbyvars,collapse=","),"\n")
+ xss = x[irows,allbyvars,with=FALSE,nomatch=nomatch,mult=mult,roll=roll,rollends=rollends]
+ } else {
+ if (verbose) cat("i clause present but columns used in by not detected. Having to subset all columns before evaluating 'by': '",deparse(by),"'\n",sep="")
+ xss = x[irows,nomatch=nomatch,mult=mult,roll=roll,rollends=rollends]
+ }
+ byval = eval(bysub, xss, parent.frame())
+ xnrow = nrow(xss)
+ # TO DO: pass xss (x subset) through into dogroups. Still need irows there (for :=), but more condense
+ # and contiguous to use xss to form .SD in dogroups than going via irows
+ }
+ if (!length(byval) && xnrow>0L) {
+ # see missing(by) up above for comments
+ # by could be NULL or character(0) for example (e.g. passed in as argument in a loop of different bys)
+ bysameorder = FALSE # 1st and only group is the entire table, so could be TRUE, but FALSE to avoid
+ # a key of empty character()
+ byval = list()
+ bynames = allbyvars = NULL
+ # the rest now fall through
+ } else bynames = names(byval)
+ if (is.atomic(byval)) {
+ if (is.character(byval) && length(byval)<=ncol(x) && !(is.name(bysub) && as.character(bysub)%chin%names(x)) ) {
+ stop("'by' appears to evaluate to column names but isn't c() or key(). Use by=list(...) if you can. Otherwise, by=eval",deparse(bysub)," should work. This is for efficiency so data.table can detect which columns are needed.")
+ } else {
+ # by may be a single unquoted column name but it must evaluate to list so this is a convenience to users. Could also be a single expression here such as DT[,sum(v),by=colA%%2]
+ byval = list(byval)
+ bysubl = c(as.name("list"),bysuborig) # for guessing the column name below
+ if (is.name(bysuborig))
+ bynames = as.character(bysuborig)
+ else
+ bynames = names(byval)
+ }
+ }
+ if (!is.list(byval)) stop("'by' or 'keyby' must evaluate to vector or list of vectors (where 'list' includes data.table and data.frame which are lists, too)")
+ for (jj in seq_len(length(byval))) {
+ if (!typeof(byval[[jj]]) %chin% c("integer","logical","character","double")) stop("column or expression ",jj," of 'by' or 'keyby' is type ",typeof(byval[[jj]]),". Do not quote column names. Usage: DT[,sum(colC),by=list(colA,month(colB))]")
+ }
+ tt = sapply(byval,length)
+ if (any(tt!=xnrow)) stop("The items in the 'by' or 'keyby' list are length (",paste(tt,collapse=","),"). Each must be same length as rows in x or number of rows returned by i (",xnrow,").")
+ if (is.null(bynames)) bynames = rep.int("",length(byval))
+ if (any(bynames=="")) {
+ if (length(bysubl)<2) stop("When 'by' or 'keyby' is list() we expect something inside the brackets")
+ for (jj in seq_along(bynames)) {
+ if (bynames[jj]=="") {
+ # Best guess. Use "month" in the case of by=month(date), use "a" in the case of by=a%%2
+ byvars = all.vars(bysubl[[jj+1L]], functions = TRUE)
+ if (length(byvars) == 1) tt = byvars
+ else {
+ tt = grep("^eval|^[^[:alpha:]. ]",byvars,invert=TRUE,value=TRUE)[1L]
+ if (!length(tt)) tt = all.vars(bysubl[[jj+1L]])[1L]
+ }
+ # fix for #497
+ if (length(byvars) > 1L && tt %in% all.vars(jsub, FALSE)) {
+ bynames[jj] = deparse(bysubl[[jj+1L]])
+ if (verbose)
+ cat("by-expression '", bynames[jj], "' is not named, and the auto-generated name '", tt, "' clashed with variable(s) in j. Therefore assigning the entire by-expression as name.\n", sep="")
+
+ }
+ else bynames[jj] = tt
+ # if user doesn't like this inferred name, user has to use by=list() to name the column
+ }
+ }
+ }
+ setattr(byval, "names", bynames) # byval is just a list not a data.table hence setattr not setnames
+ }
+
+ jvnames = NULL
+ if (is.name(jsub)) {
+ # j is a single unquoted column name
+ if (jsub!=".SD") {
+ jvnames = gsub("^[.](N|I|GRP|BY)$","\\1",as.character(jsub))
+ # jsub is list()ed after it's eval'd inside dogroups.
+ }
+ } else if (is.call(jsub) && as.character(jsub[[1L]]) %chin% c("list",".")) {
+ jsub[[1L]] = quote(list)
+ jsubl = as.list.default(jsub) # TO DO: names(jsub) and names(jsub)="" seem to work so make use of that
+ if (length(jsubl)>1) {
+ jvnames = names(jsubl)[-1L] # check list(a=sum(v),v)
+ if (is.null(jvnames)) jvnames = rep.int("", length(jsubl)-1L)
+ for (jj in seq.int(2L,length(jsubl))) {
+ if (jvnames[jj-1L] == "" && mode(jsubl[[jj]])=="name")
+ jvnames[jj-1L] = gsub("^[.](N|I|GRP|BY)$","\\1",deparse(jsubl[[jj]]))
+ # TO DO: if call to a[1] for example, then call it 'a' too
+ }
+ setattr(jsubl, "names", NULL) # drops the names from the list so it's faster to eval the j for each group. We'll put them back aftwards on the result.
+ jsub = as.call(jsubl)
+ } # else empty list is needed for test 468: adding an empty list column
+ } # else maybe a call to transform or something which returns a list.
+ av = all.vars(jsub,TRUE) # TRUE fixes bug #1294 which didn't see b in j=fns[[b]](c)
+ if (".SD" %chin% av) {
+ if (missing(.SDcols)) {
+ # here we need to use 'dupdiff' instead of 'setdiff'. Ex: setdiff(c("x", "x"), NULL) will give 'x'.
+ ansvars = dupdiff(names(x),union(bynames,allbyvars)) # TO DO: allbyvars here for vars used by 'by'. Document.
+ # just using .SD in j triggers all non-by columns in the subset even if some of
+ # those columns are not used. It would be tricky to detect whether the j expression
+ # really does use all of the .SD columns or not, hence .SDcols for grouping
+ # over a subset of columns
+
+ # all duplicate columns must be matched, because nothing is provided
+ ansvals = dupmatch(ansvars, names(x))
+ } else {
+ # FR #4979 - negative numeric and character indices for SDcols
+ colsub = substitute(.SDcols)
+ # fix for #5190. colsub[[1L]] gave error when it's a symbol.
+ if (is.call(colsub) && deparse(colsub[[1L]], 500L) %in% c("!", "-")) {
+ colm = TRUE
+ colsub = colsub[[2L]]
+ } else colm = FALSE
+ # fix for #1216, make sure the paranthesis are peeled from expr of the form (((1:4)))
+ while(is.call(colsub) && colsub[[1L]] == "(") colsub = as.list(colsub)[[-1L]]
+ if (is.call(colsub) && length(colsub) == 3L && colsub[[1L]] == ":") {
+ # .SDcols is of the format a:b
+ .SDcols = eval(colsub, setattr(as.list(seq_along(x)), 'names', names(x)), parent.frame())
+ } else {
+ .SDcols = eval(colsub, parent.frame(), parent.frame())
+ }
+ if (is.logical(.SDcols)) {
+ ansvals = which_(rep(.SDcols, length.out=length(x)), !colm)
+ ansvars = names(x)[ansvals]
+ } else if (is.numeric(.SDcols)) {
+ # if .SDcols is numeric, use 'dupdiff' instead of 'setdiff'
+ if (length(unique(sign(.SDcols))) != 1L) stop(".SDcols is numeric but has both +ve and -ve indices")
+ if (any(is.na(.SDcols)) || any(abs(.SDcols)>ncol(x)) || any(abs(.SDcols)<1L)) stop(".SDcols is numeric but out of bounds (or NA)")
+ if (colm) ansvars = dupdiff(names(x)[-.SDcols], bynames) else ansvars = names(x)[.SDcols]
+ ansvals = if (colm) setdiff(seq_along(names(x)), c(as.integer(.SDcols), which(names(x) %chin% bynames))) else as.integer(.SDcols)
+ } else {
+ if (!is.character(.SDcols)) stop(".SDcols should be column numbers or names")
+ if (any(is.na(.SDcols)) || any(!.SDcols %chin% names(x))) stop("Some items of .SDcols are not column names (or are NA)")
+ if (colm) ansvars = setdiff(setdiff(names(x), .SDcols), bynames) else ansvars = .SDcols
+ # dups = FALSE here. DT[, .SD, .SDcols=c("x", "x")] again doesn't really help with which 'x' to keep (and if '-' which x to remove)
+ ansvals = chmatch(ansvars, names(x))
+ }
+ # .SDcols might include grouping columns if users wants that, but normally we expect user not to include them in .SDcols
+ }
+ } else {
+ if (!missing(.SDcols)) warning("This j doesn't use .SD but .SDcols has been supplied. Ignoring .SDcols. See ?data.table.")
+ ansvars = setdiff(intersect(av,c(names(x),names(i),paste("i.",names(i),sep=""))), bynames)
+ if (verbose) cat("Detected that j uses these columns:",if (!length(ansvars)) "<none>" else paste(ansvars,collapse=","),"\n")
+ # using a few named columns will be faster
+ # Consider: DT[,max(diff(date)),by=list(month=month(date))]
+ # and: DT[,lapply(.SD,sum),by=month(date)]
+ # We don't want date in .SD in the latter, but we do in the former; hence the union() above.
+ ansvals = chmatch(ansvars, names(x))
+ }
+ # if (!length(ansvars)) Leave ansvars empty. Important for test 607.
+ # added 'mget' - fix for #994
+ if (any(c("get", "mget") %chin% av)) {
+ if (verbose) {
+ cat("'(m)get' found in j. ansvars being set to all columns. Use .SDcols or eval(macro) instead. Both will detect the columns used which is important for efficiency.\nOld:", paste(ansvars,collapse=","),"\n")
+ # get('varname') is too difficult to detect which columns are used in general
+ # eval(macro) column names are detected via the if jsub[[1]]==eval switch earlier above.
+ }
+ ansvars = setdiff(c(names(x), if (is.data.table(i)) c(names(i), paste("i.", names(i), sep=""))),bynames) # fix for bug #5443
+ ansvals = chmatch(ansvars, names(x))
+ if (verbose) cat("New:",paste(ansvars,collapse=","),"\n")
+ }
+
+ lhs = NULL
+ newnames = NULL
+ suppPrint = identity
+ if (length(av) && av[1L] == ":=") {
+ if (identical(attr(x,".data.table.locked"),TRUE)) stop(".SD is locked. Using := in .SD's j is reserved for possible future use; a tortuously flexible way to modify by group. Use := in j directly to modify by group by reference.")
+ suppPrint <- function(x) { .global$print=address(x); x }
+ # Suppress print when returns ok not on error, bug #2376. Thanks to: http://stackoverflow.com/a/13606880/403310
+ # All appropriate returns following this point are wrapped; i.e. return(suppPrint(x)).
+
+ # FR #4996 - verbose message and return when a join matches nothing with `:=` in j
+ if (byjoin & !notjoin) {
+ # Note: !notjoin is here only until the notjoin is implemented as a "proper" byjoin
+ if ((all(is.na(f__)) | (all(f__ == 0L) & nomatch == 0L))) {
+ if (verbose) cat("No rows pass i clause so quitting := early with no changes made.\n")
+ return(suppPrint(x))
+ }
+ }
+ if (!is.null(irows)) {
+ if (!length(irows)) {
+ if (verbose) cat("No rows pass i clause so quitting := early with no changes made.\n")
+ return(suppPrint(x))
+ } else
+ if (!with) irows <- irows[!is.na(irows)] # fixes 2445. TO DO: added a message if(verbose) or warning?
+ if (!missing(keyby)) stop("When i is present, keyby := on a subset of rows doesn't make sense. Either change keyby to by, or remove i")
+ }
+ if (is.null(names(jsub))) {
+ # regular LHS:=RHS usage, or `:=`(...) with no named arguments (an error)
+ # `:=`(LHS,RHS) is valid though, but more because can't see how to detect that, than desire
+ if (length(jsub)!=3L) stop("In `:=`(col1=val1, col2=val2, ...) form, all arguments must be named.")
+ lhs = jsub[[2]]
+ jsub = jsub[[3]]
+ if (is.name(lhs)) {
+ lhs = as.character(lhs)
+ } else {
+ # e.g. (MyVar):= or get("MyVar"):=
+ lhs = eval(lhs, parent.frame(), parent.frame())
+ }
+ } else {
+ # `:=`(c2=1L,c3=2L,...)
+ lhs = names(jsub)[-1]
+ if (any(lhs=="")) stop("In `:=`(col1=val1, col2=val2, ...) form, all arguments must be named.")
+ names(jsub)=""
+ jsub[[1]]=as.name("list")
+ }
+ av = all.vars(jsub,TRUE)
+ if (!is.atomic(lhs)) stop("LHS of := must be a symbol, or an atomic vector (column names or positions).")
+ if (is.character(lhs))
+ m = chmatch(lhs,names(x))
+ else if (is.numeric(lhs)) {
+ m = as.integer(lhs)
+ if (any(m<1L | ncol(x)<m)) stop("LHS of := appears to be column positions but are outside [1,ncol] range. New columns can only be added by name.")
+ lhs = names(x)[m]
+ } else
+ stop("LHS of := isn't column names ('character') or positions ('integer' or 'numeric')")
+ if (all(!is.na(m))) {
+ # updates by reference to existing columns
+ cols = as.integer(m)
+ newnames=NULL
+ } else {
+ # Adding new column(s). TO DO: move after the first eval in case the jsub has an error.
+ newnames=setdiff(lhs,names(x))
+ m[is.na(m)] = ncol(x)+seq_len(length(newnames))
+ cols = as.integer(m)
+ if ((ok<-selfrefok(x,verbose))==0L) # ok==0 so no warning when loaded from disk (-1) [-1 considered TRUE by R]
+ warning("Invalid .internal.selfref detected and fixed by taking a (shallow) copy of the data.table so that := can add this new column by reference. At an earlier point, this data.table has been copied by R (or been created manually using structure() or similar). Avoid key<-, names<- and attr<- which in R currently (and oddly) may copy the whole data.table. Use set* syntax instead to avoid copying: ?set, ?setnames and ?setattr. Also, in R<=v3.0.2, list(DT1,DT2) cop [...]
+ if ((ok<1L) || (truelength(x) < ncol(x)+length(newnames))) {
+ n = max(ncol(x)+100, ncol(x)+2*length(newnames))
+ name = substitute(x)
+ if (is.name(name) && ok && verbose) { # && NAMED(x)>0 (TO DO) # ok here includes -1 (loaded from disk)
+ cat("Growing vector of column pointers from truelength ",truelength(x)," to ",n,". A shallow copy has been taken, see ?alloc.col. Only a potential issue if two variables point to the same data (we can't yet detect that well) and if not you can safely ignore this. To avoid this message you could alloc.col() first, deep copy first using copy(), wrap with suppressWarnings() or increase the 'datatable.alloccol' option.\n")
+ # Verbosity should not issue warnings, so cat rather than warning.
+ # TO DO: Add option 'datatable.pedantic' to turn on warnings like this.
+
+ # TO DO ... comments moved up from C ...
+ # Note that the NAMED(dt)>1 doesn't work because .Call
+ # always sets to 2 (see R-ints), it seems. Work around
+ # may be possible but not yet working. When the NAMED test works, we can drop allocwarn argument too
+ # because that's just passed in as FALSE from [<- where we know `*tmp*` isn't really NAMED=2.
+ # Note also that this growing will happen for missing columns assigned NULL, too. But so rare, we
+ # don't mind.
+ }
+ alloc.col(x, n, verbose=verbose) # always assigns to calling scope; i.e. this scope
+ if (is.name(name)) {
+ assign(as.character(name),x,parent.frame(),inherits=TRUE)
+ } else if (is.call(name) && (name[[1L]] == "$" || name[[1L]] == "[[") && is.name(name[[2L]])) {
+ k = eval(name[[2L]], parent.frame(), parent.frame())
+ if (is.list(k)) {
+ origj = j = if (name[[1L]] == "$") as.character(name[[3L]]) else eval(name[[3L]], parent.frame(), parent.frame())
+ if (is.character(j)) {
+ if (length(j)!=1L) stop("L[[i]][,:=] syntax only valid when i is length 1, but it's length %d",length(j))
+ j = match(j, names(k))
+ if (is.na(j)) stop("Item '",origj,"' not found in names of list")
+ }
+ .Call(Csetlistelt,k,as.integer(j), x)
+ } else if (is.environment(k) && exists(as.character(name[[3L]]), k)) {
+ assign(as.character(name[[3L]]), x, k, inherits=FALSE)
+ }
+ } # TO DO: else if env$<- or list$<-
+ }
+ }
+ }
+ }
+
+ if (length(ansvars)) {
+ w = ansvals
+ if (length(rightcols) && missing(by)) w[ w %in% rightcols ] = NA
+ if (!any(wna <- is.na(w))) {
+ xcols = w
+ xcolsAns = seq_along(ansvars)
+ icols = icolsAns = integer()
+ } else {
+ if (!length(leftcols)) stop("column(s) not found: ", paste(ansvars[wna],collapse=", "))
+ xcols = w[!wna]
+ xcolsAns = which(!wna)
+ ivars = names(i)
+ ivars[leftcols] = names(x)[rightcols]
+ w2 = chmatch(ansvars[wna], ivars)
+ if (any(w2na <- is.na(w2))) {
+ ivars = paste("i.",ivars,sep="")
+ ivars[leftcols] = names(i)[leftcols]
+ w2[w2na] = chmatch(ansvars[wna][w2na], ivars)
+ if (any(w2na <- is.na(w2))) {
+ ivars[leftcols] = paste("i.",ivars[leftcols],sep="")
+ w2[w2na] = chmatch(ansvars[wna][w2na], ivars)
+ if (any(w2na <- is.na(w2))) stop("column(s) not found: ", paste(ansvars[wna][w2na],sep=", "))
+ }
+ }
+ icols = w2
+ icolsAns = which(wna)
+ }
+ }
+ } # end of if !missing(j)
+
+ SDenv = new.env(parent=parent.frame())
+ # hash=TRUE (the default) does seem better as expected using e.g. test 645. TO DO experiment with 'size' argument
+
+ if (missing(by) || (!byjoin && !length(byval))) {
+ # No grouping: 'by' = missing | NULL | character() | "" | list()
+ # Considered passing a one-group to dogroups but it doesn't do the recycling of i within group, that's done here
+ if (length(ansvars)) {
+ # TO DO: port more of this to C
+ ans = vector("list", length(ansvars))
+ if (length(i) && length(icols)) {
+ if (allLen1 && (is.na(nomatch) || !any(f__==0L))) { # nomatch=0 should drop rows in i that have no match
+ for (s in seq_along(icols)) {
+ target = icolsAns[s]
+ source = icols[s]
+ ans[[target]] = i[[source]]
+ if (address(ans[[target]]) == address(i[[source]])) ans[[target]] = copy(ans[[target]])
+ }
+ } else {
+ ii = rep.int(seq_len(nrow(i)),len__)
+ for (s in seq_along(icols)) {
+ target = icolsAns[s]
+ source = icols[s]
+ ans[[target]] = .Call(CsubsetVector,i[[source]],ii) # i.e. i[[source]][ii]
+ }
+ }
+ }
+ if (is.null(irows)) {
+ for (s in seq_along(xcols)) { # xcols means non-join x columns, since join columns come from i
+ target = xcolsAns[s]
+ source = xcols[s]
+ ans[[target]] = x[[source]]
+ # Temp fix for #921 - skip COPY until after evaluating 'jval' (scroll down).
+ # Unless 'with=FALSE' - can not be expressions but just column names.
+ if (!with && address(ans[[target]]) == address(x[[source]]))
+ ans[[target]] = copy(ans[[target]])
+ else ans[[target]] = ans[[target]]
+ }
+ } else {
+ for (s in seq_along(xcols)) {
+ target = xcolsAns[s]
+ source = xcols[s]
+ ans[[target]] = .Call(CsubsetVector,x[[source]],irows) # i.e. x[[source]][irows], but guaranteed new memory even for singleton logicals from R 3.1.0
+ }
+ }
+ # the address==address is a temp fix for R >= 3.1.0. TO DO: allow shallow copy here, then copy only when user uses :=
+ # or set* on the result by using NAMED/REFCNT on columns, with warning if they copy. Since then, even foo = DT$b
+ # would cause the next set or := to copy that column (so the warning is needed). To tackle that, we could have our
+ # own DT.NAMED attribute, perhaps.
+ # Or keep the rule that [.data.table always returns new memory, and create view() or view= as well, maybe cleaner.
+
+ setattr(ans, "names", ansvars)
+ if (haskey(x)) {
+ keylen = which.first(!key(x) %chin% ansvars)-1L
+ if (is.na(keylen)) keylen = length(key(x))
+ if (keylen > length(rightcols) && !.Call(CisOrderedSubset, irows, nrow(x))) keylen = length(rightcols)
+ if (keylen && ((is.data.table(i) && haskey(i)) || is.logical(i) || (.Call(CisOrderedSubset, irows, nrow(x)) && ((roll == FALSE) || length(irows) == 1L)))) # see #1010. don't set key when i has no key, but irows is ordered and roll != FALSE
+ setattr(ans,"sorted",head(key(x),keylen))
+ }
+ setattr(ans, "class", class(x)) # fix for #5296
+ setattr(ans, "row.names", .set_row_names(nrow(ans)))
+
+ if (!with || missing(j)) return(alloc.col(ans))
+
+ SDenv$.SD = ans
+ SDenv$.N = nrow(SDenv$.SD)
+
+ } else {
+ SDenv$.SD = null.data.table() # no columns used by j so .SD can be empty. Only needs to exist so that we can rely on it being there when locking it below for example. If .SD were used by j, of course then xvars would be the columns and we wouldn't be in this leaf.
+ SDenv$.N = if (is.null(irows)) nrow(x) else length(irows) * !identical(suppressWarnings(max(irows)), 0L)
+ # Fix for #963.
+ # When irows is integer(0), length(irows) = 0 will result in 0 (as expected).
+ # Binary search can return all 0 irows when none of the input matches. Instead of doing all(irows==0L) (previous method), which has to allocate a logical vector the size of irows, we can make use of 'max'. If max is 0, we return 0. The condition where only some irows > 0 won't occur.
+ }
+ # Temp fix for #921. Allocate `.I` only if j-expression uses it.
+ SDenv$.I = if (!missing(j) && ".I" %chin% av) seq_len(SDenv$.N) else 0L
+ SDenv$.GRP = 1L
+ setattr(SDenv$.SD,".data.table.locked",TRUE) # used to stop := modifying .SD via j=f(.SD), bug#1727. The more common case of j=.SD[,subcol:=1] was already caught when jsub is inspected for :=.
+ lockBinding(".SD",SDenv)
+ lockBinding(".N",SDenv)
+ lockBinding(".I",SDenv)
+ lockBinding(".GRP",SDenv)
+ for (ii in ansvars) assign(ii, SDenv$.SD[[ii]], SDenv)
+ # Since .SD is inside SDenv, alongside its columns as variables, R finds .SD symbol more quickly, if used.
+ # There isn't a copy of the columns here, the xvar symbols point to the SD columns (copy-on-write).
+
+ # Temp fix for #921 - check address and copy *after* evaluating 'jval'
+ jval = eval(jsub, SDenv, parent.frame())
+ # copy 'jval' when required
+ # More speedup - only check + copy if irows is NULL
+ if (is.null(irows)) {
+ if (is.atomic(jval)) {
+ jcpy = address(jval) %in% sapply(SDenv$.SD, address) # %chin% errors when RHS is list()
+ if (jcpy) jval = copy(jval)
+ } else if (address(jval) == address(SDenv$.SD)) {
+ jval = copy(jval)
+ } else if ( length(jcpy <- which(sapply(jval, address) %in% sapply(SDenv, address))) ) {
+ for (jidx in jcpy) jval[[jidx]] = copy(jval[[jidx]])
+ }
+ }
+
+ if (!is.null(lhs)) { # *** TO DO ***: use set() here now that it can add new column(s) and remove newnames and alloc logic above
+ if (verbose) cat("Assigning to ",if (is.null(irows)) "all " else paste(length(irows),"row subset of "), nrow(x)," rows\n",sep="")
+ .Call(Cassign,x,irows,cols,newnames,jval,verbose)
+ return(suppPrint(x))
+ }
+ if ((is.call(jsub) && is.list(jval) && !is.object(jval)) || !missing(by)) {
+ # is.call: selecting from a list column should return list
+ # is.object: for test 168 and 168.1 (S4 object result from ggplot2::qplot). Just plain list results should result in data.table
+
+ # Fix for #813 and #758. Ex: DT[c(FALSE, FALSE), list(integer(0), y)]
+ # where DT = data.table(x=1:2, y=3:4) should return an empty data.table!!
+ if (!is.null(irows) && (identical(irows, integer(0)) || all(irows %in% 0L))) ## TODO: any way to not check all 'irows' values?
+ if (is.atomic(jval)) jval = jval[0L] else jval = lapply(jval, `[`, 0L)
+ if (is.atomic(jval)) {
+ setattr(jval,"names",NULL)
+ jval = data.table(jval) # TO DO: should this be setDT(list(jval)) instead?
+ } else {
+ if (is.null(jvnames)) jvnames=names(jval)
+ # avoid copy if all vectors are already of same lengths, use setDT
+ lenjval = vapply(jval, length, 0L)
+ if (any(lenjval != lenjval[1L]))
+ jval = as.data.table.list(jval) # does the vector expansion to create equal length vectors
+ else setDT(jval)
+ }
+ if (is.null(jvnames)) jvnames = character(length(jval)-length(bynames))
+ ww = which(jvnames=="")
+ if (any(ww)) jvnames[ww] = paste("V",ww,sep="")
+ setnames(jval, jvnames)
+ }
+
+ # fix for bug #5114 from GSee's - .data.table.locked=TRUE. # TO DO: more efficient way e.g. address==address (identical will do that but then proceed to deep compare if !=, wheras we want just to stop?)
+ # Commented as it's taken care of above, along with #921 fix. Kept here for the bug fix info and TO DO.
+ # if (identical(jval, SDenv$.SD)) return(copy(jval))
+
+ if (is.data.table(jval)) {
+ setattr(jval, 'class', class(x)) # fix for #5296
+ if (haskey(x) && all(key(x) %chin% names(jval)) && suppressWarnings(is.sorted(jval, by=key(x)))) # TO DO: perhaps this usage of is.sorted should be allowed internally then (tidy up and make efficient)
+ setattr(jval, 'sorted', key(x))
+ }
+ return(jval)
+ }
+
+ ###########################################################################
+ # Grouping ...
+ ###########################################################################
+
+ o__ = integer()
+ if (".N" %chin% ansvars) stop("The column '.N' can't be grouped because it conflicts with the special .N variable. Try setnames(DT,'.N','N') first.")
+ if (".I" %chin% ansvars) stop("The column '.I' can't be grouped because it conflicts with the special .I variable. Try setnames(DT,'.I','I') first.")
+ SDenv$.iSD = NULL # null.data.table()
+ SDenv$.xSD = NULL # null.data.table() - introducing for FR #2693 and Gabor's post on fixing for FAQ 2.8
+
+ assign("print", function(x,...){base::print(x,...);NULL}, SDenv)
+ # Now ggplot2 returns data from print, we need a way to throw it away otherwise j accumulates the result
+
+ SDenv$.SD = null.data.table() # e.g. test 607. Grouping still proceeds even though no .SD e.g. grouping key only tables, or where j consists of .N only
+ SDenv$.N = as.integer(0) # not 0L for the reson on next line :
+ SDenv$.GRP = as.integer(1) # oddly using 1L doesn't work reliably here! Possible R bug? TO DO: create reproducible example and report. To reproduce change to 1L and run test.data.table, test 780 fails. The assign seems ineffective and a previous value for .GRP from a previous test is retained, despite just creating a new SDenv.
+
+ if (byjoin) {
+ # The groupings come instead from each row of the i data.table.
+ # Much faster for a few known groups vs a 'by' for all followed by a subset
+ if (!is.data.table(i)) stop("logicial error. i is not data.table, but mult='all' and 'by'=.EACHI")
+ byval = i
+ bynames = if (missing(on)) head(key(x),length(leftcols)) else names(on)
+ allbyvars = NULL
+ bysameorder = haskey(i) || (is.sorted(f__) && ((roll == FALSE) || length(f__) == 1L)) # Fix for #1010
+ ## 'av' correct here ?? *** TO DO ***
+ xjisvars = intersect(av, names(x)[rightcols]) # no "x." for xvars.
+ # if 'get' is in 'av' use all cols in 'i', fix for bug #5443
+ # added 'mget' - fix for #994
+ jisvars = if (any(c("get", "mget") %chin% av)) names(i) else intersect(gsub("^i[.]","", setdiff(av, xjisvars)), names(i))
+ # JIS (non join cols) but includes join columns too (as there are named in i)
+ if (length(jisvars)) {
+ tt = min(nrow(i),1L)
+ SDenv$.iSD = i[tt,jisvars,with=FALSE]
+ for (ii in jisvars) {
+ assign(ii, SDenv$.iSD[[ii]], SDenv)
+ assign(paste("i.",ii,sep=""), SDenv$.iSD[[ii]], SDenv)
+ }
+ }
+
+ } else {
+ # Find the groups, using 'byval' ...
+ if (missing(by)) stop("Internal error, by is missing")
+ if (verbose) {last.started.at=proc.time()[3];cat("Finding groups (bysameorder=",bysameorder,") ... ",sep="");flush.console()}
+ if (length(byval) && length(byval[[1]])) {
+ if (!bysameorder) {
+ o__ = forderv(byval, sort=FALSE, retGrp=TRUE) # returns integer() (not NULL) if already ordered, to save 1:xnrow for efficiency
+ bysameorder = orderedirows && !length(o__)
+ f__ = attr(o__, "starts")
+ len__ = uniqlengths(f__, xnrow)
+ if (!bysameorder) { # TO DO: lower this into forder.c
+ firstofeachgroup = o__[f__]
+ if (length(origorder <- forderv(firstofeachgroup))) {
+ f__ = f__[origorder]
+ len__ = len__[origorder]
+ }
+ }
+ if (!orderedirows && !length(o__)) o__ = 1:xnrow # temp fix. TO DO: revist orderedirows
+ } else {
+ f__ = uniqlist(byval)
+ len__ = uniqlengths(f__, xnrow)
+ # TO DO: combine uniqlist and uniquelengths into one call. Or, just set len__ to NULL when dogroups infers that.
+ }
+ } else {
+ f__=NULL
+ len__=0L
+ bysameorder=TRUE # for test 724
+ }
+ if (verbose) {cat("done in ",round(proc.time()[3]-last.started.at,3),"secs. bysameorder=",bysameorder," and o__ is length ",length(o__),"\n",sep="");flush.console}
+ # TO DO: allow secondary keys to be stored, then we see if our by matches one, if so use it, and no need to sort again. TO DO: document multiple keys.
+ }
+ alloc = if (length(len__)) seq_len(max(len__)) else 0L
+ SDenv$.I = alloc
+ if (length(xcols)) {
+ SDenv$.SD = .Call(CsubsetDT,x,alloc,xcols) # i.e. x[alloc, xcols, with=FALSE] but without recursive overhead
+ # Must not shallow copy here. This is the allocation for the largest group. Since i=alloc is passed in here, it won't shallow copy, even in future. Only DT[,xvars,with=FALSE] might ever shallow copy automatically.
+ }
+ if (nrow(SDenv$.SD)==0L) setattr(SDenv$.SD,"row.names",c(NA_integer_,0L))
+ # .set_row_names() basically other than not integer() for 0 length, otherwise dogroups has no [1] to modify to -.N
+ setattr(SDenv$.SD,".data.table.locked",TRUE) # used to stop := modifying .SD via j=f(.SD), bug#1727. The more common case of j=.SD[,subcol:=1] was already caught when jsub is inspected for :=.
+ lockBinding(".SD",SDenv)
+ lockBinding(".N",SDenv)
+ lockBinding(".GRP",SDenv)
+ lockBinding(".I",SDenv)
+ lockBinding(".iSD",SDenv)
+
+ GForce = FALSE
+ if ( (getOption("datatable.optimize")>=1 && is.call(jsub)) || (is.name(jsub) && jsub == ".SD") ) { # Ability to turn off if problems or to benchmark the benefit
+ # Optimization to reduce overhead of calling lapply over and over for each group
+ oldjsub = jsub
+ funi = 1L # Fix for #985
+ # convereted the lapply(.SD, ...) to a function and used below, easier to implement FR #2722 then.
+ .massageSD <- function(jsub) {
+ txt = as.list(jsub)[-1L]
+ if (length(names(txt))>1L) .Call(Csetcharvec, names(txt), 2L, "") # fixes bug #4839
+ fun = txt[[2L]]
+ if (is.call(fun) && fun[[1L]]=="function") {
+ # Fix for #2381: added SDenv$.SD to 'eval' to take care of cases like: lapply(.SD, function(x) weighted.mean(x, bla)) where "bla" is a column in DT
+ # http://stackoverflow.com/questions/13441868/data-table-and-stratified-means
+ # adding this does not compromise in speed (that is, not any lesser than without SDenv$.SD)
+ # replaced SDenv$.SD to SDenv to deal with Bug #5007 reported by Ricardo (Nice catch!)
+ thisfun = paste("..FUN", funi, sep="") # Fix for #985
+ assign(thisfun,eval(fun, SDenv, SDenv), SDenv) # to avoid creating function() for each column of .SD
+ lockBinding(thisfun,SDenv)
+ txt[[1L]] = as.name(thisfun)
+ } else {
+ if (is.character(fun)) fun = as.name(fun)
+ txt[[1L]] = fun
+ }
+ ans = vector("list",length(ansvars)+1L)
+ ans[[1L]] = as.name("list")
+ for (ii in seq_along(ansvars)) {
+ txt[[2L]] = as.name(ansvars[ii])
+ ans[[ii+1L]] = as.call(txt)
+ }
+ jsub = as.call(ans) # important no names here
+ jvnames = ansvars # but here instead
+ list(jsub, jvnames)
+ # It may seem inefficient to constuct a potentially long expression. But, consider calling
+ # lapply 100000 times. The C code inside lapply does the LCONS stuff anyway, every time it
+ # is called, involving small memory allocations.
+ # The R level lapply calls as.list which needs a shallow copy.
+ # lapply also does a setAttib of names (duplicating the same names over and over again
+ # for each group) which is terrible for our needs. We replace all that with a
+ # (ok, long, but not huge in memory terms) list() which is primitive (so avoids symbol
+ # lookup), and the eval() inside dogroups hardly has to do anything. All this results in
+ # overhead minimised. We don't need to worry about the env passed to the eval in a possible
+ # lapply replacement, or how to pass ... efficiently to it.
+ # Plus we optimize lapply first, so that mean() can be optimized too as well, next.
+ }
+ if (is.name(jsub)) {
+ if (jsub == ".SD") {
+ jsub = as.call(c(quote(list), lapply(ansvars, as.name)))
+ jvnames = ansvars
+ }
+ } else if ( length(jsub) == 3L && (jsub[[1L]] == "[" || jsub[[1L]] == "head") && jsub[[2L]] == ".SD" && (is.numeric(jsub[[3L]]) || jsub[[3L]] == ".N") ) {
+ # optimise .SD[1] or .SD[2L]. Not sure how to test .SD[a] as to whether a is numeric/integer or a data.table, yet.
+ jsub = as.call(c(quote(list), lapply(ansvars, function(x) { jsub[[2L]] = as.name(x); jsub })))
+ jvnames = ansvars
+ } else if (jsub[[1L]]=="lapply" && jsub[[2L]]==".SD" && length(xcols)) {
+ deparse_ans = .massageSD(jsub)
+ jsub = deparse_ans[[1L]]
+ jvnames = deparse_ans[[2L]]
+ } else if (jsub[[1L]] == "c" && length(jsub) > 1L) {
+ # TODO, TO DO: raise the checks for 'jvnames' earlier (where jvnames is set by checking 'jsub') and set 'jvnames' already.
+ # FR #2722 is just about optimisation of j=c(.N, lapply(.SD, .)) that is taken care of here.
+ # FR #735 tries to optimise j-expressions of the form c(...) as long as ... contains
+ # 1) lapply(.SD, ...), 2) simply .SD or .SD[..], 3) .N, 4) list(...) and 5) functions that normally return a single value*
+ # On 5)* the IMPORTANT point to note is that things that are not wrapped within "list(...)" should *always*
+ # return length 1 output for us to optimise. Else, there's no equivalent to optimising c(...) to list(...) AFAICT.
+ # One issue could be that these functions (e.g., mean) can be "re-defined" by the OP to produce a length > 1 output
+ # Of course this is worrying too much though. If the issue comes up, we'll just remove the relevant optimisations.
+ # For now, we optimise all functions mentioned in 'optfuns' below.
+ optfuns = c("max", "min", "mean", "length", "sum", "median", "sd", "var")
+ is_valid = TRUE
+ any_SD = FALSE
+ jsubl = as.list.default(jsub)
+ oldjvnames = jvnames
+ jvnames = NULL # TODO: not let jvnames grow, maybe use (number of lapply(.SD, .))*lenght(ansvars) + other jvars ?? not straightforward.
+ # Fix for #744. Don't use 'i' in for-loops. It masks the 'i' from the input!!
+ for (i_ in 2:length(jsubl)) {
+ this = jsub[[i_]]
+ if (is.name(this)) {
+ if (this == ".SD") { # optimise '.SD' alone
+ any_SD = TRUE
+ jsubl[[i_]] = lapply(ansvars, as.name)
+ jvnames = c(jvnames, ansvars)
+ } else if (this == ".N") {
+ # don't optimise .I in c(.SD, .I), it's length can be > 1
+ # only c(.SD, list(.I)) should be optimised!! .N is always length 1.
+ jvnames = c(jvnames, gsub("^[.]([N])$", "\\1", this))
+ } else {
+ # jvnames = c(jvnames, if (is.null(names(jsubl))) "" else names(jsubl)[i_])
+ is_valid=FALSE
+ break
+ }
+ } else if (is.call(this)) {
+ if (this[[1L]] == "lapply" && this[[2L]] == ".SD" && length(xcols)) {
+ any_SD = TRUE
+ deparse_ans = .massageSD(this)
+ funi = funi + 1L # Fix for #985
+ jsubl[[i_]] = as.list(deparse_ans[[1L]][-1L]) # just keep the '.' from list(.)
+ jvnames = c(jvnames, deparse_ans[[2L]])
+ } else if (this[[1]] == "list") {
+ # also handle c(lapply(.SD, sum), list()) - silly, yes, but can happen
+ if (length(this) > 1L) {
+ jl__ = as.list(jsubl[[i_]])[-1L] # just keep the '.' from list(.)
+ jn__ = if (is.null(names(jl__))) rep("", length(jl__)) else names(jl__)
+ idx = unlist(lapply(jl__, function(x) is.name(x) && x == ".I"))
+ if (any(idx)) jn__[idx] = ifelse(jn__[idx] == "", "I", jn__[idx])
+ jvnames = c(jvnames, jn__)
+ jsubl[[i_]] = jl__
+ }
+ } else if (is.call(this) && length(this) > 1L && as.character(this[[1L]]) %in% optfuns) {
+ jvnames = c(jvnames, if (is.null(names(jsubl))) "" else names(jsubl)[i_])
+ } else if ( length(this) == 3L && (this[[1L]] == "[" || this[[1L]] == "head") &&
+ this[[2L]] == ".SD" && (is.numeric(this[[3L]]) || this[[3L]] == ".N") ) {
+ # optimise .SD[1] or .SD[2L]. Not sure how to test .SD[a] as to whether a is numeric/integer or a data.table, yet.
+ any_SD = TRUE
+ jsubl[[i_]] = lapply(ansvars, function(x) { this[[2L]] = as.name(x); this })
+ jvnames = c(jvnames, ansvars)
+ } else if (any(all.vars(this) == ".SD")) {
+ # TODO, TO DO: revisit complex cases (as illustrated below)
+ # complex cases like DT[, c(.SD[x>1], .SD[J(.)], c(.SD), a + .SD, lapply(.SD, sum)), by=grp]
+ # hard to optimise such cases (+ difficulty in counting exact columns and therefore names). revert back to no optimisation.
+ is_valid=FALSE
+ break
+ } else { # just to be sure that any other case (I've overlooked) runs smoothly, without optimisation
+ # TO DO, TODO: maybe a message/warning here so that we can catch the overlooked cases, if any?
+ is_valid=FALSE
+ break
+ }
+ } else {
+ is_valid = FALSE
+ break
+ }
+ }
+ if (!is_valid || !any_SD) { # restore if c(...) doesn't contain lapply(.SD, ..) or if it's just invalid
+ jvnames = oldjvnames # reset jvnames
+ jsub = oldjsub # reset jsub
+ jsubl = as.list.default(jsubl) # reset jsubl
+ } else {
+ setattr(jsubl, 'names', NULL)
+ jsub = as.call(unlist(jsubl, use.names=FALSE))
+ jsub[[1L]] = quote(list)
+ }
+ }
+ if (verbose) {
+ if (!identical(oldjsub, jsub))
+ cat("lapply optimization changed j from '",deparse(oldjsub),"' to '",deparse(jsub,width.cutoff=200),"'\n",sep="")
+ else
+ cat("lapply optimization is on, j unchanged as '",deparse(jsub,width.cutoff=200),"'\n",sep="")
+ }
+ dotN <- function(x) if (is.name(x) && x == ".N") TRUE else FALSE # For #5760
+ if (getOption("datatable.optimize")>=2 && !byjoin && !length(irows) && length(f__) && length(ansvars) && !length(lhs)) {
+ # Apply GForce
+ gfuns = c("sum","mean",".N", "min", "max") # added .N for #5760
+ .ok <- function(q) {
+ if (dotN(q)) return(TRUE) # For #5760
+ ans = is.call(q) && as.character(q[[1L]]) %chin% gfuns && !is.call(q[[2L]]) && (length(q)==2 || identical("na",substring(names(q)[3L],1,2)))
+ if (is.na(ans)) ans=FALSE
+ ans
+ }
+ if (jsub[[1L]]=="list") {
+ GForce = TRUE
+ for (ii in seq_along(jsub)[-1L]) if (!.ok(jsub[[ii]])) GForce = FALSE
+ } else GForce = .ok(jsub)
+ if (GForce) {
+ if (jsub[[1L]]=="list")
+ for (ii in seq_along(jsub)[-1L]) {
+ if (dotN(jsub[[ii]])) next; # For #5760
+ jsub[[ii]][[1L]] = as.name(paste("g", jsub[[ii]][[1L]], sep=""))
+ if (length(jsub[[ii]])==3) jsub[[ii]][[3]] = eval(jsub[[ii]][[3]], parent.frame()) # tests 1187.2 & 1187.4
+ }
+ else {
+ jsub[[1L]] = as.name(paste("g", jsub[[1L]], sep=""))
+ if (length(jsub)==3) jsub[[3]] = eval(jsub[[3]], parent.frame()) # tests 1187.3 & 1187.5
+ }
+ if (verbose) cat("GForce optimized j to '",deparse(jsub,width.cutoff=200),"'\n",sep="")
+ } else if (verbose) cat("GForce is on, left j unchanged\n");
+ }
+ if (!GForce) {
+ # Still do the old speedup for mean, for now
+ nomeanopt=FALSE # to be set by .optmean() using <<- inside it
+ oldjsub = jsub
+ if (jsub[[1L]]=="list") {
+ for (ii in seq_along(jsub)[-1L]) {
+ if (dotN(jsub[[ii]])) next; # For #5760
+ if (is.call(jsub[[ii]]) && jsub[[ii]][[1L]]=="mean")
+ jsub[[ii]] = .optmean(jsub[[ii]])
+ }
+ } else if (jsub[[1L]]=="mean") {
+ jsub = .optmean(jsub)
+ }
+ if (nomeanopt) {
+ warning("Unable to optimize call to mean() and could be very slow. You must name 'na.rm' like that otherwise if you do mean(x,TRUE) the TRUE is taken to mean 'trim' which is the 2nd argument of mean. 'trim' is not yet optimized.",immediate.=TRUE)
+ }
+ if (verbose) {
+ if (!identical(oldjsub, jsub))
+ cat("Old mean optimization changed j from '",deparse(oldjsub),"' to '",deparse(jsub,width.cutoff=200),"'\n",sep="")
+ else
+ cat("Old mean optimization is on, left j unchanged.\n")
+ }
+ assign("Cfastmean", Cfastmean, SDenv)
+ assign("mean", base::mean.default, SDenv)
+ # Old comments still here for now ...
+ # Here in case nomeanopt=TRUE or some calls to mean weren't detected somehow. Better but still slow.
+ # Maybe change to :
+ # assign("mean", fastmean, SDenv) # neater than the hard work above, but slower
+ # when fastmean can do trim.
+ }
+ } else if (verbose) {
+ if (getOption("datatable.optimize")<1) cat("All optimizations are turned off\n")
+ else cat("Optimization is on but left j unchanged (single plain symbol): '",deparse(jsub,width.cutoff=200),"'\n",sep="")
+ }
+ if (byjoin) {
+ groups = i
+ grpcols = leftcols # 'leftcols' are the columns in i involved in the join (either head of key(i) or head along i)
+ jiscols = chmatch(jisvars,names(i)) # integer() if there are no jisvars (usually there aren't, advanced feature)
+ xjiscols = chmatch(xjisvars, names(x))
+ SDenv$.xSD = x[min(nrow(i), 1L), xjisvars, with=FALSE]
+ if (!missing(on)) o__ = xo else o__ = integer(0)
+ } else {
+ groups = byval
+ grpcols = seq_along(byval)
+ jiscols = NULL # NULL rather than integer() is used in C to know when using by
+ xjiscols = NULL
+ }
+ lockBinding(".xSD", SDenv)
+ grporder = o__
+ if (length(irows) && !isTRUE(irows)) {
+ # fix for bug #2758. TO DO: provide a better error message
+ if (length(irows) > 1 && length(zo__ <- which(irows == 0)) > 0) stop("i[", zo__[1], "] is 0. While grouping, i=0 is allowed when it's the only value. When length(i) > 1, all i should be > 0.")
+ if (length(o__) && length(irows)!=length(o__)) stop("Internal error: length(irows)!=length(o__)")
+ o__ = if (length(o__)) irows[o__] # better do this once up front (even though another alloc) than deep repeated branch in dogroups.c
+ else irows
+ } # else grporder is left bound to same o__ memory (no cost of copy)
+ if (is.null(lhs)) cols=NULL
+ if (!length(f__)) {
+ # for consistency of empty case in test 184
+ f__=len__=0L
+ }
+ if (GForce) {
+ thisEnv = new.env() # not parent=parent.frame() so that gsum is found
+ for (ii in ansvars) assign(ii, x[[ii]], thisEnv)
+ assign(".N", len__, thisEnv) # For #5760
+ gstart(o__, f__, len__)
+ ans = eval(jsub, thisEnv)
+ if (is.atomic(ans)) ans=list(ans) # won't copy named argument in new version of R, good
+ gend()
+ gi = if (length(o__)) o__[f__] else f__
+ g = lapply(grpcols, function(i) groups[[i]][gi])
+ ans = c(g, ans)
+ } else {
+ if (verbose) {last.started.at=proc.time()[3];cat("Starting dogroups ... ");flush.console()}
+ ans = .Call(Cdogroups, x, xcols, groups, grpcols, jiscols, xjiscols, grporder, o__, f__, len__, jsub, SDenv, cols, newnames, !missing(on), verbose)
+ if (verbose) {cat("done dogroups in",round(proc.time()[3]-last.started.at,3),"secs\n");flush.console()}
+ }
+ # TO DO: xrows would be a better name for irows: irows means the rows of x that i joins to
+ # Grouping by i: icols the joins columns (might not need), isdcols (the non join i and used by j), all __ are length x
+ # Grouping by by: i is by val, icols NULL, o__ may be subset of x, f__ points to o__ (or x if !length o__)
+ # TO DO: setkey could mark the key whether it is unique or not.
+
+ if (!is.null(lhs)) {
+ if (any(names(x)[cols] %chin% key(x)))
+ setkey(x,NULL)
+ if (!missing(keyby)) {
+ cnames = as.character(bysubl)[-1]
+ if (all(cnames %chin% names(x)))
+ setkeyv(x,cnames) # TO DO: setkey before grouping to get memcpy benefit.
+ else warning(":= keyby not straightforward character column names or list() of column names, treating as a by:",paste(cnames,collapse=","),"\n")
+ }
+ return(suppPrint(x))
+ }
+ if (is.null(ans)) {
+ ans = as.data.table.list(lapply(groups,"[",0L)) # side-effects only such as test 168
+ setnames(ans,seq_along(bynames),bynames) # TO DO: why doesn't groups have bynames in the first place?
+ return(ans)
+ }
+ setattr(ans,"row.names",.set_row_names(length(ans[[1L]])))
+ setattr(ans,"class",class(x)) # fix for #5296
+ if (is.null(names(ans))) {
+ # Efficiency gain of dropping names has been successful. Ordinarily this will run.
+ if (is.null(jvnames)) jvnames = character(length(ans)-length(bynames))
+ if (length(bynames)+length(jvnames)!=length(ans))
+ stop("Internal error: jvnames is length ",length(jvnames), " but ans is ",length(ans)," and bynames is ", length(bynames))
+ ww = which(jvnames=="")
+ if (any(ww)) jvnames[ww] = paste("V",ww,sep="")
+ setattr(ans, "names", c(bynames, jvnames))
+ } else {
+ setnames(ans,seq_along(bynames),bynames) # TO DO: reinvestigate bynames flowing from dogroups here and simplify
+ }
+ if (!missing(keyby)) {
+ setkeyv(ans,names(ans)[seq_along(byval)])
+ # but if 'bykey' and 'bysameorder' then the setattr in branch above will run instead for
+ # speed (because !missing(by) when bykey, too)
+ } else if (haskey(x) && bysameorder) {
+ setattr(ans,"sorted",names(ans)[seq_along(grpcols)])
+ }
+ alloc.col(ans) # TO DO: overallocate in dogroups in the first place and remove this line
+}
+
+.optmean <- function(expr) { # called by optimization of j inside [.data.table only. Outside for a small speed advantage.
+ if (length(expr)==2L) # no parameters passed to mean, so defaults of trim=0 and na.rm=FALSE
+ return(call(".External",quote(Cfastmean),expr[[2L]], FALSE))
+ # return(call(".Internal",expr)) # slightly faster than .External, but R now blocks .Internal in coerce.c from apx Sep 2012
+ if (length(expr)==3L && identical("na",substring(names(expr)[3L],1,2))) # one parameter passed to mean()
+ return(call(".External",quote(Cfastmean),expr[[2L]], expr[[3L]])) # faster than .Call
+ assign("nomeanopt",TRUE,parent.frame())
+ expr # e.g. trim is not optimized, just na.rm
+}
+
+# [[.data.frame is now dispatched due to inheritance.
+# The code below tried to avoid that but made things
+# very slow (462 times faster down to 1 in the timings test).
+# TO DO. Reintroduce velow but dispatch straight to
+# .C("do_subset2") or better. Tests 604-608 test
+# that this doesn't regress.
+
+#"[[.data.table" <- function(x,...) {
+# if (!cedta()) return(`[[.data.frame`(x,...))
+# .subset2(x,...)
+# #class(x)=NULL # awful, copy
+# #x[[...]]
+#}
+
+#"[[<-.data.table" <- function(x,i,j,value) {
+# if (!cedta()) return(`[[<-.data.frame`(x,i,j,value))
+# if (!missing(j)) stop("[[i,j]] assignment not available in data.table, put assignment(s) in [i,{...}] instead, more powerful")
+# cl = oldClass(x) # [[<-.data.frame uses oldClass rather than class, don't know why but we'll follow suit
+# class(x) = NULL
+# x[[i]] = value
+# class(x) = cl
+# x
+#}
+
+
+as.matrix.data.table <- function(x,...)
+{
+ dm <- dim(x)
+ cn <- names(x)
+ if (any(dm == 0L))
+ return(array(NA, dim = dm, dimnames = list(NULL, cn)))
+ p <- dm[2L]
+ n <- dm[1L]
+ collabs <- as.list(cn)
+ X <- x
+ class(X) <- NULL
+ non.numeric <- non.atomic <- FALSE
+ all.logical <- TRUE
+ for (j in seq_len(p)) {
+ if (is.ff(X[[j]])) X[[j]] <- X[[j]][] # to bring the ff into memory, since we need to create a matrix in memory
+ xj <- X[[j]]
+ if (length(dj <- dim(xj)) == 2L && dj[2L] > 1L) {
+ if (inherits(xj, "data.table"))
+ xj <- X[[j]] <- as.matrix(X[[j]])
+ dnj <- dimnames(xj)[[2]]
+ collabs[[j]] <- paste(collabs[[j]], if (length(dnj) >
+ 0L)
+ dnj
+ else seq_len(dj[2L]), sep = ".")
+ }
+ if (!is.logical(xj))
+ all.logical <- FALSE
+ if (length(levels(xj)) > 0 || !(is.numeric(xj) || is.complex(xj) || is.logical(xj)) ||
+ (!is.null(cl <- attr(xj, "class")) && any(cl %chin%
+ c("Date", "POSIXct", "POSIXlt"))))
+ non.numeric <- TRUE
+ if (!is.atomic(xj))
+ non.atomic <- TRUE
+ }
+ if (non.atomic) {
+ for (j in seq_len(p)) {
+ xj <- X[[j]]
+ if (is.recursive(xj)) {
+ }
+ else X[[j]] <- as.list(as.vector(xj))
+ }
+ }
+ else if (all.logical) {
+ }
+ else if (non.numeric) {
+ for (j in seq_len(p)) {
+ if (is.character(X[[j]]))
+ next
+ xj <- X[[j]]
+ miss <- is.na(xj)
+ xj <- if (length(levels(xj)))
+ as.vector(xj)
+ else format(xj)
+ is.na(xj) <- miss
+ X[[j]] <- xj
+ }
+ }
+ X <- unlist(X, recursive = FALSE, use.names = FALSE)
+ dim(X) <- c(n, length(X)/n)
+ dimnames(X) <- list(NULL, unlist(collabs, use.names = FALSE))
+ X
+}
+
+as.data.table.matrix <- function(x, keep.rownames=FALSE, ...)
+{
+ if (!identical(keep.rownames, FALSE)) {
+ # can specify col name to keep.rownames, #575
+ ans = data.table(rn=rownames(x), x, keep.rownames=FALSE)
+ if (is.character(keep.rownames))
+ setnames(ans, 'rn', keep.rownames[1L])
+ return(ans)
+ }
+ d <- dim(x)
+ nrows <- d[1L]
+ ir <- seq_len(nrows)
+ ncols <- d[2L]
+ ic <- seq_len(ncols)
+ dn <- dimnames(x)
+ collabs <- dn[[2L]]
+ if (any(empty <- nchar(collabs) == 0L))
+ collabs[empty] <- paste("V", ic, sep = "")[empty]
+ value <- vector("list", ncols)
+ if (mode(x) == "character") {
+ # fix for #745 - A long overdue SO post: http://stackoverflow.com/questions/17691050/data-table-still-converts-strings-to-factors
+ for (i in ic) value[[i]] <- x[, i] # <strike>for efficiency.</strike> For consistency - data.table likes and prefers "character"
+ }
+ else {
+ for (i in ic) value[[i]] <- as.vector(x[, i]) # to drop any row.names that would otherwise be retained inside every column of the data.table
+ }
+ if (length(collabs) == ncols)
+ setattr(value, "names", collabs)
+ else
+ setattr(value, "names", paste("V", ic, sep = ""))
+ setattr(value,"row.names",.set_row_names(nrows))
+ setattr(value,"class",c("data.table","data.frame"))
+ alloc.col(value)
+}
+
+# don't retain classes before "data.frame" while converting
+# from it.. like base R does. This'll break test #527 (see
+# tests and as.data.table.data.frame) I've commented #527
+# for now. This addresses #1078 and #1128
+.resetclass <- function(x, class) {
+ cx = class(x)
+ n = chmatch(class, cx)
+ cx = unique( c("data.table", "data.frame", tail(cx, length(cx)-n)) )
+}
+
+as.data.table.data.frame <- function(x, keep.rownames=FALSE, ...)
+{
+ if (!identical(keep.rownames, FALSE)) {
+ # can specify col name to keep.rownames, #575
+ ans = data.table(rn=rownames(x), x, keep.rownames=FALSE)
+ if (is.character(keep.rownames))
+ setnames(ans, 'rn', keep.rownames[1L])
+ return(ans)
+ }
+ ans = copy(x) # TO DO: change this deep copy to be shallow.
+ setattr(ans,"row.names",.set_row_names(nrow(x)))
+
+ ## NOTE: This test (#527) is no longer in effect ##
+ # for nlme::groupedData which has class c("nfnGroupedData","nfGroupedData","groupedData","data.frame")
+ # See test 527.
+ ##
+
+ # fix for #1078 and #1128, see .resetclass() for explanation.
+ setattr(ans, "class", .resetclass(x, "data.frame"))
+ alloc.col(ans)
+}
+
+as.data.table.list <- function(x, keep.rownames=FALSE, ...) {
+ if (!length(x)) return( null.data.table() )
+ n = vapply(x, length, 0L)
+ mn = max(n)
+ x = copy(x)
+ idx = which(n < mn)
+ if (length(idx)) {
+ for (i in idx) {
+ if (!is.null(x[[i]])) {# avoids warning when a list element is NULL
+ # Implementing FR #4813 - recycle with warning when nr %% nrows[i] != 0L
+ if (!n[i] && mn)
+ warning("Item ", i, " is of size 0 but maximum size is ", mn, ", therefore recycled with 'NA'")
+ else if (n[i] && mn %% n[i] != 0)
+ warning("Item ", i, " is of size ", n[i], " but maximum size is ", mn, " (recycled leaving a remainder of ", mn%%n[i], " items)")
+ x[[i]] = rep(x[[i]], length.out=mn)
+ }
+ }
+ }
+ # fix for #842
+ if (mn > 0L) {
+ nz = which(n > 0L)
+ xx = point(vector("list", length(nz)), seq_along(nz), x, nz)
+ if (!is.null(names(x)))
+ setattr(xx, 'names', names(x)[nz])
+ x = xx
+ }
+ if (is.null(names(x))) setattr(x,"names",paste("V",seq_len(length(x)),sep=""))
+ setattr(x,"row.names",.set_row_names(max(n)))
+ setattr(x,"class",c("data.table","data.frame"))
+ alloc.col(x)
+}
+
+as.data.table.data.table <- function(x, keep.rownames=FALSE, ...) {
+ # fix for #1078 and #1128, see .resetclass() for explanation.
+ setattr(x, 'class', .resetclass(x, "data.table"))
+ return(x)
+}
+
+# takes care of logical, character, numeric, integer
+as.data.table.factor <- as.data.table.ordered <-
+as.data.table.integer <- as.data.table.numeric <-
+as.data.table.logical <- as.data.table.character <-
+as.data.table.Date <- function(x, keep.rownames=FALSE, ...) {
+ if (is.matrix(x)) {
+ return(as.data.table.matrix(x, ...))
+ }
+ tt = deparse(substitute(x))[1]
+ nm = names(x)
+ # FR #2356 - transfer names of named vector as "rn" column if required
+ if (!identical(keep.rownames, FALSE) & !is.null(nm))
+ x <- list(nm, unname(x))
+ else x <- list(x)
+ if (tt == make.names(tt)) {
+ # can specify col name to keep.rownames, #575
+ nm = if (length(x) == 2L) if (is.character(keep.rownames)) keep.rownames[1L] else "rn"
+ setattr(x, 'names', c(nm, tt))
+ }
+ as.data.table.list(x, FALSE)
+}
+
+R300_provideDimnames <- function (x, sep = "", base = list(LETTERS)) # backported from R3.0.0 so data.table can depend on R 2.14.0
+{
+ dx <- dim(x)
+ dnx <- dimnames(x)
+ if (new <- is.null(dnx))
+ dnx <- vector("list", length(dx))
+ k <- length(M <- vapply(base, length, 1L))
+ for (i in which(vapply(dnx, is.null, NA))) {
+ ii <- 1L + (i - 1L)%%k
+ dnx[[i]] <- make.unique(base[[ii]][1L + 0:(dx[i] - 1L)%%M[ii]],
+ sep = sep)
+ new <- TRUE
+ }
+ if (new)
+ dimnames(x) <- dnx
+ x
+}
+
+# as.data.table.table - FR #4848
+as.data.table.table <- function(x, keep.rownames=FALSE, ...) {
+ # Fix for bug #5408 - order of columns are different when doing as.data.table(with(DT, table(x, y)))
+ val = rev(dimnames(R300_provideDimnames(x)))
+ if (is.null(names(val)) || all(nchar(names(val)) == 0L))
+ setattr(val, 'names', paste("V", rev(seq_along(val)), sep=""))
+ ans <- data.table(do.call(CJ, c(val, sorted=FALSE)), N = as.vector(x))
+ setcolorder(ans, c(rev(head(names(ans), -1)), "N"))
+ ans
+}
+
+# bug #2375. fixed. same as head.data.frame and tail.data.frame to deal with negative indices
+head.data.table <- function(x, n=6, ...) {
+ if (!cedta()) return(NextMethod())
+ stopifnot(length(n) == 1L)
+ i = seq_len(if (n<0L) max(nrow(x)+n, 0L) else min(n,nrow(x)))
+ x[i, , ]
+}
+tail.data.table <- function(x, n=6, ...) {
+ if (!cedta()) return(NextMethod())
+ stopifnot(length(n) == 1L)
+ n <- if (n<0L) max(nrow(x) + n, 0L) else min(n, nrow(x))
+ i = seq.int(to=nrow(x), length.out=n)
+ x[i]
+}
+
+"[<-.data.table" <- function (x, i, j, value) {
+ # [<- is provided for consistency, but := is preferred as it allows by group and by reference to subsets of columns
+ # with no copy of the (very large, say 10GB) columns at all. := is like an UPDATE in SQL and we like and want two symbols to change.
+ if (!cedta()) {
+ x = if (nargs()<4) `[<-.data.frame`(x, i, value=value)
+ else `[<-.data.frame`(x, i, j, value)
+ return(alloc.col(x)) # over-allocate (again). Avoid all this by using :=.
+ }
+ # TO DO: warning("Please use DT[i,j:=value] syntax instead of DT[i,j]<-value, for efficiency. See ?':='")
+ if (!missing(i)) {
+ isub=substitute(i)
+ i = eval(.massagei(isub), x, parent.frame())
+ if (is.matrix(i)) {
+ if (!missing(j)) stop("When i is matrix in DT[i]<-value syntax, it doesn't make sense to provide j")
+ x = `[<-.data.frame`(x, i, value=value)
+ return(alloc.col(x))
+ }
+ i = x[i, which=TRUE]
+ # Tried adding ... after value above, and passing ... in here (e.g. for mult="first") but R CMD check
+ # then gives "The argument of a replacement function which corresponds to the right hand side must be
+ # named 'value'". So, users have to use := for that.
+ } else i = NULL # meaning (to C code) all rows, without allocating 1L:nrow(x) vector
+ if (missing(j)) j=names(x)
+ if (!is.atomic(j)) stop("j must be atomic vector, see ?is.atomic")
+ if (any(is.na(j))) stop("NA in j")
+ if (is.character(j)) {
+ newnames = setdiff(j,names(x))
+ cols = as.integer(chmatch(j, c(names(x),newnames)))
+ # We can now mix existing columns and new columns
+ } else {
+ if (!is.numeric(j)) stop("j must be vector of column name or positions")
+ if (any(j>ncol(x))) stop("Attempt to assign to column position greater than ncol(x). Create the column by name, instead. This logic intends to catch (most likely) user errors.")
+ cols = as.integer(j) # for convenience e.g. to convert 1 to 1L
+ newnames = NULL
+ }
+ reinstatekey=NULL
+ if (haskey(x) && identical(key(x),key(value)) &&
+ identical(names(x),names(value)) &&
+ is.sorted(i) &&
+ identical(substitute(x),quote(`*tmp*`))) {
+ # DT["a",]$y <- 1.1 winds up creating `*tmp*` subset of rows and assigning _all_ the columns into x and
+ # over-writing the key columns with the same value (not just the single 'y' column).
+ # That isn't good for speed; it's an R thing. Solution is to use := instead to avoid all this, but user
+ # expects key to be retained in this case because _he_ didn't assign to a key column (the internal base R
+ # code did).
+ reinstatekey=key(x)
+ }
+ if (!selfrefok(x) || truelength(x) < ncol(x)+length(newnames)) {
+ x = alloc.col(x,length(x)+length(newnames)) # because [<- copies via *tmp* and main/duplicate.c copies at length but copies truelength over too
+ # search for one other .Call to assign in [.data.table to see how it differs
+ }
+ verbose=getOption("datatable.verbose")
+ if (!.R.subassignCopiesOthers) { # From 3.1.0, DF[2,"b"] = 7 no longer copies DF$a, but the VECSXP is copied (i.e. a shallow copy).
+ x = .Call(Cassign,copy(x),i,cols,newnames,value,verbose)
+ } else {
+ .Call(Cassign,x,i,cols,newnames,value,verbose)
+ }
+ alloc.col(x) # can maybe avoid this realloc, but this is (slow) [<- anyway, so just be safe.
+ if (length(reinstatekey)) setkeyv(x,reinstatekey)
+ invisible(x)
+ # no copy at all if user calls directly; i.e. `[<-.data.table`(x,i,j,value)
+ # or uses data.table := syntax; i.e. DT[i,j:=value]
+ # but, there is one copy by R in [<- dispatch to `*tmp*`; i.e. DT[i,j]<-value. *Update: not from R > 3.0.2, yay*
+ # That copy is via main/duplicate.c which preserves truelength but copies length amount. Hence alloc.col(x,length(x)).
+ # No warn passed to assign here because we know it'll be copied via *tmp*.
+ # := allows subassign to a column with no copy of the column at all, and by group, etc.
+}
+
+"$<-.data.table" <- function(x, name, value) {
+ if (!cedta()) {
+ ans = `$<-.data.frame`(x, name, value)
+ return(alloc.col(ans)) # over-allocate (again)
+ }
+ x = copy(x)
+ `[<-.data.table`(x,j=name,value=value) # important i is missing here
+}
+
+as.data.table <-function(x, keep.rownames=FALSE, ...)
+{
+ if (is.null(x))
+ return(null.data.table())
+ UseMethod("as.data.table")
+}
+
+as.data.table.default <- function(x, ...){
+ setDT(as.data.frame(x, ...))[]
+}
+
+as.data.frame.data.table <- function(x, ...)
+{
+ ans = copy(x)
+ setattr(ans,"row.names",.set_row_names(nrow(x))) # since R 2.4.0, data.frames can have non-character row names
+ setattr(ans,"class","data.frame")
+ setattr(ans,"sorted",NULL) # remove so if you convert to df, do something, and convert back, it is not sorted
+ setattr(ans,".internal.selfref",NULL)
+ # leave tl intact, no harm,
+ ans
+}
+
+as.list.data.table <- function(x, ...) {
+ # Similar to as.list.data.frame in base. Although a data.table/frame is a list, too, it may be
+ # being coerced to raw list type (by calling code) so that "[" and "[[" work in their raw list form,
+ # such as lapply does for data.frame. So we do have to remove the class attributes (and thus shallow
+ # copy is almost instant way to achieve that, without risking compatibility).
+ #if (sys.call(-2L)[[1L]]=="lapply")
+ # return(x)
+ ans = shallow(x)
+ setattr(ans, "class", NULL)
+ setattr(ans, "row.names", NULL)
+ setattr(ans, "sorted", NULL)
+ setattr(ans,".internal.selfref", NULL) # needed to pass S4 tests for example
+ ans
+}
+
+
+dimnames.data.table <- function(x) {
+ if (!cedta()) {
+ if (!identical(class(x),c("data.table","data.frame"))) stop("data.table inherits from data.frame (from v1.5) but this data.table does not. Has it been created manually (e.g. by using 'structure' rather than 'data.table') or saved to disk using a prior version of data.table? The correct class is c('data.table','data.frame').")
+ return(`dimnames.data.frame`(x))
+ }
+ list(NULL, names(x))
+}
+
+"dimnames<-.data.table" = function (x, value) # so that can do colnames(dt)=<..> as well as names(dt)=<..>
+{
+ if (!cedta()) return(`dimnames<-.data.frame`(x,value)) # won't maintain key column (if any). Revisit if ever causes a compatibility problem but don't think it's likely that packages change column names using dimnames<-. See names<-.data.table below.
+ if (.R.assignNamesCopiesAll) warning("This is R<3.1.0 where dimnames(x)<-value syntax deep copies the entire table. Please upgrade to R>=3.1.0 and see ?setnames which allows you to change names by name with built-in checks and warnings.")
+ if (!is.list(value) || length(value) != 2) stop("attempting to assign invalid object to dimnames of a data.table")
+ if (!is.null(value[[1L]])) stop("data.tables do not have rownames")
+ if (ncol(x) != length(value[[2]])) stop("can't assign",length(value[[2]]),"colnames to a",ncol(x),"column data.table")
+ setnames(x,as.character(value[[2]]))
+ x # this returned value is now shallow copied by R 3.1.0 via *tmp*. A very welcome change.
+}
+
+"names<-.data.table" <- function(x,value)
+{
+ # When non data.table aware packages change names, we'd like to maintain the key, too.
+ # If call is names(DT)[2]="newname", R will call this names<-.data.table function (notice no i) with 'value' already prepared to be same length as ncol
+ caller = as.character(sys.call(-2L))[1L]
+ if ( ((tt<-identical(caller,"colnames<-")) && cedta(3)) || cedta() ) {
+ if (.R.assignNamesCopiesAll)
+ warning("This is R<3.1.0 where ",if(tt)"col","names(x)<-value deep copies the entire table (several times). Please upgrade to R>=3.1.0 and see ?setnames which allows you to change names by name with built-in checks and warnings.")
+ } else x = shallow(x) ## Fix for #476 and #825. Needed for R v3.1.0+. TO DO: revisit
+ if (is.null(value))
+ setattr(x,"names",NULL) # e.g. plyr::melt() calls base::unname()
+ else
+ setnames(x,value)
+ x # this returned value is now shallow copied by R 3.1.0 via *tmp*. A very welcome change.
+}
+
+within.data.table <- function (data, expr, ...)
+# basically within.list but retains key (if any)
+# will be slower than using := or a regular query (see ?within for further info).
+{
+ if (!cedta()) return(NextMethod())
+ parent <- parent.frame()
+ e <- evalq(environment(), data, parent)
+ eval(substitute(expr), e) # might (and it's known that some user code does) contain rm()
+ l <- as.list(e)
+ l <- l[!sapply(l, is.null)]
+ nD <- length(del <- setdiff(names(data), (nl <- names(l))))
+ ans = copy(data)
+ if (length(nl)) ans[,nl] <- l
+ if (nD) ans[,del] <- NULL
+ if (haskey(data) && all(key(data) %chin% names(ans))) {
+ x = TRUE
+ for (i in key(data)) {
+ x = identical(data[[i]],ans[[i]])
+ if (!x) break
+ }
+ if (x) setattr(ans,"sorted",key(data))
+ }
+ ans
+}
+
+
+transform.data.table <- function (`_data`, ...)
+# basically transform.data.frame with data.table instead of data.frame, and retains key
+{
+ if (!cedta()) return(NextMethod())
+ e <- eval(substitute(list(...)), `_data`, parent.frame())
+ tags <- names(e)
+ inx <- chmatch(tags, names(`_data`))
+ matched <- !is.na(inx)
+ if (any(matched)) {
+ `_data`[,inx[matched]] <- e[matched]
+ `_data` <- data.table(`_data`)
+ }
+ if (!all(matched)) {
+ ans <- do.call("data.table", c(list(`_data`), e[!matched]))
+ } else {
+ ans <- `_data`
+ }
+ key.cols <- key(`_data`)
+ if (!any(tags %chin% key.cols)) {
+ setattr(ans, "sorted", key.cols)
+ }
+ ans
+}
+
+subset.data.table <- function (x, subset, select, ...)
+{
+ key.cols <- key(x)
+
+ if (missing(subset)) {
+ r <- TRUE
+ } else {
+ e <- substitute(subset)
+ r <- eval(e, x, parent.frame())
+ if (!is.logical(r))
+ stop("'subset' must evaluate to logical")
+ r <- r & !is.na(r)
+ }
+
+ if (missing(select)) {
+ vars <- seq_len(ncol(x))
+ } else {
+ nl <- as.list(seq_len(ncol(x)))
+ setattr(nl,"names",names(x))
+ vars <- eval(substitute(select), nl, parent.frame()) # e.g. select=colF:colP
+ # #891 fix - don't convert numeric vars to column names - will break when there are duplicate columns
+ key.cols <- intersect(key.cols, names(x)[vars]) ## Only keep key.columns found in the select clause
+ }
+
+ ans <- x[r, vars, with = FALSE]
+
+ if (nrow(ans) > 0L) {
+ if (!missing(select) && length(key.cols)) {
+ ## Set the key on the returned data.table as long as the key
+ ## columns that "remain" are the same as the original, or a
+ ## prefix of it.
+ is.prefix <- all(key(x)[seq_len(length(key.cols))] == key.cols)
+ if (is.prefix) {
+ setattr(ans, "sorted", key.cols)
+ }
+ }
+ } else {
+ setkey(ans,NULL)
+ }
+ ans
+}
+
+# Equivalent of 'rowSums(is.na(dt) > 0L)' but much faster and memory efficient.
+# Also called "complete.cases" in base. Unfortunately it's not a S3 generic.
+# Also handles bit64::integer64. TODO: export this?
+# For internal use only. 'by' requires integer input. No argument checks here yet.
+is_na <- function(x, by=seq_along(x)) .Call(Cdt_na, x, by)
+any_na <- function(x, by=seq_along(x)) .Call(CanyNA, x, by)
+
+na.omit.data.table <- function (object, cols = seq_along(object), invert = FALSE, ...) {
+ if (!cedta()) return(NextMethod())
+ if ( !missing(invert) && is.na(as.logical(invert)) )
+ stop("Argument 'invert' must be logical TRUE/FALSE")
+ if (is.character(cols)) {
+ old = cols
+ cols = chmatch(cols, names(object), nomatch=0L)
+ if (any(cols==0L))
+ stop("Columns ", paste(old[cols==0L], collapse=","),
+ " doesn't exist in the input data.table")
+ }
+ cols = as.integer(cols)
+ ix = .Call(Cdt_na, object, cols)
+ .Call(CsubsetDT, object, which_(ix, bool = invert), seq_along(object))
+ # compare the above to stats:::na.omit.data.frame
+}
+
+which_ <- function(x, bool = TRUE) {
+ .Call("Cwhichwrapper", x, bool)
+}
+
+is.na.data.table <- function (x) {
+ if (!cedta()) return(`is.na.data.frame`(x))
+ do.call("cbind", lapply(x, "is.na"))
+}
+
+# not longer needed as inherits ...
+# t.data.table <- t.data.frame
+# Math.data.table <- Math.data.frame
+# summary.data.table <- summary.data.frame
+
+Ops.data.table <- function(e1, e2 = NULL)
+{
+ ans = NextMethod()
+ if (cedta() && is.data.frame(ans))
+ ans = as.data.table(ans)
+ ans
+}
+
+
+split.data.table <- function(...) {
+ if (cedta() && getOption("datatable.dfdispatchwarn")) # or user can use suppressWarnings
+ warning("split is inefficient. It copies memory. Please use [,j,by=list(...)] syntax. See data.table FAQ.")
+ NextMethod() # allow user to do it though, split object will be data.table's with 'NA' repeated in row.names silently
+}
+
+# TO DO, add more warnings e.g. for by.data.table(), telling user what the data.table syntax is but letting them dispatch to data.frame if they want
+
+copy <- function(x) {
+ newx = .Call(Ccopy,x) # copies at length but R's duplicate() also copies truelength over.
+ # TO DO: inside Ccopy it could reset tl to 0 or length, but no matter as selfrefok detects it
+ # TO DO: revisit duplicate.c in R 3.0.3 and see where it's at
+ if (!is.data.table(x)) return(newx) # e.g. in as.data.table.list() the list is copied before changing to data.table
+ setattr(newx,".data.table.locked",NULL)
+ alloc.col(newx)
+}
+
+copyattr <- function(from, to) {
+ .Call(Ccopyattr, from, to)
+}
+
+point <- function(to, to_idx, from, from_idx) {
+ .Call(CpointWrapper, to, to_idx, from, from_idx)
+}
+
+.shallow <- function(x, cols = NULL, retain.key = FALSE) {
+ isnull = is.null(cols)
+ if (!isnull) cols = validate(cols, x) # NULL is default = all columns
+ ans = .Call(Cshallowwrapper, x, cols) # copies VECSXP only
+ if (retain.key && isnull) return(ans) # handle most frequent case first
+ # rest of the cases
+ cols = names(x)[cols]
+ retain.key = retain.key && identical(cols, head(key(x), length(cols)))
+ setattr(ans, 'sorted', if (haskey(x) && retain.key) cols else NULL)
+ ans
+ # TODO: check/remove attributes for secondary keys?
+}
+
+shallow <- function(x, cols=NULL) {
+ if (!is.data.table(x))
+ stop("x is not a data.table. Shallow copy is a copy of the vector of column pointers (only), so is only meaningful for data.table")
+ ans = .shallow(x, cols=cols, retain.key = TRUE)
+ ans
+}
+
+alloc.col <- function(DT, n=getOption("datatable.alloccol"), verbose=getOption("datatable.verbose"))
+{
+ name = substitute(DT)
+ if (identical(name,quote(`*tmp*`))) stop("alloc.col attempting to modify `*tmp*`")
+ ans = .Call(Calloccolwrapper,DT,as.integer(eval(n)),verbose)
+ for (i in seq_along(ans)) {
+ # clear the same excluded by copyMostAttrib(). Primarily for data.table and as.data.table, but added here centrally (see #4890).
+ setattr(ans[[i]],"names",NULL)
+ setattr(ans[[i]],"dim",NULL)
+ setattr(ans[[i]],"dimnames",NULL)
+ }
+ if (is.name(name)) {
+ name = as.character(name)
+ assign(name,ans,parent.frame(),inherits=TRUE)
+ }
+ .Call(Csetnamed,ans,0L)
+}
+
+selfrefok <- function(DT,verbose=getOption("datatable.verbose")) {
+ .Call(Cselfrefokwrapper,DT,verbose)
+}
+
+truelength <- function(x) .Call(Ctruelength,x)
+# deliberately no "truelength<-" method. alloc.col is the mechanism for that.
+# settruelength() no longer need (and so removed) now that data.table depends on R 2.14.0
+# which initializes tl to zero rather than leaving uninitialized.
+
+setattr <- function(x,name,value) {
+ # Wrapper for setAttrib internal R function
+ # Sets attribute by reference (no copy)
+ # Named setattr (rather than setattrib) at R level to more closely resemble attr<-
+ # And as from 1.7.8 is made exported in NAMESPACE for use in user attributes.
+ # User can also call `attr<-` function directly, but that copies (maybe just when NAMED>0, which is always for data.frame, I think). See "Confused by NAMED" thread on r-devel 24 Nov 2011.
+ # We tend to use setattr() internally in data.table.R because often we construct a data.table and it hasn't
+ # got names yet. setnames() is the user interface which checks integrity and doesn't let you drop names for example.
+ if (name=="names" && is.data.table(x) && length(attr(x,"names")) && !is.null(value))
+ setnames(x,value)
+ # Using setnames here so that truelength of names can be retained, to carry out integrity checks such as not
+ # creating names longer than the number of columns of x, and to change the key, too
+ # For convenience so that setattr(DT,"names",allnames) works as expected without requiring a switch to setnames.
+ else .Call(Csetattrib, x, name, value)
+ # If name=="names" and this is the first time names are assigned (e.g. in data.table()), this will be grown by alloc.col very shortly afterwards in the caller.
+
+ # fix for #1142 - duplicated levels for factors
+ if (name == "levels" && is.factor(x) && anyDuplicated(value))
+ .Call(Csetlevels, x, (value <- as.character(value)), unique(value))
+ invisible(x)
+}
+
+setnames <- function(x,old,new) {
+ # Sets by reference, maintains truelength, no copy of table at all.
+ # But also more convenient than names(DT)[i]="newname" because we can also do setnames(DT,"oldname","newname")
+ # without an onerous match() ourselves. old can be positions, too, but we encourage by name for robustness.
+ if (!is.data.frame(x)) stop("x is not a data.table or data.frame")
+ if (!length(attr(x,"names"))) stop("x has no column names") # because setnames is for user user. Internally, use setattr(x,"names",...)
+ if (length(names(x)) != length(x)) stop("dt is length ",length(dt)," but its names are length ",length(names(x)))
+ if (missing(new)) {
+ # for setnames(DT,new); e.g., setnames(DT,c("A","B")) where ncol(DT)==2
+ if (!is.character(old)) stop("Passed a vector of type '",typeof(old),"'. Needs to be type 'character'.")
+ if (length(old) != ncol(x)) stop("Can't assign ",length(old)," names to a ",ncol(x)," column data.table")
+ # note that duplicate names are permitted to be created in this usage only
+ w = which(names(x) != old)
+ if (!length(w)) return(invisible(x)) # no changes
+ new = old[w]
+ i = w
+ } else {
+ if (missing(old)) stop("When 'new' is provided, 'old' must be provided too")
+ if (!is.character(new)) stop("'new' is not a character vector")
+ if (length(new)!=length(old)) stop("'old' is length ",length(old)," but 'new' is length ",length(new))
+ if (is.numeric(old)) {
+ tt = old<1L | old>length(x) | is.na(old)
+ if (any(tt)) stop("Items of 'old' either NA or outside range [1,",length(x),"]: ",paste(old[tt],collapse=","))
+ i = as.integer(old)
+ if (any(duplicated(i))) stop("Some duplicates exist in 'old': ",paste(i[duplicated(i)],collapse=","))
+ } else {
+ if (!is.character(old)) stop("'old' is type ",typeof(old)," but should be integer, double or character")
+ if (any(duplicated(old))) stop("Some duplicates exist in 'old': ", paste(old[duplicated(old)],collapse=","))
+ i = chmatch(old,names(x))
+ if (any(is.na(i))) stop("Items of 'old' not found in column names: ",paste(old[is.na(i)],collapse=","))
+ if (any(tt<-!is.na(chmatch(old,names(x)[-i])))) stop("Some items of 'old' are duplicated (ambiguous) in column names: ",paste(old[tt],collapse=","))
+ }
+ }
+ # update the key if the column name being change is in the key
+ m = chmatch(names(x)[i], key(x))
+ w = which(!is.na(m))
+ if (length(w))
+ .Call(Csetcharvec, attr(x,"sorted"), m[w], new[w])
+
+ # update secondary keys
+ idx = attr(x,"index")
+ for (k in names(attributes(idx))) {
+ tt = strsplit(k,split="__")[[1]][-1]
+ m = chmatch(names(x)[i], tt)
+ w = which(!is.na(m))
+ if (length(w)) {
+ tt[m[w]] = new[w]
+ newk = paste("__",paste(tt,collapse="__"),sep="")
+ setattr(idx, newk, attr(idx, k))
+ setattr(idx, k, NULL)
+ }
+ }
+
+ .Call(Csetcharvec, attr(x,"names"), as.integer(i), new)
+ invisible(x)
+}
+
+setcolorder <- function(x,neworder)
+{
+ # if (!is.data.table(x)) stop("x is not a data.table")
+ if (length(neworder)!=length(x)) stop("neworder is length ",length(neworder)," but x has ",length(x)," columns.")
+ if (is.character(neworder)) {
+ if (any(duplicated(neworder))) stop("neworder contains duplicate column names")
+ if (any(duplicated(names(x)))) stop("x has some duplicated column name(s): ",paste(names(x)[duplicated(names(x))],collapse=","),". Please remove or rename the duplicate(s) and try again.")
+ o = as.integer(chmatch(neworder,names(x)))
+ if (any(is.na(o))) stop("Names in neworder not found in x: ",paste(neworder[is.na(o)],collapse=","))
+ } else {
+ if (!is.numeric(neworder)) stop("neworder is not a character or numeric vector")
+ o = as.integer(neworder)
+ m = !(o %in% seq_len(length(x)))
+ if (any(m)) stop("Column numbers in neworder out of bounds: ",paste(o[m],collapse=","))
+ }
+ .Call(Csetcolorder,x,o)
+ invisible(x)
+}
+
+set <- function(x,i=NULL,j,value) # low overhead, loopable
+{
+ if (is.atomic(value)) {
+ # protect NAMED of atomic value from .Call's NAMED=2 by wrapping with list()
+ l = vector("list",1)
+ .Call(Csetlistelt,l,1L,value) # to avoid the copy by list() in R < 3.1.0
+ value = l
+ }
+ .Call(Cassign,x,i,j,NULL,value,FALSE) # verbose=FALSE for speed to avoid getOption() TO DO: somehow read getOption("datatable.verbose") from C level
+ invisible(x)
+}
+
+chmatch <- function(x,table,nomatch=NA_integer_)
+ .Call(Cchmatchwrapper,x,table,as.integer(nomatch),FALSE)
+
+"%chin%" <- function(x,table) {
+ # TO DO if table has 'ul' then match to that
+ .Call(Cchmatchwrapper,x,table,NA_integer_,TRUE)
+}
+
+chorder <- function(x) {
+ o = forderv(x, sort=TRUE, retGrp=FALSE)
+ if (length(o)) o else seq_along(x)
+}
+
+chgroup <- function(x) {
+ # TO DO: deprecate and remove this. It's exported but doubt anyone uses it. Think the plan was to use it internally, but forderv superceded.
+ o = forderv(x, sort=FALSE, retGrp=TRUE)
+ if (length(o)) as.vector(o) else seq_along(x) # as.vector removes the attributes
+}
+
+
+.rbind.data.table <- function(..., use.names=TRUE, fill=FALSE, idcol=NULL) {
+ # See FAQ 2.23
+ # Called from base::rbind.data.frame
+ l = list(...)
+ # if (missing(use.names)) message("Columns will be bound by name for consistency with base. You can supply unnamed lists and the columns will then be joined by position, or set use.names=FALSE. Alternatively, explicitly setting use.names to TRUE will remove this message.")
+ rbindlist(l, use.names, fill, idcol)
+}
+
+rbindlist <- function(l, use.names=fill, fill=FALSE, idcol=NULL) {
+ ans = .Call("Crbindlist", l, use.names, fill)
+ if (!length(ans)) return(null.data.table())
+ setDT(ans)
+ if (!is.null(idcol)) {
+ if (isTRUE(idcol)) idcol = ".id"
+ if (!is.character(idcol))
+ stop("idcol must be a logical or character vector of length 1. If logical and 'TRUE' the id column will automatically named '.id'. Else the column will be named with the character value provided in 'idcol'.")
+ if (idcol %in% names(ans))
+ stop(idcol, " is already a column name in the result. Please provide another name for 'idcol'.")
+ nm = names(l)
+ len = vapply(l, NROW, 0L)
+ idx = which(len > 0L)
+ len = len[idx]
+ if (is.null(nm)) nm = seq_along(len)
+ else nm = nm[idx]
+ ansnames = c(idcol, names(ans))
+ set(ans, j=idcol, value=rep.int(nm, len))
+ setcolorder(ans, ansnames)
+ }
+ ans
+}
+
+vecseq <- function(x,y,clamp) .Call(Cvecseq,x,y,clamp)
+
+# .Call(Caddress, x) increments NAM() when x is vector with NAM(1). Referring object within non-primitive function is enough to increment reference.
+address <- function(x) .Call(Caddress, eval(substitute(x), parent.frame()))
+
+":=" <- function(...) stop('Check that is.data.table(DT) == TRUE. Otherwise, := and `:=`(...) are defined for use in j, once only and in particular ways. See help(":=").')
+
+setDF <- function(x, rownames=NULL) {
+ if (!is.list(x)) stop("setDF only accepts data.table, data.frame or list of equal length as input")
+ if (any(duplicated(rownames))) stop("rownames contains duplicates")
+ if (is.data.table(x)) {
+ # copied from as.data.frame.data.table
+ if (is.null(rownames)) {
+ rn <- .set_row_names(nrow(x))
+ } else {
+ if (length(rownames) != nrow(x))
+ stop("rownames incorrect length; expected ", nrow(x), " names, got ", length(rownames))
+ rn <- rownames
+ }
+ setattr(x, "row.names", rn)
+ setattr(x, "class", "data.frame")
+ setattr(x, "sorted", NULL)
+ setattr(x, ".internal.selfref", NULL)
+ } else if (is.data.frame(x)) {
+ if (!is.null(rownames)){
+ if (length(rownames) != nrow(x))
+ stop("rownames incorrect length; expected ", nrow(x), " names, got ", length(rownames))
+ setattr(x, "row.names", rownames)
+ }
+ x
+ } else {
+ n = vapply(x, length, 0L)
+ mn = max(n)
+ if (any(n<mn))
+ stop("All elements in argument 'x' to 'setDF' must be of same length")
+ xn = names(x)
+ if (is.null(xn)) {
+ setattr(x, "names", paste("V",seq_len(length(x)),sep=""))
+ } else {
+ idx = xn %chin% ""
+ if (any(idx)) {
+ xn[idx] = paste("V", seq_along(which(idx)), sep="")
+ setattr(x, "names", xn)
+ }
+ }
+ if (is.null(rownames)) {
+ rn <- .set_row_names(mn)
+ } else {
+ if (length(rownames) != mn)
+ stop("rownames incorrect length; expected ", mn, " names, got ", length(rownames))
+ rn <- rownames
+ }
+ setattr(x,"row.names", rn)
+ setattr(x,"class","data.frame")
+ }
+ invisible(x)
+}
+
+setDT <- function(x, keep.rownames=FALSE, key=NULL, check.names=FALSE) {
+ name = substitute(x)
+ if (is.name(name)) {
+ home <- function(x, env) {
+ if (identical(env, emptyenv()))
+ stop("Can not find symbol ", cname, call. = FALSE)
+ else if (exists(x, env, inherits=FALSE)) env
+ else home(x, parent.env(env))
+ }
+ cname = as.character(name)
+ envir = home(cname, parent.frame())
+ if (bindingIsLocked(cname, envir)) {
+ stop("Can not convert '", cname, "' to data.table by reference because binding is locked. It is very likely that '", cname, "' resides within a package (or an environment) that is locked to prevent modifying its variable bindings. Try copying the object to your current environment, ex: var <- copy(var) and then using setDT again.")
+ }
+ }
+ if (is.data.table(x)) {
+ # fix for #1078 and #1128, see .resetclass() for explanation.
+ setattr(x, 'class', .resetclass(x, 'data.table'))
+ if (!missing(key)) setkeyv(x, key) # fix for #1169
+ if (check.names) setattr(x, "names", make.names(names(x), unique=TRUE))
+ if (selfrefok(x) > 0) return(invisible(x)) else alloc.col(x)
+ } else if (is.data.frame(x)) {
+ rn = if (!identical(keep.rownames, FALSE)) rownames(x) else NULL
+ setattr(x, "row.names", .set_row_names(nrow(x)))
+ if (check.names) setattr(x, "names", make.names(names(x), unique=TRUE))
+ # fix for #1078 and #1128, see .resetclass() for explanation.
+ setattr(x, "class", .resetclass(x, 'data.frame'))
+ alloc.col(x)
+ if (!is.null(rn)) {
+ nm = c(if (is.character(keep.rownames)) keep.rownames[1L] else "rn", names(x))
+ x[, (nm[1L]) := rn]
+ setcolorder(x, nm)
+ }
+ } else if (is.null(x) || (is.list(x) && !length(x))) {
+ x = null.data.table()
+ } else if (is.list(x)) {
+ # copied from as.data.table.list - except removed the copy
+ n = vapply(x, length, 0L)
+ mn = max(n)
+ if (any(n<mn))
+ stop("All elements in argument 'x' to 'setDT' must be of same length")
+ xn = names(x)
+ if (is.null(xn)) {
+ setattr(x, "names", paste("V",seq_len(length(x)),sep=""))
+ } else {
+ idx = xn %chin% "" # names can be NA - test 1006 caught that!
+ if (any(idx)) {
+ xn[idx] = paste("V", seq_along(which(idx)), sep="")
+ setattr(x, "names", xn)
+ }
+ if (check.names) setattr(x, "names", make.names(xn, unique=TRUE))
+ }
+ setattr(x,"row.names",.set_row_names(max(n)))
+ setattr(x,"class",c("data.table","data.frame"))
+ alloc.col(x)
+ } else {
+ stop("Argument 'x' to 'setDT' should be a 'list', 'data.frame' or 'data.table'")
+ }
+ if (!is.null(key)) setkeyv(x, key)
+ if (is.name(name)) {
+ name = as.character(name)
+ assign(name, x, parent.frame(), inherits=TRUE)
+ } else if (is.call(name) && (name[[1L]] == "$" || name[[1L]] == "[[") && is.name(name[[2L]])) {
+ # common case is call from 'lapply()'
+ k = eval(name[[2L]], parent.frame(), parent.frame())
+ if (is.list(k)) {
+ origj = j = if (name[[1L]] == "$") as.character(name[[3L]]) else eval(name[[3L]], parent.frame(), parent.frame())
+ if (length(j) == 1L) {
+ if (is.character(j)) {
+ j = match(j, names(k))
+ if (is.na(j))
+ stop("Item '", origj, "' not found in names of input list")
+ }
+ }
+ .Call(Csetlistelt,k,as.integer(j), x)
+ } else if (is.environment(k) && exists(as.character(name[[3L]]), k)) {
+ assign(as.character(name[[3L]]), x, k, inherits=FALSE)
+ }
+ }
+ invisible(x)
+}
+
+as_list <- function(x) {
+ lx = vector("list", 1L)
+ .Call(Csetlistelt, lx, 1L, x)
+ lx
+}
+
+# FR #686
+rleid <- function(...) {
+ rleidv(list(...))
+}
+
+rleidv <- function(x, cols=seq_along(x)) {
+ if (is.atomic(x)) {
+ if (!missing(cols) && !is.null(cols))
+ stop("x is a single vector, non-NULL 'cols' doesn't make sense")
+ cols = 1L
+ x = as_list(x)
+ } else {
+ if (!length(cols))
+ stop("x is a list, 'cols' can not be 0-length")
+ if (is.character(cols))
+ cols = chmatch(cols, names(x))
+ cols = as.integer(cols)
+ }
+ x = .shallow(x, cols) # shallow copy even if list..
+ .Call(Crleid, setDT(x), -1L)
+}
+
+gsum <- function(x, na.rm=FALSE) .Call(Cgsum, x, na.rm)
+gmean <- function(x, na.rm=FALSE) .Call(Cgmean, x, na.rm)
+gmin <- function(x, na.rm=FALSE) .Call(Cgmin, x, na.rm)
+gmax <- function(x, na.rm=FALSE) .Call(Cgmax, x, na.rm)
+gstart <- function(o, f, l) .Call(Cgstart, o, f, l)
+gend <- function() .Call(Cgend)
+
+isReallyReal <- function(x) {
+ .Call(CisReallyReal, x)
+}
diff --git a/R/duplicated.R b/R/duplicated.R
new file mode 100644
index 0000000..a139bc7
--- /dev/null
+++ b/R/duplicated.R
@@ -0,0 +1,99 @@
+
+duplicated.data.table <- function(x, incomparables=FALSE, fromLast=FALSE, by=key(x), ...) {
+ if (!cedta()) return(NextMethod("duplicated"))
+ if (!identical(incomparables, FALSE)) {
+ .NotYetUsed("incomparables != FALSE")
+ }
+ if (nrow(x) == 0L || ncol(x) == 0L) return(logical(0)) # fix for bug #5582
+ if (is.na(fromLast) || !is.logical(fromLast)) stop("'fromLast' must be TRUE or FALSE")
+ query <- .duplicated.helper(x, by)
+ # fix for bug #5405 - unique on null data table returns error (because of 'forderv')
+ # however, in this case we can bypass having to go to forderv at all.
+ if (!length(query$by)) return(logical(0))
+ res <- rep.int(TRUE, nrow(x))
+
+ if (query$use.keyprefix) {
+ f = uniqlist(shallow(x, query$by))
+ if (fromLast) f = cumsum(uniqlengths(f, nrow(x)))
+ } else {
+ o = forderv(x, by=query$by, sort=FALSE, retGrp=TRUE)
+ f = attr(o,"starts")
+ if (fromLast) f = cumsum(uniqlengths(f, nrow(x)))
+ if (length(o)) f=o[f]
+ }
+ res[f] = FALSE
+ res
+}
+
+unique.data.table <- function(x, incomparables=FALSE, fromLast=FALSE, by=key(x), ...) {
+ if (!cedta()) return(NextMethod("unique"))
+ dups <- duplicated.data.table(x, incomparables, fromLast, by, ...)
+ .Call(CsubsetDT, x, which_(dups, FALSE), seq_len(ncol(x))) # more memory efficient version of which(!dups)
+ # i.e. x[!dups] but avoids [.data.table overhead when unique() is loop'd
+ # TO DO: allow logical to be passed through to C level, and allow cols=NULL to mean all, for further speed gain.
+ # See news for v1.9.3 for link to benchmark use-case on datatable-help.
+}
+
+## Specify the column names to be used in the uniqueness query, and if this
+## query can take advantage of the keys of `x` (if present).
+## returns a list
+##
+## This was dropped into a helper because initial implementation of
+## unique.data.table and duplicated.data.table both needed this. However,
+## unique.data.table has bene refactored to simply call duplicated.data.table
+## making the refactor unnecessary, but let's leave it here just in case
+.duplicated.helper <- function(x, by) {
+ use.sub.cols <- !is.null(by) # && !isTRUE(by) # Fixing bug #5424
+
+ if (use.sub.cols) {
+ ## Did the user specify (integer) indexes for the columns?
+ if (is.numeric(by)) {
+ if (any(as.integer(by) != by) || any(by<1) || any(by>ncol(x))) {
+ stop("Integer values between 1 and ncol are required for 'by' when ",
+ "column indices. It's often better to use column names.")
+ }
+ by <- names(x)[by]
+ }
+ if (!is.character(by)) {
+ stop("Only NULL, column indices or column names are allowed in by")
+ }
+ bad.cols <- setdiff(by, names(x))
+ if (length(bad.cols)) {
+ stop("by specifies column names that do not exist. First 5: ",paste(head(bad.cols,5),collapse=","))
+ }
+
+ use.keyprefix = haskey(x) &&
+ length(by) <= length(key(x)) &&
+ all(head(key(x), length(by)) == by)
+ } else {
+ ## by is not was explicitly set to
+ use.keyprefix = FALSE
+ by = names(x)
+ }
+
+ list(use.keyprefix=use.keyprefix, by=by)
+}
+
+# FR #5172 anyDuplicated.data.table
+# Note that base's anyDuplicated is faster than any(duplicated(.)) (for vectors) - for data.frames it still pastes before calling duplicated
+# In that sense, this anyDuplicated is *not* the same as base's - meaning it's not a different implementation
+# This is just a wrapper. That being said, it should be incredibly fast on data.tables (due to data.table's fast forder)
+anyDuplicated.data.table <- function(x, incomparables=FALSE, fromLast=FALSE, by=key(x), ...) {
+ if (!cedta()) return(NextMethod("anyDuplicated"))
+ dups <- duplicated(x, incomparables, fromLast, by, ...)
+ if (fromLast) idx = tail(which(dups), 1L) else idx = head(which(dups), 1L)
+ if (!length(idx)) idx=0L
+ idx
+}
+
+
+# simple straightforward helper function to get the number
+# of groups in a vector or data.table. Here by data.table,
+# we really mean `.SD` - used in a grouping operation
+uniqueN <- function(x, by = if (is.data.table(x)) key(x) else NULL) {
+ if (!is.atomic(x) && !is.data.frame(x))
+ return(length(unique(x)))
+ if (is.atomic(x)) x = as_list(x)
+ if (is.null(by)) by = seq_along(x)
+ length(attr(forderv(x, by=by, retGrp=TRUE), 'starts'))
+}
diff --git a/R/fcast.R b/R/fcast.R
new file mode 100644
index 0000000..011b0f8
--- /dev/null
+++ b/R/fcast.R
@@ -0,0 +1,228 @@
+guess <- function(x) {
+ if ("value" %chin% names(x))
+ return("value")
+ if ("(all)" %chin% names(x))
+ return("(all)")
+ var <- names(x)[ncol(x)]
+ message("Using '", var, "' as value column. Use 'value.var' to override")
+ return(var)
+}
+
+dcast <- function(data, formula, fun.aggregate = NULL, ..., margins = NULL,
+ subset = NULL, fill = NULL, value.var = guess(data)) {
+ if (is.data.table(data))
+ UseMethod("dcast", data)
+ else
+ reshape2::dcast(data, formula, fun.aggregate = fun.aggregate, ..., margins = margins,
+ subset = subset, fill = fill, value.var = value.var)
+}
+
+check_formula <- function(formula, varnames, valnames) {
+ if (is.character(formula)) formula = as.formula(formula)
+ if (class(formula) != "formula" || length(formula) != 3L)
+ stop("Invalid formula. Cast formula should be of the form LHS ~ RHS, for e.g., a + b ~ c.")
+ vars = all.vars(formula)
+ vars = vars[!vars %chin% c(".", "...")]
+ allvars = c(vars, valnames)
+ ans = deparse_formula(as.list(formula)[-1L], varnames, allvars)
+}
+
+deparse_formula <- function(expr, varnames, allvars) {
+ lvars = lapply(expr, function(this) {
+ if (is.call(this)) {
+ if (this[[1L]] == quote(`+`))
+ unlist(deparse_formula(as.list(this)[-1L], varnames, allvars))
+ else this
+ } else if (is.name(this)) {
+ if (this == quote(`...`)) {
+ subvars = setdiff(varnames, allvars)
+ lapply(subvars, as.name)
+ } else this
+ }
+ })
+ lvars = lapply(lvars, function(x) if (length(x) && !is.list(x)) list(x) else x)
+}
+
+value_vars <- function(value.var, varnames) {
+ if (is.character(value.var))
+ value.var = list(value.var)
+ value.var = lapply(value.var, unique)
+ valnames = unique(unlist(value.var))
+ iswrong = which(!valnames %in% varnames)
+ if (length(iswrong))
+ stop("value.var values [", paste(value.var[iswrong], collapse=", "), "] are not found in 'data'.")
+ value.var
+}
+
+aggregate_funs <- function(funs, vals, sep="_", ...) {
+ if (is.call(funs) && funs[[1L]] == "eval")
+ funs = eval(funs[[2L]], parent.frame(2L), parent.frame(2L))
+ if (is.call(funs) && as.character(funs[[1L]]) %in% c("c", "list"))
+ funs = lapply(as.list(funs)[-1L], function(x) {
+ if (is.call(x) && as.character(x[[1L]]) %in% c("c", "list")) as.list(x)[-1L] else x
+ })
+ else funs = list(funs)
+ if (length(funs) != length(vals)) {
+ if (length(vals) == 1L)
+ vals = replicate(length(funs), vals)
+ else stop("When 'fun.aggregate' and 'value.var' are both lists, 'value.var' must be either of length =1 or =length(fun.aggregate).")
+ }
+ dots = list(...)
+ construct_funs <- function(fun, val) {
+ if (!is.list(fun)) fun = list(fun)
+ ans = vector("list", length(fun)*length(val))
+ nms = vector("character", length(ans))
+ k = 1L
+ for (i in fun) {
+ for (j in val) {
+ expr = list(i, as.name(j))
+ if (length(dots))
+ expr = c(expr, dots)
+ ans[[k]] = as.call(expr)
+ # changed order of arguments here, #1153
+ nms[k] = paste(j, all.names(i, max.names=1L, functions=TRUE), sep=sep)
+ k = k+1L;
+ }
+ }
+ setattr(ans, 'names', nms)
+ }
+ ans = mapply(construct_funs, funs, vals, SIMPLIFY=FALSE)
+ as.call(c(quote(list), unlist(ans)))
+}
+
+dcast.data.table <- function(data, formula, fun.aggregate = NULL, sep = "_", ..., margins = NULL, subset = NULL, fill = NULL, drop = TRUE, value.var = guess(data), verbose = getOption("datatable.verbose")) {
+ if (!is.data.table(data)) stop("'data' must be a data.table.")
+ if (anyDuplicated(names(data))) stop('data.table to cast must have unique column names')
+ drop = as.logical(drop[1])
+ if (is.na(drop)) stop("'drop' must be logical TRUE/FALSE")
+ lvals = value_vars(value.var, names(data))
+ valnames = unique(unlist(lvals))
+ lvars = check_formula(formula, names(data), valnames)
+ lvars = lapply(lvars, function(x) if (!length(x)) quote(`.`) else x)
+ # tired of lapply and the way it handles environments!
+ allcols = c(unlist(lvars), lapply(valnames, as.name))
+ dat = vector("list", length(allcols))
+ for (i in seq_along(allcols)) {
+ x = allcols[[i]]
+ dat[[i]] = if (identical(x, quote(`.`))) rep(".", nrow(data))
+ else eval(x, data, parent.frame())
+ if (is.function(dat[[i]]))
+ stop("Column [", deparse(x), "] not found or of unknown type.")
+ }
+ setattr(lvars, 'names', c("lhs", "rhs"))
+ # Have to take care of duplicate names, and provide names for expression columns properly.
+ varnames = make.unique(sapply(unlist(lvars), all.vars, max.names=1L), sep=sep)
+ dupidx = which(valnames %in% varnames)
+ if (length(dupidx)) {
+ dups = valnames[dupidx]
+ valnames = tail(make.unique(c(varnames, valnames)), -length(varnames))
+ lvals = lapply(lvals, function(x) { x[x %in% dups] = valnames[dupidx]; x })
+ }
+ lhsnames = head(varnames, length(lvars$lhs))
+ rhsnames = tail(varnames, -length(lvars$lhs))
+ setattr(dat, 'names', c(varnames, valnames))
+ setDT(dat)
+ if (any(sapply(as.list(dat)[varnames], is.list))) {
+ stop("Columns specified in formula can not be of type list")
+ }
+ m <- as.list(match.call()[-1L])
+ subset <- m[["subset"]][[2L]]
+ if (!is.null(subset)) {
+ if (is.name(subset)) subset = as.call(list(quote(`(`), subset))
+ idx = which(eval(subset, data, parent.frame())) # any advantage thro' secondary keys?
+ dat = .Call(CsubsetDT, dat, idx, seq_along(dat))
+ }
+ if (!nrow(dat) || !ncol(dat)) stop("Can not cast an empty data.table")
+ fun.call = m[["fun.aggregate"]]
+ fill.default = NULL
+ if (is.null(fun.call)) {
+ oo = forderv(dat, by=varnames, retGrp=TRUE)
+ if (attr(oo, 'maxgrpn') > 1L) {
+ message("Aggregate function missing, defaulting to 'length'")
+ fun.call = quote(length)
+ }
+ }
+ if (!is.null(fun.call)) {
+ fun.call = aggregate_funs(fun.call, lvals, sep, ...)
+ errmsg = "Aggregating function(s) should take vector inputs and return a single value (length=1). However, function(s) returns length!=1. This value will have to be used to fill any missing combinations, and therefore must be length=1. Either override by setting the 'fill' argument explicitly or modify your function to handle this case appropriately."
+ if (is.null(fill)) {
+ fill.default <- suppressWarnings(dat[0][, eval(fun.call)])
+ # tryCatch(fill.default <- dat[0][, eval(fun.call)], error = function(x) stop(errmsg, call.=FALSE))
+ if (nrow(fill.default) != 1L) stop(errmsg, call.=FALSE)
+ }
+ if (!any(valnames %chin% varnames)) {
+ dat = dat[, eval(fun.call), by=c(varnames)]
+ } else {
+ dat = dat[, { .SD; eval(fun.call) }, by=c(varnames), .SDcols = valnames]
+ }
+ }
+ order_ <- function(x) {
+ o = forderv(x, retGrp=TRUE, sort=TRUE)
+ idx = attr(o, 'starts')
+ if (!length(o)) o = seq_along(x)
+ o[idx] # subsetVector retains attributes, using R's subset for now
+ }
+ cj_uniq <- function(DT) {
+ do.call("CJ", lapply(DT, function(x)
+ if (is.factor(x)) {
+ xint = seq_along(levels(x))
+ setattr(xint, 'levels', levels(x))
+ setattr(xint, 'class', class(x))
+ } else .Call(CsubsetVector, x, order_(x))
+ ))}
+ valnames = setdiff(names(dat), varnames)
+ # 'dat' != 'data'? then setkey to speed things up (slightly), else ad-hoc (for now). Still very fast!
+ if (!is.null(fun.call) || !is.null(subset))
+ setkeyv(dat, varnames)
+ if (length(rhsnames)) {
+ lhs = shallow(dat, lhsnames); rhs = shallow(dat, rhsnames); val = shallow(dat, valnames)
+ # handle drop=TRUE/FALSE - Update: Logic moved to R, AND faster than previous version. Take that... old me :-).
+ if (drop) {
+ map = setDT(lapply(list(lhsnames, rhsnames), function(cols) frankv(dat, cols=cols, ties.method="dense")))
+ maporder = lapply(map, order_)
+ mapunique = lapply(seq_along(map), function(i) .Call(CsubsetVector, map[[i]], maporder[[i]]))
+ lhs = .Call(CsubsetDT, lhs, maporder[[1L]], seq_along(lhs))
+ rhs = .Call(CsubsetDT, rhs, maporder[[2L]], seq_along(rhs))
+ } else {
+ lhs_ = cj_uniq(lhs); rhs_ = cj_uniq(rhs)
+ map = vector("list", 2L)
+ .Call(Csetlistelt, map, 1L, lhs_[lhs, which=TRUE])
+ .Call(Csetlistelt, map, 2L, rhs_[rhs, which=TRUE])
+ setDT(map)
+ mapunique = vector("list", 2L)
+ .Call(Csetlistelt, mapunique, 1L, seq_len(nrow(lhs_)))
+ .Call(Csetlistelt, mapunique, 2L, seq_len(nrow(rhs_)))
+ lhs = lhs_; rhs = rhs_
+ }
+ maplen = sapply(mapunique, length)
+ idx = do.call("CJ", mapunique)[map, I := .I][["I"]] # TO DO: move this to C and avoid materialising the Cross Join.
+ ans = .Call("Cfcast", lhs, val, maplen[[1L]], maplen[[2L]], idx, fill, fill.default, is.null(fun.call))
+ allcols = do.call("paste", c(rhs, sep=sep))
+ if (length(valnames) > 1L)
+ allcols = do.call("paste", c(CJ(valnames, allcols, sorted=FALSE), sep=sep))
+ # removed 'setcolorder()' here, #1153
+ setattr(ans, 'names', c(lhsnames, allcols))
+ setDT(ans); setattr(ans, 'sorted', lhsnames)
+ } else {
+ # formula is of the form x + y ~ . (rare case)
+ if (drop) {
+ if (is.null(subset) && is.null(fun.call)) {
+ dat = copy(dat) # can't be avoided
+ setkeyv(dat, lhsnames)
+ }
+ ans = dat
+ } else {
+ lhs = shallow(dat, lhsnames)
+ val = shallow(dat, valnames)
+ lhs_ = cj_uniq(lhs)
+ idx = lhs_[lhs, I := .I][["I"]]
+ lhs_[, I := NULL]
+ ans = .Call("Cfcast", lhs_, val, nrow(lhs_), 1L, idx, fill, fill.default, is.null(fun.call))
+ setDT(ans); setattr(ans, 'sorted', lhsnames)
+ setnames(ans, c(lhsnames, valnames))
+ }
+ if (length(valnames) == 1L)
+ setnames(ans, valnames, value.var)
+ }
+ return (ans)
+}
diff --git a/R/fmelt.R b/R/fmelt.R
new file mode 100644
index 0000000..348c9f2
--- /dev/null
+++ b/R/fmelt.R
@@ -0,0 +1,39 @@
+# Add melt generic, don't import reshape2 as it requires R >= 3.0.0.
+melt <- function(data, ..., na.rm = FALSE, value.name = "value") {
+ if (is.data.table(data))
+ UseMethod("melt", data)
+ else
+ reshape2::melt(data, ..., na.rm=na.rm, value.name=value.name)
+}
+
+patterns <- function(...) {
+ p = unlist(list(...), use.names=FALSE)
+}
+
+melt.data.table <- function(data, id.vars, measure.vars, variable.name = "variable",
+ value.name = "value", ..., na.rm = FALSE, variable.factor = TRUE, value.factor = FALSE,
+ verbose = getOption("datatable.verbose")) {
+ if (!is.data.table(data)) stop("'data' must be a data.table")
+ if (missing(id.vars)) id.vars=NULL
+ if (missing(measure.vars)) measure.vars = NULL
+ measure.sub = substitute(measure.vars)
+ if (is.call(measure.sub) && measure.sub[[1L]] == "patterns") {
+ measure.vars = lapply(eval(measure.sub), grep, names(data))
+ }
+ if (is.list(measure.vars)) {
+ if (length(value.name) == 1L)
+ value.name = paste(value.name, seq_along(measure.vars), sep="")
+ }
+ ans <- .Call("Cfmelt", data, id.vars, measure.vars,
+ as.logical(variable.factor), as.logical(value.factor),
+ variable.name, value.name, as.logical(na.rm),
+ as.logical(verbose))
+ setDT(ans)
+ if (any(duplicated(names(ans)))) {
+ cat("Duplicate column names found in molten data.table. Setting unique names using 'make.names'")
+ setnames(ans, make.unique(names(ans)))
+ }
+ setattr(ans, 'sorted', NULL)
+ ans
+}
+
diff --git a/R/foverlaps.R b/R/foverlaps.R
new file mode 100644
index 0000000..9f955b6
--- /dev/null
+++ b/R/foverlaps.R
@@ -0,0 +1,222 @@
+foverlaps <- function(x, y, by.x = if (!is.null(key(x))) key(x) else key(y), by.y = key(y), maxgap=0L, minoverlap=1L, type=c("any", "within", "start", "end", "equal"), mult=c("all", "first", "last"), nomatch=getOption("datatable.nomatch"), which = FALSE, verbose=getOption("datatable.verbose")) {
+
+ if (!is.data.table(y) || !is.data.table(x)) stop("y and x must both be data.tables. Use `setDT()` to convert list/data.frames to data.tables by reference or as.data.table() to convert to data.tables by copying.")
+ maxgap = as.integer(maxgap); minoverlap = as.integer(minoverlap);
+ which = as.logical(which); nomatch = as.integer(nomatch);
+ if (!length(maxgap) || length(maxgap) != 1L || is.na(maxgap) || maxgap < 0L)
+ stop("maxgap must be a non-negative integer value of length 1")
+ if (!length(minoverlap) || length(minoverlap) != 1L || is.na(minoverlap) || minoverlap < 1L)
+ stop("minoverlap must be a positive integer value of length 1")
+ if (!length(which) || length(which) != 1L || is.na(which))
+ stop("which must be a logical vector of length 1. Either TRUE/FALSE")
+ if (!length(nomatch) || length(nomatch) != 1L || (!is.na(nomatch) && nomatch!=0L))
+ stop("nomatch must either be NA or 0, or (ideally) NA_integer_ or 0L")
+ type = match.arg(type)
+ mult = match.arg(mult)
+ if (type == "equal")
+ stop("type = 'equal' is not implemented yet. But note that this is just the same as a normal data.table join y[x, ...], unless you are also interested in setting 'minoverlap / maxgap' arguments. But those arguments are not implemented yet as well.")
+ if (maxgap > 0L || minoverlap > 1L)
+ stop("maxgap and minoverlap arguments are not yet implemented.")
+ if (is.null(by.y))
+ stop("'y' must be keyed (i.e., sorted, and, marked as sorted). Call setkey(y, ...) first, see ?setkey. Also check the examples in ?foverlaps.")
+ if (length(by.x) < 2L || length(by.y) < 2L)
+ stop("'by.x' and 'by.y' should contain at least two column names (or numbers) each - corresponding to 'start' and 'end' points of intervals. Please see ?foverlaps and examples for more info.")
+ if (is.numeric(by.x)) {
+ if (any(by.x < 0L) || any(by.x > length(x)))
+ stop("Invalid numeric value for 'by.x'; it should be a vector with values 1 <= by.x <= length(x)")
+ by.x = names(x)[by.x]
+ }
+ if (is.numeric(by.y)) {
+ if (any(by.y < 0L) || any(by.y > length(y)))
+ stop("Invalid numeric value for 'by.x'; it should be a vector with values 1 <= by.y <= length(y)")
+ by.y = names(y)[by.y]
+ }
+ if (!length(by.x) || !is.character(by.x))
+ stop("A non-empty vector of column names is required for by.x")
+ if (!length(by.y) || !is.character(by.y))
+ stop("A non-empty vector of column names is required for by.y")
+ if (!identical(by.y, key(y)[seq_along(by.y)]))
+ stop("The first ", length(by.y), " columns of y's key is not identical to the columns specified in by.y.")
+ if (any(is.na(chmatch(by.x, names(x)))))
+ stop("Elements listed in 'by.x' must be valid names in data.table 'x'")
+ if (any(is.na(chmatch(by.y, names(y)))))
+ stop("Elements listed in 'by.y' must be valid names in data.table 'y'")
+ if (anyDuplicated(by.x) || anyDuplicated(by.y))
+ stop("Duplicate columns are not allowed in overlap joins. This may change in the future.")
+ if (length(by.x) != length(by.y))
+ stop("length(by.x) != length(by.y). Columns specified in by.x should correspond to columns specified in by.y and should be of same lengths.")
+
+ xnames = by.x; xintervals = tail(xnames, 2L);
+ ynames = by.y; yintervals = tail(ynames, 2L);
+ if (!storage.mode(x[[xintervals[1L]]]) %chin% c("double", "integer") || !storage.mode(x[[xintervals[2L]]]) %chin% c("double", "integer"))
+ stop("The last two columns in by.x should correspond to the 'start' and 'end' intervals in data.table 'x' and must be integer/numeric type.")
+ if ( any(x[[xintervals[2L]]] - x[[xintervals[1L]]] < 0L) )
+ stop("All entries in column ", xintervals[1L], " should be <= corresponding entries in column ", xintervals[2L], " in data.table 'x'")
+ if (!storage.mode(y[[yintervals[1L]]]) %chin% c("double", "integer") || !storage.mode(y[[yintervals[2L]]]) %chin% c("double", "integer"))
+ stop("The last two columns in by.y should correspond to the 'start' and 'end' intervals in data.table 'y' and must be integer/numeric type.")
+ if ( any(y[[yintervals[2L]]] - y[[yintervals[1L]]] < 0L) )
+ stop("All entries in column ", yintervals[1L], " should be <= corresponding entries in column ", yintervals[2L], " in data.table 'y'")
+ ## see NOTES below:
+ yclass = c(class(y[[yintervals[1L]]]), class(y[[yintervals[2L]]]))
+ isdouble = FALSE; isposix = FALSE
+ if ( any(c("numeric", "POSIXct") %chin% yclass) ) {
+ # next representive double > x under the given precision (48,56 or 64-bit in data.table) = x*incr
+ dt_eps <- function() {
+ bits = floor(log2(.Machine$double.eps))
+ 2 ^ (bits + (getNumericRounding() * 8L))
+ }
+ incr = 1 + dt_eps()
+ isdouble = TRUE
+ isposix = "POSIXct" %chin% yclass
+ } else incr = 1L # integer or Date class for example
+
+ ## hopefully all checks are over. Now onto the actual task at hand.
+ origx = x; x = shallow(x, by.x)
+ origy = y; y = shallow(y, by.y)
+ if (identical(by.x, key(origx)[seq_along(by.x)]))
+ setattr(x, 'sorted', by.x)
+ setattr(y, 'sorted', by.y) ## is definitely sorted on by.y
+ roll = switch(type, start=, end=, equal= 0.0,
+ any=, within= +Inf)
+ make_call <- function(names, fun=NULL) {
+ if (is.character(names))
+ names = lapply(names, as.name)
+ call = c(substitute(fun, list(fun=fun)), names)
+ if (!is.null(fun)) as.call(call) else call
+ }
+ construct <- function(icols, mcols, type=type) {
+ icall = make_call(icols)
+ setattr(icall, 'names', icols)
+ mcall = make_call(mcols, quote(c))
+ if (type %chin% c("within", "any")) {
+ mcall[[3L]] = substitute(
+ if (isposix) unclass(val)*incr # incr is okay since this won't be negative
+ else if (isdouble) {
+ # fix for #1006 - 0.0 occurs in both start and end
+ # better fix for 0.0, and other -ves. can't use 'incr'
+ # hopefully this doesn't open another can of worms
+ (val+dt_eps())*(1 + sign(val)*dt_eps())
+ }
+ else val+incr,
+ list(val = mcall[[3L]], incr = incr))
+ }
+ make_call(c(icall, pos=mcall), quote(list))
+ }
+ uycols = switch(type, start = yintervals[1L],
+ end = yintervals[2L], any =,
+ within =, equal = yintervals)
+ call = construct(head(ynames, -2L), uycols, type)
+ if (verbose) {last.started.at=proc.time()[3];cat("unique() + setkey() operations done in ...");flush.console()}
+ uy = unique(y[, eval(call)])
+ setkey(uy)[, `:=`(lookup = list(list(integer(0))), type_lookup = list(list(integer(0))), count=0L, type_count=0L)]
+ if (verbose) {cat(round(proc.time()[3]-last.started.at,3),"secs\n");flush.console}
+ matches <- function(ii, xx, del, ...) {
+ cols = setdiff(names(xx), del)
+ xx = shallow(xx, cols)
+ ans = bmerge(xx, ii, seq_along(xx), seq_along(xx), haskey(xx), integer(0), verbose=verbose, ...)
+ # vecseq part should never run here, but still...
+ if (ans$allLen1) ans$starts else vecseq(ans$starts, ans$lens, NULL)
+ }
+ indices <- function(x, y, intervals, ...) {
+ if (type == "start") {
+ sidx = eidx = matches(x, y, intervals[2L], rollends=c(FALSE,FALSE), ...) ## TODO: eidx can be set to integer(0)
+ } else if (type == "end") {
+ eidx = sidx = matches(x, y, intervals[1L], rollends=c(FALSE,FALSE), ...) ## TODO: sidx can be set to integer(0)
+ } else {
+ sidx = matches(x, y, intervals[2L], rollends=rep(type == "any", 2L), ...)
+ eidx = matches(x, y, intervals[1L], rollends=c(FALSE,TRUE), ...)
+ }
+ list(sidx, eidx)
+ }
+ # nomatch has no effect here, just for passing arguments consistently to `bmerge`
+ .Call(Clookup, uy, nrow(y), indices(uy, y, yintervals, nomatch=0L, roll=roll), maxgap, minoverlap, mult, type, verbose)
+ if (maxgap == 0L && minoverlap == 1L) {
+ iintervals = tail(names(x), 2L)
+ if (verbose) {last.started.at=proc.time()[3];cat("binary search(es) done in ...");flush.console()}
+ xmatches = indices(uy, x, xintervals, nomatch=0L, roll=roll)
+ if (verbose) {cat(round(proc.time()[3]-last.started.at,3),"secs\n");flush.console}
+ olaps = .Call(Coverlaps, uy, xmatches, mult, type, nomatch, verbose)
+ } else if (maxgap == 0L && minoverlap > 1L) {
+ stop("Not yet implemented")
+ } else if (maxgap > 0L && minoverlap == 1L) {
+ stop("Not yet implemented")
+ } else if (maxgap > 0L && minoverlap > 1L) {
+ if (maxgap > minoverlap)
+ warning("maxgap > minoverlap. maxgap will have no effect here.")
+ stop("Not yet implemented")
+ }
+ setDT(olaps)
+ setnames(olaps, c("xid", "yid"))
+ yid = NULL # for 'no visible binding for global variable' from R CMD check on i clauses below
+ # if (type == "any") setorder(olaps) # at times the combine operation may not result in sorted order
+ # CsubsetDT bug has been fixed by Matt. So back to using it! Should improve subset substantially.
+ if (which) {
+ if (mult %chin% c("first", "last"))
+ return (olaps$yid)
+ else if (!is.na(nomatch))
+ return (.Call(CsubsetDT, olaps, which(olaps$yid > 0L), seq_along(olaps)))
+ else return (olaps)
+ } else {
+ if (!is.na(nomatch))
+ olaps = .Call(CsubsetDT, olaps, which(olaps$yid > 0L), seq_along(olaps))
+ ycols = setdiff(names(origy), head(by.y, -2L))
+ idx = chmatch(ycols, names(origx), nomatch=0L)
+ ans = .Call(CsubsetDT, origx, olaps$xid, seq_along(origx))
+ if (any(idx>0L))
+ setnames(ans, names(ans)[idx], paste("i.", names(ans)[idx], sep=""))
+ xcols1 = head(by.x, -2L)
+ xcols2 = setdiff(names(ans), xcols1)
+ ans[, (ycols) := .Call(CsubsetDT, origy, olaps$yid, chmatch(ycols, names(origy)))]
+ setcolorder(ans, c(xcols1, ycols, xcols2))
+ return (ans[])
+ }
+}
+
+## Notes: (If there's a better way than the solution I propose here, I'd be glad to apply it.)
+# Side note: This post is a great read on floating points: http://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/
+
+# We add 1L to the end coordinate in case of integers to generate lookup to identify interval overlaps properly. And that is great!
+# However for double precision, there are two issues we need to take into consideration:
+
+# ---
+# Firstly, assuming 64-bit precision, we can't simply add 1L. For e.g., consider:
+ # x = data.table(start=0.88, end=0.88)
+ # y = data.table(start=0.26, end=0.61, key=c("start", "end"))
+# and we'd like to do:
+ # foverlaps(x, y, type="any")
+# Adding 1 to 0.61 will result in 1.61, and will make sure 0.88 falls between 0.26 and 1.61, and that's wrong!
+
+# POSIXct objects are internally numeric as well.
+
+# So how do we determine the "increment" (1L for integers) for numeric type?
+# To get there, we need to understand that the "increment" is a great idea. Just the value isn't correct in case of numerics. Let's consider
+# just ordinary "numeric" objects (non-POSIXct). The idea behind increment is to ensure that the "end" coordinate gets incremented to the *next
+# distinguishable representative number*!!! In case of integers, the next integer after 5L is 6L. Simple. Increment is 1L (assuming no integer
+# overflows). In case of "numeric" that "increment" is (1 + .Machine$double.eps), and this gets *multiplied* (not added) to "end" coordinate.
+# Simple again. It fixes the problem, for now (read on).
+
+# NOTE THAT simply doing ("end" + .Machine$double.eps ^ 0.5) is insufficient because this doesn't work as the numbers grow bigger. For e.g., try:
+ # identical(3e8, 3e8+.Machine$double.eps^0.5)
+ # identical(3e8, 3e8*(1+.Machine$double.eps))
+
+# The important point is that we **need to be ABSOLUTELY sure** that "end coordinate" gets incremented to it's *next representative number*. Why? Consider a 'subject' interval [4,4]. When we collapse this to get the 1D-form and take `unique`, if the "end" coordinate is not distinguishable from the start coordinate, `unique` will return just one value. And this brings us back to square-one (the reason why we needed to add one in the first place!!). For example, consider query = c(5,5) and [...]
+
+# For POSIXct objects, we could still do the same. But a) multiplication is not supported for POSIXt objects, so we'll have to unclass it, multiply and convert back.. which is not ideal - timezone, time spent on conversion etc.. and b) all.equal in base considers a tolerance of 0.001 for POSIXt objects, I'm guessing this is for "millisecond" resolution? The problem with (b) is that more than millisecond resolution will return incorrect results again.
+
+ # # More than millisecond resolution. Results are not stable.
+ # tt = c( as.POSIXct('2011-10-11 07:49:36.0003'), as.POSIXct('2011-10-11 07:49:36.0199'), as.POSIXct('2011-10-11 07:49:36.0399'))
+ # DT1 = data.table(start=tt, end=tt)
+ # DT2 = data.table(start=tt[2], end=tt[2])
+ # setkey(DT2)
+ # foverlaps(DT1, DT2, which=TRUE)
+
+# So, to put an end to this problem, we'll unclass it, multiply and convert back. In any case, the join does not depend on the timezone, as the internal numeric equivalent seems to be identical irrespective of the time zones.. So that's good news!
+# ---
+# Secondly, we've 48,56 and 64-bit precision in data.table (through get and setNumericRounding). And default right now is 48-bit. So, we've to make sure that the "end" interval should be multiplied accordingly depending on the current precision. This is taken care of by dt_eps(). Quite simple to follow, really.
+# ---
+
+# I believe this is the most correct way of doing it (probably convoluted way, and there are simpler ways... but I've not come across it).
+
+# Tests are added to ensure we cover these aspects (to my knowledge) to ensure that any undesirable changes in the future breaks those tests.
+
+# Conclusion: floating point manipulations are hell!
diff --git a/R/fread.R b/R/fread.R
new file mode 100644
index 0000000..7cf55d4
--- /dev/null
+++ b/R/fread.R
@@ -0,0 +1,121 @@
+
+fread <- function(input="",sep="auto",sep2="auto",nrows=-1L,header="auto",na.strings="NA",stringsAsFactors=FALSE,verbose=getOption("datatable.verbose"),autostart=1L,skip=0L,select=NULL,drop=NULL,colClasses=NULL,integer64=getOption("datatable.integer64"),dec=if (sep!=".") "." else ",", col.names, check.names=FALSE, encoding="unknown", strip.white=TRUE, showProgress=getOption("datatable.showProgress"),data.table=getOption("datatable.fread.datatable")) {
+ if (!is.character(dec) || length(dec)!=1L || nchar(dec)!=1) stop("dec must be a single character e.g. '.' or ','")
+ # handle encoding, #563
+ if (!encoding %in% c("unknown", "UTF-8", "Latin-1")) {
+ stop("Argument 'encoding' must be 'unknown', 'UTF-8' or 'Latin-1'.")
+ }
+ if (!strip.white %in% c(TRUE, FALSE)) {
+ stop("Argument 'strip.white' must be logical TRUE/FALSE")
+ }
+ if (getOption("datatable.fread.dec.experiment") && Sys.localeconv()["decimal_point"] != dec) {
+ oldlocale = Sys.getlocale("LC_NUMERIC")
+ if (verbose) cat("dec='",dec,"' but current locale ('",oldlocale,"') has dec='",Sys.localeconv()["decimal_point"],"'. Attempting to change locale to one that has the desired decimal point.\n",sep="")
+ on.exit(Sys.setlocale("LC_NUMERIC", oldlocale))
+ if (dec==".") {
+ tt <- Sys.setlocale("LC_NUMERIC", "C")
+ if (!identical(tt,"C")) stop("It is supposed to be guaranteed that Sys.setlocale('LC_NUMERIC', 'C') will always work!")
+ }
+ else suppressWarnings(tt <- Sys.setlocale("LC_NUMERIC", "")) # hope get lucky with system locale; i.e. France in France
+ if (Sys.localeconv()["decimal_point"] != dec) {
+ if (verbose) cat("Changing to system locale ('",tt,"') did not provide the desired dec. Now trying any provided in getOption('datatable.fread.dec.locale')\n", sep="")
+ for (i in getOption("datatable.fread.dec.locale")) {
+ if (i=="") {
+ if (verbose) cat("Ignoring ''\n")
+ next
+ } else {
+ if (verbose) cat("Trying '",i,"'\n", sep="")
+ }
+ suppressWarnings(tt <- Sys.setlocale("LC_NUMERIC", i))
+ cmd = paste("Sys.setlocale('LC_NUMERIC','",i,"')",sep="")
+ if (is.null(tt)) stop(cmd," returned NULL (locale information is unavailable). See ?Sys.setlocale.")
+ if (tt=="") {
+ if (verbose) cat(cmd, ' returned ""; i.e., this locale name is not valid on your system. It was provided by you in getOption("datatable.fread.dec.locale"). See ?Sys.setlocale and ?fread.')
+ next
+ }
+ if (toupper(tt)!=toupper(i)) {
+ warning(cmd, " returned '",tt,"' != '",i,"' (not NULL not '' and allowing for case differences). This may not be a problem but please report.")
+ }
+ if (Sys.localeconv()["decimal_point"] == dec) break
+ if (verbose) cat("Successfully changed locale but it provides dec='",Sys.localeconv()["decimal_point"],"' not the desired dec", sep="")
+ }
+ }
+ if (Sys.localeconv()["decimal_point"] != dec) {
+ stop('Unable to change to a locale which provides the desired dec. You will need to add a valid locale name to getOption("datatable.fread.dec.locale"). See the long paragraph in ?fread.', if(verbose)'' else ' Run again with verbose=TRUE to inspect.') # see issue #502
+ }
+ if (verbose) cat("This R session's locale is now '",tt,"' which provides the desired decimal point for reading numerics in the file - success! The locale will be restored to what it was ('",oldlocale,") even if the function fails for other reasons.\n")
+ }
+
+ is_url <- function(x) grepl("^(http|ftp)s?://", x)
+ is_secureurl <- function(x) grepl("^(http|ftp)s://", x)
+ is_file <- function(x) grepl("^file://", x)
+ if (!is.character(input) || length(input)!=1L) {
+ stop("'input' must be a single character string containing a file name, a command, full path to a file, a URL starting 'http[s]://', 'ftp[s]://' or 'file://', or the input data itself")
+ } else if (is_url(input) || is_file(input)) {
+ tt = tempfile()
+ on.exit(unlink(tt), add = TRUE)
+ # In text mode on Windows-only, R doubles up \r to make \r\r\n line endings. mode="wb" avoids that. See ?connections:"CRLF"
+ if (!is_secureurl(input)) {
+ download.file(input, tt, mode = "wb", quiet = !showProgress)
+ } else {
+ if (!requireNamespace("curl", quietly = TRUE))
+ stop("Input URL requires https:// connection for which fread() requires 'curl' package, but cannot be found. Please install the package using 'install.packages()'.")
+ curl::curl_download(input, tt, mode = "wb", quiet = !showProgress)
+ }
+ input = tt
+ } else if (input == "" || length(grep('\\n|\\r', input)) > 0) {
+ # text input
+ } else if (!file.exists(input)) {
+ if (length(grep(' ', input)) == 0) stop("File '",input,"' does not exist. Include one or more spaces to consider the input a system command.")
+ tt = tempfile()
+ on.exit(unlink(tt), add = TRUE)
+ if (.Platform$OS.type == "unix") {
+ if (file.exists('/dev/shm') && file.info('/dev/shm')$isdir) {
+ tt = tempfile(tmpdir = '/dev/shm')
+ }
+ system(paste('(', input, ') > ', tt, sep=""))
+ } else {
+ shell(paste('(', input, ') > ', tt, sep=""))
+ }
+ input = tt
+ }
+ if (identical(header,"auto")) header=NA
+ if (identical(sep,"auto")) sep=NULL
+ if (is.atomic(colClasses) && !is.null(names(colClasses))) colClasses = tapply(names(colClasses),colClasses,c,simplify=FALSE)
+ ans = .Call(Creadfile,input,sep,as.integer(nrows),header,na.strings,verbose,as.integer(autostart),skip,select,drop,colClasses,integer64,dec,encoding,strip.white,as.integer(showProgress))
+ nr = length(ans[[1]])
+ if ( integer64=="integer64" && !exists("print.integer64") && any(sapply(ans,inherits,"integer64")) )
+ warning("Some columns have been read as type 'integer64' but package bit64 isn't loaded. Those columns will display as strange looking floating point data. There is no need to reload the data. Just require(bit64) to obtain the integer64 print method and print the data again.")
+ setattr(ans,"row.names",.set_row_names(nr))
+
+ if (isTRUE(data.table)) {
+ setattr(ans, "class", c("data.table", "data.frame"))
+ alloc.col(ans)
+ } else {
+ setattr(ans, "class", "data.frame")
+ }
+ if (isTRUE(as.logical(check.names))) {
+ setattr(ans, 'names', make.unique(names(ans)))
+ }
+ as_factor <- function(x) {
+ lev = forderv(x, retGrp = TRUE)
+ # get levels, also take care of all sorted condition
+ if (length(lev)) lev = x[lev[attributes(lev)$starts]]
+ else lev = x[attributes(lev)$starts]
+ ans = chmatch(x, lev)
+ setattr(ans, 'levels', lev)
+ setattr(ans, 'class', 'factor')
+ }
+ if (isTRUE(as.logical(stringsAsFactors))) {
+ cols = which(vapply(ans, is.character, TRUE))
+ if (length(cols)) {
+ if (verbose) cat("Converting column(s) [", paste(names(ans)[cols], collapse = ", "), "] from 'char' to 'factor'\n", sep = "")
+ for (j in cols)
+ set(ans, j = j, value = as_factor(.subset2(ans, j)))
+ }
+ }
+ # FR #768
+ if (!missing(col.names))
+ setnames(ans, col.names) # setnames checks and errors automatically
+ ans
+}
diff --git a/R/getdots.R b/R/getdots.R
new file mode 100644
index 0000000..2f4c342
--- /dev/null
+++ b/R/getdots.R
@@ -0,0 +1,10 @@
+## NOTE: this has problems when '...' contains quoted names that have special symbols
+## for a better option see the logic in setkey
+
+getdots <- function()
+{
+ # return a string vector of the arguments in '...'
+ # My long winded way: gsub(" ","",unlist(strsplit(deparse(substitute(list(...))),"[(,)]")))[-1]
+ # Peter Dalgaard's & Brian Ripley helped out and ended up with :
+ as.character(match.call(sys.function(-1), call=sys.call(-1), expand.dots=FALSE)$...)
+}
diff --git a/R/last.R b/R/last.R
new file mode 100644
index 0000000..368ce7d
--- /dev/null
+++ b/R/last.R
@@ -0,0 +1,13 @@
+
+# data.table defined last(x) with no arguments, just for last. If you need the last 10 then use tail(x,10).
+# This last is implemented this way for compatibility with xts::last which is S3 generic taking 'n' and 'keep' arguments
+# We'd like last() on vectors to be fast, so that's a direct x[NROW(x)] as it was in data.table, otherwise use xts's.
+# If xts is loaded higher than data.table, xts::last will work but slower.
+last <- function(x, ...) {
+ if (nargs()==1L || !"package:xts" %in% search()) {
+ if (is.data.frame(x)) return(x[NROW(x),])
+ if (!length(x)) return(x) else return(x[[length(x)]]) # for vectors, [[ works like [
+ }
+ xts::last(x,...) # UseMethod("last") doesn't find xts's methods, not sure what I did wrong.
+}
+
diff --git a/R/like.R b/R/like.R
new file mode 100644
index 0000000..315cd23
--- /dev/null
+++ b/R/like.R
@@ -0,0 +1,16 @@
+like <- function(vector, pattern)
+{
+ # Intended for use with a data.table 'where'
+ # Don't use * or % like SQL's like. Uses regexpr syntax - more powerful.
+ if (is.factor(vector)) {
+ as.integer(vector) %in% grep(pattern,levels(vector))
+ } else {
+ # most usually character, but integer and numerics will be silently coerced by grepl
+ grepl(pattern,vector)
+ }
+ # returns 'logical' so can be combined with other where clauses.
+}
+
+"%like%" = like
+
+
diff --git a/R/merge.R b/R/merge.R
new file mode 100644
index 0000000..cde76d2
--- /dev/null
+++ b/R/merge.R
@@ -0,0 +1,80 @@
+merge.data.table <- function(x, y, by = NULL, by.x = NULL, by.y = NULL, all = FALSE, all.x = all,
+ all.y = all, sort = TRUE, suffixes = c(".x", ".y"), allow.cartesian=getOption("datatable.allow.cartesian"), ...) {
+ if (!sort %in% c(TRUE, FALSE))
+ stop("Argument 'sort' should be logical TRUE/FALSE")
+ if (!is.data.table(y)) {
+ y = as.data.table(y)
+ if (missing(by) && missing(by.x)) {
+ by = key(x)
+ }
+ }
+ if (any(duplicated(names(x)))) stop("x has some duplicated column name(s): ",paste(names(x)[duplicated(names(x))],collapse=","),". Please remove or rename the duplicate(s) and try again.")
+ if (any(duplicated(names(y)))) stop("y has some duplicated column name(s): ",paste(names(y)[duplicated(names(y))],collapse=","),". Please remove or rename the duplicate(s) and try again.")
+
+ ## set up 'by'/'by.x'/'by.y'
+ if ( (!is.null(by.x) || !is.null(by.y)) && length(by.x)!=length(by.y) )
+ stop("`by.x` and `by.y` must be of same length.")
+ if (!missing(by) && !missing(by.x))
+ warning("Supplied both `by` and `by.x/by.y`. `by` argument will be ignored.")
+ if (!is.null(by.x)) {
+ if ( !is.character(by.x) || !is.character(by.y))
+ stop("A non-empty vector of column names are required for `by.x` and `by.y`.")
+ if (!all(by.x %in% names(x)))
+ stop("Elements listed in `by.x` must be valid column names in x.")
+ if (!all(by.y %in% names(y)))
+ stop("Elements listed in `by.y` must be valid column names in y.")
+ by = by.x
+ names(by) = by.y
+ } else {
+ if (is.null(by))
+ by = intersect(key(x), key(y))
+ if (is.null(by))
+ by = key(x)
+ if (is.null(by))
+ stop("Can not match keys in x and y to automatically determine appropriate `by` parameter. Please set `by` value explicitly.")
+ if (length(by) == 0L || !is.character(by))
+ stop("A non-empty vector of column names for `by` is required.")
+ if (!all(by %in% intersect(colnames(x), colnames(y))))
+ stop("Elements listed in `by` must be valid column names in x and y")
+ by.x = by.y = by
+ }
+ # with i. prefix in v1.9.3, this goes away. Left here for now ...
+ ## sidestep the auto-increment column number feature-leading-to-bug by
+ ## ensuring no names end in ".1", see unit test
+ ## "merge and auto-increment columns in y[x]" in test-data.frame.like.R
+ start = setdiff(names(x), by.x)
+ end = setdiff(names(y), by.y)
+ dupnames = intersect(start, end)
+ if (length(dupnames)) {
+ start[chmatch(dupnames, start, 0L)] = paste(dupnames, suffixes[1L], sep="")
+ end[chmatch(dupnames, end, 0L)] = paste(dupnames, suffixes[2L], sep="")
+ }
+
+ dt = y[x,nomatch=ifelse(all.x,NA,0),on=by,allow.cartesian=allow.cartesian] # includes JIS columns (with a i. prefix if conflict with x names)
+
+ if (all.y && nrow(y)) { # If y does not have any rows, no need to proceed
+ # Perhaps not very commonly used, so not a huge deal that the join is redone here.
+ missingyidx = y[!x,which=TRUE,on=by,allow.cartesian=allow.cartesian]
+ if (length(missingyidx)) {
+ yy = y[missingyidx]
+ othercolsx = setdiff(names(x), by)
+ if (length(othercolsx)) {
+ tmp = rep.int(NA_integer_, length(missingyidx))
+ # TO DO: use set() here instead..
+ yy = cbind(yy, x[tmp, othercolsx, with = FALSE])
+ }
+ # empty data.tables (nrow =0, ncol>0) doesn't skip names anymore in new rbindlist
+ # takes care of #5672 without having to save names. This is how it should be, IMHO.
+ dt = rbind(dt, yy, use.names=FALSE)
+ }
+ }
+ # X[Y] sytax puts JIS i columns at the end, merge likes them alongside i.
+ newend = setdiff(names(y), by.y)
+ # fix for #1290, make sure by.y order is set properly before naming
+ setcolorder(dt, c(by.y, setdiff(names(dt), c(by.y, newend)), newend))
+ setnames(dt, c(by.x, start, end))
+ if (nrow(dt) > 0L) {
+ setkeyv(dt, if (sort) by.x else NULL)
+ }
+ dt
+}
diff --git a/R/onAttach.R b/R/onAttach.R
new file mode 100644
index 0000000..95b0c84
--- /dev/null
+++ b/R/onAttach.R
@@ -0,0 +1,8 @@
+.onAttach <- function(libname, pkgname) {
+ # Runs when attached to search() path such as by library() or require()
+ if (interactive()) {
+ packageStartupMessage('data.table ',as.character(packageVersion("data.table")),' For help type ?data.table or https://github.com/Rdatatable/data.table/wiki')
+ packageStartupMessage('The fastest way to learn (by data.table authors): https://www.datacamp.com/courses/data-analysis-the-data-table-way')
+ }
+}
+
diff --git a/R/onLoad.R b/R/onLoad.R
new file mode 100644
index 0000000..734e162
--- /dev/null
+++ b/R/onLoad.R
@@ -0,0 +1,104 @@
+.onLoad <- function(libname, pkgname) {
+ # Runs when loaded but not attached to search() path; e.g., when a package just Imports (not Depends on) data.table
+
+ "Please read FAQ 2.23 (vignette('datatable-faq')) which explains in detail why data.table adds one for loop to the start of base::cbind.data.frame and base::rbind.data.frame. If there is a better solution we will gladly change it."
+ # Commented as a character string so this message is retained and seen by anyone who types data.table:::.onLoad
+ tt = base::cbind.data.frame
+ ss = body(tt)
+ if (class(ss)!="{") ss = as.call(c(as.name("{"), ss))
+ prefix = if (!missing(pkgname)) "data.table::" else "" # R provides the arguments when it calls .onLoad, I don't in dev/test
+ if (!length(grep("data.table",ss[[2]]))) {
+ ss = ss[c(1,NA,2:length(ss))]
+ ss[[2]] = parse(text=paste("if (!identical(class(..1),'data.frame')) for (x in list(...)) { if (inherits(x,'data.table')) return(",prefix,"data.table(...)) }",sep=""))[[1]]
+ body(tt)=ss
+ (unlockBinding)("cbind.data.frame",baseenv())
+ assign("cbind.data.frame",tt,envir=asNamespace("base"),inherits=FALSE)
+ lockBinding("cbind.data.frame",baseenv())
+ }
+ tt = base::rbind.data.frame
+ ss = body(tt)
+ if (class(ss)!="{") ss = as.call(c(as.name("{"), ss))
+ if (!length(grep("data.table",ss[[2]]))) {
+ ss = ss[c(1,NA,2:length(ss))]
+ ss[[2]] = parse(text=paste("for (x in list(...)) { if (inherits(x,'data.table')) return(",prefix,".rbind.data.table(...)) }",sep=""))[[1]] # fix for #4995
+ body(tt)=ss
+ (unlockBinding)("rbind.data.frame",baseenv())
+ assign("rbind.data.frame",tt,envir=asNamespace("base"),inherits=FALSE)
+ lockBinding("rbind.data.frame",baseenv())
+ }
+ # Set options for the speed boost in v1.8.0 by avoiding 'default' arg of getOption(,default=)
+ opts = c("datatable.verbose"="FALSE", # datatable.<argument name>
+ "datatable.nomatch"="NA_integer_", # datatable.<argument name>
+ "datatable.optimize"="Inf", # datatable.<argument name>
+ "datatable.print.nrows"="100L", # datatable.<argument name>
+ "datatable.print.topn"="5L", # datatable.<argument name>
+ "datatable.allow.cartesian"="FALSE", # datatable.<argument name>
+ "datatable.dfdispatchwarn"="TRUE", # not a function argument
+ "datatable.warnredundantby"="TRUE", # not a function argument
+ "datatable.alloccol"="quote(max(100L,ncol(DT)+64L))",# argument 'n' of alloc.col. Allocate at least 64 spare slots by default. Needs to be 100L floor to save small object reallocs.
+ "datatable.integer64"="'integer64'", # datatable.<argument name> integer64|double|character
+ "datatable.showProgress"="1L", # in fread
+ "datatable.auto.index"="TRUE", # DT[col=="val"] to auto add index so 2nd time faster
+ "datatable.fread.datatable"="TRUE",
+ "datatable.old.bywithoutby"="FALSE", # temp rollback method for code migration, will be removed in future
+ "datatable.fread.dec.experiment"="TRUE", # temp. will remove once stable
+ "datatable.fread.dec.locale"=if (.Platform$OS.type=="unix") "'fr_FR.utf8'" else "'French_France.1252'",
+ "datatable.prettyprint.char" = NULL # FR #1091
+ )
+ for (i in setdiff(names(opts),names(options()))) {
+ eval(parse(text=paste("options(",i,"=",opts[i],")",sep="")))
+ }
+ # reshape2
+ # Tried this :
+ # if (!"package:reshape2" %in% search()) {
+ # # temporary until reshape2 pull request to make generic is on CRAN ...
+ # try(library(reshape2, pos="package:base", quietly=TRUE, warn.conflicts=FALSE), silent=TRUE)
+ # }
+ # which works. But then when melt in data.table is loaded, _that's_ what generates the mask message.
+ # There's also a NOTE: Package startup functions should not change the search path.
+ # Therefore, removed. Users will need to make sure reshape2 isn't loaded, or loaded behind data.table on search()
+
+ # Test R behaviour ...
+
+ x = 1:3
+ y = list(x)
+ .R.listCopiesNamed <<- (address(x) != address(y[[1]])) # FALSE from R 3.1
+
+ DF = data.frame(a=1:3, b=4:6)
+ add1 = address(DF$a)
+ add2 = address(DF$b)
+ names(DF) = c("A","B")
+ add3 = address(DF$A)
+ add4 = address(DF$B)
+ .R.assignNamesCopiesAll <<- add1 != add3 # FALSE from R 3.1
+ if ((add1 == add3) != (add2 == add4)) stop("If one column is copied surely the other should be as well, when checking .R.assignNamesCopiesAll")
+
+ DF = data.frame(a=1:3, b=4:6)
+ add1 = address(DF$a)
+ add2 = address(DF)
+ DF[2,"b"] = 7 # changed b but not a
+ add3 = address(DF$a)
+ add4 = address(DF)
+ .R.subassignCopiesOthers <<- add1 != add3 # FALSE from R 3.1
+ .R.subassignCopiesVecsxp <<- add2 != add4 # currently TRUE in R 3.1, but could feasibly change
+
+ invisible()
+}
+
+# Switch on these variables instead of getRversion(). Set to TRUE just to create them as a single logical. They are set by .onLoad() above.
+.R.listCopiesNamed = TRUE
+.R.assignNamesCopiesAll = TRUE
+.R.subassignCopiesOthers = TRUE
+.R.subassignCopiesVecsxp = TRUE
+
+getRversion <- function(...) stop("Reminder to data.table developers: don't use getRversion() internally. Add a behaviour test to .onLoad instead.")
+# 1) using getRversion() wasted time when R3.0.3beta was released without the changes we expected in getRversion()>"3.0.2".
+# 2) R-devel and ourselves may wish to tinker with R-devel, turning on and off features in the same version number. So it's better if data.table doesn't hard code expectations into the version number.
+# 3) The discipline of adding a feaure test here helps fully understand the change.
+# 4) Defining getRversion with a stop() here helps prevent new switches on getRversion() being added in future. Easily circumvented but the point is to issue the message above.
+
+.onUnload <- function(libpath) {
+ # fix for #474. the shared object name is different from package name
+ # So 'detach' doesn't find datatable.so, as it looks by default for data.table.so
+ library.dynam.unload("datatable", libpath)
+}
diff --git a/R/setkey.R b/R/setkey.R
new file mode 100644
index 0000000..6be9e53
--- /dev/null
+++ b/R/setkey.R
@@ -0,0 +1,656 @@
+setkey <- function(x, ..., verbose=getOption("datatable.verbose"), physical=TRUE)
+{
+ if (is.character(x)) stop("x may no longer be the character name of the data.table. The possibility was undocumented and has been removed.")
+ cols = as.character(substitute(list(...))[-1])
+ if (!length(cols)) cols=colnames(x)
+ else if (identical(cols,"NULL")) cols=NULL
+ setkeyv(x, cols, verbose=verbose, physical=physical)
+}
+
+set2key <- function(...) setkey(..., physical=FALSE)
+set2keyv <- function(...) setkeyv(..., physical=FALSE)
+
+setkeyv <- function(x, cols, verbose=getOption("datatable.verbose"), physical=TRUE)
+{
+ if (is.null(cols)) { # this is done on a data.frame when !cedta at top of [.data.table
+ if (physical) setattr(x,"sorted",NULL)
+ setattr(x,"index",NULL) # setkey(DT,NULL) also clears secondary keys. set2key(DT,NULL) just clears secondary keys.
+ return(invisible(x))
+ }
+ if (!is.data.table(x)) stop("x is not a data.table")
+ if (!is.character(cols)) stop("cols is not a character vector. Please see further information in ?setkey.")
+ if (physical && identical(attr(x,".data.table.locked"),TRUE)) stop("Setting a physical key on .SD is reserved for possible future use; to modify the original data's order by group. Try set2key instead. Or, set*(copy(.SD)) as a (slow) last resort.")
+ if (!length(cols)) {
+ warning("cols is a character vector of zero length. Removed the key, but use NULL instead, or wrap with suppressWarnings() to avoid this warning.")
+ setattr(x,"sorted",NULL)
+ return(invisible(x))
+ }
+ if (identical(cols,"")) stop("cols is the empty string. Use NULL to remove the key.")
+ if (any(nchar(cols)==0)) stop("cols contains some blanks.")
+ if (!length(cols)) {
+ cols = colnames(x) # All columns in the data.table, usually a few when used in this form
+ } else {
+ # remove backticks from cols
+ cols <- gsub("`", "", cols)
+ miss = !(cols %in% colnames(x))
+ if (any(miss)) stop("some columns are not in the data.table: " %+% cols[miss])
+ }
+ alreadykeyedbythiskey = identical(key(x),cols)
+ if (".xi" %chin% names(x)) stop("x contains a column called '.xi'. Conflicts with internal use by data.table.")
+ for (i in cols) {
+ .xi = x[[i]] # [[ is copy on write, otherwise checking type would be copying each column
+ if (!typeof(.xi) %chin% c("integer","logical","character","double")) stop("Column '",i,"' is type '",typeof(.xi),"' which is not supported as a key column type, currently.")
+ }
+ if (!is.character(cols) || length(cols)<1) stop("'cols' should be character at this point in setkey")
+ if (verbose) {
+ tt = system.time(o <- forderv(x, cols, sort=TRUE, retGrp=FALSE)) # system.time does a gc, so we don't want this always on, until refcnt is on by default in R
+ cat("forder took", tt["user.self"]+tt["sys.self"], "sec\n")
+ } else {
+ o <- forderv(x, cols, sort=TRUE, retGrp=FALSE)
+ }
+ if (!physical) {
+ if (is.null(attr(x,"index",exact=TRUE))) setattr(x, "index", integer())
+ setattr(attr(x,"index",exact=TRUE), paste("__",paste(cols,collapse="__"),sep=""), o)
+ return(invisible(x))
+ }
+ setattr(x,"index",NULL) # TO DO: reorder existing indexes likely faster than rebuilding again. Allow optionally. Simpler for now to clear.
+ if (length(o)) {
+ if (alreadykeyedbythiskey) warning("Already keyed by this key but had invalid row order, key rebuilt. If you didn't go under the hood please let datatable-help know so the root cause can be fixed.")
+ if (verbose) {
+ tt = system.time(.Call(Creorder,x,o))
+ cat("reorder took", tt["user.self"]+tt["sys.self"], "sec\n")
+ } else {
+ .Call(Creorder,x,o)
+ }
+ } else {
+ if (verbose) cat("x is already ordered by these columns, no need to call reorder\n")
+ } # else empty integer() from forderv means x is already ordered by those cols, nothing to do.
+ if (!alreadykeyedbythiskey) setattr(x,"sorted",cols) # the if() just to save plonking an identical vector into the attribute
+ invisible(x)
+}
+
+key <- function(x) attr(x,"sorted",exact=TRUE)
+key2 <- function(x) {
+ ans = names(attributes(attr(x,"index",exact=TRUE)))
+ if (is.null(ans)) return(ans) # otherwise character() gets returned by next line
+ gsub("^__","",ans)
+}
+get2key <- function(x, col) attr(attr(x,"index",exact=TRUE),paste("__",col,sep=""),exact=TRUE) # work in progress, not yet exported
+
+"key<-" <- function(x,value) {
+ warning("The key(x)<-value form of setkey can copy the whole table. This is due to <- in R itself. Please change to setkeyv(x,value) or setkey(x,...) which do not copy and are faster. See help('setkey'). You can safely ignore this warning if it is inconvenient to change right now. Setting options(warn=2) turns this warning into an error, so you can then use traceback() to find and change your key<- calls.")
+ setkeyv(x,value)
+ # The returned value here from key<- is then copied by R before assigning to x, it seems. That's
+ # why we can't do anything about it without a change in R itself. If we return NULL (or invisible()) from this key<-
+ # method, the table gets set to NULL. So, although we call setkeyv(x,cols) here, and that doesn't copy, the
+ # returned value (x) then gets copied by R.
+ # So, solution is that caller has to call setkey or setkeyv directly themselves, to avoid <- dispatch and its copy.
+}
+
+haskey <- function(x) !is.null(key(x))
+
+# reverse a vector by reference (no copy)
+setrev <- function(x) .Call(Csetrev, x)
+
+# reorder a vector based on 'order' (integer)
+# to be used in fastorder instead of x[o], but in general, it's better to replace vector subsetting with this..?
+# Basic checks that all items of order are in range 1:n with no NAs are now made inside Creorder.
+# FOR INTERNAL USE ONLY
+setreordervec <- function(x, order) .Call(Creorder, x, order)
+
+# sort = sort.int = sort.list = order = is.unsorted <- function(...)
+# stop("Should never be called by data.table internals. Use is.sorted() on vectors, or forder() for lists and vectors.")
+# Nice idea, but users might use these in i or j e.g. blocking order caused tests 304 to fail.
+# Maybe just a grep through *.R for use of these function internally would be better (TO DO).
+
+# Don't use base::is.unsorted internally, because :
+# 1) it returns NA if any(is.na(.)) where NAs are detected at R level, inefficiently
+# 2) it uses locale whereas in data.table we control locale sorting independently (C locale currently, but
+# "sorted" attribute will need an extra attribute "locale" so we can check if key's locale is the current locale)
+# 3) wrapper needed, used to be :
+# identical(FALSE,is.unsorted(x)) && !(length(x)==1 && is.na(x))
+# where the && was needed to maintain backwards compatibility after r-devel's change of is.unsorted(NA) to FALSE (was NA) [May 2013].
+# The others (order, sort.int etc) are turned off to protect ourselves from using them internally, for speed and for
+# consistency; e.g., consistent twiddling of numeric/integer64, NA at the beginning of integer, locale ordering of character vectors.
+
+is.sorted <- function(x, by=seq_along(x)) {
+ if (is.list(x)) {
+ warning("Use 'if (length(o<-forderv(DT,by))) ...' for efficiency in one step, so you have o as well if not sorted.")
+ # could pass through a flag for forderv to return early on first FALSE. But we don't need that internally
+ # since internally we always then need ordering, an it's better in one step. Don't want inefficiency to creep in.
+ # This is only here for user/debugging use to check/test valid keys; e.g. data.table:::is.sorted(DT,by)
+ 0 == length(forderv(x,by,retGrp=FALSE,sort=TRUE))
+ } else {
+ if (!missing(by)) stop("x is vector but 'by' is supplied")
+ .Call(Cfsorted, x)
+ }
+ # Cfsorted could be named CfIsSorted, but since "sorted" is an adjective not verb, it's clear; e.g., Cfsort would sort it ("sort" is verb).
+ # Return value of TRUE/FALSE is relied on in [.data.table quite a bit on vectors. Simple. Stick with that (rather than -1/0/+1)
+ # Important to call forder.c::fsorted here, for consistent character ordering and numeric/integer64 twiddling.
+}
+
+forderv <- function(x, by=seq_along(x), retGrp=FALSE, sort=TRUE, order=1L, na.last=FALSE)
+{
+ if (!(sort || retGrp)) stop("At least one of retGrp or sort must be TRUE")
+ na.last = as.logical(na.last)
+ if (!length(na.last)) stop('length(na.last) = 0')
+ if (length(na.last) != 1L) {
+ warning("length(na.last) > 1, only the first element will be used")
+ na.last = na.last[1L]
+ }
+ # TO DO: export and document forder
+ if (is.atomic(x)) {
+ if (!missing(by) && !is.null(by)) stop("x is a single vector, non-NULL 'by' doesn't make sense")
+ by = NULL
+ if ( !missing(order) && (length(order) != 1L || !(order %in% c(1L, -1L))) )
+ stop("x is a single vector, length(order) must be =1 and it's value should be 1 (ascending) or -1 (descending).")
+ } else {
+ if (!length(x)) return(integer(0)) # to be consistent with base::order. this'll make sure forderv(NULL) will result in error
+ # (as base does) but forderv(data.table(NULL)) and forderv(list()) will return integer(0))
+ if (is.character(by)) by=chmatch(by, names(x))
+ by = as.integer(by)
+ if ( (length(order) != 1L && length(order) != length(by)) || any(!order %in% c(1L, -1L)) )
+ stop("x is a list, length(order) must be either =1 or =length(by) and each value should be 1 or -1 for each column in 'by', corresponding to ascending or descending order, respectively. If length(order) == 1, it will be recycled to length(by).")
+ if (length(order) == 1L) order = rep(order, length(by))
+ }
+ order = as.integer(order)
+ .Call(Cforder, x, by, retGrp, sort, order, na.last) # returns integer() if already sorted, regardless of sort=TRUE|FALSE
+}
+
+forder <- function(x, ..., na.last=TRUE, decreasing=FALSE)
+{
+ if (!is.data.table(x)) stop("x must be a data.table.")
+ if (ncol(x) == 0) stop("Attempting to order a 0-column data.table.")
+ if (is.na(decreasing) || !is.logical(decreasing)) stop("'decreasing' must be logical TRUE or FALSE")
+ cols = substitute(list(...))[-1]
+ if (identical(as.character(cols),"NULL") || !length(cols)) return(NULL) # to provide the same output as base::order
+ ans = x
+ order = rep(1L, length(cols))
+ if (length(cols)) {
+ ans = vector("list", length(cols))
+ cols = as.list(cols)
+ xcols = names(x)
+ for (i in seq_along(cols)) {
+ v=cols[[i]]
+ if (i == 1L && is.call(v) && length(v) == 2L && v[[1L]] == "list") return(1L) # to be consistent with base, see comment below under while loop
+ while (is.call(v) && length(v) == 2L && v[[1L]] != "list") {
+ # take care of "--x", "{-x}", "(---+x)" etc., cases and also "list(y)". 'list(y)' is ambiguous though. In base, with(DT, order(x, list(y))) will error
+ # that 'arguments are not of same lengths'. But with(DT, order(list(x), list(y))) will return 1L, which is very strange. On top of that, with(DT,
+ # order(x, as.list(10:1)) would return 'unimplemented type list'. It's all very inconsistent. But we HAVE to be consistent with base HERE.
+ if (!as.character(v[[1L]]) %chin% c("+", "-")) break # FIX for bug #5583
+ if (v[[1L]] == "-") order[i] = -order[i]
+ v = v[[-1L]]
+ }
+ if (is.name(v)) {
+ ix <- chmatch(as.character(v), xcols, nomatch=0L)
+ if (ix != 0L) ans <- point(ans, i, x, ix) # see 'point' in data.table.R and C-version pointWrapper in assign.c - avoid copies
+ else {
+ v = as.call(list(as.name("list"), v))
+ ans <- point(ans, i, eval(v, x, parent.frame()), 1L)
+ }
+ } else {
+ if (!is.object(eval(v, x, parent.frame()))) {
+ v = as.call(list(as.name("list"), v))
+ ans = point(ans, i, eval(v, x, parent.frame()), 1L) # eval has to make a copy here (not due to list(.), but due to ex: "4-5*y"), unavoidable.
+ } else ans = point(ans, i, list(unlist(eval(v, x, parent.frame()))), 1L)
+ } # else stop("Column arguments to order by in 'forder' should be of type name/symbol (ex: quote(x)) or call (ex: quote(-x), quote(x+5*y))")
+ }
+ }
+ cols = seq_along(ans)
+ for (i in cols) {
+ if (!typeof(ans[[i]]) %chin% c("integer","logical","character","double"))
+ stop("Column '",i,"' is type '",typeof(ans[[i]]),"' which is not supported for ordering currently.")
+ }
+ o = forderv(ans, cols, sort=TRUE, retGrp=FALSE, order= if (decreasing) -order else order, na.last)
+ if (!length(o)) o = seq_along(ans[[1L]]) else o
+ o
+}
+
+fsort <- function(x, decreasing = FALSE, na.last = FALSE, ...)
+{
+ o = forderv(x, order=!decreasing, na.last=na.last)
+ return( if (length(o)) x[o] else x ) # TO DO: document the nice efficiency here
+}
+
+setorder <- function(x, ..., na.last=FALSE)
+# na.last=FALSE here, to be consistent with data.table's default
+# as opposed to DT[order(.)] where na.last=TRUE, to be consistent with base
+{
+ if (!is.data.frame(x)) stop("x must be a data.frame or data.table.")
+ cols = substitute(list(...))[-1]
+ if (identical(as.character(cols),"NULL")) return(x)
+ if (length(cols)) {
+ cols=as.list(cols)
+ order=rep(1L, length(cols))
+ for (i in seq_along(cols)) {
+ v=as.list(cols[[i]])
+ if (length(v) > 1 && v[[1L]] == "+") v=v[[-1L]]
+ else if (length(v) > 1 && v[[1L]] == "-") {
+ v=v[[-1L]]
+ order[i] = -1L
+ }
+ cols[[i]]=as.character(v)
+ }
+ cols=unlist(cols, use.names=FALSE)
+ } else {
+ cols=colnames(x)
+ order=rep(1L, length(cols))
+ }
+ setorderv(x, cols, order, na.last)
+}
+
+setorderv <- function(x, cols, order=1L, na.last=FALSE)
+{
+ if (is.null(cols)) return(x)
+ if (!is.data.frame(x)) stop("x must be a data.frame or data.table")
+ na.last = as.logical(na.last)
+ if (is.na(na.last) || !length(na.last)) stop('na.last must be logical TRUE/FALSE')
+ if (!is.character(cols)) stop("cols is not a character vector. Please see further information in ?setorder.")
+ if (!length(cols)) {
+ warning("cols is a character vector of zero length. Use NULL instead, or wrap with suppressWarnings() to avoid this warning.")
+ return(x)
+ }
+ if (any(nchar(cols)==0)) stop("cols contains some blanks.") # TODO: probably I'm checking more than necessary here.. there are checks in 'forderv' as well
+ if (!length(cols)) {
+ cols = colnames(x) # All columns in the data.table, usually a few when used in this form
+ } else {
+ # remove backticks from cols
+ cols <- gsub("`", "", cols)
+ miss = !(cols %in% colnames(x))
+ if (any(miss)) stop("some columns are not in the data.table: " %+% cols[miss])
+ }
+ if (".xi" %in% colnames(x)) stop("x contains a column called '.xi'. Conflicts with internal use by data.table.")
+ for (i in cols) {
+ .xi = x[[i]] # [[ is copy on write, otherwise checking type would be copying each column
+ if (!typeof(.xi) %chin% c("integer","logical","character","double")) stop("Column '",i,"' is type '",typeof(.xi),"' which is not supported for ordering currently.")
+ }
+ if (!is.character(cols) || length(cols)<1) stop("'cols' should be character at this point in setkey.")
+
+ o = forderv(x, cols, sort=TRUE, retGrp=FALSE, order=order, na.last=na.last)
+ if (length(o)) {
+ .Call(Creorder, x, o)
+ if (is.data.frame(x) & !is.data.table(x)) {
+ .Call(Creorder, rn <- rownames(x), o)
+ setattr(x, 'row.names', rn)
+ }
+ setattr(x, 'sorted', NULL) # if 'forderv' is not 0-length, it means order has changed. So, set key to NULL, else retain key.
+ setattr(x, 'index', NULL) # remove secondary keys too. These could be reordered and retained, but simpler and faster to remove
+ }
+ invisible(x)
+}
+
+binary <- function(x) .Call(Cbinary, x)
+
+setNumericRounding <- function(x) {.Call(CsetNumericRounding, as.integer(x)); invisible()}
+getNumericRounding <- function() .Call(CgetNumericRounding)
+
+SJ <- function(...) {
+ JDT = as.data.table(list(...))
+ setkey(JDT)
+}
+# S for Sorted, usually used in i to sort the i table
+
+# TO DO?: Use the CJ list() replication method for SJ (inside as.data.table.list?, #2109) too to avoid alloc.col
+
+CJ <- function(..., sorted = TRUE, unique = FALSE)
+{
+ # Pass in a list of unique values, e.g. ids and dates
+ # Cross Join will then produce a join table with the combination of all values (cross product).
+ # The last vector is varied the quickest in the table, so dates should be last for roll for example
+ l = list(...)
+ if (unique) l = lapply(l, unique)
+
+ # using rep.int instead of rep speeds things up considerably (but attributes are dropped).
+ j = lapply(l, class) # changed "vapply" to avoid errors with "ordered" "factor" input
+ if (length(l)==1L && sorted && length(o <- forderv(l[[1L]])))
+ l[[1L]] = l[[1L]][o]
+ else if (length(l) > 1L) {
+ n = vapply(l, length, 0L)
+ nrow = prod(n)
+ x = c(rev(take(cumprod(rev(n)))), 1L)
+ for (i in seq_along(x)) {
+ y = l[[i]]
+ if (sorted && length(o <- forderv(y))) y = y[o]
+ if (i == 1L)
+ l[[i]] = rep.int(y, times = rep.int(x[i], n[i])) # i.e. rep(y, each=x[i])
+ else if (i == length(n))
+ l[[i]] = rep.int(y, times = nrow/(x[i]*n[i]))
+ else
+ l[[i]] = rep.int(rep.int(y, times = rep.int(x[i], n[i])), times = nrow/(x[i]*n[i]))
+ if (any(class(l[[i]]) != j[[i]]))
+ setattr(l[[i]], 'class', j[[i]]) # reset "Date" class - rep.int coerces to integer
+ }
+ }
+ setattr(l, "row.names", .set_row_names(length(l[[1L]])))
+ setattr(l, "class", c("data.table", "data.frame"))
+
+ if (is.null(vnames <- names(l)))
+ vnames = vector("character", length(l))
+ if (any(tt <- vnames == "")) {
+ vnames[tt] = paste("V", which(tt), sep="")
+ setattr(l, "names", vnames)
+ }
+ l <- alloc.col(l) # a tiny bit wasteful to over-allocate a fixed join table (column slots only), doing it anyway for consistency, and it's possible a user may wish to use SJ directly outside a join and would expect consistent over-allocation.
+ if (sorted) setattr(l, 'sorted', names(l))
+ l
+}
+
+frankv <- function(x, cols=seq_along(x), order=1L, na.last=TRUE, ties.method=c("average", "first", "random", "max", "min", "dense")) {
+ ties.method = match.arg(ties.method)
+ if (!length(na.last)) stop('length(na.last) = 0')
+ if (length(na.last) != 1L) {
+ warning("length(na.last) > 1, only the first element will be used")
+ na.last = na.last[1L]
+ }
+ keep = (na.last == "keep")
+ na.last = as.logical(na.last)
+ as_list <- function(x) {
+ xx = vector("list", 1L)
+ .Call(Csetlistelt, xx, 1L, x)
+ xx
+ }
+ if (is.atomic(x)) {
+ if (!missing(cols) && !is.null(cols))
+ stop("x is a single vector, non-NULL 'cols' doesn't make sense")
+ cols = 1L
+ x = as_list(x)
+ } else {
+ if (!length(cols))
+ stop("x is a list, 'cols' can not be 0-length")
+ if (is.character(cols))
+ cols = chmatch(cols, names(x))
+ cols = as.integer(cols)
+ }
+ x = .shallow(x, cols) # shallow copy even if list..
+ setDT(x)
+ cols = seq_along(cols)
+ if (is.na(na.last)) {
+ set(x, j = "..na_prefix..", value = is_na(x, cols))
+ order = if (length(order) == 1L) c(1L, rep(order, length(cols))) else c(1L, order)
+ cols = c(ncol(x), cols)
+ nas = x[[ncol(x)]]
+ }
+ if (ties.method == "random") {
+ set(x, i = if (is.na(na.last)) which_(nas, FALSE) else NULL,
+ j = "..stats_runif..",
+ value = stats::runif(nrow(x)))
+ order = if (length(order) == 1L) c(rep(order, length(cols)), 1L) else c(order, 1L)
+ cols = c(cols, ncol(x))
+ }
+ xorder = forderv(x, by=cols, order=order, sort=TRUE, retGrp=TRUE,
+ na.last=if (identical(na.last, FALSE)) na.last else TRUE)
+ xstart = attr(xorder, 'starts')
+ xsorted = FALSE
+ if (!length(xorder)) {
+ xsorted = TRUE
+ xorder = seq_along(x[[1L]])
+ }
+ ans = switch(ties.method,
+ average = , min = , max =, dense = {
+ rank = .Call(Cfrank, xorder, xstart, uniqlengths(xstart, length(xorder)), ties.method)
+ },
+ first = , random = {
+ if (xsorted) xorder else forderv(xorder)
+ }
+ )
+ # take care of na.last="keep"
+ V1 = NULL # for R CMD CHECK warning
+ if (isTRUE(keep)) {
+ ans = (setDT(as_list(ans))[which_(nas, TRUE), V1 := NA])[[1L]]
+ } else if (is.na(na.last)) {
+ ans = ans[which_(nas, FALSE)]
+ }
+ ans
+}
+
+frank <- function(x, ..., na.last=TRUE, ties.method=c("average", "first", "random", "max", "min", "dense")) {
+ cols = substitute(list(...))[-1]
+ if (identical(as.character(cols), "NULL")) {
+ cols = NULL
+ order = 1L
+ } else if (length(cols)) {
+ cols=as.list(cols)
+ order=rep(1L, length(cols))
+ for (i in seq_along(cols)) {
+ v=as.list(cols[[i]])
+ if (length(v) > 1 && v[[1L]] == "+") v=v[[-1L]]
+ else if (length(v) > 1 && v[[1L]] == "-") {
+ v=v[[-1L]]
+ order[i] = -1L
+ }
+ cols[[i]]=as.character(v)
+ }
+ cols=unlist(cols, use.names=FALSE)
+ } else {
+ cols=colnames(x)
+ order=if (is.null(cols)) 1L else rep(1L, length(cols))
+ }
+ frankv(x, cols=cols, order=order, na.last=na.last, ties.method=ties.method)
+
+}
+
+#########################################################################################
+# Deprecated ...
+#########################################################################################
+
+
+# nocov start
+# don't include functions not used for coverage
+bench <- function(quick=TRUE, testback=TRUE, baseline=FALSE) {
+ if (baseline) testback=FALSE # when baseline return in fastorder.c is uncommented, baseline must be TRUE
+ # fastorder benchmark forwards vs backwards
+
+ Levels=Rows=SubGroupN=rand.forw=rand.back=ordT.forw=ordT.back=ordB.forw=ordB.back=rev.forw=rev.back=NULL
+ x=y=faster1=faster2=faster3=faster4=NULL # to keep R CMD check quiet
+
+ if (quick) {Sr = 1:3; Nr = 2:4} else {Sr = 1:5; Nr = 2:8}
+ ans = setkey(CJ(Levels=as.integer(10^Sr),Rows=as.integer(10^Nr)))
+
+ # TO DO: add a case S1 : 1e7 levels S2 : 1:3 Every level has 3 rows. 1e7 calls to iradix
+ # Sr: c(1:3, 10^(1:7)) CJ(Sr,Sr) all combinations
+
+ # fastorder (backwards) doesn't call isSortedList anymore (we removed that at C level). It now proceeds as if
+ # unsorted always. This is always more favourable to fastorder timings, unless, the data is actually perfectly
+ # sorted. Hence we no longer test perfectly ordered here, as that was just testing isSortedList anyway. ordT
+ # and ordB tests dominate.
+
+ ans[, SubGroupN:=format(as.integer(ceiling(Rows/Levels)), big.mark=",")]
+ ans[,Rows:=format(Rows,big.mark=",")]
+ ans[,Levels:=format(Levels,big.mark=",")]
+ ident <- function(x,y) if (length(x)==0) length(y)==0 || identical(y,seq_along(y)) else identical(x,y)
+ IS.SORTED <- function(...)suppressWarnings(is.sorted(...)) # just needed here to ensure the test data construction is working
+ for (i in 1:nrow(ans)) {
+ ttype = c("user.self","sys.self") # elapsed can sometimes be >> user.self+sys.self. TO DO: three repeats as well.
+ tol = 0.5 # we don't mind about 0.5s; benefits when almost sorted outweigh
+ S = ans[i,as.integer(gsub(",","",Levels))]
+ N = ans[i,as.integer(gsub(",","",Rows))]
+ DT = setDT(lapply(1:2, function(x){sample(S,N,replace=TRUE)}))
+
+ if (testback || baseline) ans[i, rand.back := sum(system.time(y<<-fastorder(DT, 1:2))[ttype])]
+ # in baseline mode, Cforder doesn't order, so y is needed to test baseline on ordered DT
+ ans[i, rand.forw := sum(system.time(x<<-forderv(DT))[ttype])]
+ if (testback) ans[i, faster1 := rand.forw<rand.back+tol]
+ if (testback) if (!ident(x,y)) browser()
+
+ .Call(Creorder,DT, if (baseline) y else x) # in baseline mode, x is deliberately wrong. And if testback=FALSE, we won't have y
+ if (!IS.SORTED(DT)) stop("Logical error: ordered table is not sorted according to IS.SORTED!")
+ if (baseline) ans[, rand.back := NULL]
+
+ if (FALSE) {
+ # don't test perfectly ordered case. See note above.
+ if (testback) ans[i, ord.back := sum(system.time(y<<-fastorder(DT, 1:2))[ttype])]
+ ans[i, ord.forw := sum(system.time(x<<-forderv(DT))[ttype])]
+ if (testback) ans[i, faster2 := ord.forw<ord.back+tol]
+ if (testback) if (!ident(x,y)) browser()
+ }
+
+ if (DT[[1]][1] == DT[[1]][2]) v = 2 else v = 1 # make small change to column 2, unless rows 1 and 2 aren't in the same group by column 1
+ old = DT[[v]][1:2]
+ DT[1:2, (v):=77:76] # unsorted near the top to trigger full sort, is.sorted detects quickly.
+ if (IS.SORTED(DT)) stop("Table is sorted. Change to the top didn't work.")
+
+ if (testback) ans[i, ordT.back := sum(system.time(y<<-fastorder(DT, 1:2))[ttype])] # T for Top
+ ans[i, ordT.forw := sum(system.time(x<<-forderv(DT))[ttype])]
+ if (testback) ans[i, faster2 := ordT.forw<ordT.back+tol]
+ if (testback) if (!ident(x,y)) browser()
+
+ DT[1:2, (v):=old] # undo the change at the top to make it sorted again
+ if (!IS.SORTED(DT)) stop("Logical error: reverting the small change at the top didn't make DT ordered again")
+ r = c(nrow(DT)-1, nrow(DT))
+ if (DT[[1]][r[1]] == DT[[1]][r[2]]) v = 2 else v = 1
+ old = DT[[v]][r]
+ DT[r, (v):=77:76] # unsorted near the very end, so is.sorted does full scan.
+ if (IS.SORTED(DT)) stop("Table is sorted. Change to the very bottom didn't work.")
+
+ if (testback) ans[i, ordB.back := sum(system.time(y<<-fastorder(DT, 1:2))[ttype])] # B for Bottom
+ ans[i, ordB.forw := sum(system.time(x<<-forderv(DT))[ttype])]
+ if (testback) ans[i, faster3 := ordB.forw<ordB.back+tol]
+ if (testback) if (!ident(x,y)) browser()
+
+ DT[r, (v):=old] # undo the change at the top to make it sorted again
+ if (!IS.SORTED(DT)) stop("Logical error: reverting the small change at the bottom didn't make DT ordered again")
+
+ .Call(Creorder,DT,nrow(DT):1) # Pefect reverse order, some sort algo's worst case e.g. O(n^2)
+ if (IS.SORTED(DT)) stop("Logical error: reverse order of table is sorted according to IS.SORTED!")
+ # Adding this test revealed the complexity that a reverse order vector containing ties, would not be stable if the reverse was applied. isorted fixed so that -1 returned only if strictly decreasing order
+
+ if (testback) ans[i, rev.back := sum(system.time(y<<-fastorder(DT, 1:2))[ttype])] # rev = reverse order
+ ans[i, rev.forw := sum(system.time(x<<-forderv(DT))[ttype])]
+ if (testback) ans[i, faster4 := rev.forw<rev.back+tol]
+ if (testback) if (!ident(x,y)) browser()
+
+ if (i==nrow(ans) || ans[i+1,Levels]!=ans[i,Levels]) print(ans[Levels==Levels[i]]) # print each block as we go along
+ }
+ cat("\nFinished.\n\n")
+ ans
+}
+
+# radixorder1 is used internally, only with fastorder
+# so adding a new argument is okay. added 'o' for order vector
+radixorder1 <- function(x, o=NULL) {
+ if(is.object(x)) x = xtfrm(x) # should take care of handling factors, Date's and others, so we don't need unlist
+ if (!is.null(o)) { # fix for http://stackoverflow.com/questions/21437546/data-table-1-8-11-and-aggregation-issues (moved this if-check to before checking logical)
+ x = copy(x)
+ setreordervec(x, o)
+ }
+ if(typeof(x) == "logical") return(c(which(is.na(x)),which(!x),which(x))) # logical is a special case of radix sort; just 3 buckets known up front. TO DO - could be faster in C but low priority
+ if(typeof(x) != "integer") # this allows factors; we assume the levels are sorted as we always do in data.table
+ stop("radixorder1 is only for integer 'x'")
+ base::sort.list(x, na.last=FALSE, decreasing=FALSE,method="radix")
+ # Always put NAs first, relied on in C binary search by relying on NA_integer_ being -maxint (checked in C).
+}
+
+# FOR INTERNAL use only.
+# Note that implementing just "sort" (and not order) takes half of this time. Getting order seems to be more time-consuming
+# slightly slower than R's (improperly named radix order) counting sort but:
+# 1) works for any data size - not restricted like R's radix where max-min should be <= 1e5
+# 2) with any values => also works on -ve integers, NA
+# 3) directly returns sort value instead of sort order by setting last parameter in C function to FALSE (not accessible via iradixorder)
+# 4) removed "decreasing=". Use 'setrev' instead to get the reversed order
+iradixorder <- function(x, o=NULL) {
+ # copied from radixorder1 and just changed the call to the correct function
+ # xtfrm converts date object to numeric. but this will be called only if it's integer, so do a as.integer(.)
+ if(is.object(x)) x = as.integer(xtfrm(x))
+ if(typeof(x) == "logical") {
+ if (!is.null(o)) { # since iradixorder requires a copy this check is better to be inside this if-statement unlike radixorder1
+ x = copy(x)
+ setreordervec(x, o)
+ }
+ return(c(which(is.na(x)), which(!x), which(x)))
+ }
+ if(typeof(x) != "integer") # this allows factors; we assume the levels are sorted as we always do in data.table
+ stop("iradixorder is only for integer 'x'. Try dradixorder for numeric 'x'")
+ if (length(x) == 0L) return(integer(0))
+ # OLD: passing list(x) to C to ensure copy is being made...
+ # NOTE: passing list(x) does not make a copy in >3.0.2 (devel version currently), so explicitly copying
+ x = copy(x)
+ if (!is.null(o)) setreordervec(x, o)
+ ans <- .Call(Cfastradixint, x, TRUE) # TRUE returns indices, FALSE returns sorted value directly
+ ans
+ # NA first as data.table requires
+}
+
+# FOR INTERNAL use only.
+# at least > 5-30x times faster than ordernumtol and order (depending on the number of groups to find the tolerance on)
+# real-life performances must be towards the much faster side though.
+dradixorder <- function(x, o=NULL, tol=.Machine$double.eps^0.5) {
+ if (!is.atomic(x) || typeof(x) != "double") stop("'dradixorder' is only numeric 'x'")
+ if (length(x) == 0) return(integer(0))
+ # OLD: passing list(x) to C to ensure copy is being made...
+ # NOTE: passing list(x) does not make a copy in >3.0.2 (devel version currently), so explicitly copying
+ x = copy(x)
+ if (!is.null(o)) setreordervec(x, o)
+ ans <- .Call(Cfastradixdouble, x, as.numeric(tol), TRUE) # TRUE returns order, FALSE returns sorted vector.
+ ans
+ # NA first followed by NaN as data.table requires
+}
+
+regularorder1 <- function(x) {
+ if(is.object(x)) x = xtfrm(x) # should take care of handling factors, Date's and others, so we don't need unlist
+ base::sort.list(x, na.last=FALSE, decreasing=FALSE)
+}
+
+ordernumtol <- function(x, tol=.Machine$double.eps^0.5) {
+ o = forderv(x)
+ if (length(o)) o else seq_along(x)
+ # was as follows, but we removed Crorder_tol at C level.
+ # o=seq_along(x)
+ # .Call(Crorder_tol,x,o,tol)
+ # o
+ # Retaining this function (ordernumtol) so that fastorder and bench() still works. So we can
+ # still test forwards vs backwards through columns, but just using the new forderv to sort the
+ # entire numeric column when backwards with fastorder.
+}
+
+# chorder2 to be used only with fastorder
+# neither are exported
+chorder2 <- function(x, o=NULL) {
+ if (!is.null(o)) {
+ x = copy(x)
+ setreordervec(x, o)
+ }
+ forderv(x,sort=TRUE) # was .Call(Ccountingcharacter, x, TRUE) but that's now removed
+}
+
+fastorder <- function(x, by=seq_along(x), verbose=getOption("datatable.verbose"))
+{
+ # x can be a vector, or anything that's stored as a list (inc data.frame and data.table), thus can be accessed with non-copying base::[[.
+ # When x is a list, 'by' may be integers or names
+ # This function uses the backwards approach; i.e. first orders the last column, then orders the 2nd to last column ordered by the order of
+ # the last column, and so on. This vectorized approach is much faster than base::order(...) [src/main/sort.c:ordervector(...,listgreater)]
+ # which is a comparison sort comparing 2 rows using a loop through columns for that row with a switch on each column type.
+
+ # Now here only for dev testing to compare to forderv, e.g. in bench()
+ # Always orders without testing for sortedness. This is favourable to fastorder (!), unless, data is perfectly ordered. See message in bench().
+
+ if (is.atomic(x)) { by=NULL; v = x; w = 1 } # w = 1 just for the error message below
+ else { w = last(by); v = x[[w]] }
+ o = switch(typeof(v),
+ "double" = dradixorder(v), # ordernumtol(v),
+ "character" = chorder(v),
+ # Use a radix sort (fast and stable for ties), but will fail for range > 1e5 elements (and any negatives in base)
+ tryCatch(radixorder1(v),error=function(e) {
+ if (verbose) cat("Column",w,"failed radixorder1, reverting to 'iradixorder'\n")
+ iradixorder(v) # regularorder1(v)
+ })
+ )
+ if (is.atomic(x)) return(o)
+ # If there is more than one column, run through them backwards
+ for (w in rev(take(by))) {
+ v = x[[w]] # We could make the 'copy' here followed by 'setreordervec'
+ # instead of creating 'chorder2'. But 'iradixorder' and 'dradixorder'
+ # already take a copy internally So it's better to avoid copying twice.
+ switch(typeof(v),
+ "double" = setreordervec(o, dradixorder(v, o)), # PREV: o[dradixorder(v[o])], PPREV: o[ordernumto(v[o])]
+ "character" = setreordervec(o, chorder2(v, o)), # TO DO: avoid the copy and reorder, pass in o to C like ordernumtol (still stands??)
+ tryCatch(setreordervec(o, radixorder1(v, o)), error=function(e) {
+ if (verbose) cat("Column",w,"failed radixorder1, reverting to 'iradixorder'\n")
+ setreordervec(o, iradixorder(v, o)) # PREV: o[regularorder1(v[o])]
+ # TO DO: avoid the copy and reorder, pass in o to C like ordernumtol (still holds??)
+ })
+ )
+ }
+ o
+}
+
+# nocov end
diff --git a/R/setops.R b/R/setops.R
new file mode 100644
index 0000000..ac78173
--- /dev/null
+++ b/R/setops.R
@@ -0,0 +1,44 @@
+# For internal use only (input symbol requirement is not checked)
+# cols [symbol] - columns provided to function argument
+# dt [symbol] - a data.table
+# Iff all of 'cols' is present in 'x' return col indices
+# is.data.table(dt) check should be performed in the calling function
+validate <- function(cols, dt) {
+ argcols = deparse(substitute(cols))
+ argdt = deparse(substitute(dt))
+ origcols = cols
+ if (is.character(cols)) cols = chmatch(cols, names(dt))
+ cols = as.integer(cols)
+ isna = which(!cols %in% seq_along(dt))
+ if (length(isna))
+ stop(argcols, " value(s) [", paste(origcols[isna], collapse=", "), "] not present (or out of range) in ", argdt)
+ cols
+}
+
+# setdiff for data.tables, internal at the moment #547
+setdiff_ <- function(x, y, by.x=seq_along(x), by.y=seq_along(y), use.names=FALSE) {
+ if (!is.data.table(x) || !is.data.table(y)) stop("x and y must both be data.tables")
+ if (is.null(x) || !length(x)) return(x)
+ by.x = validate(by.x, x)
+ if (is.null(y) || !length(y)) return(unique(x, by=by.x))
+ by.y = validate(by.y, y)
+ if (length(by.x) != length(by.y)) stop("length(by.x) != length(by.y)")
+ # factor in x should've factor/character in y, and viceversa
+ for (a in seq_along(by.x)) {
+ lc = by.y[a]
+ rc = by.x[a]
+ icnam = names(y)[lc]
+ xcnam = names(x)[rc]
+ if ( is.character(x[[rc]]) && !(is.character(y[[lc]]) || is.factor(y[[lc]])) ) {
+ stop("When x's column ('",xcnam,"') is character, the corresponding column in y ('",icnam,"') should be factor or character, but found incompatible type '",typeof(y[[lc]]),"'.")
+ } else if ( is.factor(x[[rc]]) && !(is.character(y[[lc]]) || is.factor(y[[lc]])) ) {
+ stop("When x's column ('",xcnam,"') is factor, the corresponding column in y ('",icnam,"') should be character or factor, but found incompatible type '",typeof(y[[lc]]),"'.")
+ } else if ( (is.integer(x[[rc]]) || is.double(x[[rc]])) && (is.logical(y[[lc]]) || is.character(y[[lc]])) ) {
+ stop("When x's column ('",xcnam,"') is integer or numeric, the corresponding column in y ('",icnam,"') can not be character or logical types, but found incompatible type '",typeof(y[[lc]]),"'.")
+ }
+ }
+ ux = unique(shallow(x, by.x))
+ uy = unique(shallow(y, by.y))
+ ix = duplicated(rbind(uy, ux, use.names=use.names, fill=FALSE))[-seq_len(nrow(uy))]
+ .Call("CsubsetDT", ux, which_(ix, FALSE), seq_along(ux)) # more memory efficient version of which(!ix)
+}
diff --git a/R/shift.R b/R/shift.R
new file mode 100644
index 0000000..ffea563
--- /dev/null
+++ b/R/shift.R
@@ -0,0 +1,14 @@
+shift <- function(x, n=1L, fill=NA, type=c("lag", "lead"), give.names=FALSE) {
+ type = match.arg(type)
+ ans = .Call(Cshift, x, as.integer(n), fill, type)
+ if (give.names) {
+ if (is.null(names(x))) {
+ xsub = substitute(x)
+ if (is.atomic(x) && is.name(xsub)) nx = deparse(xsub, 500L)
+ else nx = paste("V", if (is.atomic(x)) 1L else seq_along(x), sep="")
+ }
+ else nx = names(x)
+ setattr(ans, 'names', do.call("paste", c(CJ(nx, type, n, sorted=FALSE), sep="_")))
+ }
+ ans
+}
diff --git a/R/tables.R b/R/tables.R
new file mode 100644
index 0000000..bd80477
--- /dev/null
+++ b/R/tables.R
@@ -0,0 +1,44 @@
+
+MB = NCOL = NROW = NULL # globals to pass NOTE from R CMD check
+
+tables <- function(mb=TRUE,order.col="NAME",width=80,env=parent.frame(),silent=FALSE)
+{
+ # Prints name, size and colnames of all data.tables in the calling environment by default
+ tt = objects(envir=env, all.names=TRUE)
+ ss = which(as.logical(sapply(tt, function(x) is.data.table(get(x,envir=env)))))
+ if (!length(ss)) {
+ if (!silent) cat("No objects of class data.table exist in .GlobalEnv\n")
+ return(invisible(data.table(NULL)))
+ }
+ tab = tt[ss]
+ info = data.table(NAME=tab)
+ for (i in seq_along(tab)) {
+ DT = get(tab[i],envir=env) # doesn't copy
+ set(info,i,"NROW",nrow(DT))
+ set(info,i,"NCOL",ncol(DT))
+ if (mb) set(info,i,"MB",ceiling(as.numeric(object.size(DT))/1024^2)) # mb is an option because object.size() appears to be slow. TO DO: revisit
+ set(info,i,"COLS",paste(colnames(DT),collapse=","))
+ set(info,i,"KEY",paste(key(DT),collapse=","))
+ }
+ info[,NROW:=format(sprintf("%4s",prettyNum(NROW,big.mark=",")),justify="right")] # %4s is for minimum width
+ info[,NCOL:=format(sprintf("%4s",prettyNum(NCOL,big.mark=",")),justify="right")]
+ if (mb) {
+ total = sum(info$MB)
+ info[, MB:=format(sprintf("%2s",prettyNum(MB,big.mark=",")),justify="right")]
+ }
+ if (!order.col %in% names(info)) stop("order.col='",order.col,"' not a column name of info")
+ info = info[base::order(info[[order.col]])] # base::order to maintain locale ordering of table names
+ m = as.matrix(info)
+ colnames(m)[2] = sprintf(paste("%",nchar(m[1,"NROW"]),"s",sep=""), "NROW")
+ colnames(m)[3] = sprintf(paste("%",nchar(m[1,"NCOL"]),"s",sep=""), "NCOL")
+ if (mb) colnames(m)[4] = sprintf(paste("%",nchar(m[1,"MB"]),"s",sep=""), "MB")
+ m[,"COLS"] = substring(m[,"COLS"],1,width)
+ m[,"KEY"] = substring(m[,"KEY"],1,width)
+ if (!silent) {
+ print(m, quote=FALSE, right=FALSE)
+ if (mb) cat("Total: ",prettyNum(as.character(total),big.mark=","),"MB\n",sep="")
+ }
+ invisible(info)
+}
+
+
diff --git a/R/test.data.table.R b/R/test.data.table.R
new file mode 100644
index 0000000..4267562
--- /dev/null
+++ b/R/test.data.table.R
@@ -0,0 +1,211 @@
+
+test.data.table <- function(verbose=FALSE, pkg="pkg") {
+ if (exists("test.data.table",.GlobalEnv,inherits=FALSE)) {
+ # package developer
+ if ("package:data.table" %in% search()) stop("data.table package loaded")
+ if (.Platform$OS.type == "unix" && Sys.info()['sysname'] != "Darwin")
+ d = path.expand("~/data.table/inst/tests")
+ else {
+ if (!pkg %in% dir()) stop(paste(pkg, " not in dir()", sep=""))
+ d = paste(getwd(),"/", pkg, "/inst/tests",sep="")
+ }
+ } else {
+ # user
+ d = paste(getNamespaceInfo("data.table","path"),"/tests",sep="")
+ }
+ # for (fn in dir(d,"*.[rR]$",full=TRUE)) { # testthat runs those
+ oldenc = options(encoding="UTF-8")[[1L]] # just for tests 708-712 on Windows
+ # TO DO: reinstate solution for C locale of CRAN's Mac (R-Forge's Mac is ok)
+ # oldlocale = Sys.getlocale("LC_CTYPE")
+ # Sys.setlocale("LC_CTYPE", "") # just for CRAN's Mac to get it off C locale (post to r-devel on 16 Jul 2012)
+ olddir = setwd(d)
+ on.exit(setwd(olddir))
+ for (fn in file.path(d, 'tests.Rraw')) { # not testthat
+ cat("Running",fn,"\n")
+ oldverbose = getOption("datatable.verbose")
+ if (verbose) options(datatable.verbose=TRUE)
+ sys.source(fn,envir=new.env(parent=.GlobalEnv))
+ options(data.table.verbose=oldverbose)
+ # As from v1.7.2, testthat doesn't run the tests.Rraw (hence file name change to .Rraw).
+ # There were environment issues with system.time() (when run by test_package) that only
+ # showed up when CRAN maintainers tested on 64bit. Matt spent a long time including
+ # testing on 64bit in Amazon EC2. Solution was simply to not run the tests.R from
+ # testthat, which probably makes sense anyway to speed it up a bit (was running twice
+ # before).
+ }
+ options(encoding=oldenc)
+ # Sys.setlocale("LC_CTYPE", oldlocale)
+ invisible()
+}
+
+# Define test() and its globals here, for use in dev
+# But primarily called by test.data.table() calling inst/tests/tests.Rraw
+# Initialized at the top of tests.Raw ...
+# nfail = ntest = lastnum = 0
+# whichfail = NULL
+# .devtesting = TRUE
+
+compactprint <- function(DT, topn=2) {
+ cn = paste(" [Key=",paste(key(DT),collapse=",")," Types=",paste(substring(gsub("integer64","i64",sapply(DT,class)),1,3),collapse=","),"]",sep="")
+ print(copy(DT)[,(cn):=""], topn=topn)
+ invisible()
+}
+test <- function(num,x,y,error=NULL,warning=NULL,output=NULL) {
+ # Usage:
+ # i) tests that x equals y when both x and y are supplied, the most common usage
+ # ii) tests that x is TRUE when y isn't supplied
+ # iii) if error is supplied, y should be missing and x is tested to result in an error message matching the pattern
+ # iv) if warning is supplied, y (if supplied) is checked to equal x, and x should result in a warning message matching the pattern
+ # v) if output is supplied, x is evaluated and printed and the output is checked to match the pattern
+ # At most one of error, warning or output may be supplied, all single character strings (passed to grep)
+ # num just needs to be numeric and unique. We normally increment integers at the end, but inserts can be made using decimals e.g. 10,11,11.1,11.2,12,13,...
+ # Motivations:
+ # 1) we'd like to know all tests that fail not just stop at the first. This often helps by revealing a common feature across a set of
+ # failing tests
+ # 2) test() tests more deeply than a diff on console output and uses a data.table appropriate definition of "equals" different
+ # from all.equal and different to identical related to row.names and unused factor levels
+ # 3) each test has a unique id which we refer to in commit messages, emails etc.
+ nfail = get("nfail", parent.frame()) # to cater for both test.data.table() and stepping through tests in dev
+ whichfail = get("whichfail", parent.frame())
+ assign("ntest", get("ntest", parent.frame()) + 1, parent.frame(), inherits=TRUE) # bump number of tests run
+ assign("lastnum", num, parent.frame(), inherits=TRUE)
+ v = getOption("datatable.verbose")
+ i = interactive() # exists(".devtesting",parent.frame()) && get(".devtesting", parent.frame())
+ if (v || i) {
+ cat(if (i) "\r" else "\n\n", "Running test id ", num, " ",sep="")
+ }
+ # TO DO: every line that could possibly fail should ideally be inside test()
+ xsub = substitute(x)
+ ysub = substitute(y)
+ if (is.null(output)) err <<- try(x,TRUE)
+ else out = gsub("NULL$","",paste(capture.output(print(err<<-try(x,TRUE))),collapse=""))
+ if (!is.null(output)) {
+ output = gsub("[[]","<LBRACKET>",output)
+ output = gsub("[]]","<RBRACKET>",output)
+ output = gsub("<LBRACKET>","[[]",output)
+ output = gsub("<RBRACKET>","[]]",output)
+ output = gsub("[(]","[(]",output)
+ output = gsub("[)]","[)]",output)
+ if (!length(grep(output,out))) {
+ cat("Test",num,"didn't produce correct output:\n")
+ cat(">",deparse(xsub),"\n")
+ cat("Expected: '",output,"'\n",sep="")
+ cat("Observed: '",out,"'\n",sep="")
+ assign("nfail", nfail+1, parent.frame(), inherits=TRUE)
+ assign("whichfail", c(whichfail, num), parent.frame(), inherits=TRUE)
+ return()
+ }
+ }
+ if (!is.null(error) || !is.null(warning)) {
+ type = ifelse(!is.null(error),"error","warning")
+ patt = txt = ifelse(!is.null(error),error,warning)
+ patt = gsub("[(]","[(]",patt)
+ patt = gsub("[)]","[)]",patt)
+ patt = gsub("\\^","\\\\^", patt) # for test 923 containing 2^31 in error message
+ observedtype = ifelse(length(grep("converted from warning",err)), "warning", "error")
+ if (! (inherits(err,"try-error") &&
+ length(grep(patt,err)) &&
+ type==observedtype)) {
+ cat("Test",num,"didn't produce correct",type,":\n")
+ cat(">",deparse(xsub),"\n")
+ cat("Expected ",type,": '",txt,"'\n",sep="")
+ if (!inherits(err,"try-error"))
+ cat("Observed: no error or warning\n")
+ else
+ cat("Observed ",observedtype,": '",gsub("^[(]converted from warning[)] ","",gsub("\n$","",gsub("^Error.* : \n ","",as.character(err)))),"'\n",sep="")
+ assign("nfail", nfail+1, parent.frame(), inherits=TRUE) # Not the same as nfail <<- nfail + 1, it seems (when run via R CMD check)
+ assign("whichfail", c(whichfail, num), parent.frame(), inherits=TRUE)
+ return()
+ }
+ if (type=="warning")
+ err <- if (is.null(output)) x<-try(suppressWarnings(x),TRUE) else out<-paste(capture.output(x<-try(suppressWarnings(x),TRUE)),collapse="")
+ else return()
+ }
+ if (inherits(err,"try-error") || (!missing(y) && inherits(err<-try(y,TRUE),"try-error"))) {
+ cat("Test",num,err)
+ assign("nfail", nfail+1, parent.frame(), inherits=TRUE)
+ assign("whichfail", c(whichfail, num), parent.frame(), inherits=TRUE)
+ return()
+ }
+ if (missing(y)) {
+ if (!is.null(output)) return()
+ if (isTRUE(as.vector(x))) return() # as.vector to drop names of a named vector such as returned by system.time
+ cat("Test",num,"expected TRUE but observed:\n")
+ cat(">",deparse(xsub),"\n")
+ if (is.data.table(x)) compactprint(x) else print(x)
+ assign("nfail", nfail+1, parent.frame(), inherits=TRUE)
+ assign("whichfail", c(whichfail, num), parent.frame(), inherits=TRUE)
+ return()
+ } else {
+ if (identical(x,y)) return()
+ if (is.data.table(x) && is.data.table(y)) {
+ # TO DO: test 166 doesn't pass with these :
+ # if (!selfrefok(x)) stop("x selfref not ok")
+ # if (!selfrefok(y)) stop("y selfref not ok")
+ xc=copy(x)
+ yc=copy(y) # so we don't affect the original data which may be used in the next test
+ # drop unused levels in factors
+ if (length(x)) for (i in which(sapply(x,is.factor))) {.xi=x[[i]];xc[,(i):=factor(.xi)]}
+ if (length(y)) for (i in which(sapply(y,is.factor))) {.yi=y[[i]];yc[,(i):=factor(.yi)]}
+ setattr(xc,"row.names",NULL) # for test 165+, i.e. x may have row names set from inheritance but y won't, consider these equal
+ setattr(yc,"row.names",NULL)
+ setattr(xc,"index",NULL) # too onerous to create test RHS with the correct index as well, just check result
+ setattr(yc,"index",NULL)
+ if (identical(xc,yc) && identical(key(x),key(y))) return() # check key on original x and y because := above might have cleared it on xc or yc
+ if (isTRUE(all.equal(xc,yc)) && identical(key(x),key(y))) return()
+ }
+ if (is.factor(x) && is.factor(y)) {
+ x = factor(x)
+ y = factor(y)
+ if (identical(x,y)) return()
+ }
+ if (is.atomic(x) && is.atomic(y) && isTRUE(all.equal(x,y))) return() # For test 617 on r-prerel-solaris-sparc on 7 Mar 2013
+ }
+ cat("Test",num,"ran without errors but failed check that x equals y:\n")
+ cat("> x =",deparse(xsub),"\n")
+ if (is.data.table(x)) compactprint(x) else if (length(x)>6) {cat("First 6 of", length(x),":");print(head(x))} else print(x)
+ cat("> y =",deparse(ysub),"\n")
+ if (is.data.table(y)) compactprint(y) else if (length(y)>6) {cat("First 6 of", length(y),":");print(head(y))} else print(y)
+ assign("nfail", nfail+1, parent.frame(), inherits=TRUE)
+ assign("whichfail", c(whichfail, num), parent.frame(), inherits=TRUE)
+ invisible()
+}
+
+
+## Tests that two data.tables (`target` and `current`) are equivalent.
+## This method is used primarily to make life easy with a testing harness
+## built around test_that. A call to test_that::{expect_equal|equal} will
+## ultimately dispatch to this method when making an "equality" call.
+all.equal.data.table <- function(target, current, trim.levels=TRUE, ...) {
+ target = copy(target)
+ current = copy(current)
+ if (trim.levels) {
+ ## drop unused levels
+ if (length(target)) {
+ for (i in which(sapply(target, is.factor))) {
+ .xi = factor(target[[i]])
+ target[,(i):=.xi]
+ }
+ }
+ if (length(current)) {
+ for (i in which(sapply(current, is.factor))) {
+ .xi = factor(current[[i]])
+ current[,(i):=.xi]
+ }
+ }
+ }
+
+ ## Trim any extra row.names attributes that came from some inheritence
+ setattr(target, "row.names", NULL)
+ setattr(current, "row.names", NULL)
+
+ # all.equal uses unclass which doesn't know about external pointers; there
+ # doesn't seem to be all.equal.externalptr method in base.
+ setattr(target, ".internal.selfref", NULL)
+ setattr(current, ".internal.selfref", NULL)
+
+ all.equal.list(target, current, ...)
+}
+
+
+
diff --git a/R/timetaken.R b/R/timetaken.R
new file mode 100644
index 0000000..dc07195
--- /dev/null
+++ b/R/timetaken.R
@@ -0,0 +1,21 @@
+timetaken <- function(started.at)
+{
+ if (inherits(started.at,"POSIXct")) {
+ # started.at was Sys.time(). Slower method due to POSIXt.
+ secs <- as.double(difftime(Sys.time(), started.at, units="secs"))
+ } else {
+ # new faster method using started.at = proc.time()
+ secs = proc.time()[3] - started.at[3]
+ }
+ mins <- secs %/% 60
+ hrs <- mins %/% 60
+ days <- hrs %/% 24
+ mins = mins - hrs * 60
+ hrs = hrs - 24*days
+ if (secs >= 60) {
+ if (days >= 1) res = sprintf("%d days ", as.integer(days)) else res=""
+ paste(res,sprintf("%02.0f:%02.0f:%02.0f", hrs, mins, secs %% 60),sep="")
+ } else {
+ sprintf("%1.3fsec", secs)
+ }
+}
diff --git a/R/transpose.R b/R/transpose.R
new file mode 100644
index 0000000..b693d6b
--- /dev/null
+++ b/R/transpose.R
@@ -0,0 +1,18 @@
+transpose <- function(l, fill=NA, ignore.empty=FALSE) {
+ ans = .Call(Ctranspose, l, fill, ignore.empty)
+ if (is.data.table(l)) setDT(ans)
+ else if (is.data.frame(l)) {
+ if (is.null(names(ans)))
+ setattr(ans, "names", paste("V", seq_along(ans), sep = ""))
+ setattr(ans, "row.names", .set_row_names(length(ans[[1L]])))
+ setattr(ans, "class", "data.frame")
+ }
+ ans[]
+}
+
+tstrsplit <- function(x, ..., fill=NA, type.convert=FALSE) {
+ ans = transpose(strsplit(as.character(x), ...), fill=fill, ignore.empty = FALSE)
+ # Implementing #1094, but default FALSE
+ if(type.convert) ans = lapply(ans, type.convert, as.is = TRUE)
+ ans
+}
diff --git a/R/uniqlist.R b/R/uniqlist.R
new file mode 100644
index 0000000..8a16f3d
--- /dev/null
+++ b/R/uniqlist.R
@@ -0,0 +1,24 @@
+
+uniqlist <- function (l, order = -1L)
+{
+ # Assumes input list is ordered by each list item (or by 'order' if supplied), and that all list elements are the same length
+ # Finds the non-duplicate rows. Was called duplist but now grows vector - doesn't over-allocate result vector and
+ # is >2x times faster on numeric types
+ # TO DO: Possibly reinstate reverse argument :
+ # FALSE works in the usual duplicated() way, the first in a sequence of dups, will be FALSE
+ # TRUE has the last in a sequence of dups FALSE (so you can keep the last if thats required)
+ # l = list(...)
+ if (!is.list(l))
+ stop("l not type list")
+ if (!length(l)) return(list(0L))
+ ans <- .Call(Cuniqlist, l, as.integer(order))
+ ans
+}
+
+# implemented for returning the lengths of groups obtained from uniqlist (for internal use only)
+uniqlengths <- function(x, len) {
+ # check for type happens in C, but still converting to integer here to be sure.
+ ans <- .Call(Cuniqlengths, as.integer(x), as.integer(len))
+ ans
+}
+
diff --git a/R/utils.R b/R/utils.R
new file mode 100644
index 0000000..0e4cbbd
--- /dev/null
+++ b/R/utils.R
@@ -0,0 +1,49 @@
+# all non-exported / unused internal (utility) functions
+
+# which.first
+which.first <- function(x)
+{
+ if (!is.logical(x)) {
+ stop("x not boolean")
+ }
+ match(TRUE, x)
+}
+
+# which.last
+which.last <- function(x)
+{
+ if (!is.logical(x)) {
+ stop("x not boolean")
+ }
+ length(x) - match(TRUE, rev(x)) +1
+}
+
+# trim
+trim <- function(x) {
+ # Removes whitespace at the beginning and end of strings
+ # Assigning to x[] to retain the original dimensions, rownames and colnames
+ x[] = gsub(" +$", "", x)
+ x[] = gsub("^ +", "", x)
+ x
+}
+
+# take (I don't see it being used anywhere)
+take <- function(x, n=1)
+{
+ # returns the head of head, without the last n observations
+ # convenient when inlining expressions
+ # NROW, for vectors, returns the vector length, but we cater for non-table like lists also here
+ # TO DO: allow taking along any dimension e.g. all but last column, rather than all but last row.
+ if (is.list(x) && !is.data.frame(x) && !is.data.table(x)) l = length(x)
+ else l = NROW(x)
+ if (l < n) stop("Cannot take ",n," from ",l)
+ head(x, l-n)
+}
+# TODO: Implement take as UseMethod. Specific methods for each type.
+
+# plus
+"%+%" <- function(x,y)
+UseMethod("%+%")
+
+"%+%.default" <- function(x,y) paste(paste(x,collapse=","),paste(y,collapse=","),sep="")
+# we often construct warning msgs with a msg followed by several items of a vector, so %+% is for convenience
diff --git a/R/xts.R b/R/xts.R
new file mode 100644
index 0000000..135c1d1
--- /dev/null
+++ b/R/xts.R
@@ -0,0 +1,18 @@
+as.data.table.xts <- function(x, keep.rownames = TRUE, ...){
+ stopifnot(requireNamespace("xts"), !missing(x), xts::is.xts(x))
+ if(!keep.rownames) return(setDT(as.data.frame(x, row.names=FALSE))[])
+ if("index" %in% names(x)) stop("Input xts object should not have 'index' column because it would result in duplicate column names. Rename 'index' column in xts or use `keep.rownames=FALSE` and add index manually as another column.")
+ r = setDT(as.data.frame(x, row.names=FALSE))
+ index = NULL # fix for "no visible binding for global variable index"
+ r[, index := zoo::index(x)]
+ setcolorder(r,c("index",names(r)[names(r)!="index"]))[]
+}
+
+as.xts.data.table <- function(x){
+ stopifnot(requireNamespace("xts"), !missing(x), is.data.table(x))
+ if(!any(class(x[[1]]) %in% c("POSIXct","Date"))) stop("data.table must have a POSIXct or Date column on first position, use `setcolorder` function.")
+ colsNumeric = sapply(x, is.numeric)[-1] # exclude first col, xts index
+ if(any(!colsNumeric)) warning(paste("Following columns are not numeric and will be omitted:",paste(names(colsNumeric)[!colsNumeric],collapse=", ")))
+ r = setDF(x[,.SD,.SDcols=names(colsNumeric)[colsNumeric]])
+ xts::as.xts(r, order.by=x[[1]])
+}
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..72a00bb
--- /dev/null
+++ b/README.md
@@ -0,0 +1,3060 @@
+
+**Current stable release** (always even) : [v1.9.6 on CRAN](http://cran.r-project.org/package=data.table), released 19<sup>th</sup> Sep 2015.
+**Development version** (always odd): [v1.9.7 on GitHub](https://github.com/Rdatatable/data.table/) [](https://travis-ci.org/Rdatatable/data.table) [](http://codecov.io/github/Rdatatable/data.table?branch=master)
+ [How to install?](https://github.com/Rdatatable/data.table/wiki/Installation)
+
+<!-- Note this file is displayed on the CRAN page, as well as on GitHub. So the the link to GitHub is not to itself when viewed on the CRAN page. -->
+
+**Introduction, installation, documentation, benchmarks etc:** [HOMEPAGE](https://github.com/Rdatatable/data.table/wiki)
+
+**Guidelines for filing issues / pull requests:** Please see the project's [Contribution Guidelines](https://github.com/Rdatatable/data.table/blob/master/Contributing.md).
+
+---
+
+### Changes in v1.9.6 (on CRAN 19 Sep 2015)
+
+#### NEW FEATURES
+
+ 1. `fread`
+ * passes `showProgress=FALSE` through to `download.file()` (as `quiet=TRUE`). Thanks to a pull request from Karl Broman and Richard Scriven for filing the issue, [#741](https://github.com/Rdatatable/data.table/issues/741).
+ * accepts `dec=','` (and other non-'.' decimal separators), [#917](https://github.com/Rdatatable/data.table/issues/917). A new paragraph has been added to `?fread`. On Windows this should just-work. On Unix it may just-work but if not you will need to read the paragraph for an extra step. In case it somehow breaks `dec='.'`, this new feature can be turned off with `options(datatable.fread.dec.experiment=FALSE)`.
+ * Implemented `stringsAsFactors` argument for `fread()`. When `TRUE`, character columns are converted to factors. Default is `FALSE`. Thanks to Artem Klevtsov for filing [#501](https://github.com/Rdatatable/data.table/issues/501), and to @hmi2015 for [this SO post](http://stackoverflow.com/q/31350209/559784).
+ * gains `check.names` argument, with default value `FALSE`. When `TRUE`, it uses the base function `make.unique()` to ensure that the column names of the data.table read in are all unique. Thanks to David Arenburg for filing [#1027](https://github.com/Rdatatable/data.table/issues/1027).
+ * gains `encoding` argument. Acceptable values are "unknown", "UTF-8" and "Latin-1" with default value of "unknown". Closes [#563](https://github.com/Rdatatable/data.table/issues/563). Thanks to @BenMarwick for the original report and to the many requests from others, and Q on SO.
+ * gains `col.names` argument, and is similar to `base::read.table()`. Closes [#768](https://github.com/Rdatatable/data.table/issues/768). Thanks to @dardesta for filing the FR.
+
+ 2. `DT[column == value]` no longer recycles `value` except in the length 1 case (when it still uses DT's key or an automatic secondary key, as introduced in v1.9.4). If `length(value)==length(column)` then it works element-wise as standard in R. Otherwise, a length error is issued to avoid common user errors. `DT[column %in% values]` still uses DT's key (or an an automatic secondary key) as before. Automatic indexing (i.e., optimization of `==` and `%in%`) may still be turned off with [...]
+
+ 3. `na.omit` method for data.table is rewritten in C, for speed. It's ~11x faster on bigger data; see examples under `?na.omit`. It also gains two additional arguments a) `cols` accepts column names (or numbers) on which to check for missing values. 2) `invert` when `TRUE` returns the rows with any missing values instead. Thanks to the suggestion and PR from @matthieugomez.
+
+ 4. New function `shift()` implements fast `lead/lag` of *vector*, *list*, *data.frames* or *data.tables*. It takes a `type` argument which can be either *"lag"* (default) or *"lead"*. It enables very convenient usage along with `:=` or `set()`. For example: `DT[, (cols) := shift(.SD, 1L), by=id]`. Please have a look at `?shift` for more info.
+
+ 5. `frank()` is now implemented. It's much faster than `base::rank` and does more. It accepts *vectors*, *lists* with all elements of equal lengths, *data.frames* and *data.tables*, and optionally takes a `cols` argument. In addition to implementing all the `ties.method` methods available from `base::rank`, it also implements *dense rank*. It is also capable of calculating ranks by ordering column(s) in ascending or descending order. See `?frank` for more. Closes [#760](https://github. [...]
+
+ 6. `rleid()`, a convenience function for generating a run-length type id column to be used in grouping operations is now implemented. Closes [#686](https://github.com/Rdatatable/data.table/issues/686). Check `?rleid` examples section for usage scenarios.
+
+ 7. Efficient convertion of `xts` to data.table. Closes [#882](https://github.com/Rdatatable/data.table/issues/882). Check examples in `?as.xts.data.table` and `?as.data.table.xts`. Thanks to @jangorecki for the PR.
+
+ 8. `rbindlist` gains `idcol` argument which can be used to generate an index column. If `idcol=TRUE`, the column is automatically named `.id`. Instead you can also provide a column name directly. If the input list has no names, indices are automatically generated. Closes [#591](https://github.com/Rdatatable/data.table/issues/591). Also thanks to @KevinUshey for filing [#356](https://github.com/Rdatatable/data.table/issues/356).
+
+ 9. A new helper function `uniqueN` is now implemented. It is equivalent to `length(unique(x))` but much faster. It handles `atomic vectors`, `lists`, `data.frames` and `data.tables` as input and returns the number of unique rows. Closes [#884](https://github.com/Rdatatable/data.table/issues/884). Gains by argument. Closes [#1080](https://github.com/Rdatatable/data.table/issues/1080). Closes [#1224](https://github.com/Rdatatable/data.table/issues/1224). Thanks to @DavidArenburg, @kevinm [...]
+
+ 10. Implemented `transpose()` to transpose a list and `tstrsplit` which is a wrapper for `transpose(strsplit(...))`. This is particularly useful in scenarios where a column has to be split and the resulting list has to be assigned to multiple columns. See `?transpose` and `?tstrsplit`, [#1025](https://github.com/Rdatatable/data.table/issues/1025) and [#1026](https://github.com/Rdatatable/data.table/issues/1026) for usage scenarios. Closes both #1025 and #1026 issues.
+ * Implemented `type.convert` as suggested by Richard Scriven. Closes [#1094](https://github.com/Rdatatable/data.table/issues/1094).
+
+ 11. `melt.data.table`
+ * can now melt into multiple columns by providing a list of columns to `measure.vars` argument. Closes [#828](https://github.com/Rdatatable/data.table/issues/828). Thanks to Ananda Mahto for the extended email discussions and ideas on generating the `variable` column.
+ * also retains attributes wherever possible. Closes [#702](https://github.com/Rdatatable/data.table/issues/702) and [#993](https://github.com/Rdatatable/data.table/issues/993). Thanks to @richierocks for the report.
+ * Added `patterns.Rd`. Closes [#1294](https://github.com/Rdatatable/data.table/issues/1294). Thanks to @MichaelChirico.
+
+ 12. `.SDcols`
+ * understands `!` now, i.e., `DT[, .SD, .SDcols=!"a"]` now works, and is equivalent to `DT[, .SD, .SDcols = -c("a")]`. Closes [#1066](https://github.com/Rdatatable/data.table/issues/1066).
+ * accepts logical vectors as well. If length is smaller than number of columns, the vector is recycled. Closes [#1060](https://github.com/Rdatatable/data.table/issues/1060). Thanks to @StefanFritsch.
+
+ 13. `dcast` can now:
+ * cast multiple `value.var` columns simultaneously. Closes [#739](https://github.com/Rdatatable/data.table/issues/739).
+ * accept multiple functions under `fun.aggregate`. Closes [#716](https://github.com/Rdatatable/data.table/issues/716).
+ * supports optional column prefixes as mentioned under [this SO post](http://stackoverflow.com/q/26225206/559784). Closes [#862](https://github.com/Rdatatable/data.table/issues/862). Thanks to @JohnAndrews.
+ * works with undefined variables directly in formula. Closes [#1037](https://github.com/Rdatatable/data.table/issues/1037). Thanks to @DavidArenburg for the MRE.
+ * Naming conventions on multiple columns changed according to [#1153](https://github.com/Rdatatable/data.table/issues/1153). Thanks to @MichaelChirico for the FR.
+ * also has a `sep` argument with default `_` for backwards compatibility. [#1210](https://github.com/Rdatatable/data.table/issues/1210). Thanks to @dbetebenner for the FR.
+
+ 14. `.SDcols` and `with=FALSE` understand `colA:colB` form now. That is, `DT[, lapply(.SD, sum), by=V1, .SDcols=V4:V6]` and `DT[, V5:V7, with=FALSE]` works as intended. This is quite useful for interactive use. Closes [#748](https://github.com/Rdatatable/data.table/issues/748) and [#1216](https://github.com/Rdatatable/data.table/issues/1216). Thanks to @carbonmetrics, @jangorecki and @mtennekes.
+
+ 15. `setcolorder()` and `setorder()` work with `data.frame`s too. Closes [#1018](https://github.com/Rdatatable/data.table/issues/1018).
+
+ 16. `as.data.table.*` and `setDT` argument `keep.rownames` can take a column name as well. When `keep.rownames=TRUE`, the column will still automatically named `rn`. Closes [#575](https://github.com/Rdatatable/data.table/issues/575).
+
+ 17. `setDT` gains a `key` argument so that `setDT(X, key="a")` would convert `X` to a `data.table` by reference *and* key by the columns specified. Closes [#1121](https://github.com/Rdatatable/data.table/issues/1121).
+
+ 18. `setDF` also converts `list` of equal length to `data.frame` by reference now. Closes [#1132](https://github.com/Rdatatable/data.table/issues/1132).
+
+ 19. `CJ` gains logical `unique` argument with default `FALSE`. If `TRUE`, unique values of vectors are automatically computed and used. This is convenient, for example, `DT[CJ(a, b, c, unique=TRUE)]` instead of doing `DT[CJ(unique(a), unique(b), unique(c))]`. Ultimately, `unique = TRUE` will be default. Closes [#1148](https://github.com/Rdatatable/data.table/issues/1148).
+
+ 20. `on=` syntax: data.tables can join now without having to set keys by using the new `on` argument. For example: `DT1[DT2, on=c(x = "y")]` would join column 'y' of `DT2` with 'x' of `DT1`. `DT1[DT2, on="y"]` would join on column 'y' on both data.tables. Closes [#1130](https://github.com/Rdatatable/data.table/issues/1130) partly.
+
+ 21. `merge.data.table` gains arguments `by.x` and `by.y`. Closes [#637](https://github.com/Rdatatable/data.table/issues/637) and [#1130](https://github.com/Rdatatable/data.table/issues/1130). No copies are made even when the specified columns aren't key columns in data.tables, and therefore much more fast and memory efficient. Thanks to @blasern for the initial PRs. Also gains logical argument `sort` (like base R). Closes [#1282](https://github.com/Rdatatable/data.table/issues/1282).
+
+ 22. `setDF()` gains `rownames` argument for ready conversion to a `data.frame` with user-specified rows. Closes [#1320](https://github.com/Rdatatable/data.table/issues/1320). Thanks to @MichaelChirico for the FR and PR.
+
+ 23. `print.data.table` gains `quote` argument (defaul=`FALSE`). This option surrounds all printed elements with quotes, helps make whitespace(s) more evident. Closes [#1177](https://github.com/Rdatatable/data.table/issues/1177); thanks to @MichaelChirico for the PR.
+
+ 24. `[.data.table` now accepts single column numeric matrix in `i` argument the same way as `data.frame`. Closes [#826](https://github.com/Rdatatable/data.table/issues/826). Thanks to @jangorecki for the PR.
+
+ 25. `setDT()` gains `check.names` argument paralleling that of `fread`, `data.table`, and `base` functionality, allowing poorly declared objects to be converted to tidy `data.table`s by reference. Closes [#1338](https://github.com/Rdatatable/data.table/issues/1338); thanks to @MichaelChirico for the FR/PR.
+
+#### BUG FIXES
+
+ 1. `if (TRUE) DT[,LHS:=RHS]` no longer prints, [#869](https://github.com/Rdatatable/data.table/issues/869) and [#1122](https://github.com/Rdatatable/data.table/issues/1122). Tests added. To get this to work we've had to live with one downside: if a `:=` is used inside a function with no `DT[]` before the end of the function, then the next time `DT` or `print(DT)` is typed at the prompt, nothing will be printed. A repeated `DT` or `print(DT)` will print. To avoid this: include a `DT[]` [...]
+
+ 2. `DT[FALSE,LHS:=RHS]` no longer prints either, [#887](https://github.com/Rdatatable/data.table/issues/887). Thanks to Jureiss for reporting.
+
+ 3. `:=` no longer prints in knitr for consistency with behaviour at the prompt, [#505](https://github.com/Rdatatable/data.table/issues/505). Output of a test `knit("knitr.Rmd")` is now in data.table's unit tests. Thanks to Corone for the illustrated report.
+
+ 4. `knitr::kable()` works again without needing to upgrade from knitr v1.6 to v1.7, [#809](https://github.com/Rdatatable/data.table/issues/809). Packages which evaluate user code and don't wish to import data.table need to be added to `data.table:::cedta.pkgEvalsUserCode` and now only the `eval` part is made data.table-aware (the rest of such package's code is left data.table-unaware). `data.table:::cedta.override` is now empty and will be deprecated if no need for it arises. Thanks to [...]
+
+ 5. `fread()`:
+ * doubled quotes ("") inside quoted fields including if immediately followed by an embedded newline. Thanks to James Sams for reporting, [#489](https://github.com/Rdatatable/data.table/issues/489).
+ * quoted fields with embedded newlines in the lines used to detect types, [#810](https://github.com/Rdatatable/data.table/issues/810). Thanks to Vladimir Sitnikov for the scrambled data file which is now included in the test suite.
+ * when detecting types in the middle and end of the file, if the jump lands inside a quoted field with (possibly many) embedded newlines, this is now detected.
+ * if the file doesn't exist the error message is clearer ([#486](https://github.com/Rdatatable/data.table/issues/486))
+ * system commands are now checked to contain at least one space
+ * sep="." now works (when dec!="."), [#502](https://github.com/Rdatatable/data.table/issues/502). Thanks to Ananda Mahto for reporting.
+ * better error message if quoted field is missing an end quote, [#802](https://github.com/Rdatatable/data.table/issues/802). Thanks to Vladimir Sitnikov for the sample file which is now included in the test suite.
+ * providing sep which is not present in the file now reads as if sep="\n" rather than 'sep not found', #738. Thanks to Adam Kennedy for explaining the use-case.
+ * seg fault with errors over 1,000 characters (when long lines are included) is fixed, [#802](https://github.com/Rdatatable/data.table/issues/802). Thanks again to Vladimir Sitnikov.
+ * Missing `integer64` values are properly assigned `NA`s. Closes [#488](https://github.com/Rdatatable/data.table/issues/488). Thanks to @PeterStoyanov and @richierocks for the report.
+ * Column headers with empty strings aren't skipped anymore. [Closes #483](https://github.com/Rdatatable/data.table/issues/483). Thanks to @RobyJoehanes and @kforner.
+ * Detects separator correctly when commas also exist in text fields. Closes [#923](https://github.com/Rdatatable/data.table/issues/923). Thanks to @raymondben for the report.
+ * `NA` values in NA inflated file are read properly. [Closes #737](https://github.com/Rdatatable/data.table/issues/737). Thanks to Adam Kennedy.
+ * correctly handles `na.strings` argument for all types of columns - it detect possible `NA` values without coercion to character, like in base `read.table`. [fixes #504](https://github.com/Rdatatable/data.table/issues/504). Thanks to @dselivanov for the PR. Also closes [#1314](https://github.com/Rdatatable/data.table/issues/1314), which closes this issue completely, i.e., `na.strings = c("-999", "FALSE")` etc. also work.
+ * deals with quotes more robustly. When reading quoted fields fail, it re-attemps to read the field as if it wasn't quoted. This helps read in those fields that might have unbalanced quotes without erroring immediately, thereby closing issues [#568](https://github.com/Rdatatable/data.table/issues/568), [#1256](https://github.com/Rdatatable/data.table/issues/1256), [#1077](https://github.com/Rdatatable/data.table/issues/1077), [#1079](https://github.com/Rdatatable/data.table/issues/ [...]
+ * gains argument `strip.white` which is `TRUE` by default (unlike `base::read.table`). All unquoted columns' leading and trailing white spaces are automatically removed. If \code{FALSE}, only trailing spaces of header is removed. Closes [#1113](https://github.com/Rdatatable/data.table/issues/1113), [#1035](https://github.com/Rdatatable/data.table/issues/1035), [#1000](https://github.com/Rdatatable/data.table/issues/1000), [#785](https://github.com/Rdatatable/data.table/issues/785), [...]
+ * doesn't warn about empty lines when 'nrow' argument is specified and that many rows are read properly. Thanks to @richierocks for the report. Closes [#1330](https://github.com/Rdatatable/data.table/issues/1330).
+ * doesn't error/warn about not being able to read last 5 lines when 'nrow' argument is specified. Thanks to @robbig2871. Closes [#773](https://github.com/Rdatatable/data.table/issues/773).
+
+ 6. Auto indexing:
+ * `DT[colA == max(colA)]` now works again without needing `options(datatable.auto.index=FALSE)`. Thanks to Jan Gorecki and kaybenleroll, [#858](https://github.com/Rdatatable/data.table/issues/858). Test added.
+ * `DT[colA %in% c("id1","id2","id2","id3")]` now ignores the RHS duplicates (as before, consistent with base R) without needing `options(datatable.auto.index=FALSE)`. Thanks to Dayne Filer for reporting.
+ * If `DT` contains a column `class` (happens to be a reserved attribute name in R) then `DT[class=='a']` now works again without needing `options(datatable.auto.index=FALSE)`. Thanks to sunnyghkm for reporting, [#871](https://github.com/Rdatatable/data.table/issues/871).
+ * `:=` and `set*` now drop secondary keys (new in v1.9.4) so that `DT[x==y]` works again after a `:=` or `set*` without needing `options(datatable.auto.index=FALSE)`. Only `setkey()` was dropping secondary keys correctly. 23 tests added. Thanks to user36312 for reporting, [#885](https://github.com/Rdatatable/data.table/issues/885).
+ * Automatic indices are not created on `.SD` so that `dt[, .SD[b == "B"], by=a]` works correctly. Fixes [#958](https://github.com/Rdatatable/data.table/issues/958). Thanks to @azag0 for the nice reproducible example.
+ * `i`-operations resulting in 0-length rows ignore `j` on subsets using auto indexing. Closes [#1001](https://github.com/Rdatatable/data.table/issues/1001). Thanks to @Gsee.
+ * `POSIXct` type columns work as expected with auto indexing. Closes [#955](https://github.com/Rdatatable/data.table/issues/955). Thanks to @GSee for the minimal report.
+ * Auto indexing with `!` operator, for e.g., `DT[!x == 1]` works as intended. Closes [#932](https://github.com/Rdatatable/data.table/issues/932). Thanks to @matthieugomez for the minimal example.
+ * While fixing `#932`, issues on subsetting `NA` were also spotted and fixed, for e.g., `DT[x==NA]` or `DT[!x==NA]`.
+ * Works fine when RHS is of `list` type - quite unusual operation but could happen. Closes [#961](https://github.com/Rdatatable/data.table/issues/961). Thanks to @Gsee for the minimal report.
+ * Auto indexing errored in some cases when LHS and RHS were not of same type. This is fixed now. Closes [#957](https://github.com/Rdatatable/data.table/issues/957). Thanks to @GSee for the minimal report.
+ * `DT[x == 2.5]` where `x` is integer type resulted in `val` being coerced to integer (for binary search) and therefore returned incorrect result. This is now identified using the function `isReallyReal()` and if so, auto indexing is turned off. Closes [#1050](https://github.com/Rdatatable/data.table/issues/1050).
+ * Auto indexing errored during `DT[x %in% val]` when `val` has some values not present in `x`. Closes [#1072](https://github.com/Rdatatable/data.table/issues/1072). Thanks to @CarlosCinelli for asking on [StackOverflow](http://stackoverflow.com/q/28932742/559784).
+
+ 7. `as.data.table.list` with list input having 0-length items, e.g. `x = list(a=integer(0), b=3:4)`. `as.data.table(x)` recycles item `a` with `NA`s to fit the length of the longer column `b` (length=2), as before now, but with an additional warning message that the item has been recycled with `NA`. Closes [#847](https://github.com/Rdatatable/data.table/issues/847). Thanks to @tvinodr for the report. This was a regression from 1.9.2.
+
+ 8. `DT[i, j]` when `i` returns all `FALSE` and `j` contains some length-0 values (ex: `integer(0)`) now returns an empty data.table as it should. Closes [#758](https://github.com/Rdatatable/data.table/issues/758) and [#813](https://github.com/Rdatatable/data.table/issues/813). Thanks to @tunaaa and @nigmastar for the nice reproducible reports.
+
+ 9. `allow.cartesian` is ignored during joins when:
+ * `i` has no duplicates and `mult="all"`. Closes [#742](https://github.com/Rdatatable/data.table/issues/742). Thanks to @nigmastar for the report.
+ * assigning by reference, i.e., `j` has `:=`. Closes [#800](https://github.com/Rdatatable/data.table/issues/800). Thanks to @matthieugomez for the report.
+
+ In both these cases (and during a `not-join` which was already fixed in [1.9.4](https://github.com/Rdatatable/data.table/blob/master/README.md#bug-fixes-1)), `allow.cartesian` can be safely ignored.
+
+ 10. `names<-.data.table` works as intended on data.table unaware packages with Rv3.1.0+. Closes [#476](https://github.com/Rdatatable/data.table/issues/476) and [#825](https://github.com/Rdatatable/data.table/issues/825). Thanks to ezbentley for reporting [here](http://stackoverflow.com/q/23256177/559784) on SO and to @narrenfrei.
+
+ 11. `.EACHI` is now an exported symbol (just like `.SD`,`.N`,`.I`,`.GRP` and `.BY` already were) so that packages using `data.table` and `.EACHI` pass `R CMD check` with no NOTE that this symbol is undefined. Thanks to Matt Bannert for highlighting.
+
+ 12. Some optimisations of `.SD` in `j` was done in 1.9.4, refer to [#735](https://github.com/Rdatatable/data.table/issues/735). Due to an oversight, j-expressions of the form c(lapply(.SD, ...), list(...)) were optimised improperly. This is now fixed. Thanks to @mmeierer for filing [#861](https://github.com/Rdatatable/data.table/issues/861).
+
+ 13. `j`-expressions in `DT[, col := x$y()]` (or) `DT[, col := x[[1]]()]` are now (re)constructed properly. Thanks to @ihaddad-md for reporting. Closes [#774](https://github.com/Rdatatable/data.table/issues/774).
+
+ 14. `format.ITime` now handles negative values properly. Closes [#811](https://github.com/Rdatatable/data.table/issues/811). Thanks to @StefanFritsch for the report along with the fix!
+
+ 15. Compatibility with big endian machines (e.g., SPARC and PowerPC) is restored. Most Windows, Linux and Mac systems are little endian; type `.Platform$endian` to confirm. Thanks to Gerhard Nachtmann for reporting and the [QEMU project](http://qemu.org/) for their PowerPC emulator.
+
+ 16. `DT[, LHS := RHS]` with RHS is of the form `eval(parse(text = foo[1]))` referring to columns in `DT` is now handled properly. Closes [#880](https://github.com/Rdatatable/data.table/issues/880). Thanks to tyner.
+
+ 17. `subset` handles extracting duplicate columns in consistency with data.table's rule - if a column name is duplicated, then accessing that column using column number should return that column, whereas accessing by column name (due to ambiguity) will always extract the first column. Closes [#891](https://github.com/Rdatatable/data.table/issues/891). Thanks to @jjzz.
+
+ 18. `rbindlist` handles combining levels of data.tables with both ordered and unordered factor columns properly. Closes [#899](https://github.com/Rdatatable/data.table/issues/899). Thanks to @ChristK.
+
+ 19. Updating `.SD` by reference using `set` also errors appropriately now; similar to `:=`. Closes [#927](https://github.com/Rdatatable/data.table/issues/927). Thanks to @jrowen for the minimal example.
+
+ 20. `X[Y, .N]` returned the same result as `X[Y, .N, nomatch=0L]`) when `Y` contained rows that has no matches in `X`. Fixed now. Closes [#963](https://github.com/Rdatatable/data.table/issues/963). Thanks to [this SO post](http://stackoverflow.com/q/27004002/559784) from @Alex which helped discover the bug.
+
+ 21. `data.table::dcast` handles levels in factor columns properly when `drop = FALSE`. Closes [#893](https://github.com/Rdatatable/data.table/issues/893). Thanks to @matthieugomez for the great minimal example.
+
+ 22. `[.data.table` subsets complex and raw type objects again. Thanks to @richierocks for the nice minimal example. Closes [#982](https://github.com/Rdatatable/data.table/issues/982).
+
+ 23. Fixed a bug in the internal optimisation of `j-expression` with more than one `lapply(.SD, function(..) ..)` as illustrated [here on SO](http://stackoverflow.com/a/27495844/559784). Closes #985. Thanks to @jadaliha for the report and to @BrodieG for the debugging on SO.
+
+ 24. `mget` fetches columns from the default environment `.SD` when called from within the frame of `DT`. That is, `DT[, mget(cols)]`, `DT[, lapply(mget(cols), sum), by=.]` etc.. work as intended. Thanks to @Roland for filing this issue. Closes [#994](https://github.com/Rdatatable/data.table/issues/994).
+
+ 25. `foverlaps()` did not find overlapping intervals correctly *on numeric ranges* in a special case where both `start` and `end` intervals had *0.0*. This is now fixed. Thanks to @tdhock for the reproducible example. Closes [#1006](https://github.com/Rdatatable/data.table/issues/1006) partly.
+
+ 26. When performing rolling joins, keys are set only when we can be absolutely sure. Closes [#1010](https://github.com/Rdatatable/data.table/issues/1010), which explains cases where keys should not be retained.
+
+ 27. Rolling joins with `-Inf` and `Inf` are handled properly. Closes [#1007](https://github.com/Rdatatable/data.table/issues/1007). Thanks to @tdhock for filing [#1006](https://github.com/Rdatatable/data.table/issues/1006) which lead to the discovery of this issue.
+
+ 28. Overlapping range joins with `-Inf` and `Inf` and 0.0 in them are handled properly now. Closes [#1006](https://github.com/Rdatatable/data.table/issues/1006). Thanks to @tdhock for filing the issue with a nice reproducible example.
+
+ 29. Fixed two segfaults in `shift()` when number of rows in `x` is lesser than value for `n`. Closes [#1009](https://github.com/Rdatatable/data.table/issues/1009) and [#1014](https://github.com/Rdatatable/data.table/issues/1014). Thanks to @jangorecki and @ashinm for the reproducible reports.
+
+ 30. Attributes are preserved for `sum()` and `mean()` when fast internal (GForce) implementations are used. Closes [#1023](https://github.com/Rdatatable/data.table/issues/1023). Thanks to @DavidArenburg for the nice reproducible example.
+
+ 31. `lapply(l, setDT)` is handled properly now; over-allocation isn't lost. Similarly, `for (i in 1:k) setDT(l[[i]])` is handled properly as well. Closes [#480](https://github.com/Rdatatable/data.table/issues/480).
+
+ 32. `rbindlist` stack imbalance on all `NULL` list elements is now fixed. Closes [#980](https://github.com/Rdatatable/data.table/issues/980). Thanks to @ttuggle.
+
+ 33. List columns can be assigned to columns of `factor` type by reference. Closes [#936](https://github.com/Rdatatable/data.table/issues/936). Thanks to @richierocks for the minimal example.
+
+ 34. After setting the `datatable.alloccol` option, creating a data.table with more than the set `truelength` resulted in error or segfault. This is now fixed. Closes [#970](https://github.com/Rdatatable/data.table/issues/970). Thanks to @caneff for the nice minimal example.
+
+ 35. Update by reference using `:=` after loading from disk where the `data.table` exists within a local environment now works as intended. Closes [#479](https://github.com/Rdatatable/data.table/issues/479). Thanks to @ChongWang for the minimal reproducible example.
+
+ 36. Issues on merges involving `factor` columns with `NA` and merging `factor` with `character` type with non-identical levels are both fixed. Closes [#499](https://github.com/Rdatatable/data.table/issues/499) and [#945](https://github.com/Rdatatable/data.table/issues/945). Thanks to @AbielReinhart and @stewbasic for the minimal examples.
+
+ 37. `as.data.table(ll)` returned a `data.table` with 0-rows when the first element of the list has 0-length, for e.g., `ll = list(NULL, 1:2, 3:4)`. This is now fixed by removing those 0-length elements. Closes [#842](https://github.com/Rdatatable/data.table/issues/842). Thanks to @Rick for the nice minimal example.
+
+ 38. `as.datat.able.factor` redirects to `as.data.table.matrix` when input is a `matrix`, but also of type `factor`. Closes [#868](https://github.com/Rdatatable/data.table/issues/868). Thanks to @mgahan for the example.
+
+ 39. `setattr` now returns an error when trying to set `data.table` and/or `data.frame` as class to a *non-list* type object (ex: `matrix`). Closes [#832](https://github.com/Rdatatable/data.table/issues/832). Thanks to @Rick for the minimal example.
+
+ 40. data.table(table) works as expected. Closes [#1043](https://github.com/Rdatatable/data.table/issues/1043). Thanks to @rnso for the [SO post](http://stackoverflow.com/q/28499359/559784).
+
+ 41. Joins and binary search based subsets of the form `x[i]` where `x`'s key column is integer and `i` a logical column threw an error before. This is now fixed by converting the logical column to integer type and then performing the join, so that it works as expected.
+
+ 42. When `by` expression is, for example, `by = x %% 2`, `data.table` tries to automatically extracts meaningful column names from the expression. In this case it would be `x`. However, if the `j-expression` also contains `x`, for example, `DT[, last(x), by= x %% 2]`, the original `x` got masked by the expression in `by`. This is now fixed; by-expressions are not simplified in column names for these cases. Closes [#497](https://github.com/Rdatatable/data.table/issues/497). Thanks to @G [...]
+
+ 43. `rbindlist` now errors when columns have non-identical class attributes and are not `factor`s, e.g., binding column of class `Date` with `POSIXct`. Previously this returned incorrect results. Closes [#705](https://github.com/Rdatatable/data.table/issues/705). Thanks to @ecoRoland for the minimal report.
+
+ 44. Fixed a segfault in `melt.data.table` when `measure.vars` have duplicate names. Closes [#1055](https://github.com/Rdatatable/data.table/issues/1055). Thanks to @ChristK for the minimal report.
+
+ 45. Fixed another segfault in `melt.data.table` issue that was caught due to issue in Windows. Closes [#1059](https://github.com/Rdatatable/data.table/issues/1059). Thanks again to @ChristK for the minimal report.
+
+ 46. `DT[rows, newcol := NULL]` resulted in a segfault on the next assignment by reference. Closes [#1082](https://github.com/Rdatatable/data.table/issues/1082). Thanks to @stevenbagley for the MRE.
+
+ 47. `as.matrix(DT)` handles cases where `DT` contains both numeric and logical columns correctly (doesn't coerce to character columns anymore). Closes [#1083](https://github.com/Rdatatable/data.table/issues/1083). Thanks to @bramvisser for the [SO post](http://stackoverflow.com/questions/29068328/correlation-between-numeric-and-logical-variable-gives-intended-error).
+
+ 48. Coercion is handled properly on subsets/joins on `integer64` key columns. Closes [#1108](https://github.com/Rdatatable/data.table/issues/1108). Thanks to @vspinu.
+
+ 49. `setDT()` and `as.data.table()` both strip *all classes* preceding *data.table*/*data.frame*, to be consistent with base R. Closes [#1078](https://github.com/Rdatatable/data.table/issues/1078) and [#1128](https://github.com/Rdatatable/data.table/issues/1128). Thanks to Jan and @helix123 for the reports.
+
+ 50. `setattr(x, 'levels', value)` handles duplicate levels in `value`
+ appropriately. Thanks to Jeffrey Horner for pointing it out [here](http://jeffreyhorner.tumblr.com/post/118297392563/tidyr-challenge-help-me-do-my-job). Closes [#1142](https://github.com/Rdatatable/data.table/issues/1142).
+
+ 51. `x[J(vals), .N, nomatch=0L]` also included no matches in result, [#1074](https://github.com/Rdatatable/data.table/issues/1074). And `x[J(...), col := val, nomatch=0L]` returned a warning with incorrect results when join resulted in no matches as well, even though `nomatch=0L` should have no effect in `:=`, [#1092](https://github.com/Rdatatable/data.table/issues/1092). Both issues are fixed now. Thanks to @riabusan and @cguill95 for #1092.
+
+ 52. `.data.table.locked` attributes set to NULL in internal function `subsetDT`. Closes [#1154](https://github.com/Rdatatable/data.table/issues/1154). Thanks to @Jan.
+
+ 53. Internal function `fastmean()` retains column attributes. Closes [#1160](https://github.com/Rdatatable/data.table/issues/1160). Thanks to @renkun-ken.
+
+ 54. Using `.N` in `i`, for e.g., `DT[, head(.SD, 3)[1:(.N-1L)]]` accessed incorrect value of `.N`. This is now fixed. Closes [#1145](https://github.com/Rdatatable/data.table/issues/1145). Thanks to @claytonstanley.
+
+ 55. `setDT` handles `key=` argument properly when input is already a `data.table`. Closes [#1169](https://github.com/Rdatatable/data.table/issues/1169). Thanks to @DavidArenburg for the PR.
+
+ 56. Key is retained properly when joining on factor type columns. Closes [#477](https://github.com/Rdatatable/data.table/issues/477). Thanks to @nachti for the report.
+
+ 57. Over-allocated memory is released more robustly thanks to Karl Miller's investigation and suggested fix.
+
+ 58. `DT[TRUE, colA:=colA*2]` no longer churns through 4 unnecessary allocations as large as one column. This was caused by `i=TRUE` being recycled. Thanks to Nathan Kurz for reporting and investigating. Added provided test to test suite. Only a single vector is allocated now for the RHS (`colA*2`). Closes [#1249](https://github.com/Rdatatable/data.table/issues/1249).
+
+ 59. Thanks to @and3k for the excellent bug report [#1258](https://github.com/Rdatatable/data.table/issues/1258). This was a result of shallow copy retaining keys when it shouldn't. It affected some cases of joins using `on=`. Fixed now.
+
+ 60. `set()` and `:=` handle RHS value `NA_integer_` on factor types properly. Closes [#1234](https://github.com/Rdatatable/data.table/issues/1234). Thanks to @DavidArenburg.
+
+ 61. `merge.data.table()` didn't set column order (and therefore names) properly in some cases. Fixed now. Closes [#1290](https://github.com/Rdatatable/data.table/issues/1290). Thanks to @ChristK for the minimal example.
+
+ 62. print.data.table now works for 100+ rows as intended when `row.names=FALSE`. Closes [#1307](https://github.com/Rdatatable/data.table/issues/1307). Thanks to @jangorecki for the PR.
+
+ 63. Row numbers are not printed in scientific format. Closes [#1167](https://github.com/Rdatatable/data.table/issues/1167). Thanks to @jangorecki for the PR.
+
+ 64. Using `.GRP` unnamed in `j` now returns a variable named `GRP` instead of `.GRP` as the period was causing issues. Same for `.BY`. Closes [#1243](https://github.com/Rdatatable/data.table/issues/1243); thanks to @MichaelChirico for the PR.
+
+ 65. `DT[, 0, with=FALSE]` returns null data.table to be consistent with `data.frame`'s behaviour. Closes [#1140](https://github.com/Rdatatable/data.table/issues/1140). Thanks to @franknarf1.
+
+ 66. Evaluating quoted expressions with `.` in `by` works as intended. That is, `dt = data.table(a=c(1,1,2,2), b=1:4); expr=quote(.(a)); dt[, sum(b), eval(expr)]` works now. Closes [#1298](https://github.com/Rdatatable/data.table/issues/1298). Thanks @eddi.
+
+ 67. `as.list` method for `IDate` object works properly. Closes [#1315](https://github.com/Rdatatable/data.table/issues/1315). Thanks to @gwerbin.
+
+#### NOTES
+
+ 1. Clearer explanation of what `duplicated()` does (borrowed from base). Thanks to @matthieugomez for pointing out. Closes [#872](https://github.com/Rdatatable/data.table/issues/872).
+
+ 2. `?setnames` has been updated now that `names<-` and `colnames<-` shallow (rather than deep) copy from R >= 3.1.0, [#853](https://github.com/Rdatatable/data.table/issues/872).
+
+ 3. [FAQ 1.6](https://github.com/Rdatatable/data.table/wiki/vignettes/datatable-faq.pdf) has been embellished, [#517](https://github.com/Rdatatable/data.table/issues/517). Thanks to a discussion with Vivi and Josh O'Brien.
+
+ 4. `data.table` redefines `melt` generic and *suggests* `reshape2` instead of *import*. As a result we don't have to load `reshape2` package to use `melt.data.table` anymore. The reason for this change is that `data.table` requires R >=2.14, whereas `reshape2` R v3.0.0+. Reshape2's melt methods can be used without any issues by loading the package normally.
+
+ 5. `DT[, j, ]` at times made an additional (unnecessary) copy. This is now fixed. This fix also avoids allocating `.I` when `j` doesn't use it. As a result `:=` and other subset operations should be faster (and use less memory). Thanks to @szilard for the nice report. Closes [#921](https://github.com/Rdatatable/data.table/issues/921).
+
+ 6. Because `reshape2` requires R >3.0.0, and `data.table` works with R >= 2.14.1, we can not import `reshape2` anymore. Therefore we define a `melt` generic and `melt.data.table` method for data.tables and redirect to `reshape2`'s `melt` for other objects. This is to ensure that existing code works fine.
+
+ 7. `dcast` is also a generic now in data.table. So we can use `dcast(...)` directly, and don't have to spell it out as `dcast.data.table(...)` like before. The `dcast` generic in data.table redirects to `reshape2::dcast` if the input object is not a data.table. But for that you have to load `reshape2` before loading `data.table`. If not, reshape2's `dcast` overwrites data.table's `dcast` generic, in which case you will need the `::` operator - ex: `data.table::dcast(...)`.
+
+ NB: Ideal situation would be for `dcast` to be a generic in reshape2 as well, but it is not. We have issued a [pull request](https://github.com/hadley/reshape/pull/62) to make `dcast` in reshape2 a generic, but that has not yet been accepted.
+
+ 8. Clarified the use of `bit64::integer4` in `merge.data.table()` and `setNumericRounding()`. Closes [#1093](https://github.com/Rdatatable/data.table/issues/1093). Thanks to @sfischme for the report.
+
+ 9. Removed an unnecessary (and silly) `giveNames` argument from `setDT()`. Not sure why I added this in the first place!
+
+ 10. `options(datatable.prettyprint.char=5L)` restricts the number of characters to be printed for character columns. For example:
+
+ options(datatable.prettyprint.char = 5L)
+ DT = data.table(x=1:2, y=c("abcdefghij", "klmnopqrstuv"))
+ DT
+ # x y
+ # 1: 1 abcde...
+ # 2: 2 klmno...
+
+ 11. `rolltolast` argument in `[.data.table` is now defunct. It was deprecated in 1.9.4.
+
+ 12. `data.table`'s dependency has been moved forward from R 2.14.0 to R 2.14.1, now nearly 4 years old (Dec 2011). As usual before release to CRAN we ensure data.table passes the test suite on the stated dependency and keep this as old as possible for as long as possible. As requested by users in managed environments. For this reason we still don't use `paste0()` internally, since that was added to R 2.15.0.
+
+ 13. Warning about `datatable.old.bywithoutby` option (for grouping on join without providing `by`) being deprecated in the next release is in place now. Thanks to @jangorecki for the PR.
+
+ 14. Fixed `allow.cartesian` documentation to `nrow(x)+nrow(i)` instead of `max(nrow(x), nrow(i))`. Closes [#1123](https://github.com/Rdatatable/data.table/issues/1123).
+
+### Changes in v1.9.4 (on CRAN 2 Oct 2014)
+
+#### NEW FEATURES
+
+ 1. `by=.EACHI` runs `j` for each group in `DT` that each row of `i` joins to.
+ ```R
+ setkey(DT, ID)
+ DT[c("id1", "id2"), sum(val)] # single total across both id1 and id2
+ DT[c("id1", "id2"), sum(val), by = .EACHI] # sum(val) for each id
+ DT[c("id1", "id2"), sum(val), by = key(DT)] # same
+ ```
+ In other words, `by-without-by` is now explicit, as requested by users, [#371](https://github.com/Rdatatable/data.table/issues/371). When `i` contains duplicates, `by=.EACHI` is different to `by=key(DT)`; e.g.,
+ ```R
+ setkey(DT, ID)
+ ids = c("id1", "id2", "id1") # NB: id1 appears twice
+ DT[ids, sum(val), by = ID] # 2 rows returned
+ DT[ids, sum(val), by = .EACHI] # 3 rows in the order of ids (result 1 and 3 are not merged)
+ ```
+ `by=.EACHI` can be useful when `i` is event data, where you don't want the events aggregated by common join values but wish the output to be ordered with repeats, or simply just using join inherited columns as parameters; e.g.;
+ ```R
+ X[Y, head(.SD, i.top), by = .EACHI]
+ ```
+ where `top` is a non-join column in `Y`; i.e., join inherited column. Thanks to many, especially eddi, Sadao Milberg and Gabor Grothendieck for extended discussions. Closes [#538](https://github.com/Rdatatable/data.table/issues/538).
+
+ 2. Accordingly, `X[Y, j]` now does what `X[Y][, j]` did. To return the old behaviour: `options(datatable.old.bywithoutby=TRUE)`. This is a temporary option to aid migration and will be removed in future. See [this](http://r.789695.n4.nabble.com/changing-data-table-by-without-by-syntax-to-require-a-quot-by-quot-td4664770.html), [this](http://stackoverflow.com/questions/16093289/data-table-join-and-j-expression-unexpected-behavior) and [this](http://stackoverflow.com/a/16222108/403310) p [...]
+
+ 3. `Overlap joins` ([#528](https://github.com/Rdatatable/data.table/issues/528)) is now here, finally!! Except for `type="equal"` and `maxgap` and `minoverlap` arguments, everything else is implemented. Check out `?foverlaps` and the examples there on its usage. This is a major feature addition to `data.table`.
+
+ 4. `DT[column==value]` and `DT[column %in% values]` are now optimized to use `DT`'s key when `key(DT)[1]=="column"`, otherwise a secondary key (a.k.a. _index_) is automatically added so the next `DT[column==value]` is much faster. No code changes are needed; existing code should automatically benefit. Secondary keys can be added manually using `set2key()` and existence checked using `key2()`. These optimizations and function names/arguments are experimental and may be turned off with ` [...]
+
+ 5. `fread()`:
+ * accepts line breaks inside quoted fields. Thanks to Clayton Stanley for highlighting [here](http://stackoverflow.com/questions/21006661/fread-and-a-quoted-multi-line-column-value).
+ * accepts trailing backslash in quoted fields. Thanks to user2970844 for highlighting [here](http://stackoverflow.com/questions/24375832/fread-and-column-with-a-trailing-backslash).
+ * Blank and `"NA"` values in logical columns (`T`,`True`,`TRUE`) no longer cause them to be read as character, [#567](https://github.com/Rdatatable/data.table/issues/567). Thanks to Adam November for reporting.
+ * URLs now work on Windows. R's `download.file()` converts `\r\n` to `\r\r\n` on Windows. Now avoided by downloading in binary mode. Thanks to Steve Miller and Dean MacGregor for reporting, [#492](https://github.com/Rdatatable/data.table/issues/492).
+ * Fixed seg fault in sparse data files when bumping to character, [#796](https://github.com/Rdatatable/data.table/issues/796) and [#722](https://github.com/Rdatatable/data.table/issues/722). Thanks to Adam Kennedy and Richard Cotton for the detailed reproducible reports.
+ * New argument `fread(...,data.table=FALSE)` returns a `data.frame` instead of a `data.table`. This can be set globally: `options(datatable.fread.datatable=FALSE)`.
+
+ 6. `.()` can now be used in `j` and is identical to `list()`, for consistency with `i`.
+ ```R
+ DT[,list(MySum=sum(B)),by=...]
+ DT[,.(MySum=sum(B)),by=...] # same
+ DT[,list(colB,colC,colD)]
+ DT[,.(colB,colC,colD)] # same
+ ```
+ Similarly, `by=.()` is now a shortcut for `by=list()`, for consistency with `i` and `j`.
+
+ 7. `rbindlist` gains `use.names` and `fill` arguments and is now implemented entirely in C. Closes [#345](https://github.com/Rdatatable/data.table/issues/345):
+ * `use.names` by default is FALSE for backwards compatibility (does not bind by names by default)
+ * `rbind(...)` now just calls `rbindlist()` internally, except that `use.names` is TRUE by default, for compatibility with base (and backwards compatibility).
+ * `fill=FALSE` by default. If `fill=TRUE`, `use.names` has to be TRUE.
+ * When use.names=TRUE, at least one item of the input list has to have non-null column names.
+ * When fill=TRUE, all items of the input list has to have non-null column names.
+ * Duplicate columns are bound in the order of occurrence, like base.
+ * Attributes that might exist in individual items would be lost in the bound result.
+ * Columns are coerced to the highest SEXPTYPE when they are different, if possible.
+ * And incredibly fast ;).
+ * Documentation updated in much detail. Closes [#333](https://github.com/Rdatatable/data.table/issues/333).
+
+ 8. `bit64::integer64` now works in grouping and joins, [#342](https://github.com/Rdatatable/data.table/issues/342). Thanks to James Sams for highlighting UPCs and Clayton Stanley for [this SO post](http://stackoverflow.com/questions/22273321/large-integers-in-data-table-grouping-results-different-in-1-9-2-compared-to-1). `fread()` has been detecting and reading `integer64` for a while.
+
+ 9. `setNumericRounding()` may be used to reduce to 1 byte or 0 byte rounding when joining to or grouping columns of type 'numeric', [#342](https://github.com/Rdatatable/data.table/issues/342). See example in `?setNumericRounding` and NEWS item below for v1.9.2. `getNumericRounding()` returns the current setting.
+
+ 10. `X[Y]` now names non-join columns from `i` that have the same name as a column in `x`, with an `i.` prefix for consistency with the `i.` prefix that has been available in `j` for some time. This is now documented.
+
+ 11. For a keyed table `X` where the key columns are not at the beginning in order, `X[Y]` now retains the original order of columns in X rather than moving the join columns to the beginning of the result.
+
+ 12. It is no longer an error to assign to row 0 or row NA.
+ ```R
+ DT[0, colA := 1L] # now does nothing, silently (was error)
+ DT[NA, colA := 1L] # now does nothing, silently (was error)
+ DT[c(1, NA, 0, 2), colA:=1L] # now ignores the NA and 0 silently (was error)
+ DT[nrow(DT) + 1, colA := 1L] # error (out-of-range) as before
+ ```
+ This is for convenience to avoid the need for a switch in user code that evals various `i` conditions in a loop passing in `i` as an integer vector which may containing `0` or `NA`.
+
+ 13. A new function `setorder` is now implemented which uses data.table's internal fast order to reorder rows *by reference*. It returns the result invisibly (like `setkey`) that allows for compound statements; e.g., `setorder(DT, a, -b)[, cumsum(c), by=list(a,b)]`. Check `?setorder` for more info.
+
+ 14. `DT[order(x, -y)]` is now by default optimised to use data.table's internal fast order as `DT[forder(DT, x, -y)]`. It can be turned off by setting `datatable.optimize` to < 1L or just calling `base:::order` explicitly. It results in 20x speedup on data.table of 10 million rows with 2 integer columns, for example. To order character vectors in descending order it's sufficient to do `DT[order(x, -y)]` as opposed to `DT[order(x, -xtfrm(y))]` in base. This closes [#603](https://github. [...]
+
+ 15. `mult="all"` -vs- `mult="first"|"last"` now return consistent types and columns, [#340](https://github.com/Rdatatable/data.table/issues/340). Thanks to Michele Carriero for highlighting.
+
+ 16. `duplicated.data.table` and `unique.data.table` gains `fromLast = TRUE/FALSE` argument, similar to base. Default value is FALSE. Closes [#347](https://github.com/Rdatatable/data.table/issues/347).
+
+ 17. `anyDuplicated.data.table` is now implemented. Closes [#350](https://github.com/Rdatatable/data.table/issues/350). Thanks to M C (bluemagister) for reporting.
+
+ 18. Complex j-expressions of the form `DT[, c(..., lapply(.SD, fun)), by=grp]`are now optimised as long as `.SD` is of the form `lapply(.SD, fun)` or `.SD`, `.SD[1]` or `.SD[1L]`. This resolves [#370](https://github.com/Rdatatable/data.table/issues/370). Thanks to Sam Steingold for reporting.
+ This also completes the first two task lists in [#735](https://github.com/Rdatatable/data.table/issues/735).
+ ```R
+ ## example:
+ DT[, c(.I, lapply(.SD, sum), mean(x), lapply(.SD, log)), by=grp]
+ ## is optimised to
+ DT[, list(.I, x=sum(x), y=sum(y), ..., mean(x), log(x), log(y), ...), by=grp]
+ ## and now... these variations are also optimised internally for speed
+ DT[, c(..., .SD, lapply(.SD, sum), ...), by=grp]
+ DT[, c(..., .SD[1], lapply(.SD, sum), ...), by=grp]
+ DT[, .SD, by=grp]
+ DT[, c(.SD), by=grp]
+ DT[, .SD[1], by=grp] # Note: but not yet DT[, .SD[1,], by=grp]
+ DT[, c(.SD[1]), by=grp]
+ DT[, head(.SD, 1), by=grp] # Note: but not yet DT[, head(.SD, -1), by=grp]
+ # but not yet optimised
+ DT[, c(.SD[a], .SD[x>1], lapply(.SD, sum)), by=grp] # where 'a' is, say, a numeric or a data.table, and also for expressions like x>1
+ ```
+ The underlying message is that `.SD` is being slowly optimised internally wherever possible, for speed, without compromising in the nice readable syntax it provides.
+
+ 19. `setDT` gains `keep.rownames = TRUE/FALSE` argument, which works only on `data.frame`s. TRUE retains the data.frame's row names as a new column named `rn`.
+
+ 20. The output of `tables()` now includes `NCOL`. Thanks to @dnlbrky for the suggestion.
+
+ 21. `DT[, LHS := RHS]` (or its equivalent in `set`) now provides a warning and returns `DT` as it was, instead of an error, when `length(LHS) = 0L`, [#343](https://github.com/Rdatatable/data.table/issues/343). For example:
+ ```R
+ DT[, grep("^b", names(DT)) := NULL] # where no columns start with b
+ # warns now and returns DT instead of error
+ ```
+
+ 22. GForce now is also optimised for j-expression with `.N`. Closes [#334](https://github.com/Rdatatable/data.table/issues/334) and part of [#523](https://github.com/Rdatatable/data.table/issues/523).
+ ```R
+ DT[, list(.N, mean(y), sum(y)), by=x] # 1.9.2 - doesn't know to use GForce - will be (relatively) slower
+ DT[, list(.N, mean(y), sum(y)), by=x] # 1.9.3+ - will use GForce.
+ ```
+
+ 23. `setDF` is now implemented. It accepts a data.table and converts it to data.frame by reference, [#338](https://github.com/Rdatatable/data.table/issues/338). Thanks to canneff for the discussion [here](http://r.789695.n4.nabble.com/Is-there-any-overhead-to-converting-back-and-forth-from-a-data-table-to-a-data-frame-td4688332.html) on data.table mailing list.
+
+ 24. `.I` gets named as `I` (instead of `.I`) wherever possible, similar to `.N`, [#344](https://github.com/Rdatatable/data.table/issues/344).
+
+ 25. `setkey` on `.SD` is now an error, rather than warnings for each group about rebuilding the key. The new error is similar to when attempting to use `:=` in a `.SD` subquery: `".SD is locked. Using set*() functions on .SD is reserved for possible future use; a tortuously flexible way to modify the original data by group."` Thanks to Ron Hylton for highlighting the issue on datatable-help [here](http://r.789695.n4.nabble.com/data-table-is-asking-for-help-tp4692080.html).
+
+ 26. Looping calls to `unique(DT)` such as in `DT[,unique(.SD),by=group]` is now faster by avoiding internal overhead of calling `[.data.table`. Thanks again to Ron Hylton for highlighting in the [same thread](http://r.789695.n4.nabble.com/data-table-is-asking-for-help-tp4692080.html). His example is reduced from 28 sec to 9 sec, with identical results.
+
+ 27. Following `gsum` and `gmean`, now `gmin` and `gmax` from GForce are also implemented. Closes part of [#523](https://github.com/Rdatatable/data.table/issues/523). Benchmarks are also provided.
+ ```R
+ DT[, list(sum(x), min(y), max(z), .N), by=...] # runs by default using GForce
+ ```
+
+ 28. `setorder()` and `DT[order(.)]` handles `integer64` type in descending order as well. Closes [#703](https://github.com/Rdatatable/data.table/issues/703).
+
+ 29. `setorder()` and `setorderv()` gain `na.last = TRUE/FALSE`. Closes [#706](https://github.com/Rdatatable/data.table/issues/706).
+
+ 30. `.N` is now available in `i`, [FR#724](https://github.com/Rdatatable/data.table/issues/724). Thanks to newbie indirectly [here](http://stackoverflow.com/a/24649115/403310) and Farrel directly [here](http://stackoverflow.com/questions/24685421/how-do-you-extract-a-few-random-rows-from-a-data-table-on-the-fly).
+
+ 31. `by=.EACHI` is now implemented for *not-joins* as well. Closes [#604](https://github.com/Rdatatable/data.table/issues/604). Thanks to Garrett See for filing the FR. As an example:
+ ```R
+ DT = data.table(x=c(1,1,1,1,2,2,3,4,4,4), y=1:10, key="x")
+ DT[!J(c(1,4)), sum(y), by=.EACHI] # is equivalent to DT[J(c(2,3)), sum(y), by=.EACHI]
+ ```
+
+
+#### BUG FIXES
+
+
+ 1. When joining to fewer columns than the key has, using one of the later key columns explicitly in j repeated the first value. A problem introduced by v1.9.2 and not caught bythe 1,220 tests, or tests in 37 dependent packages. Test added. Many thanks to Michele Carriero for reporting.
+ ```R
+ DT = data.table(a=1:2, b=letters[1:6], key="a,b") # keyed by a and b
+ DT[.(1), list(b,...)] # correct result again (joining just to a not b but using b)
+ ```
+
+ 2. `setkey` works again when a non-key column is type list (e.g. each cell can itself be a vector), [#54](https://github.com/Rdatatable/data.table/issues/54). Test added. Thanks to James Sams, Michael Nelson and Musx [for the reproducible examples](http://stackoverflow.com/questions/22186798/r-data-table-1-9-2-issue-on-setkey).
+
+ 3. The warning "internal TRUE value has been modified" with recently released R 3.1 when grouping a table containing a logical column *and* where all groups are just 1 row is now fixed and tests added. Thanks to James Sams for the reproducible example. The warning is issued by R and we have asked if it can be upgraded to error (UPDATE: change now made for R 3.1.1 thanks to Luke Tierney).
+
+ 4. `data.table(list())`, `data.table(data.table())` and `data.table(data.frame())` now return a null data.table (no columns) rather than one empty column, [#48](https://github.com/Rdatatable/data.table/issues/48). Test added. Thanks to Shubh Bansal for reporting.
+
+ 5. `unique(<NULL data.table>)` now returns a null data.table, [#44](https://github.com/Rdatatable/data.table/issues/44). Thanks to agstudy for reporting.
+
+ 6. `data.table()` converted POSIXlt to POSIXct, consistent with `base:::data.frame()`, but now also provides a helpful warning instead of coercing silently, [#59](https://github.com/Rdatatable/data.table/issues/59). Thanks to Brodie Gaslam, Patrick and Ragy Isaac for reporting [here](http://stackoverflow.com/questions/21487614/error-creating-r-data-table-with-date-time-posixlt) and [here](http://stackoverflow.com/questions/21320215/converting-from-data-frame-to-data-table-i-get-an-err [...]
+
+ 7. If another class inherits from data.table; e.g. `class(DT) == c("UserClass","data.table","data.frame")` then `DT[...]` now retains `UserClass` in the result. Thanks to Daniel Krizian for reporting, [#64](https://github.com/Rdatatable/data.table/issues/44). Test added.
+
+ 8. An error `object '<name>' not found` could occur in some circumstances, particularly after a previous error. [Reported on SO](http://stackoverflow.com/questions/22128047/how-to-avoid-weird-umlaute-error-when-using-data-table) with non-ASCII characters in a column name, a red herring we hope since non-ASCII characters are fully supported in data.table including in column names. Fix implemented and tests added.
+
+ 9. Column order was reversed in some cases by `as.data.table.table()`, [#43](https://github.com/Rdatatable/data.table/issues/43). Test added. Thanks to Benjamin Barnes for reporting.
+
+ 10. `DT[, !"missingcol", with=FALSE]` now returns `DT` (rather than a NULL data.table) with warning that "missingcol" is not present.
+
+ 11. `DT[,y := y * eval(parse(text="1*2"))]` resulted in error unless `eval()` was wrapped with paranthesis. That is, `DT[,y := y * (eval(parse(text="1*2")))]`, **#5423**. Thanks to Wet Feet for reporting and to Simon O'Hanlon for identifying the issue [here on SO](http://stackoverflow.com/questions/22375404/unable-to-use-evalparse-in-data-table-function/22375557#22375557).
+
+ 12. Using `by` columns with attributes (ex: factor, Date) in `j` did not retain the attributes, also in case of `:=`. This was partially a regression from an earlier fix ([#155](https://github.com/Rdatatable/data.table/issues/155)) due to recent changes for R3.1.0. Now fixed and clearer tests added. Thanks to Christophe Dervieux for reporting and to Adam B for reporting [here on SO](http://stackoverflow.com/questions/22536586/by-seems-to-not-retain-attribute-of-date-type-columns-in-da [...]
+
+ 13. `.BY` special variable did not retain names of the grouping columns which resulted in not being able to access `.BY$grpcol` in `j`. Ex: `DT[, .BY$x, by=x]`. This is now fixed. Closes **#5415**. Thanks to Stephane Vernede for the bug report.
+
+ 14. Fixed another issue with `eval(parse(...))` in `j` along with assignment by reference `:=`. Closes [#30](https://github.com/Rdatatable/data.table/issues/30). Thanks to Michele Carriero for reporting.
+
+ 15. `get()` in `j` did not see `i`'s columns when `i` is a data.table which lead to errors while doing operations like: `DT1[DT2, list(get('c'))]`. Now, use of `get` makes *all* x's and i's columns visible (fetches all columns). Still, as the verbose message states, using `.SDcols` or `eval(macro)` would be able to select just the columns used, which is better for efficiency. Closes [#34](https://github.com/Rdatatable/data.table/issues/34). Thanks to Eddi for reporting.
+
+ 16. Fixed an edge case with `unique` and `duplicated`, which on empty data.tables returned a 1-row data.table with all NAs. Closes [#28](https://github.com/Rdatatable/data.table/issues/28). Thanks to Shubh Bansal for reporting.
+
+ 17. `dcast.data.table` resuled in error (because function `CJ()` was not visible) in packages that "import" data.table. This did not happen if the package "depends" on data.table. Closes bug [#31](https://github.com/Rdatatable/data.table/issues/31). Thanks to K Davis for the excellent report.
+
+ 18. `merge(x, y, all=TRUE)` error when `x` is empty data.table is now fixed. Closes [#24](https://github.com/Rdatatable/data.table/issues/24). Thanks to Garrett See for filing the report.
+
+ 19. Implementing #5249 closes bug [#26](https://github.com/Rdatatable/data.table/issues/26), a case where rbind gave error when binding with empty data.tables. Thanks to Roger for [reporting on SO](http://stackoverflow.com/q/23216033/559784).
+
+ 20. Fixed a segfault during grouping with assignment by reference, ex: `DT[, LHS := RHS, by=.]`, where length(RHS) > group size (.N). Closes [#25](https://github.com/Rdatatable/data.table/issues/25). Thanks to Zachary Long for reporting on datatable-help mailing list.
+
+ 21. Consistent subset rules on datat.tables with duplicate columns. In short, if indices are directly provided, 'j', or in .SDcols, then just those columns are either returned (or deleted if you provide -.SDcols or !j). If instead, column names are given and there are more than one occurrence of that column, then it's hard to decide which to keep and which to remove on a subset. Therefore, to remove, all occurrences of that column are removed, and to keep, always the first column is r [...]
+
+ Note that using `by=` to aggregate on duplicate columns may not give intended result still, as it may not operate on the proper column.
+
+ 22. When DT is empty, `DT[, newcol:=max(b), by=a]` now properly adds the column, [#49](https://github.com/Rdatatable/data.table/issues/49). Thanks to Shubh bansal for filing the report.
+
+ 23. When `j` evaluates to `integer(0)/character(0)`, `DT[, j, with=FALSE]` resulted in error, [#21](https://github.com/Rdatatable/data.table/issues/21). Thanks indirectly to Malcolm Cook for [#52](https://github.com/Rdatatable/data.table/issues/52), through which this (recent) regression (from 1.9.3) was found.
+
+ 24. `print(DT)` now respects `digits` argument on list type columns, [#37](https://github.com/Rdatatable/data.table/issues/37). Thanks to Frank for the discussion on the mailing list and to Matthew Beckers for filing the bug report.
+
+ 25. FR # 2551 implemented leniance in warning messages when columns are coerced with `DT[, LHS := RHS]`, when `length(RHS)==1`. But this was very lenient; e.g., `DT[, a := "bla"]`, where `a` is a logical column should get a warning. This is now fixed such that only very obvious cases coerces silently; e.g., `DT[, a := 1]` where `a` is `integer`. Closes [#35](https://github.com/Rdatatable/data.table/issues/35). Thanks to Michele Carriero and John Laing for reporting.
+
+ 26. `dcast.data.table` provides better error message when `fun.aggregate` is specified but it returns length != 1. Closes [#693](https://github.com/Rdatatable/data.table/issues/693). Thanks to Trevor Alexander for reporting [here on SO](http://stackoverflow.com/questions/24152733/undocumented-error-in-dcast-data-table).
+
+ 27. `dcast.data.table` tries to preserve attributes whereever possible, except when `value.var` is a `factor` (or ordered factor). For `factor` types, the casted columns will be coerced to type `character` thereby losing the `levels` attribute. Closes [#688](https://github.com/Rdatatable/data.table/issues/688). Thanks to juancentro for reporting.
+
+ 28. `melt` now returns friendly error when `meaure.vars` are not in data instead of segfault. Closes [#699](https://github.com/Rdatatable/data.table/issues/688). Thanks to vsalmendra for [this post on SO](http://stackoverflow.com/q/24326797/559784) and the subsequent bug report.
+
+ 29. `DT[, list(m1 = eval(expr1), m2=eval(expr2)), by=val]` where `expr1` and `expr2` are constructed using `parse(text=.)` now works instead of resulting in error. Closes [#472](https://github.com/Rdatatable/data.table/issues/472). Thanks to Benjamin Barnes for reporting with a nice reproducible example.
+
+ 30. A join of the form `X[Y, roll=TRUE, nomatch=0L]` where some of Y's key columns occur more than once (duplicated keys) might at times return incorrect join. This was introduced only in 1.9.2 and is fixed now. Closes [#700](https://github.com/Rdatatable/data.table/issues/472). Thanks to Michael Smith for the very nice reproducible example and nice spotting of such a tricky case.
+
+ 31. Fixed an edge case in `DT[order(.)]` internal optimisation to be consistent with base. Closes [#696](https://github.com/Rdatatable/data.table/issues/696). Thanks to Michael Smith and Garrett See for reporting.
+
+ 32. `DT[, list(list(.)), by=.]` and `DT[, col := list(list(.)), by=.]` returns correct results in R >=3.1.0 as well. The bug was due to recent (welcoming) changes in R v3.1.0 where `list(.)` does not result in a *copy*. Closes [#481](https://github.com/Rdatatable/data.table/issues/481). Also thanks to KrishnaPG for filing [#728](https://github.com/Rdatatable/data.table/issues/728).
+
+ 33. `dcast.data.table` handles `fun.aggregate` argument properly when called from within a function that accepts `fun.aggregate` argument and passes to `dcast.data.table()`. Closes [#713](https://github.com/Rdatatable/data.table/issues/713). Thanks to mathematicalcoffee for reporting [here](http://stackoverflow.com/q/24542976/559784) on SO.
+
+ 34. `dcast.data.table` now returns a friendly error when fun.aggregate value for missing combinations is 0-length, and 'fill' argument is not provided. Closes [#715](https://github.com/Rdatatable/data.table/issues/715)
+
+ 35. `rbind/rbindlist` binds in the same order of occurrence also when binding tables with duplicate names along with 'fill=TRUE' (previously, it grouped all duplicate columns together). This was the underlying reason for [#725](https://github.com/Rdatatable/data.table/issues/715). Thanks to Stefan Fritsch for the report with a nice reproducible example and discussion.
+
+ 36. `setDT` now provides a friendly error when attempted to change a variable to data.table by reference whose binding is locked (usually when the variable is within a package, ex: CO2). Closes [#475](https://github.com/Rdatatable/data.table/issues/475). Thanks to David Arenburg for filing the report [here](http://stackoverflow.com/questions/23361080/error-in-setdt-from-data-table-package) on SO.
+
+ 37. `X[!Y]` where `X` and `Y` are both data.tables ignores 'allow.cartesian' argument, and rightly so because a not-join (or anti-join) cannot exceed nrow(x). Thanks to @fedyakov for spotting this. Closes [#698](https://github.com/Rdatatable/data.table/issues/698).
+
+ 38. `as.data.table.matrix` does not convert strings to factors by default. `data.table` likes and prefers using character vectors to factors. Closes [#745](https://github.com/Rdatatable/data.table/issues/698). Thanks to @fpinter for reporting the issue on the github issue tracker and to vijay for reporting [here](http://stackoverflow.com/questions/17691050/data-table-still-converts-strings-to-factors) on SO.
+
+ 39. Joins of the form `x[y[z]]` resulted in duplicate names when all `x`, `y` and `z` had the same column names as non-key columns. This is now fixed. Closes [#471](https://github.com/Rdatatable/data.table/issues/471). Thanks to Christian Sigg for the nice reproducible example.
+
+ 40. `DT[where, someCol:=NULL]` is now an error that `i` is provided since it makes no sense to delete a column for only a subset of rows. Closes [#506](https://github.com/Rdatatable/data.table/issues/506).
+
+ 41. `forder` did not identify `-0` as `0` for numeric types. This is fixed now. Thanks to @arcosdium for nice minimal example. Closes [#743](https://github.com/Rdatatable/data.table/issues/743).
+
+ 42. Segfault on joins of the form `X[Y, c(..), by=.EACHI]` is now fixed. Closes [#744](https://github.com/Rdatatable/data.table/issues/744). Thanks to @nigmastar (Michele Carriero) for the excellent minimal example.
+
+ 43. Subset on data.table using `lapply` of the form `lapply(L, "[", Time == 3L)` works now without error due to `[.data.frame` redirection. Closes [#500](https://github.com/Rdatatable/data.table/issues/500). Thanks to Garrett See for reporting.
+
+ 44. `id.vars` and `measure.vars` default value of `NULL` was removed to be consistent in behaviour with `reshape2:::melt.data.frame`. Closes [#780](https://github.com/Rdatatable/data.table/issues/780). Thanks to @dardesta for reporting.
+
+ 45. Grouping using external variables on keyed data.tables did not return correct results at times. Thanks to @colinfang for reporting. Closes [#762](https://github.com/Rdatatable/data.table/issues/762).
+
+#### NOTES
+
+ 1. Reminder: using `rolltolast` still works but since v1.9.2 now issues the following warning:
+ > 'rolltolast' has been marked 'deprecated' in ?data.table since v1.8.8 on CRAN 3 Mar 2013, see NEWS. Please change to the more flexible 'rollends' instead. 'rolltolast' will be removed in the next version."
+
+ 2. Using `with=FALSE` with `:=` is now deprecated in all cases, given that wrapping the LHS of `:=` with parentheses has been preferred for some time.
+ ```R
+ colVar = "col1"
+ DT[, colVar:=1, with=FALSE] # deprecated, still works silently as before
+ DT[, (colVar):=1] # please change to this
+ DT[, c("col1","col2"):=1] # no change
+ DT[, 2:4 := 1] # no change
+ DT[, c("col1","col2"):=list(sum(a),mean(b)] # no change
+ DT[, `:=`(...), by=...] # no change
+ ```
+ The next release will issue a warning when `with=FALSE` is used with `:=`.
+
+ 3. `?duplicated.data.table` explained that `by=NULL` or `by=FALSE` would use all columns, however `by=FALSE` resulted in error. `by=FALSE` is removed from help and `duplicated` returns an error when `by=TRUE/FALSE` now. Closes [#38](https://github.com/Rdatatable/data.table/issues/38).
+
+ 4. More info about distinguishing small numbers from 0.0 in v1.9.2+ is [here](http://stackoverflow.com/questions/22290544/grouping-very-small-numbers-e-g-1e-28-and-0-0-in-data-table-v1-8-10-vs-v1-9-2).
+
+ 5. `?dcast.data.table` now explains how the names are generated for the columns that are being casted. Closes **#5676**.
+
+ 6. `dcast.data.table(dt, a ~ ... + b)` now generates the column names with values from `b` coming last. Closes **#5675**.
+
+ 7. Added `x[order(.)]` internal optimisation, and how to go back to `base:::order(.)` if one wants to sort by session locale to
+ `?setorder` (with alias `?order` and `?forder`). Closes [#478](https://github.com/Rdatatable/data.table/issues/478) and
+ also [#704](https://github.com/Rdatatable/data.table/issues/704). Thanks to Christian Wolf for the report.
+
+ 8. Added tests (1351.1 and 1351.2) to catch any future regressions on particular case of binary search based subset reported [here](http://stackoverflow.com/q/24729001/559784) on SO. Thanks to Scott for the post. The regression was contained to v1.9.2 AFAICT. Closes [#734](https://github.com/Rdatatable/data.table/issues/704).
+
+ 9. Added an `.onUnload` method to unload `data.table`'s shared object properly. Since the name of the shared object is 'datatable.so' and not 'data.table.so', 'detach' fails to unload correctly. This was the reason for the issue reported [here](http://stackoverflow.com/questions/23498804/load-detach-re-load-anomaly) on SO. Closes [#474](https://github.com/Rdatatable/data.table/issues/474). Thanks to Matthew Plourde for reporting.
+
+ 10. Updated `BugReports` link in DESCRIPTION. Thanks to @chrsigg for reporting. Closes [#754](https://github.com/Rdatatable/data.table/issues/754).
+
+ 11. Added `shiny`, `rmarkdown` and `knitr` to the data.table whitelist. Packages which take user code as input and run it in their own environment (so do not `Depend` or `Import` `data.table` themselves) either need to be added here, or they can define a variable `.datatable.aware <- TRUE` in their namepace, so that data.table can work correctly in those packages. Users can also add to `data.table`'s whitelist themselves using `assignInNamespace()` but these additions upstream remove t [...]
+
+ 12. Clarified `with=FALSE` as suggested in [#513](https://github.com/Rdatatable/data.table/issues/513).
+
+ 13. Clarified `.I` in `?data.table`. Closes [#510](https://github.com/Rdatatable/data.table/issues/510). Thanks to Gabor for reporting.
+
+ 14. Moved `?copy` to its own help page, and documented that `dt_names <- copy(names(DT))` is necessary for `dt_names` to be not modified by reference as a result of updating `DT` by reference (e.g. adding a new column by reference). Closes [#512](https://github.com/Rdatatable/data.table/issues/512). Thanks to Zach for [this SO question](http://stackoverflow.com/q/15913417/559784) and user1971988 for [this SO question](http://stackoverflow.com/q/18662715/559784).
+
+ 15. `address(x)` doesn't increment `NAM()` value when `x` is a vector. Using the object as argument to a non-primitive function is sufficient to increment its reference. Closes #824. Thanks to @tarakc02 for the [question on twitter](https://twitter.com/tarakc02/status/513796515026837504) and hint from Hadley.
+
+---
+
+### Changes in v1.9.2 (on CRAN 27 Feb 2014)
+
+#### NEW FEATURES
+
+ 1. Fast methods of `reshape2`'s `melt` and `dcast` have been implemented for `data.table`, **FR #2627**. Most settings are identical to `reshape2`, see `?melt.data.table.`
+ > `melt`: 10 million rows and 5 columns, 61.3 seconds reduced to 1.2 seconds.
+ > `dcast`: 1 million rows and 4 columns, 192 seconds reduced to 3.6 seconds.
+
+ * `melt.data.table` is also capable of melting on columns of type `list`.
+ * `melt.data.table` gains `variable.factor` and `value.factor` which by default are TRUE and FALSE respectively for compatibility with `reshape2`. This allows for directly controlling the output type of "variable" and "value" columns (as factors or not).
+ * `melt.data.table`'s `na.rm = TRUE` parameter is optimised to remove NAs directly during melt and therefore avoids the overhead of subsetting using `!is.na` afterwards on the molten data.
+ * except for `margins` argument from `reshape2:::dcast`, all features of dcast are intact. `dcast.data.table` can also accept `value.var` columns of type list.
+
+ > Reminder of Cologne (Dec 2013) presentation **slide 32** : ["Why not submit a dcast pull request to reshape2?"](http://datatable.r-forge.r-project.org/CologneR_2013.pdf).
+
+ 2. Joins scale better as the number of rows increases. The binary merge used to start on row 1 of i; it now starts on the middle row of i. Many thanks to Mike Crowe for the suggestion. This has been done within column so scales much better as the number of join columns increase, too.
+
+ > Reminder: bmerge allows the rolling join feature: forwards, backwards, limited and nearest.
+
+ 3. Sorting (`setkey` and ad-hoc `by=`) is faster and scales better on randomly ordered data and now also adapts to almost sorted data. The remaining comparison sorts have been removed. We use a combination of counting sort and forwards radix (MSD) for all types including double, character and integers with range>100,000; forwards not backwards through columns. This was inspired by [Terdiman](http://codercorner.com/RadixSortRevisited.htm) and [Herf's](http://stereopsis.com/radix.html) [...]
+
+ 4. `unique` and `duplicated` methods for `data.table` are significantly faster especially for type numeric (i.e. double), and type integer where range > 100,000 or contains negatives.
+
+ 5. `NA`, `NaN`, `+Inf` and `-Inf` are now considered distinct values, may be in keys, can be joined to and can be grouped. `data.table` defines: `NA` < `NaN` < `-Inf`. Thanks to Martin Liberts for the suggestions, #4684, #4815 and #4883.
+
+ 6. Numeric data is still joined and grouped within tolerance as before but instead of tolerance being `sqrt(.Machine$double.eps) == 1.490116e-08` (the same as `base::all.equal`'s default) the significand is now rounded to the last 2 bytes, apx 11 s.f. This is more appropriate for large (1.23e20) and small (1.23e-20) numerics and is faster via a simple bit twiddle. A few functions provided a 'tolerance' argument but this wasn't being passed through so has been removed. We aim to add a [...]
+
+ 7. New optimization: **GForce**. Rather than grouping the data, the group locations are passed into grouped versions of sum and mean (`gsum` and `gmean`) which then compute the result for all groups in a single sequential pass through the column for cache efficiency. Further, since the g* function is called just once, we don't need to find ways to speed up calling sum or mean repetitively for each group. Plan is to add `gmin`, `gmax`, `gsd`, `gprod`, `gwhich.min` and `gwhich.max`. Exa [...]
+ ```R
+ DT[,sum(x,na.rm=),by=...] # yes
+ DT[,list(sum(x,na.rm=),mean(y,na.rm=)),by=...] # yes
+ DT[,lapply(.SD,sum,na.rm=),by=...] # yes
+ DT[,list(sum(x),min(y)),by=...] # no. gmin not yet available, only sum and mean so far.
+ ```
+ GForce is a level 2 optimization. To turn it off: `options(datatable.optimize=1)`. Reminder: to see the optimizations and other info, set `verbose=TRUE`
+
+ 8. fread's `integer64` argument implemented. Allows reading of `integer64` data as 'double' or 'character' instead of `bit64::integer64` (which remains the default as before). Thanks to Chris Neff for the suggestion. The default can be changed globally; e.g, `options(datatable.integer64="character")`
+
+ 9. fread's `drop`, `select` and `NULL` in `colClasses` are implemented. To drop or select columns by name or by number. See examples in `?fread`.
+
+ 10. fread now detects `T`, `F`, `True`, `False`, `TRUE` and `FALSE` as type logical, consistent with `read.csv`, #4766. Thanks to Adam November for highlighting.
+
+ 11. fread now accepts quotes (both `'` and `"`) in the middle of fields, whether the field starts with `"` or not, rather than the 'unbalanced quotes' error, #2694. Thanks to baidao for reporting. It was known and documented at the top of `?fread` (now removed). If a field starts with `"` it must end with `"` (necessary to include the field separator itself in the field contents). Embedded quotes can be in column names, too. Newlines (`\n`) still can't be in quoted fields or quoted co [...]
+
+ 12. fread gains `showProgress`, default TRUE. The global option is `datatable.showProgress`.
+
+ 13. `fread("1.46761e-313\n")` detected the **ERANGE** error, so read as `character`. It now reads as numeric but with a detailed warning. Thanks to Heather Turner for the detailed report, #4879.
+
+ 14. fread now understand system commands; e.g., `fread("grep blah file.txt")`.
+
+ 15. `as.data.table` method for `table()` implemented, #4848. Thanks to Frank Pinter for suggesting [here on SO](http://stackoverflow.com/questions/18390947/data-table-of-table-is-very-different-from-data-frame-of-table).
+
+ 16. `as.data.table` methods added for integer, numeric, character, logical, factor, ordered and Date.
+
+ 17. `DT[i,:=,]` now accepts negative indices in `i`. Thanks to Eduard Antonyan. See also bug fix #2697.
+
+ 18. `set()` is now able to add new columns by reference, #2077.
+ ```R
+ DT[3:5, newCol := 5L]
+ set(DT, i=3:5, j="newCol", 5L) # same
+ ```
+
+ 19. eval will now be evaluated anywhere in a `j`-expression as long as it has just one argument, #4677. Will still need to use `.SD` as environment in complex cases. Also fixes bug [here on SO](http://stackoverflow.com/a/19054962/817778).
+
+ 20. `!` at the head of the expression will no longer trigger a not-join if the expression is logical, #4650. Thanks to Arunkumar Srinivasan for reporting.
+
+ 21. `rbindlist` now chooses the highest type per column, not the first, #2456. Up-conversion follows R defaults, with the addition of factors being the highest type. Also fixes #4981 for the specific case of `NA`'s.
+
+ 22. `cbind(x,y,z,...)` now creates a data.table if `x` isn't a `data.table` but `y` or `z` is, unless `x` is a `data.frame` in which case a `data.frame` is returned (use `data.table(DF,DT)` instead for that).
+
+ 23. `cbind(x,y,z,...)` and `data.table(x,y,z,...)` now retain keys of any `data.table` inputs directly (no sort needed, for speed). The result's key is `c(key(x), key(y), key(z), ...)`, provided, that the data.table inputs that have keys are not recycled and there are no ambiguities (i.e. duplicates) in column names.
+
+ 24. `rbind/rbindlist` will preserve ordered factors if it's possible to do so; i.e., if a compatible global order exists, #4856 & #5019. Otherwise the result will be a `factor` and a *warning*.
+
+ 25. `rbind` now has a `fill` argument, #4790. When `fill=TRUE` it will behave in a manner similar to plyr's `rbind.fill`. This option is incompatible with `use.names=FALSE`. Thanks to Arunkumar Srinivasan for the base code.
+
+ 26. `rbind` now relies exclusively on `rbindlist` to bind `data.tables` together. This makes rbind'ing factors faster, #2115.
+
+ 27. `DT[, as.factor('x'), with=FALSE]` where `x` is a column in `DT` is now equivalent to `DT[, "x", with=FALSE]` instead of ending up with an error, #4867. Thanks to tresbot for reporting [here on SO](http://stackoverflow.com/questions/18525976/converting-multiple-data-table-columns-to-factors-in-r).
+
+ 28. `format.data.table` now understands 'formula' and displays embedded formulas as expected, FR #2591.
+
+ 29. `{}` around `:=` in `j` now obtain desired result, but with a warning #2496. Now,
+ ```R
+ DT[, { `:=`(...)}] # now works
+ DT[, {`:=`(...)}, by=(...)] # now works
+ ```
+ Thanks to Alex for reporting [here on SO](http://stackoverflow.com/questions/14541959/expression-syntax-for-data-table-in-r).
+
+ 30. `x[J(2), a]`, where `a` is the key column sees `a` in `j`, #2693 and FAQ 2.8. Also, `x[J(2)]` automatically names the columns from `i` using the key columns of `x`. In cases where the key columns of `x` and `i` are identical, i's columns can be referred to by using `i.name`; e.g., `x[J(2), i.a]`. Thanks to mnel and Gabor for the discussion [here](http://r.789695.n4.nabble.com/Problem-with-FAQ-2-8-tt4668878.html).
+
+ 31. `print.data.table` gains `row.names`, default=TRUE. When FALSE, the row names (along with the :) are not printed, #5020. Thanks to Frank Erickson.
+
+ 32. `.SDcols` now is also able to de-select columns. This works both with column names and column numbers.
+ ```R
+ DT[, lapply(.SD,...), by=..., .SDcols=-c(1,3)] # .SD all but columns 1 and 3
+ DT[, lapply(.SD,...), by=..., .SDcols=-c("x", "z")] # .SD all but columns 'x' and 'z'
+ DT[..., .SDcols=c(1, -3)] # can't mix signs, error
+ DT[, .SD, .SDcols=c("x", -"z")] # can't mix signs, error
+ ```
+ Thanks to Tonny Peterson for filing FR #4979.
+
+ 33. `as.data.table.list` now issues a warning for those items/columns that result in a remainder due to recycling, #4813. `data.table()` also now issues a warning (instead of an error previously) when recycling leaves a remainder; e.g., `data.table(x=1:2, y=1:3)`.
+
+ 34. `:=` now coerces without warning when precision is not lost and `length(RHS) == 1`, #2551.
+ ```R
+ DT = data.table(x=1:2, y=c(TRUE, FALSE))
+ DT[1, x:=1] # ok, now silent
+ DT[1, y:=0] # ok, now silent
+ DT[1, y:=0L] # ok, now silent
+ ```
+
+ 35. `as.data.table.*(x, keep.rownames=TRUE)`, where `x` is a named vector now adds names of `x` into a new column with default name `rn`. Thanks to Garrett See for FR #2356.
+
+ 36. `X[Y, col:=value]` when no match exists in the join is now caught early and X is simply returned. Also a message when `datatable.verbose` is TRUE is provided. In addition, if `col` is an existing column, since no update actually takes place, the key is now retained. Thanks to Frank Erickson for suggesting, #4996.
+
+ 37. New function `setDT()` takes a `list` (named and/or unnamed) or `data.frame` and changes its type by reference to `data.table`, *without any copy*. It also has a logical argument `giveNames` which is used for a list inputs. See `?setDT` examples for more. Based on [this FR on SO](http://stackoverflow.com/questions/20345022/convert-a-data-frame-to-a-data-table-without-copy/20346697#20346697).
+
+ 38. `setnames(DT,"oldname","newname")` no longer complains about any duplicated column names in `DT` so long as oldname is unique and unambiguous. Thanks to Wet Feet for highlighting [here on SO](http://stackoverflow.com/questions/20942905/ignore-safety-check-when-using-setnames).
+
+ 39. `last(x)` where `length(x)=0` now returns 'x' instead of an error, #5152. Thanks to Garrett See for reporting.
+
+ 40. `as.ITime.character` no longer complains when given vector input, and will accept mixed format time entries; e.g., c("12:00", "13:12:25")
+
+ 41. Key is now retained in `NA` subsets; e.g.,
+ ```R
+ DT = data.table(a=1:3,b=4:6,key="a")
+ DT[NA] # 1-row of NA now keyed by 'a'
+ DT[5] # 1-row of NA now keyed by 'a'
+ DT[2:4] # not keyed as before because NA (last row of result) sorts first in keyed data.table
+ ```
+
+ 42. Each column in the result for each group has always been recycled (if necessary) to match the longest column in that group's result. If it doesn't recycle exactly, though, it was caught gracefully as an error. Now, it is recycled, with remainder with warning.
+ ```R
+ DT = data.table(a=1:2,b=1:6)
+ DT[, list(b,1:2), by=a] # now recycles the 1:2 with warning to length 3
+ ```
+
+#### BUG FIXES
+
+ 1. Long outstanding (usually small) memory leak in grouping fixed, #2648. When the last group is smaller than the largest group, the difference in those sizes was not being released. Also evident in non-trivial aggregations where each group returns a different number of rows. Most users run a grouping
+ query once and will never have noticed these, but anyone looping calls to grouping (such as when running in parallel, or benchmarking) may have suffered. Tests added. Thanks to many including vc273 and Y T for reporting [here](http://stackoverflow.com/questions/20349159/memory-leak-in-data-table-grouped-assignment-by-reference) and [here](http://stackoverflow.com/questions/15651515/slow-memory-leak-in-data-table-when-returning-named-lists-in-j-trying-to-reshap) on SO.
+
+ 2. In long running computations where data.table is called many times repetitively the following error could sometimes occur, #2647: *"Internal error: .internal.selfref prot is not itself an extptr"*. Now fixed. Thanks to theEricStone, StevieP and JasonB for (difficult) reproducible examples [here](http://stackoverflow.com/questions/15342227/getting-a-random-internal-selfref-error-in-data-table-for-r).
+
+ 3. If `fread` returns a data error (such as no closing quote on a quoted field) it now closes the file first rather than holding a lock open, a Windows only problem.
+ Thanks to nigmastar for reporting [here](http://stackoverflow.com/questions/18597123/fread-data-table-locks-files) and Carl Witthoft for the hint. Tests added.
+
+ 4. `DT[0,col:=value]` is now a helpful error rather than crash, #2754. Thanks to Ricardo Saporta for reporting. `DT[NA,col:=value]`'s error message has also been improved. Tests added.
+
+ 5. Assigning to the same column twice in the same query is now an error rather than a crash in some circumstances; e.g., `DT[,c("B","B"):=NULL]` (delete by reference the same column twice). Thanks to Ricardo (#2751) and matt_k (#2791) for reporting [here](http://stackoverflow.com/questions/16638484/remove-multiple-columns-from-data-table). Tests added.
+
+ 6. Crash and/or incorrect aggregate results with negative indexing in `i` is fixed, with a warning when the `abs(negative index) > nrow(DT)`, #2697. Thanks to Eduard Antonyan (eddi) for reporting [here](http://stackoverflow.com/questions/16046696/data-table-bug-causing-a-segfault-in-r). Tests added.
+
+ 7. `head()` and `tail()` handle negative `n` values correctly now, #2375. Thanks to Garrett See for reporting. Also it results in an error when `length(n) != 1`. Tests added.
+
+ 8. Crash when assigning empty data.table to multiple columns is fixed, #4731. Thanks to Andrew Tinka for reporting. Tests added.
+
+ 9. `print(DT, digits=2)` now heeds digits and other parameters, #2535. Thanks to Heather Turner for reporting. Tests added.
+
+ 10. `print(data.table(table(1:101)))` is now an 'invalid column' error and suggests `print(as.data.table(table(1:101)))` instead, #4847. Thanks to Frank Pinter for reporting. Test added.
+
+ 11. Crash when grouping by character column where `i` is `integer(0)` is now fixed. It now returns an appropriate empty data.table. This fixes bug #2440. Thanks to Malcolm Cook for reporting. Tests added.
+
+ 12. Grouping when i has value '0' and `length(i) > 1` resulted in crash; it is now fixed. It returns a friendly error instead. This fixes bug #2758. Thanks to Garrett See for reporting. Tests added.
+
+ 13. `:=` failed while subsetting yielded NA and `with=FALSE`, #2445. Thanks to Damian Betebenner for reporting.
+
+ 14. `by=month(date)` gave incorrect results if `key(DT)=="date"`, #2670. Tests added.
+ ```R
+ DT[,,by=month(date)] # now ok if key(DT)=="date"
+ DT[,,by=list(month(date))] # ok before whether or not key(DT)=="date"
+ ```
+
+ 15. `rbind` and `rbindlist` could crash if input columns themselves had hidden names, #4890 & #4912. Thanks to Chris Neff and Stefan Fritsch for reporting. Tests added.
+
+ 16. `data.table()`, `as.data.table()` and other paths to create a data.table now detect and drop hidden names, the root cause of #4890. It was never intended that columns could have hidden names attached.
+
+ 17. Cartesian Join (`allow.cartesian = TRUE`) when both `x` and `i` are keyed and `length(key(x)) > length(key(i))` set resulting key incorrectly. This is now fixed, #2677. Tests added. Thanks to Shir Levkowitz for reporting.
+
+ 18. `:=` (assignment by reference) loses POSIXct or ITime attribute *while grouping* is now fixed, #2531. Tests added. Thanks to stat quant for reporting [here](http://stackoverflow.com/questions/14604820/why-does-this-posixct-or-itime-loses-its-format-attribute) and to Paul Murray for reporting [here](http://stackoverflow.com/questions/15996692/cannot-assign-columns-as-date-by-reference-in-data-table) on SO.
+
+ 19. `chmatch()` didn't always match non-ascii characters, #2538 and #4818. chmatch is used internally so `DT[is.na(päs), päs := 99L]` now works. Thanks to Benjamin Barnes and Stefan Fritsch for reporting. Tests added.
+
+ 20. `unname(DT)` threw an error when `20 < nrow(DT) <= 100`, bug #4934. This is now fixed. Tests added. Thanks to Ricardo Saporta.
+
+ 21. A special case of not-join and logical TRUE, `DT[!TRUE]`, gave an error whereas it should be identical to `DT[FALSE]`. Now fixed and tests added. Thanks once again to Ricardo Saporta for filing #4930.
+
+ 22. `X[Y,roll=-Inf,rollends=FALSE]` didn't roll the middle correctly if `Y` was keyed. It was ok if `Y` was unkeyed or rollends left as the default [c(TRUE,FALSE) when roll < 0]. Thanks to user338714 for reporting [here](http://stackoverflow.com/questions/18984179/roll-data-table-with-rollends). Tests added.
+
+ 23. Key is now retained after an order-preserving subset, #295.
+
+ 24. Fixed bug #2584. Now columns that had function names, in particular "list" do not pose problems in `.SD`. Thanks to Zachary Mayer for reporting.
+
+ 25. Fixed bug #4927. Unusual column names in normal quotes, ex: `by=".Col"`, now works as expected in `by`. Thanks to Ricardo Saporta for reporting.
+
+ 26. `setkey` resulted in error when column names contained ",". This is now fixed. Thanks to Corone for reporting [here](http://stackoverflow.com/a/19166273/817778) on SO.
+
+ 27. `rbind` when at least one argument was a data.table, but not the first, returned the rbind'd data.table with key. This is now fixed, #4995. Thanks to Frank Erickson for reporting.
+
+ 28. That `.SD` doesn't retain column's class is now fixed (#2530). Thanks to Corone for reporting [here](http://stackoverflow.com/questions/14753411/why-does-data-table-lose-class-definition-in-sd-after-group-by).
+
+ 29. `eval(quote())` returned error when the quoted expression is a not-join, #4994. This is now fixed. Tests added.
+
+ 30. `DT[, lapply(.SD, function(), by=]` did not see columns of DT when optimisation is "on". This is now fixed, #2381. Tests added. Thanks to David F for reporting [here](http://stackoverflow.com/questions/13441868/data-table-and-stratified-means) on SO.
+
+ 31. #4959 - rbind'ing empty data.tables now works
+
+ 32. #5005 - some function expressions were not being correctly evaluated in j-expression. Thanks to Tonny Petersen for reporting.
+
+ 33. Fixed bug #5007, `j` did not see variables declared within a local (function) environment properly. Now, `DT[, lapply(.SD, function(x) fun_const), by=x]` where "fun_const" is a local variable within a function works as expected. Thanks to Ricardo Saporta for catching this and providing a very nice reproducible example.
+
+ 34. Fixing #5007 also fixes #4957, where `.N` was not visible during `lapply(.SD, function(x) ...)` in `j`. Thanks to juba for noticing it [here](http://stackoverflow.com/questions/19094771/replace-values-in-each-column-based-on-conditions-according-to-groups-by-rows) on SO.
+
+ 35. Fixed another case where function expressions were not constructed properly in `j`, while fixing #5007. `DT[, lapply(.SD, function(x) my_const), by=x]` now works as expected instead of ending up in an error.
+
+ 36. Fixed #4990, where `:=` did not generate a recycling warning during `by`, when `length(RHS) < group` size but not an integer multiple of group size. Now,
+ ```R
+ DT <- data.table(a=rep(1:2, c(5,2)))
+ DT[, b := c(1:2), by=a]
+ ```
+ will generate a warning (here for first group as RHS length (2) is not an integer multiple of group size (=5)).
+
+ 37. Fixed #5069 where `gdata:::write.fwf` returned an error with data.table.
+
+ 38. Fixed #5098 where construction of `j`-expression with a function with no-argument returned the function definition instead of returning the result from executing the function.
+
+ 39. Fixed #5106 where `DT[, .N, by=y]` where `y` is a vector with `length(y) = nrow(DT)`, but `y` is not a column in `DT`. Thanks to colinfang for reporting.
+
+ 40. Fixed #5104 which popped out as a side-effect of fixing #2531. `:=` while grouping and assigning columns that are factors resulted in wrong results (and the column not being added). This is now fixed. Thanks to Jonathen Owen for reporting.
+
+ 41. Fixed bug #5114 where modifying columns in particular cases resulted in ".SD is locked" error. Thanks to GSee for the bug report.
+
+ 42. Implementing FR #4979 lead to a bug when grouping with .SDcols, where .SDcols argument was variable name. This bug #5190 is now fixed.
+
+ 43. Fixed #5171 - where setting the attribute name to a non-character type resulted in a segfault. Ex: `setattr(x, FALSE, FALSE); x`. Now ends up with a friendly error.
+
+ 44. Dependent packages using `cbind` may now *Import* data.table as intended rather than needing to *Depend*. There was a missing `data.table::` prefix on a call to `key()`. Thanks to Maarten-Jan Kallen for reporting.
+
+ 45. 'chmatch' didn't handle character encodings properly when the string was identical but the encoding were different. For ex: `UTF8` and `Latin1`. This is now fixed (a part of bug #5159). Thanks to Stefan Fritsch for reporting.
+
+ 46. Joins (`X[Y]`) on character columns with different encodings now issue a warning that join may result in unexpected results for those indices with different encodings. That is, when "ä" in X's key column and "ä" in Y's key column are of different encodings, a warning is issued. This takes care of bugs #5266 and other part of #5159 for the moment. Thanks to Stefan Fritsch once again for reporting.
+
+ 47. Fixed #5117 - segfault when `rbindlist` on empty data.tables. Thanks to Garrett See for reporting.
+
+ 48. Fixed a rare segfault that occurred on >250m rows (integer overflow during memory allocation); closes #5305. Thanks to Guenter J. Hitsch for reporting.
+
+ 49. `rbindlist` with at least one factor column along with the presence of at least one empty data.table resulted in segfault (or in linux/mac reported an error related to hash tables). This is now fixed, #5355. Thanks to Trevor Alexander for [reporting on SO](http://stackoverflow.com/questions/21591433/merging-really-not-that-large-data-tables-immediately-results-in-r-being-killed) (and mnel for filing the bug report):
+
+ 50. `CJ()` now orders character vectors in a locale consistent with `setkey`, #5375. Typically this affected whether upper case letters were ordered before lower case letters; they were by `setkey()` but not by `CJ()`. This difference started in v1.8.10 with the change "CJ() is 90% faster...", see NEWS below. Test added and avenues for differences closed off and nailed down, with no loss in performance. Many thanks to Malcolm Hawkes for reporting.
+
+#### THANKS FOR BETA TESTING TO :
+
+ 1. Zach Mayer for a reproducible segfault related to radix sorting character strings longer than 20. Test added.
+
+ 2. Simon Biggs for reporting a bug in fread'ing logicals. Test added.
+
+ 3. Jakub Szewczyk for reporting that where "." is used in formula interface of `dcast.data.table` along with an aggregate function, it did not result in aggregated result, #5149. Test added.
+ ```R
+ dcast.data.table(x, a ~ ., mean, value.var="b")
+ ```
+
+ 4. Jonathan Owen for reporting that `DT[,sum(.SD),by=]` failed with GForce optimization, #5380. Added test and error message redirecting to use `DT[,lapply(.SD,sum),by=]` or `base::sum` and how to turn off GForce.
+
+ 5. Luke Tierney for guidance in finding a corruption of `R_TrueValue` which needed `--enable-strict-barier`, `gctorture2` and a hardware watchpoint to ferret out. Started after a change in Rdevel on 11 Feb 2014, r64973.
+
+ 6. Minkoo Seo for a new test on rbindlist, #4648.
+
+ 7. Gsee for reporting that `set()` and `:=` could no longer add columns by reference to an object that inherits from data.table; e.g., `class = c("myclass", data.table", "data.frame"))`, #5115.
+
+ 8. Clayton Stanley for reporting #5307 [here on SO](http://stackoverflow.com/questions/21437546/data-table-1-8-11-and-aggregation-issues). Aggregating logical types could give wrong results.
+
+ 9. New and very welcome ASAN and UBSAN checks on CRAN detected :
+ * integer64 overflow in test 899 reading integers longer than apx 18 digits
+ ```R
+ fread("Col1\n12345678901234567890")` # works as before, bumped to character
+ ```
+ * a memory fault in rbindlist when binding ordered factors, and, some items in the list of data.table/list are empty or NULL. In both cases we had anticipated and added tests for these cases, which is why ASAN and UBSAN were able to detect a problem for us.
+
+ 10. Karl Millar for reporting a similar fault that ASAN detected, #5042. Also fixed.
+
+ 11. Ricardo Saporta for finding a crash when i is empty and a join column is character, #5387. Test added.
+
+#### NOTES
+
+ 1. If `fread` detects data which would be lost if the column was read according to type supplied in `colClasses`, e.g. a numeric column specified as integer in `colClasses`, the message that it has ignored colClasses is upgraded to warning instead of just a line in `verbose=TRUE` mode.
+
+ 2. `?last` has been improved and if `xts` is needed but not installed the error message is more helpful, #2728. Thanks to Sam Steingold for reporting.
+
+ 3. `?between corrected`. It returns a logical not integer vector, #2671. Thanks to Michael Nelson for reporting.
+
+ 4. `.SD`, `.N`, `.I`, `.GRP` and `.BY` are now exported (as NULL). So that NOTEs aren't produced for them by `R CMD check` or `codetools::checkUsage` via compiler. `utils::globalVariables()` was considered, but exporting chosen. Thanks to Sam Steingold for raising, #2723.
+
+ 5. When `DT` is empty, `DT[,col:=""]` is no longer a warning. The warning was:
+ > "Supplied 1 items to be assigned to 0 items of column (1 unused)"
+
+ 6. Using `rolltolast` still works but now issues the following warning :
+ > "'rolltolast' has been marked 'deprecated' in ?data.table since v1.8.8 on CRAN 3 Mar 2013, see NEWS. Please
+ change to the more flexible 'rollends' instead. 'rolltolast' will be removed in the next version."
+
+ 7. There are now 1,220 raw tests, as reported by `test.data.table()`.
+
+ 8. `data.table`'s dependency has been moved forward from R 2.12.0 to R 2.14.0, now over 2 years old (Oct 2011). As usual before release to CRAN, we ensure data.table passes `R CMD check` on the stated dependency and keep this as old as possible for as long as possible. As requested by users in managed environments. For this reason we still don't use `paste0()` internally, since that was added to R 2.15.0.
+
+---
+
+### Changes in v1.8.10 (on CRAN 03 Sep 2013)
+
+#### NEW FEATURES
+
+ * fread :
+ * If some column names are blank they are now given default names rather than causing
+ the header row to be read as a data row. Thanks to Simon Judes for suggesting.
+
+ * "+" and "-" are now read as character rather than integer 0. Thanks to Alvaro Gonzalez and
+ Roby Joehanes for reporting, #4814.
+ http://stackoverflow.com/questions/15388714/reading-strand-column-with-fread-data-table-package
+
+ * % progress console meter has been removed. The ouput was inconvenient in batch mode, log files and
+ reports which don't handle \r. It was too difficult to detect where fread is being called from, plus,
+ removing it speeds up fread a little by saving code inside the C for loop (which is why it wasn't
+ made optional instead). Use your operating system's system monitor to confirm fread is progressing.
+ Thanks to Baptiste for highlighting :
+ http://stackoverflow.com/questions/15370993/strange-output-from-fread-when-called-from-knitr
+
+ * colClasses has been added. Same character vector format as read.csv (may be named or unnamed), but
+ additionally may be type list. Type list enables setting ranges of columns by numeric position.
+ NOTE: colClasses is intended for rare overrides, not routine use.
+
+ * fread now supports files larger than 4GB on 64bit Windows (#2767 thanks to Paul Harding) and files
+ between 2GB and 4GB on 32bit Windows (#2655 thanks to Vishal). A C call to GetFileSize() needed to
+ be GetFileSizeEx().
+
+ * When input is the data as a character string, it is no longer truncated to your system's maximum
+ path length, #2649. It was being passed through path.expand() even when it wasn't a filename.
+ Many thanks to Timothee Carayol for the reproducible report. The limit should now be R's character
+ string length limit (2^31-1 bytes = 2GB). Test added.
+
+ * New argument 'skip' overrides automatic banner skipping. When skip>=0, 'autostart' is ignored and
+ line skip+1 will be taken as the first data row, or column names according to header="auto"|TRUE|FALSE
+ as usual. Or, skip="string" uses the first line containing "string" (chosen to be a substring of the
+ column name row unlikely to appear earlier), inspired by read.xls in package gdata.
+ Thanks to Gabor Grothendieck for these suggestions.
+
+ * fread now stops reading if an empty line is encountered, with warning if any text exists after that
+ such as a footer (the first line of which will be included in the warning message).
+
+ * Now reads files that are open in Excel without having to close them first, #2661. And up to 5 attempts
+ are made every 250ms on Windows as recommended here : http://support.microsoft.com/kb/316609.
+
+ * "nan%" observed in output of fread(...,verbose=TRUE) timings are now 0% when fread takes 0.000 seconds.
+
+ * An unintended 50,000 column limit in fread has been removed. Thanks to mpmorley for reporting. Test added.
+ http://stackoverflow.com/questions/18449997/fread-protection-stack-overflow-error
+
+ * unique() and duplicated() methods gain 'by' to allow testing for uniqueness using any subset of columns,
+ not just the keyed columns (if keyed) or all columns (if not). By default by=key(dt) for backwards
+ compatibility. ?duplicated has been revised and tests added.
+ Thanks to Arunkumar Srinivasan, Ricardo Saporta, and Frank Erickson for useful discussions.
+
+ * CJ() is 90% faster on 1e6 rows (for example), #4849. The inputs are now sorted first before combining
+ rather than after combining and uses rep.int instead of rep (thanks to Sean Garborg for the ideas,
+ code and benchmark) and only sorted if is.unsorted(), #2321.
+ Reminder: CJ = Cross Join; i.e., joins to all combinations of its inputs.
+
+ * CJ() gains 'sorted' argument, by default TRUE for backwards compatibility. FALSE retains input order and is faster
+ to create the result of CJ() but then slower to join from since unkeyed.
+
+ * New function address() returns the address in RAM of its argument. Sometimes useful in determining whether a value
+ has been copied or not by R, programatically.
+ http://stackoverflow.com/a/10913296/403310
+
+#### BUG FIXES
+
+ * merge no longer returns spurious NA row(s) when y is empty and all.y=TRUE (or all=TRUE), #2633. Thanks
+ to Vinicius Almendra for reporting. Test added.
+ http://stackoverflow.com/questions/15566250/merge-data-table-with-all-true-introduces-na-row-is-this-correct
+
+ * rbind'ing data.tables containing duplicate, "" or NA column names now works, #2726 & #2384.
+ Thanks to Garrett See and Arun Srinivasan for reporting. This also affected the printing of data.tables
+ with duplicate column names since the head and tail are rbind-ed together internally.
+
+ * rbind, cbind and merge on data.table should now work in packages that Import but do not
+ Depend on data.table. Many thanks to a patch to .onLoad from Ken Williams, and related
+ posts from Victor Kryukov :
+ http://r.789695.n4.nabble.com/Import-problem-with-data-table-in-packages-tp4665958.html
+
+ * Mixing adding and updating into one DT[, `:=`(existingCol=...,newCol=...), by=...] now works
+ without error or segfault, #2778 and #2528. Many thanks to Arunkumar Srinivasan for reporting
+ and for the nice reproducible examples. Tests added.
+
+ * rbindlist() now binds factor columns correctly, #2650. Thanks to many for reporting. Tests added.
+
+ * Deleting a (0-length) factor column using :=NULL on an empty data.table now works, #4809. Thanks
+ to Frank Pinter for reporting. Test added.
+ http://stackoverflow.com/questions/18089587/error-deleting-factor-column-in-empty-data-table
+
+ * Writing FUN= in DT[,lapply(.SD,FUN=...),] now works, #4893. Thanks to Jan Wijffels for
+ reporting and Arun for suggesting and testing a fix. Committed and test added.
+ http://stackoverflow.com/questions/18314757/why-cant-i-used-fun-in-lapply-when-grouping-by-using-data-table
+
+ * The slowness of transform() on data.table has been fixed, #2599. But, please use :=.
+
+ * setkey(DT,`Colname with spaces`) now works, #2452.
+ setkey(DT,"Colname with spaces") worked already.
+
+ * mean() in j has been optimized since v1.8.2 (see NEWS below) but wasn't respecting na.rm=TRUE (the default).
+ Many thanks to Colin Fang for reporting. Test added.
+ http://stackoverflow.com/questions/18571774/data-table-auto-remove-na-in-by-for-mean-function
+
+
+USER VISIBLE CHANGES
+
+ * := on a null data.table now gives a clearer error message :
+ "Cannot use := to add columns to a null data.table (no columns), currently. You can use :=
+ to add (empty) columns to an empty data.table (1 or more columns, all 0 length), though."
+ rather than the untrue :
+ "Cannot use := to add columns to an empty data.table, currently"
+
+ * Misuse of := and `:=`() is now caught in more circumstances and gives a clearer and shorter error message :
+ ":= and `:=`(...) are defined for use in j, once only and in particular ways. See
+ help(":="). Check is.data.table(DT) is TRUE."
+
+ * data.table(NULL) now prints "Null data.table (0 rows and 0 cols)" and FAQ 2.5 has been
+ improved. Thanks to:
+ http://stackoverflow.com/questions/15317536/is-null-does-not-work-on-null-data-table-in-r-possible-bug
+
+ * The braces {} have been removed from rollends's default, to solve a trace() problem. Thanks
+ to Josh O'Brien's investigation :
+ http://stackoverflow.com/questions/15931801/why-does-trace-edit-true-not-work-when-data-table
+
+#### NOTES
+
+ * Tests 617,646 and 647 could sometimes fail (e.g. r-prerel-solaris-sparc on 7 Mar 2013)
+ due to machine tolerance. Fixed.
+
+ * The default for datatable.alloccol has changed from max(100L, 2L*ncol(DT)) to max(100L, ncol(DT)+64L).
+ And a pointer to ?truelength has been added to an error message as suggested and thanks to Roland :
+ http://stackoverflow.com/questions/15436356/potential-problems-from-over-allocating-truelength-more-than-1000-times
+
+ * For packages wishing to use data.table optionally (e.g. according to user of that package) and therefore
+ not wishing to Depend on data.table (which is the normal determination of data.table-awareness via .Depends),
+ `.datatable.aware` may be set to TRUE in such packages which cedta() will look for, as before. But now it doesn't
+ need to be exported. Thanks to Hadley Wickham for the suggestion and solution.
+
+ * There are now 1,009 raw tests, as reported by test.data.table().
+
+ * Welcome to Arunkumar Srinivasan and Ricardo Saporta who have joined the project and contributed directly
+ by way of commits above.
+
+ * v1.8.9 was on R-Forge only. v1.8.10 was released to CRAN.
+ Odd numbers are development, evens on CRAN.
+
+
+### Changes in v1.8.8 (on CRAN 06 Mar 2013)
+
+#### NEW FEATURES
+
+ * New function fread(), a fast and friendly file reader.
+ * header, skip, nrows, sep and colClasses are all auto detected.
+ * integers>2^31 are detected and read natively as bit64::integer64.
+ * accepts filenames, URLs and "A,B\n1,2\n3,4" directly
+ * new implementation entirely in C
+ * with a 50MB .csv, 1 million rows x 6 columns :
+ read.csv("test.csv") # 30-60 sec (varies)
+ read.table("test.csv",<all known tricks and known nrows>) # 10 sec
+ fread("test.csv") # 3 sec
+ * airline data: 658MB csv (7 million rows x 29 columns)
+ read.table("2008.csv",<all known tricks and known nrows>) # 360 sec
+ fread("2008.csv") # 40 sec
+ See ?fread. Many thanks to Chris Neff, Garrett See, Hideyoshi Maeda, Patrick
+ Nic, Akhil Behl and Aykut Firat for ideas, discussions and beta testing.
+ ** The fread function is still under development; e.g., dates are read as
+ ** character and embedded quotes ("\"" and """") cause problems.
+
+ * New argument 'allow.cartesian' (default FALSE) added to X[Y] and merge(X,Y), #2464.
+ Prevents large allocations due to misspecified joins; e.g., duplicate key values in Y
+ joining to the same group in X over and over again. The word 'cartesian' is used loosely
+ for when more than max(nrow(X),nrow(Y)) rows would be returned. The error message is
+ verbose and includes advice. Thanks to a question by Nick Clark, help from user1935457
+ and a detailed reproducible crash report from JR.
+ http://stackoverflow.com/questions/14231737/greatest-n-per-group-reference-with-intervals-in-r-or-sql
+ If the new option affects existing code you can set :
+ options(datatable.allow.cartesian=TRUE)
+ to restore the previous behaviour until you have time to address.
+
+ * In addition to TRUE/FALSE, 'roll' may now be a positive number (roll forwards/LOCF) or
+ negative number (roll backwards/NOCB). A finite number limits the distance a value is
+ rolled (limited staleness). roll=TRUE and roll=+Inf are equivalent.
+ 'rollends' is a new parameter holding two logicals. The first observation is rolled
+ backwards if rollends[1] is TRUE. The last observation is rolled forwards if rollends[2]
+ is TRUE. If roll is a finite number, the same limit applies to the ends.
+ New value roll='nearest' joins to the nearest value (either backwards or forwards) when
+ the value falls in a gap, and to the end value according to 'rollends'.
+ 'rolltolast' has been deprecated. For backwards compatibility it is converted to
+ {roll=TRUE;rollends=c(FALSE,FALSE)}.
+ This implements FR#2300 & FR#206 and helps several recent S.O. questions :
+ https://r-forge.r-project.org/tracker/?group_id=240&atid=978&func=detail&aid=2300
+
+#### BUG FIXES
+
+ * setnames(DT,c(NA,NA)) is now a type error rather than a segfault, #2393.
+ Thanks to Damian Betebenner for reporting.
+
+ * rbind() no longers warns about inputs having columns in a different order
+ if use.names has been explicitly set TRUE, #2385. Thanks to Simon Judes
+ for reporting.
+
+ * := by group with 0 length RHS could crash in some circumstances. Thanks to
+ Damien Challet for the reproducible example using obfuscated data and
+ pinpointing the version that regressed. Fixed and test added.
+
+ * Error 'attempting to roll join on a factor column' could occur when a non last
+ join column was a factor column, #2450. Thanks to Blue Magister for
+ highlighting.
+
+ * NA in a join column of type double could cause both X[Y] and merge(X,Y)
+ to return incorrect results, #2453. Due to an errant x==NA_REAL in the C source
+ which should have been ISNA(x). Support for double in keyed joins is a relatively
+ recent addition to data.table, but embarrassing all the same. Fixed and tests added.
+ Many thanks to statquant for the thorough and reproducible report :
+ http://stackoverflow.com/questions/14076065/data-table-inner-outer-join-to-merge-with-na
+
+ * setnames() of all column names (such as setnames(DT,toupper(names(DT)))) failed on a
+ keyed table where columns 1:length(key) were not the key. Fixed and test added.
+
+ * setkey could sort 'double' columns (such as POSIXct) incorrectly when not the
+ last column of the key, #2484. In data.table's C code :
+ x[a] > x[b]-tol
+ should have been :
+ x[a]-x[b] > -tol [or x[b]-x[a] < tol ]
+ The difference may have been machine/compiler dependent. Many thanks to statquant
+ for the short reproducible example. Test added.
+
+ * cbind(DT,1:n) returned an invalid data.table (some columns were empty) when DT
+ had one row, #2478. Grouping now warns if j evaluates to an invalid data.table,
+ to aid tracing root causes like this in future. Tests added. Many thanks to
+ statquant for the reproducible example revealed by his interesting solution
+ and to user1935457 for the assistance :
+ http://stackoverflow.com/a/14359701/403310
+
+ * merge(...,all.y=TRUE) was 'setcolorder' error if a y column name included a space
+ and there were rows in y not in x, #2555. The non syntactically valid column names
+ are now preserved as intended. Thanks to Simon Judes for reporting. Tests added.
+
+ * An error in := no longer suppresses the next print, #2376; i.e.,
+ > DT[,foo:=colnameTypo+1]
+ Error: object 'colnameTypo' not found
+ > DT # now prints DT ok
+ > DT # used to have to type DT a second time to see it
+ Many thanks to Charles, Joris Meys, and, Spacedman whose solution is now used
+ by data.table internally (http://stackoverflow.com/a/13606880/403310).
+
+#### NOTES
+
+ * print(DT,topn=2), where topn is provided explicitly, now prints the top and bottom 2 rows
+ even when nrow(x)<100 [options()$datatable.print.nrows]. And the 'topn' argument is now first
+ for easier interactive use: print(DT,2), head(DT,2) and tail(DT,2).
+
+ * The J() alias is now removed *outside* DT[...], but will still work inside DT[...];
+ i.e., DT[J(...)] is fine. As warned in v1.8.2 (see below in this file) and deprecated
+ with warning() in v1.8.4. This resolves the conflict with function J() in package
+ XLConnect (#1747) and rJava (#2045).
+ Please use data.table() directly instead of J(), outside DT[...].
+
+ * ?merge.data.table and FAQ 1.12 have been improved (#2457), and FAQ 2.24 added.
+ Thanks to dnlbrky for highlighting : http://stackoverflow.com/a/14164411/403310.
+
+ * There are now 943 raw tests, as reported by test.data.table().
+
+ * v1.8.7 was on R-Forge only. v1.8.8 was released to CRAN.
+ Odd numbers are development, evens on CRAN.
+
+
+### Changes in v1.8.6 (on CRAN 13 Nov 2012)
+
+#### BUG FIXES
+
+ * A variable in calling scope was not found when combining i, j and by in
+ one query, i used that local variable, and that query occurred inside a
+ function, #2368. This worked in 1.8.2, a regression. Test added.
+
+#### COMPATIBILITY FOR R 2.12.0-2.15.0
+
+ * setnames used paste0() to construct its error messages, a function
+ added to R 2.15.0. Reverted to use paste(). Tests added.
+
+ * X[Y] where Y is empty (test 764) failed due to reliance on a pmin()
+ enhancement in R 2.15.1. Removed reliance.
+
+#### NOTES
+
+ * test.data.table() now passes in 2.12.0, the stated dependency, as well as
+ 2.14.0, 2.15.0, 2.15.1, 2.15.2 and R-devel.
+
+ * Full R CMD check (i.e. including compatibility tests with the 9 Suggest-ed
+ packages and S4 tests run using testthat which in turn depends on packages
+ which depend on R >= 2.14.0) passes ok in 2.14.0 onwards.
+
+ * There are now 876 raw tests, as reported by test.data.table().
+
+ * v1.8.5 was on R-Forge only. v1.8.6 was released to CRAN.
+ Odd numbers are development, evens on CRAN.
+
+
+### Changes in v1.8.4 (on CRAN 9 Nov 2012)
+
+#### NEW FEATURES
+
+ * New printing options have been added :
+ options(datatable.print.nrows=100)
+ options(datatable.print.topn=10)
+ If the table to be printed has more than nrows, the top and bottom topn rows
+ are printed. Otherwise, below nrows, the entire table is printed.
+ Thanks to Allan Engelhardt and Melanie Bacou for useful discussions :
+ http://lists.r-forge.r-project.org/pipermail/datatable-help/2012-September/001303.html
+ and see FAQs 2.11 and 2.22.
+
+ * When one or more rows in i have no match to x and i is unkeyed, i is now
+ tested to see if it is sorted. If so, the key is retained. As before, when all rows of
+ i match to x, the key is retained if i matches to an ordered subset of keyed x without
+ needing to test i, even if i is unkeyed.
+
+ * by on a keyed empty table is now keyed by the by columns, for consistency with
+ the non empty case when an ordered grouping is detected.
+
+ * DT[,`:=`(col1=val1, col2=val2, ...)] is now valid syntax rather than a crash, #2254.
+ Many thanks to Thell Fowler for the suggestion.
+
+ * with=FALSE is no longer needed to use column names or positions on the LHS of :=, #2120.
+ DT[,c("newcol","existingcol"):=list(1L,NULL)] # with=FALSE not needed
+ DT[,`:=`(newcol=1L, existingcol:=NULL)] # same
+ If the LHS is held in a variable, the followed equivalent options are retained :
+ mycols = c("existingcol","newcol")
+ DT[,get("mycols"):=1L]
+ DT[,eval(mycols):=1L] # same
+ DT[,mycols:=1L,with=FALSE] # same
+ DT[,c("existingcol","newcol"):=1L] # same (with=FALSE not needed)
+
+ * Multiple LHS:= and `:=`(...) now work by group, and by without by. Implementing
+ or fixing, and thanks to, #2215 (Florian Oswald), #1710 (Farrel Buchinsky) and
+ others.
+ DT[,c("newcol1","newcol2"):=list(mean(col1),sd(col1)), by=grp]
+ DT[,`:=`(newcol1=mean(col1),
+ newcol2=sd(col1),
+ ...), by=grp] # same but easier to read
+ DT[c("grp1","grp2"), `:=`(newcol1=mean(col1),
+ newcol2=sd(col1))] # same using by-without-by
+
+ * with=FALSE now works with a symbol LHS of :=, by group (#2120) :
+ colname = "newcol"
+ DT[,colname:=f(),by=grp,with=FALSE]
+ Thanks to Alex Chernyakov :
+ http://stackoverflow.com/questions/11745169/dynamic-column-names-in-data-table-r
+ http://stackoverflow.com/questions/11680579/assign-multiple-columns-using-in-data-table-by-group
+
+ * .GRP is a new symbol available to j. Value 1 for the first group, 2 for the 2nd, etc. Thanks
+ to Josh O'Brien for the suggestion :
+ http://stackoverflow.com/questions/13018696/data-table-key-indices-or-group-counter
+
+ * .I is a new symbol available to j. An integer vector length .N. It contains the group's row
+ locations in DT. This implements FR#1962.
+ DT[,.I[which.max(colB)],by=colA] # row numbers of maxima by group
+
+ * A new "!" prefix on i signals 'not-join' (a.k.a. 'not-where'), #1384i.
+ DT[-DT["a", which=TRUE, nomatch=0]] # old not-join idiom, still works
+ DT[!"a"] # same result, now preferred.
+ DT[!J(6),...] # !J == not-join
+ DT[!2:3,...] # ! on all types of i
+ DT[colA!=6L | colB!=23L,...] # multiple vector scanning approach (slow)
+ DT[!J(6L,23L)] # same result, faster binary search
+ '!' has been used rather than '-' :
+ * to match the 'not-join'/'not-where' nomenclature
+ * with '-', DT[-0] would return DT rather than DT[0] and not be backwards
+ compatible. With '!', DT[!0] returns DT both before (since !0 is TRUE in
+ base R) and after this new feature.
+ * to leave DT[+J...] and DT[-J...] available for future use
+
+ * When with=FALSE, "!" may also be a prefix on j, #1384ii. This selects all but the named columns.
+ DF[,-match("somecol",names(DF))] # works when somecol exists. If not, NA causes an error.
+ DF[,-match("somecol",names(DF),nomatch=0)] # works when somecol exists. Empty data.frame when it doesn't, silently.
+ DT[,-match("somecol",names(DT)),with=FALSE] # same issues.
+ DT[,setdiff(names(DT),"somecol"),with=FALSE] # works but you have to know order of arguments, and no warning if doesn't exist
+ - vs -
+ DT[,!"somecol",with=FALSE] # works and easy to read. With (helpful) warning if somecol isn't there.
+ Strictly speaking, this (!j) is a "not-select" (!i is 'not-where'). This has no analogy in SQL.
+ Reminder: i is analogous to WHERE, j is analogous to SELECT and `:=` in j changes SELECT to UPDATE.
+ !j when j is column positions is very similar to -j.
+ DF[,-(2:3),drop=FALSE] # all but columns 2 and 3. Careful, brackets and drop=FALSE are required.
+ DT[,-(2:3),with=FALSE] # same
+ DT[,!2:3,with=FALSE] # same
+ copy(DT)[,2:3:=NULL,with=FALSE] # same
+ !j was introduced for column names really, not positions. It works for both, for consistency :
+ toremove = c("somecol","anothercol")
+ DT[,!toremove,with=FALSE]
+ toremove = 2:3
+ DT[,!toremove,with=FALSE] # same code works without change
+
+ * 'which' now accepts NA. This means return the row numbers in i that don't match, #1384iii.
+ Thanks to Santosh Srinivas for the suggestion.
+ X[Y,which=TRUE] # row numbers of X that do match, as before
+ X[!Y,which=TRUE] # row numbers of X that don't match
+ X[Y,which=NA] # row numbers of Y that don't match
+ X[!Y,which=NA] # row numbers of Y that do match (for completeness)
+
+ * setnames() now works on data.frame, #2273. Thanks to Christian Hudon for the suggestion.
+
+#### BUG FIXES
+
+ * A large slowdown (many minutes instead of a few secs) in X[Y] joins has been fixed, #2216.
+ This occurred where the number of rows in i was large, and at least one row joined to
+ more than one row in x. Possibly in other similar circumstances too. The workaround was
+ to set mult="first" which is no longer required. Test added.
+ Thanks to a question and report from Alex Chernyakov :
+ http://stackoverflow.com/questions/12042779/time-of-data-table-join
+
+ * Indexing columns of data.table with a logical vector and `with=FALSE` now works as
+ expected, fixing #1797. Thanks to Mani Narayanan for reporting. Test added.
+
+ * In X[Y,cols,with=FALSE], NA matches are now handled correctly. And if cols
+ includes join columns, NA matches (if any) are now populated from i. For
+ consistency with X[Y] and X[Y,list(...)]. Tests added.
+
+ * "Internal error" when combining join containing missing groups and group by
+ is fixed, #2162. For example :
+ X[Y,.N,by=NonJoinColumn]
+ where Y contains some rows that don't match to X. This bug could also result in a segfault.
+ Thanks to Andrey Riabushenko and Michael Schermerhorn for reporting. Tests added.
+
+ * On empty tables, := now changes column type and adds new 0 length columns ok, fixing
+ #2274. Tests added.
+
+ * Deleting multiple columns out-of-order is no longer a segfault, #2223. Test added.
+ DT[,c(9,2,6):=NULL]
+ Reminder: deleting columns by reference is relatively instant, regardless of table size.
+
+ * Mixing column adds and deletes in one := gave incorrect results, #2251. Thanks to
+ Michael Nelson for reporting. Test added.
+ DT[,c("newcol","col1"):=list(col1+1L,NULL)]
+ DT[,`:=`(newcol=col1+1L,col1=NULL)] # same
+
+ * Out of bound positions in the LHS of := are now caught. Root cause of crash in #2254.
+ Thanks to Thell Fowler for reporting. Tests added.
+ DT[,(ncol(DT)+1):=1L] # out of bounds error (add new columns by name only)
+ DT[,ncol(DT):=1L] # ok
+
+ * A recycled column plonk RHS of := no longer messes up setkey and := when used on that
+ object afterwards, #2298. For example,
+ DT = data.table(a=letters[3:1],x=1:3)
+ DT[,c("x1","x2"):=x] # ok (x1 and x2 are now copies of x)
+ setkey(DT,a) # now ok rather than wrong result
+ Thanks to Timothee Carayol for reporting. Tests added.
+
+ * Join columns are now named correctly when j is .SD, a subset of .SD, or similar, #2281.
+ DT[c("a","b"),.SD[...]] # result's first column now named key(DT)[1] rather than 'V1'
+
+ * Joining an empty i table now works without error (#2194). It also retains key and has a consistent
+ number and type of empty columns as the non empty by-without-by case. Tests added.
+
+ * by-without-by with keyed i where key isn't the 1:n columns of i could crash, #2314. Many thanks
+ to Garrett See for reporting with reproducible example data file. Tests added.
+
+ * DT[,col1:=X[Y,col2]] was a crash, #2311. Due to RHS being a data.table. mult="first"
+ (or drop=TRUE in future) was likely intended. Thanks to Anoop Shah for reporting with
+ reproducible example. Root cause (recycling of list columns) fixed and tests added.
+
+ * Grouping by a column which somehow has names, no longer causes an error, #2307.
+ DT = data.table(a=1:3,b=c("a","a","b"))
+ setattr(DT$b, "names", c("a","b","c")) # not recommended, just to illustrate
+ DT[,sum(a),by=b] # now ok
+
+ * gWidgetsWWW wasn't known as data.table aware, even though it mimicks executing
+ code in .GlobalEnv, #2340. So, data.table is now gWidgetsWWW-aware. Further packages
+ can be added if required by changing a new variable :
+ data.table:::cedta.override
+ by using assignInNamespace(). Thanks to Zach Waite and Yihui Xie for investigating and
+ providing reproducible examples :
+ http://stackoverflow.com/questions/13106018/data-table-error-when-used-through-knitr-gwidgetswww
+
+ * Optimization of lapply when FUN is a character function name now works, #2212.
+ DT[,lapply(.SD, "+", 1), by=id] # no longer an error
+ DT[,lapply(.SD, `+`, 1), by=id] # same, worked before
+ Thanks to Michael Nelson for highlighting. Tests added.
+
+ * Syntactically invalid column names (such as "Some rate (%)") are now preserved in X[Y] joins and
+ merge(), as intended. Thanks to George Kaupas (#2193i) and Yang Zhang (#2090) for reporting.
+ Tests added.
+
+ * merge() and setcolorder() now check for duplicate column names first rather than a less helpful
+ error later, #2193ii. Thanks to Peter Fine for reporting. Tests added.
+
+ * Column attributes (such as 'comment') are now retained by X[Y] and merge(), #2270. Thanks to
+ Allan Engelhardt for reporting. Tests added.
+
+ * A matrix RHS of := is now treated as vector, with warning if it has more than 1 column, #2333.
+ Thanks to Alex Chernyakov for highlighting. Tests added.
+ DT[,b:=scale(a)] # now works rather than creating an invalid column of type matrix
+ http://stackoverflow.com/questions/13076509/why-error-from-na-omit-after-running-scale-in-r-in-data-table
+
+ * last() is now S3 generic for compatibility with xts::last, #2312. Strictly speaking, for speed,
+ last(x) deals with vector, list and data.table inputs directly before falling back to
+ S3 dispatch. Thanks to Garrett See for reporting. Tests added.
+
+ * DT[,lapply(.SD,sum)] in the case of no grouping now returns a data.table for consistency, rather
+ than list, #2263. Thanks to Justin and mnel for highlighting. Existing test changed.
+ http://stackoverflow.com/a/12290443/403310
+
+ * L[[2L]][,newcol:=] now works, where L is a list of data.table objects, #2204. Thanks to Melanie Bacou
+ for reporting. Tests added. A warning is issued when the first column is added if L was created with
+ list(DT1,DT2) since R's list() copies named inputs. Until reflist() is implemented, this warning can be
+ ignored or suppressed.
+ http://lists.r-forge.r-project.org/pipermail/datatable-help/2012-August/001265.html
+
+ * DT[J(data.frame(...))] now works again, giving the same result as DT[data.frame(...)], #2265.
+ Thanks to Christian Hudon for reporting. Tests added.
+
+ * A memory leak has been fixed, #2191 and #2284. All data.table objects leaked the over allocated column
+ pointer slots; i.e., when a data.table went out of scope or was rm()'d this memory wasn't released and
+ gc() would report growing Vcells. For a 3 column data.table with a 100 allocation, the growth was
+ 1.5Kb per data.table on 64bit (97*8*2 bytes) and 0.75Kb on 32bit (97*4*2 bytes).
+ Many thanks to Xavier Saint-Mleux and Sasha Goodman for the reproducible examples and
+ assistance. Tests added.
+
+ * rbindlist now skips empty (0 row) items as well as NULL items. So the column types of the result are
+ now taken from the first non-empty data.table. Thanks to Garrett See for reporting. Test added.
+
+ * setnames did not update column names correctly when passed integer column positions and those
+ column names contained duplicates, fixed. This affected the column names of queries involving
+ two or more by expressions with a named list inside {}. Thanks to Steve Lianoglou for finding and
+ fixing. Tests added.
+ DT[, {list(name1=sum(v),name2=sum(w))}, by="a,b"] # now ok, no blank column names in result
+ DT[, list(name1=sum(v),name2=sum(w)), by="a,b"] # ok before
+
+#### USER VISIBLE CHANGES
+
+ * J() now issues a warning (when used *outside* DT[...]) that using it
+ outside DT[...] is deprecated. See item below in v1.8.2.
+ Use data.table() directly instead of J(), outside DT[...]. Or, define
+ an alias yourself. J() will continue to work *inside* DT[...] as documented.
+
+ * DT[,LHS:=RHS,...] no longer prints DT. This implements #2128 "Try again to get
+ DT[i,j:=value] to return invisibly". Thanks to discussions here :
+ http://stackoverflow.com/questions/11359553/how-to-suppress-output-when-using-in-r-data-table
+ http://r.789695.n4.nabble.com/Avoiding-print-when-using-tp4643076.html
+ FAQs 2.21 and 2.22 have been updated.
+
+ * DT[] now returns DT rather than an error that either i or j must be supplied.
+ So, ending with [] at the console is a convenience to print the result of :=, rather
+ than wrapping with print(); e.g.,
+ DT[i,j:=value]...oops forgot print...[]
+ is the same as :
+ print(DT[i,j:=value])
+
+ * A warning is now issued when by is set equal to the by-without-by join columns,
+ causing x to be subset and then grouped again. The warning suggests removing by or
+ changing it, #2282. This can be turned off using options(datatable.warnredundantby=FALSE)
+ in case it occurs after upgrading, until those lines can be modified.
+ Thanks to Ben Barnes for highlighting :
+ http://stackoverflow.com/a/12474211/403310
+
+ * Description of how join columns are determined in X[Y] syntax has been further clarified
+ in ?data.table. Thanks to Alex :
+ http://stackoverflow.com/questions/12920803/merge-data-table-when-the-number-of-key-columns-are-different
+
+ * ?transform and example(transform) has been fixed and embelished, #2316.
+ Thanks to Garrett See's suggestion.
+
+ * ?setattr has been updated to document that it takes any input, not just data.table, and
+ can be used on columns of a data.frame, for example.
+
+ * Efficiency warnings when joining between a factor column and a character column are now downgraded
+ to messages when verbosity is on, #2265i. Thanks to Christian Hudon for the suggestion.
+
+#### THANKS TO BETA TESTING (bugs caught in 1.8.3 before release to CRAN)
+
+ * Combining a join with mult="first"|"last" followed by by inside the same [...] gave incorrect
+ results or a crash, #2303. Many thanks to Garrett See for the reproducible example and
+ pinpointing in advance which commit had caused the problem. Tests added.
+
+ * Examples in ?data.table have been updated now that := no longer prints. Thanks to Garrett See.
+
+#### NOTES
+
+ * There are now 869 raw tests. test.data.table() should return precisely this number of
+ tests passed. If not, then somehow, a slightly stale version from R-Forge is likely
+ installed; please reinstall from CRAN.
+
+ * v1.8.3 was an R-Forge only beta release. v1.8.4 was released to CRAN.
+
+
+### Changes in v1.8.2
+
+#### NEW FEATURES
+
+ * Numeric columns (type 'double') are now allowed in keys and ad hoc
+ by. J() and SJ() no longer coerce 'double' to 'integer'. i join columns
+ which mismatch on numeric type are coerced silently to match
+ the type of x's join column. Two floating point values
+ are considered equal (by grouping and binary search joins) if their
+ difference is within sqrt(.Machine$double.eps), by default. See example
+ in ?unique.data.table. Completes FRs #951, #1609 and #1075. This paves the
+ way for other atomic types which use 'double' (such as POSIXct and bit64).
+ Thanks to Chris Neff for beta testing and finding problems with keys
+ of two numeric columns (bug #2004), fixed and tests added.
+
+ * := by group is now implemented (FR#1491) and sub-assigning to a new column
+ by reference now adds the column automatically (initialized with NA where
+ the sub-assign doesn't touch) (FR#1997). := by group can be combined with all
+ types of i, so ":= by group" includes grouping by `i` as well as by `by`.
+ Since := by group is by reference, it should be significantly faster than any
+ method that (directly or indirectly) `cbind`s the grouped results to DT, since
+ no copy of the (large) DT is made at all. It's a short and natural syntax that
+ can be compounded with other queries.
+ DT[,newcol:=sum(colB),by=colA]
+
+ * Prettier printing of list columns. The first 6 items of atomic vectors
+ are collapsed with "," followed by a trailing "," if there are more than
+ 6, FR#1608. This difference to data.frame has been added to FAQ 2.17.
+ Embedded objects (such as a data.table) print their class name only to avoid
+ seemingly mangled output, bug #1803. Thanks to Yike Lu for reporting.
+ For example:
+ > data.table(x=letters[1:3],
+ y=list( 1:10, letters[1:4], data.table(a=1:3,b=4:6) ))
+ x y
+ 1: a 1,2,3,4,5,6,
+ 2: b a,b,c,d
+ 3: c <data.table>
+
+ * Warnings added when joining character to factor, and factor to character.
+ Character to character is now preferred in joins and needs no coercion.
+ Even so, these coercions have been made much more efficient by taking
+ a shallow copy of i internally, avoiding a full deep copy of i.
+
+ * Ordered subsets now retain x's key. Always for logical and keyed i, using
+ base::is.unsorted() for integer and unkeyed i. Implements FR#295.
+
+ * mean() is now automatically optimized, #1231. This can speed up grouping
+ by 20 times when there are a large number of groups. See wiki point 3, which
+ is no longer needed to know. Turn off optimization by setting
+ options(datatable.optimize=0).
+
+ * DT[,lapply(.SD,...),by=...] is now automatically optimized, #2067. This can speed
+ up applying a function by column by group, by over 20 times. See wiki point 5
+ which is no longer needed to know. In other words:
+ DT[,lapply(.SD,sum),by=grp]
+ is now just as fast as :
+ DT[,list(x=sum(x),y=sum(y)),by=grp]
+ Don't forget to use .SDcols when a subset of columns is needed.
+
+ * The package is now Byte Compiled (when installed in R 2.14.0 or later). Several
+ internal speed improvements were made in this version too, such as avoiding
+ internal copies. If you find 1.8.2 is faster, before attributing that to Byte
+ Compilation, please install the package without Byte Compilation and compare
+ ceteris paribus. If you find cases where speed has slowed, please let us know.
+
+ * sapply(DT,class) gets a significant speed boost by avoiding a call to unclass()
+ in as.list.data.table() called by lapply(DT,...), which copied the entire object.
+ Thanks to a question by user1393348 on Stack Overflow, implementing #2000.
+ http://stackoverflow.com/questions/10584993/r-loop-over-columns-in-data-table
+
+ * The J() alias is now deprecated outside DT[...], but will still work inside
+ DT[...], as in DT[J(...)].
+ J() is conflicting with function J() in package XLConnect (#1747)
+ and rJava (#2045). For data.table to change is easier, with some efficiency
+ advantages too. The next version of data.table will issue a warning from J()
+ when used outside DT[...]. The version after will remove it. Only then will
+ the conflict with rJava and XLConnect be resolved.
+ Please use data.table() directly instead of J(), outside DT[...].
+
+ * New DT[.(...)] syntax (in the style of package plyr) is identical to
+ DT[list(...)], DT[J(...)] and DT[data.table(...)]. We plan to add ..(), too, so
+ that .() and ..() are analogous to the file system's ./ and ../; i.e., .()
+ evaluates within the frame of DT and ..() in the parent scope.
+
+ * New function rbindlist(l). This does the same as do.call("rbind",l), but much
+ faster.
+
+#### BUG FIXES
+
+ * DT[,f(.SD),by=colA] where f(x)=x[,colB:=1L] was a segfault, bug#1727.
+ This is now a graceful error to say that using := in .SD's j is
+ reserved for future use. This was already caught in most circumstances,
+ other than via f(.SD). Thanks to Leon Baum for reporting. Test added.
+
+ * If .N is selected by j it is now renamed "N" (no dot) in the output, to
+ avoid a potential conflict in subsequent grouping between a column called
+ ".N" and the special .N variable, fixing #1720. ?data.table updated and
+ FAQ 4.6 added with detailed examples. Tests added.
+
+ * Moved data.table setup code from .onAttach to .onLoad so that it
+ is also run when data.table is simply `import`ed from within a package,
+ fixing #1916 related to missing data.table options.
+
+ * Typos fixed in ?":=", thanks to Michael Weylandt for reporting.
+
+ * base::unname(DT) now works again, as needed by plyr::melt(). Thanks to
+ Christoph Jaeckel for reporting. Test added.
+
+ * CJ(x=...,y=...) now retains the column names x and y, useful when CJ
+ is used independently (since x[CJ(...)] takes join column names from x).
+ Restores behaviour lost somewhere between 1.7.1 and 1.8.0, thanks
+ to Muhammad Waliji for reporting. Tests added.
+
+ * A column plonk via set() was only possible by passing NULL as i. The default
+ for i is now NULL so that missing i invokes a column plonk, too (when length(value)
+ == nrow(DT)). A column plonk is much more efficient than creating 1:nrow(DT) and
+ passing that as i to set() or DT[i,:=] (almost infinitely faster). Thanks to
+ testing by Josh O'Brien in comments on Stack Overflow. Test added.
+
+ * Joining a factor column with unsorted and unused levels to a character column
+ now matches properly, fixing #1922. Thanks to Christoph Jäckel for the reproducible
+ example. Test added.
+
+ * 'by' on an empty table now returns an empty table (#1945) and .N, .SD and .BY are
+ now available in the empty case (also #1945). The column names and types of
+ the returned empty table are consistent with the non empty case. Thanks to
+ Malcolm Cook for reporting. Tests added.
+
+ * DT[NULL] now returns the NULL data.table, rather than an error. Test added.
+ Use DT[0] to return an empty copy of DT.
+
+ * .N, .SD and .BY are now available to j when 'by' is missing, "", character()
+ and NULL, fixing #1732. For consistency so that j works unchanged when by is
+ dynamic and passed one of those values all meaning 'don't group'. Thanks
+ to Joseph Voelkel reporting and Chris Neff for further use cases. Tests added.
+
+ * chorder(character()) was a seg fault, #2026. Fixed and test added.
+
+ * When grouping by i, if the first row of i had no match, .N was 1 rather than 0.
+ Fixed and tests added. Thanks to a question by user1165199 on Stack Overflow :
+ http://stackoverflow.com/questions/10721517/count-number-of-times-data-is-in-another-dataframe-in-r
+
+ * All object attributes are now retained by grouping; e.g., tzone of POSIXct is no
+ longer lost, fixing #1704. Test added. Thanks to Karl Ove Hufthammer for reporting.
+
+ * All object attributes are now retained by recycling assign to a new column (both
+ <- and :=); e.g., POSIXct class is no longer lost, fixing #1712. Test added. Thanks
+ to Leon Baum for reporting.
+
+ * unique() of ITime no longer coerces to integer, fixing #1719. Test added.
+
+ * rbind() of DT with an irregular list() now recycles the list items correctly,
+ #2003. Test added.
+
+ * setcolorder() now produces correct error when passed missing column names. Test added.
+
+ * merge() with common names, and, all.y=TRUE (or all=TRUE) no longer returns an error, #2011.
+ Tests added. Thanks to a question by Ina on Stack Overflow :
+ http://stackoverflow.com/questions/10618837/joining-two-partial-data-tables-keeping-all-x-and-all-y
+
+ * Removing or setting datatable.alloccol to NULL is no longer a memory leak, #2014.
+ Tests added. Thanks to a question by Vanja on Stack Overflow :
+ http://stackoverflow.com/questions/10628371/r-importing-data-table-package-namespace-unexplainable-jump-in-memory-consumpt
+
+ * DT[,2:=someval,with=FALSE] now changes column 2 even if column 1 has the same (duplicate)
+ name, #2025. Thanks to Sean Creighton for reporting. Tests added.
+
+ * merge() is now correct when all=TRUE but there are no common values in the two
+ data.tables, fixing #2114. Thanks to Karl Ove Hufthammer for reporting. Tests added.
+
+ * An as.data.frame method has been added for ITime, so that ITime can be passed to ggplot2
+ without error, #1713. Thanks to Farrel Buchinsky for reporting. Tests added.
+ ITime axis labels are still displayed as integer seconds from midnight; we don't know why ggplot2
+ doesn't invoke ITime's as.character method. Convert ITime to POSIXct for ggplot2, is one approach.
+
+ * setnames(DT,newnames) now works when DT contains duplicate column names, #2103.
+ Thanks to Timothee Carayol for reporting. Tests added.
+
+ * subset() would crash on a keyed table with non-character 'select', #2131. Thanks
+ to Benjamin Barnes for reporting. The root cause was non character inputs to chmatch
+ and %chin%. Tests added.
+
+ * Non-ascii column names now work when passed as character 'by', #2134. Thanks to
+ Karl Ove Hufthammer for reporting. Tests added.
+ DT[, mean(foo), by=ÆØÅ] # worked before
+ DT[, mean(foo), by="ÆØÅ"] # now works too
+ DT[, mean(foo), by=colA] # worked before
+ DT[, mean(foo), by="colA"] # worked before
+
+#### USER VISIBLE CHANGES
+
+ * Incorrect syntax error message for := now includes advice to check that
+ DT is a data.table rather than a data.frame. Thanks to a comment by
+ gkaupas on Stack Overflow.
+
+ * When set() is passed a logical i, the error message now includes advice to
+ wrap with which() and take the which() outside the loop (if any) if possible.
+
+ * An empty data.table (0 rows, 1+ cols) now print as "Empty data.table" rather
+ than "NULL data.table". A NULL data.table, returned by data.table(NULL) has
+ 0 rows and 0 cols. DT[0] returns an empty data.table.
+
+ * 0 length by (such as NULL and character(0)) now return a data.table when j
+ is vector, rather than vector, for consistency of return types when by
+ is dynamic and 'dont group' needs to be represented. Bug fix #1599 in
+ v1.7.0 was fixing an error in this case (0 length by).
+
+ * Default column names for unnamed columns are now consistent between 'by' and
+ non-'by'; e.g. these two queries now name the columns "V1" and "V2" :
+ DT[,list("a","b"),by=x]
+ DT[,list("a","b")] # used to name the columns 'a' and 'b', oddly.
+
+ * Typing ?merge now asks whether to display ?merge.data.frame or ?merge.data.table,
+ and ?merge.data.table works directly. Thanks to Chris Neff for suggesting.
+
+ * Description of how join columns are determined in X[Y] syntax has been clarified
+ in ?data.table. Thanks to Juliet Hannah and Yike Lu.
+
+ * DT now prints consistent row numbers when the column names are reprinted at the
+ bottom of the output (saves scrolling up). Thanks to Yike Lu for reporting #2015.
+ The tail as well as the head of large tables is now printed.
+
+
+#### THANKS TO BETA TESTING (i.e. bugs caught in 1.8.1 before release to CRAN) :
+
+ * Florian Oswald for #2094: DT[,newcol:=NA] now adds a new logical column ok.
+ Test added.
+
+ * A large slow down (2s went up to 40s) when iterating calls to DT[...] in a
+ for loop, such as in example(":="), was caught and fixed in beta, #2027.
+ Speed regression test added.
+
+ * Christoph Jäckel for #2078: by=c(...) with i clause broke. Tests added.
+
+ * Chris Neff for #2065: keyby := now keys, unless, i clause is present or
+ keyby is not straightforward column names (in any format). Tests added.
+
+ * :=NULL to delete, following by := by group to add, didn't add the column,
+ #2117. Test added.
+
+ * Combining i subset with by gave incorrect results, #2118. Tests added.
+
+ * Benjamin Barnes for #2133: rbindlist not supporting type 'logical'.
+ Tests added.
+
+ * Chris Neff for #2146: using := to add a column to the result of a simple
+ column subset such as DT[,list(x)], or after changing all column names
+ with setnames(), was an error. Fixed and tests added.
+
+#### NOTES
+
+ * There are now 717 raw tests, plus S4 tests.
+
+ * v1.8.1 was an R-Forge only beta release. v1.8.2 was released to CRAN.
+
+
+### Changes in v1.8.0
+
+#### NEW FEATURES
+
+ * character columns are now allowed in keys and are preferred to
+ factor. data.table() and setkey() no longer coerce character to
+ factor. Factors are still supported. Implements FR#1493, FR#1224
+ and (partially) FR#951.
+
+ * setkey() no longer sorts factor levels. This should be more convenient
+ and compatible with ordered factors where the levels are 'labels', in
+ some order other than alphabetical. The established advice to paste each
+ level with an ordinal prefix, or use another table to hold the factor
+ labels instead of a factor column, is no longer needed. Solves FR#1420.
+ Thanks to Damian Betebenner and Allan Engelhardt raising on datatable-help
+ and their tests have been added verbatim to the test suite.
+
+ * unique(DT) and duplicated(DT) are now faster with character columns,
+ on unkeyed tables as well as keyed tables, FR#1724.
+
+ * New function set(DT,i,j,value) allows fast assignment to elements
+ of DT. Similar to := but avoids the overhead of [.data.table, so is
+ much faster inside a loop. Less flexible than :=, but as flexible
+ as matrix subassignment. Similar in spirit to setnames(), setcolorder(),
+ setkey() and setattr(); i.e., assigns by reference with no copy at all.
+
+ M = matrix(1,nrow=100000,ncol=100)
+ DF = as.data.frame(M)
+ DT = as.data.table(M)
+ system.time(for (i in 1:1000) DF[i,1L] <- i) # 591.000s
+ system.time(for (i in 1:1000) DT[i,V1:=i]) # 1.158s
+ system.time(for (i in 1:1000) M[i,1L] <- i) # 0.016s
+ system.time(for (i in 1:1000) set(DT,i,1L,i)) # 0.027s
+
+ * New functions chmatch() and %chin%, faster versions of match()
+ and %in% for character vectors. R's internal string cache is
+ utilised (no hash table is built). They are about 4 times faster
+ than match() on the example in ?chmatch.
+
+ * Internal function sortedmatch() removed and replaced with chmatch()
+ when matching i levels to x levels for columns of type 'factor'. This
+ preliminary step was causing a (known) significant slowdown when the number
+ of levels of a factor column was large (e.g. >10,000). Exacerbated in
+ tests of joining four such columns, as demonstrated by Wes McKinney
+ (author of Python package Pandas). Matching 1 million strings of which
+ of which 600,000 are unique is now reduced from 16s to 0.5s, for example.
+ Background here :
+ http://stackoverflow.com/questions/8991709/why-are-pandas-merges-in-python-faster-than-data-table-merges-in-r
+
+ * rbind.data.table() gains a use.names argument, by default TRUE.
+ Set to FALSE to combine columns in order rather than by name. Thanks to
+ a question by Zach on Stack Overflow :
+ http://stackoverflow.com/questions/9315258/aggregating-sub-totals-and-grand-totals-with-data-table
+
+ * New argument 'keyby'. An ad hoc by just as 'by' but with an additional setkey()
+ on the by columns of the result, for convenience. Not to be confused with a
+ 'keyed by' such as DT[...,by=key(DT)] which can be more efficient as explained
+ by FAQ 3.3. Thanks to Yike Lu for the suggestion and discussion (FR#1780).
+
+ * Single by (or keyby) expressions no longer need to be wrapped in list(),
+ for convenience, implementing FR#1743; e.g., these now works :
+ DT[,sum(v),by=a%%2L]
+ DT[,sum(v),by=month(date)]
+ instead of needing :
+ DT[,sum(v),by=list(a%%2L)]
+ DT[,sum(v),by=list(month(date))]
+
+ * Unnamed 'by' expressions have always been inspected using all.vars() to make
+ a guess at a sensible column name for the result. This guess now includes
+ function names via all.vars(functions=TRUE), for convenience; e.g.,
+ DT[,sum(v),by=month(date)]
+ now returns a column called 'month' rather than 'date'. It is more robust to
+ explicitly name columns, though; e.g.,
+ DT[,sum(v),by=list("Guaranteed name"=month(date))]
+
+ * For a surprising speed boost in some circumstances, default options such as
+ 'datatable.verbose' are now set when the package loads (unless they are already
+ set, by user's profile for example). The 'default' argument of base::getOption()
+ was the culprit and has been removed internally from all 11 calls.
+
+
+#### BUG FIXES
+
+ * Fixed a `suffixes` handling bug in merge.data.table that was
+ only recently introduced during the recent "fast-merge"-ing reboot.
+ Briefly, the bug was only triggered in scenarios where both
+ tables had identical column names that were not part of `by` and
+ ended with *.1. cf. "merge and auto-increment columns in y[x]"
+ test in tests/test-data.frame-like.R for more information.
+
+ * Adding a column using := on a data.table just loaded from disk was
+ correctly detected and over allocated, but incorrectly warning about
+ a previous copy. Test 462 tested loading from disk, but suppressed
+ warnings (sadly). Fixed.
+
+ * data.table unaware packages that use DF[i] and DF[i]<-value syntax
+ were not compatible with data.table, fixed. Many thanks to Prasad Chalasani
+ for providing a reproducible example with base::droplevels(), and
+ Helge Liebert for providing a reproducible example (#1794) with stats::reshape().
+ Tests added.
+
+ * as.data.table(DF) already preserved DF's attributes but not any inherited
+ classes such as nlme's groupedData, so nlme was incompatible with
+ data.table. Fixed. Thanks to Dieter Menne for providing a reproducible
+ example. Test added.
+
+ * The internal row.names attribute of .SD (which exists for compatibility with
+ data.frame only) was not being updated for each group. This caused length errors
+ when calling any non-data.table-aware package from j, by group, when that package
+ used length of row.names. Such as the recent update to ggplot2. Fixed.
+
+ * When grouped j consists of a print of an object (such as ggplot2), the print is now
+ masked to return NULL rather than the object that ggplot2 returns since the
+ recent update v0.9.0. Otherwise data.table tries to accumulate the (albeit
+ invisible) print object. The print mask is local to grouping, not generally.
+
+ * 'by' was failing (bug #1880) when passed character column names where one or more
+ included a space. So, this now works :
+ DT[,sum(v),by="column 1"]
+ and j retains spaces in column names rather than replacing spaces with "."; e.g.,
+ DT[,list("a b"=1)]
+ Thanks to Yang Zhang for reporting. Tests added. As before, column names may be
+ back ticked in the usual R way (in i, j and by); e.g.,
+ DT[,sum(`nicely named var`+1),by=month(`long name for date column`)]
+
+ * unique() on an unkeyed table including character columns now works correctly, fixing
+ #1725. Thanks to Steven Bagley for reporting. Test added.
+
+ * %like% now returns logical (rather than integer locations) so that it can be
+ combined with other i clauses, fixing #1726. Thanks to Ivan Zhang for reporting. Test
+ added.
+
+
+#### THANKS TO
+
+ * Joshua Ulrich for spotting a missing PACKAGE="data.table"
+ in .Call in setkey.R, and suggesting as.list.default() and
+ unique.default() to avoid dispatch for speed, all implemented.
+
+
+#### USER-VISIBLE CHANGES
+
+ * Providing .SDcols when j doesn't use .SD is downgraded from error to warning,
+ and verbosity now reports which columns have been detected as used by j.
+
+ * check.names is now FALSE by default, for convenience when working with column
+ names with spaces and other special characters, which are now fully supported.
+ This difference to data.frame has been added to FAQ 2.17.
+
+
+### Changes in v1.7.10
+
+#### NEW FEATURES
+
+ * New function setcolorder() reorders the columns by name
+ or by number, by reference with no copy. This is (almost)
+ infinitely faster than DT[,neworder,with=FALSE].
+
+ * The prefix i. can now be used in j to refer to join inherited
+ columns of i that are otherwise masked by columns in x with
+ the same name.
+
+
+#### BUG FIXES
+
+ * tracemem() in example(setkey) was causing CRAN check errors
+ on machines where R is compiled without memory profiling available,
+ for efficiency. Notably, R for Windows, Ubuntu and Mac have memory
+ profiling enabled which may slow down R on those architectures even
+ when memory profiling is not being requested by the user. The call to
+ tracemem() is now wrapped with try().
+
+ * merge of unkeyed tables now works correctly after breaking in 1.7.8 and
+ 1.7.9. Thanks to Eric and DM for reporting. Tests added.
+
+ * nomatch=0 was ignored for the first group when j used join inherited
+ scope. Fixed and tests added.
+
+
+#### USER-VISIBLE CHANGES
+
+ * Updating an existing column using := after a key<- now works without warning
+ or error. This can be useful in interactive use when you forget to use setkey()
+ but don't mind about the inefficiency of key<-. Thanks to Chris Neff for
+ providing a convincing use case. Adding a new column uing := after key<-
+ issues a warning, shallow copies and proceeds, as before.
+
+ * The 'datatable.pre.suffixes' option has been removed. It was available to
+ obtain deprecated merge() suffixes pre v1.5.4.
+
+
+### Changes in v1.7.9
+
+#### NEW FEATURES
+
+ * New function setnames(), referred to in 1.7.8 warning messages.
+ It makes no copy of the whole data object, unlike names<- and
+ colnames<-. It may be more convenient as well since it allows changing
+ a column name, by name; e.g.,
+
+ setnames(DT,"oldcolname","newcolname") # by name; no match() needed
+ setnames(DT,3,"newcolname") # by position
+ setnames(DT,2:3,c("A","B")) # multiple
+ setnames(DT,c("a","b"),c("A","B")) # multiple by name
+ setnames(DT,toupper(names(DT))) # replace all
+
+ setnames() maintains truelength of the over-allocated names vector. This
+ allows := to add columns fully by reference without growing the names
+ vector. As before with names<-, if a key column's name is changed,
+ the "sorted" attribute is updated with the new column name.
+
+#### BUG FIXES
+
+ * Incompatibility with reshape() of 3 column tables fixed
+ (introduced by 1.7.8) :
+ Error in setkey(ans, NULL) : x is not a data.table
+ Thanks to Damian Betebenner for reporting and
+ reproducible example. Tests added to catch in future.
+
+ * setattr(DT,...) still returns DT, but now invisibly. It returns
+ DT back again for compound syntax to work; e.g.,
+ setattr(DT,...)[i,j,by]
+ Again, thanks to Damian Betebenner for reporting.
+
+
+### Changes in v1.7.8
+
+#### BUG FIXES
+
+ * unique(DT) now works when DT is keyed and a key
+ column is called 'x' (an internal scoping conflict
+ introduced in v1.6.1). Thanks to Steven Bagley for
+ reporting.
+
+ * Errors and seg faults could occur in grouping when
+ j contained character or list columns. Many thanks
+ to Jim Holtman for providing a reproducible example.
+
+ * Setting a key on a table with over 268 million rows
+ (2^31/8) now works (again), #1714. Bug introduced in
+ v1.7.2. setkey works up to the regular R vector limit
+ of 2^31 rows (2 billion). Thanks to Leon Baum
+ for reporting.
+
+ * Checks in := are now made up front (before starting to
+ modify the data.table) so that the data.table isn't
+ left in an invalid state should an error occur, #1711.
+ Thanks to Chris Neff for reporting.
+
+ * The 'Chris crash' is fixed. The root cause was that key<-
+ always copies the whole table. The problem with that copy
+ (other than being slower) is that R doesn't maintain the
+ over allocated truelength, but it looks as though it has.
+ key<- was used internally, in particular in merge(). So,
+ adding a column using := after merge() was a memory overwrite,
+ since the over allocated memory wasn't really there after
+ key<-'s copy.
+
+ data.tables now have a new attribute '.internal.selfref' to
+ catch and warn about such copies in future. All internal
+ use of key<- has been replaced with setkey(), or new function
+ setkeyv() which accepts a vector, and do not copy.
+
+ Many thanks to Chris Neff for extended dialogue, providing a
+ reproducible example and his patience. This problem was not just
+ in pre 2.14.0, but post 2.14.0 as well. Thanks also to Christoph
+ Jäckel, Timothée Carayol and DM for investigations and suggestions,
+ which in combination led to the solution.
+
+ * An example in ?":=" fixed, and j and by descriptions
+ improved in ?data.table. Thanks to Joseph Voelkel for
+ reporting.
+
+#### NEW FEATURES
+
+ * Multiple new columns can be added by reference using
+ := and with=FALSE; e.g.,
+ DT[,c("foo","bar"):=1L,with=FALSE]
+ DT[,c("foo","bar"):=list(1L,2L),with=FALSE]
+
+ * := now recycles vectors of non divisible length, with
+ a warning (previously an error).
+
+ * When setkey coerces a numeric or character column, it
+ no longer makes a copy of the whole table, FR#1744. Thanks
+ to an investigation by DM.
+
+ * New function setkeyv(DT,v) (v stands for vector) replaces
+ key(DT)<-v syntax. Also added setattr(). See ?copy.
+
+ * merge() now uses (manual) secondary keys, for speed.
+
+
+#### USER VISIBLE CHANGES
+
+ * The loc argument of setkey has been removed. This wasn't very
+ useful and didn't warrant a period of deprecation.
+
+ * datatable.alloccol has been removed. That warning is now
+ controlled by datatable.verbose=TRUE. One option is easer.
+
+ * If i is a keyed data.table, it is no longer an error if its
+ key is longer than x's key; the first length(key(x)) columns
+ of i's key are used to join.
+
+
+### Changes in v1.7.7
+
+#### BUG FIXES
+
+ * Previous bug fix for random crash in R <= 2.13.2
+ related to truelength and over-allocation didn't
+ work, 3rd attempt. Thanks to Chris Neff for his
+ patience and testing. This has shown up consistently
+ as error status on CRAN old-rel checks (windows and
+ mac). So if they pass, this issue is fixed.
+
+
+### Changes in v1.7.6
+
+#### NEW FEATURES
+
+ * An empty list column can now be added with :=, and
+ data.table() accepts empty list().
+ DT[,newcol:=list()]
+ data.table(a=1:3,b=list())
+ Empty list columns contain NULL for all rows.
+
+#### BUG FIXES
+
+ * Adding a column to a data.table loaded from disk could
+ result in a memory corruption in R <= 2.13.2, revealed
+ and thanks to CRAN checks on windows old-rel.
+
+ * Adding a factor column with a RHS to be recycled no longer
+ loses its factor attribute, #1691. Thanks to Damian
+ Betebenner for reporting.
+
+
+### Changes in v1.7.5
+
+#### BUG FIXES
+
+ * merge()-ing a data.table where its key is not the first
+ few columns in order now works correctly and without
+ warning, fixing #1645. Thanks to Timothee Carayol for
+ reporting.
+
+ * Mixing nomatch=0 and mult="last" (or "first") now works,
+ #1661. Thanks to Johann Hibschman for reporting.
+
+ * Join Inherited Scope now respects nomatch=0, #1663. Thanks
+ to Johann Hibschman for reporting.
+
+ * by= could generate a keyed result table with invalid key;
+ e.g., when by= expressions return NA, #1631. Thanks to
+ Muhammad Waliji for reporting.
+
+ * Adding a column to a data.table loaded from disk resulted
+ in an error that truelength(DT)<length(DT).
+
+ * CJ() bogus values and logical error fixed, #1689. Thanks to
+ Damian Betebenner and Chris Neff for reporting.
+
+ * j=list(.SD,newcol=...) now gives friendly error suggesting cbind
+ or merge afterwards until := by group is implemented, rather than
+ treating .SD as a list column, #1647. Thanks to a question by
+ Christoph_J on Stack Overflow.
+
+
+#### USER VISIBLE CHANGES
+
+ * rbind now cross-refs colnames as data.frame does, rather
+ than always binding by column order, FR#1634. A warning is
+ produced when the colnames are not in a consistent order.
+ Thanks to Damian Betebenner for highlighting. rbind an
+ unnamed list to bind columns by position.
+
+ * The 'bysameorder' argument has been removed, as intended and
+ warned in ?data.table.
+
+ * New option datatable.allocwarn. See ?truelength.
+
+#### NOTES
+
+ * There are now 472 raw tests, plus S4 tests.
+
+
+### Changes in v1.7.4
+
+#### BUG FIXES
+
+ * v1.7.3 failed CRAN checks (and could crash) in R pre-2.14.0.
+ Over-allocation in v1.7.3 uses truelength which is initialized
+ to 0 by R 2.14.0, but not initialized pre-2.14.0. This was
+ known and coded for but only tested in 2.14.0 before previous
+ release to CRAN.
+
+#### NOTES
+
+ * Two unused C variables removed to pass warning from one CRAN
+ check machine (r-devel-fedora). -Wno-unused removed from
+ Makevars to catch this in future before submitting to CRAN.
+
+
+### Changes in v1.7.3
+
+#### NEW FEATURES
+
+ * data.table now over-allocates its vector of column pointer slots
+ (100 by default). This allows := to add columns fully by
+ reference as suggested by Muhammad Waliji, #1646. When the 100
+ slots are used up, more space is automatically allocated.
+
+ Over allocation has negligible overhead. It's just the vector
+ of column pointers, not the columns themselves.
+
+ * New function alloc.col() pre-allocates column slots. Use
+ this before a loop to add many more than 100 columns, for example,
+ to avoid the warning as data.table grows its column pointer vector
+ every additional 100 columns; e.g.,
+ alloc.col(DT,10000) # reserve 10,000 column slots
+
+ * New function truelength() returns the number of column pointer
+ slots allocated, always >= length() other than just after a table
+ has been loaded from disk.
+
+ * New option 'datatable.nomatch' allows the default for nomatch
+ to be changed from NA to 0, as wished for by Branson Owen.
+
+ * cbind(DT,...) now retains DT's key, as wished for by Chris Neff
+ and partly implementing FR#295.
+
+#### BUG FIXES
+
+ * Assignment to factor columns (using :=, [<- or $<-) could cause
+ 'variable not found' errors and a seg fault in some circumstances
+ due to a new feature in v1.7.0: "Factor columns on LHS of :=, [<-
+ and $<- can now be assigned new levels", fixing #1664. Thanks to
+ Daniele Signori for reporting.
+
+ * DT[i,j]<-value no longer crashes when j is a factor column and value
+ is numeric, fixing #1656.
+
+ * An unnecessarily strict machine tolerance test failed CRAN checks
+ on Mac preventing v1.7.2 availability for Mac (only).
+
+#### USER VISIBLE CHANGES
+
+ * := now has its own help page in addition to the examples in ?data.table,
+ see help(":=").
+
+ * The error message from X[Y] when X is unkeyed has been lengthened to
+ including advice to call setkey first and see ?setkey. Thanks to a
+ comment by ilprincipe on Stack Overflow.
+
+ * Deleting a missing column is now a warning rather than error. Thanks
+ to Chris Neff for suggesting, #1642.
+
+
+### Changes in v1.7.2
+
+#### NEW FEATURES
+
+ * unique and duplicated methods now work on unkeyed tables (comparing
+ all columns in that case) and both now respect machine tolerance for
+ double precision columns, implementing FR#1626 and fixing bug #1632.
+ Their help page has been updated accordingly with detailed examples.
+ Thanks to questions by Iterator and comments by Allan Engelhardt on
+ Stack Overflow.
+
+ * A new method as.data.table.list has been added, since passing a (pure)
+ list to data.table() now creates a single list column.
+
+
+#### BUG FIXES
+
+ * Assigning to a column variable using <- or = in j now
+ works (creating a local copy within j), rather than
+ persisting from group to group and sometimes causing a crash.
+ Non column variables still persist from group to group; e.g.,
+ a group counter. This fixes the remainder of #1624 thanks to
+ Steve Lianoglou for reporting.
+
+ * A crash bug is fixed when j returns a (strictly) NULL column next
+ to a non-empty column, #1633. This case was anticipated and coded
+ for but an errant LENGTH() should have been length(). Thanks
+ to Dennis Murphy for reporting.
+
+ * The first column of data.table() can now be a list column, fixing
+ #1640. Thanks to Stavros Macrakis for reporting.
+
+
+### Changes in v1.7.1
+
+#### BUG FIXES
+
+ * .SD is now locked, partially fixing #1624. It was never
+ the intention to allow assignment to .SD. Take a 'copy(.SD)'
+ first if needed. Now documented in ?data.table and new FAQ 4.5
+ including example. Thanks to Steve Lianoglou for reporting.
+
+ * := now works with a logical i subset; e.g.,
+ DT[x==1,y:=x]
+ Thanks to Muhammad Waliji for reporting.
+
+#### USER VISIBLE CHANGES
+
+ * Error message "column <name> of i is not internally type integer"
+ is now more helpful adding "i doesn't need to be keyed, just
+ convert the (likely) character column to factor". Thanks to
+ Christoph_J for his SO question.
+
+
+### Changes in v1.7.0
+
+#### NEW FEATURES
+
+ * data.table() now accepts list columns directly rather than
+ needing to add list columns to an existing data.table; e.g.,
+
+ DT = data.table(x=1:3,y=list(4:6,3.14,matrix(1:12,3)))
+
+ Thanks to Branson Owen for reminding. As before, list columns
+ can be created via grouping; e.g.,
+
+ DT = data.table(x=c(1,1,2,2,2,3,3),y=1:7)
+ DT2 = DT[,list(list(unique(y))),by=x]
+ DT2
+ x V1
+ [1,] 1 1, 2
+ [2,] 2 3, 4, 5
+ [3,] 3 6, 7
+
+ and list columns can be grouped; e.g.,
+
+ DT2[,sum(unlist(V1)),by=list(x%%2)]
+ x V1
+ [1,] 1 16
+ [2,] 0 12
+
+ Accordingly, one item has been added to FAQ 2.17 (differences
+ between data.frame and data.table): data.frame(list(1:2,"k",1:4))
+ creates 3 columns, data.table creates one list column.
+
+ * subset, transform and within now retain keys when the expression
+ does not 'touch' key columns, implemeting FR #1341.
+
+ * Recycling list() items on RHS of := now works; e.g.,
+
+ DT[,1:4:=list(1L,NULL),with=FALSE]
+ # set columns 1 and 3 to 1L and remove columns 2 and 4
+
+ * Factor columns on LHS of :=, [<- and $<- can now be assigned
+ new levels; e.g.,
+
+ DT = data.table(A=c("a","b"))
+ DT[2,"A"] <- "c" # adds new level automatically
+ DT[2,A:="c"] # same (faster)
+ DT$A = "newlevel" # adds new level and recycles it
+
+ Thanks to Damian Betebenner and Chris Neff for highlighting.
+ To change the type of a column, provide a full length RHS (i.e.
+ 'replace' the column).
+
+#### BUG FIXES
+
+ * := with i all FALSE no longer sets the whole column, fixing
+ bug #1570. Thanks to Chris Neff for reporting.
+
+ * 0 length by (such as NULL and character(0)) now behave as
+ if by is missing, fixing bug #1599. This is useful when by
+ is dynamic and a 'dont group' needs to be represented.
+ Thanks to Chris Neff for reporting.
+
+ * NULL j no longer results in 'inconsistent types' error, but
+ instead returns no rows for that group, fixing bug #1576.
+
+ * matrix i is now an error rather than using i as if it were a
+ vector and obtaining incorrect results. It was undocumented that
+ matrix might have been an acceptable type. matrix i is
+ still acceptable in [<-; e.g.,
+ DT[is.na(DT)] <- 1L
+ and this now works rather than assigning to non-NA items in some
+ cases.
+
+ * Inconsistent [<- behaviour is now fixed (#1593) so these examples
+ now work :
+ DT[x == "a", ]$y <- 0L
+ DT["a", ]$y <- 0L
+ But, := is highly encouraged instead for speed; i.e.,
+ DT[x == "a", y:=0L]
+ DT["a", y:=0L]
+ Thanks to Leon Baum for reporting.
+
+ * unique on an unsorted table now works, fixing bug #1601.
+ Thanks to a question by Iterator on Stack Overflow.
+
+ * Bug fix #1534 in v1.6.5 (see NEWS below) only worked if data.table
+ was higher than IRanges on the search() path, despite the item in
+ NEWS stating otherwise. Fixed.
+
+ * Compatibility with package sqldf (which can call do.call("rbind",...)
+ on an empty "...") is fixed and test added. data.table was switching
+ on list(...)[[1]] rather than ..1. Thanks to RYogi for reporting #1623.
+
+#### USER VISIBLE CHANGES
+
+ * cbind and rbind are no longer masked. But, please do read FAQ 2.23,
+ 4.4 and 5.1.
+
+
+### Changes in v1.6.6
+
+#### BUG FIXES
+
+ * Tests using .Call("Rf_setAttrib",...) passed CRAN acceptance
+ checks but failed on many (but not all) platforms. Fixed.
+ Thanks to Prof Brian Ripley for investigating the issue.
+
+### Changes in v1.6.5
+
+#### NEW FEATURES
+
+ * The LHS of := may now be column names or positions
+ when with=FALSE; e.g.,
+
+ DT[,c("d","e"):=NULL,with=FALSE]
+ DT[,4:5:=NULL,with=FALSE]
+ newcolname="myname"
+ DT[,newcolname:=3.14,with=FALSE]
+
+ This implements FR#1499 'Ability to efficiently remove a
+ vector of column names' by Timothee Carayol in addition to
+ creating and assigning to multiple columns. We still plan
+ to allow multiple := without needing with=FALSE, in future.
+
+ * setkey(DT,...) now returns DT (invisibly) rather than NULL.
+ This is to allow compound statements; e.g.,
+ setkey(DT,x)["a"]
+
+ * setkey (and key<-) are now more efficient when the data happens
+ to be already sorted by the key columns; e.g., when data is
+ loaded from ordered files.
+
+ * If DT is already keyed by the columns passed to setkey (or
+ key<-), the key is now rebuilt and checked rather than skipping
+ for efficiency. This is to save needing to know to drop the key
+ first to rebuild an invalid key. Invalid keys can arise by going
+ 'under the hood'; e.g., attr(DT,"sorted")="z", or somehow ending
+ up with unordered factor levels. A warning is issued so the root
+ cause can be fixed. Thanks to Timothee Carayol for highlighting.
+
+ * A new copy() function has been added, FR#1501. This copies a
+ data.table (retaining its key, if any) and should now be used to
+ copy rather than data.table(). Reminder: data.tables are not
+ copied on write by setkey, key<- or :=.
+
+
+#### BUG FIXES
+
+ * DT[,z:=a/b] and DT[a>3,z:=a/b] work again, where a and
+ b are columns of DT. Thanks to Chris Neff for reporting,
+ and his patience.
+
+ * Numeric columns with class attributes are now correctly
+ coerced to integer by setkey and ad hoc by. The error
+ similar to 'fractional data cannot be truncated' should now
+ only occur when that really is true. A side effect of
+ this is that ad hoc by and setkey now work on IDate columns
+ which have somehow become numeric; e.g., via rbind(DF,DF)
+ as reported by Chris Neff.
+
+ * .N is now 0 (rather than 1) when no rows in x match the
+ row in i, fixing bug #1532. Thanks to Yang Zhang for
+ reporting.
+
+ * Compatibility with package IRanges has been restored. Both
+ data.table and IRanges mask cbind and rbind. When data.table's
+ cbind is found first (if it is loaded after IRanges) and the
+ first argument is not data.table, it now delegates to the next
+ package on the search path (and above that), one or more of which
+ may also mask cbind (such as IRanges), rather than skipping
+ straight to base::cbind. So, it no longer matters which way around
+ data.table and IRanges are loaded, fixing #1534. Thanks to Steve
+ Lianoglou for reporting.
+
+
+#### USER VISIBLE CHANGES
+
+ * setkey's verbose messages expanded.
+
+
+### Changes in v1.6.4
+
+#### NEW FEATURES
+
+ * DT[colA>3,which=TRUE] now returns row numbers rather
+ than a logical vector, for consistency.
+
+#### BUG FIXES
+
+ * Changing a keyed column name now updates the key, too,
+ so an invalid key no longer arises, fixing #1495.
+ Thanks to Chris Neff for reporting.
+
+ * := already warned when a numeric RHS is coerced to
+ match an integer column's type. Now it also warns when
+ numeric is coerced to logical, and integer is coerced
+ to logical, fixing #1500. Thanks to Chris Neff for
+ reporting.
+
+ * The result of DT[,newcol:=3.14] now includes the new
+ column correctly, as well as changing DT by reference,
+ fixing #1496. Thanks to Chris Neff for reporting.
+
+ * :=NULL to remove a column (instantly, regardless of table
+ size) now works rather than causing a segfault in some
+ circumstances, fixing #1497. Thanks to Timothee Carayol
+ for reporting.
+
+ * Previous within() and transform() behaviour restored; e.g.,
+ can handle multiple columns again. Thanks to Timothee Carayol
+ for reporting.
+
+ * cbind(DT,DF) now works, as does rbind(DT,DF), fixing #1512.
+ Thanks to Chris Neff for reporting. This was tricky to fix due
+ to nuances of the .Internal dispatch code in cbind and rbind,
+ preventing S3 methods from working in all cases.
+ R will now warn that cbind and rbind have been masked when
+ the data.table package is loaded. These revert to base::cbind
+ and base::rbind when the first argument is not data.table.
+
+ * Removing multiple columns now works (again) using
+ DT[,c("a","b")]=NULL, or within(DT,rm(a,b)), fixing #1510.
+ Thanks to Timothee Carayol for reporting.
+
+
+#### NOTES
+
+ * The package uses two features (packageVersion() and \href in Rd)
+ added to R 2.12.0 and is therefore dependent on that release.
+ A 'spurious warning' when checking a package using \href was
+ fixed in R 2.12.2 patched but we believe that warning can safely
+ be ignored in versions >= 2.12.0 and < 2.12.2 patched.
+
+
+### Changes in v1.6.3
+
+#### NEW FEATURES
+
+ * Ad hoc grouping now returns results in the same order each
+ group first appears in the table, rather than sorting the
+ groups. Thanks to Steve Lianoglou for highlighting. The order
+ of the rows within each group always has and always will be
+ preserved. For larger datasets a 'keyed by' is still faster;
+ e.g., by=key(DT).
+
+ * The 'key' argument of data.table() now accepts a vector of
+ column names in addition to a single comma separated string
+ of column names, for consistency. Thanks to Steve Lianoglou
+ for highlighting.
+
+ * A new argument '.SDcols' has been added to [.data.table. This
+ may be character column names or numeric positions and
+ specifies the columns of x included in .SD. This is useful
+ for speed when applying a function through a subset of
+ (possibly very many) columns; e.g.,
+ DT[,lapply(.SD,sum),by="x,y",.SDcols=301:350]
+
+ * as(character, "IDate") and as(character, "ITime") coercion
+ functions have been added. Enables the user to declaring colClasses
+ as "IDate" and "ITime" in the various read.table (and sister)
+ functions. Thanks to Chris Neff for the suggestion.
+
+ * DT[i,j]<-value is now handled by data.table in C rather
+ than falling through to data.frame methods, FR#200. Thanks to
+ Ivo Welch for raising speed issues on r-devel, to Simon Urbanek
+ for the suggestion, and Luke Tierney and Simon for information
+ on R internals.
+
+ [<- syntax still incurs one working copy of the whole
+ table (as of R 2.13.1) due to R's [<- dispatch mechanism
+ copying to `*tmp*`, so, for ultimate speed and brevity,
+ the operator := may now be used in j as follows.
+
+ * := is now available to j and means assign to the column by
+ reference; e.g.,
+
+ DT[i,colname:=value]
+
+ This syntax makes no copies of any part of memory at all.
+
+ m = matrix(1,nrow=100000,ncol=100)
+ DF = as.data.frame(m)
+ DT = as.data.table(m)
+
+ system.time(for (i in 1:1000) DF[i,1] <- i)
+ user system elapsed
+ 287.062 302.627 591.984
+
+ system.time(for (i in 1:1000) DT[i,V1:=i])
+ user system elapsed
+ 1.148 0.000 1.158 ( 511 times faster )
+
+ := in j can be combined with all types of i, such as binary
+ search, and used to add and remove columns efficiently.
+ Fast assigning within groups will be implemented in future.
+
+ Reminder that data.frame and data.table both allow columns
+ of mixed types, including columns which themselves may be
+ type list; matrix may be one (atomic) type only.
+
+ *Please note*, := is new and experimental.
+
+
+#### BUG FIXES
+
+ * merge()ing two data.table's with user-defined `suffixes`
+ was getting tripped up when column names in x ended in
+ '.1'. This resulted in the `suffixes` parameter being
+ ignored.
+
+ * Mistakenly wrapping a j expression inside quotes; e.g.,
+ DT[,list("sum(a),sum(b)"),by=grp]
+ was appearing to work, but with wrong column names. This
+ now returns a character column (the quotes should not
+ be used). Thanks to Joseph Voelkel for reporting.
+
+ * setkey has been made robust in several ways to fix issues
+ introduced in 1.6.2: #1465 ('R crashes after setkey')
+ reported by Eugene Tyurin and similar bug #1387 ('paste()
+ by group to create long comma separated strings can crash')
+ reported by Nicolas Servant and Jean-Francois Rami. This
+ bug was not reproducible so we are especially grateful for
+ the patience of these people in helping us find, fix and
+ test it.
+
+ * Combining a join, j and by together in one query now works
+ rather than giving an error, fixing bug #1468. Discovered
+ indirectly thanks to a post from Jelmer Ypma.
+
+ * Invalid keys no longer arise when a non-data.table-aware
+ package reorders the data; e.g.,
+ setkey(DT,x,y)
+ plyr::arrange(DT,y) # same as DT[order(y)]
+ This now drops the key to avoid incorrect results being
+ returned the next time the invalid key is joined to. Thanks
+ to Chris Neff for reporting.
+
+
+#### USER-VISIBLE CHANGES
+
+ * The startup banner has been shortened to one line.
+
+ * data.table does not support POSIXlt. Almost unbelievably
+ POSIXlt uses 40 bytes to store a single datetime. If it worked
+ before, that was unintentional. Please see ?IDateTime, or any
+ other date class that uses a single atomic vector. This is
+ regardless of whether the POSIXlt is a key column, or not. This
+ resolves bug #1481 by documenting non support in ?data.table.
+
+
+#### DEPRECATED & DEFUNCT
+
+ * Use of the DT() alias in j is no longer caught for backwards
+ compatibility and is now fully removed. As warned in NEWS
+ for v1.5.3, v1.4, and FAQs 2.6 and 2.7.
+
+
+### Changes in v1.6.2
+
+#### NEW FEATURES
+
+ * setkey no longer copies the whole table and should be
+ faster for large tables. Each column is reordered by reference
+ (in C) using one column of working memory, FR#1006. User
+ defined attributes on the original table are now also
+ retained (thanks to Thell Fowler for reporting).
+
+ * A new symbol .N is now available to j, containing the
+ number of rows in the group. This may be useful when
+ the column names are not known in advance, for
+ convenience generally, and for efficiency.
+
+
+### Changes in v1.6.1
+
+#### NEW FEATURES
+
+ * j's environment is now consistently reused so
+ that local variables may be set which persist
+ from group to group; e.g., incrementing a group
+ counter :
+ DT[,list(z,groupInd<-groupInd+1),by=x]
+ Thanks to Andreas Borg for reporting.
+
+ * A new symbol .BY is now available to j, containing 1 row
+ of the current 'by' variables, type list. 'by' variables
+ may also be used by name, and are now length 1 too. This
+ implements FR#1313. FAQ 2.10 has been updated accordingly.
+ Some examples :
+ DT[,sum(x)*.BY[[1]],by=eval(byexp)]
+ DT[,sum(x)*mylookuptable[J(y),z],by=y]
+ DT[,list(sum(unlist(.BY)),sum(z)),by=list(x,y%%2)]
+
+ * i may now be type list, and works the same as when i
+ is type data.table. This saves needing J() in as many
+ situations and may be a little more efficient. One
+ application is using .BY directly in j to join to a
+ relatively small lookup table, once per group, for space
+ and time efficiency. For example :
+ DT[,list(GROUPDATA[.BY]$name,sum(v)),by=grp]
+
+
+#### BUG FIXES
+
+ * A 'by' character vector of column names now
+ works when there are less rows than columns; e.g.,
+ DT[,sum(x),by=key(DT)] where nrow(DT)==1.
+ Many thanks to Andreas Borg for report, proposed
+ fix and tests.
+
+ * Zero length columns in j no longer cause a crash in
+ some circumstances. Empty columns are filled with NA
+ to match the length of the longest column in j.
+ Thanks to Johann Hibschman for bug report #1431.
+
+ * unique.data.table now calls the same internal code
+ (in C) that grouping calls. This fixes a bug when
+ unique is called directly by user, and, NA exist
+ in the key (which might be quite rare). Thanks to
+ Damian Betebenner for bug report. unique should also
+ now be faster.
+
+ * Variables in calling scope can now be used in j when
+ i is logical or integer, fixing bug #1421. Thanks
+ to Alexander Peterhansl for reporting.
+
+
+#### USER-VISIBLE CHANGES
+
+ * ?data.table now documents that logical i is not quite
+ the same as i in [.data.frame. NA are treated as FALSE,
+ and DT[NA] returns 1 row of NA, unlike [.data.frame.
+ Three points have been added to FAQ 2.17. Thanks to
+ Johann Hibschman for highlighting.
+
+ * Startup banner now uses packageStartupMessage() so the
+ banner can be suppressed by those annoyed by banners,
+ whilst still being helpful to new users.
+
+
+
+### Changes in v1.6
+
+#### NEW FEATURES
+
+ * data.table now plays nicely with S4 classes. Slots can be
+ defined to be S4 objects, S4 classes can inherit from data.table,
+ and S4 function dispatch works on data.table objects. See the
+ tests in inst/tests/test-S4.R, and from the R prompt:
+ ?"data.table-class"
+
+ * merge.data.table now works more like merge.data.frame:
+ (i) suffixes are consistent with merge.data.frame; existing users
+ may set options(datatable.pre.suffixes=TRUE) for backwards
+ compatibility.
+ (ii) support for 'by' argument added (FR #1315).
+ However, X[Y] syntax is preferred; some users never use merge.
+
+
+#### BUG FIXES
+
+ * by=key(DT) now works when the number of rows is not
+ divisible by the number of groups (#1298, an odd bug).
+ Thanks to Steve Lianoglou for reporting.
+
+ * Combining i and by where i is logical or integer subset now
+ works, fixing bug #1294. Thanks to Johann Hibschman for
+ contributing a new test.
+
+ * Variable scope inside [[...]] now works without a workaround
+ required. This can be useful for looking up which function
+ to call based on the data e.g. DT[,fns[[fn]](colA),by=ID].
+ Thanks to Damian Betebenner for reporting.
+
+ * Column names in self joins such as DT[DT] are no longer
+ duplicated, fixing bug #1340. Thanks to Andreas Borg for
+ reporting.
+
+
+#### USER-VISIBLE CHANGES
+
+ * Additions and updates to FAQ vignette. Thanks to Dennis
+ Murphy for his thorough proof reading.
+
+ * Welcome to Steve Lianoglou who joins the project
+ contributing S4-ization, testing using testthat, and more.
+
+ * IDateTime is now linked from ?data.table. data.table users
+ unaware of IDateTime, please do take a look. Tom added
+ IDateTime in v1.5 (see below).
+
+
+### Changes in v1.5.3
+
+#### NEW FEATURES
+
+ * .SD no longer includes 'by' columns, FR#978. This resolves
+ the long standing annoyance of duplicated 'by' columns
+ when the j expression returns a subset of rows from .SD.
+ For example, the following query no longer contains
+ a redundant 'colA.1' duplicate.
+ DT[,.SD[2],by=colA] # 2nd row of each group
+ Any existing code that uses .SD may require simple
+ changes to remove workarounds.
+
+ * 'by' may now be a character vector of column names.
+ This allows syntax such as DT[,sum(x),by=key(DT)].
+
+ * X[Y] now includes Y's non-join columns, as most users
+ naturally expect, FR#746. Please do use j in one step
+ (i.e. X[Y,j]) since that merges just the columns j uses and
+ is much more efficient than X[Y][,j] or merge(X,Y)[,j].
+
+ * The 'Join Inherited Scope' feature is back on, FR#1095. This
+ is consistent with X[Y] including Y's non-join columns, enabling
+ natural progression from X[Y] to X[Y,j]. j sees columns in X
+ first then Y.
+ If the same column name exists in both X and Y, the data in
+ Y can be accessed via a prefix "i." (not yet implemented).
+
+ * Ad hoc by now coerces double to integer (provided they are
+ all.equal) and character to factor, FR#1051, as setkey
+ already does.
+
+#### USER-VISIBLE CHANGES
+
+ * The default for mult is now "all", as planned and
+ prior notice given in FAQ 2.2.
+
+ * ?[.data.table has been merged into ?data.table and updated,
+ simplified, corrected and formatted.
+
+#### DEPRECATED & DEFUNCT
+
+ * The DT() alias is now fully deprecated, as warned
+ in NEWS for v1.4, and FAQs 2.6 and 2.7.
+
+
+### Changes in v1.5.2
+
+#### NEW FEATURES
+
+ * 'by' now works when DT contains list() columns i.e.
+ where each value in a column may itself be vector
+ or where each value is a different type. FR#1092.
+
+ * The result from merge() is now keyed. FR#1244.
+
+
+#### BUG FIXES
+
+ * eval of parse()-ed expressions now works without
+ needing quote() in the expression, bug #1243. Thanks
+ to Joseph Voelkel for reporting.
+
+ * the result from the first group alone may be bigger
+ than the table itself, bug #1245. Thanks to
+ Steve Lianoglou for reporting.
+
+ * merge on a data.table with a single key'd column only
+ and all=TRUE now works, bug #1241. Thanks to
+ Joseph Voelkel for reporting.
+
+ * merge()-ing by a column called "x" now works, bug
+ #1229 related to variable scope. Thanks to Steve
+ Lianoglou for reporting.
+
+
+### Changes in v1.5.1
+
+#### BUG FIXES
+
+ * Fixed inheritance for other packages importing or depending
+ on data.table, bugs #1093 and #1132. Thanks to Koert Kuipers
+ for reporting.
+
+ * data.table queries can now be used at the debugger() prompt,
+ fixing bug #1131 related to inheritance from data.frame.
+
+
+### Changes in v1.5
+
+#### NEW FEATURES
+
+ * data.table now *inherits* from data.frame, for functions and
+ packages which _only_ accept data.frame, saving time and
+ memory of conversion. A data.table is a data.frame too;
+ is.data.frame() now returns TRUE.
+
+ * Integer-based date and time-of-day classes have been
+ introduced. This allows dates and times to be used as keys
+ more easily. See as.IDate, as.ITime, and IDateTime.
+ Conversions to and from POSIXct, Date, and chron are
+ supported.
+
+ * [<-.data.table and $<-.data.table were revised to check for
+ changes to the key-ed columns. [<-.data.table also now allows
+ data.table-style indexing for i. Both of these changes may
+ introduce incompatibilities for existing code.
+
+ * Logical columns are now allowed in keys and in 'by', as are expressions
+ that evaluate to logical. Thanks to David Winsemius for highlighting.
+
+
+#### BUG FIXES
+
+ * DT[,5] now returns 5 as FAQ 1.1 says, for consistency
+ with DT[,c(5)] and DT[,5+0]. DT[,"region"] now returns
+ "region" as FAQ 1.2 says. Thanks to Harish V for reporting.
+
+ * When a quote()-ed expression q is passed to 'by' using
+ by=eval(q), the group column names now come from the list
+ in the expression rather than the name 'q' (bug #974) and,
+ multiple items work (bug #975). Thanks to Harish V for
+ reporting.
+
+ * quote()-ed i and j expressions receive similar fixes, bugs
+ #977 and #1058. Thanks to Harish V and Branson Owen for
+ reporting.
+
+ * Multiple errors (grammar, format and spelling) in intro.Rnw
+ and faqs.Rnw corrected by Dennis Murphy. Thank you.
+
+ * Memory is now reallocated in rare cases when the up front
+ allocate for the result of grouping is insufficient. Bug
+ #952 raised by Georg V, and also reported by Harish. Thank
+ you.
+
+ * A function call foo(arg=sum(b)) now finds b in DT when foo
+ contains DT[,eval(substitute(arg)),by=a], fixing bug #1026.
+ Thanks to Harish V for reporting.
+
+ * If DT contains column 'a' then DT[J(unique(a))] now finds
+ 'a', fixing bug #1005. Thanks to Branson Owen for reporting.
+
+ * 'by' on no data (for example when 'i' returns no rows) now
+ works, fixing bug #709.
+
+ * 'by without by' now heeds nomatch=NA, fixing bug #1015.
+ Thanks to Harish V for reporting.
+
+ * DT[NA] now returns 1 row of NA rather than the whole table
+ via standard NA logical recycling. A single NA logical is
+ a special case and is now replaced by NA_integer_. Thanks
+ to Branson Owen for highlighting the issue.
+
+ * NROW removed from data.table, since the is.data.frame() in
+ base::NROW now returns TRUE due to inheritance. Fixes bug
+ #1039 reported by Bradley Buchsbaum. Thank you.
+
+ * setkey() now coerces character to factor and double to
+ integer (provided they are all.equal), fixing bug #953.
+ Thanks to Steve Lianoglou for reporting.
+
+ * 'by' now accepts lists from the calling scope without the
+ work around of wrapping with as.list() or {}, fixing bug
+ #1060. Thanks to Johann Hibschman for reporting.
+
+
+#### NOTES
+
+ * The package uses the 'default' option of base::getOption,
+ and is therefore dependent on R 2.10.0. Updated DESCRIPTION
+ file accordingly. Thanks to Christian Hudon for reporting.
+
+
+### Changes in v1.4.1
+
+
+#### NEW FEATURES
+
+ * Vignettes tidied up.
+
+
+#### BUG FIXES
+
+ * Out of order levels in key columns are now sorted by
+ setkey. Thanks to Steve Lianoglou for reporting.
+
+
+
+
+### Changes in v1.4
+
+
+#### NEW FEATURES
+
+ * 'by' faster. Memory is allocated first for the result, then
+ populated directly by the result of j for each group. Can be 10
+ or more times faster than tapply() and aggregate(), see
+ timings vignette.
+
+ * j should now be a list(), not DT(), of expressions. Use of
+ j=DT(...) is caught internally and replaced with j=list(...).
+
+ * 'by' may be a list() of expressions. A single column name
+ is automatically list()-ed for convenience. 'by' may still be
+ a comma separated character string, as before.
+ DT[,sum(x),by=region] # new
+ DT[,sum(x),by=list(region,month(date))] # new
+ DT[,sum(x),by="region"] # old, ok too
+ DT[,sum(x),by="region,month(date)"] # old, ok too
+
+ * key() and key<- added. More R-style alternatives to getkey()
+ and setkey().
+
+ * haskey() added. Returns TRUE if a table has a key.
+
+ * radix sorting is now column by column where possible, was
+ previously all or nothing. Helps with keys of many columns.
+
+ * Added format method.
+
+ * 22 tests added to test.data.table(), now 149.
+
+ * Three vignettes added : FAQ, Intro & Timings
+
+
+#### DEPRECATED & DEFUNCT
+
+ * The DT alias is removed. Use 'data.table' instead to create
+ objects. See 2nd new feature above.
+
+ * RUnit framework removed.
+ test.data.table() is called from examples in .Rd so 'R CMD check'
+ will run it. Simpler. An eval(body(test.data.table))
+ is also in the .Rd, to catch namespace issues.
+
+ * Dependency on package 'ref' removed.
+
+ * Arguments removed: simplify, incbycols and byretn.
+ Grouping is simpler now, these are superfluous.
+
+
+#### BUG FIXES
+
+ * Column classes are now retained by subset and grouping.
+
+ * tail no longer fails when a column 'x' exists.
+
+
+#### KNOWN PROBLEMS
+
+ * Minor : Join Inherited Scope not working, contrary
+ to the documentation.
+
+
+#### NOTES
+
+ * v1.4 was essentially the branch at rev 44, reintegrated
+ at rev 78.
+
+
+
+
+### Changes in v1.3
+
+
+#### NEW FEATURES
+
+ * Radix sorting added. Speeds up setkey and add-hoc 'by'
+ by factor of 10 or more.
+
+ * Merge method added, much faster than base::merge method
+ of data.frame.
+
+ * 'by' faster. Logic moved from R into C. Memory is
+ allocated for the largest group only, then re-used.
+
+ * The Sub Data is accessible as a whole by j using object
+ .SD. This should only be used in rare circumstances. See FAQ.
+
+ * Methods added : duplicated, unique, transform, within,
+ [<-, t, Math, Ops, is.na, na.omit, summary
+
+ * Column name rules improved e.g. dots now allowed.
+
+ * as.data.frame.data.table rownames improved.
+
+ * 29 tests added to test.data.table(), now 127.
+
+
+#### USER-VISIBLE CHANGES
+
+ * Default of mb changed, now tables(mb=TRUE)
+
+
+#### DEPRECATED & DEFUNCT
+
+ * ... removed in [.data.table.
+ j may not be a function, so this is now superfluous.
+
+
+#### BUG FIXES
+
+ * Incorrect version warning with R 2.10+ fixed.
+
+ * j enclosure raised one level. This fixes some bugs
+ where the j expression previously saw internal variable
+ names. It also speeds up grouping a little.
+
+
+#### NOTES
+
+ * v1.3 was not released to CRAN. R-Forge repository only.
+
+
+
+### v1.2 released to CRAN in Aug 2008
+
+
diff --git a/build/vignette.rds b/build/vignette.rds
new file mode 100644
index 0000000..482882e
Binary files /dev/null and b/build/vignette.rds differ
diff --git a/inst/doc/datatable-faq.R b/inst/doc/datatable-faq.R
new file mode 100644
index 0000000..a75f9fd
--- /dev/null
+++ b/inst/doc/datatable-faq.R
@@ -0,0 +1,224 @@
+### R code from vignette source 'datatable-faq.Rnw'
+
+###################################################
+### code chunk number 1: datatable-faq.Rnw:15-18
+###################################################
+if (!exists("data.table",.GlobalEnv)) library(data.table) # see Intro.Rnw for comments on these two lines
+rm(list=as.character(tables()$NAME),envir=.GlobalEnv)
+options(width=70) # so lines wrap round
+
+
+###################################################
+### code chunk number 2: datatable-faq.Rnw:95-102
+###################################################
+DT = as.data.table(iris)
+setkey(DT,Species)
+myfunction = function(dt, expr) {
+ e = substitute(expr)
+ dt[,eval(e),by=Species]
+}
+myfunction(DT,sum(Sepal.Width))
+
+
+###################################################
+### code chunk number 3: datatable-faq.Rnw:109-115
+###################################################
+DT = as.data.table(iris)
+whatToRun = quote( .(AvgWidth = mean(Sepal.Width),
+ MaxLength = max(Sepal.Length)) )
+DT[, eval(whatToRun), by=Species]
+DT[, eval(whatToRun), by=.(FirstLetter=substring(Species,1,1))]
+DT[, eval(whatToRun), by=.(Petal.Width=round(Petal.Width,0))]
+
+
+###################################################
+### code chunk number 4: datatable-faq.Rnw:158-165
+###################################################
+X = data.table(grp=c("a","a","b","b","b","c","c"), foo=1:7)
+setkey(X,grp)
+Y = data.table(c("b","c"), bar=c(4,2))
+X
+Y
+X[Y,sum(foo*bar)]
+X[Y,sum(foo*bar),by=.EACHI]
+
+
+###################################################
+### code chunk number 5: datatable-faq.Rnw:193-196
+###################################################
+DF = data.frame(x=1:3,y=4:6,z=7:9)
+DF
+DF[,c("y","z")]
+
+
+###################################################
+### code chunk number 6: datatable-faq.Rnw:199-201
+###################################################
+DT = data.table(DF)
+DT[,c(y,z)]
+
+
+###################################################
+### code chunk number 7: datatable-faq.Rnw:204-205
+###################################################
+DT[,.(y,z)]
+
+
+###################################################
+### code chunk number 8: datatable-faq.Rnw:214-220
+###################################################
+data.table(NULL)
+data.frame(NULL)
+as.data.table(NULL)
+as.data.frame(NULL)
+is.null(data.table(NULL))
+is.null(data.frame(NULL))
+
+
+###################################################
+### code chunk number 9: datatable-faq.Rnw:224-227
+###################################################
+DT = data.table(a=1:3,b=c(4,5,6),d=c(7L,8L,9L))
+DT[0]
+sapply(DT[0],class)
+
+
+###################################################
+### code chunk number 10: datatable-faq.Rnw:249-252
+###################################################
+DT = data.table(x=rep(c("a","b"),c(2,3)),y=1:5)
+DT
+DT[,{z=sum(y);z+3},by=x]
+
+
+###################################################
+### code chunk number 11: datatable-faq.Rnw:258-263
+###################################################
+DT[,{
+ cat("Objects:",paste(objects(),collapse=","),"\n")
+ cat("Trace: x=",as.character(x)," y=",y,"\n")
+ sum(y)
+},by=x]
+
+
+###################################################
+### code chunk number 12: datatable-faq.Rnw:269-271
+###################################################
+DT[,.(g=1,h=2,i=3,j=4,repeatgroupname=x,sum(y)),by=x]
+DT[,.(g=1,h=2,i=3,j=4,repeatgroupname=x[1],sum(y)),by=x]
+
+
+###################################################
+### code chunk number 13: datatable-faq.Rnw:290-292
+###################################################
+A = matrix(1:12,nrow=4)
+A
+
+
+###################################################
+### code chunk number 14: datatable-faq.Rnw:295-296
+###################################################
+A[c(1,3),c(2,3)]
+
+
+###################################################
+### code chunk number 15: datatable-faq.Rnw:303-306
+###################################################
+B = cbind(c(1,3),c(2,3))
+B
+A[B]
+
+
+###################################################
+### code chunk number 16: datatable-faq.Rnw:309-314
+###################################################
+rownames(A) = letters[1:4]
+colnames(A) = LETTERS[1:3]
+A
+B = cbind(c("a","c"),c("B","C"))
+A[B]
+
+
+###################################################
+### code chunk number 17: datatable-faq.Rnw:317-322
+###################################################
+A = data.frame(A=1:4,B=letters[11:14],C=pi*1:4)
+rownames(A) = letters[1:4]
+A
+B
+A[B]
+
+
+###################################################
+### code chunk number 18: datatable-faq.Rnw:325-327
+###################################################
+B = data.frame(c("a","c"),c("B","C"))
+cat(try(A[B],silent=TRUE))
+
+
+###################################################
+### code chunk number 19: datatable-faq.Rnw:406-407
+###################################################
+base::cbind.data.frame
+
+
+###################################################
+### code chunk number 20: datatable-faq.Rnw:414-417
+###################################################
+foo = data.frame(a=1:3)
+cbind.data.frame = function(...)cat("Not printed\n")
+cbind(foo)
+
+
+###################################################
+### code chunk number 21: datatable-faq.Rnw:419-420
+###################################################
+rm("cbind.data.frame")
+
+
+###################################################
+### code chunk number 22: datatable-faq.Rnw:474-480
+###################################################
+DT = data.table(a=rep(1:3,1:3),b=1:6,c=7:12)
+DT
+DT[,{ mySD = copy(.SD)
+ mySD[1,b:=99L]
+ mySD },
+ by=a]
+
+
+###################################################
+### code chunk number 23: datatable-faq.Rnw:486-492
+###################################################
+DT = data.table(a=c(1,1,2,2,2),b=c(1,2,2,2,1))
+DT
+DT[,list(.N=.N),list(a,b)] # show intermediate result for exposition
+cat(try(
+ DT[,list(.N=.N),by=list(a,b)][,unique(.N),by=a] # compound query more typical
+,silent=TRUE))
+
+
+###################################################
+### code chunk number 24: datatable-faq.Rnw:496-502
+###################################################
+if (packageVersion("data.table") >= "1.8.1") {
+ DT[,.N,by=list(a,b)][,unique(N),by=a]
+}
+if (packageVersion("data.table") >= "1.9.3") {
+ DT[,.N,by=.(a,b)][,unique(N),by=a] # same
+}
+
+
+###################################################
+### code chunk number 25: datatable-faq.Rnw:520-528
+###################################################
+DT = data.table(a=1:5,b=1:5)
+suppressWarnings(
+DT[2,b:=6] # works (slower) with warning
+)
+class(6) # numeric not integer
+DT[2,b:=7L] # works (faster) without warning
+class(7L) # L makes it an integer
+DT[,b:=rnorm(5)] # 'replace' integer column with a numeric column
+
+
diff --git a/inst/doc/datatable-faq.Rnw b/inst/doc/datatable-faq.Rnw
new file mode 100644
index 0000000..097caa4
--- /dev/null
+++ b/inst/doc/datatable-faq.Rnw
@@ -0,0 +1,585 @@
+\documentclass[a4paper]{article}
+
+\usepackage[margin=3cm]{geometry}
+%%\usepackage[round]{natbib}
+\usepackage[colorlinks=true,urlcolor=blue]{hyperref}
+
+%%\newcommand{\acronym}[1]{\textsc{#1}}
+%%\newcommand{\class}[1]{\mbox{\textsf{#1}}}
+\newcommand{\code}[1]{\mbox{\texttt{#1}}}
+\newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}}
+\newcommand{\proglang}[1]{\textsf{#1}}
+\SweaveOpts{keep.source=TRUE}
+%% \VignetteIndexEntry{Frequently asked questions}
+
+<<echo=FALSE,results=hide>>=
+if (!exists("data.table",.GlobalEnv)) library(data.table) # see Intro.Rnw for comments on these two lines
+rm(list=as.character(tables()$NAME),envir=.GlobalEnv)
+options(width=70) # so lines wrap round
+@
+
+\begin{document}
+\title{FAQs about the \pkg{data.table} package in \proglang{R}}
+\date{Revised: \today\\(A later revision may be available on the \href{https://github.com/Rdatatable/data.table/wiki}{homepage})}
+\maketitle
+
+The first section, Beginner FAQs, is intended to be read in order, from start to finish.
+
+\tableofcontents
+\section{Beginner FAQs}
+
+\subsection{Why does \code{DT[,5]} return \code{5}?}
+Because by default, unlike a \code{data.frame}, the 2nd argument is an \emph{expression}
+which is evaluated within the scope of \code{DT}. 5 evaluates to 5. It is generally bad practice to refer
+to columns by number rather than name. If someone else comes along and reads your code later, they
+may have to hunt around to find out which column is number 5. Furthermore, if you or someone else
+changes the column ordering of \code{DT} higher up in your \proglang{R} program, you might get bugs if you forget to
+change all the places in your code which refer to column number 5.
+
+Say column 5 is called ''region'', just do \code{DT[,region]} instead. Notice there are no quotes around
+the column name. This is what we mean by j being evaluated within the scope of the \code{data.table}. That
+scope consists of an environment where the column names are variables.
+
+You can place \emph{any} \proglang{R} expression in \code{j}; e.g., \code{DT[,colA*colB/2]}. Further, \code{j} may be a set of \proglang{R} expressions (including calls to any \proglang{R} package) wrapped with \code{list()}, \code{.()} or an anonymous code block wrapped with braces \code{{}}. A simple example is \code{DT[,fitdistr(d1-d1,"normal")]}.
+
+Having said this, there are some circumstances where referring to a column by number is ok, such as
+a sequence of columns. In these situations just do \code{DT[,5:10,with=FALSE]} or \newline \code{DT[,c(1,4,10),with=FALSE]}.
+See \code{?data.table} for an explanation of the \code{with} argument. It lets you use \code{data.table} the same way as \code{data.frame}, when you need to.
+
+Note that \code{with()} has been a base function for a long time. That's one reason we say \code{data.table} builds
+upon base functionality. There is little new here really, \code{data.table} is just making use of \code{with()}
+and building it into the syntax.
+
+\subsection{Why does \code{DT[,"region"]} return \code{"region"}?}
+See answer to 1.1 above. Try \code{DT[,region]} instead. Or \code{DT[,"region",with=FALSE]}.
+
+
+\subsection{Why does \code{DT[,region]} return a vector? I'd like a 1-column \code{data.table}. There is no \code{drop} argument like I'm used to in \code{data.frame}.}
+Try \code{DT[,.(region)]} instead. \code{.()} is an alias for \code{list()} and ensures a \code{data.table} is returned.
+
+\subsection{Why does \code{DT[,x,y,z]} not work? I wanted the 3 columns \code{x},\code{y} and \code{z}.}
+The \code{j} expression is the 2nd argument. The correct way to do this is \code{DT[,.(x,y,z)]}.
+
+\subsection{I assigned a variable \code{mycol="x"} but then \code{DT[,mycol]} returns \code{"x"}. How do I get it to look up the column name contained in the \code{mycol} variable?}
+This is what we mean when we say the \code{j} expression 'sees' objects in the calling scope. The variable \code{mycol} does not exist as a column name of
+\code{DT} so \proglang{R} then looked in the calling scope and found \code{mycol} there and returned its value \code{"x"}. This is correct behaviour. Had \code{mycol} been a column name,
+then that column's data would have been returned. What you probably meant was \code{DT[,mycol,with=FALSE]}, which will return the \code{x} column's
+data as you wanted. Alternatively, since a \code{data.table} \emph{is} a \code{list}, too, you can write \code{DT[["x"]]} or \code{DT[[mycol]]}.
+
+\subsection{Ok but I don't know the expressions in advance. How do I programatically pass them in?}
+To create expressions use the \code{quote()} function. We refer to these as \emph{quote()-ed} expressions to
+save confusion with the double quotes used to create a character vector such as \code{c("x")}. The simplest
+quote()-ed expression is just one column name :
+
+\code{q = quote(x)}
+
+\code{DT[,eval(q)] \# returns the column x as a vector}
+
+\code{q = quote(list(x))}
+
+\code{DT[,eval(q)] \# returns the column x as a 1-column data.table}
+\newline
+Since these are \emph{expressions}, we are not restricted to column names only :
+
+\code{q = quote(mean(x))}
+
+\code{DT[,eval(q)] \# identical to DT[,mean(x)]}
+
+\code{q = quote(list(x,sd(y),mean(y*z)))}
+
+\code{DT[,eval(q)] \# identical to DT[,list(x,sd(y),mean(y*z))]}
+\newline
+However, if it's just simply a vector of column names you need, it may be simpler to pass a character vector to \code{j} and use \code{with=FALSE}.
+
+To pass an expression into your own function, one idiom is as follows :
+<<>>=
+DT = as.data.table(iris)
+setkey(DT,Species)
+myfunction = function(dt, expr) {
+ e = substitute(expr)
+ dt[,eval(e),by=Species]
+}
+myfunction(DT,sum(Sepal.Width))
+@
+
+\code{quote()} and \code{eval()} are like macros in other languages. Instead of \code{j=myfunction()} (which won't work without laboriously passing in all the arguments) it's \code{j=eval(mymacro)}. This can be more efficient than a function call, and convenient. When data.table sees \code{j=eval(mymacro)} it knows to find \code{mymacro} in calling scope so as not to be tripped up if a column name happens to be called \code{mymacro}, too.
+
+For example, let's make sure that exactly the same \code{j} is run for a set of different grouping criteria :
+
+<<>>=
+DT = as.data.table(iris)
+whatToRun = quote( .(AvgWidth = mean(Sepal.Width),
+ MaxLength = max(Sepal.Length)) )
+DT[, eval(whatToRun), by=Species]
+DT[, eval(whatToRun), by=.(FirstLetter=substring(Species,1,1))]
+DT[, eval(whatToRun), by=.(Petal.Width=round(Petal.Width,0))]
+@
+
+\subsection{What are the benefits of being able to use column names as if they are variables inside \code{DT[...]}?}
+\code{j} doesn't have to be just column names. You can write any \proglang{R} \emph{expression} of column names directly as the \code{j}; e.g.,
+\code{DT[,mean(x*y/z)]}. The same applies
+to \code{i}; e.g., \code{DT[x>1000, sum(y*z)]}.
+This runs the \code{j} expression on the set of rows where the \code{i} expression is true. You don't even need to return data; e.g., \code{DT[x>1000, plot(y,z)]}. Finally, you can do \code{j} by group by adding \code{by=}; e.g., \code{DT[x>1000, sum(y*z), by=w]}. This runs \code{j} for each group in column \code{w} but just over the rows where \code{x>1000}. By placing the 3 parts of the query (where, select and group by) inside the square brackets, \code{data.table} sees this query as [...]
+
+\subsection{OK, I'm starting to see what \code{data.table} is about, but why didn't you enhance \code{data.frame} in \proglang{R}? Why does it have to be a new package?}
+As FAQ 1.1 highlights, \code{j} in \code{[.data.table} is fundamentally different from \code{j} in \code{[.data.frame}. Even something as simple as \code{DF[,1]} would break existing code in many packages and user code. This is by design. We want it to work this way for more complicated syntax to work. There are other differences, too (see FAQ \ref{faq:SmallerDiffs}).
+
+Furthermore, \code{data.table} \emph{inherits} from \code{data.frame}. It \emph{is} a \code{data.frame}, too. A \code{data.table} can be passed to any package that only accepts \code{data.frame} and that package can use \code{[.data.frame} syntax on the \code{data.table}.
+
+We \emph{have} proposed enhancements to \proglang{R} wherever possible, too. One of these was accepted as a new feature in \proglang{R} 2.12.0 :
+\begin{quotation}unique() and match() are now faster on character vectors where
+ all elements are in the global CHARSXP cache and have unmarked
+ encoding (ASCII). Thanks to Matt Dowle for suggesting
+ improvements to the way the hash code is generated in unique.c.\end{quotation}
+
+A second proposal was to use memcpy in duplicate.c, which is much faster than a for loop in C. This would improve the \emph{way} that \proglang{R} copies data internally (on some measures by 13 times). The thread on r-devel is here : \url{http://tolstoy.newcastle.edu.au/R/e10/devel/10/04/0148.html}.
+
+\subsection{Why are the defaults the way they are? Why does it work the way it does?}
+The simple answer is because the main author originally designed it for his own use. He wanted it that way. He finds it a more natural, faster way to
+write code, which also executes more quickly.
+
+\subsection{Isn't this already done by \code{with()} and \code{subset()} in base?}
+Some of the features discussed so far are, yes. The package builds upon base functionality. It does the same sorts of things but with
+less code required and executes many times faster if used correctly.
+
+\subsection{Why does \code{X[Y]} return all the columns from \code{Y} too? Shouldn't it return a subset of \code{X}?}
+This was changed in v1.5.3. \code{X[Y]} now includes \code{Y}'s non-join columns. We refer to this feature as \emph{join inherited scope} because not only are \code{X} columns available to the j expression, so are \code{Y} columns. The downside is that \code{X[Y]} is less efficient since every item of \code{Y}'s non-join columns are duplicated to match the (likely large) number of rows in \code{X} that match. We therefore strongly encourage \code{X[Y,j]} instead of \code{X[Y]}. See next FAQ.
+
+\subsection{What is the difference between \code{X[Y]} and \code{merge(X,Y)}?}
+\code{X[Y]} is a join, looking up \code{X}'s rows using \code{Y} (or \code{Y}'s key if it has one) as an index.\newline
+\code{Y[X]} is a join, looking up \code{Y}'s rows using \code{X} (or \code{X}'s key if it has one) as an index.\newline
+\code{merge(X,Y)}\footnote{Here we mean either the \code{merge} \emph{method} for \code{data.table} or the \code{merge} method for \code{data.frame} since both methods work in the same way in this respect. See \code{?merge.data.table} and FAQ 2.24 for more information about method dispatch.} does both ways at the same time. The number of rows of \code{X[Y]} and \code{Y[X]} usually differ; whereas the number of rows returned by \code{merge(X,Y)} and \code{merge(Y,X)} is the same.
+
+\emph{BUT} that misses the main point. Most tasks require something to be done on the data after a join or merge. Why merge all the columns of data, only to use a small subset of them afterwards? You may suggest \code{merge(X[,ColsNeeded1],Y[,ColsNeeded2])}, but that takes copies of the subsets of data and it requires the programmer to work out which columns are needed. \code{X[Y,j]} in data.table does all that in one step for you. When you write \code{X[Y,sum(foo*bar)]}, \code{data.tabl [...]
+
+\subsection{Anything else about \code{X[Y,sum(foo*bar)]}?}
+This behaviour changed in v1.9.4 (Sep 2014). It now does the \code{X[Y]} join and then runs \code{sum(foo*bar)} over all the rows; i.e., \code{X[Y][,sum(foo*bar)]}. It used to run \code{j} for each \emph{group} of \code{X} that each row of \code{Y} matches to. That can still be done as it's very useful but you now need to be explicit and specify \code{by=.EACHI}; i.e., \code{X[Y,sum(foo*bar),by=.EACHI]}. We call this \emph{grouping by each i}.
+For example, and making it complicated by using \emph{join inherited scope}, too :
+<<>>=
+X = data.table(grp=c("a","a","b","b","b","c","c"), foo=1:7)
+setkey(X,grp)
+Y = data.table(c("b","c"), bar=c(4,2))
+X
+Y
+X[Y,sum(foo*bar)]
+X[Y,sum(foo*bar),by=.EACHI]
+@
+
+\subsection{That's nice. How did you manage to change it?}
+The request to change came from users. The feeling was that if a query is doing grouping then an explicit `by=` should be present for code readability reasons. An option is provided to return the old behaviour: \code{options(datatable.old.bywithoutby)}, by default FALSE. This enables upgrading to test the other new features / bug fixes in v1.9.4, with later migration of any by-without-by queries when ready (by adding \code{by=.EACHI} to them). We retained 47 pre-change tests and added th [...]
+
+Of the 66 packages on CRAN or Bioconductor that depend or import data.table at the time of releasing v1.9.4, only one was affected by the change. That could be because many packages don't have comprehensive tests, or just that grouping by each row in i wasn't being used much by downstream packages. We always test the new version with all dependent packages before release and coordinate any changes with those maintainers. So this release was quite straightforward in that regard.
+
+Another compelling reason to make the change was that previously, there was no efficient way to achieve what \code{X[Y,sum(foo*bar)]} does now. You had to write \code{X[Y][,sum(foo*bar)]}. That was suboptimal because \code{X[Y]} joined all the columns and passed them all to the second compound query without knowing that only foo and bar are needed. To solve that efficiency problem, extra programming effort was required: \code{X[Y,list(foo,bar)][,sum(foo*bar)]}. The change to \code{by=.E [...]
+
+\section{General syntax}
+
+\subsection{How can I avoid writing a really long \code{j} expression? You've said I should use the column \emph{names}, but I've got a lot of columns.}
+When grouping, the \code{j} expression can use column names as variables, as you know, but it can also use a reserved symbol \code{.SD} which refers to the {\bf S}ubset of the \code{{\bf D}ata.table} for each group (excluding the grouping columns). So to sum up all your columns it's just \code{DT[,lapply(.SD,sum),by=grp]}. It might seem tricky, but it's fast to write and fast to run. Notice you don't have to create an anonymous \code{function}. The \code{.SD} object is efficiently implem [...]
+So please don't do this, for example, \code{DT[,sum(.SD[["sales"]]),by=grp]}. That works but is inefficient and inelegant. This is what was intended: \code{DT[,sum(sales),by=grp]} and could be 100's of times faster. If you do use all the data in \code{.SD} for each group (such as in \code{DT[,lapply(.SD,sum),by=grp]}) then that's very good usage of \code{.SD}. Also see \code{?data.table} for the \code{.SDcols} argument which allows you to specify a subset of columns for \code{.SD}.
+
+\subsection{Why is the default for \code{mult} now \code{"all"}?}
+In v1.5.3 the default was changed to \code{"all"}. When \code{i} (or \code{i}'s key if it has one) has fewer columns than \code{x}'s key, \code{mult} was already set to \code{"all"} automatically. Changing the default makes this clearer and easier for users as it came up quite often.
+
+In versions up to v1.3, \code{"all"} was slower. Internally, \code{"all"} was implemented by joining using \code{"first"}, then again from scratch using \code{"last"}, after which a diff between them was performed to work out the span of the matches in \code{x} for each row in \code{i}. Most often we join to single rows, though, where \code{"first"},\code{"last"} and \code{"all"} return the same result. We preferred maximum performance for the majority of situations so the default chosen [...]
+
+In v1.4 the binary search in C was changed to branch at the deepest level to find first and last. That branch will likely occur within the same final pages of RAM so there should no longer be a speed disadvantage in defaulting \code{mult} to \code{"all"}. We warned that the default might change and made the change in v1.5.3.
+
+A future version of \code{data.table} may allow a distinction between a key and a \emph{unique key}. Internally \code{mult="all"} would perform more like \code{mult="first"} when all \code{x}'s key columns were joined to and \code{x}'s key was a unique key. \code{data.table} would need checks on insert and update to make sure a unique key is maintained. An advantage of specifying a unique key would be that \code{data.table} would ensure no duplicates could be inserted, in addition to per [...]
+
+
+\subsection{I'm using \code{c()} in the \code{j} and getting strange results.}
+This is a common source of confusion. In \code{data.frame} you are used to, for example:
+<<>>=
+DF = data.frame(x=1:3,y=4:6,z=7:9)
+DF
+DF[,c("y","z")]
+@
+which returns the two columns. In \code{data.table} you know you can use the column names directly and might try :
+<<>>=
+DT = data.table(DF)
+DT[,c(y,z)]
+@
+but this returns one vector. Remember that the \code{j} expression is evaluated within the environment of \code{DT} and \code{c()} returns a vector. If 2 or more columns are required, use \code{list()} or \code{.()} instead:
+<<>>=
+DT[,.(y,z)]
+@
+\code{c()} can be useful in a \code{data.table} too, but its behaviour is different from that in \code{[.data.frame}.
+
+\subsection{I have built up a complex table with many columns. I want to use it as a template for a new table; i.e., create a new table with no rows, but with the column names and types copied from my table. Can I do that easily?}
+Yes. If your complex table is called \code{DT}, try \code{NEWDT = DT[0]}.
+
+\subsection{Is a null data.table the same as \code{DT[0]}?}
+No. By "null data.table" we mean the result of \code{data.table(NULL)} or \code{as.data.table(NULL)}; i.e.,
+<<>>=
+data.table(NULL)
+data.frame(NULL)
+as.data.table(NULL)
+as.data.frame(NULL)
+is.null(data.table(NULL))
+is.null(data.frame(NULL))
+@
+The null \code{data.table|frame} is \code{NULL} with some attributes attached, making it not NULL anymore. In R only pure \code{NULL} is \code{NULL} as tested by \code{is.null()}. When referring to the "null data.table" we use lower case null to help distinguish from upper case \code{NULL}. To test for the null data.table, use \code{length(DT)==0} or \code{ncol(DT)==0} (\code{length} is slightly faster as it's a primitive function).
+An \emph{empty} data.table (\code{DT[0]}) has one or more columns, all of which are empty. Those empty columns still have names and types.
+<<>>=
+DT = data.table(a=1:3,b=c(4,5,6),d=c(7L,8L,9L))
+DT[0]
+sapply(DT[0],class)
+@
+
+\subsection{Why has the \code{DT()} alias been removed?}\label{faq:DTremove1}
+\code{DT} was introduced originally as a wrapper for a list of \code{j} expressions. Since \code{DT} was an alias for \code{data.table}, this was a convenient way to take care of silent recycling in cases where each item of the \code{j} list evaluated to different lengths. The alias was one reason grouping was slow, though.
+As of v1.3, \code{list()} or \code{.()} should be passed instead to the \code{j} argument. These are much faster, especially when there are many groups. Internally, this was a nontrivial change. Vector recycling is now done internally, along with several other speed enhancements for grouping.
+
+\subsection{But my code uses \code{j=DT(...)} and it works. The previous FAQ says that \code{DT()} has been removed.}\label{faq:DTremove2}
+Then you are using a version prior to 1.5.3. Prior to 1.5.3 \code{[.data.table} detected use of \code{DT()} in the \code{j} and automatically replaced it with a call to \code{list()}. This was to help the transition for existing users.
+
+\subsection{What are the scoping rules for \code{j} expressions?}
+Think of the subset as an environment where all the column names are variables. When a variable \code{foo} is used in the \code{j} of a query such as \code{X[Y,sum(foo)]}, \code{foo} is looked for in the following order :
+\begin{enumerate}
+\item The scope of \code{X}'s subset; i.e., \code{X}'s column names.
+\item The scope of each row of \code{Y}; i.e., \code{Y}'s column names (\emph{join inherited scope})
+\item The scope of the calling frame; e.g., the line that appears before the \code{data.table} query.
+\item Exercise for reader: does it then ripple up the calling frames, or go straight to \code{globalenv()}?
+\item The global environment
+\end{enumerate}
+This is \emph{lexical scoping} as explained in \href{http://cran.r-project.org/doc/FAQ/R-FAQ.html#Lexical-scoping}{R FAQ 3.3.1}.
+The environment in which the function was created is not relevant, though, because there is \emph{no function}. No anonymous \emph{function}
+is passed to the \code{j}. Instead, an anonymous \emph{body} is passed to the \code{j}; for example,
+<<>>=
+DT = data.table(x=rep(c("a","b"),c(2,3)),y=1:5)
+DT
+DT[,{z=sum(y);z+3},by=x]
+@
+Some programming languages call this a \emph{lambda}.
+
+\subsection{Can I trace the \code{j} expression as it runs through the groups?}
+Try something like this:
+<<>>=
+DT[,{
+ cat("Objects:",paste(objects(),collapse=","),"\n")
+ cat("Trace: x=",as.character(x)," y=",y,"\n")
+ sum(y)
+},by=x]
+@
+
+\subsection{Inside each group, why are the group variables length 1?}
+In the previous FAQ, \code{x} is a grouping variable
+and (as from v1.6.1) has length 1 (if inspected or used in \code{j}). It's for efficiency and convenience. Therefore, there is no difference between the following two statements:
+<<>>=
+DT[,.(g=1,h=2,i=3,j=4,repeatgroupname=x,sum(y)),by=x]
+DT[,.(g=1,h=2,i=3,j=4,repeatgroupname=x[1],sum(y)),by=x]
+@
+If you need the size of the current group, use \code{.N} rather than calling \code{length()} on any column.
+
+\subsection{Only the first 10 rows are printed, how do I print more?}
+There are two things happening here. First, if the number of rows in a \code{data.table} are large (\code{> 100} by default), then a summary of the \code{data.table} is printed to the console by default. Second, the summary of a large \code{data.table} is printed by takingthe top and bottom \code{n} rows of the \code{data.table} and only printing those. Both of these parameters (when to trigger a summary and how much of a table to use as a summary) are configurable by \proglang{R}'s \cod [...]
+
+For instance, to enforce the summary of a \code{data.table} to only happen when a \code{data.table} is greater than 50 rows, you could \code{options(datatable.print.nrows=50)}. To disable the summary-by-default completely, you could \code{options(datatable.print.nrows=Inf)}. You could also call \code{print} directly, as in \code{print(your.data.table, nrows=Inf)}.
+
+If you want to show more than just the top (and bottom) 10 rows of a \code{data.table} summary (say you like 20), set \code{options(datatable.print.topn=20)}, for example. Again, you could also just call \code{print} directly, as in \code{print(your.data.table, topn=20)}
+
+\subsection{With an \code{X[Y]} join, what if \code{X} contains a column called \code{"Y"}?}
+When \code{i} is a single name such as \code{Y} it is evaluated in the calling frame. In all other cases such as calls to \code{.()} or other expressions, \code{i} is evaluated within the scope of \code{X}. This facilitates easy \emph{self joins} such as \code{X[J(unique(colA)),mult="first"]}.
+
+\subsection{\code{X[Z[Y]]} is failing because \code{X} contains a column \code{"Y"}. I'd like it to use the table \code{Y} in calling scope.}
+The \code{Z[Y]} part is not a single name so that is evaluated within the frame of \code{X} and the problem occurs. Try \code{tmp=Z[Y];X[tmp]}. This is robust to \code{X} containing a column \code{"tmp"} because \code{tmp} is a single name. If you often encounter conflics of this type, one simple solution may be to name all tables in uppercase and all column names in lowercase, or some similar scheme.
+
+\subsection{Can you explain further why \code{data.table} is inspired by \code{A[B]} syntax in base?}
+Consider \code{A[B]} syntax using an example matrix \code{A} :
+<<>>=
+A = matrix(1:12,nrow=4)
+A
+@
+To obtain cells (1,2)=5 and (3,3)=11 many users (we believe) may try this first :
+<<>>=
+A[c(1,3),c(2,3)]
+@
+That returns the union of those rows and columns, though. To reference the cells, a 2-column matrix is required. \code{?Extract} says :
+\begin{quotation}
+When indexing arrays by [ a single argument i can be a matrix with as many columns as there are dimensions of x; the result is then a vector with elements corresponding to the sets of indices in each row of i.
+\end{quotation}
+Let's try again.
+<<>>=
+B = cbind(c(1,3),c(2,3))
+B
+A[B]
+@
+A matrix is a 2-dimension structure with row names and column names. Can we do the same with names?
+<<>>=
+rownames(A) = letters[1:4]
+colnames(A) = LETTERS[1:3]
+A
+B = cbind(c("a","c"),c("B","C"))
+A[B]
+@
+So, yes we can. Can we do the same with \code{data.frame}?
+<<>>=
+A = data.frame(A=1:4,B=letters[11:14],C=pi*1:4)
+rownames(A) = letters[1:4]
+A
+B
+A[B]
+@
+But, notice that the result was coerced to character. \proglang{R} coerced \code{A} to matrix first so that the syntax could work, but the result isn't ideal. Let's try making \code{B} a \code{data.frame}.
+<<>>=
+B = data.frame(c("a","c"),c("B","C"))
+cat(try(A[B],silent=TRUE))
+@
+So we can't subset a \code{data.frame} by a \code{data.frame} in base R. What if we want row names and column names that aren't character but integer or float? What if we want more than 2 dimensions of mixed types? Enter \code{data.table}.
+
+Furthermore, matrices, especially sparse matrices, are often stored in a 3 column tuple: (i,j,value). This can be thought of as a key-value pair where \code{i} and \code{j} form a 2-column key. If we have more than one value, perhaps of different types it might look like (i,j,val1,val2,val3,...). This looks very much like a \code{data.frame}. Hence \code{data.table} extends \code{data.frame} so that a \code{data.frame X} can be subset by a \code{data.frame Y}, leading to the \code{X[Y]} syntax.
+
+\subsection{Can base be changed to do this then, rather than a new package?}
+\code{data.frame} is used \emph{everywhere} and so it is very difficult to make \emph{any} changes to it.
+\code{data.table} \emph{inherits} from \code{data.frame}. It \emph{is} a \code{data.frame}, too. A \code{data.table} \emph{can} be passed to any package that \emph{only} accepts \code{data.frame}. When that package uses \code{[.data.frame} syntax on the \code{data.table}, it works. It works because \code{[.data.table} looks to see where it was called from. If it was called from such a package, \code{[.data.table} diverts to \code{[.data.frame}.
+
+\subsection{I've heard that \code{data.table} syntax is analogous to SQL.}
+Yes :
+\begin{itemize}
+\item{\code{i} <==> where}
+\item{\code{j} <==> select}
+\item{\code{:=} <==> update}
+\item{\code{by} <==> group by}
+\item{\code{i} <==> order by (in compound syntax)}
+\item{\code{i} <==> having (in compound syntax)}
+\item{\code{nomatch=NA} <==> outer join}
+\item{\code{nomatch=0} <==> inner join}
+\item{\code{mult="first"|"last"} <==> N/A because SQL is inherently unordered}
+\item{\code{roll=TRUE} <==> N/A because SQL is inherently unordered}
+\end{itemize}
+The general form is : \newline
+\code{\hspace*{2cm}DT[where,select|update,group by][order by][...] ... [...]}
+\newline\newline
+A key advantage of column vectors in \proglang{R} is that they are \emph{ordered}, unlike SQL\footnote{It may be a surprise to learn that \code{select top 10 * from ...} does \emph{not} reliably return the same rows over time in SQL. You do need to include an \code{order by} clause, or use a clustered index to guarantee row order; i.e., SQL is inherently unordered.}. We can use ordered functions in \code{data.table} queries such as \code{diff()} and we can use \emph{any} \proglang{R} fun [...]
+
+
+\subsection{What are the smaller syntax differences between \code{data.frame} and \code{data.table}?}\label{faq:SmallerDiffs}
+\begin{itemize}
+\item{\code{DT[3]} refers to the 3rd row, but \code{DF[3]} refers to the 3rd column}
+\item{\code{DT[3,]} == \code{DT[3]}, but \code{DF[,3]} == \code{DF[3]} (somewhat confusingly)}
+\item{For this reason we say the comma is \emph{optional} in \code{DT}, but not optional in \code{DF}}
+\item{\code{DT[[3]]} == \code{DF[3]} == \code{DF[[3]]}}
+\item{\code{DT[i,]} where \code{i} is a single integer returns a single row, just like \code{DF[i,]}, but unlike a matrix single row subset which returns a vector.}
+\item{\code{DT[,j,with=FALSE]} where \code{j} is a single integer returns a one column \code{data.table}, unlike \code{DF[,j]} which returns a vector by default}
+\item{\code{DT[,"colA",with=FALSE][[1]]} == \code{DF[,"colA"]}.}
+\item{\code{DT[,colA]} == \code{DF[,"colA"]}}
+\item{\code{DT[,list(colA)]} == \code{DF[,"colA",drop=FALSE]}}
+\item{\code{DT[NA]} returns 1 row of \code{NA}, but \code{DF[NA]} returns a copy of \code{DF} containing
+ \code{NA} throughout. The symbol \code{NA} is type logical in \proglang{R} and
+ is therefore recycled by \code{[.data.frame}. Intention was probably \code{DF[NA\_integer\_]}.
+ \code{[.data.table} does this automatically for convenience.}
+\item{\code{DT[c(TRUE,NA,FALSE)]} treats the \code{NA} as \code{FALSE}, but \code{DF[c(TRUE,NA,FALSE)]} returns
+ \code{NA} rows for each \code{NA}}
+\item{\code{DT[ColA==ColB]} is simpler than \code{DF[!is.na(ColA) \& !is.na(ColB) \& ColA==ColB,]}}
+\item{\code{data.frame(list(1:2,"k",1:4))} creates 3 columns, \code{data.table} creates one \code{list} column.}
+\item{\code{check.names} is by default \code{TRUE} in \code{data.frame} but \code{FALSE} in \code{data.table}, for convenience.}
+\item{\code{stringsAsFactors} is by default \code{TRUE} in \code{data.frame} but \code{FALSE} in \code{data.table}, for efficiency. Since a global string cache was added to R, characters items are a pointer to the single cached string and there is no longer a performance benefit of coverting to factor.}
+\item{Atomic vectors in \code{list} columns are collapsed when printed using ", " in \code{data.frame}, but "," in \code{data.table} with a trailing comma after the 6th item to avoid accidental printing of large embedded objects.}
+\end{itemize}
+In \code{[.data.frame} we very often set \code{drop=FALSE}. When we forget, bugs can arise in edge cases where single columns are selected and all of a sudden a vector is returned rather than a single column \code{data.frame}. In \code{[.data.table} we took the opportunity to make it consistent and drop \code{drop}.
+\newline\newline
+When a \code{data.table} is passed to a \code{data.table}-unaware package, that package it not concerned with any of these differences; it just works.
+
+\subsection{I'm using \code{j} for its side effect only, but I'm still getting data returned. How do I stop that?}
+In this case \code{j} can be wrapped with \code{invisible()}; e.g., \code{DT[,invisible(hist(colB)),by=colA]}\footnote{\code{hist()} returns the breakpoints in addition to plotting to the graphics device}.
+
+\subsection{Why does \code{[.data.table} now have a \code{drop} argument from v1.5?}
+So that \code{data.table} can inherit from \code{data.frame} without using \code{\dots}. If we used \code{\dots} then invalid argument names would not be caught.
+
+The \code{drop} argument is never used by \code{[.data.table}. It is a placeholder for non \code{data.table} aware packages when they use the \code{[.data.frame} syntax directly on a \code{data.table}.
+
+\subsection{Rolling joins are cool and very fast! Was that hard to program?}
+The prevailing row on or before the \code{i} row is the final row the binary search tests anyway. So \code{roll=TRUE} is essentially just a switch in the binary search C code to return that row.
+
+\subsection{Why does \code{DT[i,col:=value]} return the whole of \code{DT}? I expected either no visible value (consistent with \code{<-}), or a message or return value containing how many rows were updated. It isn't obvious that the data has indeed been updated by reference.}
+This has changed in v1.8.3 to meet your expectations. Please upgrade.
+The whole of \code{DT} is returned (now invisibly) so that compound syntax can work; e.g., \code{DT[i,done:=TRUE][,sum(done)]}. The number of rows updated is returned when verbosity is on, either on a per query basis or globally using \code{options(datatable.verbose=TRUE)}.
+
+\subsection{Ok, thanks. What was so difficult about the result of \code{DT[i,col:=value]} being returned invisibly?}
+\proglang{R} internally forces visibility on for \code{[}. The value of FunTab's eval column (see src/main/names.c) for \code{[} is 0 meaning force \code{R\_Visible} on (see R-Internals section 1.6). Therefore, when we tried \code{invisible()} or setting \code{R\_Visible} to 0 directly ourselves, \code{eval} in src/main/eval.c would force it on again.
+
+To solve this problem, the key was to stop trying to stop the print method running after a \code{:=}. Instead, inside \code{:=} we now (from v1.8.3) set a global flag which the print method uses to know whether to actually print or not.
+
+\subsection{I've noticed that \code{base::cbind.data.frame} (and \code{base::rbind.data.frame}) appear to be changed by \code{data.table}. How is this possible? Why?}
+It is a temporary, last resort solution until we discover a better way to solve the problems listed below. Essentially, the issue is that \code{data.table} inherits from \code{data.frame}, \emph{and}, \code{base::cbind} and \code{base::rbind} (uniquely) do their own S3 dispatch internally as documented by \code{?cbind}. The change is adding one \code{for} loop to the start of each function directly in base; e.g.,
+<<>>=
+base::cbind.data.frame
+@
+That modification is made dynamically; i.e., the base definition of \code{cbind.data.frame} is fetched, the \code{for} loop added to the beginning and then assigned back to base. This solution is intended to be robust to different definitions of \code{base::cbind.data.frame} in different versions of \proglang{R}, including unknown future changes. Again, it is a last resort until a better solution is known or made available. The competing requirements are :
+\begin{itemize}
+\item \code{cbind(DT,DF)} needs to work. Defining \code{cbind.data.table} doesn't work because \code{base::cbind} does its own S3 dispatch and requires that the \emph{first} \code{cbind} method for each object it is passed is \emph{identical}. This is not true in \code{cbind(DT,DF)} because the first method for \code{DT} is \code{cbind.data.table} but the first method for \code{DF} is \code{cbind.data.frame}. \code{base::cbind} then falls through to its internal \code{bind} code which ap [...]
+\item This naturally leads to trying to mask \code{cbind.data.frame} instead. Since a \code{data.table} is a \code{data.frame}, \code{cbind} would find the same method for both \code{DT} and \code{DF}. However, this doesn't work either because \code{base::cbind} appears to find methods in \code{base} first; i.e., \code{base::cbind.data.frame} isn't maskable. This is reproducible as follows :
+\end{itemize}
+<<>>=
+foo = data.frame(a=1:3)
+cbind.data.frame = function(...)cat("Not printed\n")
+cbind(foo)
+@
+<<echo=FALSE>>=
+rm("cbind.data.frame")
+@
+\begin{itemize}
+\item Finally, we tried masking \code{cbind} itself (v1.6.5 and v1.6.6). This allowed \code{cbind(DT,DF)} to work, but introduced compatibility issues with package IRanges, since IRanges also masks \code{cbind}. It worked if IRanges was lower on the search() path than \code{data.table}, but if IRanges was higher then \code{data.table}'s \code{cbind} would never be called and the strange looking matrix output occurs again (FAQ \ref{faq:cbinderror}).
+\end{itemize}
+If you know of a better solution, that still solves all the issues above, then please let us know and we'll gladly change it.
+
+\subsection{I've read about method dispatch (e.g. \code{merge} may or may not dispatch to \code{merge.data.table}) but \emph{how} does R know how to dispatch? Are dots significant or special? How on earth does R know which function to dispatch and when?}
+This comes up quite a lot, but it's really earth shatteringly simple. A function such as \code{merge} is \emph{generic} if it consists of a call to \code{UseMethod}. When you see people talking about whether or not functions are \emph{generic} functions they are merely typing the function, without \code{()} afterwards, looking at the program code inside it and if they see a call to \code{UseMethod} then it is \emph{generic}. What does \code{UseMethod} do? It literally slaps the function [...]
+
+You might now ask: where is this documented in R? Answer: it's quite clear, but, you need to first know to look in \code{?UseMethod} and \emph{that} help file contains :
+
+ "When a function calling UseMethod('fun') is applied to an object with class attribute c('first', 'second'), the system searches for a function called fun.first and, if it finds it, applies it to the object. If no such function is found a function called fun.second is tried. If no class name produces a suitable function, the function fun.default is used, if it exists, or an error results."
+
+Happily, an internet search for "How does R method dispatch work" (at the time of writing) returns the \code{?UseMethod} help page as the top link. Admittedly, other links rapidly descend into the intracies of S3 vs S4, internal generics and so on.
+
+However, features like basic S3 dispatch (pasting the function name together with the class name) is why some R folk love R. It's so simple. No complicated registration or signature is required. There isn't much needed to learn. To create the \code{merge} method for \code{data.table} all that was required, literally, was to merely create a function called \code{merge.data.table}.
+
+\section{Questions relating to compute time}
+
+\subsection{I have 20 columns and a large number of rows. Why is an expression of one column so quick?}
+Several reasons:
+\begin{itemize}
+\item Only that column is grouped, the other 19 are ignored because \code{data.table} inspects the \code{j} expression and realises it doesn't use the other columns.
+\item One memory allocation is made for the largest group only, then that memory is re-used for the other groups. There is very little garbage to collect.
+\item \proglang{R} is an in-memory column store; i.e., the columns are contiguous in RAM. Page fetches from RAM into L2 cache are minimised.
+\end{itemize}
+
+\subsection{I don't have a key on a large table, but grouping is still really quick. Why is that?}
+\code{data.table} uses radix sorting. This is significantly faster than other sort algorithms. See our presentations on our homepage for more information.
+
+This is also one reason why \code{setkey()} is quick.
+
+When no key is set, or we group in a different order from that of the key, we call it an \emph{ad hoc by}.
+
+\subsection{Why is grouping by columns in the key faster than an ad hoc by?}
+Because each group is contiguous in RAM, thereby minimising page fetches and memory can be
+copied in bulk (memcpy in C) rather than looping in C.
+
+\section{Error messages}
+\subsection{\code{Could not find function "DT"}}
+See FAQ \ref{faq:DTremove1} and FAQ \ref{faq:DTremove2}.
+
+\subsection{\code{unused argument(s) (MySum = sum(v))}}
+This error is generated by \code{DT[,MySum=sum(v)]}. \code{DT[,.(MySum=sum(v))]} was intended, or \code{DT[,j=.(MySum=sum(v))]}.
+
+\subsection{\code{'translateCharUTF8' must be called on a CHARSXP}}
+This error (and similar; e.g., \code{'getCharCE' must be called on a CHARSXP}) may be nothing do with character data or locale. Instead, this can be a symptom of an earlier memory corruption. To date these have been reproducible and fixed (quickly). Please report it to datatable-help.
+
+\subsection{\code{cbind(DT,DF) returns a strange format e.g. 'Integer,5'}} \label{faq:cbinderror}
+This occurs prior to v1.6.5, for \code{rbind(DT,DF)} too. Please upgrade to v1.6.7 or later.
+
+\subsection{\code{cannot change value of locked binding for '.SD'}}
+\code{.SD} is locked by design. See \code{?data.table}. If you'd like to manipulate \code{.SD} before using it, or returning it, and don't wish to modify \code{DT} using \code{:=}, then take a copy first (see \code{?copy}); e.g.,
+<<>>=
+DT = data.table(a=rep(1:3,1:3),b=1:6,c=7:12)
+DT
+DT[,{ mySD = copy(.SD)
+ mySD[1,b:=99L]
+ mySD },
+ by=a]
+@
+
+\subsection{\code{cannot change value of locked binding for '.N'}}
+Please upgrade to v1.8.1 or later. From this version, if \code{.N} is returned by \code{j} it is renamed to \code{N} to avoid any abiguity in any subsequent grouping between the \code{.N} special variable and a column called \code{".N"}.
+The old behaviour can be reproduced by forcing \code{.N} to be called \code{.N}, like this :
+<<>>=
+DT = data.table(a=c(1,1,2,2,2),b=c(1,2,2,2,1))
+DT
+DT[,list(.N=.N),list(a,b)] # show intermediate result for exposition
+cat(try(
+ DT[,list(.N=.N),by=list(a,b)][,unique(.N),by=a] # compound query more typical
+,silent=TRUE))
+@
+If you are already running v1.8.1 or later then the error message is now more helpful than the \code{cannot change value of locked binding} error. As you can see above, since this vignette was produced using v1.8.1 or later.
+The more natural syntax now works :
+<<>>=
+if (packageVersion("data.table") >= "1.8.1") {
+ DT[,.N,by=list(a,b)][,unique(N),by=a]
+}
+if (packageVersion("data.table") >= "1.9.3") {
+ DT[,.N,by=.(a,b)][,unique(N),by=a] # same
+}
+@
+
+\section{Warning messages}
+\subsection{\code{The following object(s) are masked from 'package:base': cbind, rbind}}
+This warning was present in v1.6.5 and v.1.6.6 only, when loading the package. The motivation was to allow \code{cbind(DT,DF)} to work, but as it transpired, broke (full) compatibility with package IRanges. Please upgrade to v1.6.7 or later.
+
+\subsection{\code{Coerced numeric RHS to integer to match the column's type}}
+Hopefully, this is self explanatory. The full message is :\newline
+
+\code{Coerced numeric RHS to integer to match the column's type; may have truncated}\newline
+\code{precision. Either change the column to numeric first by creating a new numeric}\newline
+\code{vector length 5 (nrows of entire table) yourself and assigning that (i.e. }\newline
+\code{'replace' column), or coerce RHS to integer yourself (e.g. 1L or as.integer)}\newline
+\code{to make your intent clear (and for speed). Or, set the column type correctly}\newline
+\code{up front when you create the table and stick to it, please.}\newline
+
+To generate it, try :
+<<>>=
+DT = data.table(a=1:5,b=1:5)
+suppressWarnings(
+DT[2,b:=6] # works (slower) with warning
+)
+class(6) # numeric not integer
+DT[2,b:=7L] # works (faster) without warning
+class(7L) # L makes it an integer
+DT[,b:=rnorm(5)] # 'replace' integer column with a numeric column
+@
+
+\section{General questions about the package}
+
+\subsection{v1.3 appears to be missing from the CRAN archive?}
+That is correct. v1.3 was available on R-Forge only. There were several large
+changes internally and these took some time to test in development.
+
+\subsection{Is \code{data.table} compatible with S-plus?}
+Not currently.
+\begin{itemize}
+\item A few core parts of the package are written in C and use internal \proglang{R} functions and \proglang{R} structures.
+\item The package uses lexical scoping which is one of the differences between \proglang{R} and \proglang{S-plus} explained by
+\href{http://cran.r-project.org/doc/FAQ/R-FAQ.html#Lexical-scoping}{R FAQ 3.3.1}.
+\end{itemize}
+
+\subsection{Is it available for Linux, Mac and Windows?}
+Yes, for both 32-bit and 64-bit on all platforms. Thanks to CRAN. There are no special or OS-specific libraries used.
+
+\subsection{I think it's great. What can I do?}
+Please file suggestions, bug reports and enhancement requests on \href{https://github.com/Rdatatable/data.table/issues}{GitHub}.
+This helps make the package better.
+
+Please do vote for the package on \href{http://crantastic.org/packages/data-table}{Crantastic}. This helps encourage the developers and helps other \proglang{R} users find the package. If you have time to write a comment too, that can help others in the community. Just simply clicking that you use the package, though, is much appreciated.
+
+You can submit pull requests to change the code and/or documentation yourself.
+
+\subsection{I think it's not great. How do I warn others about my experience?}
+Please put your vote and comments on \href{http://crantastic.org/packages/data-table}{Crantastic}. Please make it constructive so we have a chance to improve.
+
+\subsection{I have a question. I know the r-help posting guide tells me to contact the maintainer (not r-help), but is there a larger group of people I can ask?}
+Yes, there are two options. You can post to \href{mailto:datatable-help at lists.r-forge.r-project.org}{datatable-help}. It's like r-help, but just for this package. Or the \href{http://stackoverflow.com/questions/tagged/data.table}{\code{data.table} tag on Stack Overflow}. Feel free to answer questions in those places, too.
+
+\subsection{Where are the datatable-help archives?}
+The \href{https://github.com/Rdatatable/data.table/wiki}{homepage} contains links to the archives in several formats.
+
+\subsection{I'd prefer not to contact datatable-help, can I mail just one or two people privately?}
+Sure. You're more likely to get a faster answer from datatable-help or Stack Overflow, though. Asking publicly in those places helps build the knowledge base.
+
+\subsection{I have created a package that depends on \code{data.table}. How do I ensure my package is \code{data.table}-aware so that inheritance from \code{data.frame} works?}
+Either i) include \code{data.table} in the \code{Depends:} field of your DESCRIPTION file, or ii) include \code{data.table} in the \code{Imports:} field of your DESCRIPTION file AND \code{import(data.table)} in your NAMESPACE file.
+
+\subsection{Why is this FAQ in pdf format? Can it moved to HTML?}
+Yes we'd like to move it to a HTML vignette. Just haven't got to that yet.
+The benefits of vignettes (rather than a wiki) including the following:
+\begin{itemize}
+\item We include \proglang{R} code in the vignettes. This code is \emph{actually run} when the file is created, not copy and pasted.
+\item This document is \emph{reproducible}. Grab the .Rnw and you can run it yourself.
+\item CRAN checks the package (including running vignettes) every night on Linux, Mac and Windows, both 32bit and 64bit. Results are posted to \url{http://cran.r-project.org/web/checks/check_results_data.table.html}. Included there are results from r-devel; i.e., not yet released R. That serves as a very useful early warning system for any potential future issues as \proglang{R} itself develops.
+\item This file is bound into each version of the package. The package is not accepted on CRAN unless this file passes checks. Each version of the package will have its own FAQ file which will be relevant for that version. Contrast this to a single website, which can be ambiguous if the answer depends on the version.
+\item You can open it offline at your \proglang{R} prompt using \code{vignette()}.
+\item You can extract the code from the document and play with it using\newline \code{edit(vignette("datatable-faq"))}.
+\end{itemize}
+
+\end{document}
+
+
diff --git a/inst/doc/datatable-faq.pdf b/inst/doc/datatable-faq.pdf
new file mode 100644
index 0000000..13f3895
Binary files /dev/null and b/inst/doc/datatable-faq.pdf differ
diff --git a/inst/doc/datatable-intro-vignette.R b/inst/doc/datatable-intro-vignette.R
new file mode 100644
index 0000000..539af8f
--- /dev/null
+++ b/inst/doc/datatable-intro-vignette.R
@@ -0,0 +1,210 @@
+## ---- echo = FALSE, message = FALSE--------------------------------------
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+# options(datatable.auto.index=FALSE)
+
+## ----echo=FALSE-----------------------------------------------------------------------------------
+options(width=100)
+
+## -------------------------------------------------------------------------------------------------
+flights <- fread("flights14.csv")
+flights
+dim(flights)
+
+## -------------------------------------------------------------------------------------------------
+DT = data.table(ID = c("b","b","b","a","a","c"), a = 1:6, b = 7:12, c=13:18)
+DT
+class(DT$ID)
+
+## -------------------------------------------------------------------------------------------------
+getOption("datatable.print.nrows")
+
+## ----eval = FALSE---------------------------------------------------------------------------------
+# DT[i, j, by]
+#
+# ## R: i j by
+# ## SQL: where select | update group by
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[origin == "JFK" & month == 6L]
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[1:2]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[order(origin, -dest)]
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+odt = data.table(col=sample(1e7))
+(t1 <- system.time(ans1 <- odt[base::order(col)])) ## uses order from base R
+(t2 <- system.time(ans2 <- odt[order(col)])) ## uses data.table's forder
+(identical(ans1, ans2))
+
+## ----echo = FALSE---------------------------------------------------------------------------------
+rm(odt); rm(ans1); rm(ans2); rm(t1); rm(t2)
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, arr_delay]
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, list(arr_delay)]
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, .(arr_delay, dep_delay)]
+head(ans)
+
+## alternatively
+# ans <- flights[, list(arr_delay, dep_delay)]
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, .(delay_arr = arr_delay, delay_dep = dep_delay)]
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, sum((arr_delay + dep_delay) < 0)]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[origin == "JFK" & month == 6L,
+ .(m_arr=mean(arr_delay), m_dep=mean(dep_delay))]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[origin == "JFK" & month == 6L, length(dest)]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[origin == "JFK" & month == 6L, .N]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, c("arr_delay", "dep_delay"), with=FALSE]
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+DF = data.frame(x = c(1,1,1,2,2,3,3,3), y = 1:8)
+
+## (1) normal way
+DF[DF$x > 1, ] # data.frame needs that ',' as well
+
+## (2) using with
+DF[with(DF, x > 1), ]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# ## not run
+#
+# # returns all columns except arr_delay and dep_delay
+# ans <- flights[, !c("arr_delay", "dep_delay"), with=FALSE]
+# # or
+# ans <- flights[, -c("arr_delay", "dep_delay"), with=FALSE]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# ## not run
+#
+# # returns year,month and day
+# ans <- flights[, year:day, with=FALSE]
+# # returns day, month and year
+# ans <- flights[, day:year, with=FALSE]
+# # returns all columns except year, month and day
+# ans <- flights[, -(year:day), with=FALSE]
+# ans <- flights[, !(year:day), with=FALSE]
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, .(.N), by=.(origin)]
+ans
+
+## or equivalently using a character vector in 'by'
+# ans <- flights[, .(.N), by="origin"]
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, .N, by=origin]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[carrier == "AA", .N, by=origin]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[carrier == "AA", .N, by=.(origin,dest)]
+head(ans)
+
+## or equivalently using a character vector in 'by'
+# ans <- flights[carrier == "AA", .N, by=c("origin", "dest")]
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[carrier == "AA",
+ .(mean(arr_delay), mean(dep_delay)),
+ by=.(origin, dest, month)]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[carrier == "AA",
+ .(mean(arr_delay), mean(dep_delay)),
+ keyby=.(origin, dest, month)]
+ans
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[carrier == "AA", .N, by = .(origin, dest)]
+
+## -------------------------------------------------------------------------------------------------
+ans <- ans[order(origin, -dest)]
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[carrier == "AA", .N, by=.(origin, dest)][order(origin, -dest)]
+head(ans, 10)
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# DT[ ...
+# ][ ...
+# ][ ...
+# ]
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, .N, .(dep_delay>0, arr_delay>0)]
+ans
+
+## -------------------------------------------------------------------------------------------------
+DT
+
+DT[, print(.SD), by=ID]
+
+## -------------------------------------------------------------------------------------------------
+DT[, lapply(.SD, mean), by=ID]
+
+## -------------------------------------------------------------------------------------------------
+flights[carrier == "AA", ## Only on trips with carrier "AA"
+ lapply(.SD, mean), ## compute the mean
+ by=.(origin, dest, month), ## for every 'origin,dest,month'
+ .SDcols=c("arr_delay", "dep_delay")] ## for just those specified in .SDcols
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights[, head(.SD, 2), by=month]
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+DT[, .(val = c(a,b)), by=ID]
+
+## -------------------------------------------------------------------------------------------------
+DT[, .(val = list(c(a,b))), by=ID]
+
+## -------------------------------------------------------------------------------------------------
+## (1) look at the difference between
+DT[, print(c(a,b)), by=ID]
+
+## (2) and
+DT[, print(list(c(a,b))), by=ID]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# DT[i, j, by]
+
diff --git a/inst/doc/datatable-intro-vignette.Rmd b/inst/doc/datatable-intro-vignette.Rmd
new file mode 100644
index 0000000..7e2335f
--- /dev/null
+++ b/inst/doc/datatable-intro-vignette.Rmd
@@ -0,0 +1,716 @@
+---
+title: "Introduction to data.table"
+date: "`r Sys.Date()`"
+output:
+ rmarkdown::html_document:
+ theme: spacelab
+ highlight: pygments
+ css : css/bootstrap.css
+vignette: >
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r, echo = FALSE, message = FALSE}
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+# options(datatable.auto.index=FALSE)
+```
+This vignette introduces the *data.table* syntax, its general form, how to *subset* rows, *select and compute* on columns and perform aggregations *by group*. Familiarity with *data.frame* data structure from base R is useful, but not essential to follow this vignette.
+
+***
+
+## Data analysis using data.table
+
+Data manipulation operations such as *subset*, *group*, *update*, *join* etc., are all inherently related. Keeping these *related operations together* allows for:
+
+* *concise* and *consistent* syntax irrespective of the set of operations you would like to perform to achieve your end goal.
+
+* performing analysis *fluidly* without the cognitive burden of having to map each operation to a particular function from a set of functions available before to perform the analysis.
+
+* *automatically* optimising operations internally, and very effectively, by knowing precisely the data required for each operation and therefore very fast and memory efficient.
+
+Briefly, if you are interested in reducing *programming* and *compute* time tremendously, then this package is for you. The philosophy that *data.table* adheres to makes this possible. Our goal is to illustrate it through this series of vignettes.
+
+
+## Data {#data}
+
+In this vignette, we will use [NYC-flights14](https://github.com/arunsrinivasan/flights/wiki/NYC-Flights-2014-data) data. It contains On-Time flights data from the [Bureau of Transporation Statistics](http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236) for all the flights that departed from New York City airports in 2014 (inspired by [nycflights13](https://github.com/hadley/nycflights13)). The data is available only for Jan-Oct'14.
+
+We can use *data.table's* fast file reader `fread` to load *flights* directly as follows:
+
+```{r echo=FALSE}
+options(width=100)
+```
+
+```{r}
+flights <- fread("flights14.csv")
+flights
+dim(flights)
+```
+
+Aside: `fread` accepts `http` and `https` URLs directly as well as operating system commands such as `sed` and `awk` output. See `?fread` for examples.
+
+## Introduction
+
+In this vignette, we will
+
+1. start with basics - what is a *data.table*, its general form, how to *subset* rows, *select and compute* on columns
+
+2. and then we will look at performing data aggregations by group,
+
+## 1. Basics {#basics-1}
+
+### a) What is data.table? {#what-is-datatable-1a}
+
+*data.table* is an R package that provides **an enhanced version** of *data.frames*. In the [Data](#data) section, we already created a *data.table* using `fread()`. We can also create one using the `data.table()` function. Here is an example:
+
+```{r}
+DT = data.table(ID = c("b","b","b","a","a","c"), a = 1:6, b = 7:12, c=13:18)
+DT
+class(DT$ID)
+```
+
+You can also convert existing objects to a *data.table* using `as.data.table()`.
+
+#### Note that: {.bs-callout .bs-callout-info}
+
+* Unlike *data.frames*, columns of `character` type are *never* converted to `factors` by default.
+
+* Row numbers are printed with a `:` in order to visually separate the row number from the first column.
+
+* When the number of rows to print exceeds the global option `datatable.print.nrows` (default = `r getOption("datatable.print.nrows")`), it automatically prints only the top 5 and bottom 5 rows (as can be seen in the [Data](#data) section).
+
+ ```{.r}
+ getOption("datatable.print.nrows")
+ ```
+
+* *data.table* doesn't set or use *row names*, ever. We will see as to why in *"Keys and fast binary search based subset"* vignette.
+
+### b) General form - in what way is a data.table *enhanced*? {#enhanced-1b}
+
+In contrast to a *data.frame*, you can do *a lot more* than just subsetting rows and selecting columns within the frame of a *data.table*, i.e., within `[ ... ]`. To understand it we will have to first look at the *general form* of *data.table* syntax, as shown below:
+
+```{r eval = FALSE}
+DT[i, j, by]
+
+## R: i j by
+## SQL: where select | update group by
+```
+
+Users who have a SQL background might perhaps immediately relate to this syntax.
+
+#### The way to read it (out loud) is: {.bs-callout .bs-callout-info}
+
+Take `DT`, subset rows using `i`, then calculate `j`, grouped by `by`.
+
+#
+
+Let's begin by looking at `i` and `j` first - subsetting rows and operating on columns.
+
+### c) Subset rows in `i` {#subset-i-1c}
+
+#### -- Get all the flights with "JFK" as the origin airport in the month of June.
+
+```{r}
+ans <- flights[origin == "JFK" & month == 6L]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Within the frame of a *data.table*, columns can be referred to *as if they are variables*. Therefore, we simply refer to `dest` and `month` as if they are variables. We do not need to add the prefix `flights$` each time. However using `flights$dest` and `flights$month` would work just fine.
+
+* The *row indices* that satisfies the condition `origin == "JFK" & month == 6L` are computed, and since there is nothing else left to do, a *data.table* all columns from `flights` corresponding to those *row indices* are simply returned.
+
+* A comma after the condition is also not required in `i`. But `flights[dest == "JFK" & month == 6L, ]` would work just fine. In *data.frames* however, the comma is necessary.
+
+#### -- Get the first two rows from `flights`. {#subset-rows-integer}
+
+```{r}
+ans <- flights[1:2]
+ans
+```
+#### {.bs-callout .bs-callout-info}
+
+* In this case, there is no condition. The row indices are already provided in `i`. We therefore return a *data.table* with all columns from `flight` for those *row indices*.
+
+#### -- Sort `flights` first by column `origin` in *ascending* order, and then by `dest` in *descending* order:
+
+We can use the base R function `order()` to accomplish this.
+
+```{r}
+ans <- flights[order(origin, -dest)]
+head(ans)
+```
+#### `order()` is internally optimised {.bs-callout .bs-callout-info}
+
+* We can use "-" on a *character* columns within the frame of a *data.table* to sort in decreasing order.
+
+* In addition, `order(...)` within the frame of a *data.table* uses *data.table*'s internal fast radix order `forder()`, which is much faster than `base::order`. Here's a small example to highlight the difference.
+
+ ```{r}
+ odt = data.table(col=sample(1e7))
+ (t1 <- system.time(ans1 <- odt[base::order(col)])) ## uses order from base R
+ (t2 <- system.time(ans2 <- odt[order(col)])) ## uses data.table's forder
+ (identical(ans1, ans2))
+ ````
+
+The speedup here is **~`r round(t1[3]/t2[3])`x**. We will discuss *data.table*'s fast order in more detail in the *data.table internals* vignette.
+
+* This is so that you can improve performance tremendously while using already familiar functions.
+
+#
+```{r echo = FALSE}
+rm(odt); rm(ans1); rm(ans2); rm(t1); rm(t2)
+```
+
+### d) Select column(s) in `j` {#select-j-1d}
+
+#### -- Select `arr_delay` column, but return it as a *vector*.
+
+```{r}
+ans <- flights[, arr_delay]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Since columns can be referred to as if they are variables within the frame of data.tables, we directly refer to the *variable* we want to subset. Since we want *all the rows*, we simply skip `i`.
+
+* It returns *all* the rows for the column `arr_delay`.
+
+#### -- Select `arr_delay` column, but return as a *data.table* instead.
+
+```{r}
+ans <- flights[, list(arr_delay)]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We wrap the *variables* (column names) within `list()`, which ensures that a *data.table* is returned. In case of a single column name, not wrapping with `list()` returns a vector instead, as seen in the [previous example](#select-j-1d).
+
+* *data.table* also allows using `.()` to wrap columns with. It is an *alias* to `list()`; they both mean the same. Feel free to use whichever you prefer.
+
+ We will continue to use `.()` from here on.
+
+#
+
+*data.tables* (and *data.frames*) are internally *lists* as well, but with all its columns of equal length and with a *class* attribute. Allowing `j` to return a *list* enables converting and returning a *data.table* very efficiently.
+
+#### Tip: {.bs-callout .bs-callout-warning #tip-1}
+
+As long as `j-expression` returns a *list*, each element of the list will be converted to a column in the resulting *data.table*. This makes `j` quite powerful, as we will see shortly.
+
+#### -- Select both `arr_delay` and `dep_delay` columns.
+
+```{r}
+ans <- flights[, .(arr_delay, dep_delay)]
+head(ans)
+
+## alternatively
+# ans <- flights[, list(arr_delay, dep_delay)]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Wrap both columns within `.()`, or `list()`. That's it.
+
+#
+
+#### -- Select both `arr_delay` and `dep_delay` columns *and* rename them to `delay_arr` and `delay_dep`.
+
+Since `.()` is just an alias for `list()`, we can name columns as we would while creating a *list*.
+
+```{r}
+ans <- flights[, .(delay_arr = arr_delay, delay_dep = dep_delay)]
+head(ans)
+```
+
+That's it.
+
+### e) Compute or *do* in `j`
+
+#### -- How many trips have had total delay < 0?
+
+```{r}
+ans <- flights[, sum((arr_delay + dep_delay) < 0)]
+ans
+```
+
+#### What's happening here? {.bs-callout .bs-callout-info}
+
+* *data.table*'s `j` can handle more than just *selecting columns* - it can handle *expressions*, i.e., *compute on columns*. This shouldn't be surprising, as columns can be referred to as if they are variables. Then we should be able to *compute* by calling functions on those variables. And that's what precisely happens here.
+
+### f) Subset in `i` *and* do in `j`
+
+#### -- Calculate the average arrival and departure delay for all flights with "JFK" as the origin airport in the month of June.
+
+```{r}
+ans <- flights[origin == "JFK" & month == 6L,
+ .(m_arr=mean(arr_delay), m_dep=mean(dep_delay))]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We first subset in `i` to find matching *row indices* where `origin` airport equals *"JFK"*, and `month` equals *6*. At this point, we *do not* subset the entire *data.table* corresponding to those rows.
+
+* Now, we look at `j` and find that it uses only *two columns*. And what we have to do is to compute their `mean()`. Therefore we subset just those columns corresponding to the matching rows, and compute their `mean()`.
+
+Because the three main components of the query (`i`, `j` and `by`) are *together* inside `[...]`, *data.table* can see all three and optimise the query altogether *before evaluation*, not each separately. We are able to therefore avoid the entire subset, for both speed and memory efficiency.
+
+#### -- How many trips have been made in 2014 from "JFK" airport in the month of June?
+
+```{r}
+ans <- flights[origin == "JFK" & month == 6L, length(dest)]
+ans
+```
+
+The function `length()` requires an input argument. We just needed to compute the number of rows in the subset. We could have used any other column as input argument to `length()` really.
+
+This type of operation occurs quite frequently, especially while grouping as we will see in the next section, that *data.table* provides a *special symbol* `.N` for it.
+
+#### Special symbol `.N`: {.bs-callout .bs-callout-info #special-N}
+
+`.N` is a special in-built variable that holds the number of observations in the current group. It is particularly useful when combined with `by` as we'll see in the next section. In the absence of group by operations, it simply returns the number of rows in the subset.
+
+#
+So we can now accomplish the same task by using `.N` as follows:
+
+```{r}
+ans <- flights[origin == "JFK" & month == 6L, .N]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Once again, we subset in `i` to get the *row indices* where `origin` airport equals *"JFK"*, and `month` equals *6*.
+
+* We see that `j` uses only `.N` and no other columns. Therefore the entire subset is not materialised. We simply return the number of rows in the subset (which is just the length of row indices).
+
+* Note that we did not wrap `.N` with `list()` or `.()`. Therefore a vector is returned.
+
+We could have accomplished the same operation by doing `nrow(flights[origin == "JFK" & month == 6L])`. However, it would have to subset the entire *data.table* first corresponding to the *row indices* in `i` *and then* return the rows using `nrow()`, which is unnecessary and inefficient. We will cover this and other optimisation aspects in detail under the *data.table design* vignette.
+
+### g) Great! But how can I refer to columns by names in `j` (like in a *data.frame*)?
+
+You can refer to column names the *data.frame* way using `with = FALSE`.
+
+#### -- Select both `arr_delay` and `dep_delay` columns the *data.frame* way.
+
+```{r}
+ans <- flights[, c("arr_delay", "dep_delay"), with=FALSE]
+head(ans)
+```
+
+The argument is named `with` after the R function `with()` because of similar functionality. Suppose you've a *data.frame* `DF` and you'd like to subset all rows where `x > 1`.
+
+```{r}
+DF = data.frame(x = c(1,1,1,2,2,3,3,3), y = 1:8)
+
+## (1) normal way
+DF[DF$x > 1, ] # data.frame needs that ',' as well
+
+## (2) using with
+DF[with(DF, x > 1), ]
+```
+
+#### {.bs-callout .bs-callout-info #with_false}
+
+* Using `with()` in (2) allows using `DF`'s column `x` as if it were a variable.
+
+ Hence the argument name `with` in *data.table*. Setting `with=FALSE` disables the ability to refer to columns as if they are variables, thereby restoring the "*data.frame* mode".
+
+* We can also *deselect* columns using `-` or `!`. For example:
+
+ ```{r eval=FALSE}
+ ## not run
+
+ # returns all columns except arr_delay and dep_delay
+ ans <- flights[, !c("arr_delay", "dep_delay"), with=FALSE]
+ # or
+ ans <- flights[, -c("arr_delay", "dep_delay"), with=FALSE]
+ ```
+
+* From `v1.9.5+`, we can also select by specifying start and end column names, for e.g, `year:day` to select the first three columns.
+
+ ```{r eval=FALSE}
+ ## not run
+
+ # returns year,month and day
+ ans <- flights[, year:day, with=FALSE]
+ # returns day, month and year
+ ans <- flights[, day:year, with=FALSE]
+ # returns all columns except year, month and day
+ ans <- flights[, -(year:day), with=FALSE]
+ ans <- flights[, !(year:day), with=FALSE]
+ ```
+
+ This is particularly handy while working interactively.
+
+#
+
+`with = TRUE` is default in *data.table* because we can do much more by allowing `j` to handle expressions - especially when combined with `by` as we'll see in a moment.
+
+## 2. Aggregations
+
+We've already seen `i` and `j` from *data.table*'s general form in the previous section. In this section, we'll see how they can be combined together with `by` to perform operations *by group*. Let's look at some examples.
+
+### a) Grouping using `by`
+
+#### -- How can we get the number of trips corresponding to each origin airport?
+
+```{r}
+ans <- flights[, .(.N), by=.(origin)]
+ans
+
+## or equivalently using a character vector in 'by'
+# ans <- flights[, .(.N), by="origin"]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We know `.N` [is a special variable](#special-N) that holds the number of rows in the current group. Grouping by `origin` obtains the number of rows, `.N`, for each group.
+
+* By doing `head(flights)` you can see that the origin airports occur in the order *"JFK"*, *"LGA"* and *"EWR"*. The original order of grouping variables is preserved in the result.
+
+* Since we did not provide a name for the column returned in `j`, it was named `N`automatically by recognising the special symbol `.N`.
+
+* `by` also accepts character vector of column names. It is particularly useful to program with, for e.g., designing a function with the columns to be group by as a function argument.
+
+* When there's only one column or expression to refer to in `j` and `by`, we can drop the `.()` notation. This is purely for convenience. We could instead do:
+
+ ```{r}
+ ans <- flights[, .N, by=origin]
+ ans
+ ```
+
+ We'll use this convenient form wherever applicable hereafter.
+
+#
+
+#### -- How can we calculate the number of trips for each origin airport for carrier code *"AA"*? {#origin-.N}
+
+The unique carrier code *"AA"* corresponds to *American Airlines Inc.*
+
+```{r}
+ans <- flights[carrier == "AA", .N, by=origin]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We first obtain the row indices for the expression `carrier == "AA"` from `i`.
+
+* Using those *row indices*, we obtain the number of rows while grouped by `origin`. Once again no columns are actually materialised here, because the `j-expression` does not require any columns to be actually subsetted and is therefore fast and memory efficient.
+
+#### -- How can we get the total number of trips for each `origin, dest` pair for carrier code *"AA"*? {#origin-dest-.N}
+
+```{r}
+ans <- flights[carrier == "AA", .N, by=.(origin,dest)]
+head(ans)
+
+## or equivalently using a character vector in 'by'
+# ans <- flights[carrier == "AA", .N, by=c("origin", "dest")]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `by` accepts multiple columns. We just provide all the columns by which to group by.
+
+#### -- How can we get the average arrival and departure delay for each `orig,dest` pair for each month for carrier code *"AA"*? {#origin-dest-month}
+
+```{r}
+ans <- flights[carrier == "AA",
+ .(mean(arr_delay), mean(dep_delay)),
+ by=.(origin, dest, month)]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We did not provide column names for expressions in `j`, they were automatically generated (`V1`, `V2`).
+
+* Once again, note that the input order of grouping columns is preserved in the result.
+
+#
+
+Now what if we would like to order the result by those grouping columns `origin`, `dest` and `month`?
+
+### b) keyby
+
+*data.table* retaining the original order of groups is intentional and by design. There are cases when preserving the original order is essential. But at times we would like to automatically sort by the variables we grouped by.
+
+#### -- So how can we directly order by all the grouping variables?
+
+```{r}
+ans <- flights[carrier == "AA",
+ .(mean(arr_delay), mean(dep_delay)),
+ keyby=.(origin, dest, month)]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* All we did was to change `by` to `keyby`. This automatically orders the result by the grouping variables in increasing order. Note that `keyby()` is applied after performing the operation, i.e., on the computed result.
+
+**Keys:** Actually `keyby` does a little more than *just ordering*. It also *sets a key* after ordering by setting an *attribute* called `sorted`. But we'll learn more about `keys` in the next vignette.
+
+For now, all you've to know is you can use `keyby` to automatically order by the columns specified in `by`.
+
+### c) Chaining
+
+Let's reconsider the task of [getting the total number of trips for each `origin, dest` pair for carrier *"AA"*](#origin-dest-.N).
+
+```{r}
+ans <- flights[carrier == "AA", .N, by = .(origin, dest)]
+```
+
+#### -- How can we order `ans` using the columns `origin` in ascending order, and `dest` in descending order?
+
+We can store the intermediate result in a variable, and then use `order(origin, -dest)` on that variable. It seems fairly straightforward.
+
+```{r}
+ans <- ans[order(origin, -dest)]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Recall that we can use "-" on a *character* column in `order()` within the frame of a *data.table*. This is possible to due *data.table*'s internal query optimisation.
+
+* Also recall that `order(...)` with the frame of a *data.table* is *automatically optimised* to use *data.table*'s internal fast radix order `forder()` for speed. So you can keep using the already *familiar* base R functions without compromising in speed or memory efficiency that *data.table* offers. We will cover this in more detail in the *data.table internals* vignette.
+
+#
+
+But this requires having to assign the intermediate result and then overwriting that result. We can do one better and avoid this intermediate assignment on to a variable altogther by `chaining` expressions.
+
+```{r}
+ans <- flights[carrier == "AA", .N, by=.(origin, dest)][order(origin, -dest)]
+head(ans, 10)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We can tack expressions one after another, *forming a chain* of operations, i.e., `DT[ ... ][ ... ][ ... ]`.
+
+* Or you can also chain them vertically:
+
+ ```{r eval=FALSE}
+ DT[ ...
+ ][ ...
+ ][ ...
+ ]
+ ```
+
+### d) Expressions in `by`
+
+#### -- Can `by` accept *expressions* as well or just take columns?
+
+Yes it does. As an example, if we would like to find out how many flights started late but arrived early (or on time), started and arrived late etc...
+
+```{r}
+ans <- flights[, .N, .(dep_delay>0, arr_delay>0)]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* The last row corresponds to `dep_delay > 0 = TRUE` and `arr_delay > 0 = FALSE`. We can see that `r flights[!is.na(arr_delay) & !is.na(dep_delay), .N, .(dep_delay>0, arr_delay>0)][, N[4L]]` flights started late but arrived early (or on time).
+
+* Note that we did not provide any names to `by-expression`. And names have been automatically assigned in the result.
+
+* You can provide other columns along with expressions, for example: `DT[, .N, by=.(a, b>0)]`.
+
+### e) Multiple columns in `j` - `.SD`
+
+#### -- Do we have to compute `mean()` for each column individually?
+
+It is of course not practical to have to type `mean(myCol)` for every column one by one. What if you had a 100 columns to compute `mean()` of?
+
+How can we do this efficiently? To get there, refresh on [this tip](#tip-1) - *"As long as j-expression returns a list, each element of the list will be converted to a column in the resulting data.table"*. Suppose we can refer to the *data subset for each group* as a variable *while grouping*, then we can loop through all the columns of that variable using the already familiar base function `lapply()`. We don't have to learn any new function.
+
+#### Special symbol `.SD`: {.bs-callout .bs-callout-info #special-SD}
+
+*data.table* provides a *special* symbol, called `.SD`. It stands for **S**ubset of **D**ata. It by itself is a *data.table* that holds the data for *the current group* defined using `by`.
+
+Recall that a *data.table* is internally a list as well with all its columns of equal length.
+
+#
+
+Let's use the [*data.table* `DT` from before](#what-is-datatable-1a) to get a glimpse of what `.SD` looks like.
+
+```{r}
+DT
+
+DT[, print(.SD), by=ID]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `.SD` contains all the columns *except the grouping columns* by default.
+
+* It is also generated by preserving the original order - data corresponding to `ID = "b"`, then `ID = "a"`, and then `ID = "c"`.
+
+#
+
+To compute on (multiple) columns, we can then simply use the base R function `lapply()`.
+
+```{r}
+DT[, lapply(.SD, mean), by=ID]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `.SD` holds the rows corresponding to columns *a*, *b* and *c* for that group. We compute the `mean()` on each of these columns using the already familiar base function `lapply()`.
+
+* Each group returns a list of three elements containing the mean value which will become the columns of the resulting `data.table`.
+
+* Since `lapply()` returns a *list*, there is no need to wrap it with an additional `.()` (if necessary, refer to [this tip](#tip-1)).
+
+#
+
+We are almost there. There is one little thing left to address. In our `flights` *data.table*, we only wanted to calculate the `mean()` of two columns `arr_delay` and `dep_delay`. But `.SD` would contain all the columns other than the grouping variables by default.
+
+#### -- How can we specify just the columns we would like to compute the `mean()` on?
+
+#### .SDcols {.bs-callout .bs-callout-info}
+
+Using the argument `.SDcols`. It accepts either column names or column indices. For example, `.SDcols = c("arr_delay", "dep_delay")` ensures that `.SD` contains only these two columns for each group.
+
+Similar to the [with = FALSE section](#with_false), you can also provide the columns to remove instead of columns to keep using `-` or `!` sign as well as select consecutive columns as `colA:colB` and deselect consecutive columns as `!(colA:colB) or `-(colA:colB)`.
+
+#
+Now let us try to use `.SD` along with `.SDcols` to get the `mean()` of `arr_delay` and `dep_delay` columns grouped by `origin`, `dest` and `month`.
+
+```{r}
+flights[carrier == "AA", ## Only on trips with carrier "AA"
+ lapply(.SD, mean), ## compute the mean
+ by=.(origin, dest, month), ## for every 'origin,dest,month'
+ .SDcols=c("arr_delay", "dep_delay")] ## for just those specified in .SDcols
+```
+
+### f) Subset `.SD` for each group:
+
+#### -- How can we return the first two rows for each `month`?
+
+```{r}
+ans <- flights[, head(.SD, 2), by=month]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `.SD` is a *data.table* that holds all the rows for *that group*. We simply subset the first two rows as we have seen [here](#subset-rows-integer) already.
+
+* For each group, `head(.SD, 2)` returns the first two rows as a *data.table* which is also a list. So we do not have to wrap it with `.()`.
+
+### g) Why keep `j` so flexible?
+
+So that we have a consistent syntax and keep using already existing (and familiar) base functions instead of learning new functions. To illustrate, let us use the *data.table* `DT` we created at the very beginning under [What is a data.table?](#what-is-datatable-1a) section.
+
+#### -- How can we concatenate columns `a` and `b` for each group in `ID`?
+
+```{r}
+DT[, .(val = c(a,b)), by=ID]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* That's it. There is no special syntax required. All we need to know is the base function `c()` which concatenates vectors and [the tip from before](#tip-1).
+
+#### -- What if we would like to have all the values of column `a` and `b` concatenated, but returned as a list column?
+
+```{r}
+DT[, .(val = list(c(a,b))), by=ID]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Here, we first concatenate the values with `c(a,b)` for each group, and wrap that with `list()`. So for each group, we return a list of all concatenated values.
+
+* Note those commas are for display only. A list column can contain any object in each cell, and in this example, each cell is itself a vector and some cells contain longer vectors than others.
+
+#
+Once you start internalising usage in `j`, you will realise how powerful the syntax can be. A very useful way to understand it is by playing around, with the help of `print()`.
+
+For example:
+
+```{r}
+## (1) look at the difference between
+DT[, print(c(a,b)), by=ID]
+
+## (2) and
+DT[, print(list(c(a,b))), by=ID]
+```
+
+In (1), for each group, a vector is returned, with length = 6,4,2 here. However (2) returns a list of length 1 for each group, with its first element holding vectors of length 6,4,2. Therefore (1) results in a length of ` 6+4+2 = `r 6+4+2``, whereas (2) returns `1+1+1=`r 1+1+1``.
+
+## Summary
+
+The general form of *data.table* syntax is:
+
+```{r eval=FALSE}
+DT[i, j, by]
+```
+
+We have seen so far that,
+
+#### Using `i`: {.bs-callout .bs-callout-info}
+
+* We can subset rows similar to a *data.frame* - except you don't have to use `DT$` repetitively since columns within the frame of a *data.table* are seen as if they are *variables*.
+
+* We can also sort a *data.table* using `order()`, which internally uses *data.table*'s fast order for performance.
+
+We can do much more in `i` by keying a *data.table*, which allows blazing fast subsets and joins. We will see this in the *"Keys and fast binary search based subsets"* and *"Joins and rolling joins"* vignette.
+
+#### Using `j`: {.bs-callout .bs-callout-info}
+
+1. Select columns the *data.table* way: `DT[, .(colA, colB)]`.
+
+2. Select columns the *data.frame* way: `DT[, c("colA", "colB"), with=FALSE]`.
+
+3. Compute on columns: `DT[, .(sum(colA), mean(colB))]`.
+
+4. Provide names if necessary: `DT[, .(sA =sum(colA), mB = mean(colB))]`.
+
+5. Combine with `i`: `DT[colA > value, sum(colB)]`.
+
+#
+
+#### Using `by`: {.bs-callout .bs-callout-info}
+
+* Using `by`, we can group by columns by specifying a *list of columns* or a *character vector of column names* or even *expressions*. The flexibility of `j`, combined with `by` and `i` makes for a very powerful syntax.
+
+* `by` can handle multiple columns and also *expressions*.
+
+* We can `keyby` grouping columns to automatically sort the grouped result.
+
+* We can use `.SD` and `.SDcols` in `j` to operate on multiple columns using already familiar base functions. Here are some examples:
+
+ 1. `DT[, lapply(.SD, fun), by=., .SDcols=...]` - applies `fun` to all columns specified in `.SDcols` while grouping by the columns specified in `by`.
+
+ 2. `DT[, head(.SD, 2), by=.]` - return the first two rows for each group.
+
+ 3. `DT[col > val, head(.SD, 1), by=.]` - combine `i` along with `j` and `by`.
+
+#
+
+#### And remember the tip: {.bs-callout .bs-callout-warning}
+
+As long as `j` returns a *list*, each element of the list will become a column in the resulting *data.table*.
+
+#
+
+We will see how to *add/update/delete* columns *by reference* and how to combine them with `i` and `by` in the next vignette.
+
+***
+
diff --git a/inst/doc/datatable-intro-vignette.html b/inst/doc/datatable-intro-vignette.html
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+
+<div id="header">
+<h1 class="title">Introduction to data.table</h1>
+<h4 class="date"><em>2015-09-18</em></h4>
+</div>
+
+
+<p>This vignette introduces the <em>data.table</em> syntax, its general form, how to <em>subset</em> rows, <em>select and compute</em> on columns and perform aggregations <em>by group</em>. Familiarity with <em>data.frame</em> data structure from base R is useful, but not essential to follow this vignette.</p>
+<hr />
+<div id="data-analysis-using-data.table" class="section level2">
+<h2>Data analysis using data.table</h2>
+<p>Data manipulation operations such as <em>subset</em>, <em>group</em>, <em>update</em>, <em>join</em> etc., are all inherently related. Keeping these <em>related operations together</em> allows for:</p>
+<ul>
+<li><p><em>concise</em> and <em>consistent</em> syntax irrespective of the set of operations you would like to perform to achieve your end goal.</p></li>
+<li><p>performing analysis <em>fluidly</em> without the cognitive burden of having to map each operation to a particular function from a set of functions available before to perform the analysis.</p></li>
+<li><p><em>automatically</em> optimising operations internally, and very effectively, by knowing precisely the data required for each operation and therefore very fast and memory efficient.</p></li>
+</ul>
+<p>Briefly, if you are interested in reducing <em>programming</em> and <em>compute</em> time tremendously, then this package is for you. The philosophy that <em>data.table</em> adheres to makes this possible. Our goal is to illustrate it through this series of vignettes.</p>
+</div>
+<div id="data" class="section level2">
+<h2>Data</h2>
+<p>In this vignette, we will use <a href="https://github.com/arunsrinivasan/flights/wiki/NYC-Flights-2014-data">NYC-flights14</a> data. It contains On-Time flights data from the <a href="http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236">Bureau of Transporation Statistics</a> for all the flights that departed from New York City airports in 2014 (inspired by <a href="https://github.com/hadley/nycflights13">nycflights13</a>). The data is available only for Jan-Oct’14.</p>
+<p>We can use <em>data.table’s</em> fast file reader <code>fread</code> to load <em>flights</em> directly as follows:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights <-<span class="st"> </span><span class="kw">fread</span>(<span class="st">"flights14.csv"</span>)
+flights
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13</span>
+<span class="co"># --- </span>
+<span class="co"># 253312: 2014 10 31 1 -30 UA LGA IAH 201 1416 14</span>
+<span class="co"># 253313: 2014 10 31 -5 -14 UA EWR IAH 189 1400 8</span>
+<span class="co"># 253314: 2014 10 31 -8 16 MQ LGA RDU 83 431 11</span>
+<span class="co"># 253315: 2014 10 31 -4 15 MQ LGA DTW 75 502 11</span>
+<span class="co"># 253316: 2014 10 31 -5 1 MQ LGA SDF 110 659 8</span>
+<span class="kw">dim</span>(flights)
+<span class="co"># [1] 253316 11</span></code></pre></div>
+<p>Aside: <code>fread</code> accepts <code>http</code> and <code>https</code> URLs directly as well as operating system commands such as <code>sed</code> and <code>awk</code> output. See <code>?fread</code> for examples.</p>
+</div>
+<div id="introduction" class="section level2">
+<h2>Introduction</h2>
+<p>In this vignette, we will</p>
+<ol style="list-style-type: decimal">
+<li><p>start with basics - what is a <em>data.table</em>, its general form, how to <em>subset</em> rows, <em>select and compute</em> on columns</p></li>
+<li><p>and then we will look at performing data aggregations by group,</p></li>
+</ol>
+</div>
+<div id="basics-1" class="section level2">
+<h2>1. Basics</h2>
+<div id="what-is-datatable-1a" class="section level3">
+<h3>a) What is data.table?</h3>
+<p><em>data.table</em> is an R package that provides <strong>an enhanced version</strong> of <em>data.frames</em>. In the <a href="#data">Data</a> section, we already created a <em>data.table</em> using <code>fread()</code>. We can also create one using the <code>data.table()</code> function. Here is an example:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT =<span class="st"> </span><span class="kw">data.table</span>(<span class="dt">ID =</span> <span class="kw">c</span>(<span class="st">"b"</span>,<span class="st">"b"</span>,<span class="st">"b"</span>,<span class="st">"a"</span>,<span class="st">"a"</span>,<span class="st">"c"</span>), <span class="dt">a =</span> <span class="dv">1</span>:<span class= [...]
+DT
+<span class="co"># ID a b c</span>
+<span class="co"># 1: b 1 7 13</span>
+<span class="co"># 2: b 2 8 14</span>
+<span class="co"># 3: b 3 9 15</span>
+<span class="co"># 4: a 4 10 16</span>
+<span class="co"># 5: a 5 11 17</span>
+<span class="co"># 6: c 6 12 18</span>
+<span class="kw">class</span>(DT$ID)
+<span class="co"># [1] "character"</span></code></pre></div>
+<p>You can also convert existing objects to a <em>data.table</em> using <code>as.data.table()</code>.</p>
+<div id="note-that" class="section level4 bs-callout bs-callout-info">
+<h4>Note that:</h4>
+<ul>
+<li><p>Unlike <em>data.frames</em>, columns of <code>character</code> type are <em>never</em> converted to <code>factors</code> by default.</p></li>
+<li><p>Row numbers are printed with a <code>:</code> in order to visually separate the row number from the first column.</p></li>
+<li><p>When the number of rows to print exceeds the global option <code>datatable.print.nrows</code> (default = 100), it automatically prints only the top 5 and bottom 5 rows (as can be seen in the <a href="#data">Data</a> section).</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">getOption</span>(<span class="st">"datatable.print.nrows"</span>)
+<span class="co"># [1] 100</span></code></pre></div></li>
+<li><p><em>data.table</em> doesn’t set or use <em>row names</em>, ever. We will see as to why in <em>“Keys and fast binary search based subset”</em> vignette.</p></li>
+</ul>
+</div>
+</div>
+<div id="enhanced-1b" class="section level3">
+<h3>b) General form - in what way is a data.table <em>enhanced</em>?</h3>
+<p>In contrast to a <em>data.frame</em>, you can do <em>a lot more</em> than just subsetting rows and selecting columns within the frame of a <em>data.table</em>, i.e., within <code>[ ... ]</code>. To understand it we will have to first look at the <em>general form</em> of <em>data.table</em> syntax, as shown below:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT[i, j, by]
+
+## R: i j by
+## SQL: where select | update group by</code></pre></div>
+<p>Users who have a SQL background might perhaps immediately relate to this syntax.</p>
+<div id="the-way-to-read-it-out-loud-is" class="section level4 bs-callout bs-callout-info">
+<h4>The way to read it (out loud) is:</h4>
+<p>Take <code>DT</code>, subset rows using <code>i</code>, then calculate <code>j</code>, grouped by <code>by</code>.</p>
+</div>
+</div>
+</div>
+<div id="section" class="section level1">
+<h1></h1>
+<p>Let’s begin by looking at <code>i</code> and <code>j</code> first - subsetting rows and operating on columns.</p>
+<div id="c-subset-rows-in-i" class="section level3">
+<h3>c) Subset rows in <code id="subset-i-1c">i</code></h3>
+<div id="get-all-the-flights-with-jfk-as-the-origin-airport-in-the-month-of-june." class="section level4">
+<h4>– Get all the flights with “JFK” as the origin airport in the month of June.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[origin ==<span class="st"> "JFK"</span> &<span class="st"> </span>month ==<span class="st"> </span>6L]
+<span class="kw">head</span>(ans)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 6 1 -9 -5 AA JFK LAX 324 2475 8</span>
+<span class="co"># 2: 2014 6 1 -10 -13 AA JFK LAX 329 2475 12</span>
+<span class="co"># 3: 2014 6 1 18 -1 AA JFK LAX 326 2475 7</span>
+<span class="co"># 4: 2014 6 1 -6 -16 AA JFK LAX 320 2475 10</span>
+<span class="co"># 5: 2014 6 1 -4 -45 AA JFK LAX 326 2475 18</span>
+<span class="co"># 6: 2014 6 1 -6 -23 AA JFK LAX 329 2475 14</span></code></pre></div>
+</div>
+<div id="section-1" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Within the frame of a <em>data.table</em>, columns can be referred to <em>as if they are variables</em>. Therefore, we simply refer to <code>dest</code> and <code>month</code> as if they are variables. We do not need to add the prefix <code>flights$</code> each time. However using <code>flights$dest</code> and <code>flights$month</code> would work just fine.</p></li>
+<li><p>The <em>row indices</em> that satisfies the condition <code>origin == "JFK" & month == 6L</code> are computed, and since there is nothing else left to do, a <em>data.table</em> all columns from <code>flights</code> corresponding to those <em>row indices</em> are simply returned.</p></li>
+<li><p>A comma after the condition is also not required in <code>i</code>. But <code>flights[dest == "JFK" & month == 6L, ]</code> would work just fine. In <em>data.frames</em> however, the comma is necessary.</p></li>
+</ul>
+</div>
+<div id="subset-rows-integer" class="section level4">
+<h4>– Get the first two rows from <code>flights</code>.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[<span class="dv">1</span>:<span class="dv">2</span>]
+ans
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11</span></code></pre></div>
+</div>
+<div id="section-2" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li>In this case, there is no condition. The row indices are already provided in <code>i</code>. We therefore return a <em>data.table</em> with all columns from <code>flight</code> for those <em>row indices</em>.</li>
+</ul>
+</div>
+<div id="sort-flights-first-by-column-origin-in-ascending-order-and-then-by-dest-in-descending-order" class="section level4">
+<h4>– Sort <code>flights</code> first by column <code>origin</code> in <em>ascending</em> order, and then by <code>dest</code> in <em>descending</em> order:</h4>
+<p>We can use the base R function <code>order()</code> to accomplish this.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[<span class="kw">order</span>(origin, -dest)]
+<span class="kw">head</span>(ans)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 5 6 49 EV EWR XNA 195 1131 8</span>
+<span class="co"># 2: 2014 1 6 7 13 EV EWR XNA 190 1131 8</span>
+<span class="co"># 3: 2014 1 7 -6 -13 EV EWR XNA 179 1131 8</span>
+<span class="co"># 4: 2014 1 8 -7 -12 EV EWR XNA 184 1131 8</span>
+<span class="co"># 5: 2014 1 9 16 7 EV EWR XNA 181 1131 8</span>
+<span class="co"># 6: 2014 1 13 66 66 EV EWR XNA 188 1131 9</span></code></pre></div>
+</div>
+<div id="order-is-internally-optimised" class="section level4 bs-callout bs-callout-info">
+<h4><code>order()</code> is internally optimised</h4>
+<ul>
+<li><p>We can use “-” on a <em>character</em> columns within the frame of a <em>data.table</em> to sort in decreasing order.</p></li>
+<li><p>In addition, <code>order(...)</code> within the frame of a <em>data.table</em> uses <em>data.table</em>’s internal fast radix order <code>forder()</code>, which is much faster than <code>base::order</code>. Here’s a small example to highlight the difference.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">odt =<span class="st"> </span><span class="kw">data.table</span>(<span class="dt">col=</span><span class="kw">sample</span>(<span class="fl">1e7</span>))
+(t1 <-<span class="st"> </span><span class="kw">system.time</span>(ans1 <-<span class="st"> </span>odt[base::<span class="kw">order</span>(col)])) ## uses order from base R
+<span class="co"># user system elapsed </span>
+<span class="co"># 4.116 0.016 4.128</span>
+(t2 <-<span class="st"> </span><span class="kw">system.time</span>(ans2 <-<span class="st"> </span>odt[<span class="kw">order</span>(col)])) ## uses data.table's forder
+<span class="co"># user system elapsed </span>
+<span class="co"># 0.308 0.012 0.319</span>
+(<span class="kw">identical</span>(ans1, ans2))
+<span class="co"># [1] TRUE</span></code></pre></div></li>
+</ul>
+<p>The speedup here is <strong>~13x</strong>. We will discuss <em>data.table</em>’s fast order in more detail in the <em>data.table internals</em> vignette.</p>
+<ul>
+<li>This is so that you can improve performance tremendously while using already familiar functions.</li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-3" class="section level1">
+<h1></h1>
+<div id="d-select-columns-in-j" class="section level3">
+<h3>d) Select column(s) in <code id="select-j-1d">j</code></h3>
+<div id="select-arr_delay-column-but-return-it-as-a-vector." class="section level4">
+<h4>– Select <code>arr_delay</code> column, but return it as a <em>vector</em>.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, arr_delay]
+<span class="kw">head</span>(ans)
+<span class="co"># [1] 13 13 9 -26 1 0</span></code></pre></div>
+</div>
+<div id="section-4" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Since columns can be referred to as if they are variables within the frame of data.tables, we directly refer to the <em>variable</em> we want to subset. Since we want <em>all the rows</em>, we simply skip <code>i</code>.</p></li>
+<li><p>It returns <em>all</em> the rows for the column <code>arr_delay</code>.</p></li>
+</ul>
+</div>
+<div id="select-arr_delay-column-but-return-as-a-data.table-instead." class="section level4">
+<h4>– Select <code>arr_delay</code> column, but return as a <em>data.table</em> instead.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, <span class="kw">list</span>(arr_delay)]
+<span class="kw">head</span>(ans)
+<span class="co"># arr_delay</span>
+<span class="co"># 1: 13</span>
+<span class="co"># 2: 13</span>
+<span class="co"># 3: 9</span>
+<span class="co"># 4: -26</span>
+<span class="co"># 5: 1</span>
+<span class="co"># 6: 0</span></code></pre></div>
+</div>
+<div id="section-5" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We wrap the <em>variables</em> (column names) within <code>list()</code>, which ensures that a <em>data.table</em> is returned. In case of a single column name, not wrapping with <code>list()</code> returns a vector instead, as seen in the <a href="#select-j-1d">previous example</a>.</p></li>
+<li><p><em>data.table</em> also allows using <code>.()</code> to wrap columns with. It is an <em>alias</em> to <code>list()</code>; they both mean the same. Feel free to use whichever you prefer.</p>
+<p>We will continue to use <code>.()</code> from here on.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-6" class="section level1">
+<h1></h1>
+<p><em>data.tables</em> (and <em>data.frames</em>) are internally <em>lists</em> as well, but with all its columns of equal length and with a <em>class</em> attribute. Allowing <code>j</code> to return a <em>list</em> enables converting and returning a <em>data.table</em> very efficiently.</p>
+<div id="tip-1" class="section level4 bs-callout bs-callout-warning">
+<h4>Tip:</h4>
+<p>As long as <code>j-expression</code> returns a <em>list</em>, each element of the list will be converted to a column in the resulting <em>data.table</em>. This makes <code>j</code> quite powerful, as we will see shortly.</p>
+</div>
+<div id="select-both-arr_delay-and-dep_delay-columns." class="section level4">
+<h4>– Select both <code>arr_delay</code> and <code>dep_delay</code> columns.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, .(arr_delay, dep_delay)]
+<span class="kw">head</span>(ans)
+<span class="co"># arr_delay dep_delay</span>
+<span class="co"># 1: 13 14</span>
+<span class="co"># 2: 13 -3</span>
+<span class="co"># 3: 9 2</span>
+<span class="co"># 4: -26 -8</span>
+<span class="co"># 5: 1 2</span>
+<span class="co"># 6: 0 4</span>
+
+## alternatively
+<span class="co"># ans <- flights[, list(arr_delay, dep_delay)]</span></code></pre></div>
+</div>
+<div id="section-7" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li>Wrap both columns within <code>.()</code>, or <code>list()</code>. That’s it.</li>
+</ul>
+</div>
+</div>
+<div id="section-8" class="section level1">
+<h1></h1>
+<div id="select-both-arr_delay-and-dep_delay-columns-and-rename-them-to-delay_arr-and-delay_dep." class="section level4">
+<h4>– Select both <code>arr_delay</code> and <code>dep_delay</code> columns <em>and</em> rename them to <code>delay_arr</code> and <code>delay_dep</code>.</h4>
+<p>Since <code>.()</code> is just an alias for <code>list()</code>, we can name columns as we would while creating a <em>list</em>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, .(<span class="dt">delay_arr =</span> arr_delay, <span class="dt">delay_dep =</span> dep_delay)]
+<span class="kw">head</span>(ans)
+<span class="co"># delay_arr delay_dep</span>
+<span class="co"># 1: 13 14</span>
+<span class="co"># 2: 13 -3</span>
+<span class="co"># 3: 9 2</span>
+<span class="co"># 4: -26 -8</span>
+<span class="co"># 5: 1 2</span>
+<span class="co"># 6: 0 4</span></code></pre></div>
+<p>That’s it.</p>
+</div>
+<div id="e-compute-or-do-in-j" class="section level3">
+<h3>e) Compute or <em>do</em> in <code>j</code></h3>
+<div id="how-many-trips-have-had-total-delay-0" class="section level4">
+<h4>– How many trips have had total delay < 0?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, <span class="kw">sum</span>((arr_delay +<span class="st"> </span>dep_delay) <<span class="st"> </span><span class="dv">0</span>)]
+ans
+<span class="co"># [1] 141814</span></code></pre></div>
+</div>
+<div id="whats-happening-here" class="section level4 bs-callout bs-callout-info">
+<h4>What’s happening here?</h4>
+<ul>
+<li><em>data.table</em>’s <code>j</code> can handle more than just <em>selecting columns</em> - it can handle <em>expressions</em>, i.e., <em>compute on columns</em>. This shouldn’t be surprising, as columns can be referred to as if they are variables. Then we should be able to <em>compute</em> by calling functions on those variables. And that’s what precisely happens here.</li>
+</ul>
+</div>
+</div>
+<div id="f-subset-in-i-and-do-in-j" class="section level3">
+<h3>f) Subset in <code>i</code> <em>and</em> do in <code>j</code></h3>
+<div id="calculate-the-average-arrival-and-departure-delay-for-all-flights-with-jfk-as-the-origin-airport-in-the-month-of-june." class="section level4">
+<h4>– Calculate the average arrival and departure delay for all flights with “JFK” as the origin airport in the month of June.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[origin ==<span class="st"> "JFK"</span> &<span class="st"> </span>month ==<span class="st"> </span>6L,
+ .(<span class="dt">m_arr=</span><span class="kw">mean</span>(arr_delay), <span class="dt">m_dep=</span><span class="kw">mean</span>(dep_delay))]
+ans
+<span class="co"># m_arr m_dep</span>
+<span class="co"># 1: 5.839349 9.807884</span></code></pre></div>
+</div>
+<div id="section-9" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We first subset in <code>i</code> to find matching <em>row indices</em> where <code>origin</code> airport equals <em>“JFK”</em>, and <code>month</code> equals <em>6</em>. At this point, we <em>do not</em> subset the entire <em>data.table</em> corresponding to those rows.</p></li>
+<li><p>Now, we look at <code>j</code> and find that it uses only <em>two columns</em>. And what we have to do is to compute their <code>mean()</code>. Therefore we subset just those columns corresponding to the matching rows, and compute their <code>mean()</code>.</p></li>
+</ul>
+<p>Because the three main components of the query (<code>i</code>, <code>j</code> and <code>by</code>) are <em>together</em> inside <code>[...]</code>, <em>data.table</em> can see all three and optimise the query altogether <em>before evaluation</em>, not each separately. We are able to therefore avoid the entire subset, for both speed and memory efficiency.</p>
+</div>
+<div id="how-many-trips-have-been-made-in-2014-from-jfk-airport-in-the-month-of-june" class="section level4">
+<h4>– How many trips have been made in 2014 from “JFK” airport in the month of June?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[origin ==<span class="st"> "JFK"</span> &<span class="st"> </span>month ==<span class="st"> </span>6L, <span class="kw">length</span>(dest)]
+ans
+<span class="co"># [1] 8422</span></code></pre></div>
+<p>The function <code>length()</code> requires an input argument. We just needed to compute the number of rows in the subset. We could have used any other column as input argument to <code>length()</code> really.</p>
+<p>This type of operation occurs quite frequently, especially while grouping as we will see in the next section, that <em>data.table</em> provides a <em>special symbol</em> <code>.N</code> for it.</p>
+</div>
+<div id="special-N" class="section level4 bs-callout bs-callout-info">
+<h4>Special symbol <code>.N</code>:</h4>
+<p><code>.N</code> is a special in-built variable that holds the number of observations in the current group. It is particularly useful when combined with <code>by</code> as we’ll see in the next section. In the absence of group by operations, it simply returns the number of rows in the subset.</p>
+</div>
+</div>
+</div>
+<div id="section-10" class="section level1">
+<h1></h1>
+<p>So we can now accomplish the same task by using <code>.N</code> as follows:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[origin ==<span class="st"> "JFK"</span> &<span class="st"> </span>month ==<span class="st"> </span>6L, .N]
+ans
+<span class="co"># [1] 8422</span></code></pre></div>
+<div id="section-11" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Once again, we subset in <code>i</code> to get the <em>row indices</em> where <code>origin</code> airport equals <em>“JFK”</em>, and <code>month</code> equals <em>6</em>.</p></li>
+<li><p>We see that <code>j</code> uses only <code>.N</code> and no other columns. Therefore the entire subset is not materialised. We simply return the number of rows in the subset (which is just the length of row indices).</p></li>
+<li><p>Note that we did not wrap <code>.N</code> with <code>list()</code> or <code>.()</code>. Therefore a vector is returned.</p></li>
+</ul>
+<p>We could have accomplished the same operation by doing <code>nrow(flights[origin == "JFK" & month == 6L])</code>. However, it would have to subset the entire <em>data.table</em> first corresponding to the <em>row indices</em> in <code>i</code> <em>and then</em> return the rows using <code>nrow()</code>, which is unnecessary and inefficient. We will cover this and other optimisation aspects in detail under the <em>data.table design</em> vignette.</p>
+</div>
+<div id="g-great-but-how-can-i-refer-to-columns-by-names-in-j-like-in-a-data.frame" class="section level3">
+<h3>g) Great! But how can I refer to columns by names in <code>j</code> (like in a <em>data.frame</em>)?</h3>
+<p>You can refer to column names the <em>data.frame</em> way using <code>with = FALSE</code>.</p>
+<div id="select-both-arr_delay-and-dep_delay-columns-the-data.frame-way." class="section level4">
+<h4>– Select both <code>arr_delay</code> and <code>dep_delay</code> columns the <em>data.frame</em> way.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, <span class="kw">c</span>(<span class="st">"arr_delay"</span>, <span class="st">"dep_delay"</span>), with=<span class="ot">FALSE</span>]
+<span class="kw">head</span>(ans)
+<span class="co"># arr_delay dep_delay</span>
+<span class="co"># 1: 13 14</span>
+<span class="co"># 2: 13 -3</span>
+<span class="co"># 3: 9 2</span>
+<span class="co"># 4: -26 -8</span>
+<span class="co"># 5: 1 2</span>
+<span class="co"># 6: 0 4</span></code></pre></div>
+<p>The argument is named <code>with</code> after the R function <code>with()</code> because of similar functionality. Suppose you’ve a <em>data.frame</em> <code>DF</code> and you’d like to subset all rows where <code>x > 1</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DF =<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">x =</span> <span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">3</span>,<span class="dv">3</span>), <span class="dt">y =</span> <span class="dv">1</span>:<span class="dv">8</span>)
+
+## (1) normal way
+DF[DF$x ><span class="st"> </span><span class="dv">1</span>, ] <span class="co"># data.frame needs that ',' as well</span>
+<span class="co"># x y</span>
+<span class="co"># 4 2 4</span>
+<span class="co"># 5 2 5</span>
+<span class="co"># 6 3 6</span>
+<span class="co"># 7 3 7</span>
+<span class="co"># 8 3 8</span>
+
+## (2) using with
+DF[<span class="kw">with</span>(DF, x ><span class="st"> </span><span class="dv">1</span>), ]
+<span class="co"># x y</span>
+<span class="co"># 4 2 4</span>
+<span class="co"># 5 2 5</span>
+<span class="co"># 6 3 6</span>
+<span class="co"># 7 3 7</span>
+<span class="co"># 8 3 8</span></code></pre></div>
+</div>
+<div id="with_false" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Using <code>with()</code> in (2) allows using <code>DF</code>’s column <code>x</code> as if it were a variable.</p>
+<p>Hence the argument name <code>with</code> in <em>data.table</em>. Setting <code>with=FALSE</code> disables the ability to refer to columns as if they are variables, thereby restoring the “<em>data.frame</em> mode”.</p></li>
+<li><p>We can also <em>deselect</em> columns using <code>-</code> or <code>!</code>. For example:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## not run
+
+<span class="co"># returns all columns except arr_delay and dep_delay</span>
+ans <-<span class="st"> </span>flights[, !<span class="kw">c</span>(<span class="st">"arr_delay"</span>, <span class="st">"dep_delay"</span>), with=<span class="ot">FALSE</span>]
+<span class="co"># or</span>
+ans <-<span class="st"> </span>flights[, -<span class="kw">c</span>(<span class="st">"arr_delay"</span>, <span class="st">"dep_delay"</span>), with=<span class="ot">FALSE</span>]</code></pre></div></li>
+<li><p>From <code>v1.9.5+</code>, we can also select by specifying start and end column names, for e.g, <code>year:day</code> to select the first three columns.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## not run
+
+<span class="co"># returns year,month and day</span>
+ans <-<span class="st"> </span>flights[, year:day, with=<span class="ot">FALSE</span>]
+<span class="co"># returns day, month and year</span>
+ans <-<span class="st"> </span>flights[, day:year, with=<span class="ot">FALSE</span>]
+<span class="co"># returns all columns except year, month and day</span>
+ans <-<span class="st"> </span>flights[, -(year:day), with=<span class="ot">FALSE</span>]
+ans <-<span class="st"> </span>flights[, !(year:day), with=<span class="ot">FALSE</span>]</code></pre></div>
+<p>This is particularly handy while working interactively.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-12" class="section level1">
+<h1></h1>
+<p><code>with = TRUE</code> is default in <em>data.table</em> because we can do much more by allowing <code>j</code> to handle expressions - especially when combined with <code>by</code> as we’ll see in a moment.</p>
+<div id="aggregations" class="section level2">
+<h2>2. Aggregations</h2>
+<p>We’ve already seen <code>i</code> and <code>j</code> from <em>data.table</em>’s general form in the previous section. In this section, we’ll see how they can be combined together with <code>by</code> to perform operations <em>by group</em>. Let’s look at some examples.</p>
+<div id="a-grouping-using-by" class="section level3">
+<h3>a) Grouping using <code>by</code></h3>
+<div id="how-can-we-get-the-number-of-trips-corresponding-to-each-origin-airport" class="section level4">
+<h4>– How can we get the number of trips corresponding to each origin airport?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, .(.N), by=.(origin)]
+ans
+<span class="co"># origin N</span>
+<span class="co"># 1: JFK 81483</span>
+<span class="co"># 2: LGA 84433</span>
+<span class="co"># 3: EWR 87400</span>
+
+## or equivalently using a character vector in 'by'
+<span class="co"># ans <- flights[, .(.N), by="origin"]</span></code></pre></div>
+</div>
+<div id="section-13" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We know <code>.N</code> <a href="#special-N">is a special variable</a> that holds the number of rows in the current group. Grouping by <code>origin</code> obtains the number of rows, <code>.N</code>, for each group.</p></li>
+<li><p>By doing <code>head(flights)</code> you can see that the origin airports occur in the order <em>“JFK”</em>, <em>“LGA”</em> and <em>“EWR”</em>. The original order of grouping variables is preserved in the result.</p></li>
+<li><p>Since we did not provide a name for the column returned in <code>j</code>, it was named <code>N</code>automatically by recognising the special symbol <code>.N</code>.</p></li>
+<li><p><code>by</code> also accepts character vector of column names. It is particularly useful to program with, for e.g., designing a function with the columns to be group by as a function argument.</p></li>
+<li><p>When there’s only one column or expression to refer to in <code>j</code> and <code>by</code>, we can drop the <code>.()</code> notation. This is purely for convenience. We could instead do:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, .N, by=origin]
+ans
+<span class="co"># origin N</span>
+<span class="co"># 1: JFK 81483</span>
+<span class="co"># 2: LGA 84433</span>
+<span class="co"># 3: EWR 87400</span></code></pre></div>
+<p>We’ll use this convenient form wherever applicable hereafter.</p></li>
+</ul>
+</div>
+</div>
+</div>
+</div>
+<div id="section-14" class="section level1">
+<h1></h1>
+<div id="origin-.N" class="section level4">
+<h4>– How can we calculate the number of trips for each origin airport for carrier code <em>“AA”</em>?</h4>
+<p>The unique carrier code <em>“AA”</em> corresponds to <em>American Airlines Inc.</em></p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[carrier ==<span class="st"> "AA"</span>, .N, by=origin]
+ans
+<span class="co"># origin N</span>
+<span class="co"># 1: JFK 11923</span>
+<span class="co"># 2: LGA 11730</span>
+<span class="co"># 3: EWR 2649</span></code></pre></div>
+</div>
+<div id="section-15" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We first obtain the row indices for the expression <code>carrier == "AA"</code> from <code>i</code>.</p></li>
+<li><p>Using those <em>row indices</em>, we obtain the number of rows while grouped by <code>origin</code>. Once again no columns are actually materialised here, because the <code>j-expression</code> does not require any columns to be actually subsetted and is therefore fast and memory efficient.</p></li>
+</ul>
+</div>
+<div id="origin-dest-.N" class="section level4">
+<h4>– How can we get the total number of trips for each <code>origin, dest</code> pair for carrier code <em>“AA”</em>?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[carrier ==<span class="st"> "AA"</span>, .N, by=.(origin,dest)]
+<span class="kw">head</span>(ans)
+<span class="co"># origin dest N</span>
+<span class="co"># 1: JFK LAX 3387</span>
+<span class="co"># 2: LGA PBI 245</span>
+<span class="co"># 3: EWR LAX 62</span>
+<span class="co"># 4: JFK MIA 1876</span>
+<span class="co"># 5: JFK SEA 298</span>
+<span class="co"># 6: EWR MIA 848</span>
+
+## or equivalently using a character vector in 'by'
+<span class="co"># ans <- flights[carrier == "AA", .N, by=c("origin", "dest")]</span></code></pre></div>
+</div>
+<div id="section-16" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><code>by</code> accepts multiple columns. We just provide all the columns by which to group by.</li>
+</ul>
+</div>
+<div id="origin-dest-month" class="section level4">
+<h4>– How can we get the average arrival and departure delay for each <code>orig,dest</code> pair for each month for carrier code <em>“AA”</em>?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[carrier ==<span class="st"> "AA"</span>,
+ .(<span class="kw">mean</span>(arr_delay), <span class="kw">mean</span>(dep_delay)),
+ by=.(origin, dest, month)]
+ans
+<span class="co"># origin dest month V1 V2</span>
+<span class="co"># 1: JFK LAX 1 6.590361 14.2289157</span>
+<span class="co"># 2: LGA PBI 1 -7.758621 0.3103448</span>
+<span class="co"># 3: EWR LAX 1 1.366667 7.5000000</span>
+<span class="co"># 4: JFK MIA 1 15.720670 18.7430168</span>
+<span class="co"># 5: JFK SEA 1 14.357143 30.7500000</span>
+<span class="co"># --- </span>
+<span class="co"># 196: LGA MIA 10 -6.251799 -1.4208633</span>
+<span class="co"># 197: JFK MIA 10 -1.880184 6.6774194</span>
+<span class="co"># 198: EWR PHX 10 -3.032258 -4.2903226</span>
+<span class="co"># 199: JFK MCO 10 -10.048387 -1.6129032</span>
+<span class="co"># 200: JFK DCA 10 16.483871 15.5161290</span></code></pre></div>
+</div>
+<div id="section-17" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We did not provide column names for expressions in <code>j</code>, they were automatically generated (<code>V1</code>, <code>V2</code>).</p></li>
+<li><p>Once again, note that the input order of grouping columns is preserved in the result.</p></li>
+</ul>
+</div>
+</div>
+<div id="section-18" class="section level1">
+<h1></h1>
+<p>Now what if we would like to order the result by those grouping columns <code>origin</code>, <code>dest</code> and <code>month</code>?</p>
+<div id="b-keyby" class="section level3">
+<h3>b) keyby</h3>
+<p><em>data.table</em> retaining the original order of groups is intentional and by design. There are cases when preserving the original order is essential. But at times we would like to automatically sort by the variables we grouped by.</p>
+<div id="so-how-can-we-directly-order-by-all-the-grouping-variables" class="section level4">
+<h4>– So how can we directly order by all the grouping variables?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[carrier ==<span class="st"> "AA"</span>,
+ .(<span class="kw">mean</span>(arr_delay), <span class="kw">mean</span>(dep_delay)),
+ keyby=.(origin, dest, month)]
+ans
+<span class="co"># origin dest month V1 V2</span>
+<span class="co"># 1: EWR DFW 1 6.427673 10.0125786</span>
+<span class="co"># 2: EWR DFW 2 10.536765 11.3455882</span>
+<span class="co"># 3: EWR DFW 3 12.865031 8.0797546</span>
+<span class="co"># 4: EWR DFW 4 17.792683 12.9207317</span>
+<span class="co"># 5: EWR DFW 5 18.487805 18.6829268</span>
+<span class="co"># --- </span>
+<span class="co"># 196: LGA PBI 1 -7.758621 0.3103448</span>
+<span class="co"># 197: LGA PBI 2 -7.865385 2.4038462</span>
+<span class="co"># 198: LGA PBI 3 -5.754098 3.0327869</span>
+<span class="co"># 199: LGA PBI 4 -13.966667 -4.7333333</span>
+<span class="co"># 200: LGA PBI 5 -10.357143 -6.8571429</span></code></pre></div>
+</div>
+<div id="section-19" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li>All we did was to change <code>by</code> to <code>keyby</code>. This automatically orders the result by the grouping variables in increasing order. Note that <code>keyby()</code> is applied after performing the operation, i.e., on the computed result.</li>
+</ul>
+<p><strong>Keys:</strong> Actually <code>keyby</code> does a little more than <em>just ordering</em>. It also <em>sets a key</em> after ordering by setting an <em>attribute</em> called <code>sorted</code>. But we’ll learn more about <code>keys</code> in the next vignette.</p>
+<p>For now, all you’ve to know is you can use <code>keyby</code> to automatically order by the columns specified in <code>by</code>.</p>
+</div>
+</div>
+<div id="c-chaining" class="section level3">
+<h3>c) Chaining</h3>
+<p>Let’s reconsider the task of <a href="#origin-dest-.N">getting the total number of trips for each <code>origin, dest</code> pair for carrier <em>“AA”</em></a>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[carrier ==<span class="st"> "AA"</span>, .N, by =<span class="st"> </span>.(origin, dest)]</code></pre></div>
+<div id="how-can-we-order-ans-using-the-columns-origin-in-ascending-order-and-dest-in-descending-order" class="section level4">
+<h4>– How can we order <code>ans</code> using the columns <code>origin</code> in ascending order, and <code>dest</code> in descending order?</h4>
+<p>We can store the intermediate result in a variable, and then use <code>order(origin, -dest)</code> on that variable. It seems fairly straightforward.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>ans[<span class="kw">order</span>(origin, -dest)]
+<span class="kw">head</span>(ans)
+<span class="co"># origin dest N</span>
+<span class="co"># 1: EWR PHX 121</span>
+<span class="co"># 2: EWR MIA 848</span>
+<span class="co"># 3: EWR LAX 62</span>
+<span class="co"># 4: EWR DFW 1618</span>
+<span class="co"># 5: JFK STT 229</span>
+<span class="co"># 6: JFK SJU 690</span></code></pre></div>
+</div>
+<div id="section-20" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Recall that we can use “-” on a <em>character</em> column in <code>order()</code> within the frame of a <em>data.table</em>. This is possible to due <em>data.table</em>’s internal query optimisation.</p></li>
+<li><p>Also recall that <code>order(...)</code> with the frame of a <em>data.table</em> is <em>automatically optimised</em> to use <em>data.table</em>’s internal fast radix order <code>forder()</code> for speed. So you can keep using the already <em>familiar</em> base R functions without compromising in speed or memory efficiency that <em>data.table</em> offers. We will cover this in more detail in the <em>data.table internals</em> vignette.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-21" class="section level1">
+<h1></h1>
+<p>But this requires having to assign the intermediate result and then overwriting that result. We can do one better and avoid this intermediate assignment on to a variable altogther by <code>chaining</code> expressions.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[carrier ==<span class="st"> "AA"</span>, .N, by=.(origin, dest)][<span class="kw">order</span>(origin, -dest)]
+<span class="kw">head</span>(ans, <span class="dv">10</span>)
+<span class="co"># origin dest N</span>
+<span class="co"># 1: EWR PHX 121</span>
+<span class="co"># 2: EWR MIA 848</span>
+<span class="co"># 3: EWR LAX 62</span>
+<span class="co"># 4: EWR DFW 1618</span>
+<span class="co"># 5: JFK STT 229</span>
+<span class="co"># 6: JFK SJU 690</span>
+<span class="co"># 7: JFK SFO 1312</span>
+<span class="co"># 8: JFK SEA 298</span>
+<span class="co"># 9: JFK SAN 299</span>
+<span class="co"># 10: JFK ORD 432</span></code></pre></div>
+<div id="section-22" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We can tack expressions one after another, <em>forming a chain</em> of operations, i.e., <code>DT[ ... ][ ... ][ ... ]</code>.</p></li>
+<li><p>Or you can also chain them vertically:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT[ ...
+ ][ ...
+ ][ ...
+ ]</code></pre></div></li>
+</ul>
+</div>
+<div id="d-expressions-in-by" class="section level3">
+<h3>d) Expressions in <code>by</code></h3>
+<div id="can-by-accept-expressions-as-well-or-just-take-columns" class="section level4">
+<h4>– Can <code>by</code> accept <em>expressions</em> as well or just take columns?</h4>
+<p>Yes it does. As an example, if we would like to find out how many flights started late but arrived early (or on time), started and arrived late etc…</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, .N, .(dep_delay><span class="dv">0</span>, arr_delay><span class="dv">0</span>)]
+ans
+<span class="co"># dep_delay arr_delay N</span>
+<span class="co"># 1: TRUE TRUE 72836</span>
+<span class="co"># 2: FALSE TRUE 34583</span>
+<span class="co"># 3: FALSE FALSE 119304</span>
+<span class="co"># 4: TRUE FALSE 26593</span></code></pre></div>
+</div>
+<div id="section-23" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>The last row corresponds to <code>dep_delay > 0 = TRUE</code> and <code>arr_delay > 0 = FALSE</code>. We can see that 26593 flights started late but arrived early (or on time).</p></li>
+<li><p>Note that we did not provide any names to <code>by-expression</code>. And names have been automatically assigned in the result.</p></li>
+<li><p>You can provide other columns along with expressions, for example: <code>DT[, .N, by=.(a, b>0)]</code>.</p></li>
+</ul>
+</div>
+</div>
+<div id="e-multiple-columns-in-j---.sd" class="section level3">
+<h3>e) Multiple columns in <code>j</code> - <code>.SD</code></h3>
+<div id="do-we-have-to-compute-mean-for-each-column-individually" class="section level4">
+<h4>– Do we have to compute <code>mean()</code> for each column individually?</h4>
+<p>It is of course not practical to have to type <code>mean(myCol)</code> for every column one by one. What if you had a 100 columns to compute <code>mean()</code> of?</p>
+<p>How can we do this efficiently? To get there, refresh on <a href="#tip-1">this tip</a> - <em>“As long as j-expression returns a list, each element of the list will be converted to a column in the resulting data.table”</em>. Suppose we can refer to the <em>data subset for each group</em> as a variable <em>while grouping</em>, then we can loop through all the columns of that variable using the already familiar base function <code>lapply()</code>. We don’t have to learn any new function.</p>
+</div>
+<div id="special-SD" class="section level4 bs-callout bs-callout-info">
+<h4>Special symbol <code>.SD</code>:</h4>
+<p><em>data.table</em> provides a <em>special</em> symbol, called <code>.SD</code>. It stands for <strong>S</strong>ubset of <strong>D</strong>ata. It by itself is a <em>data.table</em> that holds the data for <em>the current group</em> defined using <code>by</code>.</p>
+<p>Recall that a <em>data.table</em> is internally a list as well with all its columns of equal length.</p>
+</div>
+</div>
+</div>
+<div id="section-24" class="section level1">
+<h1></h1>
+<p>Let’s use the <a href="#what-is-datatable-1a"><em>data.table</em> <code>DT</code> from before</a> to get a glimpse of what <code>.SD</code> looks like.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT
+<span class="co"># ID a b c</span>
+<span class="co"># 1: b 1 7 13</span>
+<span class="co"># 2: b 2 8 14</span>
+<span class="co"># 3: b 3 9 15</span>
+<span class="co"># 4: a 4 10 16</span>
+<span class="co"># 5: a 5 11 17</span>
+<span class="co"># 6: c 6 12 18</span>
+
+DT[, <span class="kw">print</span>(.SD), by=ID]
+<span class="co"># a b c</span>
+<span class="co"># 1: 1 7 13</span>
+<span class="co"># 2: 2 8 14</span>
+<span class="co"># 3: 3 9 15</span>
+<span class="co"># a b c</span>
+<span class="co"># 1: 4 10 16</span>
+<span class="co"># 2: 5 11 17</span>
+<span class="co"># a b c</span>
+<span class="co"># 1: 6 12 18</span>
+<span class="co"># Empty data.table (0 rows) of 1 col: ID</span></code></pre></div>
+<div id="section-25" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p><code>.SD</code> contains all the columns <em>except the grouping columns</em> by default.</p></li>
+<li><p>It is also generated by preserving the original order - data corresponding to <code>ID = "b"</code>, then <code>ID = "a"</code>, and then <code>ID = "c"</code>.</p></li>
+</ul>
+</div>
+</div>
+<div id="section-26" class="section level1">
+<h1></h1>
+<p>To compute on (multiple) columns, we can then simply use the base R function <code>lapply()</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT[, <span class="kw">lapply</span>(.SD, mean), by=ID]
+<span class="co"># ID a b c</span>
+<span class="co"># 1: b 2.0 8.0 14.0</span>
+<span class="co"># 2: a 4.5 10.5 16.5</span>
+<span class="co"># 3: c 6.0 12.0 18.0</span></code></pre></div>
+<div id="section-27" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p><code>.SD</code> holds the rows corresponding to columns <em>a</em>, <em>b</em> and <em>c</em> for that group. We compute the <code>mean()</code> on each of these columns using the already familiar base function <code>lapply()</code>.</p></li>
+<li><p>Each group returns a list of three elements containing the mean value which will become the columns of the resulting <code>data.table</code>.</p></li>
+<li><p>Since <code>lapply()</code> returns a <em>list</em>, there is no need to wrap it with an additional <code>.()</code> (if necessary, refer to <a href="#tip-1">this tip</a>).</p></li>
+</ul>
+</div>
+</div>
+<div id="section-28" class="section level1">
+<h1></h1>
+<p>We are almost there. There is one little thing left to address. In our <code>flights</code> <em>data.table</em>, we only wanted to calculate the <code>mean()</code> of two columns <code>arr_delay</code> and <code>dep_delay</code>. But <code>.SD</code> would contain all the columns other than the grouping variables by default.</p>
+<div id="how-can-we-specify-just-the-columns-we-would-like-to-compute-the-mean-on" class="section level4">
+<h4>– How can we specify just the columns we would like to compute the <code>mean()</code> on?</h4>
+</div>
+<div id="sdcols" class="section level4 bs-callout bs-callout-info">
+<h4>.SDcols</h4>
+<p>Using the argument <code>.SDcols</code>. It accepts either column names or column indices. For example, <code>.SDcols = c("arr_delay", "dep_delay")</code> ensures that <code>.SD</code> contains only these two columns for each group.</p>
+<p>Similar to the <a href="#with_false">with = FALSE section</a>, you can also provide the columns to remove instead of columns to keep using <code>-</code> or <code>!</code> sign as well as select consecutive columns as <code>colA:colB</code> and deselect consecutive columns as <code>!(colA:colB) or</code>-(colA:colB)`.</p>
+</div>
+</div>
+<div id="section-29" class="section level1">
+<h1></h1>
+<p>Now let us try to use <code>.SD</code> along with <code>.SDcols</code> to get the <code>mean()</code> of <code>arr_delay</code> and <code>dep_delay</code> columns grouped by <code>origin</code>, <code>dest</code> and <code>month</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[carrier ==<span class="st"> "AA"</span>, ## Only on trips with carrier "AA"
+ <span class="kw">lapply</span>(.SD, mean), ## compute the mean
+ by=.(origin, dest, month), ## for every 'origin,dest,month'
+ .SDcols=<span class="kw">c</span>(<span class="st">"arr_delay"</span>, <span class="st">"dep_delay"</span>)] ## for just those specified in .SDcols
+<span class="co"># origin dest month arr_delay dep_delay</span>
+<span class="co"># 1: JFK LAX 1 6.590361 14.2289157</span>
+<span class="co"># 2: LGA PBI 1 -7.758621 0.3103448</span>
+<span class="co"># 3: EWR LAX 1 1.366667 7.5000000</span>
+<span class="co"># 4: JFK MIA 1 15.720670 18.7430168</span>
+<span class="co"># 5: JFK SEA 1 14.357143 30.7500000</span>
+<span class="co"># --- </span>
+<span class="co"># 196: LGA MIA 10 -6.251799 -1.4208633</span>
+<span class="co"># 197: JFK MIA 10 -1.880184 6.6774194</span>
+<span class="co"># 198: EWR PHX 10 -3.032258 -4.2903226</span>
+<span class="co"># 199: JFK MCO 10 -10.048387 -1.6129032</span>
+<span class="co"># 200: JFK DCA 10 16.483871 15.5161290</span></code></pre></div>
+<div id="f-subset-.sd-for-each-group" class="section level3">
+<h3>f) Subset <code>.SD</code> for each group:</h3>
+<div id="how-can-we-return-the-first-two-rows-for-each-month" class="section level4">
+<h4>– How can we return the first two rows for each <code>month</code>?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[, <span class="kw">head</span>(.SD, <span class="dv">2</span>), by=month]
+<span class="kw">head</span>(ans)
+<span class="co"># month year day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 1 2014 1 14 13 AA JFK LAX 359 2475 9</span>
+<span class="co"># 2: 1 2014 1 -3 13 AA JFK LAX 363 2475 11</span>
+<span class="co"># 3: 2 2014 1 -1 1 AA JFK LAX 358 2475 8</span>
+<span class="co"># 4: 2 2014 1 -5 3 AA JFK LAX 358 2475 11</span>
+<span class="co"># 5: 3 2014 1 -11 36 AA JFK LAX 375 2475 8</span>
+<span class="co"># 6: 3 2014 1 -3 14 AA JFK LAX 368 2475 11</span></code></pre></div>
+</div>
+<div id="section-30" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p><code>.SD</code> is a <em>data.table</em> that holds all the rows for <em>that group</em>. We simply subset the first two rows as we have seen <a href="#subset-rows-integer">here</a> already.</p></li>
+<li><p>For each group, <code>head(.SD, 2)</code> returns the first two rows as a <em>data.table</em> which is also a list. So we do not have to wrap it with <code>.()</code>.</p></li>
+</ul>
+</div>
+</div>
+<div id="g-why-keep-j-so-flexible" class="section level3">
+<h3>g) Why keep <code>j</code> so flexible?</h3>
+<p>So that we have a consistent syntax and keep using already existing (and familiar) base functions instead of learning new functions. To illustrate, let us use the <em>data.table</em> <code>DT</code> we created at the very beginning under <a href="#what-is-datatable-1a">What is a data.table?</a> section.</p>
+<div id="how-can-we-concatenate-columns-a-and-b-for-each-group-in-id" class="section level4">
+<h4>– How can we concatenate columns <code>a</code> and <code>b</code> for each group in <code>ID</code>?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT[, .(<span class="dt">val =</span> <span class="kw">c</span>(a,b)), by=ID]
+<span class="co"># ID val</span>
+<span class="co"># 1: b 1</span>
+<span class="co"># 2: b 2</span>
+<span class="co"># 3: b 3</span>
+<span class="co"># 4: b 7</span>
+<span class="co"># 5: b 8</span>
+<span class="co"># 6: b 9</span>
+<span class="co"># 7: a 4</span>
+<span class="co"># 8: a 5</span>
+<span class="co"># 9: a 10</span>
+<span class="co"># 10: a 11</span>
+<span class="co"># 11: c 6</span>
+<span class="co"># 12: c 12</span></code></pre></div>
+</div>
+<div id="section-31" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li>That’s it. There is no special syntax required. All we need to know is the base function <code>c()</code> which concatenates vectors and <a href="#tip-1">the tip from before</a>.</li>
+</ul>
+</div>
+<div id="what-if-we-would-like-to-have-all-the-values-of-column-a-and-b-concatenated-but-returned-as-a-list-column" class="section level4">
+<h4>– What if we would like to have all the values of column <code>a</code> and <code>b</code> concatenated, but returned as a list column?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT[, .(<span class="dt">val =</span> <span class="kw">list</span>(<span class="kw">c</span>(a,b))), by=ID]
+<span class="co"># ID val</span>
+<span class="co"># 1: b 1,2,3,7,8,9</span>
+<span class="co"># 2: a 4, 5,10,11</span>
+<span class="co"># 3: c 6,12</span></code></pre></div>
+</div>
+<div id="section-32" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Here, we first concatenate the values with <code>c(a,b)</code> for each group, and wrap that with <code>list()</code>. So for each group, we return a list of all concatenated values.</p></li>
+<li><p>Note those commas are for display only. A list column can contain any object in each cell, and in this example, each cell is itself a vector and some cells contain longer vectors than others.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-33" class="section level1">
+<h1></h1>
+<p>Once you start internalising usage in <code>j</code>, you will realise how powerful the syntax can be. A very useful way to understand it is by playing around, with the help of <code>print()</code>.</p>
+<p>For example:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## (1) look at the difference between
+DT[, <span class="kw">print</span>(<span class="kw">c</span>(a,b)), by=ID]
+<span class="co"># [1] 1 2 3 7 8 9</span>
+<span class="co"># [1] 4 5 10 11</span>
+<span class="co"># [1] 6 12</span>
+<span class="co"># Empty data.table (0 rows) of 1 col: ID</span>
+
+## (2) and
+DT[, <span class="kw">print</span>(<span class="kw">list</span>(<span class="kw">c</span>(a,b))), by=ID]
+<span class="co"># [[1]]</span>
+<span class="co"># [1] 1 2 3 7 8 9</span>
+<span class="co"># </span>
+<span class="co"># [[1]]</span>
+<span class="co"># [1] 4 5 10 11</span>
+<span class="co"># </span>
+<span class="co"># [[1]]</span>
+<span class="co"># [1] 6 12</span>
+<span class="co"># Empty data.table (0 rows) of 1 col: ID</span></code></pre></div>
+<p>In (1), for each group, a vector is returned, with length = 6,4,2 here. However (2) returns a list of length 1 for each group, with its first element holding vectors of length 6,4,2. Therefore (1) results in a length of <code>6+4+2 = 12</code>, whereas (2) returns <code>1+1+1=3</code>.</p>
+<div id="summary" class="section level2">
+<h2>Summary</h2>
+<p>The general form of <em>data.table</em> syntax is:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT[i, j, by]</code></pre></div>
+<p>We have seen so far that,</p>
+<div id="using-i" class="section level4 bs-callout bs-callout-info">
+<h4>Using <code>i</code>:</h4>
+<ul>
+<li><p>We can subset rows similar to a <em>data.frame</em> - except you don’t have to use <code>DT$</code> repetitively since columns within the frame of a <em>data.table</em> are seen as if they are <em>variables</em>.</p></li>
+<li><p>We can also sort a <em>data.table</em> using <code>order()</code>, which internally uses <em>data.table</em>’s fast order for performance.</p></li>
+</ul>
+<p>We can do much more in <code>i</code> by keying a <em>data.table</em>, which allows blazing fast subsets and joins. We will see this in the <em>“Keys and fast binary search based subsets”</em> and <em>“Joins and rolling joins”</em> vignette.</p>
+</div>
+<div id="using-j" class="section level4 bs-callout bs-callout-info">
+<h4>Using <code>j</code>:</h4>
+<ol style="list-style-type: decimal">
+<li><p>Select columns the <em>data.table</em> way: <code>DT[, .(colA, colB)]</code>.</p></li>
+<li><p>Select columns the <em>data.frame</em> way: <code>DT[, c("colA", "colB"), with=FALSE]</code>.</p></li>
+<li><p>Compute on columns: <code>DT[, .(sum(colA), mean(colB))]</code>.</p></li>
+<li><p>Provide names if necessary: <code>DT[, .(sA =sum(colA), mB = mean(colB))]</code>.</p></li>
+<li><p>Combine with <code>i</code>: <code>DT[colA > value, sum(colB)]</code>.</p></li>
+</ol>
+</div>
+</div>
+</div>
+<div id="section-34" class="section level1">
+<h1></h1>
+<div id="using-by" class="section level4 bs-callout bs-callout-info">
+<h4>Using <code>by</code>:</h4>
+<ul>
+<li><p>Using <code>by</code>, we can group by columns by specifying a <em>list of columns</em> or a <em>character vector of column names</em> or even <em>expressions</em>. The flexibility of <code>j</code>, combined with <code>by</code> and <code>i</code> makes for a very powerful syntax.</p></li>
+<li><p><code>by</code> can handle multiple columns and also <em>expressions</em>.</p></li>
+<li><p>We can <code>keyby</code> grouping columns to automatically sort the grouped result.</p></li>
+<li><p>We can use <code>.SD</code> and <code>.SDcols</code> in <code>j</code> to operate on multiple columns using already familiar base functions. Here are some examples:</p>
+<ol style="list-style-type: decimal">
+<li><p><code>DT[, lapply(.SD, fun), by=., .SDcols=...]</code> - applies <code>fun</code> to all columns specified in <code>.SDcols</code> while grouping by the columns specified in <code>by</code>.</p></li>
+<li><p><code>DT[, head(.SD, 2), by=.]</code> - return the first two rows for each group.</p></li>
+<li><p><code>DT[col > val, head(.SD, 1), by=.]</code> - combine <code>i</code> along with <code>j</code> and <code>by</code>.</p></li>
+</ol></li>
+</ul>
+</div>
+</div>
+<div id="section-35" class="section level1">
+<h1></h1>
+<div id="and-remember-the-tip" class="section level4 bs-callout bs-callout-warning">
+<h4>And remember the tip:</h4>
+<p>As long as <code>j</code> returns a <em>list</em>, each element of the list will become a column in the resulting <em>data.table</em>.</p>
+</div>
+</div>
+<div id="section-36" class="section level1">
+<h1></h1>
+<p>We will see how to <em>add/update/delete</em> columns <em>by reference</em> and how to combine them with <code>i</code> and <code>by</code> in the next vignette.</p>
+<hr />
+</div>
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+$(document).ready(function () {
+ $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
+});
+
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+ (function () {
+ var script = document.createElement("script");
+ script.type = "text/javascript";
+ script.src = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+ document.getElementsByTagName("head")[0].appendChild(script);
+ })();
+</script>
+
+</body>
+</html>
diff --git a/inst/doc/datatable-intro.R b/inst/doc/datatable-intro.R
new file mode 100644
index 0000000..d7e7f67
--- /dev/null
+++ b/inst/doc/datatable-intro.R
@@ -0,0 +1,206 @@
+### R code from vignette source 'datatable-intro.Rnw'
+
+###################################################
+### code chunk number 1: datatable-intro.Rnw:15-19
+###################################################
+if (!exists("data.table",.GlobalEnv)) library(data.table)
+# In devel won't call library, but R CMD build/check will.
+rm(list=as.character(tables()$NAME),envir=.GlobalEnv)
+# for development when we Sweave this file repeatedly. Otherwise first tables() shows tables from last run
+
+
+###################################################
+### code chunk number 2: datatable-intro.Rnw:36-38
+###################################################
+DF = data.frame(x=c("b","b","b","a","a"),v=rnorm(5))
+DF
+
+
+###################################################
+### code chunk number 3: datatable-intro.Rnw:41-43
+###################################################
+DT = data.table(x=c("b","b","b","a","a"),v=rnorm(5))
+DT
+
+
+###################################################
+### code chunk number 4: datatable-intro.Rnw:47-49
+###################################################
+CARS = data.table(cars)
+head(CARS)
+
+
+###################################################
+### code chunk number 5: datatable-intro.Rnw:53-54
+###################################################
+tables()
+
+
+###################################################
+### code chunk number 6: datatable-intro.Rnw:66-67
+###################################################
+sapply(DT,class)
+
+
+###################################################
+### code chunk number 7: datatable-intro.Rnw:83-85
+###################################################
+tables()
+DT
+
+
+###################################################
+### code chunk number 8: datatable-intro.Rnw:90-92
+###################################################
+DT[2,] # select row 2
+DT[x=="b",] # select rows where column x == "b"
+
+
+###################################################
+### code chunk number 9: datatable-intro.Rnw:98-99
+###################################################
+cat(try(DT["b",],silent=TRUE))
+
+
+###################################################
+### code chunk number 10: datatable-intro.Rnw:103-105
+###################################################
+setkey(DT,x)
+DT
+
+
+###################################################
+### code chunk number 11: datatable-intro.Rnw:113-114
+###################################################
+tables()
+
+
+###################################################
+### code chunk number 12: datatable-intro.Rnw:119-120
+###################################################
+DT["b",]
+
+
+###################################################
+### code chunk number 13: datatable-intro.Rnw:125-127
+###################################################
+DT["b",mult="first"]
+DT["b",mult="last"]
+
+
+###################################################
+### code chunk number 14: datatable-intro.Rnw:132-133
+###################################################
+DT["b"]
+
+
+###################################################
+### code chunk number 15: datatable-intro.Rnw:138-148
+###################################################
+grpsize = ceiling(1e7/26^2) # 10 million rows, 676 groups
+tt=system.time( DF <- data.frame(
+ x=rep(LETTERS,each=26*grpsize),
+ y=rep(letters,each=grpsize),
+ v=runif(grpsize*26^2),
+ stringsAsFactors=FALSE)
+)
+head(DF,3)
+tail(DF,3)
+dim(DF)
+
+
+###################################################
+### code chunk number 16: datatable-intro.Rnw:157-160
+###################################################
+tt=system.time(ans1 <- DF[DF$x=="R" & DF$y=="h",]) # 'vector scan'
+head(ans1,3)
+dim(ans1)
+
+
+###################################################
+### code chunk number 17: datatable-intro.Rnw:165-167
+###################################################
+DT = as.data.table(DF) # but normally use fread() or data.table() directly, originally
+system.time(setkey(DT,x,y)) # one-off cost, usually
+
+
+###################################################
+### code chunk number 18: datatable-intro.Rnw:169-173
+###################################################
+ss=system.time(ans2 <- DT[list("R","h")]) # binary search
+head(ans2,3)
+dim(ans2)
+identical(ans1$v, ans2$v)
+
+
+###################################################
+### code chunk number 19: datatable-intro.Rnw:175-176
+###################################################
+if(!identical(ans1$v, ans2$v)) stop("vector scan vs binary search not equal")
+
+
+###################################################
+### code chunk number 20: datatable-intro.Rnw:186-189
+###################################################
+system.time(ans1 <- DT[x=="R" & y=="h",]) # works but is using data.table badly
+system.time(ans2 <- DF[DF$x=="R" & DF$y=="h",]) # the data.frame way
+mapply(identical,ans1,ans2)
+
+
+###################################################
+### code chunk number 21: datatable-intro.Rnw:204-206
+###################################################
+identical( DT[list("R","h"),],
+ DT[.("R","h"),])
+
+
+###################################################
+### code chunk number 22: datatable-intro.Rnw:208-209
+###################################################
+if(!identical(DT[list("R","h"),],DT[.("R","h"),])) stop("list != . check")
+
+
+###################################################
+### code chunk number 23: datatable-intro.Rnw:229-230
+###################################################
+DT[,sum(v)]
+
+
+###################################################
+### code chunk number 24: datatable-intro.Rnw:235-236
+###################################################
+DT[,sum(v),by=x]
+
+
+###################################################
+### code chunk number 25: datatable-intro.Rnw:241-246
+###################################################
+ttt=system.time(tt <- tapply(DT$v,DT$x,sum)); ttt
+sss=system.time(ss <- DT[,sum(v),by=x]); sss
+head(tt)
+head(ss)
+identical(as.vector(tt), ss$V1)
+
+
+###################################################
+### code chunk number 26: datatable-intro.Rnw:248-249
+###################################################
+if(!identical(as.vector(tt), ss$V1)) stop("by check failed")
+
+
+###################################################
+### code chunk number 27: datatable-intro.Rnw:257-262
+###################################################
+ttt=system.time(tt <- tapply(DT$v,list(DT$x,DT$y),sum)); ttt
+sss=system.time(ss <- DT[,sum(v),by="x,y"]); sss
+tt[1:5,1:5]
+head(ss)
+identical(as.vector(t(tt)), ss$V1)
+
+
+###################################################
+### code chunk number 28: datatable-intro.Rnw:264-265
+###################################################
+if(!identical(as.vector(t(tt)), ss$V1)) stop("group check failed")
+
+
diff --git a/inst/doc/datatable-intro.Rnw b/inst/doc/datatable-intro.Rnw
new file mode 100644
index 0000000..49627cb
--- /dev/null
+++ b/inst/doc/datatable-intro.Rnw
@@ -0,0 +1,293 @@
+\documentclass[a4paper]{article}
+
+\usepackage[margin=3cm]{geometry}
+%%\usepackage[round]{natbib}
+\usepackage[colorlinks=true,urlcolor=blue]{hyperref}
+
+%%\newcommand{\acronym}[1]{\textsc{#1}}
+%%\newcommand{\class}[1]{\mbox{\textsf{#1}}}
+\newcommand{\code}[1]{\mbox{\texttt{#1}}}
+\newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}}
+\newcommand{\proglang}[1]{\textsf{#1}}
+\SweaveOpts{keep.source=TRUE, strip.white=all}
+%% \VignetteIndexEntry{Quick introduction}
+
+<<echo=FALSE,results=hide>>=
+if (!exists("data.table",.GlobalEnv)) library(data.table)
+# In devel won't call library, but R CMD build/check will.
+rm(list=as.character(tables()$NAME),envir=.GlobalEnv)
+# for development when we Sweave this file repeatedly. Otherwise first tables() shows tables from last run
+@
+
+\begin{document}
+\title{Introduction to the \pkg{data.table} package in \proglang{R}}
+\date{Revised: \today\\(A later revision may be available on the \href{https://github.com/Rdatatable/data.table/wiki}{homepage})}
+\maketitle
+
+\section*{Introduction}
+
+This vignette is aimed at those who are already familiar with creating and subsetting \code{data.frame} in \proglang{R}. We aim for this quick introduction
+to be readable in {\bf 10 minutes}, briefly covering a few features:
+1.\,Keys; 2.\,Fast Grouping; and 3.\,Fast \emph{ordered} join.
+
+\section*{Creation}
+
+Recall that we create a \code{data.frame} using the function \code{data.frame()}:
+<<>>=
+DF = data.frame(x=c("b","b","b","a","a"),v=rnorm(5))
+DF
+@
+A \code{data.table} is created in exactly the same way:
+<<>>=
+DT = data.table(x=c("b","b","b","a","a"),v=rnorm(5))
+DT
+@
+Observe that a \code{data.table} prints the row numbers with a colon so as to visually separate the row number from the first column.
+We can easily convert existing \code{data.frame} objects to \code{data.table}.
+<<>>=
+CARS = data.table(cars)
+head(CARS)
+@
+We have just created two \code{data.table}s: \code{DT} and \code{CARS}. It is often useful to see a list of all
+\code{data.table}s in memory:
+<<>>=
+tables()
+@
+
+The MB column is useful to quickly assess memory use and to spot if any redundant tables can be
+removed to free up memory. Just like \code{data.frame}s, \code{data.table}s must fit inside RAM.
+
+Some users regularly work with 20 or more tables in memory, rather like a database.
+The result of \code{tables()} is itself a \code{data.table}, returned silently, so that \code{tables()}
+can be used in programs. \code{tables()} is unrelated to the base function \code{table()}.
+
+To see the column types :
+
+<<>>=
+sapply(DT,class)
+@
+
+You may have noticed the empty column KEY in the result of \code{tables()} above. This is the subject of the next section.
+
+
+\section*{1. Keys}
+
+Let's start by considering \code{data.frame}, specifically \code{rownames}. We know that each row has exactly one row name. However, a person (for example) has at least two names, a first name and a second name. It's useful to organise a telephone directory sorted by surname then first name.
+
+In \code{data.table}, a \emph{key} consists of one \emph{or more} columns. These columns may be integer, factor or numeric as well as character. Furthermore, the rows are sorted by the key. Therefore, a \code{data.table} can have at most one key because it cannot be sorted in more than one way. We can think of a key as like super-charged row names; i.e., mult-column and multi-type.
+
+Uniqueness is not enforced; i.e., duplicate key values are allowed. Since
+the rows are sorted by the key, any duplicates in the key will appear consecutively.
+
+Let's remind ourselves of our tables:
+<<>>=
+tables()
+DT
+@
+
+No keys have been set yet.
+
+<<>>=
+DT[2,] # select row 2
+DT[x=="b",] # select rows where column x == "b"
+@
+
+Aside: notice that we did not need to prefix \code{x} with \code{DT\$x}. In \code{data.table} queries, we can use column names as if they are variables directly.
+
+But since there are no rownames, the following does not work:
+<<>>=
+cat(try(DT["b",],silent=TRUE))
+@
+
+The error message tells us we need to use \code{setkey()}:
+<<>>=
+setkey(DT,x)
+DT
+@
+
+Notice that the rows in \code{DT} have now been re-ordered according to the values of \code{x}.
+The two \code{"a"} rows have moved to the top.
+We can confirm that \code{DT} does indeed have a key using \code{haskey()}, \code{key()},
+\code{attributes()}, or just running \code{tables()}.
+
+<<>>=
+tables()
+@
+
+Now that we are sure \code{DT} has a key, let's try again:
+
+<<>>=
+DT["b",]
+@
+
+By default all the rows in the group are returned\footnote{In contrast to a \code{data.frame} where only the first rowname is returned when the rownames contain duplicates.}. The \code{mult} argument (short for \emph{multiple}) allows the first or last row of the group to be returned instead.
+
+<<>>=
+DT["b",mult="first"]
+DT["b",mult="last"]
+@
+
+Also, the comma is optional.
+
+<<>>=
+DT["b"]
+@
+
+Let's now create a new \code{data.frame}. We will make it large enough to demonstrate the
+difference between a \emph{vector scan} and a \emph{binary search}.
+<<print=TRUE>>=
+grpsize = ceiling(1e7/26^2) # 10 million rows, 676 groups
+tt=system.time( DF <- data.frame(
+ x=rep(LETTERS,each=26*grpsize),
+ y=rep(letters,each=grpsize),
+ v=runif(grpsize*26^2),
+ stringsAsFactors=FALSE)
+)
+head(DF,3)
+tail(DF,3)
+dim(DF)
+@
+
+We might say that \proglang{R} has created a 3 column table and \emph{inserted}
+\Sexpr{format(nrow(DF),big.mark=",",scientific=FALSE)} rows. It took \Sexpr{format(tt[3],nsmall=3)} secs, so it inserted
+\Sexpr{format(as.integer(nrow(DF)/tt[3]),big.mark=",",scientific=FALSE)} rows per second. This is normal in base \proglang{R}. Notice that we set \code{stringsAsFactors=FALSE}. This makes it a little faster for a fairer comparison, but feel free to experiment.
+
+Let's extract an arbitrary group from \code{DF}:
+
+<<print=TRUE>>=
+tt=system.time(ans1 <- DF[DF$x=="R" & DF$y=="h",]) # 'vector scan'
+head(ans1,3)
+dim(ans1)
+@
+
+Now convert to a \code{data.table} and extract the same group:
+
+<<>>=
+DT = as.data.table(DF) # but normally use fread() or data.table() directly, originally
+system.time(setkey(DT,x,y)) # one-off cost, usually
+@
+<<print=TRUE>>=
+ss=system.time(ans2 <- DT[list("R","h")]) # binary search
+head(ans2,3)
+dim(ans2)
+identical(ans1$v, ans2$v)
+@
+<<echo=FALSE>>=
+if(!identical(ans1$v, ans2$v)) stop("vector scan vs binary search not equal")
+@
+
+At \Sexpr{format(ss[3],nsmall=3)} seconds, this was {\bf\Sexpr{as.integer(tt[3]/ss[3])}} times faster than \Sexpr{format(tt[3],nsmall=3)} seconds,
+and produced precisely the same result. If you are thinking that a few seconds is not much to save, it's the relative speedup that's important. The
+vector scan is linear, but the binary search is O(log n). It scales. If a task taking 10 hours is sped up by 100 times to 6 minutes, that is
+significant\footnote{We wonder how many people are deploying parallel techniques to code that is vector scanning}.
+
+We can do vector scans in \code{data.table}, too. In other words we can use data.table \emph{badly}.
+
+<<>>=
+system.time(ans1 <- DT[x=="R" & y=="h",]) # works but is using data.table badly
+system.time(ans2 <- DF[DF$x=="R" & DF$y=="h",]) # the data.frame way
+mapply(identical,ans1,ans2)
+@
+
+
+If the phone book analogy helped, the {\bf\Sexpr{as.integer(tt[3]/ss[3])}} times speedup should not be surprising. We use the key to take advantage of the fact
+that the table is sorted and use binary search to find the matching rows. We didn't vector scan; we didn't use \code{==}.
+
+When we used \code{x=="R"} we \emph{scanned} the entire column x, testing each and every value to see if it equalled "R". We did
+it again in the y column, testing for "h". Then \code{\&} combined the two logical results to create a single logical vector which was
+passed to the \code{[} method, which in turn searched it for \code{TRUE} and returned those rows. These were \emph{vectorized} operations. They
+occurred internally in R and were very fast, but they were scans. \emph{We} did those scans because \emph{we} wrote that R code.
+
+
+When \code{i} is a \code{list} (and \code{data.table} is a \code{list} too), we say that we are \emph{joining}. In this case, we are joining DT to the 1 row, 2 column table returned by \code{list("R","h")}. Since we do this a lot, there is an alias for \code{list}: \code{.()}.
+
+<<>>=
+identical( DT[list("R","h"),],
+ DT[.("R","h"),])
+@
+<<echo=FALSE>>=
+if(!identical(DT[list("R","h"),],DT[.("R","h"),])) stop("list != . check")
+@
+
+Both vector scanning and binary search are available in \code{data.table}, but one way of using \code{data.table} is much better than the other.
+
+The join syntax is a short, fast to write and easy to maintain. Passing a \code{data.table} into a \code{data.table} subset is analogous to \code{A[B]} syntax in base \proglang{R} where \code{A} is a matrix and \code{B} is a 2-column matrix\footnote{Subsetting a keyed \code{data.table} by a n-column
+\code{data.table} is consistent with subsetting a n-dimension array by a n-column matrix in base R}. In fact, the \code{A[B]} syntax in base R inspired the \code{data.table} package. There are
+other types of ordered joins and further arguments which are beyond the scope of this quick introduction.
+
+The merge method of \code{data.table} is very similar to \code{X[Y]}, but there are some differences. See FAQ 1.12.
+
+This first section has been about the first argument inside \code{DT[...]}, namely \code{i}. The next section is about the 2nd and 3rd arguments: \code{j} and \code{by}.
+
+
+\section*{2. Fast grouping}
+
+
+The second argument to \code{DT[...]} is \code{j} and may consist of
+one or more expressions whose arguments are (unquoted) column names, as if the column names were variables. Just as we saw earlier in \code{i} as well.
+
+<<>>=
+DT[,sum(v)]
+@
+
+When we supply a \code{j} expression and a 'by' expression, the \code{j} expression is repeated for each 'by' group.
+
+<<>>=
+DT[,sum(v),by=x]
+@
+
+The \code{by} in \code{data.table} is fast. Let's compare it to \code{tapply}.
+
+<<>>=
+ttt=system.time(tt <- tapply(DT$v,DT$x,sum)); ttt
+sss=system.time(ss <- DT[,sum(v),by=x]); sss
+head(tt)
+head(ss)
+identical(as.vector(tt), ss$V1)
+@
+<<echo=FALSE>>=
+if(!identical(as.vector(tt), ss$V1)) stop("by check failed")
+@
+
+At \Sexpr{sprintf("%0.3f",sss[3])} sec, this was {\bf\Sexpr{as.integer(ttt[3]/sss[3])}} times faster than
+\Sexpr{sprintf("%0.3f",ttt[3])} sec, and produced precisely the same result.
+
+Next, let's group by two columns:
+
+<<>>=
+ttt=system.time(tt <- tapply(DT$v,list(DT$x,DT$y),sum)); ttt
+sss=system.time(ss <- DT[,sum(v),by="x,y"]); sss
+tt[1:5,1:5]
+head(ss)
+identical(as.vector(t(tt)), ss$V1)
+@
+<<echo=FALSE>>=
+if(!identical(as.vector(t(tt)), ss$V1)) stop("group check failed")
+@
+
+This was {\bf\Sexpr{as.integer(ttt[3]/sss[3])}} times faster, and the syntax is a little simpler and easier to read.
+\newline
+
+
+\section*{3. Fast ordered joins}
+
+This is also known as last observation carried forward (LOCF) or a \emph{rolling join}.
+
+Recall that \code{X[Y]} is a join between \code{data.table} \code{X} and \code{data.table} \code{Y}. If \code{Y} has 2 columns, the first column is matched
+to the first column of the key of \code{X} and the 2nd column to the 2nd. An equi-join is performed by default, meaning that the values must be equal.
+
+Instead of an equi-join, a rolling join is :
+
+\code{X[Y,roll=TRUE]}
+
+As before the first column of \code{Y} is matched to \code{X} where the values are equal. The last join column in \code{Y} though, the 2nd one in
+this example, is treated specially. If no match is found, then the row before is returned, provided the first column still matches.
+
+Further controls are rolling forwards, backwards, nearest and limited staleness.
+
+For examples type \code{example(data.table)} and follow the output at the prompt.
+
+
+\end{document}
+
+
diff --git a/inst/doc/datatable-intro.pdf b/inst/doc/datatable-intro.pdf
new file mode 100644
index 0000000..a9d0572
Binary files /dev/null and b/inst/doc/datatable-intro.pdf differ
diff --git a/inst/doc/datatable-keys-fast-subset.R b/inst/doc/datatable-keys-fast-subset.R
new file mode 100644
index 0000000..16f4bdc
--- /dev/null
+++ b/inst/doc/datatable-keys-fast-subset.R
@@ -0,0 +1,165 @@
+## ---- echo = FALSE, message = FALSE--------------------------------------
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+options(datatable.auto.index=FALSE)
+
+## ----echo=FALSE-----------------------------------------------------------------------------------
+options(width=100)
+
+## -------------------------------------------------------------------------------------------------
+flights <- fread("flights14.csv")
+head(flights)
+dim(flights)
+
+## -------------------------------------------------------------------------------------------------
+set.seed(1L)
+DF = data.frame(ID1 = sample(letters[1:2], 10, TRUE),
+ ID2 = sample(1:3, 10, TRUE),
+ val = sample(10),
+ stringsAsFactors = FALSE,
+ row.names = sample(LETTERS[1:10]))
+DF
+
+rownames(DF)
+
+## -------------------------------------------------------------------------------------------------
+DF["C", ]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# rownames(DF) = sample(LETTERS[1:5], 10, TRUE)
+# # Warning: non-unique values when setting 'row.names': 'C', 'D'
+# # Error in `row.names<-.data.frame`(`*tmp*`, value = value): duplicate 'row.names' are not allowed
+
+## -------------------------------------------------------------------------------------------------
+DT = as.data.table(DF)
+DT
+
+rownames(DT)
+
+## -------------------------------------------------------------------------------------------------
+setkey(flights, origin)
+head(flights)
+
+## alternatively we can provide character vectors to the function 'setkeyv()'
+# setkeyv(flights, "origin") # useful to program with
+
+## -------------------------------------------------------------------------------------------------
+flights[.("JFK")]
+
+## alternatively
+# flights[J("JFK")] (or) flights[list("JFK")]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# flights["JFK"] ## same as flights[.("JFK")]
+
+## ----eval = FALSE---------------------------------------------------------------------------------
+# flights[c("JFK", "LGA")] ## same as flights[.(c("JFK", "LGA"))]
+
+## -------------------------------------------------------------------------------------------------
+key(flights)
+
+## -------------------------------------------------------------------------------------------------
+setkey(flights, origin, dest)
+head(flights)
+
+## or alternatively
+# setkeyv(flights, c("origin", "dest")) # provide a character vector of column names
+
+key(flights)
+
+## -------------------------------------------------------------------------------------------------
+flights[.("JFK", "MIA")]
+
+## -------------------------------------------------------------------------------------------------
+key(flights)
+
+flights[.("JFK")] ## or in this case simply flights["JFK"], for convenience
+
+## -------------------------------------------------------------------------------------------------
+flights[.(unique(origin), "MIA")]
+
+## -------------------------------------------------------------------------------------------------
+key(flights)
+flights[.("LGA", "TPA"), .(arr_delay)]
+
+## ----eval = FALSE---------------------------------------------------------------------------------
+# flights[.("LGA", "TPA"), "arr_delay", with=FALSE]
+
+## -------------------------------------------------------------------------------------------------
+flights[.("LGA", "TPA"), .(arr_delay)][order(-arr_delay)]
+
+## -------------------------------------------------------------------------------------------------
+flights[.("LGA", "TPA"), max(arr_delay)]
+
+## -------------------------------------------------------------------------------------------------
+# get all 'hours' in flights
+flights[, sort(unique(hour))]
+
+## -------------------------------------------------------------------------------------------------
+setkey(flights, hour)
+key(flights)
+flights[.(24), hour := 0L]
+key(flights)
+
+## -------------------------------------------------------------------------------------------------
+flights[, sort(unique(hour))]
+
+## -------------------------------------------------------------------------------------------------
+setkey(flights, origin, dest)
+key(flights)
+
+## -------------------------------------------------------------------------------------------------
+ans <- flights["JFK", max(dep_delay), keyby=month]
+head(ans)
+key(ans)
+
+## -------------------------------------------------------------------------------------------------
+flights[.("JFK", "MIA"), mult="first"]
+
+## -------------------------------------------------------------------------------------------------
+flights[.(c("LGA", "JFK", "EWR"), "XNA"), mult="last"]
+
+## -------------------------------------------------------------------------------------------------
+flights[.(c("LGA", "JFK", "EWR"), "XNA"), mult="last", nomatch = 0L]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# # key by origin,dest columns
+# flights[.("JFK", "MIA")]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# flights[origin == "JFK" & dest == "MIA"]
+
+## -------------------------------------------------------------------------------------------------
+set.seed(2L)
+N = 2e7L
+DT = data.table(x = sample(letters, N, TRUE),
+ y = sample(1000L, N, TRUE),
+ val=runif(N), key = c("x", "y"))
+print(object.size(DT), units="Mb")
+
+key(DT)
+
+## -------------------------------------------------------------------------------------------------
+## (1) Usual way of subsetting - vector scan approach
+t1 <- system.time(ans1 <- DT[x == "g" & y == 877L])
+t1
+head(ans1)
+dim(ans1)
+
+## -------------------------------------------------------------------------------------------------
+## (2) Subsetting using keys
+t2 <- system.time(ans2 <- DT[.("g", 877L)])
+t2
+head(ans2)
+dim(ans2)
+
+identical(ans1$val, ans2$val)
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# 1, 5, 10, 19, 22, 23, 30
+
diff --git a/inst/doc/datatable-keys-fast-subset.Rmd b/inst/doc/datatable-keys-fast-subset.Rmd
new file mode 100644
index 0000000..b9ebd7c
--- /dev/null
+++ b/inst/doc/datatable-keys-fast-subset.Rmd
@@ -0,0 +1,534 @@
+---
+title: "Keys and fast binary search based subset"
+date: "`r Sys.Date()`"
+output:
+ rmarkdown::html_document:
+ theme: spacelab
+ highlight: pygments
+ css : css/bootstrap.css
+vignette: >
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r, echo = FALSE, message = FALSE}
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+options(datatable.auto.index=FALSE)
+```
+
+This vignette is aimed at those who are already familiar with *data.table* syntax, its general form, how to subset rows in `i`, select and compute on columns, add/modify/delete columns *by reference* in `j` and group by using `by`. If you're not familar with these concepts, please read the *"Introduction to data.table"* and *"data.table reference semantics"* vignettes first.
+
+***
+
+## Data {#data}
+
+We will use the same `flights` data as in the *"Introduction to data.table"* vignette.
+
+```{r echo=FALSE}
+options(width=100)
+```
+
+```{r}
+flights <- fread("flights14.csv")
+head(flights)
+dim(flights)
+```
+
+## Introduction
+
+In this vignette, we will
+
+* first introduce the concept of `key` in *data.table*, and set and use keys to perform *fast binary search* based subsets in `i`,
+
+* see that we can combine key based subsets along with `j` and `by` in the exact same way as before,
+
+* look at other additional useful arguments - `mult` and `nomatch`,
+
+* and finally conclude by looking at the advantage of setting keys - perform *fast binary search based subsets* and compare with the traditional vector scan approach.
+
+## 1. Keys
+
+### a) What is a *key*?
+
+In the *"Introduction to data.table"* vignette, we saw how to subset rows in `i` using logical expressions, row numbers and using `order()`. In this section, we will look at another way of subsetting incredibly fast - using *keys*.
+
+But first, let's start by looking at *data.frames*. All *data.frames* have a row names attribute. Consider the *data.frame* `DF` below.
+
+```{r}
+set.seed(1L)
+DF = data.frame(ID1 = sample(letters[1:2], 10, TRUE),
+ ID2 = sample(1:3, 10, TRUE),
+ val = sample(10),
+ stringsAsFactors = FALSE,
+ row.names = sample(LETTERS[1:10]))
+DF
+
+rownames(DF)
+```
+
+We can *subset* a particular row using its row name as shown below:
+
+```{r}
+DF["C", ]
+```
+
+i.e., row names are more or less *an index* to rows of a *data.frame*. However,
+
+1. Each row is limited to *exactly one* row name.
+
+ But, a person (for example) has at least two names - a *first* and a *second* name. It is useful to organise a telephone directory by *surname* then *first name*.
+
+2. And row names should be *unique*.
+
+ ```{r eval=FALSE}
+ rownames(DF) = sample(LETTERS[1:5], 10, TRUE)
+ # Warning: non-unique values when setting 'row.names': 'C', 'D'
+ # Error in `row.names<-.data.frame`(`*tmp*`, value = value): duplicate 'row.names' are not allowed
+ ```
+
+Now let's convert it to a *data.table*.
+
+```{r}
+DT = as.data.table(DF)
+DT
+
+rownames(DT)
+```
+
+* Note that row names have been reset.
+
+* *data.tables* never uses row names. Since *data.tables* **inherit** from *data.frames*, it still has the row names attribute. But it never uses them. We'll see in a moment as to why.
+
+ If you would like to preserve the row names, use `keep.rownames = TRUE` in `as.data.table()` - this will create a new column called `rn` and assign row names to this column.
+
+Instead, in *data.tables* we set and use `keys`. Think of a `key` as **supercharged rownames**.
+
+#### Keys and their properties {.bs-callout .bs-callout-info #key-properties}
+
+1. We can set keys on *multiple columns* and the column can be of *different types* -- *integer*, *numeric*, *character*, *factor*, *integer64* etc. *list* and *complex* types are not supported yet.
+
+2. Uniqueness is not enforced, i.e., duplicate key values are allowed. Since rows are sorted by key, any duplicates in the key columns will appear consecutively.
+
+3. Setting a `key` *does two things*:
+
+ a. reorders the rows of the *data.table* by the column(s) provided *by reference*, always in *increasing* order.
+
+ b. marks those columns as *key* columns by setting an attribute called `sorted` to the *data.table*.
+
+ Since the rows are reordered, a *data.table* can have at most one key because it can not be sorted in more than one way.
+
+#
+
+For the rest of the vignette, we will work with `flights` data set.
+
+### b) Set, get and use keys on a *data.table*
+
+#### -- How can we set the column `origin` as key in the *data.table* `flights`?
+
+```{r}
+setkey(flights, origin)
+head(flights)
+
+## alternatively we can provide character vectors to the function 'setkeyv()'
+# setkeyv(flights, "origin") # useful to program with
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* You can use the function `setkey()` and provide the column names (without quoting them). This is helpful during interactive use.
+
+* Alternatively you can pass a character vector of column names to the function `setkeyv()`. This is particularly useful while designing functions to pass columns to set key on as function arguments.
+
+* Note that we did not have to assign the result back to a variable. This is because like the `:=` function we saw in the *"Introduction to data.table"* vignette, `setkey()` and `setkeyv()` modify the input *data.table* *by reference*. They return the result invisibly.
+
+* The *data.table* is now reordered (or sorted) by the column we provided - `origin`. Since we reorder by reference, we only require additional memory of one column of length equal to the number of rows in the *data.table*, and is therefore very memory efficient.
+
+* You can also set keys directly when creating *data.tables* using the `data.table()` function using `key=` argument. It takes a character vector of column names.
+
+#### set* and `:=`: {.bs-callout .bs-callout-info}
+
+In *data.table*, the `:=` operator and all the `set*` (e.g., `setkey`, `setorder`, `setnames` etc..) functions are the only ones which modify the input object *by reference*.
+
+#
+
+Once you *key* a *data.table* by certain columns, you can subset by querying those key columns using the `.()` notation in `i`. Recall that `.()` is an *alias to* `list()`.
+
+#### -- Use the key column `origin` to subset all rows where the origin airport matches *"JFK"*
+
+```{r}
+flights[.("JFK")]
+
+## alternatively
+# flights[J("JFK")] (or) flights[list("JFK")]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* The *key* column has already been set to `origin`. So it is sufficient to provide the value, here *"JFK"*, directly. The `.()` syntax helps identify that the task requires looking up the value *"JFK"* in the key column of *data.table* (here column `origin` of `flights` *data.table*).
+
+* The *row indices* corresponding to the value *"JFK"* in `origin` is obtained first. And since there is no expression in `j`, all columns corresponding to those row indices are returned.
+
+* On single column key of *character* type, you can drop the `.()` notation and use the values directly when subsetting, like subset using row names on *data.frames*.
+
+ ```{r eval=FALSE}
+ flights["JFK"] ## same as flights[.("JFK")]
+ ```
+
+* We can subset any amount of values as required
+
+ ```{r eval = FALSE}
+ flights[c("JFK", "LGA")] ## same as flights[.(c("JFK", "LGA"))]
+ ```
+
+ This returns all columns corresponding to those rows where `origin` column matches either *"JFK"* or *"LGA"*.
+
+#### -- How can we get the column(s) a *data.table* is keyed by?
+
+Using the function `key()`.
+
+```{r}
+key(flights)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* It returns a character vector of all the key columns.
+
+* If no key is set, it returns `NULL`.
+
+### c) Keys and multiple columns
+
+To refresh, *keys* are like *supercharged* rownames. We can set key on multiple columns and they can be of multiple types.
+
+#### -- How can I set keys on both `origin` *and* `dest` columns?
+
+```{r}
+setkey(flights, origin, dest)
+head(flights)
+
+## or alternatively
+# setkeyv(flights, c("origin", "dest")) # provide a character vector of column names
+
+key(flights)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* It sorts the *data.table* first by the column `origin` and then by `dest` *by reference*.
+
+#### -- Subset all rows using key columns where first key column `origin` matches *"JFK"* and second key column `dest` matches *"MIA"*
+
+```{r}
+flights[.("JFK", "MIA")]
+```
+
+#### How does the subset work here? {.bs-callout .bs-callout-info #multiple-key-point}
+
+* It is important to undertand how this works internally. *"JFK"* is first matched against the first key column `origin`. And *within those matching rows*, *"MIA"* is matched against the second key column `dest` to obtain *row indices* where both `origin` and `dest` match the given values.
+
+* Since no `j` is provided, we simply return *all columns* corresponding to those row indices.
+
+#
+
+#### -- Subset all rows where just the first key column `origin` matches *"JFK"*
+
+```{r}
+key(flights)
+
+flights[.("JFK")] ## or in this case simply flights["JFK"], for convenience
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Since we did not provide any values for the second key column `dest`, it just matches *"JFK"* against the first key column `origin` and returns all the matched rows.
+
+#### -- Subset all rows where just the second key column `dest` matches *"MIA"*
+
+```{r}
+flights[.(unique(origin), "MIA")]
+```
+
+#### What's happening here? {.bs-callout .bs-callout-info}
+
+* Read [this](#multiple-key-point) again. The value provided for the second key column *"MIA"* has to find the matching vlaues in `dest` key column *on the matching rows provided by the first key column `origin`*. We can not skip the values of key columns *before*. Therfore we provide *all* unique values from key column `origin`.
+
+* *"MIA"* is automatically recycled to fit the length of `unique(origin)` which is *3*.
+
+## 2) Combining keys with `j` and `by`
+
+All we have seen so far is the same concept -- obtaining *row indices* in `i`, but just using a different method -- using `keys`. It shouldn't be surprising that we can do exactly the same things in `j` and `by` as seen from the previous vignettes. We will highlight this with a few examples.
+
+### a) Select in `j`
+
+#### -- Return `arr_delay` column as a *data.table* corresponding to `origin = "LGA"` and `dest = "TPA"`.
+
+```{r}
+key(flights)
+flights[.("LGA", "TPA"), .(arr_delay)]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* The *row indices* corresponding to `origin == "LGA" and `dest == "TPA"` are obtained using *key based subset*.
+
+* Once we have the row indices, we look at `j` which requires only the `arr_delay` column. So we simply select the column `arr_delay` for those *row indices* in the exact same way as we have seen in *Introduction to data.table* vignette.
+
+* We could have returned the result by using `with = FALSE` as well.
+
+ ```{r eval = FALSE}
+ flights[.("LGA", "TPA"), "arr_delay", with=FALSE]
+ ```
+
+### b) Chaining
+
+#### -- On the result obtained above, use chaining to order the column in decreasing order.
+
+```{r}
+flights[.("LGA", "TPA"), .(arr_delay)][order(-arr_delay)]
+```
+
+### c) Compute or *do* in `j`
+
+#### -- Find the maximum arrival delay correspondong to `origin = "LGA"` and `dest = "TPA"`.
+
+```{r}
+flights[.("LGA", "TPA"), max(arr_delay)]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We can verify that the result is identical to first value (486) from the previous example.
+
+### d) *sub-assign* by reference using `:=` in `j`
+
+We have seen this example already in the *Reference semantics* vignette. Let's take a look at all the `hours` available in the `flights` *data.table*:
+
+```{r}
+# get all 'hours' in flights
+flights[, sort(unique(hour))]
+```
+
+We see that there are totally `25` unique values in the data. Both *0* and *24* hours seem to be present. Let's go ahead and replace *24* with *0*, but this time using *key*.
+
+```{r}
+setkey(flights, hour)
+key(flights)
+flights[.(24), hour := 0L]
+key(flights)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We first set `key` to *hour*. This reorders `flights` by the column *hour* and marks that column as the `key` column.
+
+* Now we can subset on *hour* by using the `.()` notation. We subset for the value *24* and obtain the corresponding *row indices*.
+
+* And on those row indices, we replace the `key` column with the value `0`.
+
+* Since we have replaced values on the *key* column, the *data.table* `flights` isn't sorted by `hour` anymore. Therefore, the key has been automatically removed by setting to NULL.
+
+#
+Now, there shouldn't be any *24* in the *hour* column.
+
+```{r}
+flights[, sort(unique(hour))]
+```
+
+### e) Aggregation using `by`
+
+Let's set the key back to `origin, dest` first.
+
+```{r}
+setkey(flights, origin, dest)
+key(flights)
+```
+
+#### -- Get the maximum departure delay for each `month` corresponding to `origin = "JFK"`. Order the result by `month`
+
+```{r}
+ans <- flights["JFK", max(dep_delay), keyby=month]
+head(ans)
+key(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We subset on the `key` column *origin* to obtain the *row indices* corresponding to *"JFK"*.
+
+* Once we obtain the row indices, we only need two columns - `month` to group by and `dep_delay` to obtain `max()` for each group. *data.table's* query optimisation therefore subsets just those two columns corresponding to the *row indices* obtained in `i`, for speed and memory efficiency.
+
+* And on that subset, we group by *month* and compute `max(dep_delay)`.
+
+* We use `keyby` to automatically key that result by *month*. Now we understand what that means. In addition to ordering, it also sets *month* as the `key` column.
+
+## 3) Additional arguments - `mult` and `nomatch`
+
+### a) The *mult* argument
+
+We can choose, for each query, if *"all"* the matching rows should be returned, or just the *"first"* or *"last"* using the `mult` argument. The default value is *"all"* - what we've seen so far.
+
+#### -- Subset only the first matching row from all rows where `origin` matches *"JFK"* and `dest` matches *"MIA"*
+
+```{r}
+flights[.("JFK", "MIA"), mult="first"]
+```
+
+#### -- Subset only the last matching row of all the rows where `origin` matches *"LGA", "JFK", "EWR"* and `dest` matches *"XNA"*
+
+```{r}
+flights[.(c("LGA", "JFK", "EWR"), "XNA"), mult="last"]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* The query *"JFK", "XNA"* doesn't match any rows in `flights` and therefore returns `NA`.
+
+* Once again, the query for second key column `dest`, *"XNA"*, is recycled to fit the length of the query for first key column `origin`, which is of length 3.
+
+### The *nomatch* argument
+
+We can choose if queries that do not match should return `NA` or be skipped altogether using the `nomatch` argument.
+
+#### -- From the previous example, Subset all rows only if there's a match
+
+```{r}
+flights[.(c("LGA", "JFK", "EWR"), "XNA"), mult="last", nomatch = 0L]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Default value for `nomatch` is `NA`. Setting `nomatch = 0L` skips queries with no matches.
+
+* The query “JFK”, “XNA” doesn’t match any rows in flights and therefore is skipped.
+
+## 4) binary search vs vector scans
+
+We have seen so far how we can set and use keys to subset. But what's the advantage? For example, instead of doing:
+
+```{r eval=FALSE}
+# key by origin,dest columns
+flights[.("JFK", "MIA")]
+```
+
+we could have done:
+
+```{r eval=FALSE}
+flights[origin == "JFK" & dest == "MIA"]
+```
+
+One advantage very likely is shorter syntax. But even more than that, *binary search based subsets* are **incredibly fast**.
+
+### a) Performance of binary search approach
+
+To illustrate, let's create a sample *data.table* with 20 million rows and three columns and key it by columns `x` and `y`.
+
+```{r}
+set.seed(2L)
+N = 2e7L
+DT = data.table(x = sample(letters, N, TRUE),
+ y = sample(1000L, N, TRUE),
+ val=runif(N), key = c("x", "y"))
+print(object.size(DT), units="Mb")
+
+key(DT)
+```
+
+`DT` is ~380MB. It is not really huge, but this will do to illustrate the point.
+
+From what we have seen in the Introduction to data.table section, we can subset those rows where columns `x = "g"` and `y = 877` as follows:
+
+```{r}
+## (1) Usual way of subsetting - vector scan approach
+t1 <- system.time(ans1 <- DT[x == "g" & y == 877L])
+t1
+head(ans1)
+dim(ans1)
+```
+
+Now let's try to subset by using keys.
+
+```{r}
+## (2) Subsetting using keys
+t2 <- system.time(ans2 <- DT[.("g", 877L)])
+t2
+head(ans2)
+dim(ans2)
+
+identical(ans1$val, ans2$val)
+```
+
+* The speedup is **~`r round(t1[3]/t2[3])`x**!
+
+### b) Why does keying a *data.table* result in blazing fast susbets?
+
+To understand that, let's first look at what *vector scan approach* (method 1) does.
+
+#### Vector scan approach: {.bs-callout .bs-callout-info}
+
+* The column `x` is searched for the value *"g"* row by row, on all 20 million of them. This results in a *logical vector* of size 20 million, with values `TRUE, FALSE or NA` corresponding to `x`'s value.
+
+* Similarly, the column `y` is searched for `877` on all 20 million rows one by one, and stored in another logical vector.
+
+* Element wise `&` operations are performed on the intermediate logical vectors and all the rows where the expression evaluates to `TRUE` are returned.
+
+This is what we call a *vector scan approach*. And this is quite inefficient, especially on larger tables and when one needs repeated subsetting, because it has to scan through all the rows each time.
+
+#
+
+Now let us look at binary search approach (method 2). Recall from [Properties of key](#key-properties) - *setting keys reorders the data.table by key columns*. Since the data is sorted, we don't have to *scan through the entire length of the column*! We can instead use *binary search* to search a value in `O(log n)` as opposed to `O(n)` in case of *vector scan approach*, where `n` is the number of rows in the *data.table*.
+
+#### Binary search approach: {.bs-callout .bs-callout-info}
+
+Here's a very simple illustration. Let's consider the (sorted) numbers shown below:
+
+```{r eval=FALSE}
+1, 5, 10, 19, 22, 23, 30
+```
+
+Suppose we'd like to find the matching position of the value *1*, using binary search, this is how we would proceed - because we know that the data is *sorted*.
+
+* Start with the middle value = 19. Is 1 == 19? No. 1 < 19.
+
+* Since the value we're looking for is smaller than 19, it should be somewhere before 19. So we can discard the rest of the half that are >= 19.
+
+* Our set is now reduced to *1, 5, 10*. Grab the middle value once again = 5. Is 1 == 5? No. 1 < 5.
+
+* Our set is reduced to *1*. Is 1 == 1? Yes. The corresponding index is also 1. And that's the only match.
+
+A vector scan approach on the other hand would have to scan through all the values (here, 7).
+
+#
+
+It can be seen that with every search we reduce the number of searches by half. This is why *binary search* based subsets are **incredibly fast**. Since rows of each column of *data.tables* have contiguous locations in memory, the operations are performed in a very cache efficient manner (also contributes to *speed*).
+
+In addition, since we obtain the matching row indices directly without having to create those huge logical vectors (equal to the number of rows in a *data.table*), it is quite **memory efficient** as well.
+
+## Summary
+
+In this vignette, we have learnt another method to subset rows in `i` by keying a *data.table*. Setting keys allows us to perform blazing fast subsets by using *binary search*. In particular, we have seen how to
+
+#### {.bs-callout .bs-callout-info}
+
+* set key and subset using the key on a *data.table*.
+
+* subset using keys which fetches *row indices* in `i`, but much faster.
+
+* combine key based subsets with `j` and `by`. Note that the `j` and `by` operations are exactly the same as before.
+
+Key based subsets are **incredibly fast** and are particularly useful when the task involves *repeated subsetting*.
+
+#
+
+We don't have to set and use keys for aggregation operations in general, unless the data is extremely large and/or the task requires repeated subsetting where key based subsets will be noticeably performant.
+
+However, keying *data.tables* are essential to *join* two *data.tables* which is the subject of discussion in the next vignette *"Joins and rolling joins"*. We will extend the concept of key based subsets to joining two *data.tables* based on `key` columns.
+
+***
+
diff --git a/inst/doc/datatable-keys-fast-subset.html b/inst/doc/datatable-keys-fast-subset.html
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+<div id="header">
+<h1 class="title">Keys and fast binary search based subset</h1>
+<h4 class="date"><em>2015-09-18</em></h4>
+</div>
+
+
+<p>This vignette is aimed at those who are already familiar with <em>data.table</em> syntax, its general form, how to subset rows in <code>i</code>, select and compute on columns, add/modify/delete columns <em>by reference</em> in <code>j</code> and group by using <code>by</code>. If you’re not familar with these concepts, please read the <em>“Introduction to data.table”</em> and <em>“data.table reference semantics”</em> vignettes first.</p>
+<hr />
+<div id="data" class="section level2">
+<h2>Data</h2>
+<p>We will use the same <code>flights</code> data as in the <em>“Introduction to data.table”</em> vignette.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights <-<span class="st"> </span><span class="kw">fread</span>(<span class="st">"flights14.csv"</span>)
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13</span>
+<span class="co"># 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18</span>
+<span class="kw">dim</span>(flights)
+<span class="co"># [1] 253316 11</span></code></pre></div>
+</div>
+<div id="introduction" class="section level2">
+<h2>Introduction</h2>
+<p>In this vignette, we will</p>
+<ul>
+<li><p>first introduce the concept of <code>key</code> in <em>data.table</em>, and set and use keys to perform <em>fast binary search</em> based subsets in <code>i</code>,</p></li>
+<li><p>see that we can combine key based subsets along with <code>j</code> and <code>by</code> in the exact same way as before,</p></li>
+<li><p>look at other additional useful arguments - <code>mult</code> and <code>nomatch</code>,</p></li>
+<li><p>and finally conclude by looking at the advantage of setting keys - perform <em>fast binary search based subsets</em> and compare with the traditional vector scan approach.</p></li>
+</ul>
+</div>
+<div id="keys" class="section level2">
+<h2>1. Keys</h2>
+<div id="a-what-is-a-key" class="section level3">
+<h3>a) What is a <em>key</em>?</h3>
+<p>In the <em>“Introduction to data.table”</em> vignette, we saw how to subset rows in <code>i</code> using logical expressions, row numbers and using <code>order()</code>. In this section, we will look at another way of subsetting incredibly fast - using <em>keys</em>.</p>
+<p>But first, let’s start by looking at <em>data.frames</em>. All <em>data.frames</em> have a row names attribute. Consider the <em>data.frame</em> <code>DF</code> below.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">set.seed</span>(1L)
+DF =<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">ID1 =</span> <span class="kw">sample</span>(letters[<span class="dv">1</span>:<span class="dv">2</span>], <span class="dv">10</span>, <span class="ot">TRUE</span>),
+ <span class="dt">ID2 =</span> <span class="kw">sample</span>(<span class="dv">1</span>:<span class="dv">3</span>, <span class="dv">10</span>, <span class="ot">TRUE</span>),
+ <span class="dt">val =</span> <span class="kw">sample</span>(<span class="dv">10</span>),
+ <span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>,
+ <span class="dt">row.names =</span> <span class="kw">sample</span>(LETTERS[<span class="dv">1</span>:<span class="dv">10</span>]))
+DF
+<span class="co"># ID1 ID2 val</span>
+<span class="co"># C a 3 5</span>
+<span class="co"># D a 1 6</span>
+<span class="co"># E b 2 4</span>
+<span class="co"># G a 1 2</span>
+<span class="co"># B b 1 10</span>
+<span class="co"># H a 2 8</span>
+<span class="co"># I b 1 9</span>
+<span class="co"># F b 2 1</span>
+<span class="co"># J a 3 7</span>
+<span class="co"># A b 2 3</span>
+
+<span class="kw">rownames</span>(DF)
+<span class="co"># [1] "C" "D" "E" "G" "B" "H" "I" "F" "J" "A"</span></code></pre></div>
+<p>We can <em>subset</em> a particular row using its row name as shown below:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DF[<span class="st">"C"</span>, ]
+<span class="co"># ID1 ID2 val</span>
+<span class="co"># C a 3 5</span></code></pre></div>
+<p>i.e., row names are more or less <em>an index</em> to rows of a <em>data.frame</em>. However,</p>
+<ol style="list-style-type: decimal">
+<li><p>Each row is limited to <em>exactly one</em> row name.</p>
+<p>But, a person (for example) has at least two names - a <em>first</em> and a <em>second</em> name. It is useful to organise a telephone directory by <em>surname</em> then <em>first name</em>.</p></li>
+<li><p>And row names should be <em>unique</em>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rownames</span>(DF) =<span class="st"> </span><span class="kw">sample</span>(LETTERS[<span class="dv">1</span>:<span class="dv">5</span>], <span class="dv">10</span>, <span class="ot">TRUE</span>)
+<span class="co"># Warning: non-unique values when setting 'row.names': 'C', 'D'</span>
+<span class="co"># Error in `row.names<-.data.frame`(`*tmp*`, value = value): duplicate 'row.names' are not allowed</span></code></pre></div></li>
+</ol>
+<p>Now let’s convert it to a <em>data.table</em>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT =<span class="st"> </span><span class="kw">as.data.table</span>(DF)
+DT
+<span class="co"># ID1 ID2 val</span>
+<span class="co"># 1: a 3 5</span>
+<span class="co"># 2: a 1 6</span>
+<span class="co"># 3: b 2 4</span>
+<span class="co"># 4: a 1 2</span>
+<span class="co"># 5: b 1 10</span>
+<span class="co"># 6: a 2 8</span>
+<span class="co"># 7: b 1 9</span>
+<span class="co"># 8: b 2 1</span>
+<span class="co"># 9: a 3 7</span>
+<span class="co"># 10: b 2 3</span>
+
+<span class="kw">rownames</span>(DT)
+<span class="co"># [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"</span></code></pre></div>
+<ul>
+<li><p>Note that row names have been reset.</p></li>
+<li><p><em>data.tables</em> never uses row names. Since <em>data.tables</em> <strong>inherit</strong> from <em>data.frames</em>, it still has the row names attribute. But it never uses them. We’ll see in a moment as to why.</p>
+<p>If you would like to preserve the row names, use <code>keep.rownames = TRUE</code> in <code>as.data.table()</code> - this will create a new column called <code>rn</code> and assign row names to this column.</p></li>
+</ul>
+<p>Instead, in <em>data.tables</em> we set and use <code>keys</code>. Think of a <code>key</code> as <strong>supercharged rownames</strong>.</p>
+<div id="key-properties" class="section level4 bs-callout bs-callout-info">
+<h4>Keys and their properties</h4>
+<ol style="list-style-type: decimal">
+<li><p>We can set keys on <em>multiple columns</em> and the column can be of <em>different types</em> – <em>integer</em>, <em>numeric</em>, <em>character</em>, <em>factor</em>, <em>integer64</em> etc. <em>list</em> and <em>complex</em> types are not supported yet.</p></li>
+<li><p>Uniqueness is not enforced, i.e., duplicate key values are allowed. Since rows are sorted by key, any duplicates in the key columns will appear consecutively.</p></li>
+<li><p>Setting a <code>key</code> <em>does two things</em>:</p>
+<ol style="list-style-type: lower-alpha">
+<li><p>reorders the rows of the <em>data.table</em> by the column(s) provided <em>by reference</em>, always in <em>increasing</em> order.</p></li>
+<li><p>marks those columns as <em>key</em> columns by setting an attribute called <code>sorted</code> to the <em>data.table</em>.</p></li>
+</ol>
+<p>Since the rows are reordered, a <em>data.table</em> can have at most one key because it can not be sorted in more than one way.</p></li>
+</ol>
+</div>
+</div>
+</div>
+<div id="section" class="section level1">
+<h1></h1>
+<p>For the rest of the vignette, we will work with <code>flights</code> data set.</p>
+<div id="b-set-get-and-use-keys-on-a-data.table" class="section level3">
+<h3>b) Set, get and use keys on a <em>data.table</em></h3>
+<div id="how-can-we-set-the-column-origin-as-key-in-the-data.table-flights" class="section level4">
+<h4>– How can we set the column <code>origin</code> as key in the <em>data.table</em> <code>flights</code>?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">setkey</span>(flights, origin)
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 4 0 AA EWR LAX 339 2454 18</span>
+<span class="co"># 2: 2014 1 1 -5 -17 AA EWR MIA 161 1085 16</span>
+<span class="co"># 3: 2014 1 1 191 185 AA EWR DFW 214 1372 16</span>
+<span class="co"># 4: 2014 1 1 -1 -2 AA EWR DFW 214 1372 14</span>
+<span class="co"># 5: 2014 1 1 -3 -10 AA EWR MIA 154 1085 6</span>
+<span class="co"># 6: 2014 1 1 4 -17 AA EWR DFW 215 1372 9</span>
+
+## alternatively we can provide character vectors to the function 'setkeyv()'
+<span class="co"># setkeyv(flights, "origin") # useful to program with</span></code></pre></div>
+</div>
+<div id="section-1" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>You can use the function <code>setkey()</code> and provide the column names (without quoting them). This is helpful during interactive use.</p></li>
+<li><p>Alternatively you can pass a character vector of column names to the function <code>setkeyv()</code>. This is particularly useful while designing functions to pass columns to set key on as function arguments.</p></li>
+<li><p>Note that we did not have to assign the result back to a variable. This is because like the <code>:=</code> function we saw in the <em>“Introduction to data.table”</em> vignette, <code>setkey()</code> and <code>setkeyv()</code> modify the input <em>data.table</em> <em>by reference</em>. They return the result invisibly.</p></li>
+<li><p>The <em>data.table</em> is now reordered (or sorted) by the column we provided - <code>origin</code>. Since we reorder by reference, we only require additional memory of one column of length equal to the number of rows in the <em>data.table</em>, and is therefore very memory efficient.</p></li>
+<li><p>You can also set keys directly when creating <em>data.tables</em> using the <code>data.table()</code> function using <code>key=</code> argument. It takes a character vector of column names.</p></li>
+</ul>
+</div>
+<div id="set-and" class="section level4 bs-callout bs-callout-info">
+<h4>set* and <code>:=</code>:</h4>
+<p>In <em>data.table</em>, the <code>:=</code> operator and all the <code>set*</code> (e.g., <code>setkey</code>, <code>setorder</code>, <code>setnames</code> etc..) functions are the only ones which modify the input object <em>by reference</em>.</p>
+</div>
+</div>
+</div>
+<div id="section-2" class="section level1">
+<h1></h1>
+<p>Once you <em>key</em> a <em>data.table</em> by certain columns, you can subset by querying those key columns using the <code>.()</code> notation in <code>i</code>. Recall that <code>.()</code> is an <em>alias to</em> <code>list()</code>.</p>
+<div id="use-the-key-column-origin-to-subset-all-rows-where-the-origin-airport-matches-jfk" class="section level4">
+<h4>– Use the key column <code>origin</code> to subset all rows where the origin airport matches <em>“JFK”</em></h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="st">"JFK"</span>)]
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19</span>
+<span class="co"># 4: 2014 1 1 2 1 AA JFK LAX 350 2475 13</span>
+<span class="co"># 5: 2014 1 1 -2 -18 AA JFK LAX 338 2475 21</span>
+<span class="co"># --- </span>
+<span class="co"># 81479: 2014 10 31 -4 -21 UA JFK SFO 337 2586 17</span>
+<span class="co"># 81480: 2014 10 31 -2 -37 UA JFK SFO 344 2586 18</span>
+<span class="co"># 81481: 2014 10 31 0 -33 UA JFK LAX 320 2475 17</span>
+<span class="co"># 81482: 2014 10 31 -6 -38 UA JFK SFO 343 2586 9</span>
+<span class="co"># 81483: 2014 10 31 -6 -38 UA JFK LAX 323 2475 11</span>
+
+## alternatively
+<span class="co"># flights[J("JFK")] (or) flights[list("JFK")]</span></code></pre></div>
+</div>
+<div id="section-3" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>The <em>key</em> column has already been set to <code>origin</code>. So it is sufficient to provide the value, here <em>“JFK”</em>, directly. The <code>.()</code> syntax helps identify that the task requires looking up the value <em>“JFK”</em> in the key column of <em>data.table</em> (here column <code>origin</code> of <code>flights</code> <em>data.table</em>).</p></li>
+<li><p>The <em>row indices</em> corresponding to the value <em>“JFK”</em> in <code>origin</code> is obtained first. And since there is no expression in <code>j</code>, all columns corresponding to those row indices are returned.</p></li>
+<li><p>On single column key of <em>character</em> type, you can drop the <code>.()</code> notation and use the values directly when subsetting, like subset using row names on <em>data.frames</em>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[<span class="st">"JFK"</span>] ## same as flights[.("JFK")]</code></pre></div></li>
+<li><p>We can subset any amount of values as required</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[<span class="kw">c</span>(<span class="st">"JFK"</span>, <span class="st">"LGA"</span>)] ## same as flights[.(c("JFK", "LGA"))]</code></pre></div>
+<p>This returns all columns corresponding to those rows where <code>origin</code> column matches either <em>“JFK”</em> or <em>“LGA”</em>.</p></li>
+</ul>
+</div>
+<div id="how-can-we-get-the-columns-a-data.table-is-keyed-by" class="section level4">
+<h4>– How can we get the column(s) a <em>data.table</em> is keyed by?</h4>
+<p>Using the function <code>key()</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">key</span>(flights)
+<span class="co"># [1] "origin"</span></code></pre></div>
+</div>
+<div id="section-4" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>It returns a character vector of all the key columns.</p></li>
+<li><p>If no key is set, it returns <code>NULL</code>.</p></li>
+</ul>
+</div>
+<div id="c-keys-and-multiple-columns" class="section level3">
+<h3>c) Keys and multiple columns</h3>
+<p>To refresh, <em>keys</em> are like <em>supercharged</em> rownames. We can set key on multiple columns and they can be of multiple types.</p>
+<div id="how-can-i-set-keys-on-both-origin-and-dest-columns" class="section level4">
+<h4>– How can I set keys on both <code>origin</code> <em>and</em> <code>dest</code> columns?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">setkey</span>(flights, origin, dest)
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 2 -2 -25 EV EWR ALB 30 143 7</span>
+<span class="co"># 2: 2014 1 3 88 79 EV EWR ALB 29 143 23</span>
+<span class="co"># 3: 2014 1 4 220 211 EV EWR ALB 32 143 15</span>
+<span class="co"># 4: 2014 1 4 35 19 EV EWR ALB 32 143 7</span>
+<span class="co"># 5: 2014 1 5 47 42 EV EWR ALB 26 143 8</span>
+<span class="co"># 6: 2014 1 5 66 62 EV EWR ALB 31 143 23</span>
+
+## or alternatively
+<span class="co"># setkeyv(flights, c("origin", "dest")) # provide a character vector of column names</span>
+
+<span class="kw">key</span>(flights)
+<span class="co"># [1] "origin" "dest"</span></code></pre></div>
+</div>
+<div id="section-5" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li>It sorts the <em>data.table</em> first by the column <code>origin</code> and then by <code>dest</code> <em>by reference</em>.</li>
+</ul>
+</div>
+<div id="subset-all-rows-using-key-columns-where-first-key-column-origin-matches-jfk-and-second-key-column-dest-matches-mia" class="section level4">
+<h4>– Subset all rows using key columns where first key column <code>origin</code> matches <em>“JFK”</em> and second key column <code>dest</code> matches <em>“MIA”</em></h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="st">"JFK"</span>, <span class="st">"MIA"</span>)]
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 -1 -17 AA JFK MIA 161 1089 15</span>
+<span class="co"># 2: 2014 1 1 7 -8 AA JFK MIA 166 1089 9</span>
+<span class="co"># 3: 2014 1 1 2 -1 AA JFK MIA 164 1089 12</span>
+<span class="co"># 4: 2014 1 1 6 3 AA JFK MIA 157 1089 5</span>
+<span class="co"># 5: 2014 1 1 6 -12 AA JFK MIA 154 1089 17</span>
+<span class="co"># --- </span>
+<span class="co"># 2746: 2014 10 31 -1 -22 AA JFK MIA 148 1089 16</span>
+<span class="co"># 2747: 2014 10 31 -3 -20 AA JFK MIA 146 1089 8</span>
+<span class="co"># 2748: 2014 10 31 2 -17 AA JFK MIA 150 1089 6</span>
+<span class="co"># 2749: 2014 10 31 -3 -12 AA JFK MIA 150 1089 5</span>
+<span class="co"># 2750: 2014 10 31 29 4 AA JFK MIA 146 1089 19</span></code></pre></div>
+</div>
+<div id="multiple-key-point" class="section level4 bs-callout bs-callout-info">
+<h4>How does the subset work here?</h4>
+<ul>
+<li><p>It is important to undertand how this works internally. <em>“JFK”</em> is first matched against the first key column <code>origin</code>. And <em>within those matching rows</em>, <em>“MIA”</em> is matched against the second key column <code>dest</code> to obtain <em>row indices</em> where both <code>origin</code> and <code>dest</code> match the given values.</p></li>
+<li><p>Since no <code>j</code> is provided, we simply return <em>all columns</em> corresponding to those row indices.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-6" class="section level1">
+<h1></h1>
+<div id="subset-all-rows-where-just-the-first-key-column-origin-matches-jfk" class="section level4">
+<h4>– Subset all rows where just the first key column <code>origin</code> matches <em>“JFK”</em></h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">key</span>(flights)
+<span class="co"># [1] "origin" "dest"</span>
+
+flights[.(<span class="st">"JFK"</span>)] ## or in this case simply flights["JFK"], for convenience
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 10 4 B6 JFK ABQ 280 1826 20</span>
+<span class="co"># 2: 2014 1 2 134 161 B6 JFK ABQ 252 1826 22</span>
+<span class="co"># 3: 2014 1 7 6 6 B6 JFK ABQ 269 1826 20</span>
+<span class="co"># 4: 2014 1 8 15 -15 B6 JFK ABQ 259 1826 20</span>
+<span class="co"># 5: 2014 1 9 45 32 B6 JFK ABQ 267 1826 20</span>
+<span class="co"># --- </span>
+<span class="co"># 81479: 2014 10 31 0 -18 DL JFK TPA 142 1005 8</span>
+<span class="co"># 81480: 2014 10 31 1 -8 B6 JFK TPA 149 1005 19</span>
+<span class="co"># 81481: 2014 10 31 -2 -22 B6 JFK TPA 145 1005 14</span>
+<span class="co"># 81482: 2014 10 31 -8 -5 B6 JFK TPA 149 1005 9</span>
+<span class="co"># 81483: 2014 10 31 -4 -18 B6 JFK TPA 145 1005 8</span></code></pre></div>
+</div>
+<div id="section-7" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li>Since we did not provide any values for the second key column <code>dest</code>, it just matches <em>“JFK”</em> against the first key column <code>origin</code> and returns all the matched rows.</li>
+</ul>
+</div>
+<div id="subset-all-rows-where-just-the-second-key-column-dest-matches-mia" class="section level4">
+<h4>– Subset all rows where just the second key column <code>dest</code> matches <em>“MIA”</em></h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="kw">unique</span>(origin), <span class="st">"MIA"</span>)]
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 -5 -17 AA EWR MIA 161 1085 16</span>
+<span class="co"># 2: 2014 1 1 -3 -10 AA EWR MIA 154 1085 6</span>
+<span class="co"># 3: 2014 1 1 -5 -8 AA EWR MIA 157 1085 11</span>
+<span class="co"># 4: 2014 1 1 43 42 UA EWR MIA 155 1085 15</span>
+<span class="co"># 5: 2014 1 1 60 49 UA EWR MIA 162 1085 21</span>
+<span class="co"># --- </span>
+<span class="co"># 9924: 2014 10 31 -11 -8 AA LGA MIA 157 1096 13</span>
+<span class="co"># 9925: 2014 10 31 -5 -11 AA LGA MIA 150 1096 9</span>
+<span class="co"># 9926: 2014 10 31 -2 10 AA LGA MIA 156 1096 6</span>
+<span class="co"># 9927: 2014 10 31 -2 -16 AA LGA MIA 156 1096 19</span>
+<span class="co"># 9928: 2014 10 31 1 -11 US LGA MIA 164 1096 15</span></code></pre></div>
+</div>
+<div id="whats-happening-here" class="section level4 bs-callout bs-callout-info">
+<h4>What’s happening here?</h4>
+<ul>
+<li><p>Read <a href="#multiple-key-point">this</a> again. The value provided for the second key column <em>“MIA”</em> has to find the matching vlaues in <code>dest</code> key column <em>on the matching rows provided by the first key column <code>origin</code></em>. We can not skip the values of key columns <em>before</em>. Therfore we provide <em>all</em> unique values from key column <code>origin</code>.</p></li>
+<li><p><em>“MIA”</em> is automatically recycled to fit the length of <code>unique(origin)</code> which is <em>3</em>.</p></li>
+</ul>
+</div>
+<div id="combining-keys-with-j-and-by" class="section level2">
+<h2>2) Combining keys with <code>j</code> and <code>by</code></h2>
+<p>All we have seen so far is the same concept – obtaining <em>row indices</em> in <code>i</code>, but just using a different method – using <code>keys</code>. It shouldn’t be surprising that we can do exactly the same things in <code>j</code> and <code>by</code> as seen from the previous vignettes. We will highlight this with a few examples.</p>
+<div id="a-select-in-j" class="section level3">
+<h3>a) Select in <code>j</code></h3>
+<div id="return-arr_delay-column-as-a-data.table-corresponding-to-origin-lga-and-dest-tpa." class="section level4">
+<h4>– Return <code>arr_delay</code> column as a <em>data.table</em> corresponding to <code>origin = "LGA"</code> and <code>dest = "TPA"</code>.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">key</span>(flights)
+<span class="co"># [1] "origin" "dest"</span>
+flights[.(<span class="st">"LGA"</span>, <span class="st">"TPA"</span>), .(arr_delay)]
+<span class="co"># arr_delay</span>
+<span class="co"># 1: 1</span>
+<span class="co"># 2: 14</span>
+<span class="co"># 3: -17</span>
+<span class="co"># 4: -4</span>
+<span class="co"># 5: -12</span>
+<span class="co"># --- </span>
+<span class="co"># 1848: 39</span>
+<span class="co"># 1849: -24</span>
+<span class="co"># 1850: -12</span>
+<span class="co"># 1851: 21</span>
+<span class="co"># 1852: -11</span></code></pre></div>
+</div>
+<div id="section-8" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>The <em>row indices</em> corresponding to <code>origin == "LGA" and</code>dest == “TPA”` are obtained using <em>key based subset</em>.</p></li>
+<li><p>Once we have the row indices, we look at <code>j</code> which requires only the <code>arr_delay</code> column. So we simply select the column <code>arr_delay</code> for those <em>row indices</em> in the exact same way as we have seen in <em>Introduction to data.table</em> vignette.</p></li>
+<li><p>We could have returned the result by using <code>with = FALSE</code> as well.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="st">"LGA"</span>, <span class="st">"TPA"</span>), <span class="st">"arr_delay"</span>, with=<span class="ot">FALSE</span>]</code></pre></div></li>
+</ul>
+</div>
+</div>
+<div id="b-chaining" class="section level3">
+<h3>b) Chaining</h3>
+<div id="on-the-result-obtained-above-use-chaining-to-order-the-column-in-decreasing-order." class="section level4">
+<h4>– On the result obtained above, use chaining to order the column in decreasing order.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="st">"LGA"</span>, <span class="st">"TPA"</span>), .(arr_delay)][<span class="kw">order</span>(-arr_delay)]
+<span class="co"># arr_delay</span>
+<span class="co"># 1: 486</span>
+<span class="co"># 2: 380</span>
+<span class="co"># 3: 351</span>
+<span class="co"># 4: 318</span>
+<span class="co"># 5: 300</span>
+<span class="co"># --- </span>
+<span class="co"># 1848: -40</span>
+<span class="co"># 1849: -43</span>
+<span class="co"># 1850: -46</span>
+<span class="co"># 1851: -48</span>
+<span class="co"># 1852: -49</span></code></pre></div>
+</div>
+</div>
+<div id="c-compute-or-do-in-j" class="section level3">
+<h3>c) Compute or <em>do</em> in <code>j</code></h3>
+<div id="find-the-maximum-arrival-delay-correspondong-to-origin-lga-and-dest-tpa." class="section level4">
+<h4>– Find the maximum arrival delay correspondong to <code>origin = "LGA"</code> and <code>dest = "TPA"</code>.</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="st">"LGA"</span>, <span class="st">"TPA"</span>), <span class="kw">max</span>(arr_delay)]
+<span class="co"># [1] 486</span></code></pre></div>
+</div>
+<div id="section-9" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li>We can verify that the result is identical to first value (486) from the previous example.</li>
+</ul>
+</div>
+</div>
+<div id="d-sub-assign-by-reference-using-in-j" class="section level3">
+<h3>d) <em>sub-assign</em> by reference using <code>:=</code> in <code>j</code></h3>
+<p>We have seen this example already in the <em>Reference semantics</em> vignette. Let’s take a look at all the <code>hours</code> available in the <code>flights</code> <em>data.table</em>:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># get all 'hours' in flights</span>
+flights[, <span class="kw">sort</span>(<span class="kw">unique</span>(hour))]
+<span class="co"># [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24</span></code></pre></div>
+<p>We see that there are totally <code>25</code> unique values in the data. Both <em>0</em> and <em>24</em> hours seem to be present. Let’s go ahead and replace <em>24</em> with <em>0</em>, but this time using <em>key</em>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">setkey</span>(flights, hour)
+<span class="kw">key</span>(flights)
+<span class="co"># [1] "hour"</span>
+flights[.(<span class="dv">24</span>), hour :<span class="er">=</span><span class="st"> </span>0L]
+<span class="kw">key</span>(flights)
+<span class="co"># NULL</span></code></pre></div>
+<div id="section-10" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We first set <code>key</code> to <em>hour</em>. This reorders <code>flights</code> by the column <em>hour</em> and marks that column as the <code>key</code> column.</p></li>
+<li><p>Now we can subset on <em>hour</em> by using the <code>.()</code> notation. We subset for the value <em>24</em> and obtain the corresponding <em>row indices</em>.</p></li>
+<li><p>And on those row indices, we replace the <code>key</code> column with the value <code>0</code>.</p></li>
+<li><p>Since we have replaced values on the <em>key</em> column, the <em>data.table</em> <code>flights</code> isn’t sorted by <code>hour</code> anymore. Therefore, the key has been automatically removed by setting to NULL.</p></li>
+</ul>
+</div>
+</div>
+</div>
+</div>
+<div id="section-11" class="section level1">
+<h1></h1>
+<p>Now, there shouldn’t be any <em>24</em> in the <em>hour</em> column.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[, <span class="kw">sort</span>(<span class="kw">unique</span>(hour))]
+<span class="co"># [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23</span></code></pre></div>
+<div id="e-aggregation-using-by" class="section level3">
+<h3>e) Aggregation using <code>by</code></h3>
+<p>Let’s set the key back to <code>origin, dest</code> first.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">setkey</span>(flights, origin, dest)
+<span class="kw">key</span>(flights)
+<span class="co"># [1] "origin" "dest"</span></code></pre></div>
+<div id="get-the-maximum-departure-delay-for-each-month-corresponding-to-origin-jfk.-order-the-result-by-month" class="section level4">
+<h4>– Get the maximum departure delay for each <code>month</code> corresponding to <code>origin = "JFK"</code>. Order the result by <code>month</code></h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ans <-<span class="st"> </span>flights[<span class="st">"JFK"</span>, <span class="kw">max</span>(dep_delay), keyby=month]
+<span class="kw">head</span>(ans)
+<span class="co"># month V1</span>
+<span class="co"># 1: 1 881</span>
+<span class="co"># 2: 2 1014</span>
+<span class="co"># 3: 3 920</span>
+<span class="co"># 4: 4 1241</span>
+<span class="co"># 5: 5 853</span>
+<span class="co"># 6: 6 798</span>
+<span class="kw">key</span>(ans)
+<span class="co"># [1] "month"</span></code></pre></div>
+</div>
+<div id="section-12" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We subset on the <code>key</code> column <em>origin</em> to obtain the <em>row indices</em> corresponding to <em>“JFK”</em>.</p></li>
+<li><p>Once we obtain the row indices, we only need two columns - <code>month</code> to group by and <code>dep_delay</code> to obtain <code>max()</code> for each group. <em>data.table’s</em> query optimisation therefore subsets just those two columns corresponding to the <em>row indices</em> obtained in <code>i</code>, for speed and memory efficiency.</p></li>
+<li><p>And on that subset, we group by <em>month</em> and compute <code>max(dep_delay)</code>.</p></li>
+<li><p>We use <code>keyby</code> to automatically key that result by <em>month</em>. Now we understand what that means. In addition to ordering, it also sets <em>month</em> as the <code>key</code> column.</p></li>
+</ul>
+</div>
+</div>
+<div id="additional-arguments---mult-and-nomatch" class="section level2">
+<h2>3) Additional arguments - <code>mult</code> and <code>nomatch</code></h2>
+<div id="a-the-mult-argument" class="section level3">
+<h3>a) The <em>mult</em> argument</h3>
+<p>We can choose, for each query, if <em>“all”</em> the matching rows should be returned, or just the <em>“first”</em> or <em>“last”</em> using the <code>mult</code> argument. The default value is <em>“all”</em> - what we’ve seen so far.</p>
+<div id="subset-only-the-first-matching-row-from-all-rows-where-origin-matches-jfk-and-dest-matches-mia" class="section level4">
+<h4>– Subset only the first matching row from all rows where <code>origin</code> matches <em>“JFK”</em> and <code>dest</code> matches <em>“MIA”</em></h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="st">"JFK"</span>, <span class="st">"MIA"</span>), mult=<span class="st">"first"</span>]
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 6 3 AA JFK MIA 157 1089 5</span></code></pre></div>
+</div>
+<div id="subset-only-the-last-matching-row-of-all-the-rows-where-origin-matches-lga-jfk-ewr-and-dest-matches-xna" class="section level4">
+<h4>– Subset only the last matching row of all the rows where <code>origin</code> matches <em>“LGA”, “JFK”, “EWR”</em> and <code>dest</code> matches <em>“XNA”</em></h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="kw">c</span>(<span class="st">"LGA"</span>, <span class="st">"JFK"</span>, <span class="st">"EWR"</span>), <span class="st">"XNA"</span>), mult=<span class="st">"last"</span>]
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 5 23 163 148 MQ LGA XNA 158 1147 18</span>
+<span class="co"># 2: NA NA NA NA NA NA JFK XNA NA NA NA</span>
+<span class="co"># 3: 2014 2 3 231 268 EV EWR XNA 184 1131 12</span></code></pre></div>
+</div>
+<div id="section-13" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>The query <em>“JFK”, “XNA”</em> doesn’t match any rows in <code>flights</code> and therefore returns <code>NA</code>.</p></li>
+<li><p>Once again, the query for second key column <code>dest</code>, <em>“XNA”</em>, is recycled to fit the length of the query for first key column <code>origin</code>, which is of length 3.</p></li>
+</ul>
+</div>
+</div>
+<div id="the-nomatch-argument" class="section level3">
+<h3>The <em>nomatch</em> argument</h3>
+<p>We can choose if queries that do not match should return <code>NA</code> or be skipped altogether using the <code>nomatch</code> argument.</p>
+<div id="from-the-previous-example-subset-all-rows-only-if-theres-a-match" class="section level4">
+<h4>– From the previous example, Subset all rows only if there’s a match</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[.(<span class="kw">c</span>(<span class="st">"LGA"</span>, <span class="st">"JFK"</span>, <span class="st">"EWR"</span>), <span class="st">"XNA"</span>), mult=<span class="st">"last"</span>, nomatch =<span class="st"> </span>0L]
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 5 23 163 148 MQ LGA XNA 158 1147 18</span>
+<span class="co"># 2: 2014 2 3 231 268 EV EWR XNA 184 1131 12</span></code></pre></div>
+</div>
+<div id="section-14" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Default value for <code>nomatch</code> is <code>NA</code>. Setting <code>nomatch = 0L</code> skips queries with no matches.</p></li>
+<li><p>The query “JFK”, “XNA” doesn’t match any rows in flights and therefore is skipped.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="binary-search-vs-vector-scans" class="section level2">
+<h2>4) binary search vs vector scans</h2>
+<p>We have seen so far how we can set and use keys to subset. But what’s the advantage? For example, instead of doing:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># key by origin,dest columns</span>
+flights[.(<span class="st">"JFK"</span>, <span class="st">"MIA"</span>)]</code></pre></div>
+<p>we could have done:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[origin ==<span class="st"> "JFK"</span> &<span class="st"> </span>dest ==<span class="st"> "MIA"</span>]</code></pre></div>
+<p>One advantage very likely is shorter syntax. But even more than that, <em>binary search based subsets</em> are <strong>incredibly fast</strong>.</p>
+<div id="a-performance-of-binary-search-approach" class="section level3">
+<h3>a) Performance of binary search approach</h3>
+<p>To illustrate, let’s create a sample <em>data.table</em> with 20 million rows and three columns and key it by columns <code>x</code> and <code>y</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">set.seed</span>(2L)
+N =<span class="st"> </span>2e7L
+DT =<span class="st"> </span><span class="kw">data.table</span>(<span class="dt">x =</span> <span class="kw">sample</span>(letters, N, <span class="ot">TRUE</span>),
+ <span class="dt">y =</span> <span class="kw">sample</span>(1000L, N, <span class="ot">TRUE</span>),
+ <span class="dt">val=</span><span class="kw">runif</span>(N), <span class="dt">key =</span> <span class="kw">c</span>(<span class="st">"x"</span>, <span class="st">"y"</span>))
+<span class="kw">print</span>(<span class="kw">object.size</span>(DT), <span class="dt">units=</span><span class="st">"Mb"</span>)
+<span class="co"># 381.5 Mb</span>
+
+<span class="kw">key</span>(DT)
+<span class="co"># [1] "x" "y"</span></code></pre></div>
+<p><code>DT</code> is ~380MB. It is not really huge, but this will do to illustrate the point.</p>
+<p>From what we have seen in the Introduction to data.table section, we can subset those rows where columns <code>x = "g"</code> and <code>y = 877</code> as follows:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## (1) Usual way of subsetting - vector scan approach
+t1 <-<span class="st"> </span><span class="kw">system.time</span>(ans1 <-<span class="st"> </span>DT[x ==<span class="st"> "g"</span> &<span class="st"> </span>y ==<span class="st"> </span>877L])
+t1
+<span class="co"># user system elapsed </span>
+<span class="co"># 0.636 0.020 0.652</span>
+<span class="kw">head</span>(ans1)
+<span class="co"># x y val</span>
+<span class="co"># 1: g 877 0.3946652</span>
+<span class="co"># 2: g 877 0.9424275</span>
+<span class="co"># 3: g 877 0.7068512</span>
+<span class="co"># 4: g 877 0.6959935</span>
+<span class="co"># 5: g 877 0.9673482</span>
+<span class="co"># 6: g 877 0.4842585</span>
+<span class="kw">dim</span>(ans1)
+<span class="co"># [1] 761 3</span></code></pre></div>
+<p>Now let’s try to subset by using keys.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## (2) Subsetting using keys
+t2 <-<span class="st"> </span><span class="kw">system.time</span>(ans2 <-<span class="st"> </span>DT[.(<span class="st">"g"</span>, 877L)])
+t2
+<span class="co"># user system elapsed </span>
+<span class="co"># 0 0 0</span>
+<span class="kw">head</span>(ans2)
+<span class="co"># x y val</span>
+<span class="co"># 1: g 877 0.3946652</span>
+<span class="co"># 2: g 877 0.9424275</span>
+<span class="co"># 3: g 877 0.7068512</span>
+<span class="co"># 4: g 877 0.6959935</span>
+<span class="co"># 5: g 877 0.9673482</span>
+<span class="co"># 6: g 877 0.4842585</span>
+<span class="kw">dim</span>(ans2)
+<span class="co"># [1] 761 3</span>
+
+<span class="kw">identical</span>(ans1$val, ans2$val)
+<span class="co"># [1] TRUE</span></code></pre></div>
+<ul>
+<li>The speedup is <strong>~x</strong>!</li>
+</ul>
+</div>
+<div id="b-why-does-keying-a-data.table-result-in-blazing-fast-susbets" class="section level3">
+<h3>b) Why does keying a <em>data.table</em> result in blazing fast susbets?</h3>
+<p>To understand that, let’s first look at what <em>vector scan approach</em> (method 1) does.</p>
+<div id="vector-scan-approach" class="section level4 bs-callout bs-callout-info">
+<h4>Vector scan approach:</h4>
+<ul>
+<li><p>The column <code>x</code> is searched for the value <em>“g”</em> row by row, on all 20 million of them. This results in a <em>logical vector</em> of size 20 million, with values <code>TRUE, FALSE or NA</code> corresponding to <code>x</code>’s value.</p></li>
+<li><p>Similarly, the column <code>y</code> is searched for <code>877</code> on all 20 million rows one by one, and stored in another logical vector.</p></li>
+<li><p>Element wise <code>&</code> operations are performed on the intermediate logical vectors and all the rows where the expression evaluates to <code>TRUE</code> are returned.</p></li>
+</ul>
+<p>This is what we call a <em>vector scan approach</em>. And this is quite inefficient, especially on larger tables and when one needs repeated subsetting, because it has to scan through all the rows each time.</p>
+</div>
+</div>
+</div>
+</div>
+<div id="section-15" class="section level1">
+<h1></h1>
+<p>Now let us look at binary search approach (method 2). Recall from <a href="#key-properties">Properties of key</a> - <em>setting keys reorders the data.table by key columns</em>. Since the data is sorted, we don’t have to <em>scan through the entire length of the column</em>! We can instead use <em>binary search</em> to search a value in <code>O(log n)</code> as opposed to <code>O(n)</code> in case of <em>vector scan approach</em>, where <code>n</code> is the number of rows in the <em> [...]
+<div id="binary-search-approach" class="section level4 bs-callout bs-callout-info">
+<h4>Binary search approach:</h4>
+<p>Here’s a very simple illustration. Let’s consider the (sorted) numbers shown below:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="dv">1</span>, <span class="dv">5</span>, <span class="dv">10</span>, <span class="dv">19</span>, <span class="dv">22</span>, <span class="dv">23</span>, <span class="dv">30</span></code></pre></div>
+<p>Suppose we’d like to find the matching position of the value <em>1</em>, using binary search, this is how we would proceed - because we know that the data is <em>sorted</em>.</p>
+<ul>
+<li><p>Start with the middle value = 19. Is 1 == 19? No. 1 < 19.</p></li>
+<li><p>Since the value we’re looking for is smaller than 19, it should be somewhere before 19. So we can discard the rest of the half that are >= 19.</p></li>
+<li><p>Our set is now reduced to <em>1, 5, 10</em>. Grab the middle value once again = 5. Is 1 == 5? No. 1 < 5.</p></li>
+<li><p>Our set is reduced to <em>1</em>. Is 1 == 1? Yes. The corresponding index is also 1. And that’s the only match.</p></li>
+</ul>
+<p>A vector scan approach on the other hand would have to scan through all the values (here, 7).</p>
+</div>
+</div>
+<div id="section-16" class="section level1">
+<h1></h1>
+<p>It can be seen that with every search we reduce the number of searches by half. This is why <em>binary search</em> based subsets are <strong>incredibly fast</strong>. Since rows of each column of <em>data.tables</em> have contiguous locations in memory, the operations are performed in a very cache efficient manner (also contributes to <em>speed</em>).</p>
+<p>In addition, since we obtain the matching row indices directly without having to create those huge logical vectors (equal to the number of rows in a <em>data.table</em>), it is quite <strong>memory efficient</strong> as well.</p>
+<div id="summary" class="section level2">
+<h2>Summary</h2>
+<p>In this vignette, we have learnt another method to subset rows in <code>i</code> by keying a <em>data.table</em>. Setting keys allows us to perform blazing fast subsets by using <em>binary search</em>. In particular, we have seen how to</p>
+<div id="section-17" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>set key and subset using the key on a <em>data.table</em>.</p></li>
+<li><p>subset using keys which fetches <em>row indices</em> in <code>i</code>, but much faster.</p></li>
+<li><p>combine key based subsets with <code>j</code> and <code>by</code>. Note that the <code>j</code> and <code>by</code> operations are exactly the same as before.</p></li>
+</ul>
+<p>Key based subsets are <strong>incredibly fast</strong> and are particularly useful when the task involves <em>repeated subsetting</em>.</p>
+</div>
+</div>
+</div>
+<div id="section-18" class="section level1">
+<h1></h1>
+<p>We don’t have to set and use keys for aggregation operations in general, unless the data is extremely large and/or the task requires repeated subsetting where key based subsets will be noticeably performant.</p>
+<p>However, keying <em>data.tables</em> are essential to <em>join</em> two <em>data.tables</em> which is the subject of discussion in the next vignette <em>“Joins and rolling joins”</em>. We will extend the concept of key based subsets to joining two <em>data.tables</em> based on <code>key</code> columns.</p>
+<hr />
+</div>
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+$(document).ready(function () {
+ $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
+});
+
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
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+ var script = document.createElement("script");
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diff --git a/inst/doc/datatable-reference-semantics.R b/inst/doc/datatable-reference-semantics.R
new file mode 100644
index 0000000..ebfdb79
--- /dev/null
+++ b/inst/doc/datatable-reference-semantics.R
@@ -0,0 +1,128 @@
+## ---- echo = FALSE, message = FALSE--------------------------------------
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+# options(datatable.auto.index=FALSE)
+
+## ----echo=FALSE-----------------------------------------------------------------------------------
+options(width=100)
+
+## -------------------------------------------------------------------------------------------------
+flights <- fread("flights14.csv")
+flights
+dim(flights)
+
+## -------------------------------------------------------------------------------------------------
+DF = data.frame(ID = c("b","b","b","a","a","c"), a = 1:6, b = 7:12, c=13:18)
+DF
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# DF$c <- 18:13 # (1) -- replace entire column
+# # or
+# DF$c[DF$ID == "b"] <- 15:13 # (2) -- subassign in column 'c'
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# DT[, c("colA", "colB", ...) := list(valA, valB, ...)]
+#
+# # when you have only one column to assign to you
+# # can drop the quotes and list(), for convenience
+# DT[, colA := valA]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# DT[, `:=`(colA = valA, # valA is assigned to colA
+# colB = valB, # valB is assigned to colB
+# ...
+# )]
+
+## -------------------------------------------------------------------------------------------------
+flights[, `:=`(speed = distance / (air_time/60), # speed in km/hr
+ delay = arr_delay + dep_delay)] # delay in minutes
+head(flights)
+
+## alternatively, using the 'LHS := RHS' form
+# flights[, c("speed", "delay") := list(distance/(air_time/60), arr_delay + dep_delay)]
+
+## -------------------------------------------------------------------------------------------------
+# get all 'hours' in flights
+flights[, sort(unique(hour))]
+
+## -------------------------------------------------------------------------------------------------
+# subassign by reference
+flights[hour == 24L, hour := 0L]
+
+## -------------------------------------------------------------------------------------------------
+flights[hour == 24L, hour := 0L][]
+
+## -------------------------------------------------------------------------------------------------
+# check again for '24'
+flights[, sort(unique(hour))]
+
+## -------------------------------------------------------------------------------------------------
+flights[, c("delay") := NULL]
+head(flights)
+
+## or using the functional form
+# flights[, `:=`(delay = NULL)]
+
+## ----eval=FALSE-----------------------------------------------------------------------------------
+# flights[, delay := NULL]
+
+## -------------------------------------------------------------------------------------------------
+flights[, max_speed := max(speed), by=.(origin, dest)]
+head(flights)
+
+## -------------------------------------------------------------------------------------------------
+in_cols = c("dep_delay", "arr_delay")
+out_cols = c("max_dep_delay", "max_arr_delay")
+flights[, c(out_cols) := lapply(.SD, max), by = month, .SDcols = in_cols]
+head(flights)
+
+## -------------------------------------------------------------------------------------------------
+# RHS gets automatically recycled to length of LHS
+flights[, c("speed", "max_speed", "max_dep_delay", "max_arr_delay") := NULL]
+head(flights)
+
+## -------------------------------------------------------------------------------------------------
+foo <- function(DT) {
+ DT[, speed := distance / (air_time/60)]
+ DT[, .(max_speed = max(speed)), by=month]
+}
+ans = foo(flights)
+head(flights)
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+flights[, speed := NULL]
+
+## -------------------------------------------------------------------------------------------------
+foo <- function(DT) {
+ DT <- copy(DT) ## deep copy
+ DT[, speed := distance / (air_time/60)] ## doesn't affect 'flights'
+ DT[, .(max_speed = max(speed)), by=month]
+}
+ans <- foo(flights)
+head(flights)
+head(ans)
+
+## -------------------------------------------------------------------------------------------------
+DT = data.table(x=1, y=2)
+DT_n = names(DT)
+DT_n
+
+## add a new column by reference
+DT[, z := 3]
+
+## DT_n also gets updated
+DT_n
+
+## use `copy()`
+DT_n = copy(names(DT))
+DT[, w := 4]
+
+## DT_n doesn't get updated
+DT_n
+
diff --git a/inst/doc/datatable-reference-semantics.Rmd b/inst/doc/datatable-reference-semantics.Rmd
new file mode 100644
index 0000000..2a66bc6
--- /dev/null
+++ b/inst/doc/datatable-reference-semantics.Rmd
@@ -0,0 +1,368 @@
+---
+title: "Reference semantics"
+date: "`r Sys.Date()`"
+output:
+ rmarkdown::html_document:
+ theme: spacelab
+ highlight: pygments
+ css : css/bootstrap.css
+vignette: >
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r, echo = FALSE, message = FALSE}
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+# options(datatable.auto.index=FALSE)
+```
+This vignette discusses *data.table*'s reference semantics which allows to *add/update/delete* columns of a *data.table by reference*, and also combine them with `i` and `by`. It is aimed at those who are already familiar with *data.table* syntax, its general form, how to subset rows in `i`, select and compute on columns, and perform aggregations by group. If you're not familar with these concepts, please read the *"Introduction to data.table"* vignette first.
+
+***
+
+## Data {#data}
+
+We will use the same `flights` data as in the *"Introduction to data.table"* vignette.
+
+```{r echo=FALSE}
+options(width=100)
+```
+
+```{r}
+flights <- fread("flights14.csv")
+flights
+dim(flights)
+```
+
+## Introduction
+
+In this vignette, we will
+
+1. first discuss reference semantics briefly and look at the two different forms in which the `:=` operator can be used
+
+2. then see how we can *add/update/delete* columns *by reference* in `j` using the `:=` operator and how to combine with `i` and `by`.
+
+3. and finally we will look at using `:=` for its *side-effect* and how we can avoid the side effects using `copy()`.
+
+## 1. Reference semantics
+
+All the operations we have seen so far in the previous vignette resulted in a new data set. We will see how to *add* new column(s), *update* or *delete* existing column(s) on the original data.
+
+### a) Background
+
+Before we look at *reference semantics*, consider the *data.frame* shown below:
+
+```{r}
+DF = data.frame(ID = c("b","b","b","a","a","c"), a = 1:6, b = 7:12, c=13:18)
+DF
+```
+
+When we did:
+
+```{r eval=FALSE}
+DF$c <- 18:13 # (1) -- replace entire column
+# or
+DF$c[DF$ID == "b"] <- 15:13 # (2) -- subassign in column 'c'
+```
+
+both (1) and (2) resulted in [*deep* copy of the entire *data.frame*](http://r.789695.n4.nabble.com/speeding-up-perception-td3640920.html#a3646694) in versions of `R` versions `< 3.1`. [It copied more than once](http://stackoverflow.com/q/23898969/559784). To improve performance by avoiding these redundant copies, *data.table* utilised the [available but unused `:=` operator in R](http://stackoverflow.com/q/7033106/559784).
+
+Great performance improvements were made in `R v3.1` as a result of which only a *shallow* copy is made for (1) and not *deep* copy. However, for (2) still, the entire column is *deep* copied even in `R v3.1+`. This means the more columns one subassigns to in the *same query*, the more *deep* copies R does.
+
+#### *shallow* vs *deep* copy {.bs-callout .bs-callout-info}
+
+A *shallow* copy is just a copy of the vector of column pointers (corresponding to the columns in a *data.frame* or *data.table*). The actual data is not physically copied in memory.
+
+A *deep* copy on the other hand copies the entire data to another location in memory.
+
+#
+With *data.table's* `:=` operator, absolutely no copies are made in *both* (1) and (2), irrespective of R version you are using. This is because `:=` operator updates *data.table* columns *in-place* (by reference).
+
+### b) The `:=` operator
+
+It can be used in `j` in two ways:
+
+(a) The `LHS := RHS` form
+
+ ```{r eval=FALSE}
+ DT[, c("colA", "colB", ...) := list(valA, valB, ...)]
+
+ # when you have only one column to assign to you
+ # can drop the quotes and list(), for convenience
+ DT[, colA := valA]
+ ```
+
+(b) The functional form
+
+ ```{r eval=FALSE}
+ DT[, `:=`(colA = valA, # valA is assigned to colA
+ colB = valB, # valB is assigned to colB
+ ...
+ )]
+ ```
+
+#### {.bs-callout .bs-callout-warning}
+
+Note that the code above explains how `:=` can be used. They are not working examples. We will start using them on `flights` *data.table* from the next section.
+
+#
+
+#### {.bs-callout .bs-callout-info}
+
+* Form (a) is usually easy to program with and is particularly useful when you don't know the columns to assign values to in advance.
+
+* On the other hand, form (b) is handy if you would like to jot some comments down for later.
+
+* The result is returned *invisibly*.
+
+* Since `:=` is available in `j`, we can combine it with `i` and `by` operations just like the aggregation operations we saw in the previous vignette.
+
+#
+
+In the two forms of `:=` shown above, note that we don't assign the result back to a variable. Because we don't need to. The input *data.table* is modified by reference. Let's go through examples to understand what we mean by this.
+
+For the rest of the vignette, we will work with `flights` *data.table*.
+
+## 2. Add/update/delete columns *by reference*
+
+### a) Add columns by reference {#ref-j}
+
+#### -- How can we add columns *speed* and *total delay* of each flight to `flights` *data.table*?
+
+```{r}
+flights[, `:=`(speed = distance / (air_time/60), # speed in km/hr
+ delay = arr_delay + dep_delay)] # delay in minutes
+head(flights)
+
+## alternatively, using the 'LHS := RHS' form
+# flights[, c("speed", "delay") := list(distance/(air_time/60), arr_delay + dep_delay)]
+```
+
+#### Note that {.bs-callout .bs-callout-info}
+
+* We did not have to assign the result back to `flights`.
+
+* The `flights` *data.table* now contains the two newly added columns. This is what we mean by *added by reference*.
+
+* We used the functional form so that we could add comments on the side to explain what the computation does. You can also see the `LHS := RHS` form (commented).
+
+### b) Update some rows of columns by reference - *sub-assign* by reference {#ref-i-j}
+
+Let's take a look at all the `hours` available in the `flights` *data.table*:
+
+```{r}
+# get all 'hours' in flights
+flights[, sort(unique(hour))]
+```
+
+We see that there are totally `25` unique values in the data. Both *0* and *24* hours seem to be present. Let's go ahead and replace *24* with *0*.
+
+#### -- Replace those rows where `hour == 24` with the value `0`
+
+```{r}
+# subassign by reference
+flights[hour == 24L, hour := 0L]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We can use `i` along with `:=` in `j` the very same way as we have already seen in the *"Introduction to data.table"* vignette.
+
+* Column `hour` is replaced with `0` only on those *row indices* where the condition `hour == 24L` specified in `i` evaluates to `TRUE`.
+
+* `:=` returns the result invisibly. Sometimes it might be necessary to see the result after the assignment. We can accomplish that by adding an empty `[]` at the end of the query as shown below:
+
+ ```{r}
+ flights[hour == 24L, hour := 0L][]
+ ```
+
+#
+Let's look at all the `hours` to verify.
+
+```{r}
+# check again for '24'
+flights[, sort(unique(hour))]
+```
+
+### c) Delete column by reference
+
+#### -- Remove `delay` column
+
+```{r}
+flights[, c("delay") := NULL]
+head(flights)
+
+## or using the functional form
+# flights[, `:=`(delay = NULL)]
+```
+
+#### {.bs-callout .bs-callout-info #delete-convenience}
+
+* Assigning `NULL` to a column *deletes* that column. And it happens *instantly*.
+
+* We can also pass column numbers instead of names in the `LHS`, although it is good programming practice to use column names.
+
+* When there is just one column to delete, we can drop the `c()` and double quotes and just use the column name *unquoted*, for convenience. That is:
+
+ ```{r eval=FALSE}
+ flights[, delay := NULL]
+ ```
+
+ is equivalent to the code above.
+
+### d) `:=` along with grouping using `by` {#ref-j-by}
+
+We have already seen the use of `i` along with `:=` in [Section 2b](#ref-i-j). Let's now see how we can use `:=` along with `by`.
+
+#### -- How can we add a new column which contains for each `orig,dest` pair the maximum speed?
+
+```{r}
+flights[, max_speed := max(speed), by=.(origin, dest)]
+head(flights)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We add a new column `max_speed` using the `:=` operator by reference.
+
+* We provide the columns to group by the same way as shown in the *Introduction to data.table* vignette. For each group, `max(speed)` is computed, which returns a single value. That value is recycled to fit the length of the group. Once again, no copies are being made at all. `flights` *data.table* is modified *in-place*.
+
+* We could have also provided `by` with a *character vector* as we saw in the *Introduction to data.table* vignette, e.g., `by = c("origin", "dest")`.
+
+#
+
+### e) Multiple columns and `:=`
+
+#### -- How can we add two more columns computing `max()` of `dep_delay` and `arr_delay` for each month, using `.SD`?
+
+```{r}
+in_cols = c("dep_delay", "arr_delay")
+out_cols = c("max_dep_delay", "max_arr_delay")
+flights[, c(out_cols) := lapply(.SD, max), by = month, .SDcols = in_cols]
+head(flights)
+```
+#### {.bs-callout .bs-callout-info}
+
+* We use the `LHS := RHS` form. We store the input column names and the new columns to add in separate variables and provide them to `.SDcols` and for `LHS` (for better readability).
+
+* Note that since we allow assignment by reference without quoting column names when there is only one column as explained in [Section 2c](#delete-convenience), we can not do `out_cols := lapply(.SD, max)`. That would result in adding one new column named `out_col`. Instead we should do either `c(out_cols)` or simply `(out_cols)`. Wrapping the variable name with `(` is enough to differentiate between the two cases.
+
+* The `LHS := RHS` form allows us to operate on multiple columns. In the RHS, to compute the `max` on columns specified in `.SDcols`, we make use of the base function `lapply()` along with `.SD` in the same way as we have seen before in the *"Introduction to data.table"* vignette. It returns a list of two elements, containing the maximum value corresponding to `dep_delay` and `arr_delay` for each group.
+
+#
+Before moving on to the next section, let's clean up the newly created columns `speed`, `max_speed`, `max_dep_delay` and `max_arr_delay`.
+
+```{r}
+# RHS gets automatically recycled to length of LHS
+flights[, c("speed", "max_speed", "max_dep_delay", "max_arr_delay") := NULL]
+head(flights)
+```
+
+## 3) `:=` and `copy()`
+
+`:=` modifies the input object by reference. Apart from the features we have discussed already, sometimes we might want to use the update by reference feature for its side effect. And at other times it may not be desirable to modify the original object, in which case we can use `copy()` function, as we will see in a moment.
+
+### a) `:=` for its side effect
+
+Let's say we would like to create a function that would return the *maximum speed* for each month. But at the same time, we would also like to add the column `speed` to *flights*. We could write a simple function as follows:
+
+```{r}
+foo <- function(DT) {
+ DT[, speed := distance / (air_time/60)]
+ DT[, .(max_speed = max(speed)), by=month]
+}
+ans = foo(flights)
+head(flights)
+head(ans)
+```
+#### {.bs-callout .bs-callout-info}
+
+* Note that the new column `speed` has been added to `flights` *data.table*. This is because `:=` performs operations by reference. Since `DT` (the function argument) and `flights` refer to the same object in memory, modifying `DT` also reflects on `flights`.
+
+* And `ans` contains the maximum speed for each month.
+
+### b) The `copy()` function
+
+In the previous section, we used `:=` for its side effect. But of course this may not be always desirable. Sometimes, we would like to pass a *data.table* object to a function, and might want to use the `:=` operator, but *wouldn't* want to update the original object. We can accomplish this using the function `copy()`.
+
+#### {.bs-callout .bs-callout-info}
+
+The `copy()` function *deep* copies the input object and therefore any subsequent update by reference operations performed on the copied object will not affect the original object.
+
+#
+
+There are two particular places where `copy()` function is essential:
+
+1. Contrary to the situation we have seen in the previous point, we may not want the input data.table to a function to be modified *by reference*. As an example, let's consider the task in the previous section, except we don't want to modify `flghts` by reference.
+
+ Let's first delete the `speed` column we generated in the previous section.
+
+ ```{r}
+ flights[, speed := NULL]
+ ```
+ Now, we could accomplish the task as follows:
+
+ ```{r}
+ foo <- function(DT) {
+ DT <- copy(DT) ## deep copy
+ DT[, speed := distance / (air_time/60)] ## doesn't affect 'flights'
+ DT[, .(max_speed = max(speed)), by=month]
+ }
+ ans <- foo(flights)
+ head(flights)
+ head(ans)
+ ```
+
+#### {.bs-callout .bs-callout-info}
+
+* Using `copy()` function did not update `flights` *data.table* by reference. It doesn't contain the column `speed`.
+
+* And `ans` contains the maximum speed corresponding to each month.
+
+However we could improve this functionality further by *shallow* copying instead of *deep* copying. In fact, we would very much like to [provide this functionality for `v1.9.8`](https://github.com/Rdatatable/data.table/issues/617). We will touch up on this again in the *data.table design* vignette.
+
+#
+
+2. When we store the column names on to a variable, e.g., `DT_n = names(DT)`, and then *add/update/delete* column(s) *by reference*. It would also modify `DT_n`, unless we do `copy(names(DT))`.
+
+ ```{r}
+ DT = data.table(x=1, y=2)
+ DT_n = names(DT)
+ DT_n
+
+ ## add a new column by reference
+ DT[, z := 3]
+
+ ## DT_n also gets updated
+ DT_n
+
+ ## use `copy()`
+ DT_n = copy(names(DT))
+ DT[, w := 4]
+
+ ## DT_n doesn't get updated
+ DT_n
+ ```
+
+## Summary
+
+#### The `:=` operator {.bs-callout .bs-callout-info}
+
+* It is used to *add/update/delete* columns by reference.
+
+* We have also seen how to use `:=` along with `i` and `by` the same way as we have seen in the *Introduction to data.table* vignette. We can in the same way use `keyby`, chain operations together, and pass expressions to `by` as well all in the same way. The syntax is *consistent*.
+
+* We can use `:=` for its side effect or use `copy()` to not modify the original object while updating by reference.
+
+#
+
+So far we have seen a whole lot in `j`, and how to combine it with `by` and little of `i`. Let's turn our attention back to `i` in the next vignette *"Keys and fast binary search based subset"* to peform *blazing fast subsets* by *keying data.tables*.
+
+***
+
diff --git a/inst/doc/datatable-reference-semantics.html b/inst/doc/datatable-reference-semantics.html
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+
+<div id="header">
+<h1 class="title">Reference semantics</h1>
+<h4 class="date"><em>2015-09-18</em></h4>
+</div>
+
+
+<p>This vignette discusses <em>data.table</em>’s reference semantics which allows to <em>add/update/delete</em> columns of a <em>data.table by reference</em>, and also combine them with <code>i</code> and <code>by</code>. It is aimed at those who are already familiar with <em>data.table</em> syntax, its general form, how to subset rows in <code>i</code>, select and compute on columns, and perform aggregations by group. If you’re not familar with these concepts, please read the <em>“Intro [...]
+<hr />
+<div id="data" class="section level2">
+<h2>Data</h2>
+<p>We will use the same <code>flights</code> data as in the <em>“Introduction to data.table”</em> vignette.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights <-<span class="st"> </span><span class="kw">fread</span>(<span class="st">"flights14.csv"</span>)
+flights
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13</span>
+<span class="co"># --- </span>
+<span class="co"># 253312: 2014 10 31 1 -30 UA LGA IAH 201 1416 14</span>
+<span class="co"># 253313: 2014 10 31 -5 -14 UA EWR IAH 189 1400 8</span>
+<span class="co"># 253314: 2014 10 31 -8 16 MQ LGA RDU 83 431 11</span>
+<span class="co"># 253315: 2014 10 31 -4 15 MQ LGA DTW 75 502 11</span>
+<span class="co"># 253316: 2014 10 31 -5 1 MQ LGA SDF 110 659 8</span>
+<span class="kw">dim</span>(flights)
+<span class="co"># [1] 253316 11</span></code></pre></div>
+</div>
+<div id="introduction" class="section level2">
+<h2>Introduction</h2>
+<p>In this vignette, we will</p>
+<ol style="list-style-type: decimal">
+<li><p>first discuss reference semantics briefly and look at the two different forms in which the <code>:=</code> operator can be used</p></li>
+<li><p>then see how we can <em>add/update/delete</em> columns <em>by reference</em> in <code>j</code> using the <code>:=</code> operator and how to combine with <code>i</code> and <code>by</code>.</p></li>
+<li><p>and finally we will look at using <code>:=</code> for its <em>side-effect</em> and how we can avoid the side effects using <code>copy()</code>.</p></li>
+</ol>
+</div>
+<div id="reference-semantics" class="section level2">
+<h2>1. Reference semantics</h2>
+<p>All the operations we have seen so far in the previous vignette resulted in a new data set. We will see how to <em>add</em> new column(s), <em>update</em> or <em>delete</em> existing column(s) on the original data.</p>
+<div id="a-background" class="section level3">
+<h3>a) Background</h3>
+<p>Before we look at <em>reference semantics</em>, consider the <em>data.frame</em> shown below:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DF =<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">ID =</span> <span class="kw">c</span>(<span class="st">"b"</span>,<span class="st">"b"</span>,<span class="st">"b"</span>,<span class="st">"a"</span>,<span class="st">"a"</span>,<span class="st">"c"</span>), <span class="dt">a =</span> <span class="dv">1</span>:<span class= [...]
+DF
+<span class="co"># ID a b c</span>
+<span class="co"># 1 b 1 7 13</span>
+<span class="co"># 2 b 2 8 14</span>
+<span class="co"># 3 b 3 9 15</span>
+<span class="co"># 4 a 4 10 16</span>
+<span class="co"># 5 a 5 11 17</span>
+<span class="co"># 6 c 6 12 18</span></code></pre></div>
+<p>When we did:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DF$c <-<span class="st"> </span><span class="dv">18</span>:<span class="dv">13</span> <span class="co"># (1) -- replace entire column</span>
+<span class="co"># or</span>
+DF$c[DF$ID ==<span class="st"> "b"</span>] <-<span class="st"> </span><span class="dv">15</span>:<span class="dv">13</span> <span class="co"># (2) -- subassign in column 'c'</span></code></pre></div>
+<p>both (1) and (2) resulted in <a href="http://r.789695.n4.nabble.com/speeding-up-perception-td3640920.html#a3646694"><em>deep</em> copy of the entire <em>data.frame</em></a> in versions of <code>R</code> versions <code>< 3.1</code>. <a href="http://stackoverflow.com/q/23898969/559784">It copied more than once</a>. To improve performance by avoiding these redundant copies, <em>data.table</em> utilised the <a href="http://stackoverflow.com/q/7033106/559784">available but unused <code> [...]
+<p>Great performance improvements were made in <code>R v3.1</code> as a result of which only a <em>shallow</em> copy is made for (1) and not <em>deep</em> copy. However, for (2) still, the entire column is <em>deep</em> copied even in <code>R v3.1+</code>. This means the more columns one subassigns to in the <em>same query</em>, the more <em>deep</em> copies R does.</p>
+<div id="shallow-vs-deep-copy" class="section level4 bs-callout bs-callout-info">
+<h4><em>shallow</em> vs <em>deep</em> copy</h4>
+<p>A <em>shallow</em> copy is just a copy of the vector of column pointers (corresponding to the columns in a <em>data.frame</em> or <em>data.table</em>). The actual data is not physically copied in memory.</p>
+<p>A <em>deep</em> copy on the other hand copies the entire data to another location in memory.</p>
+</div>
+</div>
+</div>
+<div id="section" class="section level1">
+<h1></h1>
+<p>With <em>data.table’s</em> <code>:=</code> operator, absolutely no copies are made in <em>both</em> (1) and (2), irrespective of R version you are using. This is because <code>:=</code> operator updates <em>data.table</em> columns <em>in-place</em> (by reference).</p>
+<div id="b-the-operator" class="section level3">
+<h3>b) The <code>:=</code> operator</h3>
+<p>It can be used in <code>j</code> in two ways:</p>
+<ol style="list-style-type: lower-alpha">
+<li><p>The <code>LHS := RHS</code> form</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT[, <span class="kw">c</span>(<span class="st">"colA"</span>, <span class="st">"colB"</span>, ...) :<span class="er">=</span><span class="st"> </span><span class="kw">list</span>(valA, valB, ...)]
+
+<span class="co"># when you have only one column to assign to you </span>
+<span class="co"># can drop the quotes and list(), for convenience</span>
+DT[, colA :<span class="er">=</span><span class="st"> </span>valA]</code></pre></div></li>
+<li><p>The functional form</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT[, <span class="st">`</span><span class="dt">:=</span><span class="st">`</span>(<span class="dt">colA =</span> valA, <span class="co"># valA is assigned to colA</span>
+ <span class="dt">colB =</span> valB, <span class="co"># valB is assigned to colB</span>
+ ...
+)]</code></pre></div></li>
+</ol>
+<div id="section-1" class="section level4 bs-callout bs-callout-warning">
+<h4></h4>
+<p>Note that the code above explains how <code>:=</code> can be used. They are not working examples. We will start using them on <code>flights</code> <em>data.table</em> from the next section.</p>
+</div>
+</div>
+</div>
+<div id="section-2" class="section level1">
+<h1></h1>
+<div id="section-3" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Form (a) is usually easy to program with and is particularly useful when you don’t know the columns to assign values to in advance.</p></li>
+<li><p>On the other hand, form (b) is handy if you would like to jot some comments down for later.</p></li>
+<li><p>The result is returned <em>invisibly</em>.</p></li>
+<li><p>Since <code>:=</code> is available in <code>j</code>, we can combine it with <code>i</code> and <code>by</code> operations just like the aggregation operations we saw in the previous vignette.</p></li>
+</ul>
+</div>
+</div>
+<div id="section-4" class="section level1">
+<h1></h1>
+<p>In the two forms of <code>:=</code> shown above, note that we don’t assign the result back to a variable. Because we don’t need to. The input <em>data.table</em> is modified by reference. Let’s go through examples to understand what we mean by this.</p>
+<p>For the rest of the vignette, we will work with <code>flights</code> <em>data.table</em>.</p>
+<div id="addupdatedelete-columns-by-reference" class="section level2">
+<h2>2. Add/update/delete columns <em>by reference</em></h2>
+<div id="ref-j" class="section level3">
+<h3>a) Add columns by reference</h3>
+<div id="how-can-we-add-columns-speed-and-total-delay-of-each-flight-to-flights-data.table" class="section level4">
+<h4>– How can we add columns <em>speed</em> and <em>total delay</em> of each flight to <code>flights</code> <em>data.table</em>?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[, <span class="st">`</span><span class="dt">:=</span><span class="st">`</span>(<span class="dt">speed =</span> distance /<span class="st"> </span>(air_time/<span class="dv">60</span>), <span class="co"># speed in km/hr</span>
+ <span class="dt">delay =</span> arr_delay +<span class="st"> </span>dep_delay)] <span class="co"># delay in minutes</span>
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour speed delay</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490 27</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909 10</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769 11</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414 -34</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857 3</span>
+<span class="co"># 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363 4</span>
+
+## alternatively, using the 'LHS := RHS' form
+<span class="co"># flights[, c("speed", "delay") := list(distance/(air_time/60), arr_delay + dep_delay)]</span></code></pre></div>
+</div>
+<div id="note-that" class="section level4 bs-callout bs-callout-info">
+<h4>Note that</h4>
+<ul>
+<li><p>We did not have to assign the result back to <code>flights</code>.</p></li>
+<li><p>The <code>flights</code> <em>data.table</em> now contains the two newly added columns. This is what we mean by <em>added by reference</em>.</p></li>
+<li><p>We used the functional form so that we could add comments on the side to explain what the computation does. You can also see the <code>LHS := RHS</code> form (commented).</p></li>
+</ul>
+</div>
+</div>
+<div id="ref-i-j" class="section level3">
+<h3>b) Update some rows of columns by reference - <em>sub-assign</em> by reference</h3>
+<p>Let’s take a look at all the <code>hours</code> available in the <code>flights</code> <em>data.table</em>:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># get all 'hours' in flights</span>
+flights[, <span class="kw">sort</span>(<span class="kw">unique</span>(hour))]
+<span class="co"># [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24</span></code></pre></div>
+<p>We see that there are totally <code>25</code> unique values in the data. Both <em>0</em> and <em>24</em> hours seem to be present. Let’s go ahead and replace <em>24</em> with <em>0</em>.</p>
+<div id="replace-those-rows-where-hour-24-with-the-value-0" class="section level4">
+<h4>– Replace those rows where <code>hour == 24</code> with the value <code>0</code></h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># subassign by reference</span>
+flights[hour ==<span class="st"> </span>24L, hour :<span class="er">=</span><span class="st"> </span>0L]</code></pre></div>
+</div>
+<div id="section-5" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We can use <code>i</code> along with <code>:=</code> in <code>j</code> the very same way as we have already seen in the <em>“Introduction to data.table”</em> vignette.</p></li>
+<li><p>Column <code>hour</code> is replaced with <code>0</code> only on those <em>row indices</em> where the condition <code>hour == 24L</code> specified in <code>i</code> evaluates to <code>TRUE</code>.</p></li>
+<li><p><code>:=</code> returns the result invisibly. Sometimes it might be necessary to see the result after the assignment. We can accomplish that by adding an empty <code>[]</code> at the end of the query as shown below:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[hour ==<span class="st"> </span>24L, hour :<span class="er">=</span><span class="st"> </span>0L][]
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour speed</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857</span>
+<span class="co"># --- </span>
+<span class="co"># 253312: 2014 10 31 1 -30 UA LGA IAH 201 1416 14 422.6866</span>
+<span class="co"># 253313: 2014 10 31 -5 -14 UA EWR IAH 189 1400 8 444.4444</span>
+<span class="co"># 253314: 2014 10 31 -8 16 MQ LGA RDU 83 431 11 311.5663</span>
+<span class="co"># 253315: 2014 10 31 -4 15 MQ LGA DTW 75 502 11 401.6000</span>
+<span class="co"># 253316: 2014 10 31 -5 1 MQ LGA SDF 110 659 8 359.4545</span>
+<span class="co"># delay</span>
+<span class="co"># 1: 27</span>
+<span class="co"># 2: 10</span>
+<span class="co"># 3: 11</span>
+<span class="co"># 4: -34</span>
+<span class="co"># 5: 3</span>
+<span class="co"># --- </span>
+<span class="co"># 253312: -29</span>
+<span class="co"># 253313: -19</span>
+<span class="co"># 253314: 8</span>
+<span class="co"># 253315: 11</span>
+<span class="co"># 253316: -4</span></code></pre></div></li>
+</ul>
+</div>
+</div>
+</div>
+</div>
+<div id="section-6" class="section level1">
+<h1></h1>
+<p>Let’s look at all the <code>hours</code> to verify.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># check again for '24'</span>
+flights[, <span class="kw">sort</span>(<span class="kw">unique</span>(hour))]
+<span class="co"># [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23</span></code></pre></div>
+<div id="c-delete-column-by-reference" class="section level3">
+<h3>c) Delete column by reference</h3>
+<div id="remove-delay-column" class="section level4">
+<h4>– Remove <code>delay</code> column</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[, <span class="kw">c</span>(<span class="st">"delay"</span>) :<span class="er">=</span><span class="st"> </span><span class="ot">NULL</span>]
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour speed</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857</span>
+<span class="co"># 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363</span>
+
+## or using the functional form
+<span class="co"># flights[, `:=`(delay = NULL)]</span></code></pre></div>
+</div>
+<div id="delete-convenience" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Assigning <code>NULL</code> to a column <em>deletes</em> that column. And it happens <em>instantly</em>.</p></li>
+<li><p>We can also pass column numbers instead of names in the <code>LHS</code>, although it is good programming practice to use column names.</p></li>
+<li><p>When there is just one column to delete, we can drop the <code>c()</code> and double quotes and just use the column name <em>unquoted</em>, for convenience. That is:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[, delay :<span class="er">=</span><span class="st"> </span><span class="ot">NULL</span>]</code></pre></div>
+<p>is equivalent to the code above.</p></li>
+</ul>
+</div>
+</div>
+<div id="d-along-with-grouping-using-by" class="section level3">
+<h3>d) <code>:=</code> along with grouping using <code id="ref-j-by">by</code></h3>
+<p>We have already seen the use of <code>i</code> along with <code>:=</code> in <a href="#ref-i-j">Section 2b</a>. Let’s now see how we can use <code>:=</code> along with <code>by</code>.</p>
+<div id="how-can-we-add-a-new-column-which-contains-for-each-origdest-pair-the-maximum-speed" class="section level4">
+<h4>– How can we add a new column which contains for each <code>orig,dest</code> pair the maximum speed?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[, max_speed :<span class="er">=</span><span class="st"> </span><span class="kw">max</span>(speed), by=.(origin, dest)]
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour speed max_speed</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490 526.5957</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909 526.5957</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769 526.5957</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414 517.5000</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857 526.5957</span>
+<span class="co"># 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363 518.4507</span></code></pre></div>
+</div>
+<div id="section-7" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We add a new column <code>max_speed</code> using the <code>:=</code> operator by reference.</p></li>
+<li><p>We provide the columns to group by the same way as shown in the <em>Introduction to data.table</em> vignette. For each group, <code>max(speed)</code> is computed, which returns a single value. That value is recycled to fit the length of the group. Once again, no copies are being made at all. <code>flights</code> <em>data.table</em> is modified <em>in-place</em>.</p></li>
+<li><p>We could have also provided <code>by</code> with a <em>character vector</em> as we saw in the <em>Introduction to data.table</em> vignette, e.g., <code>by = c("origin", "dest")</code>.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-8" class="section level1">
+<h1></h1>
+<div id="e-multiple-columns-and" class="section level3">
+<h3>e) Multiple columns and <code>:=</code></h3>
+<div id="how-can-we-add-two-more-columns-computing-max-of-dep_delay-and-arr_delay-for-each-month-using-.sd" class="section level4">
+<h4>– How can we add two more columns computing <code>max()</code> of <code>dep_delay</code> and <code>arr_delay</code> for each month, using <code>.SD</code>?</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">in_cols =<span class="st"> </span><span class="kw">c</span>(<span class="st">"dep_delay"</span>, <span class="st">"arr_delay"</span>)
+out_cols =<span class="st"> </span><span class="kw">c</span>(<span class="st">"max_dep_delay"</span>, <span class="st">"max_arr_delay"</span>)
+flights[, <span class="kw">c</span>(out_cols) :<span class="er">=</span><span class="st"> </span><span class="kw">lapply</span>(.SD, max), by =<span class="st"> </span>month, .SDcols =<span class="st"> </span>in_cols]
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour speed max_speed</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490 526.5957</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909 526.5957</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769 526.5957</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414 517.5000</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857 526.5957</span>
+<span class="co"># 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363 518.4507</span>
+<span class="co"># max_dep_delay max_arr_delay</span>
+<span class="co"># 1: 973 996</span>
+<span class="co"># 2: 973 996</span>
+<span class="co"># 3: 973 996</span>
+<span class="co"># 4: 973 996</span>
+<span class="co"># 5: 973 996</span>
+<span class="co"># 6: 973 996</span></code></pre></div>
+</div>
+<div id="section-9" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We use the <code>LHS := RHS</code> form. We store the input column names and the new columns to add in separate variables and provide them to <code>.SDcols</code> and for <code>LHS</code> (for better readability).</p></li>
+<li><p>Note that since we allow assignment by reference without quoting column names when there is only one column as explained in <a href="#delete-convenience">Section 2c</a>, we can not do <code>out_cols := lapply(.SD, max)</code>. That would result in adding one new column named <code>out_col</code>. Instead we should do either <code>c(out_cols)</code> or simply <code>(out_cols)</code>. Wrapping the variable name with <code>(</code> is enough to differentiate between the two cases.</p></li>
+<li><p>The <code>LHS := RHS</code> form allows us to operate on multiple columns. In the RHS, to compute the <code>max</code> on columns specified in <code>.SDcols</code>, we make use of the base function <code>lapply()</code> along with <code>.SD</code> in the same way as we have seen before in the <em>“Introduction to data.table”</em> vignette. It returns a list of two elements, containing the maximum value corresponding to <code>dep_delay</code> and <code>arr_delay</code> for each gro [...]
+</ul>
+</div>
+</div>
+</div>
+<div id="section-10" class="section level1">
+<h1></h1>
+<p>Before moving on to the next section, let’s clean up the newly created columns <code>speed</code>, <code>max_speed</code>, <code>max_dep_delay</code> and <code>max_arr_delay</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># RHS gets automatically recycled to length of LHS</span>
+flights[, <span class="kw">c</span>(<span class="st">"speed"</span>, <span class="st">"max_speed"</span>, <span class="st">"max_dep_delay"</span>, <span class="st">"max_arr_delay"</span>) :<span class="er">=</span><span class="st"> </span><span class="ot">NULL</span>]
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13</span>
+<span class="co"># 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18</span></code></pre></div>
+<div id="and-copy" class="section level2">
+<h2>3) <code>:=</code> and <code>copy()</code></h2>
+<p><code>:=</code> modifies the input object by reference. Apart from the features we have discussed already, sometimes we might want to use the update by reference feature for its side effect. And at other times it may not be desirable to modify the original object, in which case we can use <code>copy()</code> function, as we will see in a moment.</p>
+<div id="a-for-its-side-effect" class="section level3">
+<h3>a) <code>:=</code> for its side effect</h3>
+<p>Let’s say we would like to create a function that would return the <em>maximum speed</em> for each month. But at the same time, we would also like to add the column <code>speed</code> to <em>flights</em>. We could write a simple function as follows:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">foo <-<span class="st"> </span>function(DT) {
+ DT[, speed :<span class="er">=</span><span class="st"> </span>distance /<span class="st"> </span>(air_time/<span class="dv">60</span>)]
+ DT[, .(<span class="dt">max_speed =</span> <span class="kw">max</span>(speed)), by=month]
+}
+ans =<span class="st"> </span><span class="kw">foo</span>(flights)
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour speed</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9 413.6490</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11 409.0909</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19 423.0769</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7 395.5414</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13 424.2857</span>
+<span class="co"># 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18 434.3363</span>
+<span class="kw">head</span>(ans)
+<span class="co"># month max_speed</span>
+<span class="co"># 1: 1 535.6425</span>
+<span class="co"># 2: 2 535.6425</span>
+<span class="co"># 3: 3 549.0756</span>
+<span class="co"># 4: 4 585.6000</span>
+<span class="co"># 5: 5 544.2857</span>
+<span class="co"># 6: 6 608.5714</span></code></pre></div>
+<div id="section-11" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Note that the new column <code>speed</code> has been added to <code>flights</code> <em>data.table</em>. This is because <code>:=</code> performs operations by reference. Since <code>DT</code> (the function argument) and <code>flights</code> refer to the same object in memory, modifying <code>DT</code> also reflects on <code>flights</code>.</p></li>
+<li><p>And <code>ans</code> contains the maximum speed for each month.</p></li>
+</ul>
+</div>
+</div>
+<div id="b-the-copy-function" class="section level3">
+<h3>b) The <code>copy()</code> function</h3>
+<p>In the previous section, we used <code>:=</code> for its side effect. But of course this may not be always desirable. Sometimes, we would like to pass a <em>data.table</em> object to a function, and might want to use the <code>:=</code> operator, but <em>wouldn’t</em> want to update the original object. We can accomplish this using the function <code>copy()</code>.</p>
+<div id="section-12" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<p>The <code>copy()</code> function <em>deep</em> copies the input object and therefore any subsequent update by reference operations performed on the copied object will not affect the original object.</p>
+</div>
+</div>
+</div>
+</div>
+<div id="section-13" class="section level1">
+<h1></h1>
+<p>There are two particular places where <code>copy()</code> function is essential:</p>
+<ol style="list-style-type: decimal">
+<li><p>Contrary to the situation we have seen in the previous point, we may not want the input data.table to a function to be modified <em>by reference</em>. As an example, let’s consider the task in the previous section, except we don’t want to modify <code>flghts</code> by reference.</p>
+<p>Let’s first delete the <code>speed</code> column we generated in the previous section.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">flights[, speed :<span class="er">=</span><span class="st"> </span><span class="ot">NULL</span>]</code></pre></div>
+<p>Now, we could accomplish the task as follows:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">foo <-<span class="st"> </span>function(DT) {
+ DT <-<span class="st"> </span><span class="kw">copy</span>(DT) ## deep copy
+ DT[, speed :<span class="er">=</span><span class="st"> </span>distance /<span class="st"> </span>(air_time/<span class="dv">60</span>)] ## doesn't affect 'flights'
+ DT[, .(<span class="dt">max_speed =</span> <span class="kw">max</span>(speed)), by=month]
+}
+ans <-<span class="st"> </span><span class="kw">foo</span>(flights)
+<span class="kw">head</span>(flights)
+<span class="co"># year month day dep_delay arr_delay carrier origin dest air_time distance hour</span>
+<span class="co"># 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9</span>
+<span class="co"># 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11</span>
+<span class="co"># 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19</span>
+<span class="co"># 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7</span>
+<span class="co"># 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13</span>
+<span class="co"># 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18</span>
+<span class="kw">head</span>(ans)
+<span class="co"># month max_speed</span>
+<span class="co"># 1: 1 535.6425</span>
+<span class="co"># 2: 2 535.6425</span>
+<span class="co"># 3: 3 549.0756</span>
+<span class="co"># 4: 4 585.6000</span>
+<span class="co"># 5: 5 544.2857</span>
+<span class="co"># 6: 6 608.5714</span></code></pre></div></li>
+</ol>
+<div id="section-14" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Using <code>copy()</code> function did not update <code>flights</code> <em>data.table</em> by reference. It doesn’t contain the column <code>speed</code>.</p></li>
+<li><p>And <code>ans</code> contains the maximum speed corresponding to each month.</p></li>
+</ul>
+<p>However we could improve this functionality further by <em>shallow</em> copying instead of <em>deep</em> copying. In fact, we would very much like to <a href="https://github.com/Rdatatable/data.table/issues/617">provide this functionality for <code>v1.9.8</code></a>. We will touch up on this again in the <em>data.table design</em> vignette.</p>
+</div>
+</div>
+<div id="section-15" class="section level1">
+<h1></h1>
+<ol start="2" style="list-style-type: decimal">
+<li><p>When we store the column names on to a variable, e.g., <code>DT_n = names(DT)</code>, and then <em>add/update/delete</em> column(s) <em>by reference</em>. It would also modify <code>DT_n</code>, unless we do <code>copy(names(DT))</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT =<span class="st"> </span><span class="kw">data.table</span>(<span class="dt">x=</span><span class="dv">1</span>, <span class="dt">y=</span><span class="dv">2</span>)
+DT_n =<span class="st"> </span><span class="kw">names</span>(DT)
+DT_n
+<span class="co"># [1] "x" "y"</span>
+
+## add a new column by reference
+DT[, z :<span class="er">=</span><span class="st"> </span><span class="dv">3</span>]
+
+## DT_n also gets updated
+DT_n
+<span class="co"># [1] "x" "y" "z"</span>
+
+## use `copy()`
+DT_n =<span class="st"> </span><span class="kw">copy</span>(<span class="kw">names</span>(DT))
+DT[, w :<span class="er">=</span><span class="st"> </span><span class="dv">4</span>]
+
+## DT_n doesn't get updated
+DT_n
+<span class="co"># [1] "x" "y" "z"</span></code></pre></div></li>
+</ol>
+<div id="summary" class="section level2">
+<h2>Summary</h2>
+<div id="the-operator" class="section level4 bs-callout bs-callout-info">
+<h4>The <code>:=</code> operator</h4>
+<ul>
+<li><p>It is used to <em>add/update/delete</em> columns by reference.</p></li>
+<li><p>We have also seen how to use <code>:=</code> along with <code>i</code> and <code>by</code> the same way as we have seen in the <em>Introduction to data.table</em> vignette. We can in the same way use <code>keyby</code>, chain operations together, and pass expressions to <code>by</code> as well all in the same way. The syntax is <em>consistent</em>.</p></li>
+<li><p>We can use <code>:=</code> for its side effect or use <code>copy()</code> to not modify the original object while updating by reference.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-16" class="section level1">
+<h1></h1>
+<p>So far we have seen a whole lot in <code>j</code>, and how to combine it with <code>by</code> and little of <code>i</code>. Let’s turn our attention back to <code>i</code> in the next vignette <em>“Keys and fast binary search based subset”</em> to peform <em>blazing fast subsets</em> by <em>keying data.tables</em>.</p>
+<hr />
+</div>
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+$(document).ready(function () {
+ $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
+});
+
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+ (function () {
+ var script = document.createElement("script");
+ script.type = "text/javascript";
+ script.src = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+ document.getElementsByTagName("head")[0].appendChild(script);
+ })();
+</script>
+
+</body>
+</html>
diff --git a/inst/doc/datatable-reshape.R b/inst/doc/datatable-reshape.R
new file mode 100644
index 0000000..884d818
--- /dev/null
+++ b/inst/doc/datatable-reshape.R
@@ -0,0 +1,66 @@
+## ---- echo = FALSE, message = FALSE--------------------------------------
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+
+## ----echo=FALSE-----------------------------------------------------------------------------------
+options(width=100)
+
+## -------------------------------------------------------------------------------------------------
+DT = fread("melt_default.csv")
+DT
+## dob stands for date of birth.
+
+str(DT)
+
+## -------------------------------------------------------------------------------------------------
+DT.m1 = melt(DT, id.vars = c("family_id", "age_mother"),
+ measure.vars = c("dob_child1", "dob_child2", "dob_child3"))
+DT.m1
+str(DT.m1)
+
+## -------------------------------------------------------------------------------------------------
+DT.m1 = melt(DT, measure.vars = c("dob_child1", "dob_child2", "dob_child3"),
+ variable.name = "child", value.name = "dob")
+DT.m1
+
+## -------------------------------------------------------------------------------------------------
+dcast(DT.m1, family_id + age_mother ~ child, value.var = "dob")
+
+## -------------------------------------------------------------------------------------------------
+dcast(DT.m1, family_id ~ ., fun.agg = function(x) sum(!is.na(x)), value.var = "dob")
+
+## -------------------------------------------------------------------------------------------------
+DT = fread("melt_enhanced.csv")
+DT
+## 1 = female, 2 = male
+
+## -------------------------------------------------------------------------------------------------
+DT.m1 = melt(DT, id = c("family_id", "age_mother"))
+DT.m1[, c("variable", "child") := tstrsplit(variable, "_", fixed=TRUE)]
+DT.c1 = dcast(DT.m1, family_id + age_mother + child ~ variable, value.var = "value")
+DT.c1
+
+str(DT.c1) ## gender column is character type now!
+
+## -------------------------------------------------------------------------------------------------
+colA = paste("dob_child", 1:3, sep="")
+colB = paste("gender_child", 1:3, sep="")
+DT.m2 = melt(DT, measure = list(colA, colB), value.name = c("dob", "gender"))
+DT.m2
+
+str(DT.m2) ## col type is preserved
+
+## -------------------------------------------------------------------------------------------------
+DT.m2 = melt(DT, measure = patterns("^dob", "^gender"), value.name = c("dob", "gender"))
+DT.m2
+
+## -------------------------------------------------------------------------------------------------
+## new 'cast' functionality - multiple value.vars
+DT.c2 = dcast(DT.m2, family_id + age_mother ~ variable, value.var = c("dob", "gender"))
+DT.c2
+
diff --git a/inst/doc/datatable-reshape.Rmd b/inst/doc/datatable-reshape.Rmd
new file mode 100644
index 0000000..bfde7d4
--- /dev/null
+++ b/inst/doc/datatable-reshape.Rmd
@@ -0,0 +1,254 @@
+---
+title: "Efficient reshaping using data.tables"
+date: "`r Sys.Date()`"
+output:
+ rmarkdown::html_document:
+ theme: spacelab
+ highlight: pygments
+ css : css/bootstrap.css
+vignette: >
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r, echo = FALSE, message = FALSE}
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+```
+
+This vignette discusses the default usage of reshaping functions `melt` (wide to long) and `dcast` (long to wide) for *data.tables* as well as the **new extended functionalities** of melting and casting on *multiple columns* available from `v1.9.6`.
+
+***
+
+```{r echo=FALSE}
+options(width=100)
+```
+
+## Data
+
+We will load the data sets directly within sections.
+
+## Introduction
+
+The `melt` and `dcast` functions for *data.tables* are extensions of the corresponding functions from the [reshape2](http://cran.r-project.org/package=reshape2) package.
+
+In this vignette, we will
+
+1. first briefly look at the default *melting* and *casting* of *data.tables* to convert them from *wide* to *long* format and vice versa,
+
+2. then look at scenarios where the current functionalities becomes cumbersome and inefficient,
+
+3. and finally look at the new improvements to both `melt` and `dcast` methods for *data.tables* to handle multiple columns simultaneously.
+
+The extended functionalities are in line with *data.table's* philosophy of performing operations efficiently and in a straightforward manner.
+
+#### Note: {.bs-callout .bs-callout-info}
+
+From `v1.9.6` on, you don't have to load `reshape2` package to use these functions for *data.tables*. You just need to load `data.table`. If you've to load `reshape2` for melting or casting matrices and/or data.frames, then make sure to load it *before* loading `data.table`.
+
+
+## 1. Default functionality
+
+### a) `melt`ing *data.tables* (wide to long)
+
+Suppose we have a `data.table` (artificial data) as shown below:
+
+```{r}
+DT = fread("melt_default.csv")
+DT
+## dob stands for date of birth.
+
+str(DT)
+```
+
+
+
+#
+
+#### - Convert `DT` to *long* form where each `dob` is a separate observation.
+
+We could accomplish this using `melt()` by specifying `id.vars` and `measure.vars` arguments as follows:
+
+```{r}
+DT.m1 = melt(DT, id.vars = c("family_id", "age_mother"),
+ measure.vars = c("dob_child1", "dob_child2", "dob_child3"))
+DT.m1
+str(DT.m1)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `measure.vars` specify the set of columns we would like to collapse (or combine) together.
+
+* We can also specify column *indices* instead of *names*.
+
+* By default, `variable` column is of type `factor`. Set `variable.factor` argument to `FALSE` if you'd like to return a *character* vector instead. `variable.factor` argument is only available in `melt` from `data.table` and not in the [`reshape2` package](http://github.com/hadley/reshape).
+
+* By default, the molten columns are automatically named `variable` and `value`.
+
+* `melt` preserves column attributes in result.
+
+#
+
+#### - Name the `variable` and `value` columns to `child` and `dob` respectively
+
+
+```{r}
+DT.m1 = melt(DT, measure.vars = c("dob_child1", "dob_child2", "dob_child3"),
+ variable.name = "child", value.name = "dob")
+DT.m1
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* By default, when one of `id.vars` or `measure.vars` is missing, the rest of the columns are *automatically assigned* to the missing argument.
+
+* When neither `id.vars` nor `measure.vars` are specified, as mentioned under `?melt`, all *non*-`numeric`, `integer`, `logical` columns will be assigned to `id.vars`.
+
+ In addition, a warning message is issued highlighting the columns that are automatically considered to be `id.vars`.
+
+### b) `Cast`ing *data.tables* (long to wide)
+
+In the previous section, we saw how to get from wide form to long form. Let's see the reverse operation in this section.
+
+#### - How can we get back to the original data table `DT` from `DT.m`?
+
+That is, we'd like to collect all *child* observations corresponding to each `family_id, age_mother` together under the same row. We can accomplish it using `dcast` as follows:
+
+```{r}
+dcast(DT.m1, family_id + age_mother ~ child, value.var = "dob")
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `dcast` uses *formula* interface. The variables on the *LHS* of formula represents the *id* vars and *RHS* the *measure* vars.
+
+* `value.var` denotes the column to be filled in with while casting to wide format.
+
+* `dcast` also tries to preserve attributes in result wherever possible.
+
+#
+
+#### - Starting from `DT.m`, how can we get the number of children in each family?
+
+You can also pass a function to aggregate by in `dcast` with the argument `fun.aggregate`. This is particularly essential when the formula provided does not identify single observation for each cell.
+
+```{r}
+dcast(DT.m1, family_id ~ ., fun.agg = function(x) sum(!is.na(x)), value.var = "dob")
+```
+
+Check `?dcast` for other useful arguments and additional examples.
+
+## 2. Limitations in current `melt/dcast` approaches
+
+So far we've seen features of `melt` and `dcast` that are based on `reshape2` package, but implemented efficiently for *data.table*s, using internal `data.table` machinery (*fast radix ordering*, *binary search* etc..).
+
+However, there are situations we might run into where the desired operation is not expressed in a straightforward manner. For example, consider the *data.table* shown below:
+
+```{r}
+DT = fread("melt_enhanced.csv")
+DT
+## 1 = female, 2 = male
+```
+
+And you'd like to combine (melt) all the `dob` columns together, and `gender` columns together. Using the current functionalty, we can do something like this:
+
+```{r}
+DT.m1 = melt(DT, id = c("family_id", "age_mother"))
+DT.m1[, c("variable", "child") := tstrsplit(variable, "_", fixed=TRUE)]
+DT.c1 = dcast(DT.m1, family_id + age_mother + child ~ variable, value.var = "value")
+DT.c1
+
+str(DT.c1) ## gender column is character type now!
+```
+
+#### Issues {.bs-callout .bs-callout-info}
+
+1. What we wanted to do was to combine all the `dob` and `gender` type columns together respectively. Instead we are combining *everything* together, and then splitting them again. I think it's easy to see that it's quite roundabout (and inefficient).
+
+ As an analogy, imagine you've a closet with four shelves of clothes and you'd like to put together the clothes from shelves 1 and 2 together (in 1), and 3 and 4 together (in 3). What we are doing is more or less to combine all the clothes together, and then split them back on to shelves 1 and 3!
+
+2. The columns to *melt* may be of different types, as in this case (character and integer types). By *melting* them all together, the columns will be coerced in result, as explained by the warning message above and shown from output of `str(DT.c1)`, where `gender` has been converted to *character* type.
+
+3. We are generating an additional column by splitting the `variable` column into two columns, whose purpose is quite cryptic. We do it because we need it for *casting* in the next step.
+
+4. Finally, we cast the data set. But the issue is it's a much more computationally involved operation than *melt*. Specifically, it requires computing the order of the variables in formula, and that's costly.
+
+#
+
+In fact, `base::reshape` is capable of performing this operation in a very straightforward manner. It is an extremely useful and often underrated function. You should definitely give it a try!
+
+## 3. Enhanced (new) functionality
+
+### a) Enhanced `melt`
+
+Since we'd like for *data.tables* to perform this operation straightforward and efficient using the same interface, we went ahead and implemented an *additional functionality*, where we can `melt` to multiple columns *simultaneously*.
+
+#### - `melt` multiple columns simultaneously
+
+The idea is quite simple. We pass a list of columns to `measure.vars`, where each element of the list contains the columns that should be combined together.
+
+```{r}
+colA = paste("dob_child", 1:3, sep="")
+colB = paste("gender_child", 1:3, sep="")
+DT.m2 = melt(DT, measure = list(colA, colB), value.name = c("dob", "gender"))
+DT.m2
+
+str(DT.m2) ## col type is preserved
+```
+
+#### - Using `patterns()`
+
+Usually in these problems, the columns we'd like to melt can be distinguished by a common pattern. We can use the function `patterns()`, implemented for convenience, to provide regular expressions for the columns to be combined together. The above operation can be rewritten as:
+
+```{r}
+DT.m2 = melt(DT, measure = patterns("^dob", "^gender"), value.name = c("dob", "gender"))
+DT.m2
+```
+
+That's it!
+
+#### {.bs-callout .bs-callout-info}
+
+* We can remove the `variable` column if necessary.
+
+* The functionality is implemented entirely in C, and is therefore both *fast* and *memory efficient* in addition to being *straightforward*.
+
+### b) Enhanced `dcast`
+
+Okay great! We can now melt into multiple columns simultaneously. Now given the data set `DT.m2` as shown above, how can we get back to the same format as the original data we started with?
+
+If we use the current functionality of `dcast`, then we'd have to cast twice and bind the results together. But that's once again verbose, not straightforward and is also inefficient.
+
+#### - Casting multiple `value.var`s simultaneously
+
+We can now provide **multiple `value.var` columns** to `dcast` for *data.tables* directly so that the operations are taken care of internally and efficiently.
+
+```{r}
+## new 'cast' functionality - multiple value.vars
+DT.c2 = dcast(DT.m2, family_id + age_mother ~ variable, value.var = c("dob", "gender"))
+DT.c2
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Attributes are preserved in result wherever possible.
+
+* Everything is taken care of internally, and efficiently. In addition to being fast, it is also very memory efficient.
+
+#
+
+#### Multiple functions to `fun.aggregate`: {.bs-callout .bs-callout-info}
+
+You can also provide *multiple functions* to `fun.aggregate` to `dcast` for *data.tables*. Check the examples in `?dcast` which illustrates this functionality.
+
+#
+
+***
+
diff --git a/inst/doc/datatable-reshape.html b/inst/doc/datatable-reshape.html
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+<div id="header">
+<h1 class="title">Efficient reshaping using data.tables</h1>
+<h4 class="date"><em>2015-09-18</em></h4>
+</div>
+
+
+<p>This vignette discusses the default usage of reshaping functions <code>melt</code> (wide to long) and <code>dcast</code> (long to wide) for <em>data.tables</em> as well as the <strong>new extended functionalities</strong> of melting and casting on <em>multiple columns</em> available from <code>v1.9.6</code>.</p>
+<hr />
+<div id="data" class="section level2">
+<h2>Data</h2>
+<p>We will load the data sets directly within sections.</p>
+</div>
+<div id="introduction" class="section level2">
+<h2>Introduction</h2>
+<p>The <code>melt</code> and <code>dcast</code> functions for <em>data.tables</em> are extensions of the corresponding functions from the <a href="http://cran.r-project.org/package=reshape2">reshape2</a> package.</p>
+<p>In this vignette, we will</p>
+<ol style="list-style-type: decimal">
+<li><p>first briefly look at the default <em>melting</em> and <em>casting</em> of <em>data.tables</em> to convert them from <em>wide</em> to <em>long</em> format and vice versa,</p></li>
+<li><p>then look at scenarios where the current functionalities becomes cumbersome and inefficient,</p></li>
+<li><p>and finally look at the new improvements to both <code>melt</code> and <code>dcast</code> methods for <em>data.tables</em> to handle multiple columns simultaneously.</p></li>
+</ol>
+<p>The extended functionalities are in line with <em>data.table’s</em> philosophy of performing operations efficiently and in a straightforward manner.</p>
+<div id="note" class="section level4 bs-callout bs-callout-info">
+<h4>Note:</h4>
+<p>From <code>v1.9.6</code> on, you don’t have to load <code>reshape2</code> package to use these functions for <em>data.tables</em>. You just need to load <code>data.table</code>. If you’ve to load <code>reshape2</code> for melting or casting matrices and/or data.frames, then make sure to load it <em>before</em> loading <code>data.table</code>.</p>
+</div>
+</div>
+<div id="default-functionality" class="section level2">
+<h2>1. Default functionality</h2>
+<div id="a-melting-data.tables-wide-to-long" class="section level3">
+<h3>a) <code>melt</code>ing <em>data.tables</em> (wide to long)</h3>
+<p>Suppose we have a <code>data.table</code> (artificial data) as shown below:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT =<span class="st"> </span><span class="kw">fread</span>(<span class="st">"melt_default.csv"</span>)
+DT
+<span class="co"># family_id age_mother dob_child1 dob_child2 dob_child3</span>
+<span class="co"># 1: 1 30 1998-11-26 2000-01-29 NA</span>
+<span class="co"># 2: 2 27 1996-06-22 NA NA</span>
+<span class="co"># 3: 3 26 2002-07-11 2004-04-05 2007-09-02</span>
+<span class="co"># 4: 4 32 2004-10-10 2009-08-27 2012-07-21</span>
+<span class="co"># 5: 5 29 2000-12-05 2005-02-28 NA</span>
+## dob stands for date of birth.
+
+<span class="kw">str</span>(DT)
+<span class="co"># Classes 'data.table' and 'data.frame': 5 obs. of 5 variables:</span>
+<span class="co"># $ family_id : int 1 2 3 4 5</span>
+<span class="co"># $ age_mother: int 30 27 26 32 29</span>
+<span class="co"># $ dob_child1: chr "1998-11-26" "1996-06-22" "2002-07-11" "2004-10-10" ...</span>
+<span class="co"># $ dob_child2: chr "2000-01-29" NA "2004-04-05" "2009-08-27" ...</span>
+<span class="co"># $ dob_child3: chr NA NA "2007-09-02" "2012-07-21" ...</span>
+<span class="co"># - attr(*, ".internal.selfref")=<externalptr></span></code></pre></div>
+</div>
+</div>
+<div id="section" class="section level1">
+<h1></h1>
+<div id="convert-dt-to-long-form-where-each-dob-is-a-separate-observation." class="section level4">
+<h4>- Convert <code>DT</code> to <em>long</em> form where each <code>dob</code> is a separate observation.</h4>
+<p>We could accomplish this using <code>melt()</code> by specifying <code>id.vars</code> and <code>measure.vars</code> arguments as follows:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT.m1 =<span class="st"> </span><span class="kw">melt</span>(DT, <span class="dt">id.vars =</span> <span class="kw">c</span>(<span class="st">"family_id"</span>, <span class="st">"age_mother"</span>),
+ <span class="dt">measure.vars =</span> <span class="kw">c</span>(<span class="st">"dob_child1"</span>, <span class="st">"dob_child2"</span>, <span class="st">"dob_child3"</span>))
+DT.m1
+<span class="co"># family_id age_mother variable value</span>
+<span class="co"># 1: 1 30 dob_child1 1998-11-26</span>
+<span class="co"># 2: 2 27 dob_child1 1996-06-22</span>
+<span class="co"># 3: 3 26 dob_child1 2002-07-11</span>
+<span class="co"># 4: 4 32 dob_child1 2004-10-10</span>
+<span class="co"># 5: 5 29 dob_child1 2000-12-05</span>
+<span class="co"># 6: 1 30 dob_child2 2000-01-29</span>
+<span class="co"># 7: 2 27 dob_child2 NA</span>
+<span class="co"># 8: 3 26 dob_child2 2004-04-05</span>
+<span class="co"># 9: 4 32 dob_child2 2009-08-27</span>
+<span class="co"># 10: 5 29 dob_child2 2005-02-28</span>
+<span class="co"># 11: 1 30 dob_child3 NA</span>
+<span class="co"># 12: 2 27 dob_child3 NA</span>
+<span class="co"># 13: 3 26 dob_child3 2007-09-02</span>
+<span class="co"># 14: 4 32 dob_child3 2012-07-21</span>
+<span class="co"># 15: 5 29 dob_child3 NA</span>
+<span class="kw">str</span>(DT.m1)
+<span class="co"># Classes 'data.table' and 'data.frame': 15 obs. of 4 variables:</span>
+<span class="co"># $ family_id : int 1 2 3 4 5 1 2 3 4 5 ...</span>
+<span class="co"># $ age_mother: int 30 27 26 32 29 30 27 26 32 29 ...</span>
+<span class="co"># $ variable : Factor w/ 3 levels "dob_child1","dob_child2",..: 1 1 1 1 1 2 2 2 2 2 ...</span>
+<span class="co"># $ value : chr "1998-11-26" "1996-06-22" "2002-07-11" "2004-10-10" ...</span>
+<span class="co"># - attr(*, ".internal.selfref")=<externalptr></span></code></pre></div>
+</div>
+<div id="section-1" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p><code>measure.vars</code> specify the set of columns we would like to collapse (or combine) together.</p></li>
+<li><p>We can also specify column <em>indices</em> instead of <em>names</em>.</p></li>
+<li><p>By default, <code>variable</code> column is of type <code>factor</code>. Set <code>variable.factor</code> argument to <code>FALSE</code> if you’d like to return a <em>character</em> vector instead. <code>variable.factor</code> argument is only available in <code>melt</code> from <code>data.table</code> and not in the <a href="http://github.com/hadley/reshape"><code>reshape2</code> package</a>.</p></li>
+<li><p>By default, the molten columns are automatically named <code>variable</code> and <code>value</code>.</p></li>
+<li><p><code>melt</code> preserves column attributes in result.</p></li>
+</ul>
+</div>
+</div>
+<div id="section-2" class="section level1">
+<h1></h1>
+<div id="name-the-variable-and-value-columns-to-child-and-dob-respectively" class="section level4">
+<h4>- Name the <code>variable</code> and <code>value</code> columns to <code>child</code> and <code>dob</code> respectively</h4>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT.m1 =<span class="st"> </span><span class="kw">melt</span>(DT, <span class="dt">measure.vars =</span> <span class="kw">c</span>(<span class="st">"dob_child1"</span>, <span class="st">"dob_child2"</span>, <span class="st">"dob_child3"</span>),
+ <span class="dt">variable.name =</span> <span class="st">"child"</span>, <span class="dt">value.name =</span> <span class="st">"dob"</span>)
+DT.m1
+<span class="co"># family_id age_mother child dob</span>
+<span class="co"># 1: 1 30 dob_child1 1998-11-26</span>
+<span class="co"># 2: 2 27 dob_child1 1996-06-22</span>
+<span class="co"># 3: 3 26 dob_child1 2002-07-11</span>
+<span class="co"># 4: 4 32 dob_child1 2004-10-10</span>
+<span class="co"># 5: 5 29 dob_child1 2000-12-05</span>
+<span class="co"># 6: 1 30 dob_child2 2000-01-29</span>
+<span class="co"># 7: 2 27 dob_child2 NA</span>
+<span class="co"># 8: 3 26 dob_child2 2004-04-05</span>
+<span class="co"># 9: 4 32 dob_child2 2009-08-27</span>
+<span class="co"># 10: 5 29 dob_child2 2005-02-28</span>
+<span class="co"># 11: 1 30 dob_child3 NA</span>
+<span class="co"># 12: 2 27 dob_child3 NA</span>
+<span class="co"># 13: 3 26 dob_child3 2007-09-02</span>
+<span class="co"># 14: 4 32 dob_child3 2012-07-21</span>
+<span class="co"># 15: 5 29 dob_child3 NA</span></code></pre></div>
+</div>
+<div id="section-3" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>By default, when one of <code>id.vars</code> or <code>measure.vars</code> is missing, the rest of the columns are <em>automatically assigned</em> to the missing argument.</p></li>
+<li><p>When neither <code>id.vars</code> nor <code>measure.vars</code> are specified, as mentioned under <code>?melt</code>, all <em>non</em>-<code>numeric</code>, <code>integer</code>, <code>logical</code> columns will be assigned to <code>id.vars</code>.</p>
+<p>In addition, a warning message is issued highlighting the columns that are automatically considered to be <code>id.vars</code>.</p></li>
+</ul>
+</div>
+<div id="b-casting-data.tables-long-to-wide" class="section level3">
+<h3>b) <code>Cast</code>ing <em>data.tables</em> (long to wide)</h3>
+<p>In the previous section, we saw how to get from wide form to long form. Let’s see the reverse operation in this section.</p>
+<div id="how-can-we-get-back-to-the-original-data-table-dt-from-dt.m" class="section level4">
+<h4>- How can we get back to the original data table <code>DT</code> from <code>DT.m</code>?</h4>
+<p>That is, we’d like to collect all <em>child</em> observations corresponding to each <code>family_id, age_mother</code> together under the same row. We can accomplish it using <code>dcast</code> as follows:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">dcast</span>(DT.m1, family_id +<span class="st"> </span>age_mother ~<span class="st"> </span>child, <span class="dt">value.var =</span> <span class="st">"dob"</span>)
+<span class="co"># family_id age_mother dob_child1 dob_child2 dob_child3</span>
+<span class="co"># 1: 1 30 1998-11-26 2000-01-29 NA</span>
+<span class="co"># 2: 2 27 1996-06-22 NA NA</span>
+<span class="co"># 3: 3 26 2002-07-11 2004-04-05 2007-09-02</span>
+<span class="co"># 4: 4 32 2004-10-10 2009-08-27 2012-07-21</span>
+<span class="co"># 5: 5 29 2000-12-05 2005-02-28 NA</span></code></pre></div>
+</div>
+<div id="section-4" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p><code>dcast</code> uses <em>formula</em> interface. The variables on the <em>LHS</em> of formula represents the <em>id</em> vars and <em>RHS</em> the <em>measure</em> vars.</p></li>
+<li><p><code>value.var</code> denotes the column to be filled in with while casting to wide format.</p></li>
+<li><p><code>dcast</code> also tries to preserve attributes in result wherever possible.</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div id="section-5" class="section level1">
+<h1></h1>
+<div id="starting-from-dt.m-how-can-we-get-the-number-of-children-in-each-family" class="section level4">
+<h4>- Starting from <code>DT.m</code>, how can we get the number of children in each family?</h4>
+<p>You can also pass a function to aggregate by in <code>dcast</code> with the argument <code>fun.aggregate</code>. This is particularly essential when the formula provided does not identify single observation for each cell.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">dcast</span>(DT.m1, family_id ~<span class="st"> </span>., <span class="dt">fun.agg =</span> function(x) <span class="kw">sum</span>(!<span class="kw">is.na</span>(x)), <span class="dt">value.var =</span> <span class="st">"dob"</span>)
+<span class="co"># family_id .</span>
+<span class="co"># 1: 1 2</span>
+<span class="co"># 2: 2 1</span>
+<span class="co"># 3: 3 3</span>
+<span class="co"># 4: 4 3</span>
+<span class="co"># 5: 5 2</span></code></pre></div>
+<p>Check <code>?dcast</code> for other useful arguments and additional examples.</p>
+</div>
+<div id="limitations-in-current-meltdcast-approaches" class="section level2">
+<h2>2. Limitations in current <code>melt/dcast</code> approaches</h2>
+<p>So far we’ve seen features of <code>melt</code> and <code>dcast</code> that are based on <code>reshape2</code> package, but implemented efficiently for <em>data.table</em>s, using internal <code>data.table</code> machinery (<em>fast radix ordering</em>, <em>binary search</em> etc..).</p>
+<p>However, there are situations we might run into where the desired operation is not expressed in a straightforward manner. For example, consider the <em>data.table</em> shown below:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT =<span class="st"> </span><span class="kw">fread</span>(<span class="st">"melt_enhanced.csv"</span>)
+DT
+<span class="co"># family_id age_mother dob_child1 dob_child2 dob_child3 gender_child1 gender_child2 gender_child3</span>
+<span class="co"># 1: 1 30 1998-11-26 2000-01-29 NA 1 2 NA</span>
+<span class="co"># 2: 2 27 1996-06-22 NA NA 2 NA NA</span>
+<span class="co"># 3: 3 26 2002-07-11 2004-04-05 2007-09-02 2 2 1</span>
+<span class="co"># 4: 4 32 2004-10-10 2009-08-27 2012-07-21 1 1 1</span>
+<span class="co"># 5: 5 29 2000-12-05 2005-02-28 NA 2 1 NA</span>
+## 1 = female, 2 = male </code></pre></div>
+<p>And you’d like to combine (melt) all the <code>dob</code> columns together, and <code>gender</code> columns together. Using the current functionalty, we can do something like this:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT.m1 =<span class="st"> </span><span class="kw">melt</span>(DT, <span class="dt">id =</span> <span class="kw">c</span>(<span class="st">"family_id"</span>, <span class="st">"age_mother"</span>))
+<span class="co"># Warning in melt.data.table(DT, id = c("family_id", "age_mother")): 'measure.vars' [dob_child1,</span>
+<span class="co"># dob_child2, dob_child3, gender_child1, gender_child2, gender_child3] are not all of the same</span>
+<span class="co"># type. By order of hierarchy, the molten data value column will be of type 'character'. All measure</span>
+<span class="co"># variables not of type 'character' will be coerced to. Check DETAILS in ?melt.data.table for more on</span>
+<span class="co"># coercion.</span>
+DT.m1[, <span class="kw">c</span>(<span class="st">"variable"</span>, <span class="st">"child"</span>) :<span class="er">=</span><span class="st"> </span><span class="kw">tstrsplit</span>(variable, <span class="st">"_"</span>, <span class="dt">fixed=</span><span class="ot">TRUE</span>)]
+DT.c1 =<span class="st"> </span><span class="kw">dcast</span>(DT.m1, family_id +<span class="st"> </span>age_mother +<span class="st"> </span>child ~<span class="st"> </span>variable, <span class="dt">value.var =</span> <span class="st">"value"</span>)
+DT.c1
+<span class="co"># family_id age_mother child dob gender</span>
+<span class="co"># 1: 1 30 child1 1998-11-26 1</span>
+<span class="co"># 2: 1 30 child2 2000-01-29 2</span>
+<span class="co"># 3: 1 30 child3 NA NA</span>
+<span class="co"># 4: 2 27 child1 1996-06-22 2</span>
+<span class="co"># 5: 2 27 child2 NA NA</span>
+<span class="co"># 6: 2 27 child3 NA NA</span>
+<span class="co"># 7: 3 26 child1 2002-07-11 2</span>
+<span class="co"># 8: 3 26 child2 2004-04-05 2</span>
+<span class="co"># 9: 3 26 child3 2007-09-02 1</span>
+<span class="co"># 10: 4 32 child1 2004-10-10 1</span>
+<span class="co"># 11: 4 32 child2 2009-08-27 1</span>
+<span class="co"># 12: 4 32 child3 2012-07-21 1</span>
+<span class="co"># 13: 5 29 child1 2000-12-05 2</span>
+<span class="co"># 14: 5 29 child2 2005-02-28 1</span>
+<span class="co"># 15: 5 29 child3 NA NA</span>
+
+<span class="kw">str</span>(DT.c1) ## gender column is character type now!
+<span class="co"># Classes 'data.table' and 'data.frame': 15 obs. of 5 variables:</span>
+<span class="co"># $ family_id : int 1 1 1 2 2 2 3 3 3 4 ...</span>
+<span class="co"># $ age_mother: int 30 30 30 27 27 27 26 26 26 32 ...</span>
+<span class="co"># $ child : chr "child1" "child2" "child3" "child1" ...</span>
+<span class="co"># $ dob : chr "1998-11-26" "2000-01-29" NA "1996-06-22" ...</span>
+<span class="co"># $ gender : chr "1" "2" NA "2" ...</span>
+<span class="co"># - attr(*, ".internal.selfref")=<externalptr> </span>
+<span class="co"># - attr(*, "sorted")= chr "family_id" "age_mother" "child"</span></code></pre></div>
+<div id="issues" class="section level4 bs-callout bs-callout-info">
+<h4>Issues</h4>
+<ol style="list-style-type: decimal">
+<li><p>What we wanted to do was to combine all the <code>dob</code> and <code>gender</code> type columns together respectively. Instead we are combining <em>everything</em> together, and then splitting them again. I think it’s easy to see that it’s quite roundabout (and inefficient).</p>
+<p>As an analogy, imagine you’ve a closet with four shelves of clothes and you’d like to put together the clothes from shelves 1 and 2 together (in 1), and 3 and 4 together (in 3). What we are doing is more or less to combine all the clothes together, and then split them back on to shelves 1 and 3!</p></li>
+<li><p>The columns to <em>melt</em> may be of different types, as in this case (character and integer types). By <em>melting</em> them all together, the columns will be coerced in result, as explained by the warning message above and shown from output of <code>str(DT.c1)</code>, where <code>gender</code> has been converted to <em>character</em> type.</p></li>
+<li><p>We are generating an additional column by splitting the <code>variable</code> column into two columns, whose purpose is quite cryptic. We do it because we need it for <em>casting</em> in the next step.</p></li>
+<li><p>Finally, we cast the data set. But the issue is it’s a much more computationally involved operation than <em>melt</em>. Specifically, it requires computing the order of the variables in formula, and that’s costly.</p></li>
+</ol>
+</div>
+</div>
+</div>
+<div id="section-6" class="section level1">
+<h1></h1>
+<p>In fact, <code>base::reshape</code> is capable of performing this operation in a very straightforward manner. It is an extremely useful and often underrated function. You should definitely give it a try!</p>
+<div id="enhanced-new-functionality" class="section level2">
+<h2>3. Enhanced (new) functionality</h2>
+<div id="a-enhanced-melt" class="section level3">
+<h3>a) Enhanced <code>melt</code></h3>
+<p>Since we’d like for <em>data.tables</em> to perform this operation straightforward and efficient using the same interface, we went ahead and implemented an <em>additional functionality</em>, where we can <code>melt</code> to multiple columns <em>simultaneously</em>.</p>
+<div id="melt-multiple-columns-simultaneously" class="section level4">
+<h4>- <code>melt</code> multiple columns simultaneously</h4>
+<p>The idea is quite simple. We pass a list of columns to <code>measure.vars</code>, where each element of the list contains the columns that should be combined together.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">colA =<span class="st"> </span><span class="kw">paste</span>(<span class="st">"dob_child"</span>, <span class="dv">1</span>:<span class="dv">3</span>, <span class="dt">sep=</span><span class="st">""</span>)
+colB =<span class="st"> </span><span class="kw">paste</span>(<span class="st">"gender_child"</span>, <span class="dv">1</span>:<span class="dv">3</span>, <span class="dt">sep=</span><span class="st">""</span>)
+DT.m2 =<span class="st"> </span><span class="kw">melt</span>(DT, <span class="dt">measure =</span> <span class="kw">list</span>(colA, colB), <span class="dt">value.name =</span> <span class="kw">c</span>(<span class="st">"dob"</span>, <span class="st">"gender"</span>))
+DT.m2
+<span class="co"># family_id age_mother variable dob gender</span>
+<span class="co"># 1: 1 30 1 1998-11-26 1</span>
+<span class="co"># 2: 2 27 1 1996-06-22 2</span>
+<span class="co"># 3: 3 26 1 2002-07-11 2</span>
+<span class="co"># 4: 4 32 1 2004-10-10 1</span>
+<span class="co"># 5: 5 29 1 2000-12-05 2</span>
+<span class="co"># 6: 1 30 2 2000-01-29 2</span>
+<span class="co"># 7: 2 27 2 NA NA</span>
+<span class="co"># 8: 3 26 2 2004-04-05 2</span>
+<span class="co"># 9: 4 32 2 2009-08-27 1</span>
+<span class="co"># 10: 5 29 2 2005-02-28 1</span>
+<span class="co"># 11: 1 30 3 NA NA</span>
+<span class="co"># 12: 2 27 3 NA NA</span>
+<span class="co"># 13: 3 26 3 2007-09-02 1</span>
+<span class="co"># 14: 4 32 3 2012-07-21 1</span>
+<span class="co"># 15: 5 29 3 NA NA</span>
+
+<span class="kw">str</span>(DT.m2) ## col type is preserved
+<span class="co"># Classes 'data.table' and 'data.frame': 15 obs. of 5 variables:</span>
+<span class="co"># $ family_id : int 1 2 3 4 5 1 2 3 4 5 ...</span>
+<span class="co"># $ age_mother: int 30 27 26 32 29 30 27 26 32 29 ...</span>
+<span class="co"># $ variable : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...</span>
+<span class="co"># $ dob : chr "1998-11-26" "1996-06-22" "2002-07-11" "2004-10-10" ...</span>
+<span class="co"># $ gender : int 1 2 2 1 2 2 NA 2 1 1 ...</span>
+<span class="co"># - attr(*, ".internal.selfref")=<externalptr></span></code></pre></div>
+</div>
+<div id="using-patterns" class="section level4">
+<h4>- Using <code>patterns()</code></h4>
+<p>Usually in these problems, the columns we’d like to melt can be distinguished by a common pattern. We can use the function <code>patterns()</code>, implemented for convenience, to provide regular expressions for the columns to be combined together. The above operation can be rewritten as:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">DT.m2 =<span class="st"> </span><span class="kw">melt</span>(DT, <span class="dt">measure =</span> <span class="kw">patterns</span>(<span class="st">"^dob"</span>, <span class="st">"^gender"</span>), <span class="dt">value.name =</span> <span class="kw">c</span>(<span class="st">"dob"</span>, <span class="st">"gender"</span>))
+DT.m2
+<span class="co"># family_id age_mother variable dob gender</span>
+<span class="co"># 1: 1 30 1 1998-11-26 1</span>
+<span class="co"># 2: 2 27 1 1996-06-22 2</span>
+<span class="co"># 3: 3 26 1 2002-07-11 2</span>
+<span class="co"># 4: 4 32 1 2004-10-10 1</span>
+<span class="co"># 5: 5 29 1 2000-12-05 2</span>
+<span class="co"># 6: 1 30 2 2000-01-29 2</span>
+<span class="co"># 7: 2 27 2 NA NA</span>
+<span class="co"># 8: 3 26 2 2004-04-05 2</span>
+<span class="co"># 9: 4 32 2 2009-08-27 1</span>
+<span class="co"># 10: 5 29 2 2005-02-28 1</span>
+<span class="co"># 11: 1 30 3 NA NA</span>
+<span class="co"># 12: 2 27 3 NA NA</span>
+<span class="co"># 13: 3 26 3 2007-09-02 1</span>
+<span class="co"># 14: 4 32 3 2012-07-21 1</span>
+<span class="co"># 15: 5 29 3 NA NA</span></code></pre></div>
+<p>That’s it!</p>
+</div>
+<div id="section-7" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>We can remove the <code>variable</code> column if necessary.</p></li>
+<li><p>The functionality is implemented entirely in C, and is therefore both <em>fast</em> and <em>memory efficient</em> in addition to being <em>straightforward</em>.</p></li>
+</ul>
+</div>
+</div>
+<div id="b-enhanced-dcast" class="section level3">
+<h3>b) Enhanced <code>dcast</code></h3>
+<p>Okay great! We can now melt into multiple columns simultaneously. Now given the data set <code>DT.m2</code> as shown above, how can we get back to the same format as the original data we started with?</p>
+<p>If we use the current functionality of <code>dcast</code>, then we’d have to cast twice and bind the results together. But that’s once again verbose, not straightforward and is also inefficient.</p>
+<div id="casting-multiple-value.vars-simultaneously" class="section level4">
+<h4>- Casting multiple <code>value.var</code>s simultaneously</h4>
+<p>We can now provide <strong>multiple <code>value.var</code> columns</strong> to <code>dcast</code> for <em>data.tables</em> directly so that the operations are taken care of internally and efficiently.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## new 'cast' functionality - multiple value.vars
+DT.c2 =<span class="st"> </span><span class="kw">dcast</span>(DT.m2, family_id +<span class="st"> </span>age_mother ~<span class="st"> </span>variable, <span class="dt">value.var =</span> <span class="kw">c</span>(<span class="st">"dob"</span>, <span class="st">"gender"</span>))
+DT.c2
+<span class="co"># family_id age_mother dob_1 dob_2 dob_3 gender_1 gender_2 gender_3</span>
+<span class="co"># 1: 1 30 1998-11-26 2000-01-29 NA 1 2 NA</span>
+<span class="co"># 2: 2 27 1996-06-22 NA NA 2 NA NA</span>
+<span class="co"># 3: 3 26 2002-07-11 2004-04-05 2007-09-02 2 2 1</span>
+<span class="co"># 4: 4 32 2004-10-10 2009-08-27 2012-07-21 1 1 1</span>
+<span class="co"># 5: 5 29 2000-12-05 2005-02-28 NA 2 1 NA</span></code></pre></div>
+</div>
+<div id="section-8" class="section level4 bs-callout bs-callout-info">
+<h4></h4>
+<ul>
+<li><p>Attributes are preserved in result wherever possible.</p></li>
+<li><p>Everything is taken care of internally, and efficiently. In addition to being fast, it is also very memory efficient.</p></li>
+</ul>
+</div>
+</div>
+</div>
+</div>
+<div id="section-9" class="section level1">
+<h1></h1>
+<div id="multiple-functions-to-fun.aggregate" class="section level4 bs-callout bs-callout-info">
+<h4>Multiple functions to <code>fun.aggregate</code>:</h4>
+<p>You can also provide <em>multiple functions</em> to <code>fun.aggregate</code> to <code>dcast</code> for <em>data.tables</em>. Check the examples in <code>?dcast</code> which illustrates this functionality.</p>
+</div>
+</div>
+<div id="section-10" class="section level1">
+<h1></h1>
+<hr />
+</div>
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+$(document).ready(function () {
+ $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
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diff --git a/inst/tests/1206FUT.txt b/inst/tests/1206FUT.txt
new file mode 100644
index 0000000..c31c7db
--- /dev/null
+++ b/inst/tests/1206FUT.txt
@@ -0,0 +1,309 @@
+DATE COM COM_MM COM_YY OPEN_1 OPEN_I1 OPEN_2 OPEN_I2 HIGH HIGH_I LOW LOW_I CLSE_1 CLSE_I1 CLSE_2 CLSE_I2 SETTLE VOLUME OINT DEL RECTYP
+20121206 AP 000879.17 000879.99 000876.41 000878.69
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+20121206 HK 012232.40 012278.00 012200.81 012215.93
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+20121206 IN 005926.30 005942.55 005838.90 005930.90
+20121206 NK 009535.69 009565.43 009503.31 009545.16
+20121206 SG 000350.41 000350.59 000348.32 000350.03
+20121206 ST 003081.25 003082.88 003065.79 003078.20
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diff --git a/inst/tests/2008head.csv b/inst/tests/2008head.csv
new file mode 100644
index 0000000..28dc1f1
--- /dev/null
+++ b/inst/tests/2008head.csv
@@ -0,0 +1,500 @@
+Year,Month,DayofMonth,DayOfWeek,DepTime,CRSDepTime,ArrTime,CRSArrTime,UniqueCarrier,FlightNum,TailNum,ActualElapsedTime,CRSElapsedTime,AirTime,ArrDelay,DepDelay,Origin,Dest,Distance,TaxiIn,TaxiOut,Cancelled,CancellationCode,Diverted,CarrierDelay,WeatherDelay,NASDelay,SecurityDelay,LateAircraftDelay
+2008,1,3,4,2003,1955,2211,2225,WN,335,N712SW,128,150,116,-14,8,IAD,TPA,810,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,754,735,1002,1000,WN,3231,N772SW,128,145,113,2,19,IAD,TPA,810,5,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,628,620,804,750,WN,448,N428WN,96,90,76,14,8,IND,BWI,515,3,17,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,926,930,1054,1100,WN,1746,N612SW,88,90,78,-6,-4,IND,BWI,515,3,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1829,1755,1959,1925,WN,3920,N464WN,90,90,77,34,34,IND,BWI,515,3,10,0,,0,2,0,0,0,32
+2008,1,3,4,1940,1915,2121,2110,WN,378,N726SW,101,115,87,11,25,IND,JAX,688,4,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1937,1830,2037,1940,WN,509,N763SW,240,250,230,57,67,IND,LAS,1591,3,7,0,,0,10,0,0,0,47
+2008,1,3,4,1039,1040,1132,1150,WN,535,N428WN,233,250,219,-18,-1,IND,LAS,1591,7,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,617,615,652,650,WN,11,N689SW,95,95,70,2,2,IND,MCI,451,6,19,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1620,1620,1639,1655,WN,810,N648SW,79,95,70,-16,0,IND,MCI,451,3,6,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,706,700,916,915,WN,100,N690SW,130,135,106,1,6,IND,MCO,828,5,19,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1644,1510,1845,1725,WN,1333,N334SW,121,135,107,80,94,IND,MCO,828,6,8,0,,0,8,0,0,0,72
+2008,1,3,4,1426,1430,1426,1425,WN,829,N476WN,60,55,39,1,-4,IND,MDW,162,9,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,715,715,720,710,WN,1016,N765SW,65,55,37,10,0,IND,MDW,162,7,21,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1702,1700,1651,1655,WN,1827,N420WN,49,55,35,-4,2,IND,MDW,162,4,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1029,1020,1021,1010,WN,2272,N263WN,52,50,37,11,9,IND,MDW,162,6,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1452,1425,1640,1625,WN,675,N286WN,228,240,213,15,27,IND,PHX,1489,7,8,0,,0,3,0,0,0,12
+2008,1,3,4,754,745,940,955,WN,1144,N778SW,226,250,205,-15,9,IND,PHX,1489,5,16,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1323,1255,1526,1510,WN,4,N674AA,123,135,110,16,28,IND,TPA,838,4,9,0,,0,0,0,0,0,16
+2008,1,3,4,1416,1325,1512,1435,WN,54,N643SW,56,70,49,37,51,ISP,BWI,220,2,5,0,,0,12,0,0,0,25
+2008,1,3,4,706,705,807,810,WN,68,N497WN,61,65,51,-3,1,ISP,BWI,220,3,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1657,1625,1754,1735,WN,623,N724SW,57,70,47,19,32,ISP,BWI,220,5,5,0,,0,7,0,0,0,12
+2008,1,3,4,1900,1840,1956,1950,WN,717,N786SW,56,70,49,6,20,ISP,BWI,220,2,5,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1039,1030,1133,1140,WN,1244,N714CB,54,70,47,-7,9,ISP,BWI,220,2,5,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,801,800,902,910,WN,2101,N222WN,61,70,53,-8,1,ISP,BWI,220,3,5,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1520,1455,1619,1605,WN,2553,N394SW,59,70,50,14,25,ISP,BWI,220,2,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1422,1255,1657,1610,WN,188,N215WN,155,195,143,47,87,ISP,FLL,1093,6,6,0,,0,40,0,0,0,7
+2008,1,3,4,1954,1925,2239,2235,WN,1754,N243WN,165,190,155,4,29,ISP,FLL,1093,3,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,636,635,921,945,WN,2275,N454WN,165,190,147,-24,1,ISP,FLL,1093,5,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,734,730,958,1020,WN,550,N712SW,324,350,314,-22,4,ISP,LAS,2283,2,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2107,1945,2334,2230,WN,362,N798SW,147,165,134,64,82,ISP,MCO,972,6,7,0,,0,5,0,0,0,59
+2008,1,3,4,1008,1005,1234,1255,WN,543,N736SA,146,170,135,-21,3,ISP,MCO,972,5,6,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,712,710,953,1000,WN,1112,N795SW,161,170,142,-7,2,ISP,MCO,972,5,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1312,1300,1546,1550,WN,1397,N247WN,154,170,140,-4,12,ISP,MCO,972,7,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1449,1430,1715,1720,WN,3398,N707SA,146,170,134,-5,19,ISP,MCO,972,6,6,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1634,1555,1859,1845,WN,3480,N443WN,145,170,134,14,39,ISP,MCO,972,5,6,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,831,830,935,955,WN,300,N753SW,124,145,112,-20,1,ISP,MDW,765,5,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1812,1650,1927,1815,WN,422,N779SW,135,145,118,72,82,ISP,MDW,765,6,11,0,,0,3,0,0,0,69
+2008,1,3,4,1127,1105,1235,1230,WN,1837,N704SW,128,145,114,5,22,ISP,MDW,765,9,5,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1424,1355,1531,1520,WN,2871,N709SW,127,145,113,11,29,ISP,MDW,765,8,6,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1326,1230,1559,1530,WN,1056,N459WN,153,180,143,29,56,ISP,PBI,1052,5,5,0,,0,0,0,0,0,29
+2008,1,3,4,1749,1725,2019,2030,WN,2175,N621SW,150,185,138,-11,24,ISP,PBI,1052,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,726,720,958,1020,WN,3319,N206WN,152,180,140,-22,6,ISP,PBI,1052,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,646,640,929,955,WN,3667,N280WN,163,195,151,-26,6,ISP,RSW,1101,3,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1153,1140,1428,1440,WN,2006,N241WN,155,180,143,-12,13,ISP,TPA,1034,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1528,1510,1802,1810,WN,3858,N200WN,154,180,144,-8,18,ISP,TPA,1034,4,6,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,634,635,907,935,WN,3928,N459WN,153,180,142,-28,-1,ISP,TPA,1034,3,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,831,830,1148,1140,WN,534,N286WN,137,130,123,8,1,JAN,BWI,888,5,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1450,1435,1806,1745,WN,3244,N475WN,136,130,121,21,15,JAN,BWI,888,7,8,0,,0,0,0,6,0,15
+2008,1,3,4,2245,1730,2354,1850,WN,186,N792SW,69,80,59,304,315,JAN,HOU,359,3,7,0,,0,282,0,0,0,22
+2008,1,3,4,615,615,724,735,WN,971,N202WN,69,80,60,-11,0,JAN,HOU,359,2,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1150,1145,1303,1305,WN,2124,N646SW,73,80,63,-2,5,JAN,HOU,359,3,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2025,1940,2135,2100,WN,3154,N252WN,70,80,60,35,45,JAN,HOU,359,3,7,0,,0,26,0,0,0,9
+2008,1,3,4,1038,945,1314,1225,WN,1035,N346SW,96,100,81,49,53,JAN,MCO,587,8,7,0,,0,7,0,0,0,42
+2008,1,3,4,1900,1850,2123,2045,WN,205,N299WN,143,115,97,38,10,JAN,MDW,666,40,6,0,,0,1,0,28,0,9
+2008,1,3,4,700,700,851,900,WN,449,N528SW,111,120,99,-9,0,JAN,MDW,666,6,6,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,948,925,959,940,WN,3430,N487WN,71,75,59,19,23,JAX,BHM,365,3,9,0,,0,0,0,0,0,19
+2008,1,3,4,646,620,725,655,WN,1580,N243WN,99,95,77,30,26,JAX,BNA,484,6,16,0,,0,26,0,4,0,0
+2008,1,3,4,1110,1040,1136,1110,WN,2195,N479WN,86,90,72,26,30,JAX,BNA,484,5,9,0,,0,0,0,0,10,16
+2008,1,3,4,1535,1535,1603,1610,WN,2804,N255WN,88,95,74,-7,0,JAX,BNA,484,5,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1919,1915,1942,1950,WN,3428,N215WN,83,95,71,-8,4,JAX,BNA,484,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1053,1055,1245,1240,WN,433,N264LV,112,105,96,5,-2,JAX,BWI,663,7,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1433,1440,1623,1625,WN,1331,N714CB,110,105,95,-2,-7,JAX,BWI,663,2,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2015,2010,2158,2155,WN,3504,N436WN,103,105,91,3,5,JAX,BWI,663,5,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2139,2130,2244,2240,WN,378,N726SW,65,70,54,4,9,JAX,FLL,318,3,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1500,1500,1602,1615,WN,640,N399WN,62,75,49,-13,0,JAX,FLL,318,5,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,850,850,1000,1000,WN,1396,N387SW,70,70,51,0,0,JAX,FLL,318,10,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,646,645,752,755,WN,2189,N405WN,66,70,46,-3,1,JAX,FLL,318,13,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1221,1220,1328,1330,WN,3312,N685SW,67,70,54,-2,1,JAX,FLL,318,5,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1738,1730,1841,1840,WN,3948,N467WN,63,70,49,1,8,JAX,FLL,318,6,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1813,1735,1936,1905,WN,54,N643SW,143,150,125,31,38,JAX,HOU,816,6,12,0,,0,11,0,0,0,20
+2008,1,3,4,802,750,1001,955,WN,2272,N263WN,119,125,104,6,12,JAX,IND,688,7,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1820,1825,1946,1955,WN,549,N363SW,86,90,75,-9,-5,JAX,ORF,543,3,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,821,820,953,945,WN,3604,N257WN,92,85,80,8,1,JAX,ORF,543,3,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1734,1650,1941,1905,WN,23,N521SW,127,135,113,36,44,JAX,PHL,742,4,10,0,,0,3,0,0,0,33
+2008,1,3,4,712,700,926,915,WN,1232,N663SW,134,135,120,11,12,JAX,PHL,742,5,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1318,1310,1410,1400,WN,977,N376SW,52,50,39,10,8,JAX,TPA,180,4,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,958,900,1052,950,WN,1574,N791SW,54,50,36,62,58,JAX,TPA,180,4,14,0,,0,0,0,4,0,58
+2008,1,3,4,1859,1850,1950,1945,WN,2019,N392SW,51,55,38,5,9,JAX,TPA,180,4,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1538,1445,1753,1710,WN,500,N799SW,75,85,60,43,53,LAS,ABQ,487,4,11,0,,0,15,0,0,0,28
+2008,1,3,4,933,935,1151,1200,WN,778,N607SW,78,85,63,-9,-2,LAS,ABQ,487,5,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2248,2125,102,2345,WN,890,N618WN,74,80,60,77,83,LAS,ABQ,487,4,10,0,,0,7,0,0,0,70
+2008,1,3,4,1327,1230,1550,1500,WN,1171,N682SW,83,90,65,50,57,LAS,ABQ,487,3,15,0,,0,50,0,0,0,0
+2008,1,3,4,624,625,846,850,WN,1320,N456WN,82,85,61,-4,-1,LAS,ABQ,487,4,17,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1614,1600,1833,1825,WN,1925,N509SW,79,85,60,8,14,LAS,ABQ,487,4,15,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1917,1915,2136,2140,WN,2457,N293,79,85,60,-4,2,LAS,ABQ,487,5,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1832,1655,148,30,WN,302,N473WN,256,275,243,78,97,LAS,ALB,2237,3,10,0,,0,8,0,0,0,70
+2008,1,3,4,1229,1155,1633,1555,WN,1079,N351SW,124,120,91,38,34,LAS,AMA,758,5,28,0,,0,3,0,4,0,31
+2008,1,3,4,1256,1240,1724,1720,WN,155,N238WN,148,160,131,4,16,LAS,AUS,1090,3,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2118,2015,144,50,WN,2021,N499WN,146,155,127,54,63,LAS,AUS,1090,5,14,0,,0,23,0,0,0,31
+2008,1,3,4,905,850,1334,1330,WN,3222,N309SW,149,160,135,4,15,LAS,AUS,1090,4,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1739,1640,114,25,WN,1018,N245WN,275,285,253,49,59,LAS,BDL,2298,9,13,0,,0,19,0,0,0,30
+2008,1,3,4,906,905,1426,1430,WN,3948,N467WN,200,205,183,-4,1,LAS,BHM,1618,2,15,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,816,815,1339,1340,WN,249,N256WN,203,205,185,-1,1,LAS,BNA,1588,4,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1325,1240,1841,1810,WN,419,N275WN,196,210,178,31,45,LAS,BNA,1588,6,12,0,,0,2,0,0,0,29
+2008,1,3,4,1506,1440,2030,2010,WN,2032,N271WN,204,210,183,20,26,LAS,BNA,1588,6,15,0,,0,18,0,0,0,2
+2008,1,3,4,2039,1930,155,55,WN,3940,N434WN,196,205,177,60,69,LAS,BNA,1588,5,14,0,,0,0,0,22,0,38
+2008,1,3,4,924,920,1209,1210,WN,71,N312SW,105,110,84,-1,4,LAS,BOI,520,3,18,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1611,1535,1849,1825,WN,538,N619SW,98,110,73,24,36,LAS,BOI,520,3,22,0,,0,9,0,0,0,15
+2008,1,3,4,1824,1715,117,25,WN,2383,N290WN,233,250,221,52,69,LAS,BUF,1987,2,10,0,,0,48,0,0,0,4
+2008,1,3,4,826,825,930,925,WN,136,N493WN,64,60,46,5,1,LAS,BUR,223,2,16,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2118,2015,2224,2115,WN,219,N383SW,66,60,46,69,63,LAS,BUR,223,3,17,0,,0,17,0,6,0,46
+2008,1,3,4,1818,1740,1916,1840,WN,391,N608SW,58,60,46,36,38,LAS,BUR,223,2,10,0,,0,20,0,0,0,16
+2008,1,3,4,650,650,748,750,WN,670,N777QC,58,60,47,-2,0,LAS,BUR,223,3,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2146,2055,2250,2155,WN,815,N626SW,64,60,48,55,51,LAS,BUR,223,3,13,0,,0,4,0,4,0,47
+2008,1,3,4,2241,1910,2340,2010,WN,1072,N369SW,59,60,43,210,211,LAS,BUR,223,3,13,0,,0,114,0,0,0,96
+2008,1,3,4,1409,1355,1513,1500,WN,1328,N396SW,64,65,50,13,14,LAS,BUR,223,2,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1100,1050,1157,1155,WN,1586,N293,57,65,46,2,10,LAS,BUR,223,2,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1306,1250,1406,1355,WN,1838,N509SW,60,65,47,11,16,LAS,BUR,223,2,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1726,1630,1832,1740,WN,2284,N409WN,66,70,46,52,56,LAS,BUR,223,2,18,0,,0,1,0,0,0,51
+2008,1,3,4,915,915,1017,1020,WN,2699,N278WN,62,65,46,-3,0,LAS,BUR,223,2,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1229,1220,1342,1325,WN,2874,N695SW,73,65,47,17,9,LAS,BUR,223,2,24,0,,0,9,0,8,0,0
+2008,1,3,4,1459,1455,1556,1555,WN,3237,N284WN,57,60,42,1,4,LAS,BUR,223,2,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,908,845,1628,1610,WN,1774,N220WN,260,265,244,18,23,LAS,BWI,2106,6,10,0,,0,18,0,0,0,0
+2008,1,3,4,1817,1730,122,50,WN,2632,N784SW,245,260,230,32,47,LAS,BWI,2106,5,10,0,,0,7,0,0,0,25
+2008,1,3,4,1248,1245,2009,2015,WN,3759,N463WN,261,270,243,-6,3,LAS,BWI,2106,4,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,956,945,1658,1645,WN,818,N244WN,242,240,213,13,11,LAS,CLE,1825,8,21,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1849,1740,121,30,WN,564,N455WN,212,230,195,51,69,LAS,CMH,1772,2,15,0,,0,10,0,0,0,41
+2008,1,3,4,1210,1200,1905,1850,WN,991,N297WN,235,230,201,15,10,LAS,CMH,1772,6,28,0,,0,10,0,5,0,0
+2008,1,3,4,701,700,941,955,WN,85,N688SW,100,115,84,-14,1,LAS,DEN,629,9,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1047,1030,1328,1320,WN,157,N481WN,101,110,79,8,17,LAS,DEN,629,9,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2232,2115,108,5,WN,632,N203WN,96,110,77,63,77,LAS,DEN,629,6,13,0,,0,0,0,7,0,56
+2008,1,3,4,1512,1315,1802,1610,WN,706,N491WN,110,115,88,112,117,LAS,DEN,629,8,14,0,,0,0,0,0,0,112
+2008,1,3,4,2025,1955,2301,2245,WN,908,N480WN,96,110,78,16,30,LAS,DEN,629,6,12,0,,0,7,0,0,0,9
+2008,1,3,4,1439,1425,1720,1720,WN,1582,N318SW,101,115,85,0,14,LAS,DEN,629,7,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1627,1600,1916,1900,WN,2907,N647SW,109,120,80,16,27,LAS,DEN,629,8,21,0,,0,2,0,0,0,14
+2008,1,3,4,1745,1710,2017,1945,WN,2192,N313SW,92,95,78,32,35,LAS,ELP,584,2,12,0,,0,10,0,0,0,22
+2008,1,3,4,1049,1040,1320,1320,WN,2280,N352SW,91,100,73,0,9,LAS,ELP,584,3,15,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1117,1050,1325,1325,WN,1636,N321SW,128,155,116,0,27,LAS,GEG,806,3,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1725,1620,1940,1850,WN,2803,N660SW,135,150,116,50,65,LAS,GEG,806,4,15,0,,0,50,0,0,0,0
+2008,1,3,4,1824,1735,2303,2230,WN,719,N414WN,159,175,143,33,49,LAS,HOU,1235,3,13,0,,0,12,0,0,0,21
+2008,1,3,4,1150,1140,1644,1640,WN,1776,N408WN,174,180,147,4,10,LAS,HOU,1235,3,24,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,749,740,1227,1235,WN,3244,N475WN,158,175,146,-8,9,LAS,HOU,1235,3,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2051,2010,134,55,WN,3746,N321SW,163,165,150,39,41,LAS,HOU,1235,4,9,0,,0,17,0,0,0,22
+2008,1,3,4,1555,1525,2307,2245,WN,1027,N230WN,252,260,232,22,30,LAS,IAD,2066,7,13,0,,0,6,0,0,0,16
+2008,1,3,4,2255,1820,509,55,WN,1924,N761RR,194,215,176,254,275,LAS,IND,1591,9,9,0,,0,0,0,0,8,246
+2008,1,3,4,1129,1050,1757,1725,WN,3920,N464WN,208,215,179,32,39,LAS,IND,1591,8,21,0,,0,32,0,0,0,0
+2008,1,3,4,1602,1510,2357,2255,WN,3144,N272WN,295,285,269,62,52,LAS,ISP,2283,4,22,0,,0,13,0,10,0,39
+2008,1,3,4,1738,1715,1838,1820,WN,82,N499WN,60,65,42,18,23,LAS,LAX,236,6,12,0,,0,0,0,0,12,6
+2008,1,3,4,2207,2150,2306,2255,WN,135,N244WN,59,65,40,11,17,LAS,LAX,236,7,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1851,1825,2004,1930,WN,317,N335SW,73,65,41,34,26,LAS,LAX,236,16,16,0,,0,18,0,8,0,8
+2008,1,3,4,701,645,802,745,WN,337,N488WN,61,60,42,17,16,LAS,LAX,236,7,12,0,,0,16,0,1,0,0
+2008,1,3,4,1556,1455,1704,1600,WN,354,N496WN,68,65,41,64,61,LAS,LAX,236,9,18,0,,0,61,0,3,0,0
+2008,1,3,4,1254,1250,1404,1355,WN,784,N401WN,70,65,43,9,4,LAS,LAX,236,7,20,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,840,840,946,945,WN,790,N351SW,66,65,43,1,0,LAS,LAX,236,11,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1540,1525,1648,1630,WN,1061,N422WN,68,65,47,18,15,LAS,LAX,236,5,16,0,,0,10,0,3,0,5
+2008,1,3,4,750,750,901,855,WN,1653,N288WN,71,65,52,6,0,LAS,LAX,236,10,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1228,1200,1340,1310,WN,1928,N792SW,72,70,43,30,28,LAS,LAX,236,7,22,0,,0,0,0,2,0,28
+2008,1,3,4,2047,2005,2209,2115,WN,2367,N352SW,82,70,45,54,42,LAS,LAX,236,7,30,0,,0,15,0,12,0,27
+2008,1,3,4,2158,2120,2303,2225,WN,2896,N335SW,65,65,44,38,38,LAS,LAX,236,11,10,0,,0,13,0,0,0,25
+2008,1,3,4,1028,1025,1132,1135,WN,3655,N465WN,64,70,45,-3,3,LAS,LAX,236,8,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1423,1420,1530,1525,WN,3917,N609SW,67,65,44,5,3,LAS,LAX,236,11,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1147,1100,1541,1500,WN,2382,N404WN,114,120,90,41,47,LAS,LBB,775,4,20,0,,0,25,0,0,0,16
+2008,1,3,4,859,850,1343,1345,WN,565,N787SA,164,175,148,-2,9,LAS,LIT,1295,4,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1140,1110,1537,1510,WN,43,N617SW,117,120,95,27,30,LAS,MAF,796,2,20,0,,0,27,0,0,0,0
+2008,1,3,4,1226,1210,1721,1700,WN,213,N302SW,175,170,139,21,16,LAS,MCI,1140,4,32,0,,0,1,0,5,0,15
+2008,1,3,4,2118,2005,154,50,WN,411,N763SW,156,165,131,64,73,LAS,MCI,1140,7,18,0,,0,14,0,0,0,50
+2008,1,3,4,2003,1905,26,2350,WN,1004,N676SW,143,165,130,36,58,LAS,MCI,1140,5,8,0,,0,4,0,0,0,32
+2008,1,3,4,1622,1510,2059,2000,WN,1405,N775SW,157,170,130,59,72,LAS,MCI,1140,4,23,0,,0,59,0,0,0,0
+2008,1,3,4,648,650,1123,1135,WN,2972,N634SW,155,165,137,-12,-2,LAS,MCI,1140,4,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1003,940,1441,1430,WN,3068,N449WN,158,170,136,11,23,LAS,MCI,1140,5,17,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1319,1300,2033,2025,WN,380,N444WN,254,265,234,8,19,LAS,MCO,2039,6,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1506,1500,2122,2030,WN,74,N764SW,256,210,177,52,6,LAS,MDW,1521,62,17,0,,0,6,0,46,0,0
+2008,1,3,4,648,645,1205,1210,WN,227,N723SW,197,205,182,-5,3,LAS,MDW,1521,5,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1052,1050,1603,1620,WN,335,N712SW,191,210,175,-17,2,LAS,MDW,1521,6,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1424,1415,1941,1945,WN,396,N407WN,197,210,177,-4,9,LAS,MDW,1521,8,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1211,1145,1750,1720,WN,693,N232WN,219,215,175,30,26,LAS,MDW,1521,23,21,0,,0,0,0,4,0,26
+2008,1,3,4,814,755,1334,1320,WN,823,N725SW,200,205,181,14,19,LAS,MDW,1521,6,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,934,935,1453,1505,WN,830,N436WN,199,210,177,-12,-1,LAS,MDW,1521,6,16,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1708,1700,2214,2230,WN,1865,N745SW,186,210,169,-16,8,LAS,MDW,1521,6,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1919,1845,31,5,WN,2025,N740SW,192,200,171,26,34,LAS,MDW,1521,8,13,0,,0,4,0,0,0,22
+2008,1,3,4,1945,1815,52,2335,WN,2450,N405WN,187,200,168,77,90,LAS,MDW,1521,8,11,0,,0,13,0,0,0,64
+2008,1,3,4,2057,1930,202,50,WN,2794,N468WN,185,200,171,72,87,LAS,MDW,1521,7,7,0,,0,60,0,0,0,12
+2008,1,3,4,1354,1310,1907,1845,WN,3232,N201LV,193,215,177,22,44,LAS,MDW,1521,6,10,0,,0,4,0,0,0,18
+2008,1,3,4,1703,1645,33,35,WN,767,N222WN,270,290,257,-2,18,LAS,MHT,2356,4,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1030,1030,1545,1545,WN,762,N295WN,195,195,174,0,0,LAS,MSY,1501,3,18,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1903,1825,5,2340,WN,3327,N282WN,182,195,166,25,38,LAS,MSY,1501,4,12,0,,0,3,0,0,0,22
+2008,1,3,4,1108,1110,1244,1240,WN,125,N782SA,96,90,80,4,-2,LAS,OAK,407,4,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,NA,700,NA,830,WN,126,,NA,90,NA,NA,NA,LAS,OAK,407,NA,NA,1,A,0,NA,NA,NA,NA,NA
+2008,1,3,4,1354,1345,1525,1520,WN,127,N784SW,91,95,74,5,9,LAS,OAK,407,4,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1944,1740,2110,1915,WN,223,N223WN,86,95,72,115,124,LAS,OAK,407,5,9,0,,0,21,0,0,0,94
+2008,1,3,4,1251,1205,1425,1345,WN,237,N281WN,94,100,76,40,46,LAS,OAK,407,3,15,0,,0,2,0,0,0,38
+2008,1,3,4,1548,1505,1728,1635,WN,280,N460WN,100,90,78,53,43,LAS,OAK,407,4,18,0,,0,14,0,10,0,29
+2008,1,3,4,821,820,955,950,WN,864,N640SW,94,90,72,5,1,LAS,OAK,407,3,19,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2216,2125,2348,2255,WN,900,N679AA,92,90,78,53,51,LAS,OAK,407,4,10,0,,0,11,0,2,0,40
+2008,1,3,4,2010,1900,2203,2030,WN,962,N378SW,113,90,77,93,70,LAS,OAK,407,28,8,0,,0,45,0,23,0,25
+2008,1,3,4,1433,1420,1608,1555,WN,1011,N248WN,95,95,79,13,13,LAS,OAK,407,4,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,935,925,1106,1055,WN,1258,N774SW,91,90,71,11,10,LAS,OAK,407,3,17,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2156,2155,2334,2325,WN,1950,N408WN,98,90,83,9,1,LAS,OAK,407,5,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2035,2020,2209,2150,WN,3135,N695SW,94,90,81,19,15,LAS,OAK,407,3,10,0,,0,15,0,4,0,0
+2008,1,3,4,602,600,733,730,WN,3233,N354SW,91,90,76,3,2,LAS,OAK,407,4,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1152,1145,1616,1605,WN,86,N627SW,144,140,116,11,7,LAS,OKC,987,4,24,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1435,1315,1859,1755,WN,479,N622SW,144,160,132,64,80,LAS,OMA,1099,3,9,0,,0,38,0,0,0,26
+2008,1,3,4,2049,1920,106,2355,WN,875,N217JC,137,155,125,71,89,LAS,OMA,1099,2,10,0,,0,32,0,0,0,39
+2008,1,3,4,913,910,1342,1350,WN,2111,N673AA,149,160,131,-8,3,LAS,OMA,1099,4,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1613,1610,1721,1705,WN,187,N690SW,68,55,38,16,3,LAS,ONT,197,4,26,0,,0,0,0,16,0,0
+2008,1,3,4,2015,1945,2101,2040,WN,725,N259WN,46,55,34,21,30,LAS,ONT,197,4,8,0,,0,3,0,0,0,18
+2008,1,3,4,655,655,742,750,WN,731,N244WN,47,55,35,-8,0,LAS,ONT,197,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1438,1435,1533,1530,WN,872,N788SA,55,55,33,3,3,LAS,ONT,197,6,16,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1102,1030,1152,1125,WN,906,N301SW,50,55,36,27,32,LAS,ONT,197,3,11,0,,0,27,0,0,0,0
+2008,1,3,4,2218,2120,2308,2210,WN,1493,N356SW,50,50,36,58,58,LAS,ONT,197,5,9,0,,0,0,0,20,0,38
+2008,1,3,4,851,835,938,930,WN,1670,N742SW,47,55,34,8,16,LAS,ONT,197,5,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1230,1215,1331,1310,WN,2018,N313SW,61,55,36,21,15,LAS,ONT,197,4,21,0,,0,0,0,6,0,15
+2008,1,3,4,1838,1800,1930,1855,WN,2730,N273WN,52,55,32,35,38,LAS,ONT,197,3,17,0,,0,0,0,0,0,35
+2008,1,3,4,1358,1330,1449,1425,WN,3070,N660SW,51,55,37,24,28,LAS,ONT,197,4,10,0,,0,4,0,0,0,20
+2008,1,3,4,1757,1655,108,25,WN,156,N466WN,251,270,237,43,62,LAS,ORF,2155,4,10,0,,0,13,0,0,0,30
+2008,1,3,4,1816,1755,2022,2015,WN,700,N678AA,126,140,112,7,21,LAS,PDX,762,4,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,755,755,958,1020,WN,1669,N484WN,123,145,108,-22,0,LAS,PDX,762,6,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1328,1255,1549,1520,WN,2354,N258WN,141,145,106,29,33,LAS,PDX,762,22,13,0,,0,24,0,0,0,5
+2008,1,3,4,2134,2040,2343,2300,WN,2550,N608SW,129,140,112,43,54,LAS,PDX,762,3,14,0,,0,12,0,0,0,31
+2008,1,3,4,1127,1045,1856,1835,WN,1285,N435WN,269,290,245,21,42,LAS,PHL,2176,5,19,0,,0,18,0,0,0,3
+2008,1,3,4,1720,1700,41,45,WN,1843,N484WN,261,285,244,-4,20,LAS,PHL,2176,4,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,559,600,800,805,WN,102,N639SW,61,65,43,-5,-1,LAS,PHX,256,4,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,712,705,932,915,WN,243,N228WN,80,70,49,17,7,LAS,PHX,256,7,24,0,,0,0,0,10,0,7
+2008,1,3,4,1327,1320,1533,1530,WN,287,N747SA,66,70,50,3,7,LAS,PHX,256,5,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1817,1810,2016,2015,WN,504,N338SW,59,65,47,1,7,LAS,PHX,256,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1807,1740,2011,1950,WN,559,N603SW,64,70,49,21,27,LAS,PHX,256,5,10,0,,0,2,0,0,0,19
+2008,1,3,4,1712,1545,1914,1750,WN,668,N221WN,62,65,48,84,87,LAS,PHX,256,4,10,0,,0,6,0,0,0,78
+2008,1,3,4,1206,1205,1418,1415,WN,752,N680AA,72,70,47,3,1,LAS,PHX,256,4,21,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1134,1110,1343,1315,WN,781,N378SW,69,65,46,28,24,LAS,PHX,256,8,15,0,,0,9,0,4,0,15
+2008,1,3,4,1003,1005,1208,1215,WN,871,N327SW,65,70,49,-7,-2,LAS,PHX,256,5,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,815,820,1034,1025,WN,1034,N420WN,79,65,49,9,-5,LAS,PHX,256,4,26,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2156,2110,2353,2310,WN,1403,N258WN,57,60,43,43,46,LAS,PHX,256,5,9,0,,0,2,0,0,0,41
+2008,1,3,4,2140,2040,2334,2240,WN,1682,N281WN,54,60,40,54,60,LAS,PHX,256,3,11,0,,0,2,0,0,0,52
+2008,1,3,4,631,635,832,840,WN,2103,N291WN,61,65,44,-8,-4,LAS,PHX,256,5,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1521,1450,1725,1655,WN,2726,N777QC,64,65,46,30,31,LAS,PHX,256,5,13,0,,0,16,0,0,0,14
+2008,1,3,4,2100,1945,2255,2150,WN,2991,N752SW,55,65,43,65,75,LAS,PHX,256,4,8,0,,0,48,0,0,0,17
+2008,1,3,4,1657,1630,1905,1845,WN,3517,N487WN,68,75,49,20,27,LAS,PHX,256,5,14,0,,0,2,0,0,0,18
+2008,1,3,4,926,915,1129,1120,WN,3598,N790SW,63,65,44,9,11,LAS,PHX,256,5,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1413,1415,1612,1620,WN,3849,N269WN,59,65,44,-8,-2,LAS,PHX,256,4,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1825,1745,114,50,WN,512,N794SW,229,245,210,24,40,LAS,PIT,1910,6,13,0,,0,10,0,0,0,14
+2008,1,3,4,927,915,1631,1620,WN,594,N715SW,244,245,218,11,12,LAS,PIT,1910,7,19,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1630,1600,23,2350,WN,416,N401WN,293,290,266,33,30,LAS,PVD,2363,4,23,0,,0,6,0,3,0,24
+2008,1,3,4,1136,1120,1852,1840,WN,170,N448WN,256,260,231,12,16,LAS,RDU,2027,6,19,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1824,1725,121,45,WN,1509,N403WN,237,260,222,36,59,LAS,RDU,2027,2,13,0,,0,27,0,0,0,9
+2008,1,3,4,1716,1630,1830,1755,WN,239,N267WN,74,85,60,35,46,LAS,RNO,345,4,10,0,,0,11,0,0,0,24
+2008,1,3,4,1928,1855,2034,2015,WN,298,N450WN,66,80,54,19,33,LAS,RNO,345,4,8,0,,0,9,0,0,0,10
+2008,1,3,4,709,710,816,830,WN,376,N342SW,67,80,55,-14,-1,LAS,RNO,345,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1741,1730,1905,1850,WN,675,N286WN,84,80,57,15,11,LAS,RNO,345,14,13,0,,0,0,0,4,0,11
+2008,1,3,4,1609,1540,1732,1700,WN,709,N428WN,83,80,55,32,29,LAS,RNO,345,7,21,0,,0,26,0,3,0,3
+2008,1,3,4,913,840,1029,955,WN,891,N400WN,76,75,61,34,33,LAS,RNO,345,3,12,0,,0,8,0,1,0,25
+2008,1,3,4,2215,2200,2334,2315,WN,1341,N442WN,79,75,57,19,15,LAS,RNO,345,4,18,0,,0,6,0,4,0,9
+2008,1,3,4,2137,2040,2251,2155,WN,2273,N286WN,74,75,56,56,57,LAS,RNO,345,4,14,0,,0,0,0,0,0,56
+2008,1,3,4,624,625,729,740,WN,2538,N230WN,65,75,52,-11,-1,LAS,RNO,345,4,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1100,1020,1210,1145,WN,2719,N466WN,70,85,54,25,40,LAS,RNO,345,4,12,0,,0,0,0,1,0,24
+2008,1,3,4,1313,1255,1437,1415,WN,3430,N487WN,84,80,57,22,18,LAS,RNO,345,4,23,0,,0,8,0,4,0,10
+2008,1,3,4,1415,1350,1527,1510,WN,3835,N610WN,72,80,56,17,25,LAS,RNO,345,4,12,0,,0,9,0,0,0,8
+2008,1,3,4,1108,1110,1221,1230,WN,3947,N277WN,73,80,56,-9,-2,LAS,RNO,345,3,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1348,1330,1455,1435,WN,341,N315SW,67,65,51,20,18,LAS,SAN,258,2,14,0,,0,8,0,2,0,10
+2008,1,3,4,1221,1220,1329,1325,WN,535,N428WN,68,65,48,4,1,LAS,SAN,258,4,16,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2140,2050,2240,2150,WN,708,N692SW,60,60,45,50,50,LAS,SAN,258,3,12,0,,0,21,0,0,0,29
+2008,1,3,4,805,805,909,905,WN,711,N207WN,64,60,45,4,0,LAS,SAN,258,3,16,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1643,1625,1751,1735,WN,715,N902WN,68,70,46,16,18,LAS,SAN,258,4,18,0,,0,4,0,0,0,12
+2008,1,3,4,946,940,1045,1045,WN,1037,N734SA,59,65,46,0,6,LAS,SAN,258,2,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1915,1905,2016,2005,WN,1254,N490WN,61,60,44,11,10,LAS,SAN,258,4,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1839,1740,1945,1840,WN,2186,N692SW,66,60,44,65,59,LAS,SAN,258,4,18,0,,0,5,0,6,0,54
+2008,1,3,4,2207,2125,2307,2225,WN,2211,N646SW,60,60,49,42,42,LAS,SAN,258,3,8,0,,0,17,0,0,0,25
+2008,1,3,4,1503,1425,1604,1530,WN,2357,N414WN,61,65,48,34,38,LAS,SAN,258,3,10,0,,0,9,0,0,0,25
+2008,1,3,4,1613,1515,1727,1615,WN,2363,N405WN,74,60,47,72,58,LAS,SAN,258,3,24,0,,0,0,0,19,0,53
+2008,1,3,4,2248,2155,2346,2255,WN,2449,N454WN,58,60,45,51,53,LAS,SAN,258,3,10,0,,0,12,0,0,0,39
+2008,1,3,4,612,615,713,715,WN,2712,N253WN,61,60,46,-2,-3,LAS,SAN,258,3,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1118,1110,1225,1215,WN,3423,N298WN,67,65,50,10,8,LAS,SAN,258,3,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1456,1445,1920,1920,WN,120,N277WN,144,155,126,0,11,LAS,SAT,1069,4,14,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2022,1935,36,5,WN,802,N902WN,134,150,122,31,47,LAS,SAT,1069,2,10,0,,0,23,0,0,0,8
+2008,1,3,4,1049,1040,1509,1510,WN,2113,N350SW,140,150,127,-1,9,LAS,SAT,1069,4,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,753,750,1215,1220,WN,2906,N794SW,142,150,125,-5,3,LAS,SAT,1069,2,15,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1831,1810,110,45,WN,495,N768SW,219,215,192,25,21,LAS,SDF,1624,9,18,0,,0,18,0,4,0,3
+2008,1,3,4,1704,1645,1929,1930,WN,340,N634SW,145,165,129,-1,19,LAS,SEA,866,7,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2017,2005,2241,2245,WN,563,N285WN,144,160,129,-4,12,LAS,SEA,866,5,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,639,640,859,925,WN,1769,N731SA,140,165,124,-26,-1,LAS,SEA,866,5,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1117,1050,1330,1330,WN,2465,N431WN,133,160,119,0,27,LAS,SEA,866,7,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1426,1355,1605,1530,WN,488,N615SW,99,95,75,35,31,LAS,SFO,414,3,21,0,,0,0,0,22,0,13
+2008,1,3,4,1009,910,1148,1045,WN,619,N491WN,99,95,76,63,59,LAS,SFO,414,3,20,0,,0,0,0,54,0,9
+2008,1,3,4,2021,1700,2303,1835,WN,2005,N302SW,162,95,73,268,201,LAS,SFO,414,4,85,0,,0,192,0,67,0,9
+2008,1,3,4,2025,1905,2208,2040,WN,2788,N602SW,103,95,88,88,80,LAS,SFO,414,4,11,0,,0,0,0,63,0,25
+2008,1,3,4,603,605,729,740,WN,2886,N246LV,86,95,70,-11,-2,LAS,SFO,414,4,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2301,2105,59,2240,WN,3061,N447WN,118,95,98,139,116,LAS,SFO,414,4,16,0,,0,0,0,131,0,8
+2008,1,3,4,1518,1215,1646,1350,WN,3575,N492WN,88,95,72,176,183,LAS,SFO,414,4,12,0,,0,0,0,176,0,0
+2008,1,3,4,1307,1235,1432,1400,WN,12,N361SW,85,85,71,32,32,LAS,SJC,386,4,10,0,,0,4,0,0,0,28
+2008,1,3,4,615,610,746,730,WN,276,N472WN,91,80,74,16,5,LAS,SJC,386,4,13,0,,0,0,0,16,0,0
+2008,1,3,4,2017,1945,2144,2105,WN,400,N429WN,87,80,70,39,32,LAS,SJC,386,4,13,0,,0,2,0,7,0,30
+2008,1,3,4,1855,1855,2028,2020,WN,484,N673AA,93,85,67,8,0,LAS,SJC,386,14,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2114,2040,2241,2200,WN,774,N437WN,87,80,69,41,34,LAS,SJC,386,8,10,0,,0,34,0,7,0,0
+2008,1,3,4,1605,1520,1739,1645,WN,783,N306SW,94,85,68,54,45,LAS,SJC,386,5,21,0,,0,10,0,9,0,35
+2008,1,3,4,956,1000,1134,1125,WN,1945,N271WN,98,85,70,9,-4,LAS,SJC,386,15,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,731,730,848,855,WN,2387,N776WN,77,85,66,-7,1,LAS,SJC,386,4,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1734,1655,1857,1820,WN,3844,N446WN,83,85,66,37,39,LAS,SJC,386,5,12,0,,0,9,0,0,0,28
+2008,1,3,4,1139,1100,1408,1320,WN,161,N455WN,89,80,54,48,39,LAS,SLC,368,6,29,0,,0,39,0,9,0,0
+2008,1,3,4,2104,1905,2314,2120,WN,415,N395SW,70,75,58,114,119,LAS,SLC,368,2,10,0,,0,25,0,0,0,89
+2008,1,3,4,919,830,1132,1050,WN,643,N756SA,73,80,55,42,49,LAS,SLC,368,4,14,0,,0,3,0,0,0,39
+2008,1,3,4,2113,2050,2328,2305,WN,1166,N351SW,75,75,58,23,23,LAS,SLC,368,3,14,0,,0,4,0,0,0,19
+2008,1,3,4,1712,1555,1935,1820,WN,1585,N766SW,83,85,62,75,77,LAS,SLC,368,4,17,0,,0,0,0,6,0,69
+2008,1,3,4,1419,1355,1627,1615,WN,1660,N613SW,68,80,55,12,24,LAS,SLC,368,2,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2242,2140,54,2355,WN,2533,N385SW,72,75,55,59,62,LAS,SLC,368,4,13,0,,0,0,0,10,0,49
+2008,1,3,4,611,615,819,835,WN,3147,N296WN,68,80,55,-16,-4,LAS,SLC,368,3,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1949,1910,2110,2035,WN,94,N715SW,81,85,66,35,39,LAS,SMF,397,5,10,0,,0,25,0,0,0,10
+2008,1,3,4,1409,1350,1539,1520,WN,584,N324SW,90,90,74,19,19,LAS,SMF,397,6,10,0,,0,19,0,0,0,0
+2008,1,3,4,637,640,759,805,WN,624,N336SW,82,85,65,-6,-3,LAS,SMF,397,5,12,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1821,1730,1946,1855,WN,641,N626SW,85,85,67,51,51,LAS,SMF,397,6,12,0,,0,9,0,0,0,42
+2008,1,3,4,916,905,1046,1035,WN,2121,N632SW,90,90,68,11,11,LAS,SMF,397,4,18,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1134,1115,1302,1250,WN,2622,N445WN,88,95,66,12,19,LAS,SMF,397,5,17,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1527,1515,1648,1645,WN,3053,N418WN,81,90,68,3,12,LAS,SMF,397,4,9,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2115,2100,2236,2225,WN,3862,N640SW,81,85,64,11,15,LAS,SMF,397,4,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1934,1935,2054,2035,WN,201,N251WN,80,60,43,19,-1,LAS,SNA,226,29,8,0,,0,0,0,19,0,0
+2008,1,3,4,1305,1305,1358,1410,WN,300,N753SW,53,65,42,-12,0,LAS,SNA,226,4,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1917,1820,2014,1925,WN,433,N264LV,57,65,40,49,57,LAS,SNA,226,7,10,0,,0,16,0,0,0,33
+2008,1,3,4,1125,1010,1224,1110,WN,1016,N765SW,59,60,45,74,75,LAS,SNA,226,5,9,0,,0,9,0,0,0,65
+2008,1,3,4,1505,1500,1605,1605,WN,1479,N498WN,60,65,43,0,5,LAS,SNA,226,4,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,747,750,843,850,WN,3250,N245WN,56,60,42,-7,-3,LAS,SNA,226,4,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1143,1050,1641,1600,WN,773,N472WN,178,190,151,41,53,LAS,STL,1372,3,24,0,,0,2,0,0,0,39
+2008,1,3,4,1558,1530,2049,2040,WN,1050,N212WN,171,190,150,9,28,LAS,STL,1372,4,17,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1940,1855,29,2355,WN,1583,N600WN,169,180,157,34,45,LAS,STL,1372,3,9,0,,0,22,0,0,0,12
+2008,1,3,4,1820,1725,112,35,WN,3403,N737JW,232,250,217,37,55,LAS,TPA,1984,5,10,0,,0,29,0,0,0,8
+2008,1,3,4,1143,1035,1621,1505,WN,574,N314SW,158,150,126,76,68,LAS,TUL,1076,3,29,0,,0,0,0,14,0,62
+2008,1,3,4,2211,2115,17,2330,WN,486,N264LV,66,75,53,47,56,LAS,TUS,365,4,9,0,,0,5,0,0,0,42
+2008,1,3,4,1821,1725,2032,1940,WN,637,N342SW,71,75,56,52,56,LAS,TUS,365,4,11,0,,0,24,0,0,0,28
+2008,1,3,4,715,720,926,935,WN,1059,N637SW,71,75,55,-9,-5,LAS,TUS,365,3,13,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1341,1320,1556,1535,WN,1918,N267WN,75,75,55,21,21,LAS,TUS,365,3,17,0,,0,8,0,0,0,13
+2008,1,3,4,2108,2030,2313,2240,WN,3142,N446WN,65,70,53,33,38,LAS,TUS,365,4,8,0,,0,8,0,0,0,25
+2008,1,3,4,1118,1050,1333,1305,WN,3572,N489WN,75,75,54,28,28,LAS,TUS,365,3,18,0,,0,0,0,0,0,28
+2008,1,3,4,1735,1710,2010,1955,WN,230,N364SW,95,105,81,15,25,LAX,ABQ,677,4,10,0,,0,0,0,4,0,11
+2008,1,3,4,2105,2025,2342,2310,WN,361,N690SW,97,105,82,32,40,LAX,ABQ,677,6,9,0,,0,0,0,11,0,21
+2008,1,3,4,1328,1315,1611,1600,WN,397,N424WN,103,105,80,11,13,LAX,ABQ,677,18,5,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,944,940,1218,1225,WN,3245,N780SW,94,105,82,-7,4,LAX,ABQ,677,4,8,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1024,1015,1504,1505,WN,304,N326SW,160,170,148,-1,9,LAX,AUS,1242,5,7,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,820,815,1357,1400,WN,133,N749SW,217,225,200,-3,5,LAX,BNA,1797,6,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1632,1540,2207,2125,WN,1107,N708SA,215,225,203,42,52,LAX,BNA,1797,4,8,0,,0,0,0,18,0,24
+2008,1,3,4,1327,1255,1902,1840,WN,1324,N249WN,215,225,200,22,32,LAX,BNA,1797,4,11,0,,0,10,0,0,0,12
+2008,1,3,4,1330,1320,1609,1615,WN,330,N236WN,99,115,86,-6,10,LAX,ELP,714,3,10,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1925,1735,2201,2025,WN,1671,N684WN,96,110,85,96,110,LAX,ELP,714,3,8,0,,0,5,0,0,0,91
+2008,1,3,4,1005,945,1250,1235,WN,2635,N335SW,105,110,88,15,20,LAX,ELP,714,3,14,0,,0,7,0,0,0,8
+2008,1,3,4,1149,1145,1639,1650,WN,205,N299WN,170,185,156,-11,4,LAX,HOU,1390,3,11,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,1742,1625,2231,2125,WN,528,N496WN,169,180,157,66,77,LAX,HOU,1390,4,8,0,,0,11,0,0,0,55
+2008,1,3,4,807,805,1301,1310,WN,1513,N305SW,174,185,163,-9,2,LAX,HOU,1390,5,6,0,,0,NA,NA,NA,NA,NA
+2008,1,3,4,2020,1935,109,35,WN,3394,N667SW,169,180,159,34,45,LAX,HOU,1390,3,7,0,,0,6,0,0,0,28
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+2008,1,3,4,1925,1825,2024,1930,WN,2359,N385SW,59,65,49,54,60,LAX,SJC,308,3,7,0,,0,0,0,6,0,48
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+2008,1,3,4,1357,1330,1626,1615,WN,554,N626SW,89,105,79,11,27,LAX,SLC,590,3,7,0,,0,NA,NA,NA,NA,NA
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+2008,1,3,4,821,810,936,930,WN,337,N488WN,75,80,59,6,11,LAX,SMF,373,5,11,0,,0,NA,NA,NA,NA,NA
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+2008,1,3,4,949,900,1103,1020,WN,1486,N613SW,74,80,61,43,49,LAX,SMF,373,5,8,0,,0,40,0,0,0,3
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+2008,1,3,4,915,915,910,915,WN,1028,N214WN,55,60,45,-5,0,LBB,ELP,295,3,7,0,,0,NA,NA,NA,NA,NA
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diff --git a/inst/tests/ch11b.dat b/inst/tests/ch11b.dat
new file mode 100644
index 0000000..1570e0b
--- /dev/null
+++ b/inst/tests/ch11b.dat
@@ -0,0 +1,100 @@
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diff --git a/inst/tests/doublequote_newline.csv b/inst/tests/doublequote_newline.csv
new file mode 100644
index 0000000..cdd0ff5
--- /dev/null
+++ b/inst/tests/doublequote_newline.csv
@@ -0,0 +1,40 @@
+A,B
+1,a
+1,a
+1,a
+1,a
+1,a
+1,a
+1,a
+1,"embedded ""field""
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+2,"not this one"
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diff --git a/inst/tests/fread_line_error.csv b/inst/tests/fread_line_error.csv
new file mode 100644
index 0000000..1e290cf
--- /dev/null
+++ b/inst/tests/fread_line_error.csv
@@ -0,0 +1,69 @@
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+0,6-6-0 5:6:1.4 LNI,,,,,,ZG U#5,V/FH [EN],3,8,1,,,,,,,,,,1,MM [JE] O A H,.W0_6_0Y_1_5_7_B-0-S-5_7VSF-W-8-7.L
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+6,6-4-0 3:6:4.0 TMP,8,,0,7,6,GH X#8,C/KM Y F V ,3,1,0,"Q #0 - HG A-Q C Q#4 - HS O A-C V#6 - KX I#8 - RJ W-O#2 - SH I-I#3 - NU P#0 - WB A#2 - UZ F#5 - FT V#8 - HS K T X#4 - VS Q#3 - RC E#6 - SJ C#1 - HV P S#2 - HY V P#3 - JH Z#0 - LE Z X#3 - RF Y Z#5 - XU Y U#4 - GW B L#6 - MT V J L#4 - US D I D#5 - HD R#3 - CI I M#1 - QR D B W#4 - JF L T#6 - HL L K#3 - HU K/D#2 - JH C#8 - SY B W#4 - LB H L#1 - WL M C#1 - UX F N#4 - UK Y#8 - DI Q#0 - CY N A#4 - VA S M M#5 - XM C 2#0 - AH Y 8#3 - EF K 2#0 [...]
+8,3-6-5 3:1:6.5 CXO,,,,,,BG Y#2,X/DO [ZM >],6,8,1,,,,,,,,,,2,,.W5_2_1A_7_0_8_T-8-S-8_8QMR-V-3-7.S
+1,8-0-1 1:1:0.4 YYA,,,,,,RK V#5,Z/PP [PG],1,0,0,,,,,,,,,,2,,.U3_0_6Z_6_1_6_N-4-J-4_7OCG-L-8-2.N
+7,7-8-4 3:8:4.4 UNN,,,,,,ED G#3,TH V S,8,1,1,,,,,,,,,,3,,#S,B,K.X,W.B,Z.T,Z.I.G,J.D.E,V,R,K.D,Y.Y,M,X.R,I.Z.U,R.X.O,C.J.F,T.S.G,D.U,Q.Y,N.Z,W.LEU,J,W,J
+0,7-0-1 8:1:6.0 WNL,3,8,8,0,5,IO Z#4,G/C,3,4,1,C,,GG~YA OO~DY,,WH~3~XU,,,,,0,,.Q1_6_8P_3_3_7_B-3-L-4_1IVB-T-8-7.Q
+1,5-2-3 2:0:6.8 OZK,0,,8,2,0,LO M#5,J/TZ [JB WX],0,7,8,H HD GG: TZ KO,MV~UI EB~KI,WQ~CV RX~IY,MA~3~EY,LH~0~JJ,,,,,3,,.M4_5_3S_2_3_1_H-0-Z-1_4IXO-G-6-1.F
+0,8-6-8 8:6:7.4 QMK,1,6,2,0,7,XU S#3,H/FJ P O U ,1,3,1,H KY XN: FK DQ,WD~DC QY~PC,VI~UA FC~QL,ZJ~1~HF,XM~1~SI,,,,,1,,.W5_4_8L_4_7_7_P-6-D-0_7KGP-K-7-5.Q
+6,0-3-0 4:6:8.8 VWA,8,,7,0,6,EA Y#8,P/CJ [IC],2,0,2,Y DL: TU,MP~CA JO~NW,ZK~NC EH~PE,AN~1~JD,HT~3~RI,,,,,8,LV [EO] M C R,.J0_8_1V_5_6_5_Q-2-M-6_8LIG-V-4-2.G
+6,8-4-3 3:4:2.6 JMA,2,6,1,2,2,FP W#4,H/ZB AR H WJ GS,0,8,2,R PD: AQ,,"DG DC (8-4-0) 7 BEDE ZASLK TA, MCQABRT, BO 8~DQKU NV~FE",,"YY ZT (7-7-3) 3 XJRK SZYWB DB, IFXPFAV, YT 1~4~TQ",,,,,6,,.F6_6_4Y_5_0_6_U-8-I-5_4BXI-Q-8-5.V
+7,6-1-8 2:5:4.3 SIM,7,0,8,7,4,UV M#0,J/RM Z 'SM SX',1,6,5,A RF ZX,,AF 5~MRCV QY KI~VG,,LG 3~1~CK,,,,,4,,.Q5_1_5P_3_1_3_A-6-F-0_7QBH-N-5-8.J
+2,4-8-7 7:4:5.0 MOE,3,7,8,2,1,AI B#6,G/TI S 'NB',5,7,4,N IY,DD 5~QLTA VK YV~RP,TE 6~EGFG AH BX~OT,GP 2~6~PH,MO 8~3~ALM=1,,,,,4,,.E1_7_7F_5_7_3_W-0-R-7_2CXX-Z-7-2.Z
+2,0-2-3 8:2:1.7 WUW,8,1,5,3,0,FU L#3,C/ON [GA T],5,6,7,A HK E,,AI 7~FVYO IQ BE~JK___X/X/K-M-V.M,,IP 1~5~KDZ=0___N/Y/M-J-Y.F,,,,,4,,.V5_1_8L_7_7_8_Z-3-X-4_8SQA-A-1-7.A
+6,3-6-3 4:5:0.6 POL,6,,3,2,7,WU E#8,J/TD C B B ,5,2,6,"X #2 - TH X-H E Q#8 - TW I D-I I#4 - OP K#2 - FC N-F#6 - UL C-C#5 - KI J#7 - SP Y#4 - DI K#7 - NX G S W#0 - YE Q#3 - NY M#2 - SM Q#5 - GC X A#1 - BC P U#8 - EW S#1 - KS U L N#6 - AX U I#1 - XY P P#8 - CI A B#4 - WW G A Q#0 - AY K J K#8 - AA Q#3 - VY E P#0 - GF P E U#1 - XB P A#7 - IU N A#3 - MK M/O#0 - OR A#0 - OX Z V#7 - WE E V#2 - OH X U#4 - BI A Q#4 - CK V E#0 - QP N#2 - KZ P#1 - GQ H X#7 - GB Q F H#2 - DS R 1#2 - BM D 0#5 - OZ Y [...]
+0,8-3-5 8:3:1.7 HOA,,,,,,FJ V#7,Z/KQ [AA >],6,2,1,,,,,,,,,,5,,.R7_1_7O_6_1_7_S-7-H-8_7YYL-Y-2-1.Q
+8,2-1-5 3:7:4.6 ZNW,,,,,,IV J#7,M/XA [OO],0,1,2,,,,,,,,,,5,,.P5_6_3S_5_3_0_P-0-N-3_2URR-O-1-7.C
+8,2-5-0 5:0:3.0 FXN,,,,,,AT F#1,WX I I,6,8,3,,,,,,,,,,8,,#V,I,N.K,L.Y,C.V,D.B.T,K.E.M,L,Q,R.Y,J.L,L,V.Z,E.Y.R,F.F.G,O.W.R,N.U.Z,E.P,B.T,Q.Y,Z.UOD,S,Y,J
+8,3-3-3 1:8:2.3 TOO,3,8,2,1,3,YA I#5,S/I,2,5,4,H,,KT~IW UG~LN,,LE~4~IY,,,,,4,,.B5_3_2M_5_7_4_L-1-D-2_7JLS-R-6-5.I
+8,7-1-7 3:7:8.2 UNV,7,,6,7,0,LF C#6,B/RM [LV DA],1,0,8,K OM HO: FF OM,QV~VO PL~EB,AR~NU FL~JR,LS~4~TQ,QD~5~BX,,,,,7,,.H0_0_7O_3_6_3_X-4-J-0_0JQM-D-6-8.R
+8,8-4-0 5:5:1.0 APJ,3,7,4,2,5,MM H#1,N/II G J V ,3,1,6,I EQ AB: VG SF,KE~KA XO~HU,SA~RA RM~KE,LK~3~GN,QC~4~RD,,,,,5,,.W7_4_7E_5_3_5_W-8-Y-5_5HNH-S-6-4.I
+7,7-8-4 2:6:2.5 QJJ,0,,6,1,4,VD P#5,K/HJ [PE],7,6,2,W NU: TF,SX~ET CT~AF,SN~IM RY~KL,JT~6~SY,PN~4~UA,,,,,1,YC [CH] M H D,.H4_2_1K_8_1_5_C-0-C-4_6DDT-U-7-7.B
+2,3-5-7 2:0:6.4 PJM,6,5,8,7,1,QV V#8,H/MX JC A AW KL,8,1,4,F QN: GH,XJ~SV ZM~WF,VR BU~QE WI~PE,OW~7~DR,ME PO~2~UCE=5,,,,,8,AU L A Z,.M7_1_7Z_1_5_2_H-7-B-7_1HTC-V-2-4.W
+6,8-6-3 7:3:0.2 YJX,5,8,4,6,7,SD Z#4,J/QJ M 'PC ZU',8,7,4,R DJ: EI,BQ~BS IV~UC,YU SV~BE HL~XR,XZ~2~ML,IQ FE~6~TUE=7,,,,,3,YD [OA TT] U V K,.V2_7_7C_8_4_1_S-7-A-7_1RXT-N-1-2.Z
+2,2-2-5 7:6:6.8 KLO,0,8,6,4,2,QP U#0,O/ZU D 'QY',6,8,2,I DG: QJ,NW~DN BR~GY,SD TJ~NA YZ~BZ,MY~4~QF,CV MU~3~PXJ=5,,,,,3,ZA [KM] H T E,.Q8_6_4E_4_7_0_K-8-N-7_7YTY-M-3-8.Y
+6,8-4-3 1:4:7.1 AQO,1,3,4,0,1,PW R#0,V/YX E F,6,7,4,J VI: DH,FC~FA NG~CS,QX CZ~UU IV~JM,LI~4~HQ,EA DF~7~QVI=7,,,,,1,KE M H[F_K] L B B,.A8_2_2P_7_3_3_H-6-S-5_8HFI-N-3-2.L
+6,3-7-6 8:4:2.1 UCA,7,0,3,4,7,EC G#2,B/NE T N,5,4,3,D SR: BO,VT~UI LY~BJ,NL GZ~TL UV~DW,LG~3~WZ,OK AQ~7~BHS=8,,,,,8,"P",.H4_1_7V_0_0_2_K-3-E-8_1HSG-W-6-7.W
diff --git a/inst/tests/issue_1095_fread.txt b/inst/tests/issue_1095_fread.txt
new file mode 100644
index 0000000..7c02cc5
--- /dev/null
+++ b/inst/tests/issue_1095_fread.txt
@@ -0,0 +1,100 @@
+2013130413CN02422 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,03/22/2013,F ,1309,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,06, 2,4 PARTRIDGE WAY (DRIVEWAY) , , , , , ,09,01,02,01,01,01, , , , , , , , , , 0, , , ,N,NONE ,84049
+2013130413CN02826 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,04/02/2013,TU,1658,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,11, 1,375 SR 34 (PARKING LOT) , , , , , ,09,01,02,01,01,01, , , , , , , , , , 0, , , ,N,NONE ,84057
+2013130413CN03163 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,04/11/2013,TH,0853,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,08, 2,410 SR 34 (PARKING LOT) , , , , , ,09,01,02,02,01,01,05,01, , , , , , , , 0, , , ,N,NONE ,84057
+2013130413CN04103 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,05/09/2013,TH,2007,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,Y,N,08, 2,420 SR 34 (PARKING LOT) , , , , , ,09,01,02,01,01,01,05,01, , , , , , , , 0, , , ,N,NONE ,84054
+2013130413CN05020 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,06/08/2013,SA,0911,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,08, 2,410 ST RT 34 (PARKING LOT) , , , , , ,09,01,02,01,01,05, ,01, , , , , , , , 0, , , ,N,NONE ,84055
+2013130413CN05207 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,06/15/2013,SA,0148,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,Y,N,06, 2,6 RT 537 WEST (PARKING LOT) , , , , , ,09,01,02,01,06,01, , , , , , , , , , 0, , , ,N,NONE ,84049
+2013130413CN05391 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,06/21/2013,F ,1437,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,03, 2,273 RT 34 (PARKING LOT) , , , , , ,09,01,02,01,01,01,05,01, , , , , , , , 0, , , ,N,NONE ,84027
+2013130413CN05395 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,06/21/2013,F ,1541,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,06, 2,1 TRUMP NATIONAL BLVD , , , , , ,09,01,05,01,01,01,05,01, , , , , , , , 0, , , ,N,NONE ,84046
+2013130413CN05534 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,06/26/2013,W ,1727,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,06, 2,9 PROFESSIONAL CIR (PARKING LOT) , , , , , ,09,01,02,01,01,01, , , , , , , , , ,25, , , ,N,NONE ,84050
+2013130413CN05748 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,07/03/2013,W ,2035,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,11, 1,WHITE OAK DRIVE , , , , , ,07,04,02,02,03,02, , , 350,FE,W,ACORN PLACE , , , ,25, , , ,N,200 FEET OF GROSS ON THE PROPERTY OF 7 WHITE DRIVE. DANIEL CLIFFORD HOMEOWNER. ,84046
+2013130413CN06672 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,08/03/2013,SA,2330,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,08, 2,420 SR 34 (PARKING LOT) , , , , , ,09,01,02,01,06,01,05, , , , , , , , , 0, , , ,N,NONE ,84050
+2013130413CN07165 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,08/21/2013,W ,0344,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,11, 1,BUCKS MILL RD , , , , , ,07,01,02,01,05,01,05, , 100,FE,S,CR 537 , , , ,25,50, , ,N,JCP&L POLE JC971CN ,84057
+2013130413CN07206 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,08/22/2013,TH,1204,01,COLTS NECK PD , , 0, 1, 0, 0,I,B,N,N,01, 2,FIVE POINT RD , , , , , ,07,01,02,01,01,01,05, , 528,FE,S,CR 537 , , , ,35,50, , ,N, ,84050
+2013130413CN07311 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,08/25/2013,S ,1737,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,08, 2,320 SR 34 (PARKING LOT) , , , , , ,09,01,02,01,01,01, , , , , , , , , , 0, , , ,N,NONE ,84036
+2013130413CN08261 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,09/25/2013,W ,0954,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,08, 2,410 SR 34 (PARKING LOT) , , , , , ,09,01,02,01,01,01,05,01, , , , , , , , 0, , , ,N,NONE ,84054
+2013130413CN08334 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,09/27/2013,F ,1057,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,Y,N,08, 2,340 RT 34 (PARKING LOT) , , , , , ,09,01,02,01,01,01,05,01, , , , , , , , 0, , , ,N,NONE ,84045
+2013130413CN09374 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,11/01/2013,F ,2140,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,08, 2,59 FIVE POINT RD , , , , , ,09,01,02,02,06,01,05, , , , , , , , , 0, , , ,N,NONE ,84057
+2013130413CN09445 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,11/03/2013,S ,1301,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,11, 1,15 BRANDYWINE LN , , , , , ,09,01,02,01,01,01, , , , , , , , , , 0, , , ,N,NONE ,84044
+2013130413CN09783 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,11/13/2013,W ,1331,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,08, 2,IRETON KEY , , , , , ,09,01,02,01,01,01,05,01, 150,FE,E,COLTS NECK BLVD , , , ,25,25, , ,N,NONE ,84045
+2013130413CN10896 ,MONMOUTH ,ATLANTIC HIGHLANDS BORO ,12/23/2013,M ,2140,01,COLTS NECK PD , , 0, 0, 0, 0,P,B,N,N,06, 2,420 SR 34 (PARKING LOT) , , , , , ,09,01,02,02,07,02,05, , , , , , , , , 0, , , ,N,NONE ,84057
+2013130513-000795 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,01/26/2013,SA,1502,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,03, 2,BAYSHORE PLAZA PARKING LOT ,E, , , , ,09,01,02,01,01,01,05,01, , , , , , , ,25, , , ,N,? ,137
+2013130513-001195 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,02/06/2013,W ,1451,01,ATLANTIC HIGHLANDS PD , , 0, 1, 0, 1,I,B,N,N,08, 2,11 STATE HIGHWAY RT 36 PARKING LOT , , , , , ,09,01,02,01,01,01,05,01, , ,W,FIRST AVENUE , , , ,45,25, , ,N, ,136
+2013130513-001416 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,02/13/2013,W ,0944,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,11, 1,9 STATE HIGHWAY 36 PARKING LOT ,W, , , , ,09,01,02,01,01,01,05,01, 500,FE,W,FIRST AVENUE , , , ,10,25, , ,N, ,134
+2013130513-003219 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,04/07/2013,S ,1555,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,06, 2,BAY SHORE PLAZA , , , , , ,09,01,02,01,01,01,05,01, , ,S,STATE HIGHWAY 36 , , , ,25,45, , ,N, ,137
+2013130513-003432 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,04/13/2013,SA,1236,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,08, 2,9 STATE HIGHWAY 36 , , , , , ,09,01,02,01,01,01,05,01, , ,W,FIRST AVENUE , , , ,25,25, , ,N, ,#136
+2013130513-004366 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,05/10/2013,F ,1727,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,08, 2,9 STATE HIGHWAY 36 , , , , , ,09,01,02,01,01,01,05,01, , ,W,FIRST AVENUE , , , ,25,25, , ,N, ,#136
+2013130513-004558 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,05/16/2013,TH,0757,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,08, 2,96 EAST AVENUE , , , , , ,09,01,02,01,01,01,05,01, , ,E,MANYMIND AVENUE , , , ,25,25, , ,N, ,136
+2013130513-005034 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,05/28/2013,TU,1703,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,08, 2,999 STATE HIGHWAY 36 PARKING LOT ,N, , , , ,09,01,02,02,01,02,05,01, 50,FE,S,WEST GARFIEND AVENUE , , , ,10,25, , ,N, ,134
+2013130513-005212 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,06/02/2013,S ,1607,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,08, 2,BAYSHORE PLAZA 9 STATE HIGHWAY 36 ,N, , , , ,09,01,02,01,01,01,05,01, , ,S,STATE HIGHWAY ROUTE 36 , , , ,25,45, , ,N, ,130
+2013130513-005725 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,06/17/2013,M ,0929,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,08, 2,PARKING LOT OF 153 GRAND AVENUE , , , , , ,09,01,02,01,01,01,05,01, 50,FE,W,GRAND AVENUE , , , ,25,25, , ,N, ,128
+2013130513-005873 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,06/21/2013,F ,1205,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,08, 2,BAYSHORE PLAZA PARKING LOT , , , , , ,09,01,02,01,01,01,05,01, 150,FE,S,ROUTE 36 , , , ,25,45, , ,N, ,128
+2013130513-007238 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,07/30/2013,TU,1528,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,08, 2,9 STATE HIGHWAY 36 PARKING LOT , , , , , ,09,01,02,01,01,01,05,01, , ,W,FIRST AVENUE , , , ,15,25, , ,N, ,#136
+2013130513-007722 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,08/14/2013,W ,0904,01,ATLANTIC HIGHLANDS PD ,01 , 0, 0, 0, 0,P,B,N,N,08, 3,96 EAST AVENUE ,S, , , , ,09,01,02,01,01,01,05,01, , , , , , , , 0, , , ,N, ,133
+2013130513-05217 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,06/02/2013,S ,1817,01,ATLANTIC HIGHLANDS PD , , 0, 0, 0, 0,P,B,N,N,06, 2,9 STATE HIGHWAY 36 PARKING LOT , , , , , ,09,01,02,01,01,01,05,01, , ,W,FIRST AVENUE , , , ,25,25, , ,N, ,137
+2013130513002144 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,01/13/2013,S ,1902,01,EDISON TWP PD , , 0, 0, 0, 0,P,B,N,N,06, 2,450 RARTTAN CENTER ,S, , , , ,09,01,02,01,06,01,05,01, , , , , , , ,25, , , ,N, ,397
+2013130513AV00256 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,01/26/2013,SA,1500,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,I,N,N,11, 1,NJ 71 , , 71, ,00000071__ , 6.23,02,01,02,01,01,01,02,01, ,AT, ,LINCOLN AVE , , , ,30, , , ,N,NONE ,86109
+2013130513AV00866 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,03/03/2013,S ,1732,01,AVON-BY-THE-SEA PD ,AVON BY THE SE?, 0, 0, 0, 0,P,B,N,N,06, 2,MONMOUTH COUNTY 18 III ,N, 18,3,130000183_ , 3.92,05,01,02,01,03,01,02,01, 15,FE,N,WOODLAND AVE , , , ,25,25, , ,N,NONE ,86110
+2013130513AV00888 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,03/22/2013,F ,1441,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,B,N,N,01, 2,NJ 71 ,N, 71, ,00000071__ , 6.14,02,01,02,01,01,01,02,01, 50,FE,S,WASHINGTON AVE , , , ,30,25, , ,N,NONE ,86110
+2013130513AV00889 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,03/23/2013,SA,1429,01,AVON-BY-THE-SEA PD , , 0, 1, 0, 0,I,B,N,N,01, 2,NJ 71 ,S, 71, ,00000071__ , ,02,02,02,01,01,01,05,01, ,FE,N,CR 17 / SYLVANIA AVE , , , ,30,25, , ,N, ,86102
+2013130513AV01190 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,04/20/2013,SA,1345,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,B,N,N,06, 2,STANTON PL , , , , , ,07,02,02,01,01,01,05,01, 2,FE,W,BRIDGE AVE , , , ,25,25, , ,N,NONE ,86107
+2013130513AV01578 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,05/23/2013,TH,1855,01,AVON-BY-THE-SEA PD , , 0, 1, 0, 0,I,B,N,N,03, 2,SECOND LINCOLN , , , , , ,07,01,02,01,01,01,05,01, , , , , , , ,25, , , ,N,NONE ,88109
+2013130513AV01760 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,06/05/2013,W ,1708,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,B,N,N,08, 2,MARINE PLACE , , , , , ,07,01,02,01,01,01,05,01, , ,S,MONMOUTH COUNTY 17 , , , ,10,25, , ,N,NONE ,86122
+2013130513AV03315 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,09/06/2013,F ,1536,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,I,N,N,11, 1,NJ 71 , , 71, ,00000071__ , 6.23,02,01,02,01,01,01,01,01, ,AT, ,LINCOLN AVE , , , ,30,25, , ,N,NONE ,86118
+2013130513AV03328 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,09/07/2013,SA,1427,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,I,N,N,11, 1,NJ 71 , , 71, ,00000071__ , 6.23,02,01,02,01,01,01,02,01, ,AT, ,LINCOLN AVE , , , ,30, , , ,N,NONE ,86109
+2013130513AV03500 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,09/22/2013,S ,1244,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,B,N,N,06, 2,SECOND AVE , , , , , ,07,01,02,01,01,01,05,01, , , , , , , ,25, , , ,N,NONE ,86109
+2013130513AV03501 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,09/22/2013,S ,1821,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,B,N,N,08, 2,NJ 71 , , 71, ,00000071__ , ,02,01,02,01,02,01,02,01, , , , , , , ,25, , , ,N,NONE ,86109
+2013130513AV03893 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,11/01/2013,F ,1518,01,AVON-BY-THE-SEA PD , , 0, 0, 0, 0,P,I,N,N,06, 2,THIRD AVE ,S, , , , ,07,01,02,00,00,00,04,01, ,AT, ,WOODLAND AVENUE , , , ,25,25, , ,N,NONE ,86110
+2013130513MU01012 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,02/06/2013,W ,1122,99,MONMOUTH UNIVERSITY PD , , 0, 0, 0, 0,P,B,N,N,01, 2,SCHOLALS WAY , , , , , ,09,04,02,01,01,01,05,01, , , , , , , ,15, , , ,N, ,2458
+2013130513MU01525 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,02/21/2013,TH,2150,99,MONMOUTH UNIVERSITY PD , , 0, 0, 0, 0,P,B,N,N,06, 2,LOT 20 , , , , , ,09,01,02,01,06,01,05,01, , , , , , , , 0, , , ,Y,NONE ,5778
+2013130513MV00844 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,01/31/2013,TH,1825,99,MONMOUTH UNIVERSITY PD , , 0, 0, 0, 0,P,B,N,N,08, 2,MV LOT 13 ,N, , , , ,09,01,02,01,06,01,05,01, 200,FE,S,HAWK ROAD , , , ,15, , , ,N,NONE ,6933
+20131305MU1301104 ,MONMOUTH ,AVON-BY-THE-SEA BORO ,02/09/2013,SA,0212,99,MONMOUTH UNIVERSITY PD , , 0, 0, 0, 0,P,B,N,N,08, 2,PARKING LOT #3 , , , , , ,09,02,02,03,06,07,05,01, , , , , , , ,15, , , ,N,NONE ,4803
+2013130613-10004 ,MONMOUTH ,BELMAR BORO ,08/31/2013,SA,1159,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,08, 2,NORTH BLVD , , , , , ,07,05,02,01,01,01,05,01, 300,FE,W,MONMOUTH COUNTY 18 , , , ,25,25, , ,N, ,614
+2013130613-10013 ,MONMOUTH ,BELMAR BORO ,08/31/2013,SA,1347,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,01, 2,MONMOUTH COUNTY 18 III ,N, 18,3,130000183_ , 3.36,05,01,02,01,01,01,04,01, ,AT, ,2ND AVE / RIVER AVE , , , ,35,25, , ,N, ,152
+2013130613-10257 ,MONMOUTH ,BELMAR BORO ,09/04/2013,W ,1735,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,14, 1,MONMOUTH COUNTY 18 III , , 18,3,130000183_ , 2.51,05,01,02,01,01,01,04,01, 50,FE,N,15TH AVE , , , ,25,25, , ,N, ,152
+2013130613-10259 ,MONMOUTH ,BELMAR BORO ,09/04/2013,W ,2117,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,01, 2,NJ 71 , , 71, ,00000071__ , 5.54,02,01,02,01,07,01,05,01, 150,FE,W,CR 30 / 8TH AVE / MAIN ST , , , ,25, , , ,N,NONE ,920
+2013130613-1026 ,MONMOUTH ,BELMAR BORO ,02/15/2013,F ,2233,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,06, 2,18TH AVE , , , ,13471063__ , .50,07,01,02,01,06,01,04, , 10,FE,E,A STREET , , , ,25,25, , ,N, ,908
+2013130613-10316 ,MONMOUTH ,BELMAR BORO ,09/05/2013,TH,1314,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,11, 1,NJ 35 , , 35, ,00000035__ , ,02,01,02,01,01,01,04,01, 75,FE,S,MACLEARIE PARK EXIT DRIVEWAY , , , ,35, , , ,N, ,137
+2013130613-10350 ,MONMOUTH ,BELMAR BORO ,09/07/2013,SA,1035,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,06, 2,10TH AVE , , , ,13061064__ , .11,07,01,02,01,01,01,02,05, ,AT, ,BELMAR PLAZA , , , ,25, , , ,N, ,129
+2013130613-10353 ,MONMOUTH ,BELMAR BORO ,09/07/2013,SA,1214,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,01, 2,NJ 35 ,N, 35, ,00000035__ , 21.24,02,01,02,01,01,01,04,01, 50,FE,S,10TH AVE , , , ,35,25, , ,N, ,152
+2013130613-10358 ,MONMOUTH ,BELMAR BORO ,09/07/2013,SA,1445,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,01, 2,NJ 35 ,N, 35, ,00000035__ , 21.25,02,01,02,01,01,01,02,01, 15,FE,S,10TH AVE , , , ,35,25, , ,N,NONE ,904
+2013130613-10522 ,MONMOUTH ,BELMAR BORO ,09/11/2013,W ,1623,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,07, 2,NJ 35 , , 35, ,00000035__ , 21.18,02,01,02,01,01,01,03,01, ,AT, ,11TH AVE , , , ,35,25, , ,N,NONE ,128
+2013130613-10528 ,MONMOUTH ,BELMAR BORO ,09/11/2013,W ,1823,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,01, 2,MONMOUTH COUNTY 18 III , , 18,3,130000183_ , 2.84,05,01,02,01,01,01,05,01, 100,FE,S,9TH AVE , , , ,25,25, , ,N,NONE ,128
+2013130613-10617 ,MONMOUTH ,BELMAR BORO ,09/14/2013,SA, ,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,08, 2,BRIARWOOD RD , , , , , ,07,01,02,01,01,01,05,01, 300,FE,S,MONMOUTH COUNTY 16 , , , ,25,25, , ,N, ,614
+2013130613-1066 ,MONMOUTH ,BELMAR BORO ,02/17/2013,S ,1540,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,08, 2,FIFTH AVE , , , , , ,07,01,02,01,01,01,05,01, 50,FE,W,MONMOUTH COUNTY 18 , , , ,25,25, , ,N, ,904
+2013130613-10835 ,MONMOUTH ,BELMAR BORO ,09/20/2013,F ,1618,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,03, 2,NJ 35 , , 35, ,00000035__ , 20.48,02,01,02,01,03,01,05,01, ,AT, ,CR 18 / 16TH AVE / BELMAR AVE , , , ,35,25, , ,N, ,153
+2013130613-1086 ,MONMOUTH ,BELMAR BORO ,02/18/2013,M ,1116,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,02, 2,10TH AVE , , , ,13061064__ , .15,07,01,02,01,01,01,02,01, 170,FE,W,MONMOUTH COUNTY 30 , , , ,25,30, , ,N, ,136
+2013130613-10886 ,MONMOUTH ,BELMAR BORO ,09/22/2013,S ,0943,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,04, 2,FIFTEENTH AVE , , , , , ,07,01,02,01,01,01,05,01, 400,FE,W,""A"" ST , , , ,25,25, , ,N, ,614
+2013130613-10906 ,MONMOUTH ,BELMAR BORO ,09/22/2013,S ,1636,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,01, 2,NJ 71 ,S, 71, ,00000071__ , 4.91,02,01,02,01,03,01,05,01, 100,FE,N,NJ 18 / 16TH AVE , , , ,30,25, , ,N,NONE ,904
+2013130613-11065 ,MONMOUTH ,BELMAR BORO ,09/28/2013,SA,1613,01,BELMAR PD , , 0, 2, 0, 0,I,I,N,N,02, 2,NJ 35 ,N, 35, ,00000035__ , 21.18,02,01,02,01,01,01,04,01, ,AT,W,11TH AVE , , , ,35,25, , ,N, ,152
+2013130613-11072 ,MONMOUTH ,BELMAR BORO ,09/28/2013,SA,2217,01,BELMAR PD , , 0, 1, 0, 0,I,I,N,N,01, 2,NJ 71 ,S, 71, ,00000071__ , 5.57,02,01,02,01,07,01,05,01, ,AT, ,CR 30 / 8TH AVE / MAIN ST , , , ,30,25, , ,Y,NONE ,939
+2013130613-11167 ,MONMOUTH ,BELMAR BORO ,10/02/2013,W ,1218,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,08, 2,9TH AVE , , , ,13061065__ , .01,07,01,02,01,01,01,05,01, 50,FE,E,MONMOUTH COUNTY 30 , , , ,25,35, , ,N, ,152
+2013130613-11169 ,MONMOUTH ,BELMAR BORO ,10/02/2013,W ,1315,01,BELMAR PD , , 0, 1, 0, 1,I,B,N,N,13, 1,EIGHTH AVE , , , , , ,07,01,02,01,01,01,05,01, 25,FE,E,MONMOUTH COUNTY 30 , , , ,25,35, , ,N, ,152
+2013130613-11170 ,MONMOUTH ,BELMAR BORO ,10/02/2013,W ,1317,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,01, 2,NJ 35 , , 35, ,00000035__ , 21.25,02,01,02,01,01,01,02,01, 20,FE,S,10TH AVE , , , ,35, , , ,N, ,134
+2013130613-1135 ,MONMOUTH ,BELMAR BORO ,02/20/2013,W ,1213,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,02, 2,10TH AVE , , , ,13061064__ , .13,07,01,02,01,01,01,02,01, 246,FE,W,MONMOUTH COUNTY 30 , , , ,25,30, , ,N, ,1?6
+2013130613-11352 ,MONMOUTH ,BELMAR BORO ,10/10/2013,TH,1950,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,06, 2,FIFTEENTH AVE , , , , , ,07,01,02,02,05,01,05,01, 150,FE,E,13TH ST , , , ,25, , , ,N,NONE ,128
+2013130613-11450 ,MONMOUTH ,BELMAR BORO ,10/15/2013,TU,1741,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,03, 2,NJ 71 , , 71, ,00000071__ , 4.89,02,01,02,01,01,01,04,01, ,AT, ,NJ 18 / 16TH AVE , , , ,30,25, , ,N,FIRE HYDRANT ,152
+2013130613-11474 ,MONMOUTH ,BELMAR BORO ,10/16/2013,W ,1751,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,02, 2,10TH AVE , , , ,13061064__ , .11,07,01,02,01,01,05,05,01, ,AT, ,BELMAR PLAZA , , , ,25,25, , ,N, ,152
+2013130613-11528 ,MONMOUTH ,BELMAR BORO ,10/18/2013,F ,1848,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,03, 2,NJ 35 , , 35, ,00000035__ , 21.25,02,01,02,01,06,01,05,01, ,AT, ,10TH AVE , , , ,35,25, , ,N, ,153
+2013130613-11671 ,MONMOUTH ,BELMAR BORO ,10/25/2013,F ,1809,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,03, 2,NJ 35 , , 35, ,00000035__ , 21.18,02,01,02,01,01,01,03,01, ,AT, ,11TH AVE , , , ,35,25, , ,N, ,153
+2013130613-11770 ,MONMOUTH ,BELMAR BORO ,10/29/2013,TU,1802,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,01, 2,NJ 35 ,N, 35, ,00000035__ , 21.27,02,01,02,01,06,01,02,01, 100,FE,N,10TH AVE , , , ,35,25, , ,N,NONE ,128
+2013130613-11802 ,MONMOUTH ,BELMAR BORO ,10/31/2013,TH,0930,01,BELMAR PD , , 0, 1, 0, 0,I,B,Y,N,01, 3,7TH AVE , , , , , ,07,01,02,01,01,01,05,01, 30,FE,E,A STREET , , , ,25,25, , ,N,NONE ,144
+2013130613-11983 ,MONMOUTH ,BELMAR BORO ,11/08/2013,F ,1114,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,02, 2,10TH AVE , , , ,13061064__ , .18,07,01,02,01,01,01,02,01, 10,FE,W,CR 30 , , , ,25,30, , ,N, ,135
+2013130613-12022 ,MONMOUTH ,BELMAR BORO ,11/10/2013,S ,1045,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,08, 2,10TH AVE , , , ,13061064__ , .18,07,01,02,01,01,01,04,01, 25,FE,E,CR 30 , , , ,25,35, , ,N, ,152
+2013130613-12086 ,MONMOUTH ,BELMAR BORO ,11/14/2013,TH,1432,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,01, 2,NJ 35 ,N, 35, ,00000035__ , ,02,01,02,01,01,01,04,01, 250,FE,S,NJ 71 , , , ,35,25, , ,N, ,152
+2013130613-1227 ,MONMOUTH ,BELMAR BORO ,02/24/2013,S ,1838,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,01, 2,NJ 35 ,N, 35, ,00000035__ , 21.41,02,01,02,01,06,01,04,01, ,AT, ,NJ 71 / 8TH AVE , , , ,35,25, , ,N, ,151
+2013130613-12316 ,MONMOUTH ,BELMAR BORO ,11/24/2013,S ,1605,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,06, 2,MONMOUTH COUNTY 30 II , , 30,2,130000302_ , 4.53,05,01,02,01,01,01,05,01, 50,FE,N,9TH AVE , , , ,30,25, , ,N, ,153
+2013130613-12506 ,MONMOUTH ,BELMAR BORO ,12/03/2013,TU,0803,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,02, 2,11 TH AVE , , , , , ,07,01,02,01,01,01,05,01, 30,FE, ,CR 30 , , , ,25,30, , ,N,NONE ,135
+2013130613-12568 ,MONMOUTH ,BELMAR BORO ,12/06/2013,F ,1509,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,03, 2,NJ 71 , , 71, ,00000071__ , 5.04,02,01,02,02,01,02,05,01, ,AT, ,13TH AVE , , , ,30,25, , ,N, ,153
+2013130613-12659 ,MONMOUTH ,BELMAR BORO ,12/10/2013,TU,1840,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,01, 2,MONMOUTH COUNTY 30 II , , 30,2,130000302_ , 4.40,05,01,02,02,06,01,05,01, 100,FE,N,11TH AVE , , , , 0, , , ,N, ,129
+2013130613-12680 ,MONMOUTH ,BELMAR BORO ,12/11/2013,W ,2256,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,06, 2,MONMOUTH COUNTY 18 I , , 18,1,130000181_ , 8.18,05,01,02,01,06,01,05,01, 200,FE,W,D ST / NEWMAN ST , , , ,25,25, , ,N, ,153
+2013130613-12889 ,MONMOUTH ,BELMAR BORO ,12/22/2013,S ,0827,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,06, 2,THIRTEENTH AVE , , , , , ,07,01,02,01,06,01,05,01, 50,FE,W,BAYVIEW , , , ,25,25, , ,N, ,153
+2013130613-12893 ,MONMOUTH ,BELMAR BORO ,12/22/2013,S ,1147,01,BELMAR PD , , 0, 1, 0, 0,I,B,N,N,14, 1,MONMOUTH COUNTY 30 II , , 30,2,130000302_ , 4.35,05,01,02,01,01,01,05,01, 200,FE,N,12TH AVE , , , ,25,25, , ,N,(NONE) ,128
+2013130613-12983 ,MONMOUTH ,BELMAR BORO ,12/26/2013,TH,1518,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,01, 2,NJ 35 ,S, 35, ,00000035__ , 20.98,02,04,02,01,01,01,04,01, ,AT, ,K ST , , , ,35,35, , ,N, ,955
+2013130613-1299 ,MONMOUTH ,BELMAR BORO ,02/27/2013,W ,1905,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,06, 2,MONMOUTH COUNTY 18 I , , 18,1,130000181_ , 7.90,05,01,02,01,07,01,05,01, 50,FE,E,RAILROAD AVE , , , ,25,25, , ,N,NONE ,147
+2013130613-13066 ,MONMOUTH ,BELMAR BORO ,12/29/2013,S ,1359,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,03, 2,TWELFTH AVE , , , , , ,07,01,02,02,01,02,05,01, ,AT, ,RAILROAD AVENUE , , , ,25,25, , ,N, ,128
+2013130613-1432 ,MONMOUTH ,BELMAR BORO ,03/03/2013,S ,1607,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,06, 2,D ST , , , , , ,07,01,02,01,01,01,05,01, 30,FE,N,11TH AVE , , , ,25,25, , ,N,NONE ,128
+2013130613-1450 ,MONMOUTH ,BELMAR BORO ,03/03/2013,S ,1946,01,BELMAR PD , , 0, 0, 0, 0,P,B,Y,N,06, 2,FIFTEENTH AVE ,W, , , , ,07,02,02,01,06,01,04,01, 150,FE,E,E STREET , , , ,25,25, , ,N, ,152
+2013130613-1792 ,MONMOUTH ,BELMAR BORO ,03/16/2013,SA,0856,01,BELMAR PD , , 0, 0, 0, 0,P,B,N,N,11, 2,NJ 35 ,N, 35, ,00000035__ , 20.70,02,04,02,01,01,01,04,01, 100,FE,S,MAPLEWOOD RD , , , ,35,25, , ,N, ,151
+2013130613-200 ,MONMOUTH ,BELMAR BORO ,01/09/2013,W ,1551,01,BELMAR PD , , 0, 0, 0, 0,P,I,N,N,09, 2,NJ 71 , , 71, ,00000071__ , 5.46,02,01,02,01,01,01,02,01, ,AT, ,W RAILROAD AVE , , , ,25,25, , ,N,NONE ,128
+2013130613-2046 ,MONMOUTH ,BELMAR BORO ,03/26/2013,TU,1515,01,BELMAR PD , , 0, 1, 0, 0,I,I,N,N,01, 2,MONMOUTH COUNTY 30 II , , 30,2,130000302_ , 4.45,05,01,02,01,01,05,05,01, ,AT, ,10TH AVE , , , ,30, , , ,N, ,135
diff --git a/inst/tests/issue_1113_fread.txt b/inst/tests/issue_1113_fread.txt
new file mode 100644
index 0000000..e8f7ddc
--- /dev/null
+++ b/inst/tests/issue_1113_fread.txt
@@ -0,0 +1,4 @@
+ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 THETA8 THETA9 THETA10 THETA11 THETA12 THETA13 THETA14 THETA15 THETA16 THETA17 SIGMA(1,1) OMEGA(1,1) OMEGA(2,1) OMEGA(2,2) OMEGA(3,1) OMEGA(3,2) OMEGA(3,3) OMEGA(4,1) OMEGA(4,2) OMEGA(4,3) OMEGA(4,4) OMEGA(5,1) OMEGA(5,2) OMEGA(5,3) OMEGA(5,4) OMEGA(5,5) OMEGA(6,1) OMEGA(6,2) OMEGA(6,3) OMEGA(6,4) [...]
+ -25000 -2.50000E+00 2.30000E+00 6.20000E-01 5.30000E-01 -3.00000E+00 -8.10000E-01 8.30000E+00 4.10000E+00 -3.50000E+00 -3.40000E+00 -9.90000E-01 -2.30000E-02 6.80000E-01 9.10000E-01 2.30000E+00 1.00000E-01 1.00000E-01 1.33669E-04 4.00000E-01 1.00000E-02 4.00000E-01 1.00000E-02 1.00000E-02 4.00000E-01 1.00000E-02 1.00000E-02 1.00000E-02 4.00000E-01 1.00000E-02 1.00000E-02 1.00000E-02 1.00000E-02 4.00000E-01 1.00000E-02 1.00000E-02 1.00000E-02 1.00000E- [...]
+ -24999 -2.49472E+00 2.15887E+00 6.23667E-01 2.86888E-01 -2.60190E+00 -4.83346E-01 8.65879E+00 3.82465E+00 -3.69026E+00 -3.55164E+00 -9.91289E-01 -2.35327E-02 8.94482E-01 1.04624E+00 2.30139E+00 -3.68905E-01 4.83838E-03 1.24727E-02 6.69962E-01 -1.82743E-01 8.87432E-01 -1.35321E-01 2.17601E-01 2.36157E-01 -8.46622E-01 2.63270E-01 1.65191E-01 1.24854E+00 1.17863E-01 -2.82260E-01 -1.82098E-01 -1.48054E-01 4.15367E-01 4.96165E-01 -8.13818E-01 -1.68830E-01 -7.29928E- [...]
+ -24998 -2.47806E+00 2.16679E+00 5.44640E-01 1.40853E-01 -2.97418E+00 -8.07030E-01 8.59191E+00 4.05605E+00 -3.34552E+00 -3.14465E+00 -1.29517E+00 -3.53758E-01 6.90913E-01 1.10941E+00 2.65561E+00 1.81350E-01 -7.13636E-02 6.39860E-03 7.58939E-01 -7.41282E-02 4.50564E-01 -1.56544E-01 5.50040E-02 4.00618E-01 -8.38686E-01 1.99437E-01 3.24735E-01 1.13226E+00 -4.37298E-02 -9.98388E-02 -5.41804E-03 1.95687E-02 1.24519E-01 3.99408E-01 -3.52834E-01 -1.41210E-01 -5.96706E- [...]
diff --git a/inst/tests/issue_1330_fread.txt b/inst/tests/issue_1330_fread.txt
new file mode 100644
index 0000000..db8dfbc
--- /dev/null
+++ b/inst/tests/issue_1330_fread.txt
@@ -0,0 +1,8 @@
+a b
+1 1
+2 2
+
+3 3
+4 4
+
+5 5
diff --git a/inst/tests/issue_563_fread.txt b/inst/tests/issue_563_fread.txt
new file mode 100644
index 0000000..e9f7808
--- /dev/null
+++ b/inst/tests/issue_563_fread.txt
@@ -0,0 +1,5 @@
+A,B
+ą,ž
+ū,į
+ų,ė
+š,ę
diff --git a/inst/tests/issue_773_fread.txt b/inst/tests/issue_773_fread.txt
new file mode 100644
index 0000000..673608c
--- /dev/null
+++ b/inst/tests/issue_773_fread.txt
@@ -0,0 +1,28 @@
+AAA|BBB|CCC
+4|5|6
+7|8|9
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+1|2|3
+31|32|33
+21|22|23
+ZZZ|YYY
+10|11
+1|2
+1|2
+1|2
+1|2
diff --git a/inst/tests/issue_785_fread.txt b/inst/tests/issue_785_fread.txt
new file mode 100644
index 0000000..5238654
--- /dev/null
+++ b/inst/tests/issue_785_fread.txt
@@ -0,0 +1,5 @@
+IFLxID IFLxName Ifcd Tdate Ttime UpdateMillisec Cp Chg ChgPct Cq Cm Oc S5 S4 S3 S2 S1 B1 B2 B3 B4 B5 Sv5 Sv4 Sv3 Sv2 Sv1 Bv1 Bv2 Bv3 Bv4 Bv5 BS Bsratio PreClosePrc OpenPrc Hp Lp ClosePrc UpperLmtPrc LowerLmtPrc Tq Tm PreOpnIntrst OpnIntrst PreStlmtPrc StlmtPrc PreDelta Delta SettleGroupID SettleID
+IFL1 abcd IF1005 20100421 100048 500 3227.20 10.60 0.330 5 4840740.000 abc 3228.00 3227.80 3227.60 3227.40 3227.20 3227.00 3226.80 3226.60 3226.40 3226.20 13.00 17.00 10.00 14.00 2.00 7.00 4.00 1.00 20.00 4.00 B 0.508 3214.60 3215.00 3231.20 3212.20 0.00 3538.20 2895.00 18165 17570483640.00 4496 5970 3216.60 0.00 0.000 0.000 0
+IFL1 efgh IF1005 20100421 093725 500 3221.40 4.80 0.149 1 966420.000 def 3222.60 3222.40 3222.20 3222.00 3221.80 3221.40 3221.20 3221.00 3220.80 3220.60 15.00 4.00 14.00 2.00 5.00 4.00 14.00 16.00 1.00 3.00 S 0.505 3214.60 3215.00 3226.40 3212.20 0.00 3538.20 2895.00 8230 7952392200.00 4496 5427 3216.60 0.00 0.000 0.000 0
+IFL1 ijkl IF1005 20100421 093726 0 3221.80 5.20 0.162 1 966540.000 ghi 3222.60 3222.40 3222.20 3222.00 3221.80 3221.40 3221.20 3221.00 3220.80 3220.60 15.00 4.00 14.00 2.00 4.00 4.00 14.00 16.00 1.00 3.00 B 0.505 3214.60 3215.00 3226.40 3212.20 0.00 3538.20 2895.00 8231 7953358740.00 4496 5428 3216.60 0.00 0.000 0.000 0
+IFL1 mnop IF1005 20100421 093726 500 3221.80 5.20 0.162 3 2899620.000 jkl 3222.60 3222.40 3222.20 3222.00 3221.80 3221.40 3221.20 3221.00 3220.80 3220.60 15.00 4.00 15.00 2.00 1.00 4.00 14.00 16.00 1.00 3.00 B 0.505 3214.60 3215.00 3226.40 3212.20 0.00 3538.20 2895.00 8234 7956258360.00 4496 5429 3216.60 0.00 0.000 0.000 0
diff --git a/inst/tests/quoted_multiline.csv b/inst/tests/quoted_multiline.csv
new file mode 100644
index 0000000..2c3e9b3
--- /dev/null
+++ b/inst/tests/quoted_multiline.csv
@@ -0,0 +1,126 @@
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+ VWJ UB KX (BNUYFQ/BHOPM/PHRZH)
+ QGP QQ UI PKY KV FTUWO QKHPS
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diff --git a/inst/tests/russellCRCRLF.csv b/inst/tests/russellCRCRLF.csv
new file mode 100644
index 0000000..068735f
--- /dev/null
+++ b/inst/tests/russellCRCRLF.csv
@@ -0,0 +1,20 @@
+"Index Name","Date","Value Without Dividends","Value With Dividends"
+"Russell Microcap� Value Index","06/30/2000",395.77,356.90
+"Russell Microcap� Value Index","07/03/2000",397.94,359.39
+"Russell Microcap� Value Index","07/05/2000",396.27,357.91
+"Russell Microcap� Value Index","07/06/2000",398.75,360.17
+"Russell Microcap� Value Index","07/07/2000",399.20,360.58
+"Russell Microcap� Value Index","07/10/2000",400.47,361.73
+"Russell Microcap� Value Index","07/11/2000",400.67,361.91
+"Russell Microcap� Value Index","07/12/2000",404.90,365.79
+"Russell Microcap� Value Index","07/13/2000",406.70,367.43
+"Russell Microcap� Value Index","07/14/2000",407.68,368.32
+"Russell Microcap� Value Index","07/17/2000",408.67,369.22
+"Russell Microcap� Value Index","07/18/2000",407.84,368.48
+"Russell Microcap� Value Index","07/19/2000",404.81,365.75
+"Russell Microcap� Value Index","07/20/2000",406.55,367.35
+"Russell Microcap� Value Index","07/21/2000",402.66,363.85
+"Russell Microcap� Value Index","07/24/2000",398.75,360.32
+"Russell Microcap� Value Index","07/25/2000",399.36,360.88
+"Russell Microcap� Value Index","07/26/2000",400.21,361.66
+"Russell Microcap� Value Index","07/27/2000",396.02,357.97
diff --git a/inst/tests/russellCRLF.csv b/inst/tests/russellCRLF.csv
new file mode 100644
index 0000000..c40a0fd
--- /dev/null
+++ b/inst/tests/russellCRLF.csv
@@ -0,0 +1,20 @@
+"Index Name","Date","Value Without Dividends","Value With Dividends"
+"Russell Microcap� Value Index","06/30/2000",395.77,356.90
+"Russell Microcap� Value Index","07/03/2000",397.94,359.39
+"Russell Microcap� Value Index","07/05/2000",396.27,357.91
+"Russell Microcap� Value Index","07/06/2000",398.75,360.17
+"Russell Microcap� Value Index","07/07/2000",399.20,360.58
+"Russell Microcap� Value Index","07/10/2000",400.47,361.73
+"Russell Microcap� Value Index","07/11/2000",400.67,361.91
+"Russell Microcap� Value Index","07/12/2000",404.90,365.79
+"Russell Microcap� Value Index","07/13/2000",406.70,367.43
+"Russell Microcap� Value Index","07/14/2000",407.68,368.32
+"Russell Microcap� Value Index","07/17/2000",408.67,369.22
+"Russell Microcap� Value Index","07/18/2000",407.84,368.48
+"Russell Microcap� Value Index","07/19/2000",404.81,365.75
+"Russell Microcap� Value Index","07/20/2000",406.55,367.35
+"Russell Microcap� Value Index","07/21/2000",402.66,363.85
+"Russell Microcap� Value Index","07/24/2000",398.75,360.32
+"Russell Microcap� Value Index","07/25/2000",399.36,360.88
+"Russell Microcap� Value Index","07/26/2000",400.21,361.66
+"Russell Microcap� Value Index","07/27/2000",396.02,357.97
diff --git a/inst/tests/test-S4.R b/inst/tests/test-S4.R
new file mode 100644
index 0000000..13bc507
--- /dev/null
+++ b/inst/tests/test-S4.R
@@ -0,0 +1,56 @@
+context("S4 Compatability")
+
+## S4 class definitions to test
+setClass("Data.Table", contains="data.table")
+setClass("S4Composition", representation(data="data.table"))
+
+test_that("data.table can be a parent class", {
+ ids <- sample(letters[1:3], 10, replace=TRUE)
+ scores<- rnorm(10)
+ dt <- data.table(id=ids, score=scores)
+ dt.s4 <- new("Data.Table", data.table(id=ids, score=scores))
+
+ expect_true(isS4(dt.s4))
+ expect_true(inherits(dt.s4, 'data.table'))
+
+ ## pull out data from S4 as.list, and compare to list from dt
+ dt.s4.list <- dt.s4 at .Data
+ names(dt.s4.list) <- names(dt.s4)
+ expect_identical(dt.s4.list, as.list(dt), info="Underlying data not identical")
+})
+
+test_that("simple S4 conversion-isms work", {
+ df = data.frame(a=sample(letters, 10), b=1:10)
+ dt = as.data.table(df)
+ expect_equal(as(df, 'data.table'), dt)
+ expect_identical(as(dt, 'data.frame'), df)
+})
+
+test_that("data.table can be used in an S4 slot", {
+ ## A class with a data.table slot
+ dt <- data.table(a=sample(letters[1:3], 10, replace=TRUE), score=rnorm(10))
+ dt.comp <- new("S4Composition", data=dt)
+ expect_equal(dt.comp at data, dt)
+})
+
+test_that("S4 methods dispatch properly on data.table slots", {
+ ## Make toy accessor functions and compare results against normal data.table
+ ## access
+ dt <- data.table(a=sample(letters[1:3], 10, replace=TRUE), score=rnorm(10))
+ dt.comp <- new("S4Composition", data=dt)
+ setGeneric("dtGet", function(x, what) standardGeneric("dtGet"))
+ setMethod("dtGet", c(x="S4Composition", what="missing"),
+ function(x, what) {
+ x at data
+ })
+ setMethod("dtGet", c(x="S4Composition", what="ANY"),
+ function(x, what) {
+ x at data[[what]]
+ })
+
+ expect_equal(dtGet(dt.comp), dt, label='actually')
+ expect_identical(dtGet(dt.comp, 1), dt[[1]])
+ expect_identical(dtGet(dt.comp, 'b'), dt$b)
+})
+
+
diff --git a/inst/tests/test-data.frame-like.R b/inst/tests/test-data.frame-like.R
new file mode 100644
index 0000000..9994da1
--- /dev/null
+++ b/inst/tests/test-data.frame-like.R
@@ -0,0 +1,160 @@
+context("data.frame like functions (merge, subset, transform)")
+
+###############################################################################
+## Merge
+test_that("`x` columns are valid (bug #1299)", {
+ d1 <- data.table(x=c(1,3,8), y1=rnorm(3), key="x")
+ d2 <- data.table(x=c(3,8,10), y2=rnorm(3), key="x")
+ ans1 <- merge(d1, d2, by="x")
+ ans2 <- cbind(d1[2:3], y2=d2[1:2]$y2)
+ setkey(ans2, x)
+ expect_equal(ans1, ans2, info="Original test #230")
+})
+
+test_that("`xkey` column names are valid in merge (bug#1299", {
+ d1 <- data.table(xkey=c(1,3,8), y1=rnorm(3), key="xkey")
+ d2 <- data.table(xkey=c(3,8,10), y2=rnorm(3), key="xkey")
+ ans2 <- cbind(d1[2:3], y2=d2[1:2]$y2)
+ setkey(ans2, xkey)
+ expect_equal(merge(d1, d2, by="xkey"), ans2, info="Original test #238")
+})
+
+test_that("one column merges work (bug #1241)", {
+ dt <- data.table(a=rep(1:2,each=3), b=1:6, key="a")
+ y <- data.table(a=c(0,1), bb=c(10,11), key="a")
+ expect_equal(merge(y, dt), data.table(a=1L, bb=11L, b=1:3, key="a"),
+ info="Original test #231")
+ expect_equal(merge(y, dt, all=TRUE),
+ data.table(a=rep(c(0L,1L,2L),c(1,3,3)),
+ bb=rep(c(10L,11L,NA_integer_),c(1,3,3)),
+ b=c(NA_integer_,1:6), key="a"),
+ info="Original test #232")
+
+ ## y with only a key column
+ y <- data.table(a=c(0,1), key="a")
+ expect_equal(merge(y,dt), data.table(a=1L, b=1:3, key="a"),
+ info="Original test #233")
+ expect_equal(merge(y, dt, all=TRUE),
+ data.table(a=rep(c(0L,1L,2L),c(1,3,3)),
+ b=c(NA_integer_,1:6),key="a"),
+ info="Original test #234")
+})
+
+test_that("merging data.tables is almost like merging data.frames", {
+ d1 <- data.table(a=sample(letters, 10), b=sample(1:100, 10), key='a')
+ d2 <- data.table(a=d1$a, b=sample(1:50, 10), c=rnorm(10), key='a')
+ dtm <- merge(d1, d2, by='a', suffixes=c(".xx", ".yy"))
+ dtm.df <- as.data.frame(dtm)
+ dfm <- merge(as.data.frame(d1), as.data.frame(d2), by='a', suffixes=c('.xx', '.yy'))
+
+ expect_equal(unname(dtm.df), unname(dfm),
+ info="Testing contents/data after merge")
+ expect_equal(colnames(dtm), colnames(dfm),
+ info="Original test #255 (testing suffixes parameter)")
+})
+
+test_that("`suffixes` behavior can be toggled to pre 1.5.4 behavior", {
+ dt1 <- data.table(a=letters[1:5], b=1:5, key="a")
+ dt2 <- data.table(a=letters[3:8], b=1:6, key="a")
+
+ # options(datatable.pre.suffixes=FALSE)
+ # Option removed in 1.7.10
+ expect_equal(colnames(merge(dt1, dt2)), c("a", "b.x", "b.y"))
+
+ #options(datatable.pre.suffixes=TRUE)
+ #expect_equal(colnames(merge(dt1, dt2)), c("a", "b", "b.1"),
+ # info="Pre 1.5.4 behavior not working")
+
+ #options(datatable.pre.suffixes=FALSE)
+})
+
+test_that("merge and auto-increment columns in y[x]", {
+ ## merging tables that have common column names that end in *.1 gets
+ ## tricky, because the y[x] mojo does some magic to increment the *.1
+ ## in the x (I think) and keep *.1 in the y
+ x <- data.table(a=letters[1:10], b=1:10, b.1=1:10 * 10, key="a")
+ y <- data.table(a=letters[1:10], b=letters[11:20], b.1=rnorm(10), key="a")
+ M <- merge(x, y)
+ m <- merge(as.data.frame(x), as.data.frame(y), by="a")
+
+ expect_is(M, 'data.table')
+ expect_is(m, 'data.frame')
+ expect_true(all(names(M) %in% union(names(M), names(m))))
+ for (name in names(m)) {
+ expect_equal(M[[name]], m[[name]])
+ }
+
+
+ ## Original example that smoked out the bug
+ M <- data.table(a=letters[1:10], b=1:10)
+ m <- as.data.frame(M)
+ ms <- lapply(1:3, function(x) data.table(a=letters[1:10], b=1:10 * 10^x))
+
+ for (i in 1:3) {
+ M <- merge(M, ms[[i]], by='a', suffixes=c("", sprintf(".%d", i)))
+ }
+
+ for (i in 1:3) {
+ m <- merge(m, as.data.frame(ms[[i]]), by='a',
+ suffixes=c("", sprintf(".%d", i)))
+ }
+
+ expect_is(M, 'data.table')
+ expect_is(m, 'data.frame')
+ expect_true(all(names(M) %in% union(names(M), names(m))))
+ for (name in names(m)) {
+ expect_equal(M[[name]], m[[name]])
+ }
+})
+
+###############################################################################
+## subset
+test_that("simple subset maintains keys", {
+ dt <- data.table(a=sample(c('a', 'b', 'c'), 20, replace=TRUE),
+ b=sample(c('a', 'b', 'c'), 20, replace=TRUE),
+ c=sample(20), key='a')
+ sub <- subset(dt, a == 'b')
+ expect_equal(key(dt), key(sub))
+})
+
+test_that("subset using 'select' maintains key appropriately", {
+ dt <- data.table(a=sample(c('a', 'b', 'c'), 20, replace=TRUE),
+ b=sample(c('a', 'b', 'c'), 20, replace=TRUE),
+ c=sample(20), key=c('a', 'b'))
+
+ sub.1 <- subset(dt, a == 'a', select=c('c', 'b', 'a'))
+ expect_equal(key(sub.1), key(dt), info="reordering columns")
+
+ sub.2 <- subset(dt, a == 'a', select=c('a', 'c'))
+ expect_equal(key(sub.2), 'a', info="selected columns are prefix of key")
+
+ sub.3 <- subset(dt, a == 'a', select=c('b', 'c'))
+ expect_true(is.null(key(sub.3)),
+ info="selected columns do not from a key prefix")
+
+ sub.4 <- subset(dt, a == 'cc')
+ expect_equal(nrow(sub.4), 0)
+ expect_true(is.null(key(sub.4)))
+})
+
+###############################################################################
+## transform
+test_that("transform maintains keys", {
+ dt <- data.table(a=sample(c('a', 'b', 'c'), 20, replace=TRUE),
+ b=sample(c('a', 'b', 'c'), 20, replace=TRUE),
+ c=sample(20), key=c('a', 'b'))
+
+ t1 <- transform(dt, d=c+4)
+ expect_equal(key(t1), key(dt))
+ expect_equal(t1$d, dt$c + 4, info="transform was successful")
+
+ t2 <- transform(dt, d=c+4, a=sample(c('x', 'y', 'z'), 20, replace=TRUE))
+ expect_true(is.null(key(t2)), info="transforming a key column nukes the key")
+
+ ## This is probably not necessary, but let's just check that transforming
+ ## a key column doesn't twist around the rows in the result.
+ for (col in c('b', 'c')) {
+ msg <- sprintf("mutating-key-transform maintains other columns [%s]", col)
+ expect_equal(t2[[col]], dt[[col]], info=msg)
+ }
+})
diff --git a/inst/tests/tests.Rraw b/inst/tests/tests.Rraw
new file mode 100644
index 0000000..a00c2b9
--- /dev/null
+++ b/inst/tests/tests.Rraw
@@ -0,0 +1,6994 @@
+
+# This file should be clean of non-ASCII characters; e.g. CRAN Solaris
+# Use: grep --color='auto' -P -n "[\x80-\xFF]" tests.Rraw
+
+if (!exists("test.data.table",.GlobalEnv,inherits=FALSE)) {
+ require(data.table) # in dev the package should not be loaded
+ options(warn=0) # use require() so it warns but doesn't halt if not available
+ require(plyr)
+ require(ggplot2)
+ require(hexbin)
+ require(nlme)
+ require(xts)
+ require(bit64)
+ require(gdata)
+ require(GenomicRanges)
+ require(caret)
+ require(knitr)
+ require(plm)
+ # reshape2 ahead of reshape ...
+ try(detach(package:reshape2),silent=TRUE)
+ try(detach(package:reshape),silent=TRUE)
+ library(reshape2, pos="package:base", logical.return=TRUE)
+ library(reshape, pos="package:base", logical.return=TRUE)
+ .devtesting=FALSE
+} else {
+ # Matt has suppressMessages(require(bit64)) in .Rprofile
+ if ("package:reshape2" %in% search()) {
+ detach(package:reshape2)
+ library(reshape2, pos="package:base", logical.return=TRUE)
+ }
+ if ("package:reshape" %in% search()) {
+ detach(package:reshape)
+ library(reshape, pos="package:base", logical.return=TRUE)
+ }
+ .devtesting=TRUE
+}
+options(warn=2)
+
+nfail = ntest = lastnum = 0
+whichfail = NULL
+
+.timingtests = FALSE
+started.at = Sys.time()
+oldbwb = options(datatable.old.bywithoutby=FALSE) # in case user set it, or set in dev
+
+if (!.devtesting) {
+ test = data.table:::test
+ compactprint = data.table:::compactprint
+ is.sorted = data.table:::is.sorted
+ forderv = data.table:::forderv
+ forder = data.table:::forder
+ null.data.table = data.table:::null.data.table
+ ordernumtol = data.table:::ordernumtol # TO DO: deprecated, remove
+ iradixorder = data.table:::iradixorder # TO DO: deprecated, remove
+ dradixorder = data.table:::dradixorder # TO DO: deprecated, remove
+ uniqlist = data.table:::uniqlist
+ uniqlengths = data.table:::uniqlengths
+ setrev = data.table:::setrev
+ setreordervec = data.table:::setreordervec
+ selfrefok = data.table:::selfrefok
+ setattr = data.table::setattr # so as not to use bit::setattr
+ .R.listCopiesNamed = data.table:::.R.listCopiesNamed
+ .R.assignNamesCopiesAll = data.table:::.R.assignNamesCopiesAll
+ .R.subassignCopiesOthers = data.table:::.R.subassignCopiesOthers
+ .R.subassignCopiesVecsxp = data.table:::.R.subassignCopiesVecsxp
+ setdiff_ = data.table:::setdiff_
+ frankv = data.table:::frankv
+ is_na = data.table:::is_na
+ shallow = data.table:::shallow # until exported
+ chmatch2 = data.table:::chmatch2
+ which_ = data.table:::which_
+ shift = data.table:::shift
+ any_na = data.table:::any_na
+ replace_dot = data.table:::replace_dot
+ isReallyReal = data.table:::isReallyReal
+ between = data.table::between
+ which.first = data.table:::which.first
+ which.last = data.table:::which.last
+ trim = data.table:::trim
+ `%+%.default` = data.table:::`%+%.default`
+ .shallow = data.table:::.shallow
+ getdots = data.table:::getdots
+}
+
+# test for covering tables.R 100%, we need to run tables() before creating any data.tables to return null data.table
+test(0, tables(), null.data.table(), output = "No objects of class")
+
+TESTDT = data.table(a=as.integer(c(1,3,4,4,4,4,7)), b=as.integer(c(5,5,6,6,9,9,2)), v=1:7)
+setkey(TESTDT,a,b)
+# i.e. a b v
+# [1,] 1 5 1
+# [2,] 3 5 2
+# [3,] 4 6 3
+# [4,] 4 6 4
+# [5,] 4 9 5
+# [6,] 4 9 6
+# [7,] 7 2 7
+INT = function(...) { as.integer(c(...)) }
+##########################
+
+test(1, TESTDT[SJ(4,6),v,mult="first"], 3L)
+test(2, TESTDT[SJ(4,6),v,mult="last"], 4L)
+test(3, TESTDT[SJ(c(4,4,4),c(6,6,7)),v,mult="last",roll=TRUE], INT(4,4,4))
+test(4, TESTDT[SJ(c(4,4,4),c(9,9,10)),v,mult="last",roll=TRUE], INT(6,6,6))
+test(5, TESTDT[SJ(c(4,4,4),c(6,6,7)),v,mult="last",roll=TRUE,rollends=FALSE], INT(4,4,4))
+test(6, TESTDT[SJ(c(4,4,4),c(9,9,10)),v,mult="last",roll=TRUE,rollends=FALSE], INT(6,6,NA))
+test(7, TESTDT[SJ(c(4,4,4),c(9,9,10)),v,mult="first",roll=TRUE,rollends=FALSE], INT(5,5,NA))
+test(8, TESTDT[SJ(c(-9,1,4,4,8),c(1,4,4,10,1)),v], INT(NA,NA,NA,NA,NA))
+test(9, TESTDT[SJ(c(-9,1,4,4,8),c(1,4,4,10,1)),v,roll=TRUE], INT(NA,NA,NA,6,NA))
+test(10, TESTDT[SJ(c(-9,1,4,4,8),c(1,4,4,10,1)),v,roll=TRUE,rollends=FALSE], INT(NA,NA,NA,NA,NA))
+test(11, TESTDT[SJ(c(-3,2,4,4,5,7,8)),v,mult="first"], INT(NA,NA,3,3,NA,7,NA))
+test(12, TESTDT[SJ(c(-3,2,4,4,5,7,8)),v,mult="first",roll=TRUE], INT(NA,1,3,3,6,7,7))
+test(13, TESTDT[SJ(c(-3,2,4,4,5,7,8)),v,mult="last"], INT(NA,NA,6,6,NA,7,NA))
+test(14, TESTDT[SJ(c(-3,2,4,4,5,7,8)),v,mult="last",roll=TRUE], INT(NA,1,6,6,6,7,7))
+test(15, TESTDT[SJ(c(-3,2,4,4,5,7,8)),v,mult="last",nomatch=0], INT(6,6,7))
+test(16, TESTDT[SJ(c(4)),v], INT(3,4,5,6))
+#test(17, suppressWarnings(TESTDT[SJ(c(4,4)),v,mult="all",incbycols=FALSE][[1]]), INT(3:6,3:6))
+test(18, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",nomatch=0,by=.EACHI][[2]], INT(3:6))
+test(185, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",nomatch=NA], INT(NA,NA,3:6,NA))
+test(19, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",roll=TRUE,nomatch=0], INT(1,3:6,7))
+test(186, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",roll=TRUE,nomatch=NA], INT(NA,1,3:6,7))
+test(20, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=0], INT(1,3:6))
+test(187, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=NA], INT(NA,1,3:6,NA))
+test(21, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",nomatch=0], INT(1,3:4))
+test(188, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",nomatch=NA, allow.cartesian=TRUE], INT(NA,1,NA,3:4,NA,NA,NA))
+test(22, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,nomatch=0], INT(1,3:4,4,6))
+test(189, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,nomatch=NA, allow.cartesian=TRUE], INT(NA,1,NA,3:4,4,6,NA))
+test(23, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=0], INT(1,3:4,4))
+test(190, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=NA,allow.cartesian=TRUE], INT(NA,1,NA,3:4,4,NA,NA))
+test(24, TESTDT[SJ(c(1,NA,4,NA,NA,4,4),c(5,5,6,6,7,9,10)),v,mult="all",roll=TRUE,nomatch=0], INT(1,3:4,5:6,6))
+test(191, TESTDT[SJ(c(1,NA,4,NA,NA,4,4),c(5,5,6,6,7,9,10)),v,mult="all",roll=TRUE,nomatch=NA,allow.cartesian=TRUE], INT(NA,NA,NA,1,3:4,5:6,6))
+# Note that the NAs get sorted to the beginning by the SJ().
+
+# i.e. a b v (same test matrix, repeating here for easier reading of the test cases below)
+# [1,] 1 5 1
+# [2,] 3 5 2
+# [3,] 4 6 3
+# [4,] 4 6 4
+# [5,] 4 9 5
+# [6,] 4 9 6
+# [7,] 7 2 7
+test(25, TESTDT[SJ(4,6),v,mult="first"], 3L)
+test(26, TESTDT[SJ(4,6),v,mult="last"], 4L)
+test(27, TESTDT[J(c(4,4,4),c(7,6,6)),v,mult="last",roll=TRUE], INT(4,4,4))
+test(28, TESTDT[J(c(4,4,4),c(10,9,9)),v,mult="last",roll=TRUE], INT(6,6,6))
+test(29, TESTDT[J(c(4,4,4),c(7,6,6)),v,mult="last",roll=TRUE,rollends=FALSE], INT(4,4,4))
+test(30, TESTDT[J(c(4,4,4),c(10,9,9)),v,mult="last",roll=TRUE,rollends=FALSE], INT(NA,6,6))
+test(31, TESTDT[J(c(4,4,4),c(10,9,9)),v,mult="first",roll=TRUE,rollends=FALSE], INT(NA,5,5))
+test(32, TESTDT[J(c(8,1,4,4,-9),c(1,4,4,10,1)),v], INT(NA,NA,NA,NA,NA))
+test(33, TESTDT[J(c(8,1,4,4,-9),c(1,4,4,10,1)),v,roll=TRUE], INT(NA,NA,NA,6,NA))
+test(34, TESTDT[J(c(8,1,4,4,-9),c(1,4,7,10,1)),v,roll=TRUE,rollends=FALSE], INT(NA,NA,4,NA,NA))
+test(35, TESTDT[J(c(5,4,-3,8,4,7,2)),v,mult="first"], INT(NA,3,NA,NA,3,7,NA))
+test(36, TESTDT[J(c(5,4,-3,8,4,7,2)),v,mult="first",roll=TRUE], INT(6,3,NA,7,3,7,1))
+test(37, TESTDT[J(c(5,4,-3,8,4,7,2)),v,mult="last"], INT(NA,6,NA,NA,6,7,NA))
+test(38, TESTDT[J(c(5,4,-3,8,4,7,2)),v,mult="last",roll=TRUE], INT(6,6,NA,7,6,7,1))
+test(39, TESTDT[J(c(5,4,-3,8,4,7,2)),v,mult="last",nomatch=0], INT(6,6,7))
+test(40, TESTDT[J(c(4)),v,mult="all"], INT(3,4,5,6))
+test(41, TESTDT[J(c(4,4)),v,mult="all", allow.cartesian=TRUE], INT(3:6,3:6))
+test(42, TESTDT[J(c(8,2,4,-3)),v,mult="all",nomatch=0], INT(3:6))
+test(192, TESTDT[J(c(8,2,4,-3)),v,mult="all",nomatch=NA], INT(NA,NA,3:6,NA))
+test(43, TESTDT[J(c(8,2,4,-3)),v,mult="all",roll=TRUE,nomatch=0], INT(7,1,3:6))
+test(193, TESTDT[J(c(8,2,4,-3)),v,mult="all",roll=TRUE,nomatch=NA], INT(7,1,3:6,NA))
+#test(44, suppressWarnings(TESTDT[J(c(8,4,2,-3)),v,mult="all",roll=TRUE,rollends=FALSE,incbycols=FALSE]), INT(3:6,1))
+test(45, TESTDT[J(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",nomatch=0], INT(1,3:4))
+test(194, TESTDT[J(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",nomatch=NA,allow.cartesian=TRUE], INT(NA,1,NA,3:4,NA,NA,NA))
+test(46, TESTDT[J(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,nomatch=0], INT(1,3:4,4,6))
+test(195, TESTDT[J(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,nomatch=NA,allow.cartesian=TRUE], INT(NA,1,NA,3:4,4,6,NA))
+test(47, TESTDT[J(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=0], INT(1,3:4,4))
+test(196, TESTDT[J(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=NA,allow.cartesian=TRUE], INT(NA,1,NA,3:4,4,NA,NA))
+test(48, TESTDT[J(c(-9,NA,4,NA,1,4,4),c(1,5,9,6,5,9,10)),v,mult="all",roll=TRUE,nomatch=0], INT(5:6,1,5:6,6)) # this time the NAs stay where they are. Compare to test 24 above.
+test(197, TESTDT[J(c(-9,NA,4,NA,1,4,4),c(1,5,9,6,5,9,10)),v,mult="all",roll=TRUE,nomatch=NA,allow.cartesian=TRUE], INT(NA,NA,5:6,NA,1,5:6,6))
+test(49, TESTDT[J(c(4,1,0,5,3,7,NA,4,1),c(6,5,1,10,5,2,1,6,NA)),v,nomatch=0], INT(3,4,1,2,7,3,4))
+test(198, TESTDT[J(c(4,1,0,5,3,7,NA,4,1),c(6,5,1,10,5,2,1,6,NA)),v,nomatch=NA,allow.cartesian=TRUE], INT(3,4,1,NA,NA,2,7,NA,3,4,NA))
+test(50, TESTDT[J(c(4,1,0,5,3,7,NA,4,1),c(6,5,1,10,5,2,1,6,NA)),v,mult="last",nomatch=0], INT(4,1,2,7,4))
+test(199, TESTDT[J(c(4,1,0,5,3,7,NA,4,1),c(6,5,1,10,5,2,1,6,NA)),v,mult="last",nomatch=NA], INT(4,1,NA,NA,2,7,NA,4,NA))
+
+TESTDT[, a:=letters[a]]
+setkey(TESTDT,a,b)
+# i.e. a b v
+# [1,] a 5 1
+# [2,] c 5 2
+# [3,] d 6 3
+# [4,] d 6 4
+# [5,] d 9 5
+# [6,] d 9 6
+# [7,] g 2 7
+test(51, TESTDT[SJ(c("d","d","e","g"),c(6,7,1,2)),v,mult="all",roll=TRUE,nomatch=0], INT(3:4,4,7))
+test(200, TESTDT[SJ(c("d","d","e","g"),c(6,7,1,2)),v,mult="all",roll=TRUE,nomatch=NA], INT(3:4,4,NA,7))
+test(52, TESTDT[J(c("g","d","e","d"),c(6,6,1,2)),v,mult="all",roll=TRUE,nomatch=0], INT(7,3:4))
+test(201, TESTDT[J(c("g","d","e","d"),c(6,6,1,2)),v,mult="all",roll=TRUE,nomatch=NA], INT(7,3:4,NA,NA))
+
+TESTDT[, b:=letters[b]]
+setkey(TESTDT,a,b)
+# i.e.
+# a b v
+# [1,] a e 1
+# [2,] c e 2
+# [3,] d f 3
+# [4,] d f 4
+# [5,] d i 5
+# [6,] d i 6
+# [7,] g b 7
+test(53, TESTDT[SJ(c("d","d","e","g"),c("f","g","a","b")),v,mult="last"], INT(4,NA,NA,7))
+test(54, TESTDT[J(c("g","d","e","d"),c("b","g","a","f")),v,mult="last"], INT(7,NA,NA,4)) # this tests (d,g) ok even though there is an NA in last match in the roll.
+test(55, TESTDT[SJ(c("d","d","e","g"),c("f","g","a","b")),v,mult="first"], INT(3,NA,NA,7))
+test(56, TESTDT[J(c("g","d","e","d"),c("b","g","a","f")),v,mult="first"], INT(7,NA,NA,3))
+test(57, TESTDT[J(c("g","d","d","d","e","d"),c("b","g","k","b","a","f")),v,roll=TRUE], INT(7,4,6,NA,NA,3,4))
+# test 58 removed. Tested this failed (rolling join on factors) pre character columns, now works.
+test(59, TESTDT[J(c("g","d","d","d","e","d"),c("b","g","k","b","a","f")),v,roll=TRUE,rollends=FALSE], INT(7,4,NA,NA,NA,3,4))
+# test 60 removed. Tested this failed (rolling join on factors) pre character columns, now works.
+
+# Tests 61-66 were testing sortedmatch which is now replaced by chmatch for characters, and removed
+# for integers until needed.
+
+# Test 67 removed. No longer use factors so debate/problem avoided.
+# [.factor and c.factor are no longer present in data.table, not even hidden away
+# X = factor(letters[1:10])
+# test(67, levels(X[4:6]), letters[4:6])
+
+test(68, "TESTDT" %in% tables(silent=TRUE)[,NAME]) # NAME is returned as a column in which we look for the string
+test(69, "TESTDT" %in% tables(silent=TRUE)[,as.character(NAME)]) # an old test (from when NAME was factor) but no harm in keeping it
+test(69.1, names(tables(silent=TRUE)), c("NAME","NROW","NCOL","MB","COLS","KEY"))
+test(69.2, names(tables(silent=TRUE,mb=FALSE)), c("NAME","NROW","NCOL","COLS","KEY"))
+
+a = "d"
+# Variable Twister. a in this scope has same name as a inside DT scope.
+# Aug 2010 : As a result of bug 1005, and consistency with 'j' and 'by' we now allow self joins (test 183) in 'i'.
+test(70, TESTDT[eval(J(a)),v,by=.EACHI], data.table(a="d",v=3:6,key="a")) # the eval() enabled you to use the 'a' in the calling scope, not 'a' in the TESTDT. TO DO: document this.
+test(71, TESTDT[eval(SJ(a)),v,by=.EACHI], data.table(a="d",v=3:6,key="a"))
+test(72, TESTDT[eval(CJ(a)),v,by=.EACHI], data.table(a="d",v=3:6,key="a"))
+
+test(73, TESTDT[,v], 1:7)
+test(74, TESTDT[,3], 3)
+test(74.5, TESTDT[,3L], 3L)
+test(75, TESTDT[,"v"], "v")
+test(76, TESTDT[,2:3], 2:3) # See ?[.data.table that with=FALSE is required for the likely intended result
+test(77, TESTDT[,2:3,with=FALSE], data.table(b=c("e","e","f","f","i","i","b"),v=1:7))
+test(78, TESTDT[,c("b","v"),with=FALSE], data.table(b=c("e","e","f","f","i","i","b"),v=1:7))
+colsVar = c("b","v")
+test(79, TESTDT[,colsVar], colsVar)
+test(80, TESTDT[,colsVar,with=FALSE], data.table(b=c("e","e","f","f","i","i","b"),v=1:7))
+
+# works in test.data.table, but not eval(body(test.data.table)) when in R CMD check ... test(81, TESTDT[1:2,c(a,b)], factor(c("a","c","e","e")))
+# It is expected the above to be common source of confusion. c(a,b) is evaluated within
+# the frame of TESTDT, and c() creates one vector, not 2 column subset as in data.frame's.
+# If 2 columns were required use list(a,b). c() can be useful too, but is different.
+
+test(82, TESTDT[,c("a","b")], c("a","b"))
+test(83, TESTDT[,list("a","b")], data.table(V1="a",V2="b"))
+test(83.1, TESTDT[,list("sum(a),sum(b)")], data.table("sum(a),sum(b)"))
+test(83.2, TESTDT[,list("sum(a),sum(b)"),by=a], {tt=data.table(a=c("a","c","d","g"),V1="sum(a),sum(b)",key="a");tt$V1=as.character(tt$V1);tt})
+test(84, TESTDT[1:2,list(a,b)], data.table(a=c("a","c"), b=c("e","e"), key = 'a,b'))
+# test(85, TESTDT[1:2,DT(a,b)], data.table(a=c("a","c"), b=c("e","e"))) #DT() now deprecated
+
+test(86, TESTDT[,sum(v),by="b"], data.table(b=c("e","f","i","b"),V1=INT(3,7,11,7))) # TESTDT is key'd by a,b, so correct that grouping by b should not be key'd in the result by default
+test(87, TESTDT[,list(MySum=sum(v)),by="b"], data.table(b=c("e","f","i","b"),MySum=INT(3,7,11,7)))
+test(88, TESTDT[,list(MySum=sum(v),Sq=v*v),by="b"][1:3], data.table(b=c("e","e","f"),MySum=INT(3,3,7),Sq=INT(1,4,9))) # silent repetition of MySum to match the v*v vector
+# Test 89 dropped. Simplify argument no longer exists. by is now fast and always returns a data.table ... test(89, TESTDT[,sum(v),by="b",simplify=FALSE], list(7L,3L,7L,11L))
+
+# Test 88.5 contributed by Johann Hibschman (for bug fix #1294) :
+test(88.5, TESTDT[a=="d",list(MySum=sum(v)),by=list(b)], data.table(b=c("f","i"), MySum=INT(7,11), key="b"))
+
+setkey(TESTDT,b)
+test(90, TESTDT[J(c("f","i")),sum(v),by=.EACHI], data.table(b=c("f","i"),V1=c(7L,11L),key="b"))
+test(90.5, TESTDT[J(c("i","f")),sum(v),by=.EACHI], data.table(b=c("i","f"),V1=c(11L,7L))) # test not keyed
+test(91, TESTDT[SJ(c("f","i")),sum(v),by=.EACHI], data.table(b=c("f","i"),V1=c(7L,11L),key="b"))
+# Test 92 dropped same reason as 89 ... test(TESTDT[92, J(c("f","i")),sum(v),mult="all",simplify=FALSE], list(7L,11L))
+
+test(93, TESTDT[c("f","i"), which=TRUE], 4:7)
+test(94, TESTDT[c("i","f"), mult="last", which=TRUE], INT(7,5))
+
+test(95, TESTDT["f",v], 3:4)
+test(96, TESTDT["f",v,by=.EACHI], data.table(b="f",v=3:4,key="b"))
+test(97, TESTDT[c("f","i","b"),list(GroupSum=sum(v)),by=.EACHI], data.table(b=c("f","i","b"), GroupSum=c(7L,11L,7L)))
+# that line above doesn't create a key on the result so that the order fib is preserved.
+test(98, TESTDT[SJ(c("f","i","b")),list(GroupSum=sum(v)),by=.EACHI], data.table(b=c("b","f","i"), GroupSum=c(7L,7L,11L), key="b"))
+# line above is the way to group, sort by group and setkey on the result by group.
+
+dt <- data.table(A = rep(1:3, each=4), B = rep(11:14, each=3), C = rep(21:22, 6), key = "A,B")
+test(99, unique(dt), data.table(dt[c(1L, 4L, 5L, 7L, 9L, 10L)], key="A,B"))
+
+# test [<- for column assignment
+dt1 <- dt2 <- dt
+test(100, {dt1[,"A"] <- 3L; dt1}, {dt2$A <- 3L; dt2})
+
+# test transform and within
+test(101, within(dt, {D <- B^2}), transform(dt, D = B^2))
+test(102, within(dt, {A <- B^2}), transform(dt, A = B^2))
+
+# test .SD object
+test(103, dt[, sum(.SD$B), by = "A"], dt[, sum(B), by = "A"])
+test(104, dt[, transform(.SD, D = min(B)), by = "A"], dt[, list(B,C,D=min(B)), by = "A"])
+
+# test numeric and comparison operations on a data table
+test(105, all(dt + dt > dt))
+test(106, all(dt + dt > 1))
+test(107, dt + dt, dt * 2L)
+
+# test a few other generics:
+test(108, dt, data.table(t(t(dt)),key="A,B"))
+test(109, all(!is.na(dt)))
+dt2 <- dt
+dt2$A[1] <- NA # removes key
+test(110, sum(is.na(dt2)), 1L)
+test(111, {setkey(dt,NULL);dt}, na.omit(dt))
+test(112, dt2[2:nrow(dt2),A], na.omit(dt2)$A)
+
+# test [<- assignment:
+dt2[is.na(dt2)] <- 1L
+test(113, {setkey(dt,NULL);dt}, dt2) # key should be dropped because we assigned to a key column
+# want to discourage this going forward (inefficient to create RHS like this)
+# dt2[, c("A", "B")] <- dt1[, c("A", "B"), with = FALSE]
+# test(114, dt1, dt2)
+## doesn't work, yet:
+## dt2[rep(TRUE, nrow(dt)), c("A", "B")] <- dt1[, c("A", "B"), with = FALSE]
+## dt2[rep(TRUE, nrow(dt)), c("A")] <- dt1[, c("A"), with = FALSE]
+## test(dt, dt2)) stop("Test 112 failed")
+
+# test the alternate form of setkey:
+dt1 = copy(dt)
+dt2 = copy(dt)
+setkeyv(dt1, "A")
+setkey(dt2, A)
+test(115, dt1, dt2)
+
+# Test dogroups works correctly for character/factor columns
+test(116, TESTDT[,a[1],by="b"], data.table(b=c("b","e","f","i"), V1=c("g","a","d","d"), key="b"))
+test(117, TESTDT[,list(a[1],v[1]),by="b"], data.table(b=c("b","e","f","i"), V1=c("g","a","d","d"), V2=INT(7,1,3,5), key="b"))
+
+# We no longer check i for out of bounds, for consistency with data.frame and e.g. cbind(DT[w],DT[w+1]). NA rows should be returned for i>nrow
+test(118, TESTDT[8], data.table(a=as.character(NA), b=as.character(NA), v=as.integer(NA), key="b"))
+test(119, TESTDT[6:9], data.table(a=c("d","d",NA,NA), b=c("i","i",NA,NA), v=c(5L,6L,NA,NA)))
+
+n=10000
+grp1=sample(1:50,n,replace=TRUE)
+grp2=sample(1:50,n,replace=TRUE)
+dt=data.table(x=rnorm(n),y=rnorm(n),grp1=grp1,grp2=grp2)
+tt = system.time(ans <- dt[,list(.Internal(mean(x)),.Internal(mean(y))),by="grp1,grp2"])
+# test(120, tt[1] < 0.5) # actually takes more like 0.068 << 0.5, but the micro EC2 instance can be slow sometimes.
+# TO DO: incorporate performance testing into R CMD check (using testthat?), that somehow copes with running on slow machines.
+i = sample(nrow(ans),1)
+test(121, all.equal(ans[i,c(V1,V2)], dt[grp1==ans[i,grp1] & grp2==ans[i,grp2], c(mean(x),mean(y))]))
+# To DO: add a data.frame aggregate method here and check data.table is faster
+
+# Tests of 0 and 1 row tables
+TESTDT = data.table(NULL)
+test(122, TESTDT[1], TESTDT)
+test(123, TESTDT[0], TESTDT)
+test(124, TESTDT[1:10], TESTDT)
+test(125, TESTDT["k"], error="x must be keyed")
+# test 126 no longer needed now that test() has 'error' argument
+
+TESTDT = data.table(a=3L,v=2L,key="a") # testing 1-row table
+test(127, TESTDT[J(3)], TESTDT)
+test(128, TESTDT[J(4)], data.table(a=4L,v=NA_integer_,key="a")) # see tests 206-207 too re the [NA]
+test(129, TESTDT[J(4),roll=TRUE], data.table(a=4L,v=2L,key="a")) # the i values are in the result now (which make more sense for rolling joins, the x.a can still be accessed if need be)
+test(130, TESTDT[J(4),roll=TRUE,rollends=FALSE], data.table(a=4L,v=NA_integer_,key="a"))
+test(131, TESTDT[J(-4),roll=TRUE], data.table(a=-4L,v=NA_integer_,key="a"))
+
+test(132, ncol(TESTDT[0]), 2L)
+test(133, TESTDT[0][J(3)], data.table(a=3L,v=NA_integer_,key="a")) # These need to retain key for consistency (edge cases of larger sorted i)
+
+# tests on data table names, make.names is now FALSE by default from v1.8.0
+x = 2L; `1x` = 4L
+dt = data.table(a.1 = 1L, b_1 = 2L, "1b" = 3L, `a 1` = 4L, x, `1x`, 2*x)
+test(134, names(dt), c("a.1", "b_1", "1b", "a 1", "x", "V6", "V7"))
+dt = data.table(a.1 = 1L, b_1 = 2L, "1b" = 3L, `a 1` = 4L, x, `1x`, 2*x, check.names=TRUE)
+test(134.5, names(dt), c("a.1", "b_1", "X1b", "a.1.1", "x", "V6", "V7"))
+
+dt = data.table(a.1 = 1L, b_1 = 2L, "1b" = 3L, `a 1` = 4L, x, `1x`, 2*x, check.names = FALSE)
+test(135, names(dt), c("a.1", "b_1", "1b", "a 1", "x", "V6", "V7")) # the last two terms differ from data.frame()
+
+test(136, dt[,b_1, by="a.1"], data.table(a.1=1L,"b_1"=2L))
+test(137, dt[,`a 1`, by="a.1"], data.table(a.1=1L,"a 1"=4L, check.names=FALSE))
+test(138, dt[,a.1, by="`a 1`"], data.table(`a 1`=4L,a.1=1L, check.names=FALSE))
+
+# tests with NA's in factors
+dt = data.table(a = c(NA, letters[1:5]), b = 1:6)
+test(139, dt[,sum(b), by="a"], data.table(a = c(NA, letters[1:5]), V1 = 1:6))
+
+# tests to make sure rbind and grouping keep classes
+dt = data.table(a = rep(as.Date("2010-01-01"), 4), b = rep("a",4))
+test(140, rbind(dt,dt), data.table(a = rep(as.Date("2010-01-01"), 8), b = rep("a",8)))
+test(141, dt[,list(a=a), by="b"], dt[,2:1, with = FALSE])
+
+dt$a <- structure(as.integer(dt$a), class = "Date")
+test(142, dt[,list(b=b), by="a"], dt)
+
+dt = data.table(x=1:5,y=6:10)
+test(143, tail(dt), dt) # tail was failing if a column name was called x.
+
+dt <- data.table(a = rep(1:3, each = 4), b = LETTERS[1:4], b2 = LETTERS[1:4])
+test(144, dt[, .SD[3,], by=b], data.table(b=LETTERS[1:4],a=3L,b2=LETTERS[1:4]))
+
+DT = data.table(x=rep(c("a","b"),c(2,3)),y=1:5)
+xx = capture.output(ans <- DT[,{print(x);sum(y)},by=x,verbose=FALSE])
+test(145, xx, c("[1] \"a\"","[1] \"b\""))
+test(146, ans, data.table(x=c("a","b"),V1=c(3L,12L)))
+
+test(147, DT[,MySum=sum(v)], error="unused argument") # user meant DT[,list(MySum=sum(v))]. FR#204 done.
+
+dt = data.table(a=c(1L,4L,5L), b=1:3, key="a")
+test(148, dt[CJ(2:3),roll=TRUE], data.table(a=c(2L,3L),b=c(1L,1L),key="a"))
+test(149, dt[J(2:3),roll=TRUE], data.table(a=c(2L,3L),b=c(1L,1L))) # in future this will detect the subset is ordered and retain the key
+
+# 150:158 test out of order factor levels in key columns (now allowed from v1.8.0)
+dt = data.table(x=factor(c("c","b","a"),levels=c("b","a","c")),y=1:3)
+setkey(dt,x)
+test(150.1, dt["b",y,verbose=TRUE], output="Coercing character column i.'V1' to factor") # changed i.V1 to i.x as per FR #2693
+test(150.2, dt["b",y], 2L)
+# from Tom's post :
+a = data.table(a=rep(1:5, 2), b=factor(letters[rep(1:5, each =2)], levels=letters[5:1]), key="b")
+test(151.1, a[J("b"),a,verbose=TRUE], output="Coercing character column i.'V1' to factor") # message back to `i.V1` now. 'b' still accessible to satisfy FR #2693, checked on next line
+test(151.2, a[J("b"),a], 3:4)
+# stretch tests further, two out of order levels, one gets key'd the other not :
+a = data.table(x=factor(letters[rep(1:5, each =2)], levels=letters[5:1]),
+ y=factor(letters[rep(c(6,9,7,10,8), each =2)], levels=letters[10:6]),
+ z=1:10)
+test(152, is.sorted(levels(a$x)), FALSE)
+test(153, is.sorted(levels(a$y)), FALSE)
+test(154, a[,sum(z),by=x][1,paste(x,V1)], "a 3") # ad hoc by doesn't sort the groups so 'a' (5th level) should be first
+setkey(a,x) # 'e' (level 1) should come first now.
+test(155, is.sorted(levels(a$x)), FALSE)
+test(156, is.sorted(levels(a$y)), FALSE)
+test(157, a[,sum(z),by=x][1,paste(x,V1)], "e 19") # 1st level is now first
+test(158, a[,sum(z),by=y][1,paste(y,V1)], "h 19") # not 'f'
+test(158.5, a[,sum(z),keyby=y][1,paste(y,V1)], "j 15") # not 'f' either
+
+
+# tests of by expression variables
+DT = data.table( a=1:5, b=11:50, d=c("A","B","C","D"), f=1:5, grp=1:5 )
+f = quote( list(d) )
+test(159, DT[,mean(b),by=eval(f)], DT[,mean(b),by=list(d)]) # column f doesn't get in the way of expression f
+foo = function( grp ) {
+ DT[,mean(b),by=eval(grp)]
+}
+test(160, foo(quote(list(d))), DT[,mean(b),by=list(d)])
+test(161, foo(quote(list(d,a))), DT[,mean(b),by=list(d,a)])
+test(162, foo(quote(list(f))), DT[,mean(b),by=list(f)])
+test(163, foo(quote(list(grp))), DT[,mean(b),by=list(grp)]) # grp local variable in foo doesn't conflict with column grp
+test(164, foo(f), DT[,mean(b),by=d])
+
+# checks that data.table inherits methods from data.frame in base ok
+test(165, subset(DT,a>2), DT[a>2])
+test(166, suppressWarnings(split(DT,DT$grp)[[2]]), DT[grp==2])
+
+if ("package:ggplot2" %in% search()) {
+ test(167,names(print(ggplot(DT,aes(b,f))+geom_point())),c("data","panel","plot"))
+ # The names() is a stronger test that it has actually plotted, but also because test() sees the invisible result
+ test(167.1,DT[,print(ggplot(.SD,aes(b,f))+geom_point()),by=list(grp%%2L)],data.table(grp=integer())) # %%2 because there are 5 groups in DT data at this stage, just need 2 to test
+ # New test reported by C Neff on 11 Oct 2011
+ if ("package:hexbin" %in% search())
+ test(167.2, names(print(ggplot(DT) + geom_hex(aes(b, f)) + facet_wrap(~grp))), c("data","panel","plot"))
+ else
+ cat("Test 167.2 not run. If required call library(hexbin) first.\n")
+
+ # Test plotting ITime with ggplot2 which seems to require an as.data.frame method for ITime, #1713
+ datetimes = c("2011 NOV18 09:29:16", "2011 NOV18 10:42:40", "2011 NOV18 23:47:12",
+ "2011 NOV19 01:06:01", "2011 NOV19 11:35:34", "2011 NOV19 11:51:09")
+ DT = IDateTime(strptime(datetimes,"%Y %b%d %H:%M:%S"))
+ test(168, print(DT[,qplot(idate,itime)])$ranges, print(qplot(DT$idate,DT$itime))$ranges)
+ test(168.1, print(DT[,qplot(idate,as.POSIXct(itime,tzone=""))])$ranges, print(qplot(idate,as.POSIXct(itime,tzone=""),data=DT))$ranges)
+
+ try(graphics.off(),silent=TRUE)
+ #try(graphics.off(),silent=TRUE) # R CMD check doesn't like graphics it seems, even when inside try()
+} else {
+ cat("Tests 167-168 not run. If required call library(ggplot2) first.\n")
+ # ggplot takes a long time so we don't include these by default
+ # From examples, the library(ggplot2) is done first, so that 'R CMD check' does include tests 167-168
+}
+
+# test of . in formula, using inheritance
+DT = data.table(y=1:100,x=101:200,y=201:300,grp=1:5)
+test(169,DT[,as.list(lm(y~0+.,.SD)$coef),by=grp][2,x]-2<1e-10, TRUE)
+
+DT <- data.table( a=1:4, d=c("A","B","C","D") )
+g <- quote( list( d ) )
+test(170, DT[,list(d)], DT[,eval(g)])
+
+DT = data.table(A=c(25L,85L,25L,25L,85L), B=c("a","a","b","c","c"), C=c(2,65,9,82,823))
+test(171.1, DT[B=="b"][A==85], output="Empty data.table (0 rows) of 3 cols: A,B,C")
+test(171.2, DT[B=="b"][A==85,C], numeric())
+test(171.3, DT[ , data.table( A, C )[ A==25, C ] + data.table( A, C )[ A==85, C ], by=B ], data.table(B=c("a","c"),V1=c(67,905)))
+test(172, DT[ , list(3,data.table( A, C )[ A==25, C ] + data.table( A, C )[ A==85, C ]), by=B ], data.table(B=c("a","b","c"),V1=3,V2=c(67,NA,905)))
+
+# Test growing result in memory. Usually the guess is good though.
+# This example returns no rows for first group so guess for up-front allocate needs a reallocate
+DT = data.table(A=c(1L,1L,2L,2L,3L,3L), B=1:6)
+test(173, DT[,B[B>3],by=A][,V1], c(4L,5L,6L))
+
+# Example taken from Harish post to datatable-help on 11 July
+DT <- data.table(
+ A=c("a","a","b","b","d","c","a","d"),
+ B=c("x1","x2","x2","x1","x2","x1","x1","x2"),
+ C=c(5,2,3,4,9,5,1,9)
+ )
+test(174, DT[,C[C-min(C)<3],by=list(A,B)][,V1], c(1,2,3,4,9,9,5))
+test(175, DT[,C[C-min(C)<5],by=list(A,B)][,V1], c(5,1,2,3,4,9,9,5))
+
+# Tests of data.table sub-assignments: $<-.data.table & [<-.data.table
+DT = data.table(a = c("A", "Z"), b = 1:10, key = "a")
+DT[J("A"),2] <- 100L # without L generates nice warning :-)
+DT[J("A"),"b"] <- 1:5
+DT[1:3,"b"] <- 33L
+test(176, DT, data.table(a = rep(c("A", "Z"), each = 5),
+ b = as.integer(c(rep(33, 3), 4:5, seq(2, 10, by = 2))),
+ key = "a"))
+DT[J("A"),"a"] <- "Z"
+test(177, DT, data.table(a="Z", b=as.integer(c(rep(33, 3), 4:5, seq(2, 10, by = 2))))) # i.e. key dropped and column a still factor
+
+DT <- data.table(a = c("A", "Z"), b = 1:10, key = "a")
+DT$b[1:5] <- 1:5
+DT$b[1:3] <- 33
+test(178, DT, data.table(a = rep(c("A", "Z"), each = 5),
+ b = c(rep(33, 3), 4:5, seq(2, 10, by = 2)),
+ key = "a"))
+DT$a <- 10:1
+test(179, key(DT), NULL )
+
+# Test logical in a key
+DT = data.table(a=rep(1:3,each=2),b=c(TRUE,FALSE),v=1:6)
+setkey(DT,a,b)
+test(180, DT[J(2,FALSE),v], 4L)
+test(181, DT[,sum(v),by=b][,V1], c(12L,9L))
+
+# Test fix for bug 1026 reported by Harish V
+# this test needed a unique var name to generate error 'object 'b' not found'.
+# Otherwise it finds 'b' in local scope.
+setnames(DT,2,"buniquename314")
+bar = function( data, fcn ) {
+ q = substitute( fcn )
+ xx = data[,eval(q),by=a]
+ yy = data[,eval(substitute(fcn)),by=a]
+ identical(xx,yy)
+}
+test(182, bar( DT, sum(buniquename314) ), TRUE)
+
+# Test bug 1005 reported by Branson Owen
+DT = data.table(A = c("o", "x"), B = 1:10, key = "A")
+test(183, DT[J(unique(A)), B], DT$B)
+
+# Test bug 709 which returned an error here. And return type now empty table, #1945 in 1.8.1.
+xx = data.table(a=1:5,b=6:10)
+test(184, xx[a>6,sum(b),by=a], data.table(a=integer(),V1=integer()))
+
+# Tests of bug 1015 highlight by Harish
+# See thread "'by without by' now heeds nomatch=NA"
+# Tests 185-201 were added in above next to originals
+x <- data.table(a=c("a","b","d","e"),b=c("A","A","B","B"),d=c(1,2,3,4), key="a,b")
+y <- data.table(g=c("a","b","c","d"),h=c("A","A","A","A"))
+test(202, x[y], x[y,mult="all"])
+test(203, x[y,d], c(1,2,NA,NA))
+test(204, x[y,list(d)]$d, x[y,d])
+test(205, x[y,list(d),mult="all"][,d], c(1,2,NA,NA))
+
+# Test [NA] returns one NA row. NA is type *logical* so prior to
+# change in v1.5, NA would get silently recycled and the whole table would
+# be returned all NA (rarely useful and often confusing, but consistent
+# with data.frame).
+TESTDT = data.table(a=1:3,v=1:3,key="a")
+test(206, TESTDT[NA], data.table(a=NA_integer_,v=NA_integer_,key="a")) # NA are now allowed in keys, so retains key
+setkey(TESTDT,NULL)
+test(207, TESTDT[NA], data.table(a=NA_integer_,v=NA_integer_))
+
+# With inheritance, NROW and NCOL in base work nicely. No need for them in data.table.
+test(208, NROW(TESTDT), 3L)
+test(209, nrow(TESTDT), 3L)
+test(210, NCOL(TESTDT), 2L)
+test(211, ncol(TESTDT), 2L)
+
+# Test infinite recursion error is trapped when a pre-1.5 data.table
+# is used with 1.5 (bug #1008)
+DT = data.table(a=1:6,key="a")
+test(212, DT[J(3)]$a, 3L) # correct class c("data.table","data.frame")
+class(DT) = "data.table" # incorrect class, but as from 1.8.1 it works. By accident when moving from colnames() to names(), it was dimnames() doing the check, but rather than add a check that identical(class(DT),c("data.frame","data.table")) at the top of [.data.table, we'll leave it flexible to user (user might not want to inherit from data.frame for some reason).
+test(213, DT[J(3)]$a, 3L)
+
+# setkey now auto coerces double and character for convenience, and
+# to solve bug #953
+DF = data.frame(a=LETTERS[1:10], b=1:10, stringsAsFactors=FALSE)
+DT = data.table(DF)
+setkey(DT,a) # used to complain about character
+test(215, DT["C",b], 3L)
+DT = data.table(DF,key="a")
+test(216, DT["C",b], 3L)
+DT = data.table(a=c(1,2,3),v=1:3,key="a")
+test(217, DT[J(2),v], 2L)
+DT = data.table(a=c(1,2.1,3),v=1:3,key="a")
+test(218, DT[J(2.1),v], 2L)
+
+# tests of quote()-ed expressions in i. Bug #1058
+DT = data.table(a=1:5,b=6:10,key="a")
+q = quote(a>3)
+test(220, DT[eval(q),b], 9:10)
+test(221, DT[eval(parse(text="a>4")),b], 10L)
+test(222, DT[eval(parse(text="J(2)")),b], 7L)
+
+# lists in calling scope should be ok as single names passed to by, bug #1060
+DT = data.table(a=1:2,b=rnorm(10))
+byfact = DT[,a] # vector, ok before fix but check anyway
+test(223, DT[,mean(b),by=byfact], DT[,mean(b),by=list(byfact)])
+byfact = DT[,list(a)] # this caused next line to fail before fix
+test(224, DT[,mean(b),by=byfact], DT[,mean(b),by=as.list(byfact)])
+test(225, DT[,mean(b),by=byfact], DT[,mean(b),by={byfact}])
+
+# tests for building expressions via parse, bug #1243
+dt1key<-data.table(A1=1:100,onekey=rep(1:2,each=50))
+setkey(dt1key,onekey)
+ASumExpr<-parse(text="quote(sum(A1))") # no need for quote but we test it anyway because that was work around when test 227 failed
+ASumExprNoQ<-parse(text="sum(A1)")
+ans = dt1key[,sum(A1),by=onekey]
+test(226,ans,dt1key[,eval(eval(ASumExpr)),by=onekey])
+test(227,ans,dt1key[,eval(ASumExprNoQ),by=onekey])
+
+# test for uncommon grouping pattern on 1-row data.table, bug #1245
+DT = data.table(a=1L,b=2L)
+test(228,DT[,list(1:2),by=a],data.table(a=c(1L,1L),V1=1:2))
+
+# special case j=.SD, bug #1247
+DT = data.table(a=rep(1:2,each=2),b=1:4)
+test(229,DT[,.SD,by=a],DT)
+setkey(DT,a)
+test(229.1,DT[,.SD,by=key(DT)],DT)
+
+# merge bug with column 'x', bug #1229
+d1 <- data.table(x=c(1,3,8),y1=rnorm(3), key="x")
+d2 <- data.table(x=c(3,8,10),y2=rnorm(3), key="x")
+ans1=merge(d1, d2, by="x")
+ans2=cbind(d1[2:3],y2=d2[1:2]$y2);setkey(ans2,x)
+test(230, ans1, ans2)
+
+# one column merge, bug #1241
+DT = data.table(a=rep(1:2,each=3),b=1:6,key="a")
+y = data.table(a=c(0,1),bb=c(10,11),key="a")
+test(231,merge(y,DT),data.table(a=1L,bb=11,b=1:3,key="a"))
+test(232,merge(y,DT,all=TRUE),data.table(a=rep(c(0L,1L,2L),c(1,3,3)),bb=rep(c(10,11,NA_real_),c(1,3,3)),b=c(NA_integer_,1:6),key="a"))
+y = data.table(a=c(0,1),key="a") # y with only a key column
+test(233,merge(y,DT),data.table(a=1L,b=1:3,key="a"))
+test(234,merge(y,DT,all=TRUE),data.table(a=rep(c(0L,1L,2L),c(1,3,3)),b=c(NA_integer_,1:6),key="a"))
+
+# 'by' when DT contains list columns
+DT = data.table(a=c(1,1,2,3,3),key="a")
+DT$b=list(1:2,1:3,1:4,1:5,1:6)
+test(235,DT[,mean(unlist(b)),by=a],data.table(a=c(1,2,3),V1=c(1.8,2.5,mean(c(1:5,1:6))),key="a"))
+test(236,DT[,sapply(b,mean),by=a],data.table(a=c(1,1,2,3,3),V1=c(1.5,2.0,2.5,3.0,3.5),key="a"))
+
+# when i is a single name, it no longer evaluates within data.table scope
+DT = data.table(a=1:5,b=rnorm(5),key="a")
+a = list(4)
+test(237,DT[a],DT[J(4)])
+
+# repeat earlier test with xkey instead of x. xkey is internal to merge; the bigger problem Tom mentioned.
+d1 <- data.table(xkey=c(1,3,8),y1=rnorm(3), key="xkey")
+d2 <- data.table(xkey=c(3,8,10),y2=rnorm(3), key="xkey")
+ans2=cbind(d1[2:3],y2=d2[1:2]$y2);setkey(ans2,xkey)
+test(238, merge(d1, d2, by="xkey"), ans2)
+
+# Join Inherited Scope, and X[Y] including Y's non-join columns
+X=data.table(a=rep(1:3,c(3,3,2)),foo=1:8,key="a")
+Y=data.table(a=2:3,bar=6:7)
+test(239, X[Y,sum(foo),by=.EACHI], data.table(a=2:3,V1=c(15L,15L),key="a"))
+test(240, X[Y,sum(foo*bar),by=.EACHI], data.table(a=2:3,V1=c(90L,105L),key="a"))
+test(241, X[Y], data.table(a=rep(2:3,3:2),foo=4:8,bar=rep(6:7,3:2),key="a"))
+test(242, X[Y,list(foo,bar),by=.EACHI][,sum(foo*bar)], 195L)
+test(243, X[Y][,sum(foo*bar)], 195L)
+# not sure about these yet :
+# test(244, X[Y,sum(foo*bar),mult="first"], data.table(a=2:3,V1=c(24L,49L)))
+# test(245, X[Y,sum(foo*bar),mult="last"], data.table(a=2:3,V1=c(36L,56L)))
+
+# joining to less than all X's key colums (in examples but can't see formal test)
+X=data.table(a=rep(LETTERS[1:2],2:3),b=1:5,v=10:14,key="a,b")
+test(246.1, X["A"], X[1:2]) # checks that X[1:2] retains key, too
+test(246.2, key(X["A"]), c("a","b"))
+test(247, X["C"]$v, NA_integer_)
+test(248, nrow(X["C",nomatch=0]), 0L)
+
+x=data.table( a=c("a","b","c"), b=1:3, key="a" )
+y=data.table( a=c("b","d","e"), d=c(8,9,10) )
+test(249, x[y], data.table(a=c("b","d","e"),b=c(2L,NA,NA),d=c(8,9,10))) # keeps i join cols
+test(250, x[y,mult="first"], data.table(a=c("b","d","e"),b=c(2L,NA,NA),d=c(8,9,10))) # same
+
+x=data.table( a=c("a","b","b","c"), b=1:4, key="a" )
+y=data.table(a=c("b","d","b"), d=c(8,9,10))
+test(251, x[y, allow.cartesian=TRUE], data.table(a=c("b","b","d","b","b"),b=c(2:3,NA,2:3),d=c(8,8,9,10,10)))
+
+# auto coerce float to int in ad hoc by (just like setkey), FR#1051
+DT = data.table(a=INT(1,1,1,2,2),v=1:5)
+test(252, DT[,sum(v),by=a], data.table(a=1:2,V1=c(6L,9L)))
+
+# check that by retains factor columns, since character is now default
+DT = data.table(a=factor(c("A","A","A","B","B")),v=1:5)
+test(253, DT[,sum(v),by=a], data.table(a=factor(c("A","B")),V1=c(6L,9L)))
+
+# fix for bug #1298 with by=key(DT) and divisibility error.
+DT=data.table(a=c(1,1,1,2,2),b=1:5,key="a")
+test(254, DT[,sum(b),by=key(DT)]$V1, c(6L,9L))
+
+# for for bug #1294 (combining scanning i and by)
+# also see test 88.5 contributed by Johann Hibschman above.
+DT = data.table(a=1:12,b=1:2,c=1:4)
+test(255, DT[a>5,sum(c),by=b]$V1, c(12L, 7L))
+
+# fix for bug #1301 (all.vars() doesn't appear to find fn in fns[[fn]] usage)
+DT = data.table(a=1:6,b=1:2,c=letters[1:2],d=1:6)
+fns = list(a=max,b=min)
+test(256, DT[,fns[[b[1]]](d),by=c]$V1, c(5L,2L))
+test(257, DT[,fns[[c[1]]](d),by=c]$V1, c(5L,2L))
+fns=c(max,min)
+
+DT = data.table(ID=1:10, SCORE_1=1:10, SCORE_2=11:20, SCORE_3=30:21, fn=c(rep(1, 5), rep(2, 5)))
+test(258, DT[,fns[[fn]](SCORE_1,SCORE_2,SCORE_3),by=ID]$V1, c(30:26,6:10))
+test(259, DT[,as.list(fns[[fn]](SCORE_1,SCORE_2,SCORE_3)),by=ID]$V1, c(30:26,6:10))
+test(260, DT[,list(fns[[fn]](SCORE_1,SCORE_2,SCORE_3)),by=ID]$V1, c(30:26,6:10))
+
+# fix for bug #1340 - Duplicate column names in self-joins (but print ok)
+DT <- data.table(id=1:4, x1=c("a","a","b","c"), x2=c(1L,2L,3L,3L), key="x1")
+test(261, DT[DT, allow.cartesian=TRUE][id < i.id]$i.x2, 2L)
+
+# "<-" within j now assigns in the same environment for 1st group, as the rest
+# Thanks to Andeas Borg for highlighting on 11 May
+
+dt <- data.table(x=c(0,0,1,0,1,1), y=c(0,1,0,1,0,1), z=1:6)
+groupInd = 0
+test(262, dt[,list(z,groupInd<-groupInd+1),by=list(x,y)]$V2, c(1,2,2,3,3,4))
+test(263, groupInd, 0)
+test(264, dt[,list(z,groupInd<<-groupInd+1),by=list(x,y)]$V2, c(1,2,2,3,3,4))
+test(265, groupInd, 4)
+
+# Tests for passing 'by' expressions that evaluate to character column
+# names in the edge case of 1 row; the character 'by' vector could
+# feasibly be intended to be grouping values. Bug 1404; thanks to Andreas Borg
+# for the detailed report, suggested fix and tests.
+
+DT = data.frame(x=1,y="a",stringsAsFactors=FALSE)
+DT = as.data.table(DT)
+test(266,class(DT$y),"character") # just to check we setup the test correctly
+test(267,DT[,sum(x),by=y]$V1,1)
+test(268,DT[,sum(x),by="y"]$V1,1)
+colvars="y"
+test(269,DT[,sum(x),by=colvars]$V1,1)
+setkey(DT,y)
+test(270,DT[,sum(x),by=key(DT)]$V1,1)
+
+DT = data.table(x=1,y=2)
+setkeyv(DT,names(DT))
+test(271, DT[,length(x),by=key(DT)]$V1, 1L)
+
+DT = data.table(x=c(1,2,1), y=c(2,3,2), z=1:3)
+setkeyv(DT,names(DT))
+test(272, DT[,sum(z),by=key(DT)]$V1, c(1L,3L,2L))
+
+
+# Tests for .BY and implicit .BY
+# .BY is a single row, and by variables are now, too. FAQ 2.10 has been changed accordingly.
+DT = data.table(a=1:6,b=1:2)
+test(273, DT[,sum(a)*b,by=b]$V1, c(9L,24L))
+test(274, DT[,sum(a)*.BY[[1]],by=b], data.table(b=1:2,V1=c(9L,24L)))
+test(275, DT[,sum(a)*bcalc,by=list(bcalc=b+1L)], data.table(bcalc=2:3,V1=c(18L,36L)))
+test(276, DT[,sapply(.SD,sum)*b,by=b], data.table(b=1:2,V1=c(9L,24L))) # .SD should no longer include b, unlike v1.6 and before
+test(277, DT[,sapply(.SD,sum)*bcalc,by=list(bcalc=b+1L)], data.table(bcalc=2:3,V1=c(18L,36L))) # cols used in by expressions are excluded from .SD, but can still be used in j (by name only and may vary within the group e.g. DT[,max(diff(date)),by=month(date)]
+test(278, DT[,sum(a*b),by=list(bcalc=b+1L)], data.table(bcalc=2:3,V1=c(9L,24L)))
+
+
+# Test x==y where either column contain NA.
+DT = data.table(x=c(1,2,NA,3,4),y=c(0,2,3,NA,4),z=1:5)
+test(279, DT[x==y,sum(z)], 7L)
+# In data.frame the equivalent is :
+# > DF = as.data.frame(DT)
+# > DF[DF$x==DF$y,]
+# x y z
+# 2 2 2 2
+# NA NA NA NA
+# NA.1 NA NA NA
+# 5 4 4 5
+# > DF[!is.na(DF$x) & !is.na(DF$y) & DF$x==DF$y,]
+# x y z
+# 2 2 2 2
+# 5 4 4 5
+
+
+# Test that 0 length columns are expanded with NA to match non-0 length columns, bug fix #1431
+DT = data.table(pool = c(1L, 1L, 2L), bal = c(10, 20, 30))
+test(280, DT[, list(bal[0], bal[1]), by=pool], data.table(pool=1:2, V1=NA_real_, V2=c(10,30)))
+test(281, DT[, list(bal[1], bal[0]), by=pool], data.table(pool=1:2, V1=c(10,30), V2=NA_real_))
+# Test 2nd group too (the 1st is special) ...
+test(282, DT[, list(bal[ifelse(pool==1,1,0)], bal[1]), by=pool], data.table(pool=1:2, V1=c(10,NA), V2=c(10,30)))
+
+# More tests based on Andreas Borg's post of 11 May 2011.
+DT = data.table(x=INT(0,0,1,0,1,1), y=INT(1,1,0,1,1,1), z=1:6)
+ans = data.table(x=c(0L,1L,1L),y=c(1L,0L,1L),V1=c(1L,1L,2L),V2=c(7L,3L,11L))
+test(283, DT[,list(sum(x[1], y[1]),sum(z)), by=list(x,y)], ans)
+test(284, DT[,list(sum(unlist(.BY)),sum(z)),by=list(x,y)], ans)
+groupCols = c("x", "y")
+test(285, DT[,list(sum(unlist(.BY)),sum(z)),by=groupCols], ans)
+groupExpr = quote(list(x,y))
+test(286, DT[,list(sum(unlist(.BY)),sum(z)),by=groupExpr], ans)
+
+# Bug fix from Damian B on 25 June 2011 :
+DT = data.table(X=c(NA,1,2,3), Y=c(NA,2,1,3))
+setkeyv(DT,c("X","Y"))
+test(287, unique(DT), DT)
+
+# Bug fix #1421: using vars in calling scope in j when i is logical or integer.
+DT = data.table(A=c("a","b","b"),B=c(4,5,NA))
+myvar = 6
+test(288, DT[A=="b",B*myvar], c(30,NA))
+
+# Test new feature in 1.6.1 that i can be plain list (such as .BY)
+DT = data.table(grp=c("a","a","a","a","b","b","b"),v=1:7)
+mysinglelookup = data.table(grp=c("a","b"),s=c(42,84),grpname=c("California","New York"),key="grp")
+setkey(mysinglelookup,grp)
+test(289, DT[,sum(v*mysinglelookup[.BY]$s),by=grp], data.table(grp=c("a","b"),V1=c(420,1512)))
+# In v1.6.2 we will change so that single name j returns a vector, regardless of grouping
+test(290, DT[,list(mysinglelookup[.BY]$grpname,sum(v)),by=grp], data.table(grp=c("a","b"),V1=c("California","New York"),V2=c(10L,18L)))
+
+# Test user defined attributes are retained, see comment in FR#1006
+DT = data.table(a=as.numeric(1:2),b=3:4)
+setattr(DT,"myuserattr",42)
+setkey(DT,a) # a is numeric so a change of type to integer occurs, too, via := which checks selfref is ok
+test(291, attr(DT,"myuserattr"), 42)
+
+# Test new .N symbol
+DT = data.table(a=INT(1,1,1,1,2,2,2),b=INT(3,3,3,4,4,4,4))
+test(292, DT[,.N,by=list(a,b)], data.table(a=c(1L,1L,2L),b=c(3L,4L,4L),N=c(3L,1L,3L)))
+test(293, DT[,list(a+b,.N),by=list(a,b)], data.table(a=c(1L,1L,2L),b=c(3L,4L,4L),V1=4:6,N=c(3L,1L,3L)))
+
+# Test that setkey and := syntax really are by reference, even within functions. You
+# really do need to take a copy first to a new name; force(x) isn't enough.
+
+DT = data.table(a=1:3,b=4:6)
+f = function(x){ force(x)
+ setkey(x) }
+f(DT)
+test(294,key(DT),c("a","b")) # The setkey didn't copy to a local variable. Need to copy first to local variable (with a new name) if required.
+
+f = function(x){ force(x)
+ x[,a:=42L] }
+f(DT)
+test(295,DT,data.table(a=42L,b=4:6)) # := was by reference (fast) and dropped the key, too, because assigned to key column
+
+DT = data.table(a=1:3,b=4:6)
+f = function(x){ x = copy(x)
+ setkey(x) }
+f(DT)
+test(295.1,key(DT),NULL)
+setkey(DT,a)
+f = function(x){ x = copy(x)
+ x[,b:=10:12][J(2),b] } # test copy retains key
+test(295.2,f(DT),11L)
+test(295.3,DT,data.table(a=1:3,b=4:6,key="a")) # The := was on the local copy
+
+
+
+# new feature added 1.6.3, that key can be vector.
+test(296,data.table(a=1:3,b=4:6,key="a,b"),data.table(a=1:3,b=4:6,key=c("a","b")))
+
+# test .SDcols (not speed, just operation)
+DT = data.table(grp=1:3,A1=1:9,A2=10:18,A3=19:27,B1=101:109,B2=110:118,B3=119:127,key="grp")
+test(297,DT[,list(A1=sum(A1),A2=sum(A2),A3=sum(A3)),by=grp], DT[,lapply(.SD,sum),by=grp,.SDcols=2:4])
+
+DT = data.table(a=1:3,b=4:6)
+test(298, {DT$b<-NULL;DT}, data.table(a=1:3)) # delete column
+test(299, DT$c <- as.character(DT$c), error="zero length") # to simulate RHS which could (due to user error) be non NULL but zero length. This copies DT too, so the next test checks that a subsequent := detects and fixes that.
+test(299.1, DT[,c:=42L], data.table(a=1:3,c=42L), warning="Invalid .internal.selfref detected and fixed")
+test(299.2, truelength(DT)>length(DT)) # the := over-allocated, by 100 by default, but user may have changed default so just check '>'
+# FR #2551 - old 299.3 and 299.5 are changed to include length(RHS) > 1 to issue the warning
+test(299.3, DT[2:3,c:=c(42, 42)], data.table(a=1:3,c=42L), warning="Coerced 'double' RHS to 'integer' to match the column's type.*length 3 (nrows of entire table)")
+# FR #2551 - length(RHS) = 1 - no warning for type conversion
+test(299.7, DT[2,c:=42], data.table(a=1:3,c=42L))
+# also see tests 302 and 303. (Ok, new test file for fast assign would be tidier).
+test(299.4, DT[,c:=rep(FALSE,nrow(DT))], data.table(a=1:3,c=FALSE)) # replace c column with logical
+test(299.5, DT[2:3,c:=c(42,0)], data.table(a=1:3,c=c(FALSE,TRUE,FALSE)), warning="Coerced 'double' RHS to 'logical' to match the column's type.*length 3 (nrows of entire table)")
+# FR #2551 is now changed to fit in / fix bug #5442. Stricter warnings are in place now. Check tests 1294.1-34 below.
+test(299.8, DT[2,c:=42], data.table(a=1:3,c=c(FALSE,TRUE,FALSE)), warning="Coerced 'double' RHS to 'logical' to match")
+test(299.9, DT[2,c:=42L], data.table(a=1:3,c=c(FALSE,TRUE,FALSE)), warning="Coerced 'integer' RHS to 'logical' to match")
+test(299.6, DT[2:3,c:=c(0L, 0L)], data.table(a=1:3,c=FALSE), warning="Coerced 'integer' RHS to 'logical' to match the column's type.*length 3 (nrows of entire table)")
+
+
+# Test bug fix #1468, combining i and by.
+DT = data.table(a=1:3,b=1:9,v=1:9,key="a,b")
+test(300, DT[J(1),sum(v),by=b], data.table(b=c(1L,4L,7L),V1=c(1L,4L,7L),key="b"))
+test(300.1, DT[J(1:2),sum(v),by=b], data.table(b=c(1L,4L,7L,2L,5L,8L),V1=c(1L,4L,7L,2L,5L,8L)))
+
+# Test ad hoc by of more than 100,000 levels, see 2nd part of bug #1387 (100,000 from the limit of base::sort.list radix)
+# This does need to be this large, like this in CRAN checks, because sort.list(method="radix") has this limit, which
+# this tests. But it's well under 10 seconds.
+DT = data.table(A=1:10,B=rnorm(10),C=factor(paste("a",1:100010,sep="")))
+test(301, nrow(DT[,sum(B),by=C])==100010)
+DT = data.table(A=1:10,B=rnorm(10),C=paste("a",1:100010,sep=""))
+test(301.1, nrow(DT[,sum(B),by=C])==100010)
+
+# Test fast assign
+DT = data.table(a=c(1L,2L,2L,3L),b=4:7,key="a")
+DT[2,b:=42L] # needs to be on its own line to test DT symbol is changed by reference
+test(302, DT, data.table(a=c(1L,2L,2L,3L),b=c(4L,42L,6L,7L),key="a"))
+DT[J(2),b:=84L]
+test(303, DT, data.table(a=c(1L,2L,2L,3L),b=c(4L,84L,84L,7L),key="a"))
+
+# Test key is dropped when non-dt-aware packages reorder rows of data.table (for example)
+if ("package:plyr" %in% search()) {
+ DT = data.table(a=1:10,b=1:2,key="a")
+ test(304, arrange(DT,b), data.table(a=INT(1,3,5,7,9,2,4,6,8,10),b=INT(1,1,1,1,1,2,2,2,2,2))) # testing no key here, too
+} else {
+ cat("Test 304 not run. If required call library(plyr) first.\n")
+}
+
+# Test that changing colnames keep key in sync.
+# TO DO: will have to do this for secondary keys, too, when implemented.
+DT = data.table(x=1:10,y=1:10,key="x")
+setnames(DT,c("a","b"))
+test(305, key(DT), "a")
+setnames(DT,"a","R")
+test(306, key(DT), "R")
+
+setnames(DT,"b","S")
+test(307, key(DT), "R")
+setnames(DT,c("a","b"))
+test(308, key(DT), "a")
+setnames(DT,1,"R")
+test(309, key(DT), "R")
+
+# Test :=NULL
+DT = data.table(x=1:5,y=6:10,z=11:15,key="y")
+test(310, DT[,x:=NULL], data.table(y=6:10,z=11:15,key="y")) # delete first
+test(311, DT[,y:=NULL], data.table(z=11:15)) # deleting key column also removes key
+test(312, DT[,z:=NULL], data.table(NULL)) # deleting all
+test(313, DT[,a:=1:3], error="") # cannot := a new column to NULL data.table, currently. Must use data.table()
+DT = data.table(a=20:22)
+test(314, {DT[,b:=23:25];DT[,c:=26:28]}, data.table(a=20:22,b=23:25,c=26:28)) # add in series
+test(315, DT[,c:=NULL], data.table(a=20:22,b=23:25)) # delete last
+test(316, DT[,c:=NULL], data.table(a=20:22,b=23:25), warning="Adding new column 'c' then assigning NULL")
+
+
+# Test adding, removing and updating columns via [<- in one step
+DT = data.table(a=1:6,b=1:6,c=1:6)
+DT[,c("a","c","d","e")] <- list(NULL,11:16,42L,21:26)
+test(317, DT, data.table(b=1:6,c=11:16,d=42L,e=21:26))
+
+# Other assignments (covers DT[x==2, y:=5] too, #1502)
+DT[e<24,"b"] <- 99L
+test(318, DT, data.table(b=c(99L,99L,99L,4L,5L,6L),c=11:16,d=42L,e=21:26))
+test(319, DT[b!=99L,b:=99L], data.table(b=99L,c=11:16,d=42L,e=21:26))
+
+# previous within functionality restored, #1498
+DT = data.table(a=1:10)
+test(320, within(DT, {b <- 1:10; c <- a + b})[,list(a,b,c)], data.table(a=1:10,b=1:10,c=as.integer(seq(2,20,length=10))))
+# not sure why within makes columns in order a,c,b, but it seems to be a data.frame thing, too.
+test(321, transform(DT,b=42L,e=a), data.table(a=1:10,b=42L,e=1:10))
+DT = data.table(a=1:5, b=1:5)
+test(322, within(DT, rm(b)), data.table(a=1:5))
+
+# check that cbind dispatches on first argument as expected
+test(323, cbind(DT,DT), data.table(a=1:5,b=1:5,a=1:5,b=1:5)) # no check.names as from v1.8.0 (now we have :=, cbind is used far less anyway)
+test(324, cbind(DT,data.frame(c=1:5)), data.table(a=1:5,b=1:5,c=1:5))
+test(325, rbind(DT,DT), data.table(a=c(1:5,1:5),b=1:5))
+test(326, rbind(DT,data.frame(a=6:10,b=6:10)), data.table(a=1:10,b=1:10))
+
+# test removing multiple columns, and non-existing ones, #1510
+DT = data.table(a=1:5, b=6:10, c=11:15)
+test(327, within(DT,rm(a,b)), data.table(c=11:15))
+test(328, within(DT,rm(b,c)), data.table(a=1:5))
+test(329, within(DT,rm(b,a)), data.table(c=11:15))
+test(330, within(DT,rm(b,c,d)), data.table(a=1:5), warning="object 'd' not found")
+DT[,c("b","a")]=NULL
+test(332, DT, data.table(c=11:15))
+test(333, within(DT,rm(c)), data.table(NULL))
+DT = data.table(a=1:5, b=6:10, c=11:15)
+DT[,2:1]=NULL
+test(334, DT, data.table(c=11:15))
+test(335, DT[,2:1]<-NULL, error="Attempt to assign to column")
+
+DT = data.table(a=1:2, b=1:6)
+test(336, DT[,z:=a/b], data.table(a=1:2,b=1:6,z=(1:2)/(1:6)))
+test(337, DT[3:4,z:=a*b], data.table(a=1:2,b=1:6,z=c(1,1,3,8,1/5,2/6)), warning="Coerced 'integer' RHS to 'double' to match the colum")
+
+
+# test eval of LHS of := (using with=FALSE gives a warning here from v1.9.3)
+DT = data.table(a=1:3, b=4:6)
+test(338, DT[,2:=42L], data.table(a=1:3,b=42L))
+test(339, DT[,2:1:=list(10:12,3L)], data.table(a=3L,b=10:12))
+test(340, DT[,"a":=7:9], data.table(a=7:9,b=10:12))
+test(341, DT[,c("a","b"):=1:3], data.table(a=1:3,b=1:3))
+mycols = "a"
+test(342, DT[,(mycols):=NULL], data.table(b=1:3))
+mynewcol = "newname"
+test(343, DT[,(mynewcol):=21L], data.table(b=1:3,newname=21L))
+mycols = 1:2
+test(344, DT[,(mycols):=NULL], data.table(NULL))
+
+
+# It seems that the .Internal rbind of two data.frame coerces IDate to numeric. Tried defining
+# "[<-.IDate" as per Tom's suggestion, and c.IDate to no avail (maybe because the .Internal code
+# in bind.c doesn't look up package methods?). Anyway, as from 1.8.1, double are allowed in keys, so
+# these still work but for a different reason than before 1.8.1: the results are IDate stored as double,
+# rather than before when is worked because by and setkey coerced double to integer.
+DF = data.frame(x=as.IDate(c("2010-01-01","2010-01-02")), y=1:6)
+DT = as.data.table(rbind(DF,DF))
+test(345, DT[,sum(y),by=x], {.x=as.IDate(c("2010-01-01","2010-01-02"));mode(.x)="double";data.table(x=.x,V1=c(18L,24L))})
+test(346, setkey(DT,x)[J(as.IDate("2010-01-02"))], {.x=as.IDate(rep("2010-01-02",6L));mode(.x)="double";data.table(x=.x,y=rep(c(2L,4L,6L),2),key="x")})
+
+# Test that invalid keys are reset, without user needing to remove key using key(DT)=NULL first
+DT = data.table(a=letters[1:3],b=letters[6:4],key="a")
+attr(DT,"sorted")="b" # user can go under the hood
+test(347, setkey(DT,b), data.table(a=letters[3:1],b=letters[4:6],key="b"),
+ warning="Already keyed by this key but had invalid row order, key rebuilt")
+
+# Test .N==0 with nomatch=NA|0, # tests for #963 added as well
+DT = data.table(a=1:2,b=1:6,key="a")
+test(349, DT[J(2:3),.N,nomatch=NA,by=.EACHI]$N, c(3L,0L))
+test(350, DT[J(2:3),.N,nomatch=0], c(3L))
+# Test first .N==0 with nomatch=NA|0
+test(350.1, DT[J(2:3),.N], c(4L))
+test(350.2, DT[J(4),.N], 1L)
+test(350.3, DT[J(4),.N,nomatch=0L], 0L)
+test(350.4, DT[J(4:5),.N,nomatch=0L], 0L)
+test(350.5, DT[J(0:4),.N,by=.EACHI]$N, c(0L,3L,3L,0L,0L))
+test(350.6, DT[c(0,0,0), .N], 0L)
+
+# Test recycling list() on RHS of :=
+DT = data.table(a=1:3,b=4:6,c=7:9,d=10:12)
+test(351, DT[,c("a","b"):=list(13:15)], data.table(a=13:15,b=13:15,c=7:9,d=10:12))
+test(352, DT[,letters[1:4]:=list(1L,NULL)], data.table(a=c(1L,1L,1L),c=c(1L,1L,1L)))
+
+# Test assigning new levels into factor columns
+DT = data.table(f=factor(c("a","b")),x=1:4)
+test(353, DT[2,f:="c"], data.table(f=factor(c("a","c","a","b")),x=1:4))
+test(354, DT[3,f:=factor("foo")], data.table(f=factor(c("a","c","foo","b")),x=1:4))
+
+# Test growVector logic when adding levels (don't need to grow levels for character cols)
+newlevels = as.character(as.hexmode(1:2000))
+DT = data.table(f=factor("000"),x=1:2010)
+test(355, DT[11:2010,f:=newlevels], data.table(f=factor(c(rep("000",10),newlevels)),x=1:2010))
+
+DT = data.table(f=c("a","b"),x=1:4)
+# Test coercing factor to character column
+test(355.5, DT[3,f:=factor("foo")], data.table(f=c("a","b","foo","b"),x=1:4))
+test(355.6, DT[4,f:=factor("bar"),verbose=TRUE], data.table(f=c("a","b","foo","bar"),x=1:4), output="Coerced factor to character to match the column")
+
+
+# See datatable-help post and NEWS item for 1.6.7
+DT = data.table(X=factor(letters[1:10]), Y=1:10)
+DT$X = "Something Different"
+test(356, DT, data.table(X=factor("Something Different",levels=c(letters[1:10],"Something Different")), Y=1:10))
+
+DT = data.table(X=letters[1:10], Y=1:10)
+DT$X = "Something Different"
+test(356.5, DT, data.table(X="Something Different", Y=1:10))
+
+# Bug fix 1570
+DT = data.table(x=1:5,y=1:5)
+test(357, DT[x==0, y:=5L], data.table(x=1:5,y=1:5))
+test(358, DT[FALSE, y:=5L], data.table(x=1:5,y=1:5))
+
+# Bug fix 1599
+DT = data.table(a=1:2,b=1:6)
+test(359, DT[,sum(b),by=NULL], data.table(V1=21L))
+test(360, DT[,sum(b),by=character(0)], data.table(V1=21L))
+
+# Bug fix 1576 : NULL j results in 'inconsistent types' error
+DT = data.table(a=1:3,b=1:9)
+ans = data.table(a=c(1L,3L),V1=c(12L,18L))
+test(361, DT[,if (a==2) NULL else sum(b),by=a], ans)
+test(362, DT[,if (a==2) data.table(NULL) else sum(b),by=a], ans)
+test(363, DT[,if (a==2) as.list(NULL) else sum(b),by=a], ans)
+test(364, DT[,if (a==2) integer(0) else sum(b),by=a], ans)
+
+# Test that data.table() can create list() columns directly
+# NB: test 235 above ('by' when DT contains list columns) created the list column in two steps, no longer necessary
+DT = data.table(a=1:2,b=list("h",7:8))
+test(365, DT[1,b], list("h")) # should it be a special case for 1-item results to unlist? Don't think so: in keeping with no drop=TRUE principle
+test(366, DT[2,b], list(7:8))
+DT = data.table(a=1:4,b=list("h",7:8),c=list(matrix(1:12,3),data.table(a=letters[1:3],b=list(1:2,3.4,"k"),key="a")))
+test(367, DT[3,b], list("h"))
+test(368, DT[4,b], list(7:8))
+test(369, DT[3,c[[1]][2,3]], 8L)
+test(370, DT[4,c[[1]]["b",b]][[1]], 3.4)
+
+# Test returning a list() column via grouping
+DT = data.table(x=INT(1,1,2,2,2),y=1:5)
+test(371, DT[,list(list(unique(y))),by=x], data.table(x=1:2,V1=list(1:2,3:5)))
+
+# Test matrix i is an error
+test(372, DT[matrix(1:2,ncol=2)], error="i is invalid type (matrix)")
+
+# Tests from bug fix #1593
+DT = data.table(x=letters[1:2], y=1:4)
+DT[x == "a", ]$y <- 0L
+test(373, DT, data.table(x=letters[1:2], y=c(0L,2L,0L,4L)))
+DT = data.table(x=letters[1:2], y=1:4, key="x")
+DT["a", ]$y <- 0L
+test(374, DT, data.table(x=letters[1:2], y=c(0L,2L,0L,4L), key="x"))
+DT = data.table(x=letters[1:2], y=1:4)
+DT[c(1,3), ]$y <- 0L
+test(375, DT, data.table(x=letters[1:2], y=c(0L,2L,0L,4L)))
+
+# Test unique on unsorted tables (and tolerance on numeric columns, too)
+DT = data.table(a=c(2,1,2),b=c(1,2,1))
+test(376, unique(DT), data.table(a=c(2,1),b=c(1,2)))
+# From the SO thread :
+M = matrix(sample(2, 120, replace = TRUE), ncol = 3)
+DF = as.data.frame(M)
+DT = as.data.table(M)
+test(377, as.data.table(unique(DF)), unique(DT))
+
+# Test compatibility with sqldf. sqldf() does a do.call("rbind" with empty input,
+# so this tests ..1 when NULL (which was insufficiently list(...)[[1]] in 1.6.6).
+# We now test this directly rather than using sqldf, because we couldn't get 'R CMD check'
+# past "(converted from warning) closing unused connection 3 (/tmp/RtmpYllyW2/file55822c52)"
+test(378, cbind(), NULL)
+test(379, rbind(), NULL)
+
+DT = data.table(a=rep(1:3,1:3),b=1:6)
+test(380, DT[,{.SD$b[1]=10L;.SD}, by=a], error="locked binding") # .SD locked for 1st group
+test(381, DT[,{if (a==2) {.SD$b[1]=10L;.SD} else .SD}, by=a], error="locked binding") # .SD locked in 2nd group onwards too
+
+# test that direct := is trapped, but := within a copy of .SD is allowed (FAQ 4.5). See also tests 556-557.
+test(382, DT[,b:=.N*2L,by=a], data.table(a=rep(1:3,1:3),b=rep(2L*(1:3),1:3)))
+test(383, DT[,{z=10L;b:=z},by=a], error=":= and `:=`(...) are defined for use in j, once only and in particular ways")
+test(384, DT[,{mySD=copy(.SD);mySD[1,b:=99L];mySD},by=a], data.table(a=rep(1:3,1:3),b=c(99L,99L,4L,99L,6L,6L)))
+
+# somehow missed testing := on logical subset with mixed TRUE/FALSE, reported by Muhammad Waliji
+DT = data.table(x=1:2, y=1:6)
+test(385, DT[x==1, y := x], data.table(x=1:2,y=c(1L,2L,1L,4L,1L,6L)))
+test(386, DT[c(FALSE,TRUE),y:=99L], data.table(x=1:2,y=c(1L,99L,1L,99L,1L,99L)))
+
+# test that column names have the appearance of being local in j (can assign to them ok), bug #1624
+DT = data.table(name=c(rep('a', 3), rep('b', 2), rep('c', 5)), flag=FALSE)
+test(387, DT[,{flag[1]<-TRUE;list(flag=flag)}, by=name], DT[c(1,4,6),flag:=TRUE])
+DT = data.table(score=1:10, name=c(rep('a', 4), rep('b',2), rep('c', 3), 'd'))
+test(388, DT[,{ans = score[1]
+ score[1] <- -score[1]
+ ans
+ },by=name],
+ data.table(name=letters[1:4],V1=c(1L,5L,7L,10L)))
+
+# Tests 389-394 (character grouping and sorting) now at the start of this file, so that any
+# errors elsewhere show up in the last 13 lines displayed by CRAN checks.
+
+# Test unique.data.table for numeric columns within tolerance, for consistency with
+# with unique.data.frame which does this using paste.
+DT = data.table(a=tan(pi*(1/4 + 1:10)),b=42L)
+# tan(...) from example in ?all.equal.
+test(395, all.equal(DT$a, rep(1,10)))
+test(396, length(unique(DT$a))>1) # 10 unique values on all CRAN machines (as of Nov 2011) other than mac (5 unique)
+test(397, unique(DT), DT[1]) # before v1.7.2 unique would return all 10 rows. For stability within tolerance, data.table has it's own modified numeric sort.
+test(398, duplicated(DT), c(FALSE,rep(TRUE,9)))
+
+DT = data.table(a=c(3.142, 4.2, 4.2, 3.142, 1.223, 1.223), b=rep(1,6))
+test(399, unique(DT), DT[c(1,2,5)])
+test(400, duplicated(DT), c(FALSE,FALSE,TRUE,TRUE,FALSE,TRUE))
+
+DT[c(2,4,5),a:=NA]
+test(401, unique(DT), DT[c(1,2,3,6)])
+test(402, duplicated(DT), c(FALSE,FALSE,FALSE,TRUE,TRUE,FALSE))
+
+# Test NULL columns next to non-NULL, #1633
+DT = data.table(a=1:3,b=4:6)
+test(403, DT[,list(3,if(a==2)NULL else b),by=a], data.table(a=1:3,V1=3,V2=c(4L,NA_integer_,6L)))
+test(404, DT[,list(3,if(a==1)NULL else b),by=a], error="Please use a typed empty vector instead.*such as integer.*or numeric")
+test(405, DT[,list(3,if(a==1)numeric() else b),by=a], error="Column 2 of result for group.*integer.*double.*types must be consistent for each group")
+test(406, DT[,list(3,if(a==1)integer() else b),by=a], data.table(a=1:3,V1=3,V2=c(NA_integer_,5:6)))
+
+# Test that first column can be list, #1640
+test(407, data.table(list(1:2,3:5)), as.data.table(list(list(1:2,3:5))))
+
+# With over-allocation, null data.table has truelength 100. Replaced the calls to structure() in the
+# code to new null.data.table(), so test internal function. User may have changed default, so this
+# doesn't test "100" explicitly.
+test(408, null.data.table(), data.table(NULL))
+test(408.5, data.table(), data.table(NULL))
+
+# Test that adding a column using := is fully by reference rather than a shallow copy, #1646
+DT = data.table(1:2,3:4) # list vector truelength 100
+DT2 = DT
+DT2[,y:=10L]
+test(409, DT, DT2)
+test(410, DT, data.table(1:2,3:4,y=10L))
+DT2[1,V1:=99L]
+test(411, DT, DT2)
+test(412, DT, data.table(c(99L,2L),3:4,y=10L))
+
+# Test that cbind dispatched to data.table() and retains keys
+DT = data.table(x=c("a","b"),y=1:4,key="x")
+test(413.1, key(cbind(DT,DT)), NULL) # key dropped because name "x" ambiguous
+DT1 = data.table(z = c(1,2), w = 1:4, key = "z")
+test(413.2, key(cbind(DT,DT1)), c("x", "z"))
+test(413.3, key(cbind(colA=10:13, DT)), "x") # data.table() dispatched even though 1st argument isn't data.table
+test(413.4, key(cbind(colA=10:17, DT)), NULL) # DT recycled so key is dropped
+test(413.5, key(cbind(colA=1, DT)), "x") # DT not recycled so key retained
+test(414.1, key(cbind(DT,as.data.frame(DT1))), "x")
+
+test(414.2, cbind(as.data.frame(DT),DT1), data.frame(DT,DT1))
+# cbind(DF,...) should return a data.frame for consistency with base. Package treemap (at least) depends
+# on this in the return() in treepalette().
+# Use data.table(DF,DT) if a data.table result is required.
+
+
+# Test friendly error when := is used in wrong place
+test(415, x:=1, error="defined for use in j, once only and in particular ways")
+
+# Somehow never tested that X[Y] is error if X is unkeyed.
+DT = data.table(a=1:3,b=4:6)
+test(416, DT[J(2)], error="x must be keyed")
+
+# Test shallow copy warning from := adding a column, and (TO DO) only when X is NAMED.
+DT = data.table(a=1:3,b=4:6)
+test(417, alloc.col(DT,3), DT, warning="Attempt to reduce allocation from.*to 3 ignored. Can only increase allocation via shallow copy")
+old = getOption("datatable.alloccol") # search for "r-devel" note in this file why not in one step here
+options(datatable.alloccol=3L)
+DT = data.table(a=1:3,b=4:6)
+options(datatable.alloccol=old)
+DT2 = DT
+test(418, length(DT)==2 && truelength(DT)==3)
+DT[,c:=7L] # uses final slot
+test(419, DT, DT2)
+test(420, length(DT)==3 && truelength(DT)==3 && length(DT2)==3 && truelength(DT2)==3)
+test(421, DT[,d:=8L,verbose=TRUE], output="Growing vector of column pointers from")
+test(422, length(DT)==4)
+test(423, truelength(DT)>=4) # with default alloccol, new tl will be 103. But user might have set that higher and then be running test.data.table(), or user might have set alloccol to just ncol(DT)+1. Hence just >=4.
+
+# Test crash bug fixed, #1656, introduced with the 1.7.0 feature
+DT <- data.table(a = factor(c("A", "Z")), b = 1:4)
+DT[1,1] <- "Z"
+test(424, DT, data.table(a=factor(c("Z","Z","A","Z")),b=1:4))
+test(425, DT[1,1] <- 1, 1, warning="Coerced 'double' RHS to 'integer'")
+test(426, DT, data.table(a=factor(c("A","Z")),b=1:4))
+DT[1,1] <- 2L
+test(427, DT, data.table(a=factor(c("Z","Z","A","Z")),b=1:4))
+DT[1,a:="A"]
+test(428, DT, data.table(a=factor(c("A","Z","A","Z")),b=1:4))
+DT[1,a:=2L]
+test(429, DT, data.table(a=factor(c("Z","Z","A","Z")),b=1:4))
+test(430, DT[1,1]<- 3L, NA_integer_, warning="RHS contains 3 which is outside the levels range.*1,2.*of column 1, NAs generated")
+test(431, DT[1,1:=4L], data.table(a=factor(c(NA,"Z","A","Z")),b=1:4), warning="RHS contains 4 which is outside the levels range.*1,2.*of column 1, NAs generated")
+
+# simple realloc test
+if (is.null(getOption("datatable.alloccol"))) {
+ DT = data.table(a=1:3,b=4:6)
+ test(432, truelength(DT), 100L)
+ alloc.col(DT,200)
+ test(433, truelength(DT), 200L)
+ DT = alloc.col(DT,300) # superfluous in this example, but shouldn't fail
+ test(434, truelength(DT), 300L)
+ DT2 = alloc.col(DT,400)
+ test(435, truelength(DT), 400L)
+ test(436, truelength(DT2), 400L)
+}
+
+# test that alloc.col assigns to wherever object is
+DT = data.table(a=1:3,b=4:6)
+f = function() {
+ alloc.col(DT,200) # DT isn't local so (via inherits=TRUE) it finds in frame above
+ invisible()
+}
+f()
+test(437, truelength(DT), 200L)
+
+# quick test that [<- over allocates (again) after the copy of length via *tmp*
+DT = data.table(a=1:3,b=4:6)
+tl = truelength(DT)
+DT$foo = 7L
+test(438, truelength(DT), tl)
+DT[,"bar"] = 8L
+test(439, truelength(DT), tl)
+test(440, DT, data.table(a=1:3,b=4:6,foo=7L,bar=8L))
+
+# Test rbind works by colname now, for consistency with base, FR#1634
+DT = data.table(a=1:3,b=4:6)
+test(441, rbind(DT,list(a=4L,b=7L)), data.table(a=1:4,b=4:7))
+test(442, rbind(DT,data.frame(a=4L,b=7L)), data.table(a=1:4,b=4:7))
+test(443, rbind(DT,data.table(a=4L,b=7L)), data.table(a=1:4,b=4:7))
+test(444, rbind(DT,list(b=7L,a=4L)), data.table(a=1:4,b=4:7)) # rbind should by default check row names. Don't warn here. Add clear documentation instead.
+test(445, rbind(DT,data.frame(b=7L,a=4L)), data.table(a=1:4,b=4:7))
+test(446, rbind(DT,data.table(b=7L,a=4L)), data.table(a=1:4,b=4:7))
+test(450, rbind(DT,list(c=4L,a=7L)), error="This could be because the items in the list may not ")
+test(451, rbind(DT,data.frame(c=4L,a=7L)), error="This could be because the items in the list may not ")
+test(452, rbind(DT,data.table(c=4L,a=7L)), error="This could be because the items in the list may not ")
+test(453, rbind(DT,list(4L,7L)), data.table(a=1:4,b=4:7))
+
+# Test new use.names argument in 1.8.0
+test(453.1, rbind(DT,list(FOO=4L,BAR=7L),use.names=FALSE), data.table(a=1:4,b=4:7))
+test(453.2, rbind(DT,data.table(b=4:5,a=7:8), use.names=FALSE), data.table(a=1:5,b=4:8))
+
+# Test the linked reported bug, #1645
+A1 = data.table(b='hello', a='foo', key='a')
+A2 = data.table(a=c('foo', 'bar'), key='a')
+test(454, merge(A1, A2, all.y=TRUE, by='a'), data.table(a=c("bar","foo"),b=c(NA,"hello"),key="a"))
+A1 = data.table(a='foo', b='hello', key='a')
+test(455, merge(A1, A2, all.y=TRUE, by='a'), data.table(a=c("bar","foo"),b=c(NA,"hello"),key="a"))
+
+# Test mixing nomatch=0 and mult="last", bug #1661
+DT = data.table(id=c(1L, 2L, 2L, 3L), val=1:4, key="id")
+test(456, DT[J(c(1,2,4)), mult="last", nomatch=0], data.table(id=1:2,val=c(1L,3L),key="id"))
+
+# Test join inherited scope respexts nomatch=0, #1663
+DT2 = data.table(id=c(1L,2L,4L), val2=c(11,12,14),key="id")
+test(457, DT[DT2, list(val, val2), nomatch=0, by=.EACHI], data.table(id=c(1L,2L,2L),val=1:3,val2=c(11,12,12),key="id"))
+
+# Test bysameorder edge cases, #1631
+DT = data.table(a=1:3,v=4:9,key="a")
+test(458, DT[,sum(v),by=list(a%%2L)], data.table(a=c(1L,0L),V1=c(26L,13L)))
+test(459, DT[, list(sum(v)), list(ifelse(a == 2, NA, 1L))], data.table(ifelse=c(1L,NA_integer_),V1=c(26L,13L)))
+test(460, DT[, list(sum(v)), list(ifelse(a == 2, 1, NA))], data.table(ifelse=c(NA_real_,1),V1=c(26L,13L)))
+test(461, DT[,sum(v),by=a], data.table(a=1:3,V1=c(11L,13L,15L),key="a"))
+
+# Test loading from file (which resets tl to 0 in R 2.14.0+, and unitialized random number in 2.13.2-)
+f = tempfile()
+save(list="DT",file=f)
+load(f)
+test(462, DT[,foo:=10L], data.table(a=1:3,v=4:9,foo=10L,key="a"))
+unlink(f)
+
+# Test CJ problems with v1.7.4, #1689
+test(463, all(sapply(CJ(1:2,1:3),length)==6L))
+DT = data.table(x=1:4,y=1:2,cnt=1L,key="x,y")
+test(464, DT[CJ(1:4,1:4)]$cnt, INT(1,rep(NA,4),1,NA,NA,1,rep(NA,4),1,NA,NA))
+test(465, DT[CJ(1:4,1:4), sum(cnt>0), by=.EACHI]$y, rep(1:4,4))
+f1 = factor(c("READING","MATHEMATICS"))
+f2 = factor(c("2010_2011","2009_2010","2008_2009"), levels=paste(2006:2010,2007:2011,sep="_"))
+test(466, all(sapply(CJ(f1, f2),length)==6L))
+
+# Test list(.SD,newcol=..) gives error with guidance
+DT = data.table(a=1:2,v=3:6)
+test(467, DT[,list(newcol=7L,.SD),by=a], error="Error.*use := by group instead")
+
+# Test empty list column
+DT = data.table(a=1:3,b=4:6)
+test(468, DT[,foo:=list()], data.table(a=1:3,b=4:6,foo=list()))
+# Test plonk list
+test(469, DT[,bar:=list(1,"a",3.14)], data.table(a=1:3,b=4:6,foo=list(),bar=list(1,"a",3.14)))
+# Test plonk list variable (to catch deparse treating j=list() specially)
+x = list(2,"b",2.718)
+test(470, DT[,baz:=x], data.table(a=1:3,b=4:6,foo=list(),bar=list(1,"a",3.14),baz=list(2,"b",2.718)))
+# Test recycling list
+DT = data.table(a=1:4,b=5:8)
+test(471, DT[,foo:=list("a",2:3)], data.table(a=1:4,b=5:8,foo=list("a",2:3,"a",2:3)))
+# Test recycling singleton list
+DT[,foo:=NULL]
+test(472, DT[,foo:=list(list(2:3))], data.table(a=1:4,b=5:8,foo=list(2:3,2:3,2:3,2:3)))
+
+# Test adding new column with a recycled factor, #1691
+DT = data.table(a=1:4,b=5:8)
+DT[,c:=factor("a")]
+test(473, DT, data.table(a=1:4,b=5:8,c=factor(c("a","a","a","a"))))
+DT[,d:=factor(c("a","b"))]
+test(474, DT, data.table(a=1:4,b=5:8,c=factor(c("a","a","a","a")),d=factor(c("a","b","a","b"))))
+
+# Test scoping error introduced at 1.6.1, unique(DT) when key column is 'x'
+DT=data.table(x=c("a", "a", "b", "b"), y=c("a", "a", "b", "b"), key="x")
+test(475, unique(DT), data.table(x=c("a","b"),y=c("a","b"),key="x"))
+
+# Test character and list columns in tables with many small groups
+N = if (.devtesting) 1000L else 100L
+DT = data.table(grp=1:(2*N),char=sample(as.hexmode(1:N),4*N,replace=TRUE),int=sample(1:N,4*N,replace=TRUE))
+ans = DT[,list(p=paste(unique(char),collapse=","),
+ i=list(unique(int))), by=grp]
+test(476, nrow(as.matrix(ans)), 2L*N) # The as.matrix triggers the "'getCharCE' must be called on a CHARSXP", or similar symptom of earlier corruption, before fix in dogroups.c.
+
+# Test that plonking from calling scope works, even after removing, and column copy via := is ok too.
+DT = data.table(a=1:3)
+foo = 4:6
+DT[,foo:=foo]
+rm(foo)
+gc()
+DT[,foo2:=foo]
+DT[2,foo:=10L]
+DT[3,foo2:=11L]
+gc()
+test(477, DT, data.table(a=1:3,foo=c(4L,10L,6L),foo2=c(4L,5L,11L)))
+test(478, DT[,foo:=foo], DT) # does nothing, with no warning, consistent with base R `a<-a`.
+
+# Test that recycling now works with oversized inputs and % != 0 length, both with warnings.
+DT = data.table(x=1:4)
+test(479, DT[, a:=5:7], data.table(x=1:4,a=c(5:7,5L)), warning="Supplied 3 items to be assigned to 4 items of column 'a' (recycled leaving remainder of 1 items)")
+
+# Test that multiple columns can be added
+DT = data.table(x=1:4)
+test(481, DT[, c("foo","bar"):=list(10L,11:14)], data.table(x=1:4,foo=10L,bar=11:14))
+
+# and combined with update and add in one step
+test(482, DT[, c("foo","baz"):=list(12L,15:18)], data.table(x=1:4,foo=12L,bar=11:14,baz=15:18))
+
+# Test that errors in := do not leave DT in bad state, #1711
+DT = data.table(x=1:4)
+test(483, DT[,c("foo","bar"):=list(20L,numeric())], error="RHS of assignment to new column.*bar.*is zero length but not empty list")
+test(484, DT, data.table(x=1:4)) # i.e. DT as it was before, without foo being added as it did in v1.7.7-
+
+# Test i's key longer than x's
+d1 <- data.table(a=1:2, b=11:14, key="a,b")
+d2 <- data.table(A=0:1, B=1:4, key="A")
+test(485, d2[d1, allow.cartesian=TRUE], data.table(A=INT(1,1,1,1,2,2),B=INT(2,4,2,4,NA,NA),b=INT(11,11,13,13,12,14),key="A"))
+test(486, d2[d1,sum(B),by=.EACHI], data.table(A=INT(1,1,2,2),V1=INT(6,6,NA,NA),key="A")) # no allow.cartesian needed due to by-without-by
+
+if ("package:reshape" %in% search()) {
+ DT <- data.table(ID=rep(1:3, each=3), TIME=rep(1:3, 3), X=1:9)
+ test(487, data.table(reshape(DT, idvar="ID", timevar="TIME", direction="wide")),
+ data.table(ID=1:3,X.1=INT(1,4,7),X.2=INT(2,5,8),X.3=INT(3,6,9)))
+ # The data.table() around reshape is to drop reshape's attributes.
+ DT <- data.table(ID=rep(1:3, each=3), TIME=rep(1:3, 3), X=1:9, Y=10:18)
+ test(488, data.table(reshape(DT, idvar="ID", timevar="TIME", direction="wide")),
+ data.table(ID=1:3,X.1=INT(1,4,7),Y.1=INT(10,13,16),X.2=INT(2,5,8),Y.2=INT(11,14,17),X.3=INT(3,6,9),Y.3=INT(12,15,18)))
+} else {
+ cat("Tests 487 and 488 not run. If required call library(reshape) first.\n")
+}
+
+# Test warnings for names<- and colnames<-, but only warnings when caller is data.table aware.
+DT = data.table(a=1:3,b=4:6)
+test(489, names(DT)[1]<-"A", "A", warning=if (base::getRversion()>="3.1.0") NULL else "Please upgrade")
+test(490, names(DT), c("A","b"))
+test(491, colnames(DT)[2]<-"B", "B", warning=if (base::getRversion()>="3.1.0") NULL else "Please upgrade")
+test(492, names(DT), c("A","B"))
+
+# Check setnames out of bounds errors
+test(493, setnames(DT,"foo","bar"), error="not found.*foo")
+test(494, setnames(DT,3,"bar"), error="outside range.*3")
+
+# Test new function setcolorder()
+DT = data.table(a=1:2,b=3:4,c=5:6)
+test(495, setcolorder(DT,c(2,1,3)), data.table(b=3:4,a=1:2,c=5:6))
+test(496, setcolorder(DT,c(2,1,3)), data.table(a=1:2,b=3:4,c=5:6))
+test(497, setcolorder(DT,c("c","a","b")), data.table(c=5:6,a=1:2,b=3:4))
+test(498, setcolorder(DT,"a"), error="neworder is length")
+test(498.1, setcolorder(DT,c("d","a","b")), error="Names in neworder not found in x: d")
+
+
+# test first group listens to nomatch when j uses join inherited scope.
+x <- data.table(x=c(1,3,8),x1=10:12, key="x")
+y <- data.table(x=c(3,8,10),y1=10:12, key="x")
+test(499, y[x,x1,nomatch=0,by=.EACHI], data.table(x=c(3,8),x1=11:12, key="x"))
+test(500, y[x,x1,nomatch=NA,by=.EACHI], data.table(x=c(1,3,8),x1=10:12, key="x"))
+
+# Test merge bug of unkeyed tables introduced in 1.6.8 and 1.6.9 reported by Eric, and ...
+dt1 <- data.table(l = factor(c("a","b","a","b")))
+dt2 <- data.table(l = factor(c("a","b")), L = factor(c("A","B")))
+test(501, setkey(merge(dt1,dt2,by="l"),NULL), as.data.table(merge(as.data.frame(dt1), as.data.frame(dt2), by="l")))
+
+dt1 <- data.table(l = c("a","b","a","b"))
+dt2 <- data.table(l = c("a","b"), L = c("A","B"))
+test(501.5, setkey(merge(dt1,dt2,by="l"),NULL), as.data.table(merge(as.data.frame(dt1), as.data.frame(dt2), by="l")))
+
+# ... similar example from DM
+dtA = data.table(i = 1:8, j = rep(1:2, 4), k = rep(1:4, 2), A = 10:17)
+dtB = data.table(j = rep(1:2, 2), k = 1:4, B = 18:21)
+test(502, merge(dtA, dtB, by = c("j","k"), all.x = TRUE),
+ data.table(j=rep(1:2,each=4), k=rep(INT(1,3,2,4),each=2), i=INT(1,5,3,7,2,6,4,8),
+ A=INT(10,14,12,16,11,15,13,17), B=rep(INT(18,20,19,21),each=2), key="j,k"))
+test(503, dtA$i, 1:8) # check that merge didn't change the order of dtA by reference
+test(504, dtB$k, 1:4) # or dtB
+
+# Test new i. JIS prefix in 1.7.10
+DT = data.table(a=1:2,b=1:4,key="a")
+test(505, DT[J(a=1,b=6),sum(i.b*b),by=.EACHI]$V1, 24) # 24 now 'double' because i.b is 'double'
+
+# Test := after a key<-
+DT = data.table(a=3:1,b=4:6)
+test(506, key(DT)<-"a", "a", warning="can copy the whole table")
+test(508, DT, data.table(a=1:3,b=6:4,key="a"))
+test(509, DT[,b:=10L], data.table(a=1:3,b=10L,key="a"))
+test(510, DT[,c:=11L], data.table(a=1:3,b=10L,c=11L,key="a")) # Used to be warning about invalid .internal.selfref detected and fixed. As from v1.8.3 data.table() returns a NAMED==0 object, and key<- appears not to copy that. But within functions, key<- would still copy. TO DO: add tests....
+#test(511,)
+
+# Test new functons chmatch and %chin%
+y=letters
+x=c(sample(letters,12),"foo","bar")
+test(512, chmatch(x,y), match(x,y))
+test(513, chmatch(x,y,nomatch=0), match(x,y,nomatch=0))
+test(514, x %chin% y, x %in% y)
+
+# Test new function set() in v1.8.0
+DT = data.table(a=1:3,b=4:6)
+test(515, set(DT,2,1,3), data.table(a=c(1L,3L,3L),b=4:6), warning="Coerced i")
+test(516, set(DT,"2",1,3), error="i is type 'character'")
+test(517, set(DT,2L,1,3), DT, warning="Coerced j")
+# FR #2551 implemented - removed warning from 518
+# test(518, set(DT,2L,1L,3), DT, warning="Coerced 'double' RHS to 'integer'")
+test(518, set(DT,2L,1L,3), DT)
+test(519, set(DT,2L,1L,3L), data.table(a=INT(1,3,3),b=4:6))
+test(520, set(DT,2L,"a",2L), data.table(a=1:3,b=4:6))
+test(521, set(DT,2:3,"b",7:8), data.table(a=1:3,b=INT(4,7,8)))
+test(522, set(DT,2L,"foo",7L), data.table(a=1:3,b=INT(4,7,8), foo=INT(NA,7,NA))) # error="foo.*is not a column name[.] Cannot add columns with set.*use := instead")
+test(523, set(DT,2L,c("a","a"),list(9L,10L)), error="Can't assign to the same column twice in the same query (duplicates detected).")
+test(523.1, set(DT,2L,"a",10L), data.table(a=INT(1,10,3),b=INT(4,7,8), foo=INT(NA,7,NA)))
+setkey(DT,b)
+test(524, set(DT,2L,"a",2L), data.table(a=1:3, b=INT(4,7,8), foo=INT(NA,7,NA), key="b"))
+test(525, set(DT,1L,"b",6L), data.table(a=1:3, b=6:8, foo=INT(NA,7,NA)))
+test(525.1, set(DT,j="b",value=9:11), data.table(a=1:3, b=9:11, foo=INT(NA,7,NA))) # plonk syntax via missing i (fixed in 1.8.1)
+test(525.2, set(DT,NULL,"b",12:14), data.table(a=1:3, b=12:14, foo=INT(NA,7,NA))) # plonk syntax via NULL i
+
+# NEW ADDITIONAL TESTS FOR set() - bug #2077 - for using set to add columns by reference
+DT1 <- data.table(x = 1, y = 1:10, fac = sample(LETTERS[1:3], 10, replace = TRUE)) # from SO
+DT2 <- copy(DT1)
+mul=c(5.3,2.8)
+for (j in seq_along(mul)) set(DT1, i=NULL, j=paste("dot", j, sep=""), mul[j]*DT1[[j]])
+DT2[, `:=`(dot1=5.3*x, dot2=2.8*y)]
+test(1096.1, DT1, DT2)
+set(DT1, i=NULL, j="dot2", value=NULL) # remove "dot2"
+test(1096.2, DT1, DT2[, list(x,y,fac, dot1)])
+DT2[, dot2 := NULL][5:9, `:=`(bla1 = 0L, x = 3L, bla2 = 2L)]
+set(DT1, i=5:9, j=c("bla1", "x", "bla2"), value=list(0L, 3L, 2L))
+test(1096.3, DT1, DT2) # more testing with many columns including existing columns
+test(1096.4, set(DT1, i=NULL, j=7L, value=5L), error="Item 1 of column numbers in j is 7 which is outside range.*1.*6.*Use column names instead in j to add new columns.")
+
+# Test that data.frame incompability is fixed, came to light in Feb 2012
+DT = data.table(name=c('a','b','c'), value=1:3)
+test(526, base::droplevels(DT[ name != 'a' ]), data.table(name=c('b','c'),value=2:3)) # base:: because we'll implement a fast droplevels, too.
+
+if ("package:nlme" %in% search()) {
+ # commented out to be consistent with base R, as #1078 and #1128 are more common cases..
+ # until we can find a workaround for this, I'm commenting this one..
+ # Search for "Fix for #1078" for the tests..
+ # test(527, {x=Orthodont;tt=lme(distance ~ age, data=x); tt[["data"]]=NULL; tt},
+ # {x=as.data.table(Orthodont);tt=lme(distance ~ age, data=x);tt[["data"]]=NULL;tt})
+ test(528, {x=iris;tt=groupedData( Sepal.Length ~ Sepal.Width | Species, data=x);attr(tt,"class")=NULL;attr(tt,"FUN")=NULL;tt},
+ {x=as.data.table(iris);tt=groupedData( Sepal.Length ~ Sepal.Width | Species, data=x);attr(tt,"class")=NULL;attr(tt,"FUN")=NULL;attr(tt,".internal.selfref")=NULL;tt})
+}
+
+# Speed test of chmatch vs match.
+# sortedmatch was 40 times slower and the wrong approach, removed in v1.8.0.
+# Example from Tom in Jan 2011 who first found and raised the issue with sortedmatch.
+if (.timingtests) {
+ cat("Running 30sec (max) test ...");flush.console()
+ n = 1e6
+ a = as.character(as.hexmode(sample(n,replace=TRUE)))
+ b = as.character(as.hexmode(sample(n,replace=TRUE)))
+ test(529, system.time(ans1<-match(a,b))["user.self"] > system.time(ans2<-chmatch(a,b))["user.self"])
+ test(530, ans1, ans2)
+ # sorting a and b no longer makes a difference since both match and chmatch work via hash in some way or another
+ cat("done\n")
+}
+
+# Test that .set_row_names() is maintained on .SD for each group
+DT = data.table(a=INT(1,1,2,2,2,3,3,3,3),b=1:9)
+test(531, DT[,length(rownames(.SD)),by=a], data.table(a=1:3,V1=2:4))
+
+# Test column names with spaces, bug#1880, and check.names default is now FALSE, too
+# Thanks to Yang Zhang for the tests.
+DT = data.table("a b"=INT(1,1,2,2,2),c=1:5)
+test(532, DT[,sum(c),by="a b"], data.table("a b"=1:2,V1=c(3L,12L)))
+test(533, names(data.table('a b'=1)[, list('c d'=`a b`)]), "c d")
+test(534, names(transform(data.table('a b'=1), `c d`=`a b`)), c("a b","c d"))
+
+# Test keyby, new in v1.8.0
+DT = data.table(a=INT(1,3,1,2,3,2),b=1:2,c=1:3,v=1:6)
+test(535, DT[,sum(v),by=a, keyby=a], error="not both")
+test(536, DT[,sum(v),by=a], data.table(a=c(1L,3L,2L),V1=c(4L,7L,10L))) # retains appearance order
+ans = data.table(a=1:3,V1=c(4L,10L,7L),key="a")
+test(537, DT[,sum(v),keyby=a], ans)
+test(538, DT[,sum(v),keyby="a"], ans)
+var="a"
+test(539, DT[,sum(v),keyby=eval(var)], ans)
+a=quote(a%%2L)
+test(540, DT[,sum(v),by=eval(a)], data.table(a=1:0,V1=c(11L,10L)))
+test(541, DT[,sum(v),keyby=eval(a)], data.table(a=0:1,V1=c(10L,11L),key="a"))
+
+test(542, DT[,sum(v),keyby=c("a","b","c")]$V1, INT(1,3,4,6,5,2))
+test(543, DT[,sum(v),keyby="a,b,c"]$V1, INT(1,3,4,6,5,2))
+test(544, DT[,sum(v),keyby=c("a","b,c")], error="but one or more items include a comma")
+
+# Test single expressions passed to by, FR#1743 in v1.8.0
+DT = data.table(a=1:4,date=as.IDate("2012-02-28")+0:3,v=5:8)
+test(545, DT[,sum(v),by=a%%2L], data.table(a=1:0,V1=c(12L,14L)))
+test(546, DT[,sum(v),by=month(date)], data.table(month=2:3,V1=c(11L,15L)))
+
+# Test that factor levels no longer need to be sorted, and that 'ordered' class is retained.
+# Posted by Allan Engelhardt ...
+x = factor(LETTERS[1:3], levels=rev(LETTERS), ordered=TRUE)
+DT = data.table(A=x,B=x,v=1:3, key="A")
+test(547,is.ordered(DT$A) && is.ordered(DT$B))
+test(548.1, DT["A",v,verbose=TRUE], output="Coercing character column i.'V1' to factor to match type of x.'A'") # msg back to i.V1 after a change to i.A for FR #2693. That still works, just differently which no longer overwrites names(i)
+test(548.2, DT["A",v],1L)
+# Posted by Damian Betebenner ...
+set.seed(123)
+my.course.sample = sample(1:5, 10, replace=TRUE)
+Y = factor(my.course.sample, levels=1:5, labels=c("Basic Math", "Calculus", "Geometry", "Algebra I", "Algebra II"))
+DT = data.table(ID=1:10, COURSE=Y)
+test(549, DT[,sum(ID),by=COURSE]$V1, INT(1,2,29,17,6))
+setkey(DT, COURSE)
+test(550, DT[,sum(ID),by=key(DT)]$V1, INT(6,1,29,2,17))
+
+# Another test of DT[i] syntax from datatable-unaware packages, #1794 from ilprincipe.
+DF = structure(list(sample = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("panel.1yr", "panel.2yr", "panel.3yr", "panel.inc", "pre.inc", "pre.prev", "post.inc", "post.prev"), class = "factor"), base = c(2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002), ref = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("2004", "2002-2004", "2001", "2000", "2009", "2008"), c [...]
+DT = as.data.table(DF)
+test(551, nrow(reshape(DT, v.names = c("all", "di.recip", "irr"),
+ timevar = "treated", idvar = c("sample", "var", "distance"),
+ direction = "wide" )), 8L)
+
+# Test bug report #1275 from S Bagley :
+DT = data.table(a=c("1","1"), b=c(2,2))
+test(552, is.character(DT$a))
+test(553, unique(DT), data.table(a="1",b=2))
+
+# Test bug #1726 from Ivan Zhang.
+DT = data.table(V1=c('a', 'b', 'a'), V2 = c('hello', 'ello', 'llo'))
+test(554, nrow(DT[V1=='a' & V2 %like% 'll']), 2L)
+test(555, nrow(DT[V1=='a' & V2 %like% 'ello']), 1L)
+
+# Test can't := to .SD, #1727
+DT = data.table(x = 1:5, y = rnorm(5))
+test(556, DT[,.SD[,z:=rnorm(1)],by=x], error="[.]SD is locked.*reserved for possible future use")
+f = function(.SD) .SD[,z:=rnorm(1)]
+test(557, DT[, f(.SD), by=x], error="[.]SD is locked.*reserved for possible future use")
+
+# Test printing on nested data.table, bug #1803
+DT = data.table(x=letters[1:3],y=list(1:10,letters[1:4],data.table(a=1:3,b=4:6)))
+test(558, capture.output(print(DT)),
+ c(" x y","1: a 1,2,3,4,5,6,","2: b a,b,c,d","3: c <data.table>"))
+test(559, setkey(DT,x)["a",y][[1]], 1:10) # y is symbol representing list column, specially detected in dogroups
+
+# Test renaming of .N to N
+DT = data.table(a=INT(1,1,2,2,2),b=INT(1,2,2,2,1))
+test(560.1, DT[,.N,a][,.N], 2L)
+test(560.2, DT[,.N,a][,N], 2:3)
+test(561, DT[,.N,a][,N], 2:3)
+test(562, DT[,list(.N),a][,N], 2:3)
+test(563, DT[,.N,a][,unique(.N),a]$V1, c(1L,1L))
+test(564, DT[,.N,a][,unique(N),a]$V1, 2:3)
+test(565, DT[,.N,a][N>2], data.table(a=2L, N=3L))
+test(566, DT[,list(.N=.N),a][.N>2], data.table(a=2L,.N=3L))
+test(567, DT[,.N,list(a,b)][,N,by=a]$N, c(1L,1L,2L,1L))
+test(568, DT[,.N,list(a,b)][,unique(N),by=a]$V1, c(1L,2L,1L))
+test(569, DT[,list(.N=.N),list(a,b)][,.N,a], error="The column '.N' can't be grouped because")
+test(570, DT[,list(.N=.N),list(a,b)][,unique(.N),a], error="The column '.N' can't be grouped because")
+
+# Test spaces in by="..." format, datatable-help on 31 March
+DT = data.table("a "=1:2, "b"=3:4," b"=5:6, v=1:6)
+test(571, DT[,sum(v),by="b, b"], data.table("b"=3:4, " b"=5:6, V1=c(9L,12L)))
+test(572, DT[,sum(v),by="a , b"], data.table("a "=1:2, " b"=5:6, V1=c(9L,12L)))
+test(573, DT[,sum(v),by="b, a"], error="object ' a' not found")
+
+# Test base::unname, used by melt, and only supported by data.table for DF compatibility for non-dtaware packages
+DT = data.table(a=1:3, b=4:6)
+test(574, dim(unname(DT)), 3:2)
+
+# Test that CJ retains explicit names (useful if used independently)
+test(575, CJ(x=c(1L,2L), y=c("a","b")), data.table(x=c(1L,1L,2L,2L),y=c("a","b","a","b"),key="x,y"))
+test(576, CJ(c(1L,2L), y=c("a","b")), data.table(V1=c(1L,1L,2L,2L),y=c("a","b","a","b"),key="V1,y"))
+test(577, CJ(x=c(1L,2L), c("a","b")), data.table(x=c(1L,1L,2L,2L),V2=c("a","b","a","b"),key="x,V2"))
+
+# Test factor to character join when factor contains unused and reverse order levels :
+X = data.table(a=LETTERS[1:4],v=1:4,key="a")
+Y = data.table(a=factor(c("D","B"),levels=rev(LETTERS)),key="a")
+test(578, X[Y,verbose=TRUE], output="Coercing factor column i.'a' to character to match type of x.'a'")
+test(579, X[Y], data.table(a=c("D","B"), v=c(4L,2L)))
+
+# Test that logical i in set() returns helpful error
+DT = data.table(a=1:3,b=4:6)
+test(580, set(DT,a<3,"b",0L), error="simply wrap with which(), and take the which() outside the loop if possible for efficiency")
+
+# Test by on empty tables (and when i returns no rows), #1945
+DT = data.table(a=1:3,v=1:6)
+test(581, DT[a<1,sum(v),by=a], data.table(a=integer(),V1=integer()))
+test(582, DT[a<1,sum(v),by=list(a)], data.table(a=integer(),V1=integer()))
+test(583, DT[a<1], DT[0])
+test(584, DT[a<1], output="Empty data.table (0 rows) of 2 cols: a,v")
+test(585, DT[a<1,list(v)], output="Empty data.table (0 rows) of 1 col: v")
+test(586, data.table(a=integer(),V1=integer()), output="Empty data.table (0 rows) of 2 cols: a,V1")
+
+# Test that .N is available in by on empty table, also in #1945
+test(587, DT[a<1,list(sum(v),.N),by=a], data.table(a=integer(),V1=integer(),N=integer()))
+
+# Realised that DT[NULL] returned an error.
+test(588, DT[NULL], data.table(NULL))
+
+# Test that .N, .SD and .BY are available when by is missing and when by is 0 length
+DT = data.table(x=rep(1:3,each=3), y=c(1,3,6), v=1:9)
+test(589, DT[,sapply(.SD,sum)*.N], c(x=162, y=270, v=405))
+test(590, DT[,sapply(.SD,sum)*.N,by=NULL], data.table(V1=c(162,270,405)))
+test(591, DT[,sapply(.SD,sum)*.N,by=character()], data.table(V1=c(162,270,405)))
+test(592, DT[,sapply(.SD,sum)*.N,by=""], data.table(V1=c(162,270,405)))
+test(593, DT[,lapply(.SD,sum)], data.table(x=18L, y=30, v=45L)) # bug fix #2263 in v1.8.3: now data.table result for consistency
+test(594, DT[,lapply(.SD,sum),by=NULL], data.table(x=18L, y=30, v=45L))
+test(595, DT[,lapply(.SD,sum),by=character()], data.table(x=18L, y=30, v=45L))
+test(596, DT[,lapply(.SD,sum),by=""], data.table(x=18L, y=30, v=45L))
+
+# Test keys of two numeric columns, bug#2004
+DT = data.table(x=0.0,y=c(0.0,0.1,0.0,0.2,0.0))
+test(597, unique(DT), DT[c(1,2,4)])
+test(598, DT[,list(count=.N),by=c("x","y")], data.table(x=0.0,y=c(0.0,0.1,0.2),count=c(3L,1L,1L)))
+
+# And that numeric NAs sort stably to the beginning. Whether NAs are allowed in keys, another issue but
+# ordernumtol needs to deal with NA anyway for add hoc by and unique.
+DT = data.table( c(1.34, 1.34, 1.34, NA, 2.22, 2.22, 1.34, NA, NA, 1.34, 0.999), c(75.1, NA, 75.1, 75.1, 2.3, 2.4, 2.5, NA, 1.1, NA, 7.9 ))
+test(599, DT[c(8,9,4,11,2,10,7,1,3,5,6)], setkey(setkey(DT),NULL))
+
+set.seed(1)
+DT = data.table(x=rep(c(1,2), each=10), y=rnorm(20))
+setkey(DT, x, y)
+test(600, is.sorted(DT$x))
+test(601, !is.sorted(DT$y))
+test(602, base::order(DT$x,DT$y), 1:20)
+
+# Test that as.list.data.table no longer copies via unclass, so speeding up sapply(DT,class) and lapply(.SD,...) etc, #2000
+N = if (.devtesting) 1e6 else 1e4
+DT = data.table(a=1:N,b=1:N,c=1:N,d=1:N) # 15MB in dev testing, but test with N=1e7
+if (.devtesting) test(603, system.time(sapply(DT,class))["user.self"] < 0.1)
+
+# Tests on loopability, i.e. that overhead of [.data.table isn't huge, as in speed example in example(":=")
+# These are just to catch slow down regressions where instead of 1s it takes 40s
+if (.devtesting) { # TO DO: find more robust way to turn these on for CRAN checks
+test(604, system.time(for (i in 1:1000) nrow(DT))["user.self"] < 0.5)
+test(605, system.time(for (i in 1:1000) ncol(DT))["user.self"] < 0.5)
+test(606, system.time(for (i in 1:1000) length(DT[[1L]]))["user.self"] < 0.5) # much faster than nrow, TO DO: replace internally
+}
+# TO DO: move to stress test script off CRAN ...
+# DT = as.data.table(matrix(1L,nrow=100000,ncol=100))
+# test(607, system.time(for (i in 1:1000) DT[i,V1:=i])["user.self"] < 10) # 10 to be very wide margin for CRAN
+# test(608, DT[1:1000,V1], 1:1000)
+
+# Crash bug of chorder(character()), #2026
+test(609, chorder(character()), base::order(character()))
+test(610, chorder(""), base::order(""))
+# Extra tests of chorder and chgroup
+x = sample(LETTERS)
+test(610.1, chorder(x), base::order(x))
+test(610.2, chgroup(x), seq_along(x))
+x = sample(LETTERS,1000,replace=TRUE)
+test(610.3, chorder(x), base::order(x))
+test(610.4, unique(x[chgroup(x)]), unique(x))
+
+# := by group
+DT = data.table(a=1:3,b=(1:9)/10)
+test(611, DT[,v:=sum(b),by=a], data.table(a=1:3,b=(1:9)/10,v=c(1.2,1.5,1.8)))
+setkey(DT,a)
+test(612, DT[,v:=min(b),by=a], data.table(a=1:3,b=(1:9)/10,v=(1:3)/10,key="a"))
+# Assign to subset ok (NA initialized in the other items) ok :
+test(613, DT[J(2),w:=8.3]$w, rep(c(NA,8.3,NA),each=3))
+test(614, DT[J(3),x:=9L]$x, rep(c(NA_integer_,NA_integer_,9L),each=3))
+test(615, DT[J(2),z:=list(list(c(10L,11L)))]$z, rep(list(NULL, 10:11, NULL),each=3))
+# Combining := by group with i
+test(616, DT[a>1,p:=sum(b)]$p, rep(c(NA,3.3),c(3,6)))
+test(617, DT[a>1,q:=sum(b),by=a]$q, rep(c(NA,1.5,1.8),each=3))
+
+# Empty i clause, #2034. Thanks to Chris for testing, tests from him.
+test(618, copy(DT)[a>3,r:=sum(b)], DT)
+test(619, copy(DT)[J(-1),r:=sum(b)], DT)
+test(620, copy(DT)[J(-1),r:=sum(b),nomatch=0], DT)
+DT = data.table(x=letters, key="x")
+test(621, copy(DT)[J("bb"), x:="foo"], DT) # when no update, key should be retained
+test(622, copy(DT)[J("bb"), x:="foo",nomatch=0], DT)
+
+set.seed(2)
+DT = data.table(a=rnorm(5)*10, b=1:5)
+test(623, DT[,s:=sum(b),by=round(a)%%2]$s, c(10L,5L,5L,10L,10L))
+
+# Tests on POSIXct attributes
+
+DT = data.table(a=c(1,1,2,2,2))
+test(624, attributes(DT[,as.POSIXct("2011-12-13 18:50",tz="EST"),by=a][[2]]), list(class=c("POSIXct","POSIXt"),tzone="EST"))
+
+DT = data.table(x = rnorm(5))
+DT$time1 <- Sys.time() # recycle via *tmp*
+DT$time2 <- rep(Sys.time(), 5) # plonk via *tmp*
+DT[,time3:=Sys.time()] # recycle
+DT[,time4:=rep(Sys.time(),5)] # plonk
+test(625, all(sapply(DT,is,"POSIXct")[-1]))
+
+# unique on ITime doesn't lose attributes, #1719
+t = as.ITime(strptime(c("09:10:00","09:11:00","09:11:00","09:12:00"),"%H:%M:%S"))
+test(626, unique(t), t[c(1,2,4)])
+test(627, class(unique(t)), "ITime")
+
+# Test recycling list() rbind - with recent C-level changes, this seems not possible (like rbindlist)
+# old test commented.
+# test(628, rbind(data.table(a=1:3,b=5:7,c=list(1:2,1:3,1:4)), list(4L,8L,as.list(1:3))),
+# data.table(a=c(1:3,rep(4L,3L)),b=c(5:7,rep(8L,3L)),c=list(1:2,1:3,1:4,1L,2L,3L)))
+test(628, rbind(data.table(a=1:3,b=5:7,c=list(1:2,1:3,1:4)), list(4L,8L,as.list(1:3))), error = "inconsistent with first column of that item which is length")
+# Test switch in .rbind.data.table for factor columns
+test(628.5, rbind(data.table(a=1:3,b=factor(letters[1:3]),c=factor("foo")), list(4L,factor("d"),factor("bar"))),
+ data.table(a=1:4,b=factor(letters[1:4]),c=factor(c(rep("foo",3),"bar"), levels = c("foo", "bar"))))
+
+# Test merge with common names and all.y=TRUE, #2011
+DT1 = data.table(a=c(1,3,4,5), total=c(2,1,3,1), key="a")
+DT2 = data.table(a=c(2,3,5), total=c(5,1,2), key="a")
+# 629+630 worked before anyway. 631+632 test the bug fix.
+adf=as.data.frame
+adt=as.data.table
+
+test(629, merge(DT1,DT2), data.table(a=c(3,5),total.x=c(1,1),total.y=c(1,2),key="a"))
+test(629.1, merge(DT1,DT2), setkey(adt(merge(adf(DT1),adf(DT2),by="a")),a))
+
+test(630, merge(DT1,DT2,all.x=TRUE), data.table(a=c(1,3,4,5),total.x=c(2,1,3,1),total.y=c(NA,1,NA,2),key="a"))
+test(630.1, merge(DT1,DT2,all.x=TRUE), setkey(adt(merge(adf(DT1),adf(DT2),by="a",all.x=TRUE)),a))
+
+test(631, merge(DT1,DT2,all.y=TRUE), data.table(a=c(2,3,5),total.x=c(NA,1,1),total.y=c(5,1,2),key="a"))
+test(631.1, merge(DT1,DT2,all.y=TRUE), setkey(adt(merge(adf(DT1),adf(DT2),by="a",all.y=TRUE)),a))
+
+test(632, merge(DT1,DT2,all=TRUE), data.table(a=c(1,2,3,4,5),total.x=c(2,NA,1,3,1),total.y=c(NA,5,1,NA,2),key="a"))
+test(632.1, merge(DT1,DT2,all=TRUE), setkey(adt(merge(adf(DT1),adf(DT2),by="a",all=TRUE)),a))
+
+# Test that unsettting datatable.alloccol is caught, #2014
+old = getOption("datatable.alloccol")
+options(datatable.alloccol=NULL) # the return value here seems to be TRUE rather than the old expression TO DO: follow up with r-devel
+test(633, data.table(a=1:3), error="n must be integer length 1")
+options(datatable.alloccol=old)
+
+# Test that with=FALSE by number isn't messed up by dup column names, #2025
+DT = data.table(a=1:3,a=4:6)
+test(634, DT[,2:=200L], data.table(a=1:3,a=200L))
+
+# Test names when not all items are named, #2029
+DT = data.table(x=1:3,y=1:3)
+test(635, names(DT[,list(x,y,a=y)]), c("x","y","a"))
+test(636, names(DT[,list(x,a=y)]), c("x","a"))
+
+# Test := by key, and that := to the key by key unsets the key. Make it non-trivial in size too.
+set.seed(1)
+DT = data.table(a=sample(1:100,1e6,replace=TRUE),b=sample(1:1000,1e6,replace=TRUE),key="a")
+test(637, DT[,m:=sum(b),by=a][1:3], data.table(a=1L,b=c(156L,808L,848L),m=DT[J(1),sum(b)],key="a"))
+test(638, key(DT[J(43L),a:=99L]), NULL)
+setkey(DT,a)
+test(639, key(DT[,a:=99L,by=a]), NULL)
+
+# Test printing is right aligned without quotes etc, and rownames are repeated ok for more than 20 rows
+DT=data.table(a=8:10,b=c("xy","x","xyz"),c=c(1.1,22.1,0))
+test(640, capture.output(print(DT)), c(" a b c","1: 8 xy 1.1","2: 9 x 22.1","3: 10 xyz 0.0"))
+DT=data.table(a=letters,b=1:26)
+test(641, tail(capture.output(print(DT[1:20])),2), c("19: s 19","20: t 20"))
+test(642, tail(capture.output(print(DT[1:21])),2), c("21: u 21"," a b"))
+DT=data.table(a=as.character(as.hexmode(1:500)), b=1:500)
+test(643, capture.output(print(DT)), c(" a b"," 1: 001 1"," 2: 002 2"," 3: 003 3"," 4: 004 4"," 5: 005 5"," --- ","496: 1f0 496","497: 1f1 497","498: 1f2 498","499: 1f3 499","500: 1f4 500"))
+
+# Test inconsistent length of columns error.
+DT = list(a=3:1,b=4:3)
+setattr(DT,"class",c("data.table","data.frame"))
+test(644, setkey(DT,a), error="Column 2 is length 2 which differs from length of column 1 (3)")
+test(645, setkey(DT,b), error="Column 2 is length 2 which differs from length of column 1 (3)")
+
+# Test faster mean. Example from (now not needed as much) data.table wiki point 3.
+# Example is a lot of very small groups.
+set.seed(100)
+n=1e4 # small n so as not to overload daily CRAN checks.
+DT=data.table(grp1=sample(1:750, n, replace=TRUE),
+ grp2=sample(1:750, n, replace=TRUE),
+ x=rnorm(n),
+ y=rnorm(n))
+DT[c(2,5),x:=NA] # seed chosen to get a group of size 2 and 3 in the first 5 to easily inspect.
+DT[c(3,4),y:=NA]
+tt1 = system.time(ans1<-DT[,list(mean(x),mean(y)),by=list(grp1,grp2)]) # 1.1s
+tt2 = system.time(ans2<-DT[,list(.Internal(mean(x)),.Internal(mean(y))),by=list(grp1,grp2)]) # 1.1s
+basemean = base::mean # to isolate time of `::` itself
+tt3 = system.time(ans3<-DT[,list(basemean(x),basemean(y)),by=list(grp1,grp2)]) # 11s
+test(646, ans1, ans2)
+test(647, ans1, ans3)
+# this'll error with `valgrind` because of the 'long double' usage in gsumm.c (although I wonder if we need long double precision).
+# http://valgrind.org/docs/manual/manual-core.html#manual-core.limits
+# http://comments.gmane.org/gmane.comp.debugging.valgrind/10340
+test(648, any(is.na(ans1$V1)) && !any(is.nan(ans1$V1)))
+if (.devtesting) test(649, tt1["user.self"] < 10*tt2["user.self"]) # should be same speed, but *10 as large margin
+if (.devtesting) test(650, tt1["user.self"] < tt3["user.self"]/2) # 10 times faster, but test 2 times faster as large margin
+
+tt1 = system.time(ans1<-DT[,list(mean(x,na.rm=TRUE),mean(y,na.rm=TRUE)),by=list(grp1,grp2)]) # 2.0s
+tt2 = system.time(ans2<-DT[,list(mean.default(x,na.rm=TRUE),mean.default(y,na.rm=TRUE)),by=list(grp1,grp2)]) # 5.0s
+test(651, ans1, ans2)
+test(652, any(is.nan(ans1$V1)))
+if (.devtesting) test(653, tt1["user.self"] < tt2["user.self"])
+
+# See FR#2067. Here we're just testing the optimization of mean and lapply, should be comparable to above
+tt2 = system.time(ans2<-DT[,lapply(.SD,mean,na.rm=TRUE),by=list(grp1,grp2)])
+setnames(ans2,"x","V1")
+setnames(ans2,"y","V2")
+test(654, ans1, ans2)
+test(655, abs(tt1["user.self"] - tt2["user.self"])<2.0) # unoptimized tt2 takes 30 seconds rather than 2. The difference between tt1 and tt2 is under 0.2 seconds usually, so 2.0 is very large margin for error to ensure it's not 30secs.
+
+test(656, DT[,mean(x),by=grp1,verbose=TRUE], output="GForce optimized j to.*gmean")
+test(657, DT[,list(mean(x)),by=grp1,verbose=TRUE], output="GForce optimized j to.*gmean")
+test(658, DT[,list(mean(x),mean(y)),by=grp1,verbose=TRUE], output="GForce optimized j to.*gmean")
+tt = capture.output(DT[,list(mean(x),mean(y)),by=list(grp1,grp2),verbose=TRUE])
+test(659, !length(grep("Wrote less rows", tt))) # first group is one row with this seed. Ensure we treat this as aggregate case rather than allocate too many rows.
+
+# Test .N for logical i subset
+DT = data.table(a=1:10, b=rnorm(10))
+test(660, DT[a==8L, .N], 1L)
+
+# Test that growing is sensible in worst case
+DT = data.table(a=rep(1:10,1:10),b=rnorm(55))
+tt = capture.output(DT[,sum(b)*b,by=a,verbose=TRUE])
+test(661, length(grep("growing from",tt))<3) # was 6 when we simply grew enough for latest result
+
+# Test that adding a new logical column is supported, #2094
+DT=data.table(a=1:3)
+test(662, DT[,newcol:=NA], data.table(a=1:3,newcol=NA))
+test(663, sapply(DT,class), c(a="integer",newcol="logical"))
+
+# Test that setting names in the presence of dups is ok, #2103
+DT = data.table(a=1:3, b=2:4, a=3:5)
+test(664, setnames(DT, c('d','e','f')), data.table(d=1:3,e=2:4,f=3:5))
+
+# Test by=c(...) in combination with i subset, #2078
+DT = data.table(a=1:3,b=1:6,key="a")
+test(665, DT[a<3,sum(b),by=c("a"),verbose=TRUE], DT[a<3,sum(b),by="a"], output="i clause present and columns used in by detected")
+test(666, DT[a<3,sum(b),by=key(DT),verbose=TRUE], DT[a<3,sum(b),by=a], output="i clause present and columns used in by detected")
+test(667, DT[a<3,sum(b),by=paste("a")], error='Otherwise, by=eval(paste("a")) should work')
+test(668, DT[a<3,sum(b),by=eval(paste("a"))], DT[a<3,sum(b),by=a])
+test(669, DT[a<3,sum(b),by=c(2)], error="must evaluate to 'character'")
+
+# Test := keyby does key, #2065
+DT = data.table(x=1:2, y=1:6)
+ans = data.table(x=rep(1:2,each=3),y=c(1L,3L,5L,2L,4L,6L),z=rep(c(9L,12L),each=3),key="x")
+test(670, DT[,z:=sum(y),keyby=x], ans)
+DT = data.table(x=1:2, y=1:6)
+test(671, DT[,z:=sum(y),keyby="x"], ans)
+DT = data.table(x=1:2, y=1:6)
+test(672, DT[,z:=sum(y),keyby=x%%2], data.table(x=1:2,y=1:6,z=c(9L,12L)), warning=":= keyby not straightforward character column names or list() of column names, treating as a by")
+DT = data.table(x=1:2, y=1:6)
+test(673, DT[,z:=sum(y),by=x%%2], data.table(x=1:2,y=1:6,z=c(9L,12L)))
+DT = data.table(x=1:2, y=1:6)
+test(674, DT[x>1,z:=sum(y),keyby=x], error="When i is present, keyby := on a subset of rows doesn't make sense. Either change keyby to by, or remove i")
+
+# Test new .()
+DT = data.table(x=1:2, y=1:6, key="x")
+test(675, DT[.(1L)], DT[1:3])
+
+# Test new rbindlist
+l = list(data.table(a=1:2, b=7:8),
+ data.table(a=3:4, 9:10),
+ data.table(5:6, 11:12),
+ data.table(b=13:14),
+ list(15:16,17L),
+ list(c(18,19),20:21))
+test(676, rbindlist(l[1:3]), data.table(a=1:6,b=7:12))
+test(677, rbindlist(l[c(10,1,10,2,10)]), data.table(a=1:4,b=7:10)) # NULL items ignored
+test(678, rbindlist(l[c(1,4)]), error="Item 2 has 1 columns, inconsistent with item 1 which has 2")
+test(679, rbindlist(l[c(1:2,5)]), error="Column 2 of item 3 is length 1, inconsistent with first column of that item which is length 2.")
+test(680, rbindlist(l[c(2,6)]), data.table(a=c(3,4,18,19), V2=c(9:10,20:21))) # coerces 18 and 19 to numeric (with eddi's changes in commit 1012 - highest type is preserved now) --- Caught and changed by Arun on 26th Jan 2014 (in commit 1099).
+### ----> Therefore this TO DO may not be necessary here anymore (added by Arun 26th Jan 2014) ---> # TO DO when options(datatable.pedantic=TRUE): test(680.5, rbindlist(l[c(2,6)]), warning="Column 1 of item 2 is type 'double', inconsistent with column 1 of item 1's type ('integer')")
+test(681, rbindlist(list(data.table(a=letters[1:2],b=c(1.2,1.3),c=1:2), list("c",1.4,3L), NULL, list(letters[4:6],c(1.5,1.6,1.7),4:6))), data.table(a=letters[1:6], b=seq(1.2,1.7,by=0.1), c=1:6))
+test(682, rbindlist(NULL), data.table(NULL))
+test(683, rbindlist(list()), data.table(NULL))
+test(684, rbindlist(list(NULL)), data.table(NULL))
+test(685, rbindlist(list(data.table(NULL))), data.table(NULL))
+
+# Test merge when no overlap of data in by columns when all=TRUE, #2114
+DF1=data.frame(foo=letters[1:5], bar=1:5, stringsAsFactors=FALSE)
+DF2=data.frame(foo=letters[6:10], baz=6:10, stringsAsFactors=FALSE)
+DT1=as.data.table(DF1)
+DT2=as.data.table(DF2)
+test(686, merge(DF1, DF2, by="foo", all=TRUE), as.data.frame(merge(DT1,DT2,by="foo",all=TRUE)))
+DF1=data.frame(foo=letters[1:5], bar=1:5, stringsAsFactors=TRUE)
+DF2=data.frame(foo=letters[6:10], baz=6:10, stringsAsFactors=TRUE)
+DT1=as.data.table(DF1)
+DT2=as.data.table(DF2)
+test(687, merge(DF1, DF2, by="foo", all=TRUE), as.data.frame(merge(DT1,DT2,by="foo",all=TRUE)))
+
+# And a more basic test that #2114 revealed that factor to factor join was leaving NA in the i
+# factor columns, caught in 1.8.1 beta before release to CRAN.
+DT = data.table(a=factor(letters[1:4]), b=5:8, key="a")
+test(688, DT[J(factor("b"))], data.table(a=factor("b"), b=6L, key="a"))
+
+# Test removing a column followed by adding a new column using := by group, #2117
+DT = data.table(a=1:3,b=4:6)
+DT[,b:=NULL]
+test(689, DT[,b:=.N,by=a], data.table(a=1:3, b=1L))
+test(690, DT[,c:=2,by=a], data.table(a=1:3, b=1L, c=2))
+
+# Test combining i with by, with particular out of order circumstances, #2118
+set.seed(1)
+DT=data.table(a=sample(1:5,20,replace=TRUE),b=1:4,c=1:10)
+test(691, DT[a>2,sum(c),by=b], DT[a>2][,sum(c),by=b])
+test(692, DT[a>2,sum(c),by=b%%2L], data.table(b=1:0,V1=c(34L,42L)))
+test(693, DT[a>2,sum(c),by=(b+1)%%2], data.table(b=c(0,1),V1=c(34L,42L)))
+setkey(DT,b)
+test(694, DT[a>2,sum(c),by=b], DT[a>2][,sum(c),by=b])
+test(695, DT[a>2,sum(c),by=b%%2L], data.table(b=1:0,V1=c(34L,42L)))
+test(696, DT[a>2,sum(c),by=(b+1)%%2], data.table(b=c(0,1),V1=c(34L,42L)))
+
+# Test subset and %chin% crash with non-character input, #2131
+test(697, 4 %chin% letters, error="type")
+test(698, 4L %chin% letters, error="type")
+test(699, "a" %chin% 4, error="type")
+DT = data.table(aa=1:6,bb=7:12)
+test(700, subset(DT,select="aa"), DT[,list(aa)])
+test(701, subset(DT,select=aa), DT[,list(aa)])
+test(702, subset(DT,select=c(aa)), DT[,list(aa)])
+setkey(DT,aa)
+test(703, subset(DT,select="aa"), data.table(aa=1:6,key="aa"))
+test(704, subset(DT,select=aa), data.table(aa=1:6,key="aa"))
+test(705, subset(DT,select=c(aa)), data.table(aa=1:6,key="aa"))
+
+# Test rbinding of logical columns, #2133
+DT1 = data.table(A=1:3,B=letters[1:3],C=c(TRUE,TRUE,FALSE))
+DT2 = data.table(A=4:5,B=letters[4:5],C=c(TRUE,FALSE))
+test(706, rbind(DT1,DT2), data.table(A=1:5, B=letters[1:5], C=c(TRUE,TRUE,FALSE,TRUE,FALSE)))
+test(707, rbindlist(list(DT1,DT2)), rbind(DT1,DT2))
+
+# Test non ascii characters when passed as character by, #2134
+# *****
+# TO DO: reinstate. Temporarily removed to pass CRAN's Mac using C locale (R-Forge's Mac is ok)
+# *****
+
+# Test := adding column after a setnames of all column names (which [,list(x)] does), #2146
+DT = data.table(x=1:5)[,list(x)]
+test(713, DT[,y:=5], data.table(x=1:5,y=5))
+setnames(DT,c("A","B"))
+test(714, DT[,z:=6:10], data.table(A=1:5,B=5,z=6:10))
+
+# Test J alias is now removed outside DT[...] from v1.8.7 (to resolve rJava::J conflict)
+test(715, J(a=1:3,b=4), data.table(a=1:3,b=4), error="could not find function.*J")
+
+# Test get in j
+DT = data.table(a=1:3,b=4:6)
+test(716, DT[,get("b")], 4:6) # TO DO: add warning about inefficiency when datatable.pedantic=TRUE
+test(717, DT[,get("b"),verbose=TRUE], output="ansvars being set to all columns")
+
+# Test that j can be a logical index when `with=FALSE` (#1797)
+DT = data.table(a=1:10, b=rnorm(10), c=letters[1:10])
+test(718, DT[, c(FALSE, TRUE, FALSE), with=FALSE], DT[, 2, with=FALSE])
+test(719, nrow(DT[, c(FALSE, FALSE, FALSE), with=FALSE]), 0L)
+
+# Test combining join with missing groups with group by, #2162
+DT = data.table(a = 1, b = 2, c = 4, key="a")
+test(720, DT[list(c(5,6,7)), .N, by=b], data.table(b=NA_real_,N=3L))
+test(721, DT[list(c(5,6,7))][, .N, by=b], DT[list(c(5,6,7)), .N, by=b])
+test(722, DT[list(c(5,6,7)), .N, by=b, mult="first"], data.table(b=NA_real_,N=3L))
+test(723, DT[list(c(5,6,7)), .N, by=b, nomatch=0], data.table(b=numeric(),N=integer(),key="b")) # Key here is correct. by is ordered (albeit empty)
+test(724, DT[list(c(5,6,7)), .N, by=b, nomatch=0], DT[list(c(5,6,7)),nomatch=0][,.N,by=b]) # Splitting should always be consistent
+
+# another test linked from #2162
+DT = data.table(x=rep(c("a","b","c"),each=3), y=c(1L,3L,6L), v=1:9, key="x")
+test(725, DT[c("a","b","d"),list(v)], DT[J(c("a","b","d")),"v",with=FALSE]) # unfiled bug fix for NA matches; see NEWS 1.8.3
+test(726, DT[c("a", "b", "d"), sum(v), by=y, nomatch=0], data.table(y=INT(1,3,6),V1=INT(5,7,9)))
+test(727, DT[c("a", "b", "d"), sum(v), by=y], data.table(y=INT(1,3,6,NA),V1=INT(5,7,9,NA)))
+test(728, DT[c("a", "b", "d"), sum(v), by=y], DT[J(c("a", "b", "d"))][, sum(v), by=y])
+
+# explicit verbose=FALSE needed here because tests are run a second time with verbose=TRUE
+test(729.1, capture.output(DT[c("a", "b", "d"), print(.SD), by=.EACHI, verbose=FALSE]),
+ capture.output(suppressWarnings(DT[c("a", "b", "d"), print(.SD), by=x, verbose=FALSE])))
+test(729.2, capture.output(DT[c("a", "b"), print(.SD), by=y, verbose=FALSE]), # TO DO: why doesn't last group have x=d, maybe groups=i in dogroups
+ capture.output(DT[c("a", "b"),verbose=FALSE][, print(.SD), by=y, verbose=FALSE]))
+
+test(729.3, DT[c("b","d"),.SD,by=.EACHI], data.table(x=c("b","b","b","d"),y=INT(1,3,6,NA),v=INT(4,5,6,NA))) # no debate here
+test(729.4, DT[c("b","d"),.SD, by=y], DT[c("b","d")][,.SD, by=y][4L,x:=NA_character_]) # the i groups when no match don't get carried through (would be hard to implement this and very unlikely to be useful. Just break into compound query, if needed to be used in j, to get them to carry through. TO DO: add to FAQ.
+
+# That unnamed i gets x's join column names when j is .SD (or any named list, which verbose warns is inefficient), #2281
+test(729.5, DT[c("a","b"),.SD], data.table(x=rep(c("a","b"),each=3),y=INT(1,3,6),v=1:6,key="x"))
+
+# check := when combining join with missing groups and then group by
+test(730, DT[c("b","a"),w:=sum(v),by=y]$w, INT(5,7,9,5,7,9,NA,NA,NA)) # by over a different column than was joined to
+test(731, DT["d",w:=99,by=y]$w, INT(5,7,9,5,7,9,NA,NA,NA)) # do nothing for missing group, before getting as far as type error
+test(732, DT["d",w:=99L,by=y]$w, INT(5,7,9,5,7,9,NA,NA,NA)) # do nothing for missing group
+test(733, DT[c("c","e","b"),w:=sum(v),by=y%%2L]$w, INT(5,7,9,24,24,15,24,24,15))
+
+# Test column type change in the 0 row case (#2274)
+DT = data.table(a=1:3,b=4:6)[0]
+test(734, DT[,b:=as.character(b)], data.table(a=integer(),b=character()))
+test(735, DT[,c:=double()], data.table(a=integer(),b=character(),c=double()))
+
+# Deleting multiple columns out-of-order, #2223
+DT = data.table(a=1:3,b=4:6,c=7:9,d=10:12,e=13:15,f=16:18,g=19:21)
+test(736, DT[,c("b","d","g","f","c"):=NULL], data.table(a=1:3,e=13:15)) # test redundant with=FALSE is ok
+DT = data.table(a=1:3,b=4:6,c=7:9,d=10:12,e=13:15,f=16:18,g=19:21)
+test(737, DT[,c("b","d","g","f","c"):=NULL], data.table(a=1:3,e=13:15)) # with no longer needed
+
+# Mixing column adds and deletes in one := gave incorrect results, #2251.
+DT = data.table(c1=1:2)
+test(738, DT[,c("c2", "c1"):=list(c1+1L, NULL)], data.table(c2=2:3))
+
+# `:=`(c1=v1,v2=v2,...) is now valid , #2254
+DT = data.table( c1=1:3 )
+test(739, DT[,`:=`(c2=4:6, c3=7:9)], data.table(c1=1:3,c2=4:6,c3=7:9))
+test(740, DT[,`:=`(4:6,c3=7:9)], error="all arguments must be named")
+test(741, DT[,`:=`(4:6,7:9,10:12)], error="all arguments must be named") # test the same error message in the other branch
+
+# that out of bounds LHS is caught, root cause of #2254
+test(742, DT[,3:6:=1L], error="outside.*range")
+test(743, DT[,2:3:=99L], data.table(c1=1:3,c2=99L,c3=99L))
+test(744, DT[,(ncol(DT)+1):=1L], error="outside.*range")
+test(745, DT[,ncol(DT):=1L], data.table(c1=1:3,c2=99L,c3=1L))
+
+# multiple LHS with by without by, #2215
+DT = data.table(a=letters[c(1:3,3L)],key="a")
+test(746, DT["a",c("new1","new2"):=list(4L, 5L)],
+ data.table(a=letters[c(1:3,3L)],new1=INT(4,NA,NA,NA),new2=INT(5,NA,NA,NA),key="a"))
+test(747, DT[,new1:=4:6], data.table(a=letters[c(1:3,3L)],new1=INT(4L,5L,6L,4L),new2=INT(5,NA,NA,NA),key="a"), warning="recycled leaving remainder of 1 item")
+suppressWarnings(DT[,new1:=4:6])
+test(748, DT[c("c","b"),`:=`(new3=.N,new2=sum(new1)+1L),by=.EACHI], data.table(a=letters[c(1:3,3L)],new1=INT(4,5,6,4),new2=INT(5,6,11,11),new3=INT(NA,1,2,2),key="a"))
+
+# and multiple LHS by group, #1710
+DT = data.table(a=rep(6:8,1:3),b=1:6)
+test(749, DT[,c("c","d","e"):=list(.N,sum(b),a*10L),by=a], data.table(a=rep(6:8,1:3),b=1:6,c=rep(1:3,1:3),d=INT(rep(c(1,5,15),1:3)),e=rep(6:8,1:3)*10L))
+test(750, DT[a<8,`:=`(f=b+sum(d),g=.N),by=c][,6:7,with=FALSE], data.table(f=INT(2,12,13,NA,NA,NA),g=INT(1,2,2,NA,NA,NA)))
+
+# varname holding colnames, by group, linked from #2120.
+DT = data.table(a=rep(1:3,1:3),b=1:6)
+colname = "newcol"
+test(751, DT[,(colname):=sum(b),by=a], data.table(a=rep(1:3,1:3),b=1:6,newcol=INT(1,5,5,15,15,15)))
+
+# Add tests for nested := in j by group, #1987
+DT = data.table(a=rep(1:3,2:4),b=1:9)
+test(752, DT[,head(.SD,2)[,new:=1:.N],by=a], data.table(a=rep(1:3,each=2),b=c(1:4,6:7),new=1:2))
+
+# Test duplicate() of recycled plonking RHS, #2298
+DT = data.table(a=letters[3:1],x=1:3)
+test(753, setkey(DT[,c("x1","x2"):=x],a), data.table(a=letters[1:3],x=3:1,x1=3:1,x2=3:1,key="a"))
+DT = data.table(a=letters[3:1],x=1:3,y=4:6)
+test(754, setkey(DT[,c("x1","y1","x2","y2"):=list(x,y)],a), data.table(a=letters[1:3],x=3:1,y=6:4,x1=3:1,y1=6:4,x2=3:1,y2=6:4,key="a"))
+# And non-recycling i.e. that a single column copy does copy the column
+DT = data.table(a=1:3)
+test(754.1, DT[,b:=a][1,a:=4L][2,b:=5L], data.table(a=INT(4,2,3),b=INT(1,5,3)))
+test(754.2, DT[,b:=a][3,b:=6L], data.table(a=INT(4,2,3),b=INT(4,2,6)))
+test(754.3, DT[,a:=as.character(a),verbose=TRUE], output="Direct plonk.*no copy")
+RHS = as.integer(DT$a)
+test(754.4, DT[,a:=RHS,verbose=TRUE], output="RHS for item 1 has been duplicated")
+
+# Used to test warning on redundant by (#2282) but by=.EACHI has now superseded
+DT = data.table(a=letters[1:3],b=rep(c("d","e"),each=3),x=1:6,key="a,b")
+test(755, DT[c("b","c"),sum(x),by=.EACHI], data.table(a=c("b","c"),V1=c(7L,9L),key="a"))
+test(756, DT[c("b","c"),sum(x),by=a], data.table(a=c("b","c"),V1=c(7L,9L),key="a"))
+test(757, DT[list(c("b","c"),"d"),sum(x),by=a], data.table(a=c("b","c"),V1=2:3,key="a")) # 'by' less than number of join columns
+
+# join then by when mult=="last"|"first", #2303 (crash in dev 1.8.3 only)
+DT = data.table(a=1:3,b=1:6,c=7:12,key="a")
+test(758, DT[J(c(1L,1L)),sum(c),by=b,mult="last"], DT[J(c(1L,1L)),mult="last"][,sum(c),by=b])
+test(759, DT[J(1L),c,by=b,mult="last"], DT[J(1L),mult="last"][,c,by=b])
+test(760, DT[2:5,sum(c),by=b], DT[2:5][,sum(c),by=b])
+test(761, DT[2:5,sum(c),by=b%%2], DT[2:5][,sum(c),by=b%%2])
+
+# joining from empty i table, #2194
+DT = data.table(a=1:3,b=4:6,key="a")
+test(762, DT[J(integer()),b,by=.EACHI], data.table(a=integer(),b=integer(),key="a"))
+test(763, DT[J(integer()),1L,by=b], data.table(b=integer(),V1=integer(),key="b")) # ordered by is detected now (empty is ordered), otherwise a join to the result would fail just because it's empty which wouldn't be consistent with non empty case
+test(764, DT[J(integer()),b,mult="last"], integer())
+test(765, DT[J(2L),b,mult="last"], 5L)
+test(766, DT[J(5L),b,nomatch=0,by=.EACHI], data.table(a=integer(),b=integer(),key="a"))
+test(767, DT[J(5:6),b,nomatch=0,by=.EACHI], data.table(a=integer(),b=integer(),key="a"))
+
+# Crash on by-without-by with mixed type non join i columns, #2314. Despite not being used by j they were still being assigned to .BY.
+DT = data.table(iris,key="Species")
+Y = data.table(date=as.POSIXct("2011-01-01"),num=as.numeric(1:26))
+Y[,get("letters"):=LETTERS]
+Y[,A:=1:26]
+Y[,p:=factor(p)] # coerce type to match DT$Species to save warning. Crash was related to .BY internally, not the coercion.
+setkey(Y,p)
+for (i in 1:10){DT[Y,Petal.Width];DT[Y];NULL} # reliable crash in 1.8.2 (tested).
+test(768, DT[Y,Petal.Width,by=.EACHI], data.table(Species=factor(LETTERS),Petal.Width=NA_real_,key="Species"))
+DT = data.table(a=1:3,b=1:6,c=7:12, key="a")
+test(769, DT[,.BY[[1]]==a,by=a], data.table(a=1:3,V1=TRUE,key="a"))
+test(770, DT[J(2:3),.BY[[1]]==b,by=.EACHI], data.table(a=INT(2,2,3,3),V1=c(TRUE,FALSE),key="a"))
+
+# A data.table RHS of := caused a crash, #2311.
+a = data.table(first=1:6, third=c(1,1,1,3,3,4), key="first")
+b = data.table(first=c(3,4,4,5,6,7,8), second=1:7, key="first")
+test(771, b[,third:=a[b,third,by=.EACHI]], b, warning="Supplied 2 items.*to 7.*recycled leaving remainder of 1 item")
+test(772, copy(b)[,third:=as.list(a[b,third,by=.EACHI])], b, warning="Supplied 2 items.*to 7.*recycled leaving remainder of 1 item")
+test(773, b[4,third[[1]]], c(1,3,3,3,4,NA,NA))
+test(774.1, b[,third:=a[b,third,mult="first"]], ans<-data.table(first=c(3,4,4,5,6,7,8), second=1:7, third=c(1,3,3,3,4,NA,NA), key="first"))
+test(774.2, b[,third:=a[b,third]], ans) # mult="first" no longer needed as from v1.9.3. It now does what was naturally expected.
+
+
+# That names are dropped. (Names on the column vectors don't display. They increase size and aren't much use.)
+DT = data.table(a=1:3,b=LETTERS[1:3])
+map = c("A"="Foo",B="Bar",C="Baz")
+DT[,b:=map[b]]
+test(775, names(DT$b), NULL)
+
+# Test that names of named vectors don't carry through, #2307.
+DT = data.table(a=1:3,b=c("a"="a","b"="a","c"="b"))
+test(776, names(DT$b), NULL) # From v1.8.11, data.table() drops vector names
+DT = data.table(a=1:3,b=c("a","a","b"))
+setattr(DT$b, "names", c("a","b","c")) # Force names in there to test #2307
+test(777, names(DT$b), c("a","b","c"))
+test(778, DT[,sum(a),by=b], data.table(b=c("a","b"),V1=c(3L,3L))) #2307 retained names length 3 on the length 2 vector result causing it not to print.
+test(779, print(DT[,sum(a),by=b]), output=" b V11: a 32: b 3$")
+
+# Test new .GRP binding
+test(780, data.table(a=1:3,b=1:6)[,i:=.GRP,by=a][,i2:=.GRP], data.table(a=1:3,b=1:6,i=rep(1:3,2),i2=1L))
+
+# Test new .I binding
+DT = data.table(a=1:4,b=1:8)
+test(781, DT[,.I,by=a]$I, INT(1,5,2,6,3,7,4,8))
+test(782, DT[,.I[which.max(b)],by=a], data.table(a=1:4,V1=5:8))
+setkey(DT,a)
+test(783, DT[,.I,by=a]$I, 1:8)
+test(784, DT[,.I[which.max(b)],by=a], data.table(a=1:4,V1=INT(2,4,6,8),key="a"))
+test(785, DT[J(2:4),.I,by=a%%2L], data.table(a=rep(0:1,c(4,2)),I=INT(3,4,7,8,5,6)))
+test(786, DT[J(c(3,2,4)),list(.I,.GRP),by=.EACHI], data.table(a=rep(c(3L,2L,4L),each=2),I=INT(5,6,3,4,7,8),GRP=rep(1:3,each=2L)))
+test(787, DT[J(3:2),`:=`(i=.I,grp=.GRP),by=.EACHI][,list(i,grp)], data.table(i=INT(NA,NA,3:6,NA,NA),grp=INT(NA,NA,2,2,1,1,NA,NA)))
+
+# New not-join (a.k.a. not-select, since not just for data.table i but integer, logical and character too)
+DT = data.table(A=rep(1:3,each=2),B=1:6,key="A")
+test(788, DT[!J(2)], data.table(A=c(1L,1L,3L,3L),B=c(1L,2L,5L,6L),key="A"))
+test(789, DT[!(2:6)], DT[1])
+test(790, DT[!(2:6)], DT[!2:6]) # nicer than DT[-2:6] applying - to 2 first
+test(791, DT[!6], DT[1:5])
+test(792, DT[!c(TRUE,FALSE)], DT[c(FALSE,TRUE)])
+test(793, setkey(DT[,A:=letters[A]],A)[!c("b","c")], DT["a"])
+test(794, DT[!"b"], DT[c("a","c")])
+test(795, DT[!0], DT)
+test(796, DT[!NULL], DT[NULL])
+test(797, DT[!integer()], DT)
+test(798, DT[!-1], DT[1])
+test(799, DT[--1], DT[1])
+myi = c("a","c")
+test(800, DT[!myi], DT["b"])
+test(801, DT[!"c",sum(B),by=A], data.table(A=c("a","b"),V1=c(3L,7L),key="A"))
+test(802, DT[!"missing",sum(B),by=A], DT[,sum(B),by=A])
+test(803, DT[!c("a","missing","b","missing2"),sum(B),by=A], DT["c",sum(B),by=.EACHI])
+# Combining not-join with which
+test(804, DT[!"b",which=TRUE], INT(1:2,5:6)) # row numbers in DT that don't match
+# New which=NA value
+test(805, DT[c("b","foo","c"),which=NA], 2L) # row numbers in i that don't match
+test(806, DT[!c("b","foo","c"),which=NA], c(1L,3L)) # row numbers in i that do match
+test(807, DT[!c("b","foo","c"),nomatch=0], error="not-join.*prefix is present on i.*Please remove nomatch")
+test(808, DT[c("b","foo","c"),which=TRUE,nomatch=NA], INT(3:4,NA,5:6))
+test(809, DT[c("b","foo","c"),which=TRUE,nomatch=0], INT(3:4,5:6))
+test(810, DT[c("b","foo","c"),which=NA,nomatch=NA], 2L)
+test(811, DT[c("b","foo","c"),which=NA,nomatch=0], error="which=NA with nomatch=0 would always return an empty vector[.] Please change or remove either which or nomatch")
+
+# New notj for column names and positions when with=FALSE, #1384
+DT = data.table(a=1:3,b=4:6,c=7:9)
+test(812, DT[,!"b",with=FALSE], DT[,-match("b",names(DT)),with=FALSE])
+test(813, DT[,"foo",with=FALSE], error="column(s) not found: foo")
+test(814, DT[,!"foo",with=FALSE], DT, warning="column(s) not removed because not found: foo")
+test(815, DT[,!c("b","foo"),with=FALSE], DT[,list(a,c)], warning="column(s) not removed because not found: foo")
+test(816, DT[,!2:3,with=FALSE], DT[,-(2:3),with=FALSE]) # for consistency, but ! is really for character column names
+mycols = "b"
+test(817, DT[,!mycols,with=FALSE], DT[,list(a,c)])
+mycols = 2
+test(818, DT[,!mycols,with=FALSE], DT[,list(a,c)])
+
+# Test X[Y] slowdown, #2216
+X = CJ(a=seq_len(1e3),b=seq_len(1e3))
+Y = copy(X)
+X[4,b:=3L] # create a dup group, to force allLen1=FALSE
+setkey(X)
+test(819, system.time(X[Y,allow.cartesian=TRUE])["user.self"] < 5)
+# Many minutes in 1.8.2! Now well under 1s, but 5s for wide tolerance for CRAN. We like CRAN to tell us if any changes
+# in R or elsewhere cause the 2 minute bug to return. Hence not excluded by an if(.devtesting)
+
+test(820, system.time(X[Y,mult="first"])["user.self"] < 5)
+
+# Optimization of lapply(,"+"), #2212
+DT = data.table(a=rep(1:3,each=2L),b=1:6,c=7:12)
+ans = data.table(a=rep(1:3,each=2L),b=INT(2,3,5,6,8,9),c=INT(8,9,11,12,14,15))
+test(821, DT[,lapply(.SD, "+", a), by=a], ans)
+test(822, DT[,lapply(.SD, `+`, a), by=a], ans)
+ans = data.table(a=1:3,b=INT(4,9,14),c=INT(16,21,26))
+test(823, DT[,lapply(.SD, "sum", a), by=a], ans)
+test(824, DT[,lapply(.SD, sum, a), by=a], ans)
+test(825, DT[,lapply(.SD, `sum`, a), by=a], ans)
+DT[2,b:=NA_integer_]
+test(825.1, DT[,lapply(.SD, function(x)sum(x)), by=a], data.table(a=1:3,b=INT(NA,7,11),c=INT(15,19,23)))
+test(825.2, DT[,lapply(.SD,function(x,...)sum(x,...),na.rm=TRUE),by=a], data.table(a=1:3,b=INT(1,7,11),c=INT(15,19,23)))
+test(825.3, DT[,lapply(.SD,sum,na.rm=TRUE),by=a], data.table(a=1:3,b=INT(1,7,11),c=INT(15,19,23)))
+
+# Test illegal names in merge are ok and setcolorder length error, #2193i and #2090
+DT1 = data.table(a=letters[1:5], "Illegal(name%)"=1:5, key="a")
+DT2 = data.table(a=letters[1:5], b=6L, key="a")
+test(826, merge(DT1,DT2), cbind(DT1,b=6L))
+test(827, merge(DT2,DT1), cbind(DT2,"Illegal(name%)"=1:5))
+a=data.table('User ID'=c(1,2,3), 'Blah Blah'=c(1,2,3), key='User ID') #2090's test
+b=data.table('User ID'=c(1,2,3), 'Yadda Yadda'=c(1,2,3), key='User ID')
+test(827.1, names(a[b]), c("User ID","Blah Blah","Yadda Yadda"))
+
+# setcolorder and merge check for dup column names, #2193(ii)
+setnames(DT2,"b","a")
+test(828, setcolorder(DT2,c("a","b")), error="x has some duplicated column name(s): a. Please remove or rename")
+test(829, merge(DT1,DT2), error="y has some duplicated column name(s): a. Please remove or rename")
+test(830, merge(DT2,DT1), error="x has some duplicated column name(s): a. Please remove or rename")
+
+# attribs such as "comments" should be retained, #2270
+DT1 <- data.table(id = seq.int(1, 10), A = LETTERS[1:10], key = "id")
+comment(DT1$A) <- "first comment" # copies, setattr would be better as on next line
+DT2 <- data.table(id = seq.int(2, 10, 2), b = letters[1:5], key = "id")
+setattr(DT2$b,"comment","second comment")
+test(831, comment(DT1[DT2]$A), "first comment")
+test(832, comment(DT2[DT1]$b), "second comment")
+test(833, sapply(merge(DT1,DT2),comment), list(id=NULL, A="first comment", b="second comment"))
+test(834, comment(DT1[2:3]$A), "first comment")
+
+# Test that matrix RHS of := is caught, #2333
+DT = data.table(a=1:3)
+DT[,a:=scale(a)] # 1 column matrix auto treated as vector
+test(835, na.omit(DT), DT)
+test(836, DT[,a:=as.integer(a)], data.table(a=INT(-1,0,1)))
+test(837, DT[,a:=cbind(1,2)], data.table(a=c(1L,2L,1L)), warning="2 column matrix RHS of := will be treated as one vector")
+DT = data.table(a=1:3,b=1:6)
+test(838, DT[,c:=scale(b), by=a][,c:=as.integer(1000*c)], data.table(a=1:3,b=1:6,c=rep(as.integer(1000*scale(1:2)), each=3)))
+
+# Test data.table's last(). (last is used internally in data.table, too).
+test(839, last(1:10), 10L) # If xts is loaded, this'll just test xts's last. Ok as they're consistent, for vectors.
+DT = data.table(a=1:3,b=4:6)
+test(840, last(DT), DT[3L]) # xts's last returns a one row data.table ok. So this test is ok too, whether or not xts is loaded.
+ # But not true when DT is a one column data.table/data.frame, see below.
+if ("package:xts" %in% search()) { # e.g. when run via R CMD check
+ x = xts(1:100, Sys.Date()+1:100)
+ test(841, last(x,10), x[91:100,])
+ # The important thing this tests is that data.table's last() dispatches to xts's method when data.table is loaded above xts.
+ # But that isn't tested by R CMD check because xts is loaded above data.table, there.
+ # So to make this test relevant, in a fresh R session type: "require(xts);require(data.table);test.data.table()"
+ # rather than: "require(data.table);require(xts);test.data.table()"
+ # Which was the main thrust of bug#2312 fixed in v1.8.3
+} else {
+ cat("Test 841 not run. If required call library(xts) first.\n")
+ # So these won't run from R CMD check (deliberately, for now) ...
+ test(842, last(list("a",1:2,89)), 89) # xts's last returns a one item list here. Would prefer it to return the item itself.
+ DT = data.table(a=1:3)
+ test(842.1, last(DT), DT[3L])
+ # xts's last returns a 3L atomic here for 1 column data.frame, strangely. We wish for the last row, consistently. I tried
+ # providing a last.data.table method and using Enhances and Imports in DESCRIPTION with import() and S3method() in
+ # NAMESPACE but nothing I tried made last.data.table available to xts's last if xts was loaded above data.table (which was
+ # frustrating to test as well, see comment to test 839 above).
+}
+
+# Test L[[1L]][,:=] updates by reference, #2204
+l = list(data.table(a=1:3), data.table(b=4:6))
+test(843, l[[2L]][,c:=7:9], data.table(b=4:6,c=7:9))
+test(844, l, list(data.table(a=1:3), data.table(b=4:6,c=7:9)))
+names(l) = c("foo","bar") # R >= 3.1 no longer copies all the contents, yay
+test(845, l[["foo"]][2,d:=4], data.table(a=1:3,d=c(NA,4L,NA)),
+ warning= if (!.R.assignNamesCopiesAll) NULL else "Invalid .internal.selfref detected and fixed")
+l = list(data.table(a=1:3), data.table(b=4:6))
+setattr(l,"names",c("foo","bar"))
+test(846, l[["foo"]][2,d:=4], data.table(a=1:3,d=c(NA,4,NA)))
+test(847, l, list(foo=data.table(a=1:3,d=c(NA,4,NA)), bar=data.table(b=4:6)))
+old = getOption("datatable.alloccol")
+options(datatable.alloccol=2L) # the return value here seems to be TRUE rather than the old expression TO DO: follow up with r-devel
+l = list(foo=data.table(a=1:3,b=4:6),bar=data.table(c=7:9,d=10:12)) # list() doesn't copy the NAMED==0 objects here
+test(848, truelength(l[[1L]]), 2L)
+test(849, {l[[1L]][,e:=13:15]; l[[1L]]}, data.table(a=1:3,b=4:6)[,e:=13:15])
+test(850, truelength(l[[1L]]), 102L)
+test(851, truelength(l[[2L]]), 2L)
+l[["bar"]][,f:=16:18]
+test(852, truelength(l[[2L]]), 102L)
+options(datatable.alloccol=old)
+# Now create the list from named objected
+DT1 = data.table(a=1:3, b=4:6)
+DT2 = data.table(c=7:9)
+l = list(DT1, DT2)
+if (!.R.listCopiesNamed) {
+ # From R>=3.1, list() no longer copies NAMED inputs (a very welcome change in Rdevel, r63767)
+ test(853, address(DT1) == address(l[[1L]]))
+ w = NULL
+} else {
+ test(853, address(DT1) != address(l[[1L]]))
+ w = "Invalid .internal.selfref detected and fixed.*R's list() used to copy named objects"
+}
+test(854, l[[1]][,d:=10:12], data.table(a=1:3,b=4:6,d=10:12), warning = w)
+test(855, l[[1]], data.table(a=1:3,b=4:6,d=10:12))
+
+# Test setnames on data.frame, #2273.
+DF = data.frame(foo=1:2,bar=3:4)
+setnames(DF,c("baz","qux"))
+test(856, DF, data.frame(baz=1:2,qux=3:4))
+test(857.1, set(DF,NULL,"quux",5:6), error="set() on a data.frame is for changing existing columns, not adding new ones")
+test(857.2, set(DF,NULL,3L,5:6), error="set() on a data.frame is for changing existing columns, not adding new ones")
+test(858.1, set(DF,NULL,"qux",5:6), data.frame(baz=1:2, qux=5:6))
+test(858.2, set(DF,NULL,2L,7:8), data.frame(baz=1:2, qux=7:8))
+
+# Test DT[J(data.frame())], #2265
+DT = data.table(foo=c(1,2,3), bar=c(1.1,2.2,3.3), key="foo")
+i = data.frame(foo=1)
+test(859, DT[i], DT[J(i)])
+test(860, DT[i], DT[data.table(i)])
+
+# test no memory leak, #2191 and #2284
+# These take a few seconds each, and it's important to run these on CRAN to check no leak
+gc(); before = gc()["Vcells","(Mb)"]
+for (i in 1:2000) { DT = data.table(1:3); rm(DT) } # in 1.8.2 would leak 3MB
+gc(); after = gc()["Vcells","(Mb)"]
+test(861, after < before+0.5) # close to 0.0 difference, but 0.5 for safe margin
+
+gc(); before = gc()["Vcells","(Mb)"]
+DF = data.frame(x=1:20, y=runif(20))
+for (i in 1:2000) { DT = as.data.table(DF); rm(DT) }
+gc(); after = gc()["Vcells","(Mb)"]
+test(862, after < before+0.5)
+
+gc(); before = gc()["Vcells","(Mb)"]
+DT = data.table(x=1:20, y=runif(20))
+for (i in 1:2000) { x <- DT[1:5,]; rm(x) }
+gc(); after = gc()["Vcells","(Mb)"]
+test(863, after < before+0.5)
+
+# rbindlist should look for the first non-empty data.table - New changes (from Arun). Explanation below:
+# Even if data.table is empty, as long as there are column names, they should be considered.
+# Ex: What if all data.tables are empty? What'll be the column name then?
+# If there are no names, then the first non-empty set of names will be allocated. I think this is the way to do it.. TODO: Should write to Matt about it.
+test(864.1, rbindlist(list(data.table(foo=logical(0),bar=logical(0)), DT<-data.table(baz=letters[1:3],qux=4:6))), setnames(DT, c("foo", "bar")))
+test(864.2, rbindlist(list(list(logical(0),logical(0)), DT<-data.table(baz=letters[1:3],qux=4:6))), DT)
+test(864.3, rbindlist(list(data.table(logical(0),logical(0)), DT<-data.table(baz=letters[1:3],qux=4:6))), setnames(DT, c("V1", "V2")))
+
+# Steve's find that setnames failed for numeric 'old' when pointing to duplicated names
+DT = data.table(a=1:3,b=1:3,v=1:6,w=1:6)
+test(865, ans1<-DT[,{list(name1=sum(v),name2=sum(w))},by="a,b",verbose=TRUE],
+ output="result of j is a named list. It's very inefficient.*removed and put back")
+test(866, names(ans1), c("a","b","name1","name2"))
+test(867, names(ans2<-DT[,list(name1=sum(v),name2=sum(w)),by="a,b"]), c("a","b","name1","name2")) # list names extracted here
+test(868, ans1, ans2)
+# and related to setnames, too
+DT = data.table(a=1:3,b=1:6,key="a")
+test(869, DT[J(2,42,84),print(.SD),by=.EACHI], output=" b1: 22: 5.*Empty data.table (0 rows) of 3 cols: a,V2,V3")
+
+# Test setnames with duplicate colnames
+DT = data.table(a=1:3,b=4:6,b=7:9)
+test(870, setnames(DT,"b","foo"), error="Some items of 'old' are duplicated (ambiguous) in column names: b")
+test(871, setnames(DT,c("bar","bar"),c("x","y")), error="Some duplicates exist in 'old': bar")
+test(872, setnames(DT,3,"c"), data.table(a=1:3,b=4:6,c=7:9))
+test(873, setnames(DT,"foo","bar"), error="Items of 'old' not found in column names: foo")
+test(874, setnames(DT,c(1,1),c("foo","bar")), error="Some duplicates exist in 'old': 1")
+test(875, setnames(DT,"c","b"), data.table(a=1:3,b=4:6,b=7:9))
+test(875.1, setnames(DT,"a","c"), data.table(c=1:3,b=4:6,b=7:9)) # 'a' isn't duplicated so not a problem as from v1.8.11
+test(875.2, setnames(DT,c("c","b"), c("C","B")), error="Some items of 'old' are duplicated (ambiguous) in column names: b") # check error msg when 2nd one in old is the problem
+
+# Test local var problem introduced in v1.8.3
+DT = data.table(a=1:3,b=1:6)
+f = function() {
+ localvar = 2
+ print(DT[a>localvar])
+ print(DT[a>localvar,sum(b)])
+ print(DT[a>localvar,sum(b),by=a]) # bug fix 2368
+}
+test(876, f(), output=" a b1: 3 32: 3 6.*[1] 9.* a V11: 3 9")
+
+# segfault when assigning NA names, #2393
+DT = data.table(a=1:3, b=4:6)
+test(877, setnames(DT, c(NA, NA)), error="Passed a vector of type 'logical'. Needs to be type 'character'")
+
+# test no warning when use.names explicitly set, #2385 - changed 'warning' to 'message' as we just check if usenames is missing, due to C-level changes.
+# commented the message for now until confirmation with Matt.
+test(878, rbind(data.table(a=1:3,b=4:6), data.table(b=7:9,a=4:6)), data.table(a=1:6,b=4:9)) #, message="Columns will be bound by name for consistency with base")
+test(879, rbind(data.table(a=1:3,b=4:6), data.table(b=7:9,a=4:6), use.names=TRUE), data.table(a=1:6,b=4:9))
+
+# Test fread()
+n=110 # 110 just to be over the 100 limit for printing head, as a convenience
+DT = data.table( a=sample(1:1000,n,replace=TRUE),
+ b=sample(1:1000,n,replace=TRUE)-500L,
+ c=rnorm(n),
+ d=sample(c("foo","bar","baz","qux","quux"),n,replace=TRUE),
+ e=rnorm(n),
+ f=sample(1:1000,n,replace=TRUE) )
+DT[2,b:=NA_integer_]
+DT[4,c:=NA_real_]
+DT[3,d:=NA_character_]
+DT[5,d:=""]
+DT[2,e:=+Inf]
+DT[3,e:=-Inf]
+DT[4,e:=NaN] # write.table writes NaN as NA, though, and all.equal considers NaN==NA. fread would read NaN as NaN if "NaN" was in file
+write.table(DT,f<-tempfile(),sep=",",row.names=FALSE,quote=FALSE)
+test(880, fread(f), as.data.table(read.csv(f,stringsAsFactors=FALSE)))
+test(881, fread(f), DT)
+# test that columns are not coerced if nastring=NULL
+DT[3,d:="NA"]
+test(882, fread(f,na.strings=NULL)[['d']], DT[['d']])
+DT[3,d:=NA_character_]
+unlink(f)
+write.table(DT,f<-tempfile(),sep=",",row.names=FALSE,quote=TRUE)
+test(883, fread(f), as.data.table(read.csv(f,stringsAsFactors=FALSE)))
+test(884, fread(f), DT)
+unlink(f)
+
+# Test short files.
+# All the unlinks and using a new file each time are to work around apparent Windows issues it seems when writing, appending
+# rereading (possibly via the MapViewOfFile) the same file that has just been appended to. These apparent issues have only
+# showed up on winbuilder so far, so might be in combination with the D: tempdir() there; perhaps D: is on a network drive or something.
+cat("",file=f<-tempfile()); test(885, fread(f), error="empty"); unlink(f)
+test(885.1, fread(""), error="empty")
+test(886, fread("\n"), error="empty")
+test(887, fread(" \n\t \t \n \n "), error="empty")
+cat("A", file=f<-tempfile()); test(888, fread(f), data.table(A=logical())); unlink(f)
+test(889, fread("A\n"), data.table(A=logical()))
+cat("AB,CDE",file=f<-tempfile()); test(890, fread(f), data.table(AB=logical(),CDE=logical())); unlink(f)
+test(891, fread("AB,CDE\n"), data.table(AB=logical(),CDE=logical()))
+cat("3.14",file=f<-tempfile()); test(892, fread(f), data.table(V1=3.14)); unlink(f)
+cat("A,3",file=f<-tempfile()); test(893, fread(f), data.table(V1="A",V2=3L)); unlink(f)
+if (.Platform$OS.type=="unix") test(893.5, fread("A,B\r\n\r\n"), data.table(A=logical(),B=logical()))
+for (nc in c(0,1,2)) { # 0 means all cols here
+for (nr in c(0,1,2,3,5,10,18,19,20,21,22,28,29,30,31,32,38,39,40,41,42)) { # 30 and 40 are trigger points for auto skip
+for (eol in if (.Platform$OS.type=="unix") c("\n","\r\n") else "\n") {
+ headDT = head(DT,nr)[,seq_len(if (nc==0) ncol(DT) else nc),with=FALSE]
+ if (nr==0) for (j in seq_len(ncol(headDT))) set(headDT,j=j,value=logical()) # when read back in empty cols are the lowest type (logical)
+ f = tempfile()
+ cat(names(headDT),sep=",",file=f) # no \n at the end here
+ for (i in seq_len(nr)) {
+ cat(eol,file=f,append=TRUE) # on unix we simulate windows too. on windows \n will write \r\n (and \r\n will write \r\r\n)
+ write.table(headDT[i],file=f,quote=FALSE,sep=",",eol="",row.names=FALSE,col.names=FALSE,append=TRUE)
+ # loop approach is to get no \n after last line
+ }
+ test(894+nr/100+nc/1000, fread(f), headDT)
+ file.copy(f,f2<-tempfile()); unlink(f) # again trying to work around apparent issue on Windows
+ cat(eol,file=f2,append=TRUE) # now a 'normal' file ending with \n
+ test(895+nr/100+nc/1000, fread(f2), headDT)
+ file.copy(f2,f3<-tempfile()); unlink(f2)
+ cat(eol,file=f3,append=TRUE) # extra \n should be ignored
+ test(896+nr/100+nc/1000, fread(f3), headDT)
+ unlink(f)
+ unlink(f2)
+ unlink(f3)
+}}}
+if ("package:bit64" %in% search()) {
+ DT = data.table( a=sample(1:1000,n,replace=TRUE),
+ b=sample(as.integer64(2)^35 * 1:10, n, replace=TRUE),
+ c=sample(c("foo","bar","baz"),n,replace=TRUE) )
+ write.table(DT,f<-tempfile(),sep=",",row.names=FALSE,quote=FALSE)
+ test(897, class(DT$b), "integer64")
+ test(898, fread(f), DT)
+ unlink(f)
+
+ # Test all mid read bump coercions
+ DT[,a2:=as.integer64(a)][,a3:=as.double(a)][,a4:=gsub(" ","",format(a))]
+ DT[,b2:=as.double(b)][,b3:=gsub(" ","",format(b))]
+ DT[,r:=a/100][,r2:=gsub(" ","",format(r))]
+ DT[12, a2:=as.integer64(12345678901234)] # start on row 12 to avoid first 5, middle 5 and last 5 test rows
+ DT[13, a3:=3.14]
+ DT[14, a4:="123A"]
+ DT[15, b2:=1234567890123.45]
+ DT[16, b3:="12345678901234567890A"] # A is needed otherwise read as double with loss of precision (TO DO: should detect and bump to STR)
+ DT[17, r2:="3.14A"]
+ write.table(DT,f<-tempfile(),sep=",",row.names=FALSE,quote=FALSE)
+ test(899, fread(f), DT, warning="Bumped column.*to type character.*may not be lossless")
+ unlink(f)
+} else {
+ cat("Tests 897-899 not run. If required call library(bit64) first.\n")
+}
+
+# getwd() has been set by test.data.table() to the location of this tests.Rraw file. Test files should be in the same directory.
+f = "ch11b.dat" # http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat
+test(900, fread(f), as.data.table(read.table(f)))
+
+f = "1206FUT.txt" # a CRLF line ending file (DOS)
+test(901.1, DT<-fread(f,strip.white=FALSE), setDT(read.table(f,sep="\t",header=TRUE,colClasses=as.vector(sapply(DT,class)))))
+test(901.2, DT<-fread(f), setDT(read.table(f,sep="\t",header=TRUE,colClasses=as.vector(sapply(DT,class)),strip.white=TRUE)))
+
+# Tests the coerce of column 23 to character on line 179 due to the 'A' for the first time :
+f = "2008head.csv"
+test(902, fread(f), as.data.table(read.csv(f,stringsAsFactors=FALSE)), warning="Bumped column 23 to type character.*may not be lossless")
+
+test(903, fread("A,B\n1,3,foo,5\n2,4,barbaz,6"), data.table(1:2,3:4,c("foo","barbaz"),5:6),
+ warning="Starting data input on line 2 and discarding line 1 because.*: A,B") # invalid colnames (too short)
+test(904, fread("A,B,C,D\n1,3,foo,5\n2,4,barbaz,6"), DT<-data.table(A=1:2,B=3:4,C=c("foo","barbaz"),D=5:6)) # ok
+test(905, fread('A,B,C,D\n1,3,foo,5\n2,4,"barbaz",6'), DT)
+test(906, fread('A,B,C,D\n1,3,foo,5\n2,4,"ba,r,baz",6'), DT[2,C:="ba,r,baz"])
+test(907, fread('A,B,C,D\n1,3,foo,5\n2,4,"ba,\\"r,baz",6'), DT[2,C:='ba,\\"r,baz']) # \" protected ok, but \ needs taking off too (TO DO)
+test(908, fread("A,B,C\n1,3,\n2,4,\n"), data.table(A=1:2,B=3:4,C=NA)) # where NA is type logical
+
+test(909, fread("
+Date and Time,Open,High,Low,Close,Volume
+2007/01/01 22:51:00,5683,5683,5673,5673,64
+2007/01/01 22:52:00,5675,5676,5674,5674,17
+2007/01/01 22:53:00,5674,5674,5673,5674,42
+")$Open, c(5683L,5675L,5674L)) # , is higher than ' ' in the hierarchy of separators, so ',' is auto detected here.
+
+# blanks when testing if header row is all character
+test(910, fread("
+02-FEB-2009,09:55:04:962,26022009,2500,PE,36,500,44,200,11850,1100,,2865.60
+02-FEB-2009,09:55:04:987,26022009,2800,PE,108.75,200,111,50,11700,1450,,2865.60
+02-FEB-2009,09:55:04:939,26022009,3100,CE,31.1,3000,36.55,200,3500,5250,,2865.60
+")$V13, rep(2865.60,3))
+
+test(911, fread("02-FEB-2009,09:55:04:962,26022009,2500,PE,36,500,44,200,11850,1100,,2865.60
+02-FEB-2009,09:55:04:987,26022009,2800,PE,108.75,200,111,50,11700,1450,,2865.60
+02-FEB-2009,09:55:04:939,26022009,3100,CE,31.1,3000,36.55,200,3500,5250,,2865.60")$V13, rep(2865.60,3))
+
+# Check manually setting separator
+txt = "A;B;C|D,E\n1;3;4|5,6\n2;4;6|8,10\n"
+test(912, names(fread(txt)), c("A;B;C|D","E"))
+test(913, fread(txt,sep=";"), data.table(A=1:2,B=3:4,"C|D,E"=c("4|5,6","6|8,10")))
+test(914, fread(txt,sep="*"), data.table("A;B;C|D,E"=c("1;3;4|5,6","2;4;6|8,10")))
+test(915, fread(txt,sep="\n"), data.table("A;B;C|D,E"=c("1;3;4|5,6","2;4;6|8,10"))) # like a fast readLines
+
+# Crash bug when RHS is 0 length and := by group, fixed in 1.8.7
+DT = data.table(a=1:3,b=1:6)
+test(916, DT[,newcol:=logical(0),by=a], data.table(a=1:3,b=1:6,newcol=NA))
+
+# roll join error when non last join column is factor, #2450
+X = data.table(id=2001:2004, uid=c(1001,1002,1001,1001), state=factor(c('CA','CA','CA','MA')), ts=c(51,52,53,54), key='state,uid,ts')
+Y = data.table(id=3001:3004, uid=c(1001,1003,1002,1001), state=factor(c('CA','CA','CA','CA')), ts=c(51,57,59,59), key='state,uid,ts')
+test(917, X[Y,roll=TRUE], data.table(id=INT(2001,2003,2002,NA), uid=c(1001,1001,1002,1003), state=factor('CA'), ts=c(51,59,59,57), i.id=INT(3001,3004,3003,3002), key='state,uid,ts'))
+
+# NA in join column of type double, #2453.
+X = data.table(name=c("Joh","Raf","Jon","Ste","Rob","Smi"),depID=c(NA,31,33,33,34,34),key="depID")
+Y = data.table(depID=c(31,33,34,35),depName=c("Sal","Eng","Cle","Mar"),key="depID")
+test(918, Y[X], data.table(depID=c(NA,31,33,33,34,34),depName=c(NA,"Sal","Eng","Eng","Cle","Cle"),name=c("Joh","Raf","Jon","Ste","Rob","Smi"),key='depID')) # Y[X] same as merge.data.frame(X,Y,all.x=TRUE)
+test(919, X[Y], data.table(name=c("Raf","Jon","Ste","Rob","Smi",NA), depID=c(31,33,33,34,34,35), depName=c("Sal","Eng","Eng","Cle","Cle","Mar"),key='depID'))
+test(920, X[Y,nomatch=0], data.table(name=c("Raf","Jon","Ste","Rob","Smi"),depID=c(31,33,33,34,34),depName=c("Sal","Eng","Eng","Cle","Cle"),key='depID'))
+test(921, Y[X,nomatch=0], data.table(depID=c(31,33,33,34,34),depName=c("Sal","Eng","Eng","Cle","Cle"),name=c("Raf","Jon","Ste","Rob","Smi"),key='depID'))
+
+# setnames bug on keyed table, when full vector is given and target key isn't the positions in columns 1:length(key)
+DT = data.table(a=1:2,b=3:4,c=5:6,key="b")
+test(922, setnames(DT,c("A","B","C")), data.table(A=1:2,B=3:4,C=5:6,key="B"))
+
+# vecseq overflow, crash bug #2464
+DT = data.table(x=rep(1L,50000),key="x")
+test(923, DT[DT], error="Join results in more than 2^31 rows (internal vecseq reached physical limit). Very likely misspecified join.")
+X = data.table(x=1:2,y=1:6,key="x")
+test(924.1, X[J(c(1,1,1))], X[rep(1:3,3)])
+test(924.2, X[J(c(1,1,1,1))], error="Join results in 12 rows; more than 10 = nrow(x)[+]nrow(i). Check for duplicate key values in i each of")
+
+
+# sorting of 'double' columns not correct for ties (tolerance nuance in C code), #2484
+DT = data.table(X=as.POSIXct( c(rep("15DEC2008:00:00:00",10),"15DEC2008:00:00:00",rep("17DEC2008:00:00:00",2)),format="%d%b%Y:%H:%M:%S"),Y=c(1534,61,74,518,519,1519,1520,1524,3127,29250,30609,43,7853))
+setkey(DT,X,Y)
+test(925, DT[,base::order(X,Y)], 1:nrow(DT))
+
+# Test new dogroup warning for zero length columns in result when other columns are >1, #2478
+DT = data.table(a=1:3,b=1:6)
+test(926, DT[, if(a==2L) list(42:43,NULL) else list(42L,3.14), by=a], data.table(a=INT(1,2,2,3),V1=INT(42,42,43,42),V2=c(3.14,NA,NA,3.14)), warning="Item 2 of j's result for group 2 is zero length. This will be filled with 2 NAs to match the")
+test(927, DT[, if(a==2L) list(42:43,numeric()) else list(42L,3.14), by=a], data.table(a=INT(1,2,2,3),V1=INT(42,42,43,42),V2=c(3.14,NA,NA,3.14)), warning="Item 2 of j's result for group 2 is zero length. This will be filled with 2 NAs to match the")
+
+# And the root cause of #2478: that cbind(DT,1:3) created invalid data.table with empty column
+test(928, cbind(data.table(a=1L),b=1:3), data.table(a=1L,b=1:3))
+# FR #4813 implementation resulted in changing 929 error to warning
+# test(929, cbind(data.table(a=1L,b=2:3),c=1:3), error="argument 1 (nrow 2) cannot be recycled without remainder to match longest nrow (3)")
+test(929, cbind(data.table(a=1L,b=2:3),c=1:3), data.table(a=1L, b=c(2L,3L,2L), c=1:3), warning="Item 1 is of size 2 but maximum size is 3")
+test(930, cbind(data.table(a=1L,b=2:3),c=1:4), data.table(a=1L,b=INT(2,3,2,3),c=1:4))
+DT = data.table(x=c(1,1,1,1,2,2,3),y=c(1,1,2,3,1,1,2))
+DT[,rep:=1L][c(2,7),rep:=c(2L,3L)] # duplicate row 2 and triple row 7
+DT[,num:=1:.N] # to group each row by itself
+test(931, DT[,cbind(.SD,dup=1:rep),by="num"], data.table(num=INT(1,2,2,3:7,7,7),x=c(1,1,1,1,1,2,2,3,3,3),y=c(1,1,1,2,3,1,1,2,2,2),rep=INT(1,2,2,1,1,1,1,3,3,3), dup=INT(1,1,2,1,1,1,1,1,2,3)))
+
+# New roll=+/- and rollends
+DT = data.table(a=INT(1,3,4,4,4,4,7), b=INT(5,5,6,6,9,9,2), v=1:7, key="a,b")
+test(932, DT[J(c(0,2,6,8)), roll=+Inf, rollends=TRUE, v], INT(1,1,6,7))
+test(933, DT[J(c(0,2,6,8)), roll=-Inf, rollends=TRUE, v], INT(1,2,7,7))
+test(934, DT[J(c(0,2,6,8)), roll=+Inf, v], INT(NA,1,6,7))
+test(935, DT[J(c(0,2,6,8)), roll=-Inf, v], INT(1,2,7,NA))
+test(936, DT[J(c(-10,-1,2,12,13)), roll=5, rollends=TRUE, v], INT(NA,1,1,7,NA))
+test(937, DT[J(c(-10,-1,2,12,13)), roll=-5, rollends=TRUE, v], INT(NA,1,2,7,NA))
+test(938, DT[J(c(-10,2,6,7,8)), roll="nearest", v], INT(1,1,7,7,7))
+test(939, DT[J(c(-10,2,6,7,8)), roll="nearest", rollends=c(TRUE,FALSE), v], INT(1,1,7,7,NA))
+test(940, DT[J(c(-10,2,6,7,8)), roll="nearest", rollends=c(FALSE,TRUE), v], INT(NA,1,7,7,7))
+test(941, DT[J(c(-10,2,6,7,8)), roll="nearest", rollends=FALSE, v], INT(NA,1,7,7,NA))
+
+# merge all=TRUE with space in a y column name, #2555
+X = data.table(a=1:3,b=4:6)
+Y = data.table(a=2:4,"d 1"=5:7) # space in Y's column name
+test(942, merge(X,Y,all=TRUE,by="a"), data.table(a=1:4,b=INT(4:6,NA),"d 1"=INT(NA,5:7),key="a"))
+test(943, merge(X,Y,all.y=TRUE,by="a"), data.table(a=2:4,b=INT(5:6,NA),"d 1"=5:7,key="a"))
+
+# Test error message say NULL rather than empty table
+DT = data.table(NULL)
+test(944, DT[,a:=1L], error = "Cannot use := to add columns to a null data.table.*You can use")
+DT = data.table(a=numeric())
+test(945, DT[,b:=a+1], data.table(a=numeric(),b=numeric()))
+
+# fread blank column names get default names
+test(946, fread('A,B,,D\n1,3,foo,5\n2,4,bar,6\n'), data.table(A=1:2,B=3:4,c("foo","bar"),D=5:6))
+test(947, fread('0,2,,4\n1,3,foo,5\n2,4,bar,6\n'), data.table(0:2,2:4,c("","foo","bar"),4:6))
+test(948, fread('A,B,C\nD,E,F\n',header=TRUE), data.table(A="D",B="E",C=FALSE))
+test(949, fread('A,B,\nD,E,F\n',header=TRUE), data.table(A="D",B="E",V3=FALSE))
+
+# +/- with no numbers afterwards should read as character
+test(950, fread('A,B,C\n1,+,4\n2,-,5\n3,-,6\n'), data.table(A=1:3,B=c("+","-","-"),C=4:6))
+
+# catching misuse of `:=`
+x = data.table(a=1:5)
+test(951, x[,{b=a+3; `:=`(c=b)}], error="defined for use in j, once only and in particular ways")
+
+# fread colClasses
+input = 'A,B,C\n01,foo,3.140\n002,bar,6.28000\n'
+test(952, fread(input, colClasses=c(C="character")), data.table(A=1:2,B=c("foo","bar"),C=c("3.140","6.28000")))
+test(953, fread(input, colClasses=c(C="character",A="numeric")), data.table(A=c(1.0,2.0),B=c("foo","bar"),C=c("3.140","6.28000")))
+test(954, fread(input, colClasses=c(C="character",A="double")), data.table(A=c(1.0,2.0),B=c("foo","bar"),C=c("3.140","6.28000")))
+test(955, fread(input, colClasses=list(character="C",double="A")), data.table(A=c(1.0,2.0),B=c("foo","bar"),C=c("3.140","6.28000")))
+test(956, fread(input, colClasses=list(character=2:3,double="A")), data.table(A=c(1.0,2.0),B=c("foo","bar"),C=c("3.140","6.28000")))
+test(957, fread(input, colClasses=list(character=1:3)), data.table(A=c("01","002"),B=c("foo","bar"),C=c("3.140","6.28000")))
+test(958, fread(input, colClasses="character"), data.table(A=c("01","002"),B=c("foo","bar"),C=c("3.140","6.28000")))
+test(959, fread(input, colClasses=c("character","double","numeric"), verbose=TRUE),
+ warning = "Column 2 ('B') has been detected as type 'character'. Ignoring request from colClasses to read as 'numeric' (a lower type) since NAs (or loss of precision) may result",
+ output = "Detected 3 columns", # including output= just so that verbose output is captured, just the warning will be checked.
+ data.table(A=c("01","002"),B=c("foo","bar"),C=c(3.14,6.28)))
+
+test(960, fread(input, colClasses=c("character","double")), error="colClasses is unnamed and length 2 but there are 3 columns. See")
+test(961, fread(input, colClasses=1:3), error="colClasses is not type list or character vector")
+test(962, fread(input, colClasses=list(1:3)), error="colClasses is type list but has no names")
+test(963, fread(input, colClasses=list(character="D")), error="Column name 'D' in colClasses..1.. not found")
+test(964, fread(input, colClasses=c(D="character")), error="Column name 'D' in colClasses..1.. not found")
+test(965, fread(input, colClasses=list(character=0)), error="Column number 0 (colClasses..1...1.) is out of range .1,ncol=3.")
+test(966, fread(input, colClasses=list(character=2:4)), error="Column number 4 (colClasses..1...3.) is out of range .1,ncol=3.")
+
+# Character input more than 4096 bytes (used to be passed through path.expand which imposed the limit), #2649
+test(967, nrow(fread( paste( rep('a\tb\n', 10000), collapse=''), header=FALSE)), 10000L)
+
+# Test fread warns about removal of any footer (and autostart skips up over it)
+test(968, fread("A,B\n1,3\n2,4\n\nRowcount: 2\n"), data.table(A=1:2,B=3:4), warning="Stopped reading at empty line 4.*discarded.*Rowcount: 2")
+test(969, fread("A,B\n1,3\n2,4\n\n\nRowcount: 2"), data.table(A=1:2,B=3:4), warning="Stopped reading at empty line 4.*discarded.*Rowcount: 2")
+test(970, fread("A,B\n1,3\n2,4\n\n\nRowcount: 2\n\n"), data.table(A=1:2,B=3:4), warning="Stopped reading at empty line 4.*discarded.*Rowcount: 2")
+
+# fread skip override
+input = "some,bad,data\nA,B,C\n1,3,5\n2,4,6\n"
+test(971, fread(input), data.table(some=c("A",1:2),bad=c("B",3:4),data=c("C",5:6)))
+test(972, fread(input, skip=1), data.table(A=1:2,B=3:4,C=5:6))
+test(973, fread(input, skip=2), data.table(V1=1:2,V2=3:4,V3=5:6))
+test(974, fread(input, skip=2, header=TRUE), data.table("1"=2L,"3"=4L,"5"=6L))
+test(975, fread(input, skip="B"), data.table(A=1:2,B=3:4,C=5:6))
+input = "\n\nA,B\n1,3\n2,4\n\nC,D\n5,7\n6,8\n\nE,F\n9,11\n10,12\n" # 3 tables in one file
+test(976, fread(input), data.table(A=1:2,B=3:4), warning="Stopped reading at empty line 6.*discarded.*C,D")
+test(977, fread(input, autostart=8), data.table(C=5:6,D=7:8), warning="Stopped reading at empty line 10.*discarded.*E,F")
+test(978, fread(input, skip="D"), data.table(C=5:6,D=7:8), warning="Stopped reading at empty line 10.*discarded.*E,F")
+
+# mixed add and update in same `:=` bug/crash, #2528 and #2778
+DT = data.table(x=rep(1:2, c(3,2)), y=6:10)
+DT[, z:=.GRP, by=x] # first assignment
+test(979, DT[, `:=`(z=.GRP, w=2), by=x], data.table(x=INT(1,1,1,2,2),y=6:10,z=INT(1,1,1,2,2),w=2)) # mixed update and add
+# and example from http://stackoverflow.com/a/14732348/403310 :
+dt1 = fread("Date,Time,A,B
+01/01/2013,08:00,10,30
+01/01/2013,08:30,15,25
+01/01/2013,09:00,20,20
+02/01/2013,08:00,25,15
+02/01/2013,08:30,30,10
+02/01/2013,09:00,35,5")
+dt2 = fread("Date,A,B,C
+01/01/2013,100,300,1
+02/01/2013,200,400,2")
+setkey(dt1, "Date")
+setkey(dt2, "Date")
+test(980, dt1[dt2, `:=`(A=A+i.A, B=B+i.B, C=i.C)][,list(A,B,C)],
+ data.table(A=INT(110,115,120,225,230,235),B=INT(330,325,320,415,410,405),C=rep(1:2,each=3)))
+DT = data.table(A=1:2,B=3:4,C=5:6)
+test(981, DT[,`:=`(D=B+4L,B=0:1,E=A*2L,F=A*3L,C=C+1L,G=C*2L),by=A],
+ data.table(A=1:2,B=0L,C=6:7,D=7:8,E=c(2L,4L),F=c(3L,6L),G=c(10L,12L)), warning="RHS 2 is length 2")
+DT = data.table(A=1:2,B=3:4,C=5:6)
+test(982, DT[,`:=`(D=B+4L,B=0L,E=A*2L,F=A*3L,C=C+1L,G=C*2L),by=A],
+ data.table(A=1:2,B=0L,C=6:7,D=7:8,E=c(2L,4L),F=c(3L,6L),G=c(10L,12L))) # Also note that G is not yet iterative. In future: c(12,14)
+
+# rbindlist binding factors, #2650
+test(983, rbindlist(list(data.table(factor(c("A","A","B","C","A"))), data.table(factor(c("B","F","A","G"))))), data.table(V1=factor(c("A","A","B","C","A","B","F","A","G"))))
+test(984, rbindlist(list(data.table(factor(c("A","B"))), data.table(c("C","A")))), data.table(factor(c("A","B","C","A"))))
+test(985, rbindlist(list(data.table(c("A","B")), data.table(factor(c("C","A"))))), data.table(factor(c("A","B","C","A"))))
+# with NA
+test(985.1, rbindlist(list(data.table(factor(c("A","B"))), data.table(factor(c("C",NA))))), data.table(factor(c("A","B","C",NA))))
+test(985.2, rbindlist(list(data.table(c("A","B")), data.table(factor(c("C",NA))))), data.table(factor(c("A","B","C",NA))))
+
+## Allow unique/duplicated to accept custom colum combination to query for
+## uniqueness
+dt <- data.table(A = rep(1:3, each=4), B = rep(11:14, each=3), C = rep(21:22, 6), key = "A,B")
+df <- as.data.frame(dt)
+test(986, unique(dt), dt[!duplicated(df[, key(dt)]),])
+test(987, unique(dt, by='A'), dt[!duplicated(df[, 'A'])])
+test(988, unique(dt, by='B'), dt[!duplicated(df[, 'B'])])
+test(989, unique(dt, by='C'), dt[!duplicated(df[, 'C'])])
+test(990, unique(dt, by=c('B', 'C')), dt[!duplicated(df[, c('B', 'C')])])
+test(991, unique(dt, by=NULL), dt[!duplicated(df)])
+test(991.1, unique(dt, by=4), error="Integer values between 1 and ncol are required")
+test(991.2, unique(dt, by=c(1,3.1)), error="Integer values between 1 and ncol are required")
+test(991.3, unique(dt, by=2:3), dt[!duplicated(df[,c('B','C')])])
+test(991.4, unique(dt, by=c('C','D','E')), error="by specifies column names that do not exist. First 5: D,E")
+
+# :=NULL on factor column in empty data.table, #4809
+DT = data.table(A = integer(), B = factor())
+test(992, DT[, B:=NULL], data.table(A=integer()))
+
+# That including FUN= works in j=lapply, #4839
+DT = as.data.table(iris)
+test(993, DT[, lapply(.SD, function(x) sum(!is.na(x), na.rm=TRUE)), by = Species],
+ DT[, lapply(.SD, FUN=function(x) sum(!is.na(x), na.rm=TRUE)), by = Species])
+
+# fread more than 50,000 columns, the R_PPSSIZE limit in Defn.h
+# Takes too long for routine use. TO DO: move to a long running stress test script
+#M = matrix(1,nrow=3,ncol=200000)
+#f = tempfile()
+#write.csv(M,f,row.names=FALSE)
+#test(994, fread(f)[[200000]], rep(1L,3))
+#unlink(f)
+
+# CJ with `sorted = FALSE` option
+DT <- data.table(x=rep(3:5, each=4), y=rep(1:6, each=2), z=1:12)
+setkey(DT, x, y)
+OUT <- DT[J(c(5,5,3,3), c(5,1,5,1))]
+test(995, DT[CJ(c(5,3), c(5,1), sorted=FALSE)], OUT)
+
+# CJ with ordered factor
+xx <- factor(letters[1:2], ordered=TRUE)
+yy <- sample(2)
+test(996, CJ(xx, yy), setkey(data.table(rep(xx, each=2), rep(base::sort.int(yy), 2))))
+
+# That CJ orders NA consistently with setkey and historically, now it doesn't use setkey.
+# NA must always come first in data.table throughout, since binary search relies on that internally.
+test(997, DT <- CJ(c(1,3,NA,2), 5:6), setkey(setkey(copy(DT),NULL))) # double setkey to really rebuild key
+test(998, DT <- CJ(as.integer(c(1,3,NA,2)), 5:6), setkey(setkey(copy(DT),NULL)))
+test(999, DT <- CJ(c("A","B",NA,"C"), 5:6), setkey(setkey(copy(DT),NULL)))
+test(1000, DT <- CJ(c(1,NA,3), c("B",NA,"A"), c(5L,NA_integer_)), setkey(setkey(copy(DT),NULL)))
+test(1001, DT <- CJ(c(1,NA,3)), setkey(setkey(copy(DT),NULL))) # The 1 column case is switched inside CJ() so test that too.
+
+# merge all=TRUE when y is empty, #2633
+a = data.table(P=1:2,Q=3:4,key='P')
+b = data.table(P=2:3,R=5:6,key='P')
+test(1002, merge(a,b[0],all=TRUE), data.table(merge.data.frame(a,b[0],all=TRUE),key='P'))
+a = data.table(c=c(1,2),key='c')
+b = data.table(c=3,key='c')
+test(1003, merge(a,b[0],all=TRUE), data.table(merge.data.frame(a,b[0],all=TRUE),key='c'))
+
+# setkey with backticks, #2452
+DT = data.table("Date and Time"=1:3,x=4:6)
+test(1004, setkey(copy(DT),`Date and Time`), setkey(DT,"Date and Time"))
+
+# rbinding with duplicate names, NA or "", #2384 and #2726
+DT = data.table(a=1:3,b=4:6,b=7:9,c=10:12)
+test(1005, rbind(DT,DT), data.table(a=rep(1:3,2),b=rep(4:6,2),b=rep(7:9,2),c=rep(10:12,2)))
+M <- mtcars
+colnames(M)[11] <- NA
+test(1006, print(as.data.table(M), nrows=10), output="gear NA.*1: 21.0")
+
+# rbinding factor with non-factor/character
+DT1 <- data.table(x=1:5, y=factor("a"))
+DT2 <- data.table(x=1:5, y=2)
+test(1007, rbindlist(list(DT1, DT2)), data.table(x = c(1:5, 1:5), y = factor(c(rep('a', 5), rep('2', 5)), levels = c('a', '2'))))
+test(1008, rbindlist(list(DT2, DT1)), data.table(x = c(1:5, 1:5), y = factor(c(rep('2', 5), rep('a', 5)))))
+
+# rbindlist different types
+DT1 <- data.table(a = 1L, b = 2L)
+DT2 <- data.table(a = 2L, b = 'a')
+DT3 <- data.table(a = 2L, b = 2.5)
+test(1008.1, rbindlist(list(DT1, DT2)), data.table(a = c(1L,2L), b = c('2', 'a')))
+test(1008.2, rbindlist(list(DT1, DT3)), data.table(a = c(1L,2L), b = c(2, 2.5)))
+
+# optimized mean() respects na.rm=TRUE by default, as intended
+DT = data.table(a=c(NA,NA,FALSE,FALSE), b=c(1,1,2,2))
+test(1009, DT[,list(mean(a), sum(a)),by=b], data.table(b=c(1,2),V1=c(NA,0),V2=c(NA,0)))
+
+# an fread error shouldn't hold a lock on the file on Windows.
+f = tempfile()
+cat('A,B\n"aa",2\n"bb,2\n"cc",3\n', file=f) # NB: deliberate missing quote after bb.
+test(1010, fread(f), data.table(A=c("aa", "\"bb", "cc"), B=c(2L,2L,3L)))
+cat('dd",4\n',file=f,append=TRUE) # tests file lock on Windows after error
+test(1011, fread(f), data.table(A=c("aa", "\"bb", "cc", "dd\""), B=c(2L,2L,3L,4L)))
+cat('A,B\n"aa",1\n"bb",2\n"cc",3\n', file=f) # testing overwrite
+test(1012, fread(f), data.table(A=c("aa","bb","cc"),B=1:3))
+unlink(f) # testing file can be removed after error
+
+# integer64 control to fread
+test(1013, fread("A,B\n123,123\n", integer64="integer"), error="integer64='%s' which isn't 'integer64'|'double'|'numeric'|'character'")
+test(1014, fread("A,B\n123456789123456,21\n", integer64="character"), data.table(A="123456789123456",B=21L))
+test(1015, fread("A,B\n123456789123456,21\n", integer64="double"), data.table(A=as.double("123456789123456"),B=21L))
+# and that mid read bumps respect integer64 control too ..
+x = sample(1:1000,100,replace=TRUE)
+DT = data.table( A=as.character(x), B=1:100)
+DT[15, A:="123456789123456"] # row 15 is outside the top, middle and last 5 rows.
+write.table(DT,f<-tempfile(),sep=",",row.names=FALSE,quote=FALSE)
+test(1016, fread(f,integer64="numeric"), copy(DT)[,A:=as.numeric(A)])
+test(1017, fread(f,integer64="character"), DT, warning="Bumped column.*to type character.*may not be lossless")
+unlink(f)
+
+# ERANGE warning, #4879
+tt = try(fread("1.46761e-313\n"), silent=TRUE) # options(warn=2) while this test file runs
+if (inherits(tt,"try-error")) {
+ # All CRAN machines including SPARC
+ test(1018, fread("1.46761e-313\n"), data.table(V1=as.numeric("1.46761e-313")), warning="strtod() returned ERANGE")
+} else {
+ # on PowerPC only via QEMU emulation :
+ test(1018, fread("1.46761e-313\n"), data.table("1.46761e-313"=logical()))
+}
+test(1019, fread("1.23456789123456789123456789\n"), data.table(V1=as.numeric("1.23456789123456789123456789"))) # no warning, as standard
+
+# crash assigning to row 0, #2754
+DT = data.table(A=1:5,B=6:10)
+test(1020, DT[0,A:=6L], DT)
+test(1021, DT[NA,A:="foo"], DT)
+test(1022, DT[5:0,A:=21L], data.table(A=21L, B=6:10))
+test(1023, DT[c(1,2,NA,3), B:=42L], data.table(A=21L, B=c(42L,42L,42L,9:10)))
+test(1024, DT[6,A:=0L], error="i[[]1[]] is 6 which is out of range [[]1,nrow=5[]]")
+
+# crash assigning to duplicated column names/numbers, #2751
+test(1024.1, DT[,c("B","B"):=NULL], error="Can't assign to the same column twice in the same query (duplicates detected).")
+test(1024.2, DT[,c(1,2,1):=NULL], error="Can't assign to the same column twice in the same query (duplicates detected).")
+
+# as.data.table.table, #4848
+DF <- data.frame(x = c(1,1,2,NA,1,2), y = c("b", "b", "b", "a", "c", "a"), z = c(1,1,1,1,1,2), stringsAsFactors=FALSE )
+tab1 <- as.data.table(as.data.frame(table(DF$x), stringsAsFactors=FALSE)); setattr(tab1, 'names', c("V1", "N"))
+tab2 <- as.data.table(as.data.frame(table(DF$x, DF$y), stringsAsFactors=FALSE)); setattr(tab2, 'names', c("V1", "V2", "N"))
+tab3 <- as.data.table(as.data.frame(table(DF$x, DF$y, DF$z), stringsAsFactors=FALSE)); setattr(tab3, 'names', c("V1", "V2", "V3", "N"))
+test(1025, as.data.table(table(DF$x)), tab1)
+test(1026, as.data.table(table(DF$x, DF$y)), tab2)
+test(1027, as.data.table(table(DF$x, DF$y, DF$z)), tab3)
+# catch printing of data.table(table()), #4847 (as.data.table should be used instead)
+# new, updated 14th Feb, 2015. data.table(table) now redirects to as.data.table
+test(1027.1, data.table(table(1:99)), as.data.table(table(1:99)))
+# data.table() and rbindlist() in v1.8.11 now catch and removes the dim attribute. For it on to test print catches it :
+test(1027.2, {DT<-data.table(table(1:99));setattr(DT[[1]],"dim",99L);print(DT)}, error="Invalid column: it has dimensions. Can't format it. If it's the result of data.table(table()), use as.data.table(table()) instead.")
+
+# as.data.table.x where x is integer, numeric, etc...
+set.seed(45)
+test(1028, as.data.table(x<-sample(5)), data.table(V1=x))
+test(1029, as.data.table(x<-as.numeric(x)), data.table(V1=x))
+test(1030, as.data.table(x<-as.Date(x, origin="2013-01-01")), data.table(V1=x))
+test(1031, as.data.table(x<-factor(sample(5))), data.table(V1=x))
+test(1032, as.data.table(x<-factor(x, ordered=TRUE)), data.table(V1=x))
+test(1033, as.data.table(x<-as.logical(sample(0:1, 5, TRUE))), data.table(V1=x))
+test(1034, as.data.table(x<-as.character(sample(letters, 5))), data.table(V1=x))
+
+#########################################
+# All melt.data.table tests go in here #
+#########################################
+if ("package:reshape2" %in% search()) {
+
+ set.seed(45)
+ DT <- data.table(
+ i_1 = c(1:5, NA),
+ i_2 = c(NA,6,7,8,9,10),
+ f_1 = factor(sample(c(letters[1:3], NA), 6, TRUE)),
+ c_1 = sample(c(letters[1:3], NA), 6, TRUE),
+ d_1 = as.Date(c(1:3,NA,4:5), origin="2013-09-01"),
+ d_2 = as.Date(6:1, origin="2012-01-01"))
+ DT[, l_1 := DT[, list(c=list(rep(i_1, sample(5,1)))), by = i_1]$c] # generate list cols
+ DT[, l_2 := DT[, list(c=list(rep(c_1, sample(5,1)))), by = i_1]$c]
+
+ test(1035, melt(DT, id=1:2, measure=3:4), melt(DT, id=c("i_1", "i_2"), measure=c("f_1", "c_1")))
+
+ ans1 = cbind(DT[, c(1,2,8), with=FALSE], variable=factor("l_1"))
+ ans1[, value := DT$l_1]
+ test(1036, melt(DT, id=c("i_1", "i_2", "l_2"), measure=c("l_1")), ans1)
+
+ # melt retains attributes if all are of same type (new)
+ ans2 = data.table(c_1=DT$c_1, variable=rep(c("d_1", "d_2"), each=6), value=as.Date(c(DT$d_1, DT$d_2)))[!is.na(value)]
+ test(1037, melt(DT, id=4, measure=5:6, na.rm=TRUE, variable.factor=FALSE), ans2)
+
+ DT2 <- data.table(x=1:5, y=1+5i) # unimplemented class
+ test(1038, melt(DT2, id=1), error="Unknown column type 'complex'")
+
+ # more tests
+ DT[, f_2 := factor(c("z", "a", "x", "z", "a", "a"), ordered=TRUE)]
+ DT[, id := 1:6]
+ ans1 = cbind(melt(DT, id="id", measure=5:6, value.name="value1"), melt(DT, id=integer(0), measure=7:8, value.name="value2")[, variable:=NULL])
+ levels(ans1$variable) = as.character(1:2)
+ test(1038.2, ans1, melt(DT, id="id", measure=list(5:6, 7:8)))
+ test(1038.3, ans1, melt(DT, id="id", measure=list(5:6, 7:8), na.rm=TRUE)) # should've no effect
+ test(1038.7, ans1, melt(DT, id="id", measure=patterns("d_", "l_")))
+ # melt retains ordered factors!
+ test(1038.4, melt(DT, id="id", measure=c("f_1", "f_2"), value.factor=TRUE)$value, factor(c(as.character(DT$f_1), as.character(DT$f_2)), ordered=TRUE))
+ # if measure is integer(0) just returns a duplicated data.table with all idcols
+ test(1038.5, melt(DT, id=1:6, measure=integer(0)), shallow(DT, 1:6))
+ # measure.var list with single entry recycles to maximum length
+ ans = cbind(melt(DT, id="id", measure=c("c_1", "c_1"))[, variable := NULL], melt(DT, id=integer(0), measure=c("f_1", "f_2")))
+ setnames(ans, c("id", "value1", "variable", "value2"))
+ setcolorder(ans, c("id", "variable", "value1", "value2"))
+ levels(ans$variable) = as.character(1:2)
+ test(1038.6, melt(DT, id="id", measure=list(c("c_1", "c_1"), c("f_1", "f_2"))), ans)
+
+ # test to ensure attributes on non-factor id-columns are preserved after melt
+ DT <- data.table(x=1:3, y=letters[1:3], z1=8:10, z2=11:13)
+ setattr(DT$x, 'foo', 'bla1')
+ setattr(DT$y, 'bar', 1:4)
+ test(1222.1, attr(melt(DT, id=1:2)$x, "foo"), "bla1")
+ test(1222.2, attr(melt(DT, id=1:2)$y, "bar"), 1:4)
+
+ # bug #699 - melt segfaults when vars are not in dt
+ x = data.table(a=c(1,2),b=c(2,3),c=c(3,4))
+ test(1316.1, melt(x, id="d"), error="Column 'd' not found in 'data'")
+ test(1316.2, melt(x, measure="d"), error="Column 'd' not found in 'data'")
+ test(1316.3, melt(x, id="a", measure="d"), error="Column 'd' not found in 'data'")
+ test(1316.4, melt(x, id="d", measure="a"), error="Column 'd' not found in 'data'")
+
+ # fix for #780.
+ DT = data.table(x=rep(c("a","b","c"),each=3), y=c(1,3,6), v=1:9)
+ foo = function(input, by, var) {
+ melt(input, id.vars = by, measure.vars=var)
+ }
+ test(1371.1, foo(DT, by="x"), data.table(x=rep(DT$x, 2L), variable=factor(rep(c("y", "v"), each=9L), levels=c("y", "v")), value=c(DT$y, DT$v)), warning="are not all of the same type. By order of hierarchy, the molten data value column will be of type 'double'")
+ test(1371.2, foo(DT), data.table(x=rep(DT$x, 2L), variable=factor(rep(c("y", "v"), each=9L), levels=c("y", "v")), value=c(DT$y, DT$v)), warning="To be consistent with reshape2's melt, id.vars and")
+ # Fix for #1055
+ DT <- data.table(A = 1:2, B = 3:4, D = 5:6, D = 7:8)
+ test(1495, melt(DT, id=1:2), data.table(A=1:2, B=3:4,
+ variable=factor(rep(1L, 4L), labels="D"), value=5:8))
+
+ # segfault of unprotected var caught with the help of address sanitizer
+ set.seed(1)
+ val = sample(c(1:5, NA), 1e6L, TRUE)
+ dt <- setDT(replicate(100L, val, simplify=FALSE))
+ ## to ensure there's no segfault...
+ ans <- melt(dt, measure.vars=names(dt), na.rm=TRUE)
+ test(1509, ans, ans)
+}
+
+# sorting and grouping of Inf, -Inf, NA and NaN, #4684, #4815 & #4883
+DT <- data.table(x = rep(c(1, NA, NaN, Inf, -Inf), each=2))
+OUT <- data.table(x=c(1, NA, NaN, Inf, -Inf), N=2L)
+test(1039, DT[, .N, by=x], OUT)
+DT <- data.table(y =c(NA, Inf, NA, -Inf, -Inf, NaN, Inf, 1, NaN, 1))
+OUT <- data.table(y = c(NA, Inf, -Inf, NaN, 1), N=2L)
+test(1040, DT[, .N, by=y], OUT)
+
+# rbindlist on *data.frame* input, #4648. Somehow not test for this. (Although, #4648 was the same as #2650 fixed in v1.8.9).
+l <- list(u1=data.frame(i1=c('a', 'b', 'c'), val=1:3, stringsAsFactors=TRUE),
+ u2=data.frame(i1=c('d', 'e'), val=4:5, stringsAsFactors=TRUE))
+test(1041, rbindlist(l), data.table(i1=factor(letters[1:5]),val=1:5))
+
+# negative indexing in *i* leads to crash/wrong aggregates when dogroups is called. bug #2697
+DT = data.table(x = c(1,2,3,4,5), group = c(1,1,2,2,3))
+test(1042, DT[-5, mean(x), by = group], data.table(group=1:2, V1=c(1.5, 3.5)))
+# Test when abs(negative index) > nrow(dt) - should warn
+test(1042.1, DT[-10], DT, warning="Item 1 of i is -10 but there are only 5 rows. Ignoring this and 0 more like it out of 1.")
+test(1042.2, DT[c(-5, -10), mean(x), by = group], data.table(group=c(1,2),V1=c(1.5,3.5)), warning="Item 2 of i is -10 but there are only 5 rows. Ignoring this and 0 more like it out of 2.")
+# Test #1043 TO DO - mixed negatives
+test(1043, DT[c(1, -5)], error="Item 2 of i is -5 and item 1 is 1. Cannot mix positives and negatives.")
+
+# crash (floating point exception), when assigning null data.table() to multiple cols, #4731
+DT = data.table(x=1:5,y=6:10)
+test(1044, DT[3,c("x","y"):=data.table()],error="Supplied 2 columns to be assigned an empty list.*use NULL instead.*list(list())")
+test(1045, DT[3,c("x","y"):=list()],error="Supplied 2 columns to be assigned an empty list.*use NULL instead.*list(list())")
+
+# negative indexing with head() and tail(). bug #2375
+d1 = data.table(date = c(1,2,3,4,5), value = c(1,2,3,4,5))
+d2 = data.frame(d1)
+test(1046, head(d1, -2), as.data.table(head(d2, -2)))
+test(1047, head(d1, 2), as.data.table(head(d2, 2)))
+test(1048, head(d1, -10), as.data.table(head(d2, -10)))
+test(1049, head(d1, 10), as.data.table(head(d2, 10)))
+test(1050, tail(d1, -2), as.data.table(tail(d2, -2)))
+test(1051, tail(d1, 2), as.data.table(tail(d2, 2)))
+test(1052, tail(d1, -10), as.data.table(tail(d2, -10)))
+test(1053, tail(d1, 10), as.data.table(tail(d2, 10)))
+
+# negative indexing with `:=` - new feature through fixing of #2697, performs as intended for negative subscripts.
+x <- data.table(letters=letters[1:5], number=1:5)
+test(1054, x[-(1:3), number := 1L], x[4:5, number := 1L])
+test(1055, x[0, number := 1L], x)
+
+# print.data.table heeds digits=2 etc, #2535
+DT = data.table(x=rep(c("a","b","c"),each=3), y=(30/7)^(2:10))[, logy := log(y)]
+test(1056, print(DT, digits=2), output=" x y logy1: a 18 2.92: a 79 4.43: a 337 5.8")
+test(1057, print(DT, digits=2, big.mark=","), output=" x y logy1: a 18 2.9.*6: b 26,556 10.27: c 113,811 11.6")
+
+# bug #2758 fix - segfault with zeros in i and factors in by
+x <- data.table(letters=letters[1:5], factor=factor(letters[1:5]), number=1:5)
+test(1058, x[c(0, 3), list(letters, number), by=factor], error="While grouping, i=0 is allowed")
+test(1059, x[c(3, 0), list(letters, number), by=factor], error="While grouping, i=0 is allowed")
+test(1060, x[c(0, 3), number:=5L, by=factor], error="While grouping, i=0 is allowed")
+test(1061, x[c(0, 3), number:=5L], data.table(letters=letters[1:5], factor=factor(letters[1:5]), number=c(1:2,5L,4:5)))
+
+# bug #2440 fix - seqfault when j refers to grouping variable when results are empty
+DT = data.table(x=rep(c("a","b"),each=3),v=c(42,42,42,4,5,6))
+test(1062, DT[x %in% c('z'),list(x2=x),by=x], output="Empty data.table (0 rows) of 2 cols: x,x2")
+test(1063, DT[x %in% c('z'),list(vpaste=paste(v,collapse=','),x2=paste(x,x)),by=x], output="Empty data.table (0 rows) of 3 cols: x,vpaste,x2")
+test(1064, DT[integer(0), list(x2=x), by=x], output="Empty data.table (0 rows) of 2 cols: x,x2")
+
+# bug #2445 fix - := fails when subsetting yields NAs and with=FALSE
+X = data.table(A=1:3, B=1:6, key="A")
+var <- "B"
+test(1065, X[J(2:5), (var):=22L], data.table(A=rep(1:3, each=2), B=c(1L,4L,rep(22L,4)), key="A"))
+
+# fread single unnamed colClasses
+f = "A,B,C,D\n1,3,5,7\n2,4,6,8\n"
+test(1066, fread(f,colClasses=c("integer","integer","character")), error="colClasses is unnamed and length 3 but there are 4 columns")
+test(1067, fread(f,colClasses=c("integer","numeric","character","character")), data.table(A=1:2,B=c(3,4),C=c("5","6"),D=c("7","8")))
+test(1068, fread(f,colClasses="character"), data.table(A=c("1","2"),B=c("3","4"),C=c("5","6"),D=c("7","8")))
+
+# fread select and drop
+test(1069, fread(f,drop=c("D","B")), data.table(A=1:2,C=5:6))
+test(1070, fread(f,drop="E"), fread(f), warning="Column name 'E' in 'drop' not found")
+test(1071, fread(f,select="B",colClasses=list(numeric="C")), data.table(B=3:4))
+test(1072, fread(f,select="B",drop="C"), error="not both")
+test(1073, fread(f,drop=2:3), fread(f,select=c(1,4))) # tests coercing numeric select as well
+
+# that problem printing duplicate columns doesn't return, #4788
+DT = data.table(V1 = c(1:1000), V2 = c(10001:11000))
+test(1074, DT[, sum(V2), by = V1], output="1000: 1000 11000") # x has two columns both called V1 here
+
+# add test from #2446. Already fixed but add anyway. "names in neworder not found in x: 'colnames with spaces' from merge() when all.y=TRUE"
+X = data.table(a=1:3,b=4:6,"c d"=7:9)
+Y = data.table(e=10:12,a=2:4)
+test(1075, merge(X,Y,by="a",all=TRUE), data.table(a=c(1:4),b=c(4:6,NA),"c d"=c(7:9,NA),e=c(NA,10:12),key="a"))
+
+# Fixes #2670. `by` sometimes incorrect for expressions of keyed columns. When by is used like `by=month(date)`, with key column set to "date", grouping+aggregation would be wrong.
+DT = data.table(date=as.Date("2013-01-01")+seq(1,1000,by=10),1:100)
+setkey(DT,date)
+test(1076, DT[,sum(V2),by=month(date)], DT[, sum(V2), by=list(month(date))])
+# just to be sure, second test with another function using sample.
+setkey(DT, V2)
+ff <- function(x) { set.seed(45); (sample(x)-1) %/% 10}
+test(1077, DT[, sum(V2),by=ff(V2)], DT[, sum(V2),by=list(ff(V2))])
+
+# rbindlist should discard names on columns, #4890
+d = data.frame(x=1:5)
+f = function(x) {suppressWarnings(DF<-data.frame(x=x, y=1:10)); setattr(DF$x,"names","a");DF}
+l = apply(d, 1, f)
+test(1078.1, length(names(l[[1]]$x)), 10) # test this test is creating names on the column
+test(1078.2, length(names(l[[2]]$x)), 10)
+a = rbindlist(l)
+test(1078.3, a$x, rep(1:5,each=10)) # a$x would segfault before the fix to rbindlist
+
+# data.table() shouldn't retain column names, root cause of #4890
+x = 1:5
+names(x) = letters[1:5]
+test(1079.1, DF<-data.frame(x=x, y=1:10), data.frame(x=rep(1:5,2),y=1:10), warning="row names.*discarded")
+test(1079.2, lapply(DF, names), list(x=NULL, y=NULL))
+test(1079.3, DT<-data.table(x=x, y=1:10), data.table(x=rep(1:5,2),y=1:10))
+test(1079.4, lapply(DT, names), list(x=NULL, y=NULL))
+# test from similar #4912 for completeness
+z = c(a=1,b=2,c=3)
+a = data.table(z,x=1:3)
+b = rbind(a, data.table(z=2,x=1))
+test(1080, b$z, c(1,2,3,2))
+
+# mid row logical detection
+test(1081, fread("A,B,C\n1,T,2\n"), data.table(A=1L,B=TRUE,C=2L))
+
+# cartesian join answer's key should contain only the columns considered in binary search. Fixes #2677
+set.seed(45)
+n <- 10
+DT1 <- data.table(a=sample(1:3, n, replace=TRUE), b=sample(1:3, n, replace=TRUE), c=sample(1:10, n,replace=TRUE), key=c("a", "b", "c"))
+DT2 <- data.table(p=sample(1:3, n, replace=TRUE), q=sample(1:3, n, replace=TRUE), r=sample(1:n), w=sample(1:n))
+setkey(DT2, p,q)
+ans <- DT1[DT2, nomatch=0, allow.cartesian=TRUE] # NB: DT2 contains duplicate key values so columns c ends up not being sorted
+test(1082.1, key(ans), c("a","b"))
+test(1082.2, setkeyv(ans, key(ans)), ans) # i.e. key is valid, otherwise re-built warning will be caught
+check <- setkey(as.data.table(aggregate(r ~a+b+c, ans, length)), a, b)
+test(1083, setkeyv(ans[, list(r = .N), by=key(DT1)], key(ans)), check) # if the key is set properly, then and only then will the aggregation results match with "check"
+
+# Tests for #2531. `:=` loses POSIXct or ITime attribute:
+# first test from this SO post: http://stackoverflow.com/questions/15996692/cannot-assign-columns-as-date-by-reference-in-data-table
+dt <- data.table(date = as.IDate(sample(10000:11000, 10), origin = "1970-01-01"))
+dt[, group := rep(1:2, 5)]
+dt[, min.group.date := as.IDate(min(date)), by = group]
+test(1084, class(dt$min.group.date), c("IDate", "Date"))
+
+dt <- data.table(date = as.IDate(sample(10000:11000, 10), origin = "1970-01-01"))
+dt[, group := rep(1:2, 5)]
+dt[, min.group.date := min(date), by = group] # don't need to wrap it with as.IDate(.)
+test(1085, class(dt$min.group.date), c("IDate", "Date"))
+
+# second test from this SO post: http://stackoverflow.com/questions/14604820/why-does-this-posixct-or-itime-loses-its-format-attribute
+DT = data.table(x=as.POSIXct(c("2009-02-17 17:29:23.042", "2009-02-17 17:29:25.160")), y=c(1L,2L))
+DT[,x1:=as.ITime(x)]
+DT[,`:=`(last.x=tail(x,1L),last.x1=tail(x1,1L)),by=y]
+test(1086, class(DT$last.x), c("POSIXct", "POSIXt"))
+test(1087, class(DT$last.x1), "ITime")
+
+# Tests 1088-1093 were non-ASCII. Now in DtNonAsciiTests
+
+# print of unnamed DT with >20 <= 100 rows, #4934
+DT <- data.table(x=1:25, y=letters[1:25])
+DT.unnamed <- unname(copy(DT))
+test(1094, print(DT.unnamed), output="NA NA 1: 1 a 2: 2 b 3: 3 c")
+
+# DT[!TRUE] or DT[!TRUE, which=TRUE], #4930. !TRUE still can be a recycling operation with !(all TRUE)
+DT <- data.table(x=1:3, y=4:6)
+test(1095.1, DT[!TRUE], DT[FALSE])
+test(1095.2, DT[!TRUE, which=TRUE], DT[FALSE, which=TRUE])
+
+######### incremented tests by 1 as I've used 1096 for FR #2077 (above along with already existing tests 522): ###########
+# roll backwards when i is keyed and rollends=FALSE
+# http://stackoverflow.com/questions/18984179/roll-data-table-with-rollends
+dt1 = data.table(Date=as.Date(c("2013-01-03","2013-01-07")),key="Date")[,ind:=.I]
+dt2 = data.table(Date=seq(from=as.Date("2013-01-01"),to=as.Date("2013-01-10"), by="1 day"),key="Date")
+test(1097, dt1[dt2,roll=-Inf,rollends=FALSE]$ind, INT(NA,NA,1,2,2,2,2,NA,NA,NA)) # now ok
+test(1098, dt1[dt2,roll=-Inf,rollends=TRUE]$ind, INT(1,1,1,2,2,2,2,2,2,2)) # ok before
+test(1099, dt1[dt2,roll=-Inf,rollends=c(TRUE,FALSE)]$ind, INT(1,1,1,2,2,2,2,NA,NA,NA)) # ok before
+test(1100, dt1[dt2,roll=-Inf,rollends=c(FALSE,TRUE)]$ind, INT(NA,NA,1,2,2,2,2,2,2,2)) # now ok
+
+#########################################
+# All dcast.data.table tests go in here #
+#########################################
+if ("package:reshape2" %in% search()) {
+
+ names(ChickWeight) <- tolower(names(ChickWeight))
+ DT <- melt(as.data.table(ChickWeight), id=2:4) # calls melt.data.table
+
+ # changed 'mean' to 'sum' to avoid valgrind floating point precision based error.
+ test(1101, as.data.frame(dcast(DT, time ~ variable, fun=sum)), dcast(as.data.frame(DT), time~variable, fun=sum))
+ test(1102, as.data.frame(dcast(DT, diet ~ variable, fun=sum)), dcast(as.data.frame(DT), diet~variable, fun=sum))
+ x1 <- as.data.frame(dcast(DT, diet+chick ~ time, drop=FALSE))
+ x1$chick <- factor(x1$chick, levels=levels(x1$chick), ordered=FALSE)
+ x2 <- dcast(as.data.frame(DT), diet+chick~time, drop=FALSE)
+ test(1103, x1,x2)
+ x1 <- as.data.frame(dcast(DT, diet+chick ~ time, drop=FALSE, fill=0))
+ x1$chick <- factor(x1$chick, levels=levels(x1$chick), ordered=FALSE)
+ x2 <- dcast(as.data.frame(DT), diet+chick~time, drop=FALSE, fill=0)
+ test(1104.1, x1,x2)
+
+ # add test for 'subset' in dcast
+ x1 <- dcast(as.data.frame(DT), time + chick ~ variable+diet, fun=sum, subset=.(time> 20))
+ x2 <- as.data.frame(dcast(DT, time + chick ~ variable+diet, fun=sum, subset=.(time> 20)))
+ test(1104.2, x1, x2)
+
+ # testing without aggregation
+ x <- data.table(a=5:1, b=runif(5))
+ test(1104.3, as.data.frame(dcast(x, a ~ b, value.var="b")), dcast(as.data.frame(x), a ~ b, value.var="b"))
+
+ # Fix for case 2 in bug report #5149 - dcast dint aggregate properly when formula RHS has "."
+ set.seed(45)
+ DT = data.table(x=rep(1:5, each=3), y=runif(15, 0, 1))
+ ans = setDT(dcast(as.data.frame(DT), x ~ ., mean, value.var="y"))
+ setkey(ans, x)
+ test(1148.1, dcast(DT, x ~ ., mean, value.var="y"), ans)
+ # also quashed another bug with `.` in formula (when there's no aggregate function):
+ DT <- data.table(a=sample(5), b=runif(5), c=5:1)
+ ans1 = setDT(dcast(as.data.frame(DT), a ~ ., value.var="c"))
+ ans2 = setDT(dcast(as.data.frame(DT), b+a ~ ., value.var="c"))
+ setkey(ans1, "a")
+ setkey(ans2, "b", "a")
+ test(1148.2, dcast(DT, a ~ ., value.var="c"), ans1)
+ test(1148.3, dcast(DT, b+a~., value.var="c"), ans2)
+
+ # more tests for `dcast` with formula being character and errors when formula is a hybrid
+ set.seed(1)
+ x <- data.table(a=rep(1:5, each=5), b=runif(25))
+ ### adding all extra arguments for no verbose during "test.data.table()" to all dcast tests
+ test(1150.1, dcast(x, " a~ . ", value.var="b", fun=length), data.table(a=1:5, `.`=5L, key="a"))
+ test(1150.2, dcast(x, "a ~ c ", value.var="b"), error="not found or of unknown type")
+ test(1150.3, dcast(x, a ~ a, value.var="c"), error="are not found in 'data'")
+
+ # fix for #5379 - issue when factor columns on formula LHS along with `drop=FALSE`
+ set.seed(1L)
+ df <- data.frame(a=factor(sample(letters[1:3], 10, replace=TRUE), letters[1:5]),
+ b=factor(sample(tail(letters, 5), 10, replace=TRUE)))
+ dt <- as.data.table(df)
+ test(1198.1, setkey(setDT(dcast(as.data.frame(df), a~b, drop=FALSE, value.var="b", fun=length)), a), dcast(dt, a~b, drop=FALSE, fun=length, value.var="b"))
+
+ # reverse the levels
+ set.seed(1L)
+ df <- data.frame(a=factor(sample(letters[1:3], 10, replace=TRUE), letters[5:1]),
+ b=factor(sample(tail(letters, 5), 10, replace=TRUE)))
+ dt <- as.data.table(df)
+ test(1198.2, setkey(setDT(dcast(as.data.frame(df), a~b, drop=FALSE, value.var="b", fun=length)), a), dcast(dt, a~b, drop=FALSE, value.var="b", fun=length))
+
+ # more factor cols
+ set.seed(1L)
+ df <- data.frame(a1=factor(sample(letters[1:3], 10, replace=TRUE), letters[1:5]), # factor col 1
+ a2=factor(sample(letters[6:10], 10, replace=TRUE), letters[6:10]), # factor col 2
+ a3=sample(letters[1:3], 10, TRUE), # no factor
+ b=factor(sample(tail(letters, 5), 10, replace=TRUE)))
+ dt <- as.data.table(df)
+ ans <- dcast(dt, a1+a2+a3~b, drop=FALSE, value.var="b")
+ ans[, c(4:7) := lapply(.SD, as.character), .SDcols=4:7]
+ test(1198.3, setkey(setDT(dcast(as.data.frame(df), a1+a2+a3~b, drop=FALSE, value.var="b")), a1,a2,a3), ans)
+
+ # dcast bug fix for 'subset' argument (it doesn't get key set before to run C-fcast):
+ dt <- data.table(x=c(1,1,1,2,2,2,1,1), y=c(1,2,3,1,2,1,1,2), z=c(1,2,3,NA,4,5,NA,NA))
+ test(1252, dcast(dt, x~y, value.var="z", subset=.(!is.na(z))), data.table(x=c(1,2), `1`=c(1,5), `2`=c(2,4), `3`=c(3,NA), key="x"))
+
+ # FR #5675 and DOC #5676
+ set.seed(1L)
+ dt <- data.table(a=sample(10), b=2013:2014, variable=rep(c("c", "d"), each=10), value=runif(20))
+ ans1 <- names(dcast(dt, a ~ ... + b, value.var="value"))
+ test(1286, ans1, c("a", "c_2013", "c_2014", "d_2013", "d_2014"))
+
+ # bug git #693 - dcast error message improvement:
+ dt <- data.table(x=c(1,1), y=c(2,2), z = 3:4)
+ test(1314, dcast(dt, x ~ y, value.var="z", fun.aggregate=identity), error="should take vector inputs and return a single value")
+
+ # bug #688 - preserving attributes
+ DT = data.table(id = c(1,1,2,2), ty = c("a","b","a","b"), da = as.Date("2014-06-20"))
+ test(1315, dcast(DT, formula = id ~ ty, value.var="da"), data.table(id=c(1,2), a=as.Date("2014-06-20"), b=as.Date("2014-06-20"), key="id"))
+
+ # issues/713 - dcast and fun.aggregate
+ DT <- data.table(id=rep(1:2, c(3,4)), k=c(rep(letters[1:3], 2), 'c'), v=1:7)
+ foo <- function (tbl, fun.aggregate) {
+ dcast(tbl, id ~ k, value.var='v', fun.aggregate=fun.aggregate, fill=NA_integer_)
+ }
+ test(1345, foo(DT, last), dcast(DT, id ~ k, value.var='v', fun.aggregate=last, fill=NA_integer_))
+
+ # more minor changes to dcast (subset argument handling symbol - removing any surprises with data.table's typical scoping rules) - test for that.
+ DT <- data.table(id=rep(1:2, c(3,4)), k=c(rep(letters[1:3], 2), 'c'), v=1:7)
+ bla <- c(TRUE, rep(FALSE, 6L))
+ # calling `subset=.(bla)` gives eval error when testing... not sure what's happeing! using values directly instead for now.
+ test(1346.1, dcast(DT, id ~ k, value.var="v", subset=.(c(TRUE, rep(FALSE, 6L)))), dcast(DT[1L], id ~ k, value.var="v"))
+ DT[, bla := !bla]
+ test(1346.2, dcast(DT, id ~ k, value.var="v", subset=.(bla), fun.aggregate=length), dcast(DT[(bla)], id ~ k, value.var="v", fun.aggregate=length))
+
+ # issues/715
+ DT <- data.table(id=rep(1:2, c(3,2)), k=c(letters[1:3], letters[1:2]), v=1:5)
+ test(1347.1, dcast(DT, id ~ k, fun.aggregate=last, value.var="v"), error="should take vector inputs and return a single value")
+ test(1347.2, dcast(DT, id ~ k, fun.aggregate=last, value.var="v", fill=NA_integer_), data.table(id=1:2, a=c(1L, 4L), b=c(2L,5L), c=c(3L,NA_integer_), key="id"))
+
+ # Fix for #893
+ dt <- data.table(
+ x = factor("a", levels = c("a", "b")),
+ y = factor("b", levels = c("a", "b")),
+ z = 1
+ )
+ test(1457, dcast(dt, y ~ x, drop = FALSE, value.var="z"),
+ data.table(dcast(as.data.frame(dt), y ~ x, drop = FALSE, value.var="z"), key="y"))
+
+ # dcast.data.table new tests
+ # Fix for #1070 (special case of ... on LHS)
+ dt <- data.table(label= month.abb[1:5], val=0)
+ test(1501.1, dcast(dt,... ~ label, value.var="val", sum),
+ data.table(`.`=".", Apr=0, Feb=0, Jan=0, Mar=0, May=0, key="."))
+ # Fix for #862 (optional prefixes)
+ dt <- data.table(name=c("Betty","Joe","Frank","Wendy","Sally"),
+ address=c(rep("bla1",2), rep("bla2",2), "bla3"))
+ test(1501.2, dcast(dt, address ~ paste("cust", dt[, seq_len(.N), by=address]$V1, sep=""), value.var="name"), data.table(address=paste("bla",1:3,sep=""), cust1=c("Betty", "Frank", "Sally"),
+ cust2=c("Joe", "Wendy", NA), key="address"))
+
+ # Fix for #1037 (optional prefixes + undefined variables)
+ dt <- structure(list(V1 = c(0L, 1L, 2L, 3L, 4L, 0L, 1L, 2L, 3L, 4L),
+ V2 = c(1.052, 0.542, 0.496, 0.402, 0.278, 5.115, 4.329, 4.121,
+ 4.075, 4.0088)), .Names = c("V1", "V2"), class = "data.frame", row.names = c(NA, -10L))
+ setDT(dt)
+ ans1 = dcast(as.data.frame(dt), cumsum(V1 == 0) ~ V1, value.var = 'V2')
+ ans2 = dcast(dt, cumsum(V1 == 0) ~ V1, value.var = 'V2')
+ setkey(setnames(setDT(ans1), names(ans2)), V1)
+ test(1501.3, ans1, ans2)
+
+ # Implement #716 and #739 (multiple value.var and fun.aggregate)
+ # multiple value.var
+ dt = data.table(x=sample(5,20,TRUE), y=sample(2,20,TRUE),
+ z=sample(letters[1:2], 20,TRUE), d1 = runif(20), d2=1L)
+ ans21 <- dcast(as.data.frame(dt), x + y ~ z, fun=sum, value.var="d1")
+ ans22 <- dcast(as.data.frame(dt), x + y ~ z, fun=sum, value.var="d2")
+ ans23 <- dcast(as.data.frame(dt), x + y ~ z, fun=mean, value.var="d1")
+ ans24 <- dcast(as.data.frame(dt), x + y ~ z, fun=mean, value.var="d2")
+
+ ans1 <- dcast(dt, x + y ~ z, fun=sum, value.var=c("d1","d2"))
+ ans2 <- cbind(ans21, ans22[, 3:4])
+ setkey(setnames(setDT(ans2), names(ans1)), x, y)
+ test(1501.4, ans1, ans2)
+ # multiple fun.agg
+ ans1 <- dcast(dt, x + y ~ z, fun=list(sum, mean), value.var="d1")
+ ans2 <- cbind(ans21, ans23[, 3:4])
+ setkey(setnames(setDT(ans2), names(ans1)), x, y)
+ test(1501.5, ans1, ans2)
+ # multiple fun.agg and value.var (all combinations)
+ ans1 <- dcast(dt, x + y ~ z, fun=list(sum, mean), value.var=c("d1", "d2"))
+ ans2 <- cbind(ans21, ans22[, 3:4], ans23[, 3:4], ans24[, 3:4])
+ setkey(setnames(setDT(ans2), names(ans1)), x, y)
+ test(1501.6, ans1, ans2)
+ # multiple fun.agg and value.var (one-to-one)
+ ans1 <- dcast(dt, x + y ~ z, fun=list(sum, mean), value.var=list("d1", "d2"))
+ ans2 <- cbind(ans21, ans24[, 3:4])
+ setkey(setnames(setDT(ans2), names(ans1)), x, y)
+ test(1501.7, ans1, ans2)
+
+ # Additional test after fixing fun.agg creation - using the example here: https://github.com/Rdatatable/data.table/issues/716
+ DT = data.table(x=1:5, y=paste("v", 1:5, sep=""),
+ v1=6:10, v2=11:15,
+ k1=letters[1:5], k2=letters[6:10])
+ DT.m = melt(DT, id=1:2, measure=list(3:4, 5:6))
+ ans1 <- dcast(DT.m, x ~ y, fun.aggregate =
+ list(sum, function(x) paste(x, collapse="")), value.var=list("value1", "value2"))
+ ans21 <- dcast(as.data.frame(DT.m), x ~ y, fun.agg=sum, value.var="value1")
+ ans22 <- dcast(as.data.frame(DT.m), x ~ y, fun.agg=function(x) paste(x, collapse=""), value.var="value2")
+ ans2 <- cbind(ans21, ans22[, -1L])
+ setkey(setnames(setDT(ans2), names(ans1)), x)
+ test(1501.8, ans1, ans2)
+
+ # more testing on fun.aggregate
+ dt = as.data.table(airquality)
+ ans = suppressWarnings(melt(dt, id=c("Month", "Day"), na.rm=TRUE))
+ ans = ans[ , .(min=min(value), max=max(value)), by=.(Month, variable)]
+ ans = melt(ans, id=1:2, variable.name="variable2")
+ ans = dcast(ans, Month ~ variable + variable2)
+ setnames(ans, c("Month", paste(names(ans)[-1L], ".", sep="_")))
+ valvars = c("Ozone", "Solar.R", "Wind", "Temp")
+ ans2 <- suppressWarnings(dcast(dt, Month ~ ., fun=list(min, max), na.rm=TRUE, value.var=valvars))
+ setcolorder(ans, names(ans2))
+ test(1501.9, setkey(ans, Month), ans2[, names(ans2)[-1L] := lapply(.SD, as.numeric), .SDcols=-1L])
+
+ # test for #1210, sep argument for dcast
+ dt = data.table(x=sample(5,20,TRUE), y=sample(2,20,TRUE),
+ z=sample(letters[1:2], 20,TRUE), d1 = runif(20), d2=1L)
+ test(1501.10, names(dcast(dt, x ~ y + z, fun=length, value.var = "d2", sep=".")),
+ c("x", "1.a", "1.b", "2.a", "2.b"))
+}
+
+# test for freading commands
+x1 <- data.table(a = c(1:5), b = c(1:5))
+f <- tempfile()
+write.csv(x1, f, row.names = FALSE)
+if (.Platform$OS.type == "unix") {
+ test(1105, x1[a != 3], fread(paste('grep -v 3 ', f, sep="")))
+} else {
+ # x2 <- fread(paste('more ', f, sep=""))
+ # Doesn't work on winbuilder. Relies on 'more' available in DOS via Cygwin?
+ # Error:
+ # Syntax error: end of file unexpected (expecting ")")
+ # Error: (converted from warning) running command 'sh.exe -c (more D:\temp\RtmpgB8D2P\file1ed828a511cd) > D:\temp\RtmpgB8D2P\file1ed84f9f44f8' had status 2
+ # test(1105, x1, x2)
+}
+unlink(f)
+
+# test for "key" argument of [.data.table
+#x1 <- data.table(a = c(1:5), b = c(5:1))
+#x1[J(2), key = 'a']
+#test(1106, key(x1) == 'a')
+#x1[, a, key = NULL]
+#test(1107, is.null(key(x1)))
+
+# test that eval works inside expressions
+DT <- data.table(a = c(1:5))
+s <- quote(a)
+test(1108, DT[, sum(eval(s))], DT[, sum(a)])
+
+# test that boolean expression does not trigger a not-join
+DT <- data.table(a = 1:3, b = c(TRUE,FALSE,NA))
+test(1109, DT[b != TRUE], DT[!(b == TRUE)])
+
+# commented for now (by Arun)
+# # test the speed of simple comparison
+# DT <- data.table(a = 1:1e7)
+# t1 = system.time(DT[a == 100])[3]
+# t2 = system.time(DT[which(a == 100)])[3]
+# # make sure we're at most 30% slower than "which" (should pass most of the time)
+# test(1110, (t1 - t2)/t2 < 0.3)
+
+# test that a column named list is ok (this also affects other functions in by, might be worth adding a test for that)
+DT <- data.table(list = 1:6, a = 1:2)
+test(1111, DT[, lapply(.SD, sum), by = a], DT[, list(list = sum(list)), by = a])
+
+# fix for #4995. "rbind" retains key when the first argument isn't a data.table (.rbind.data.table is never run is the issue)
+DT <- data.table(name=c('Guff','Aw'),id=101:102,id2=1:2,key='id')
+y <- rbind(list('No','NON',0L),DT,list('Extra','XTR',3L))
+test(1112, key(y), NULL)
+
+# fix for http://stackoverflow.com/questions/14753411/why-does-data-table-lose-class-definition-in-sd-after-group-by
+# where, .SD loses class information.
+format.myclass <- function(x, ...){
+ paste("!!", x, "!!", sep = "")
+}
+DT <- data.table(L = rep(letters[1:3],3), N = 1:9)
+setattr(DT$N, "class", "myclass")
+test(1113, class(DT[, .SD, by = L]$N), class(DT$N))
+setkey(DT, L)
+test(1114, class(DT[, .SD, by = L]$N), class(DT$N))
+test(1115, class(DT[J(unique(L)), .SD, by=.EACHI]$N), class(DT$N))
+
+# Fix for #4994 - not-join quoted expression dint work...
+dt = data.table(a = 1:2, key = 'a')
+dt1 = data.table(a = 1)
+expr = quote(!dt1)
+test(1116, dt[eval(expr)], dt[2])
+expr = quote(!1)
+test(1117, dt[eval(expr)], dt[2])
+
+# Fix for #2381 - optimisation of `DT[, lapply(.SD, function(x) FUN(x, bla)), by=key(DT)]` where "bla" is a column in DT dint work.
+set.seed(45)
+dt <- data.table(x=rep(1:4, each=4), b1=sample(16), b2=runif(16))
+setkey(dt, x)
+test(1118, dt[, lapply(.SD, function(y) weighted.mean(y, b2, na.rm=TRUE)), by=x], dt[, lapply(.SD, weighted.mean, b2, na.rm=TRUE), by=x])
+
+# a(nother) test of #295
+DT <- data.table(x=5:1, y=1:5, key="y")
+test(1119, is.null(key(DT[, list(z = y, y = 1/y)])))
+
+
+## various ordered factor rbind tests
+DT = data.table(ordered('a', levels = c('a','b','c')))
+DT1 = data.table(factor('a', levels = c('b','a','f')))
+DT2 = data.table(ordered('b', levels = c('b','d','c')))
+DT3 = data.table(c('foo', 'bar'))
+DT4 = data.table(ordered('a', levels = c('b', 'a')))
+
+test(1120, rbind(DT, DT1, DT2, DT3), data.table(ordered(c('a','a','b', 'foo', 'bar'), levels = c('a','b','d','c','f', 'foo', 'bar'))))
+test(1121, rbindlist(list(DT, DT1, DT2, DT3)), data.table(ordered(c('a','a','b', 'foo', 'bar'), levels = c('a','b','d','c','f', 'foo', 'bar'))))
+test(1122, rbind(DT, DT4), data.table(factor(c('a','a'), levels = c('a','b','c'))), warning="ordered factor levels cannot be combined, going to convert to simple factor instead")
+test(1123, rbindlist(list(DT, DT4)), data.table(factor(c('a','a'), levels = c('a','b','c'))), warning="ordered factor levels cannot be combined, going to convert to simple factor instead")
+test(1124, rbind(DT1, DT1), data.table(factor(c('a','a'), levels = c('b','a','f'))))
+test(1125, rbindlist(list(DT1, DT1)), data.table(factor(c('a','a'), levels = c('b','a','f'))))
+
+## test rbind(..., fill = TRUE)
+DT = data.table(a = 1:2, b = 1:2)
+DT1 = data.table(a = 3:4, c = 1:2)
+
+test(1126, rbind(DT, DT1, fill = TRUE), data.table(a = 1:4, b = c(1, 2, NA, NA), c = c(NA, NA, 1, 2)))
+
+## check for #4959 - rbind'ing empty data.table's
+DT = data.table(a=character())
+#test(1127, rbind(DT, DT), DT)
+
+## check for #5005
+DT = data.table(a=0:2,b=3:5,key="a")
+test(1128, DT[, (function(){b})()], DT[, b])
+
+## Fix for FR #4867
+DT <- data.table(x=1:5, y=6:10)
+test(1129.1, DT[, as.factor(c("x", "y")), with=FALSE], DT)
+test(1129.2, DT[, as.factor(c("x", "x")), with=FALSE], DT[, list(x, x)])
+
+# Fix for a specific case that results in error in `construct` function in data.table.R (found and fixed during #5007 bug fix)
+MyValueIsTen <- 10
+set.seed(1)
+DT <- data.table(ID=sample(LETTERS[1:3], 6, TRUE), Value1=rnorm(6), Value2=runif(6))
+cols <- c("Value1", "Value2")
+DT2 <- copy(DT)
+test(1130, DT[, (cols) := lapply(.SD, function(x) MyValueIsTen), by=ID], DT2[, (cols) := 10])
+
+# Fix for #5007 - The value MyValueIsTen = 10 was never recognised (value within the function environment)
+MyValueIsTen <- 5
+set.seed(1)
+DT <- data.table(ID=sample(LETTERS[1:3], 6, TRUE), Value1=rnorm(6), Value2=runif(6))
+My_Fun <- function(x=copy(DT)) {
+ MyValueIsTen <- 10
+ cols <- c("Value1", "Value2")
+ x[, (cols) := lapply(.SD, function(x) MyValueIsTen), by=ID]
+}
+DT[, (cols) := 10]
+test(1131, My_Fun(), DT)
+
+# Test for #4957 - where `j` doesn't know `.N` when used with `lapply(.SD, function(x) ...)`
+test(1132, DT[, lapply(.SD, function(x) .N), by=ID], data.table(ID=c("A", "B", "C"), Value1=2L, Value2=2L))
+
+# Test for #4990 - `:=` does not generate recycling warning during 'by':
+DT <- data.table(x=c(1,1,1,1,1,2,2))
+# on a new column
+test(1133.1, DT[, new := c(1,2), by=x], data.table(x=c(1,1,1,1,1,2,2), new=c(1,2,1,2,1,1,2)), warning="Supplied 2 items to be assigned to group 1 of size 5 in column 'new'")
+# on an already existing column
+test(1133.2, DT[, new := c(1,2), by=x], data.table(x=c(1,1,1,1,1,2,2), new=c(1,2,1,2,1,1,2)), warning="Supplied 2 items to be assigned to group 1 of size 5 in column 'new'")
+
+# Fix for FR #2496 - catch `{` in `:=` expression in `j`:
+DT <- data.table(x=c("A", "A", "B", "B"), val =1:4)
+DT2 <- copy(DT)[, a := 1L]
+test(1134.1, DT[, {a := 1L}], DT2, warning="Caught and removed")
+test(1134.2, DT[, {b := 2L}, by=x], DT2[, b:=2L, by=x], warning="Caught and removed")
+
+# fix for bug #5069
+if ("package:gdata" %in% search()) {
+ DT <- data.table(a = c('asdfasdf','asdf','asdgasdgasdgasdg','sdg'), b = runif(4,0,1))
+ test(1135, write.fwf(DT, f<-tempfile()), NULL)
+ unlink(f)
+}
+
+# FR #2693 and Gabor's suggestions from here: http://r.789695.n4.nabble.com/Problem-with-FAQ-2-8-tt4668878.html (correcting software according to FAQ 2.8)
+d1 <- data.table(id1 = c(1L, 2L, 2L, 3L), val = 1:4, key = "id1")
+d2 <- data.table(id2 = c(1L, 2L, 4L), val2 = c(11, 12, 14),key = "id2")
+d3 <- copy(d2)
+setnames(d3, names(d1))
+
+test(1136.1, d1[d2, id1], INT(1,2,2,4))
+test(1136.2, d1[d2, id1], d1[d2][,id1])
+test(1136.3, d1[d2, id2], INT(1,2,2,4))
+test(1136.4, d1[d2, id2], d1[d2, list(id1,id2,val,val2)][,id2])
+test(1136.5, d1[d3, i.id1], INT(1,2,2,4))
+test(1136.6, d1[d3, i.id1], d1[d3, list(id1,i.id1)][,i.id1])
+test(1136.7, d1[d2, val], c(1:3, NA))
+test(1136.8, d1[d2, val2], c(11,12,12,14))
+test(1136.9, d1[d3, list(id1, val, i.val)], data.table(id1=INT(1,2,2,4), val=c(1:3, NA), i.val=c(11,12,12,14), key="id1"))
+test(1136.11, d1[d3, list(id1, i.id1, val, i.val)], data.table(id1=INT(1,2,2,4),
+ i.id1=INT(1,2,2,4), val=c(1:3, NA), i.val=c(11,12,12,14), key="id1"))
+test(1136.12, d1[d2], data.table(id1=INT(1,2,2,4), val=c(1:3, NA), val2=c(11,12,12,14), key="id1"))
+
+test(1136.13, d1[J(2), id1], INT(2,2))
+test(1136.14, d1[J(2), i.id1], error="not found")
+
+DT <- data.table(x=c("A", "A", "C", "C"), y=1:4, key="x")
+test(1136.15, DT["C", i.x], error="not found")
+
+# test for FR #4979
+DT <- data.table(x=1:5, y=6:10, z=11:15)
+test(1137.1, DT[, .SD, .SDcols=-1L], DT[, 2:3, with=FALSE])
+test(1137.2, DT[, .SD, .SDcols=-(1:2)], DT[, 3, with=FALSE])
+test(1137.3, DT[, .SD, .SDcols=-"y"], DT[, c(1,3), with=FALSE])
+test(1137.4, DT[, .SD, .SDcols=-c("y", "x")], DT[, 3, with=FALSE])
+test(1137.5, DT[, .SD, .SDcols=-which(names(DT) %in% c("x", "y", "z"))], null.data.table())
+test(1137.6, DT[, .SD, .SDcols=c(1, -2)], error=".SDcols is numeric but has both")
+test(1137.7, DT[, .SD, .SDcols=c("x", -"y")], error="invalid argument to unary")
+test(1137.8, DT[, .SD, .SDcols=c(-1, "x")], error="Some items of .SDcols are")
+
+DT <- data.table(x=1:5, y=6:10, z=11:15, zz=letters[1:5])
+test(1137.9, DT[, .SD, .SDcols=-grep("^z", names(DT))], DT[, 1:2, with=FALSE])
+test(1137.10, DT[, .SD, .SDcols=-grep("^z", names(DT), value=TRUE)], DT[, 1:2, with=FALSE])
+test(1137.11, DT[, .SD, .SDcols=-grep("^z", names(DT), value=TRUE, invert=TRUE)], DT[, 3:4, with=FALSE])
+
+set.seed(45)
+DT = data.table(x=c("A", "A", "C", "C"), y=1:4, z=runif(4))
+test(1137.12, DT[, lapply(.SD, sum), by=x, .SDcols=-"y"], DT[, lapply(.SD, sum), by=x, .SDcols="z"])
+
+# test for FR #353 / R-Forge #5020 - print.data.table gets new argument "row.names", default=TRUE. if FALSE, the row-names don't get printed
+# Thanks to Eddi for `capture.output` function!
+DT <- data.table(x=1:5, y=6:10)
+test(1138.1, capture.output(print(DT, row.names=FALSE)), c(" x y", " 1 6", " 2 7", " 3 8", " 4 9", " 5 10"))
+DT <- data.table(x=1:101, y=6:106) # bug described in #1307
+test(1138.2, capture.output(print(DT, row.names=FALSE)), c(" x y", " 1 6", " 2 7", " 3 8", " 4 9", " 5 10", "--- ", " 97 102", " 98 103", " 99 104", " 100 105", " 101 106"))
+
+# test for FR #2591 (format.data.table issue with column of class "formula")
+DT <- data.table(x=c(a~b, c~d+e), y=1:2)
+test(1139, capture.output(print(DT)), c(" x y", "1: a ~ b 1", "2: c ~ d + e 2"))
+
+# FR #4813 - provide warnings if there are remainders for both as.data.table.list(.) and data.table(.)
+X = list(a = 1:2, b = 1:3)
+test(1140, as.data.table(X), data.table(a=c(1,2,1), b=c(1,2,3)), warning="Item 1 is of size 2 but maximum")
+test(1141.1, data.table(a=1:2, b=1:3), data.table(a=c(1L,2L,1L), b=1:3), warning="Item 1 is of size 2 but maximum")
+test(1141.2, data.table(a=1:2, data.table(x=1:5, y=6:10)), data.table(a=c(1L,2L,1L,2L,1L), x=1:5, y=6:10), warning="Item 1 is of size 2 but maximum")
+test(1141.3, data.table(a=1:5, data.table(x=c(1,2), y=c(3,4))), data.table(a=c(1:5), x=c(1,2,1,2,1), y=c(3,4,3,4,3)), warning="Item 2 is of size 2 but maximum")
+
+# Fix for bug #5098 - DT[, foo()] returns function definition.
+DT <- data.table(a=1:2)
+foo <- function() sum(1:5)
+test(1142, DT[, foo()], 15L)
+
+# Fix for bug #5106 - DT[, .N, by=y] was slow when "y" is not a column in DT
+DT <- data.table(x=sample.int(10, 1e6, replace=TRUE))
+y <- DT$x
+te1 <- system.time(ans1 <- DT[, .N, by=x])[["elapsed"]]
+te2 <- system.time(ans2 <- DT[, .N, by=y])[["elapsed"]]
+test(1143.1, ans1, setnames(ans2, "y", "x"))
+test(1143.2, abs(te1-te2) < 1, TRUE)
+
+# Fix for bug #5104 - side-effect of fixing #2531 - `:=` with grouping (by) and assigning factor columns
+DT <- data.table(x=c(1,1,1,2,2), y=factor(letters[1:5]))
+test(1144.1, DT[, z := y, by=x], data.table(x=c(1,1,1,2,2), y=factor(letters[1:5]), z=factor(letters[1:5])))
+# Added 3 more tests to close bug #5437 - partial regression due to recent changes (in 1.9.2)
+# This should catch any attributes being lost hereafter.
+DT<-data.table(X=factor(2006:2012),Y=rep(1:7,2))
+test(1144.2, DT[, Z:=paste(X,.N,sep=" - "), by=list(X)], data.table(X=factor(2006:2012),Y=rep(1:7,2), Z=paste(as.character(2006:2012), 2L, sep=" - ")))
+DT = data.table(x=as.POSIXct(c("2009-02-17 17:29:23.042", "2009-02-17 17:29:25.160")), y=c(1L,2L))
+test(1144.4, DT[, list(lx=x[.N]), by=x], data.table(x=DT$x, lx=DT$x))
+ans = copy(DT)
+test(1144.3, DT[,`:=`(lx=tail(x,1L)), by=y], ans[, lx := x])
+
+# FR #2356 - retain names of named vector as column with keep.rownames=TRUE
+x <- 1:5
+setattr(x, 'names', letters[1:5])
+test(1144.1, as.data.table(x, keep=TRUE), data.table(rn=names(x), x=unname(x)))
+x <- as.numeric(x)
+setattr(x, 'names', letters[1:5])
+test(1144.2, as.data.table(x, keep=TRUE), data.table(rn=names(x), x=unname(x)))
+x <- as.character(x)
+setattr(x, 'names', letters[1:5])
+test(1144.3, as.data.table(x, keep=TRUE), data.table(rn=names(x), x=unname(x)))
+x <- as.factor(x)
+setattr(x, 'names', letters[1:5])
+test(1144.4, as.data.table(x, keep=TRUE), data.table(rn=names(x), x=unname(x)))
+x <- as.Date(1:5, origin="2013-01-01")
+setattr(x, 'names', letters[1:5])
+test(1144.5, as.data.table(x, keep=TRUE), data.table(rn=names(x), x=unname(x)))
+
+# Fix for bug #5114 - .data.table.locked ISSUE
+DT <- data.table(x=1:5, y=6:10)
+xx <- DT[, .SD, .SDcols="y"]
+test(1145, xx[, y := as.numeric(y)], data.table(y = as.numeric(6:10)))
+
+# Fix for bug #5115 - set not adding columns on class that builds on data.table
+DT <- as.data.table(BOD)
+ans = copy(DT)[, Time := as.numeric(Time)]
+setattr(DT, "class", c("myclass", class(DT)))
+setattr(ans, 'class', class(DT))
+test(1146.1, DT[, Time:= as.numeric(Time)], ans)
+DF <- as.data.frame(DT)
+test(1146.2, {set(DF, i=NULL, j=1L, value=seq_len(nrow(DF)));setattr(DF,"reference",NULL);DF}, data.frame(Time=1:nrow(BOD), demand=BOD$demand))
+test(1146.3, set(DF, i=NULL, j="bla", value=seq_len(nrow(DF))), error="set() on a data.frame is for changing existing columns, not adding new ones. Please use a data.table for that.")
+
+# Feature - implemented fast radix order for numeric types (both +ve and -ve numerics).
+# note that if "x" is already a list, then the values will be modified by reference!
+# Note: 'ordernumtol' doesn't distinguish between NA and NaN whereas this one does!
+# R-wrapper is dradixorder
+set.seed(45)
+x <- rnorm(1e6)*1e4
+test(1147.1, base::order(x), dradixorder(x, tol=numeric(0))) # base::order doesn't test with tolerance
+test(1147.2, ordernumtol(x), dradixorder(x))
+tol = .Machine$double.eps^0.5
+x <- c(8, NaN, Inf, -7.18918, 5.18909+0.07*tol, NA, -7.18918111, -Inf, NA, 5.18909, NaN, 5.18909-1.2*tol, 5.18909-0.04*tol)
+test(1147.3, dradixorder(x), c(6L, 9L, 2L, 11L, 8L, 7L, 4L, 12L, 5L, 10L, 13L, 1L, 3L))
+
+# test for `iradixorder` when input is integer(0) and numeric(0)
+test(1149.1, iradixorder(integer(0)), integer(0))
+test(1149.2, iradixorder(numeric(0)), error="iradixorder is only for integer")
+
+# test uniqlengths
+set.seed(45)
+x <- sample(c(NA_integer_, 1:1e5), 1e7, TRUE)
+ox <- forderv(x)
+o1 <- uniqlist(list(x), ox)
+test(1151.1, c(diff(o1), length(x)-tail(o1, 1L)+1L), uniqlengths(o1, length(x)))
+o1 <- uniqlist(list(x))
+test(1151.2, c(diff(o1), length(x)-tail(o1, 1L)+1L), uniqlengths(o1, length(x)))
+
+# #5190 fix - grouping with .SDcols gave "symbol not subsettable error" - consequence of FR #4979 implementation
+dt = data.table(grp = sample(letters[1:3],20, replace = TRUE), v1 = rnorm(20), v2 = rnorm(20))
+sd.cols <- "v1"
+test(1152, dt[, lapply(.SD, mean), by=grp, .SDcols=sd.cols], dt[, list(v1=mean(v1)), by=grp])
+
+# #5171 fix - setattr attribute non-character led to segfault
+x <- FALSE
+test(1153, setattr(x, FALSE, FALSE), error="Attribute name must be")
+
+# Fixed binary search capabilities for NA (for int and double) and NaN (for double):
+set.seed(1)
+DT <- data.table(x=sample(c(NA, NaN, Inf, 1:10), 100, TRUE), y=sample(c(NA, 1:10), 100, TRUE), z=sample(c(NA_character_, letters[1:10]), 100, TRUE))
+setkey(DT, x)
+test(1154.1, DT[J(NaN)], DT[is.nan(x)])
+test(1154.2, DT[J(NA_real_)], DT[is.na(x) & !is.nan(x)])
+setkey(DT, y)
+test(1154.3, setcolorder(DT[J(NA_integer_)], c("x", "y", "z")), DT[is.na(y)])
+setkey(DT, z)
+test(1154.4, setcolorder(DT[J(NA_character_)], c("x", "y", "z")), DT[is.na(z)])
+
+# Fixing the binary search above for NA/NaN also fixes BUG #4918
+dt1 <- data.table(x = c('red','orange','green'), y=c(1,2,NA), key='y')
+dt2 <- data.table(y = c(1,2,3,NA), z = c('a','b','c','missing data'), key='y')
+test(1155.1, merge(dt1, dt2, by=c('y')), data.table(y=dt1$y, x=dt1$x, z=dt2$z[1:3], key="y"))
+test(1155.2, dt2[dt1], data.table(y=dt1$y, z=dt2$z[1:3], x=dt1$x, key="y"))
+test(1155.3, dt1[dt2, nomatch=0L], data.table(x=dt1$x, y=dt1$y, z=dt2$z[1:3], key="y"))
+
+# NaN wasn't properly searched for in some cases. Fixed that. Here's the fix!
+dt <- structure(list(x = c(NaN, NaN, NaN, NaN, NaN, NA, NA, -3, -3,
+-3, -2, -2, -1, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 3, 3), y = c(16L,
+25L, 23L, 17L, 21L, 11L, 13L, 15L, 1L, 6L, 4L, 18L, 7L, 3L, 12L,
+24L, 2L, 10L, 20L, 14L, 9L, 19L, 8L, 22L, 5L)), .Names = c("x",
+"y"), row.names = c(NA, -25L), class = c("data.table", "data.frame"
+))
+setkey(dt, x)
+test(1155.4, dt[J(NaN)], dt[is.nan(x)])
+test(1155.5, dt[J(NA_real_)], dt[is.na(x) & !is.nan(x)])
+
+
+# Fix for (usually small) memory leak when grouping, #2648.
+# Deliberate worst case: largest group (100000 rows) followed last by a small group (1 row).
+DT = data.table(A=rep(1:2,c(100000,1)), B=runif(100001))
+before = gc()["Vcells",2]
+for (i in 1:50) DT[, sum(B), by=A]
+after = gc()["Vcells",2]
+test(1157, after < before+1) # +1 = 1MB
+# Before the patch, Vcells grew dramatically from 6MB to 60MB. Now stable at 6MB. Increase 50 to 1000 and it grew to over 1GB for this case.
+
+# Similar for when dogroups writes less rows than allocated, #2648.
+DT = data.table(k = 1:50, g = 1:20, val = rnorm(1e4))
+before = gc()["Vcells",2]
+for (i in 1:50) DT[ , unlist(.SD), by = 'k']
+after = gc()["Vcells",2]
+test(1158, after < before+1)
+
+# tests for 'setDT' - convert list, DF to DT without copy
+x <- data.frame(a=1:4, b=5:8)
+test(1159.1, setDT(x), data.table(a=1:4, b=5:8))
+x <- list(1:4, 5:8)
+test(1159.2, setDT(x), data.table(1:4, 5:8))
+x <- list(a=1:4, b=5:8)
+test(1159.3, setDT(x), data.table(a=1:4, b=5:8))
+x <- list(a=1:4, 5:8)
+test(1159.4, setDT(x), setnames(data.table(1:4, 5:8), c("a", "V1")))
+x <- data.table(a=1:4, b=5:8)
+test(1159.5, setDT(x), data.table(a=1:4, b=5:8))
+x <- 1:5
+test(1159.6, setDT(x), error="Argument 'x' to 'setDT' should be a")
+x <- list(1, 2:3)
+test(1159.7, setDT(x), error="All elements in argument 'x' to 'setDT'")
+
+# tests for setrev
+x <- sample(10)
+y <- rev(x)
+setrev(x)
+test(1160.1, y, x)
+x <- sample(c(1:10, NA), 21, TRUE)
+y <- rev(x)
+setrev(x)
+test(1160.2, y, x)
+x <- sample(runif(10))
+y <- rev(x)
+setrev(x)
+test(1160.3, y, x)
+x <- sample(c(runif(10), NA, NaN), 21, TRUE)
+y <- rev(x)
+setrev(x)
+test(1160.4, y, x)
+x <- sample(letters)
+y <- rev(x)
+setrev(x)
+test(1160.5, y, x)
+x <- as.logical(sample(0:1, 20, TRUE))
+y <- rev(x)
+setrev(x)
+test(1160.6, y, x)
+x <- list(1:10)
+test(1160.7, setrev(x), error="Input 'x' must be a vector")
+
+# tests for setreordervec
+# integer
+x <- sample(c(-10:10, NA), 100, TRUE)
+o <- base::order(x, na.last=FALSE)
+y <- copy(x)
+setreordervec(y, o)
+test(1161.1, x[o], y)
+# numeric
+x <- sample(c(NA, rnorm(10)), 100, TRUE)
+o <- base::order(x, na.last=FALSE)
+y <- copy(x)
+setreordervec(y, o)
+test(1161.2, x[o], y)
+# character
+x <- sample(c(NA, letters), 100, TRUE)
+o <- base::order(x, na.last=FALSE)
+y <- copy(x)
+setreordervec(y, o)
+test(1161.3, x[o], y)
+
+# tests for setreordervec
+DT <- data.table(x=sample(c(NA, -10:10), 2e2, TRUE),
+ y=sample(c(NA, NaN, -Inf, Inf, -10:10), 2e2, TRUE),
+ z=sample(c(NA, letters), 2e2, TRUE))
+# when not sorted, should return FALSE
+test(1162.1, is.sorted(DT[[1L]]), FALSE)
+setkey(DT, x)
+test(1162.2, is.sorted(DT[[1L]]), TRUE)
+
+test(1162.3, is.sorted(DT[[2L]]), FALSE)
+setkey(DT, y)
+test(1162.4, is.sorted(DT[[2L]]), TRUE)
+
+test(1162.5, is.sorted(DT[[3L]]), FALSE)
+setkey(DT, z)
+test(1162.6, is.sorted(DT[[3L]]), TRUE)
+
+setkey(DT, x, y)
+test(1162.7, length(forderv(DT, by=1:2)), 0)
+setkey(DT, x, z)
+test(1162.8, length(forderv(DT, by=c(1L, 3L))), 0)
+setkey(DT, y, z)
+test(1162.9, length(forderv(DT, by=2:3)), 0)
+setkey(DT)
+# test number 1162.10 skipped because if it fails it confusingly prints out as 1662.1 not 1662.10
+test(1162.11, length(forderv(DT, by=1:3)), 0)
+test(1162.12, is.sorted(DT, by=1:3), TRUE, warning="Use.*forderv.*for efficiency in one step, so you have o as well if not sorted")
+test(1162.13, is.sorted(DT, by=2:1), FALSE, warning="Use.*forderv.*for efficiency in one step, so you have o as well if not sorted")
+
+# FR #5152 - last on length=0 arguments
+x <- character(0)
+test(1163, last(x), character(0))
+
+# Test 1164 was a non-ASCII test, now in DtNonAsciiTests
+
+# Bug fix for #5117 - segfault when rbindlist on empty data.tables
+x <- as.data.table(BOD)
+y <- copy(x)
+test(1165, x[Time>100], rbindlist(list(x[Time > 100], y[Time > 200])))
+
+# Bug fix for the #5300 - rbind(DT, NULL) should not result in error, but BOD has an attribute as well, which won't be preserved (due to C-impl). Changing test.
+setattr(x <- as.data.table(BOD), 'reference', NULL)
+test(1166, x, rbind(x, NULL))
+
+# fix for bug #5307 - ordering with multiple columns in which at least one of them is a logical column
+foo = data.table(a=rep(c(0L,1L,0L,1L),2), b=rep(c(TRUE,TRUE,FALSE,FALSE),2), c=1L)
+test(1167, foo[, .N, by=list(b,a)], data.table(b=c(TRUE, TRUE, FALSE, FALSE), a=c(0L,1L,0L,1L), N=2L))
+
+# fix for bug #5355 - rbindlist with factor columns and empty data.tables resulted in error.
+A <- data.table(x=factor(1), key='x')
+B <- data.table(x=factor(), key='x')
+test(1168.1, rbindlist(list(B,A)), data.table(x=factor(1)))
+
+# fix for bug #5120, it's related to rbind and factors as well - more or less similar to 1168.1 (#5355). Seems to have been fixed with that commit. Just adding test here.
+tmp1 <- as.data.table(structure(list(Year = 2013L, Maturity = structure(1L, .Label = c("<1",
+"1.0 - 1.5", "1.5 - 2.0", "2.0 - 2.5", "2.5 - 3.0", "3.0 - 4.0",
+"4.0 - 5.0", ">5.0"), class = "factor"), Quality = structure(2L, .Label = c(">BBB",
+"BBB", "BB", "B", "CCC", "<CCC", "NR", "CASH"), class = c("ordered",
+"factor")), Ct = 2L, Wt = 1.56, CtTotRet = 1.08, TotRet = 69.2307692307692), .Names = c("Year",
+"Maturity", "Quality", "Ct", "Wt", "CtTotRet", "TotRet"), class = c("data.table",
+"data.frame"), row.names = c(NA, -1L)))
+
+tmp2 <- as.data.table(structure(list(Year = 2013L, Maturity = "TOTAL", Quality = "TOTAL",
+Ct = 214L, Wt = 100.001, CtTotRet = 406.26, TotRet = 406.255937440626), .Names = c("Year",
+"Maturity", "Quality", "Ct", "Wt", "CtTotRet", "TotRet"), class = c("data.table",
+"data.frame"), row.names = c(NA, -1L)))
+
+ans <- rbind(tmp1, tmp2)
+test(1168.2, as.data.frame(ans), rbind(as.data.frame(tmp1), as.data.frame(tmp2)))
+
+# checks of "" and NA_character_ ordering.
+test(1169, forderv(c(NA,"","a","NA")), INT(1,2,4,3)) # data.table does ascii ordering currently, so N comes before a
+test(1170, length(forderv(c(NA,"","NA","a"))), 0)
+test(1171, forderv(c("",NA,"a","NA")), INT(2,1,4,3))
+test(1172, length(forderv(NA_character_)), 0)
+test(1173, length(forderv(c(NA_character_,NA_character_))), 0)
+test(1174, length(forderv(c(NA_character_,NA_character_,NA_character_))), 0)
+test(1175, length(forderv("")), 0)
+test(1176, length(forderv(c("",""))), 0)
+test(1177, length(forderv(c("","",""))), 0)
+test(1178, forderv(c("",NA,"")), INT(2,1,3))
+
+# Test no invalid sort order warning when key is ok and 2nd colum is character/double
+DT = CJ(a=rep(1:3),b=c("a","b"))
+test(1179.1, key(DT), c("a","b"))
+test(1179.2, setkey(DT), DT) # i.e. no warning
+DT = CJ(a=rep(1:3),b=c(3.14,3.15))
+test(1180.1, key(DT), c("a","b"))
+test(1180.2, setkey(DT), DT) # i.e. no warning
+
+# test for iradix (NA and negatives). Tests need large range to trigger iradix.
+test(1181, forderv(INT(1,3,5000000,NA)), INT(4,1,2,3))
+test(1182, forderv(INT(1,-1,5000000,NA)), INT(4,2,1,3))
+test(1183, forderv(INT(-3,-7,1,-6000000,NA,3,5000000,NA,8)), INT(5,8,4,2,1,3,6,9,7))
+
+# tests of gsum and gmean with NA
+DT = data.table(x=rep(c("a","b","c","d"),each=3), y=c(1L,3L,6L), v=as.numeric(1:12))
+set(DT,c(3L,8L),"y",NA)
+set(DT,c(5L,9L),"v",NA)
+set(DT,10:12,"y",NA)
+set(DT,10:12,"v",NA)
+options(datatable.optimize=1) # turn off GForce
+test(1184.1, DT[, sum(v), by=x, verbose=TRUE], output="dogroups")
+test(1184.2, DT[, mean(v), by=x, verbose=TRUE], output="dogroups")
+test(1185.1, DT[, list(sum(y), sum(v), sum(y,na.rm=TRUE), sum(v,na.rm=TRUE)), by=x],
+ data.table(x=c("a","b","c","d"), V1=c(NA,10L,NA,NA), V2=c(6,NA,NA,NA), V3=c(4L,10L,7L,0L), V4=c(6,10,15,0)))
+options(datatable.optimize=0) # turn off fastmean optimization to get the answer to match to
+test(1185.2, ans <- DT[, list(mean(y), mean(v), mean(y,na.rm=TRUE), mean(v,na.rm=TRUE)), by=x, verbose=TRUE], output="All optimizations.*off")
+options(datatable.optimize=1) # turn on old fastmean optimization only
+test(1185.3, DT[, list(mean(y), mean(v), mean(y,na.rm=TRUE), mean(v,na.rm=TRUE)), by=x, verbose=TRUE], ans, output="Old mean.*changed j")
+options(datatable.optimize=Inf) # turn on GForce
+test(1185.4, DT[, list(mean(y), mean(v), mean(y,na.rm=TRUE), mean(v,na.rm=TRUE)), by=x, verbose=TRUE], ans, output="GForce optimized j to")
+test(1186, DT[, sum(v), by=x, verbose=TRUE], output="GForce optimized j to")
+test(1187.1, DT[, list(sum(y), sum(v), sum(y,na.rm=TRUE), sum(v,na.rm=TRUE)), by=x],
+ data.table(x=c("a","b","c","d"), V1=c(NA,10L,NA,NA), V2=c(6,NA,NA,NA), V3=c(4L,10L,7L,0L), V4=c(6,10,15,0)))
+MyVar = TRUE
+test(1187.2, DT[, list(sum(y,na.rm=MyVar), mean(y,na.rm=MyVar)), by=x, verbose=TRUE], output="GForce optimized j to",
+ DT[, list(sum(y,na.rm=TRUE), mean(y,na.rm=TRUE)), by=x])
+test(1187.3, DT[, mean(y,na.rm=MyVar), by=x, verbose=TRUE], output="GForce optimized j to",
+ DT[, mean(y,na.rm=TRUE), by=x])
+MyVar = FALSE
+test(1187.4, DT[, list(sum(y,na.rm=MyVar), mean(y,na.rm=MyVar)), by=x, verbose=TRUE], output="GForce optimized j to",
+ DT[, list(sum(y,na.rm=FALSE), mean(y,na.rm=FALSE)), by=x])
+test(1187.5, DT[, mean(y,na.rm=MyVar), by=x, verbose=TRUE], output="GForce optimized j to",
+ DT[, mean(y,na.rm=FALSE), by=x])
+
+
+# test from Zach Mayer
+a <- c("\"\"\"\") \" \" \" \" \"", "\"\"\"\") \" \" \" \" \"", "\"\"\"\") \" \" \" \" ,\"")
+test(1188, forderv(a), INT(1,3,2))
+
+# test as.ITime vectorization
+x = c("18:00", "18:00:12")
+test(1189, all(as.character(as.ITime(x)) == c("18:00:00", "18:00:12")))
+
+# that CJ() orders in the same order as setkey, #5375
+DT = CJ(c("Corp", "CORP"), 1:3)
+test(1190, setkey(DT), DT) # tests no warning here from setkey, was "key rebuilt" due to inconsistent locale sorting in v1.8.10
+
+# non-exact recycling in j results. Was caught with error in v1.8.10, now recycles with remainder and warning
+DT = data.table(a=1:2,b=1:6)
+test(1191, DT[, list(b,1:2), by=a], data.table(a=INT(1,1,1,2,2,2),b=INT(1,3,5,2,4,6),V2=INT(1,2,1,1,2,1)),
+ warning="Recycled leaving remainder of 1 item.*This warning is once only")
+
+# that twiddle is used consistently, and tolerance has gone.
+# nice example from : http://stackoverflow.com/questions/21885290/data-table-roll-nearest-returns-multiple-results
+x = 0.0275016249293408
+DT = data.table(rnk = c(0, 0.0909090909090909, 0.181818181818182, 0.272727272727273),
+ val = c(0.0233775088495975, 0.0270831481152598, 0.0275016216267234, 0.0275016249293408))
+# 2 byte rounding is about 11 s.f., so val[3] and val[4] are considered different
+test(1192, DT[,.N,keyby=val], setkey(DT,val)[,.N,by=val]) # tests uniqlist uses twiddle
+test(1193, DT[,.N,by=val]$N, INT(1,1,1,1))
+test(1194, DT[.(x),.N], 1) # tests bmerge uses twiddle
+DT[3, val:=0.0275016249291963]
+setkey(DT, NULL) # val[3] and val[4] are now equal, within 2 byte rounding
+test(1195, DT[,.N,keyby=val], setkey(DT,val)[,.N,by=val])
+test(1196, DT[,.N,by=val]$N, INT(1,1,2))
+test(1197, DT[.(x),.N], 2)
+
+DT = data.table(id=1:2, val1=6:1, val2=6:1) # 5380
+test(1199, DT[, sum(.SD), by=id], error="GForce sum can only be applied to columns, not .SD or similar.*looking for.*lapply(.SD")
+
+# Selection of columns, copy column to maintain the same as R <= 3.0.2, in Rdevel, for now
+# Otherwise e.g. setkey changes the original columns too. TO DO: could allow shallow copy, perhaps.
+DT = data.table(a=1:3, b=6:4, c=7:9)
+test(1200, address(DT[,"b",with=FALSE]$b) != address(DT$b))
+test(1201, address(DT[,c("b","c"),with=FALSE]$c) != address(DT$c))
+test(1202, address(DT[,list(b)]$b) != address(DT$b))
+test(1203, address(DT[,list(b,c)]$c) != address(DT$c))
+test(1204, address(DT[1:3,"b",with=FALSE]$b) != address(DT$b))
+test(1205, address(DT[TRUE,"b",with=FALSE]$b) != address(DT$b))
+
+# Somehow we didn't test DT[order(...)] anywhere yet (other than via plyr's arrange in test 304)
+DT = data.table(a=6:1, b=1:2)
+test(1206, DT[order(b,a)], data.table(a=INT(2,4,6,1,3,5),b=INT(1,1,1,2,2,2)))
+
+# Test joining to Inf, -Inf and mixed non-finites, and grouping
+DT = data.table(A=c(1,2,-Inf,+Inf,3,-1.1,NaN,NA,3.14,NaN,2.8,NA), B=1:12, key="A")
+for (i in 1:2) {
+ setNumericRounding(if (i==1L) 0L else 2L)
+ test(1207+i*0.1, DT[.(c(NA_real_,Inf)),B], INT(8,12,4))
+ test(1208+i*0.1, DT[.(c(Inf,NA_real_)),B], INT(4,8,12))
+ test(1209+i*0.1, DT[.(c(NaN,NA_real_)),B], INT(7,10,8,12))
+ test(1210+i*0.1, DT[.(c(NA_real_,NaN)),B], INT(8,12,7,10))
+ test(1211+i*0.1, DT[,sum(B),by=A]$V1, INT(20,17,3,6,1,2,11,5,9,4))
+ test(1212+i*0.1, DT[,sum(B),by=list(g=abs(trunc(A)))], data.table(g=c(NA,NaN,Inf,1,2,3),V1=INT(20,17,7,7,13,14)))
+ test(1213+i*0.1, DT[,sum(B),keyby=list(g=abs(trunc(A)))], data.table(g=c(NA,NaN,1,2,3,Inf),V1=INT(20,17,7,13,14,7),key="g"))
+ # test(1214+i*0.1, DT[.(-200.0),roll=TRUE]$B, 3L) # TO DO: roll to -Inf. Also remove -Inf and test rolling to NaN and NA
+}
+
+# that fread reads unescaped (but balanced) quotes in the middle of fields ok, #2694
+test(1215,
+ fread('N_ID VISIT_DATE REQ_URL REQType\n175931 2013-03-08T23:40:30 http://aaa.com/rest/api2.do?api=getSetMobileSession&data={"imei":"60893ZTE-CN13cd","appkey":"android_client","content":"Z0JiRA0qPFtWM3BYVltmcx5MWF9ZS0YLdW1ydXoqPycuJS8idXdlY3R0TGBtU 1'),
+ data.table(N_ID=175931L, VISIT_DATE="2013-03-08T23:40:30", REQ_URL='http://aaa.com/rest/api2.do?api=getSetMobileSession&data={"imei":"60893ZTE-CN13cd","appkey":"android_client","content":"Z0JiRA0qPFtWM3BYVltmcx5MWF9ZS0YLdW1ydXoqPycuJS8idXdlY3R0TGBtU', REQType=1L)
+)
+test(1216.1, fread('A,B,C\n1.2,Foo"Bar,"a"b\"c"d"\nfo"o,bar,"b,az""\n'), data.table(A = c("1.2", "fo\"o"), B = c("Foo\"Bar", "bar"), C = c("\"a\"b\"c\"d\"", "\"b")), error="Expecting 3 cols, but line 3 contains")
+test(1216.2, fread('A,B,C\n1.2,Foo"Bar,"a"b\"c"d""\nfo"o,bar,"b,az""\n'), data.table(A = c("1.2", "fo\"o"), B = c("Foo\"Bar", "bar"), C = c("a\"b\"c\"d\"", "\"b")), error="Expecting 3 cols, but line 3 contains")
+test(1216.3, fread('A,B,C\n1.2,Foo"Bar,"a"b\"c"d""\nfo"o,bar,"b,"az""\n'),
+ data.table(A=c('1.2','fo"o'), B=c('Foo"Bar','bar'),C=c('a"b"c"d"','b,"az"')))
+test(1217, fread('"One,Two","Three",Four\n12,3,4\n56,7,8\n'), # quoted column names including the separator
+ data.table("One,Two"=c(12L,56L),Three=c(3L,7L),Four=c(4L,8L)))
+
+# joining from empty character, #5387
+DT = data.table(a=1:3, b=c("a","b","c"), key="b")
+test(1218, DT[ DT[FALSE] ], data.table(a=integer(), b=character(), i.a=integer(), key="b"))
+
+# set() multiple columns
+DT = data.table(a=1:3,b=4:6,c=7:9)
+newVals = data.table(10:12,13:15)
+test(1219, set(DT,j=2:3,value=newVals), data.table(a=1:3,b=10:12,c=13:15))
+newVals = list(16:18,19:21)
+test(1220, set(DT,j=2:3,value=newVals), data.table(a=1:3,b=16:18,c=19:21))
+
+# Test non-join key columns used in j work again (spotted straight away by Michele on datatable-help when v1.9.2 was released).
+# Introduced at commit 1030. Very extensive new tests 1136* still all pass (great stuff Arun).
+DT = data.table(a=1:2,b=letters[1:6],key="a,b")
+test(1221, DT[.(1),b], c("a","c","e"))
+
+###########################################################################################
+# extensive testing of forderv with decreasing order of sorting (total of >700 tests so far - without NaN/NA
+###########################################################################################
+# - Generate a random seed each time; the randomness allows catching errors quicker
+# - But save the seed so that we can generate the same data back if any error occurs
+seed = as.integer(Sys.time()) # sample(9999L, 1L) temporary fix, because all the set.seed(.) used above makes this sample() step deterministic (always seed=9107)
+seedInfo = paste("forder decreasing argument test: seed = ", seed," ", sep="")
+# no NaN (because it's hard to match with base:::order) ## TODO: add tests with NaN
+set.seed(seed)
+foo <- function(n) apply(matrix(sample(letters, n*8L, TRUE), ncol=8L), 1, paste, sep="")
+i1 = as.integer(sample(c(-100:100), 1e3, TRUE))
+i2 = as.integer(sample(c(-100:100, -1e6, 1e6), 1e3, TRUE))
+d1 = as.numeric(sample(c(-100:100,Inf,-Inf), 1e3, TRUE))
+d2 = as.numeric(rnorm(1e3))
+c1 = sample(c(letters), 1e3, TRUE)
+c2 = sample(foo(200), 1e3, TRUE)
+
+DT = data.table(i1, i2, d1, d2, c1, c2)
+# randomise col order as well
+colorder=sample(ncol(DT))
+setcolorder(DT, names(DT)[colorder])
+seedInfo = paste(seedInfo, "colorder = ", paste(colorder, collapse=","), sep="")
+ans = vector("list", length(names(DT)))
+
+test_no = 1223.0
+oldnfail = nfail
+for (i in seq_along(names(DT))) {
+ cj = as.matrix(do.call(CJ, split(rep(c(1L,-1L), each=i), 1:i)))
+ ans[[i]] = combn(names(DT), i, function(x) {
+ tmp = apply(cj, 1, function(y) {
+ test_no <<- signif(test_no+.001, 7)
+ ll = as.call(c(as.name("order"),
+ lapply(seq_along(x), function(j) {
+ if (y[j] == 1L)
+ as.name(x[j])
+ else {
+ if (class(DT[[x[j]]]) =="character")
+ as.call(c(as.name("-"), as.call(list(as.name("xtfrm"), as.name(x[j])))))
+ else
+ as.call(list(as.name("-"), as.name(x[j])))
+ }
+ })
+ )
+ )
+ test(test_no, forderv(DT, by=x, order=y), with(DT, eval(ll)))
+ })
+ dim(tmp)=NULL
+ list(tmp)
+ })
+}
+ans = NULL
+if (nfail > oldnfail) cat(seedInfo, "\n") # to reproduce
+
+# fix for bug #5405 - unique on null data.table should return null data.table
+test(1224, unique(data.table(NULL)), data.table(NULL))
+
+# forderv should return 'integer(0)' when 'x' is not atomic and of 0 length (to be consistent with base:::order)
+test(1225.1, forderv(list()), integer(0))
+test(1225.2, forderv(data.table(NULL)), integer(0))
+
+# fix for bug #5377 - data.table(null list, data.frame, data.table) should return null data.table
+test(1226.1, data.table(list()), null.data.table())
+test(1226.2, data.table(data.frame(NULL)), null.data.table())
+test(1226.3, data.table(data.table(NULL)), null.data.table())
+test(1226.4, data.table(data.frame()), null.data.table())
+test(1226.5, data.table(data.table()), null.data.table())
+
+# fix for bug #5321 - POSIXlt issue.
+setDT(DT1 <- data.frame(id=1:3, d=strptime(c("06:02:36", "06:02:48", "07:03:12"), "%H:%M:%S")))
+test(1227, data.table(id=1:3, d=strptime(c("06:02:36", "06:02:48", "07:03:12"), "%H:%M:%S")), DT1, warning="POSIXlt column type detected and converted to")
+
+# fix for bug #5296 - retaining class of original data.table after passing through `[.data.table`
+DT <- data.table(a=1:2,b=3:4)
+setattr(DT, "class", c("newclass", class(DT)))
+test(1228.1, class(DT), class(DT[a>1]))
+test(1228.2, class(DT), class(DT[, list(b)]))
+test(1228.3, class(DT), class(DT[, "b", with=FALSE]))
+test(1228.4, class(DT), class(DT[, sum(b), by=a]))
+test(1228.5, class(DT), class(DT[a>1, sum(b), by=a]))
+test(1228.6, class(DT), class(DT[a>1, c:=sum(b), by=a]))
+
+# test 1229 was non-ASCII, now in package DtNonAsciiTests
+
+# Test that ad hoc by detects if ordered and dogroups switches to memcpy if contiguous, #1050
+DT = data.table(a=1:3,b=1:6,key="a")
+options(datatable.optimize=1) # turn off GForce, to test dogroups
+test(1230, DT[, sum(b), by=a, verbose=TRUE], output="memcpy contiguous groups")
+setkey(DT,NULL)
+test(1231, DT[, sum(b), by=a, verbose=TRUE], output="memcpy contiguous groups")
+test(1232, DT[, sum(b), by=a+1, verbose=TRUE], output="memcpy contiguous groups")
+test(1233, DT[, sum(b), by=a%%2, verbose=TRUE], output="collecting discontiguous groups")
+test(1234, DT[, sum(a), by=b, verbose=TRUE], output="collecting discontiguous groups")
+setkey(DT,a)
+test(1235, DT[.(2:3),sum(b),by=.EACHI,verbose=TRUE], data.table(a=2:3,V1=c(7L,9L),key="a"), output="memcpy contiguous groups")
+test(1236, DT[.(3:2),sum(b),by=.EACHI,verbose=TRUE], data.table(a=3:2,V1=c(9L,7L)), output="memcpy contiguous groups")
+test(1237, DT[.(3:2),sum(b),keyby=.EACHI,verbose=TRUE], data.table(a=2:3,V1=c(7L,9L),key="a"), output="memcpy contiguous groups")
+options(datatable.optimize=Inf)
+
+# check that key is not preserved when length of fastorder is > 0
+DT <- data.table(x=1:5, y=6:10, key="x")
+test(1238.1, key(setorder(DT, x)), "x")
+test(1238.2, key(setorder(DT, -x)), NULL)
+
+# Fix for bug #5366 - setkey fails when non-key columns are of type list.
+DT <- data.table(x=5:1, y=as.list(1:5))
+test(1239.1, setkey(DT, x), setattr(data.table(x=1:5, y=as.list(5:1)), 'sorted', 'x'))
+DT <- data.table(x=5:1, y=as.list(1:5))
+test(1239.2, setorder(DT, x), data.table(x=1:5, y=as.list(5:1)))
+
+# Fix for bug #5408 - order of as.data.table.table is different when doing as.data.table(with(DT, table(x,y)))
+set.seed(123)
+DT <- data.table(XX = sample(LETTERS[1:5], 1000, replace = TRUE), yy = sample(1:5, 1000, replace = TRUE))
+ans1 <- as.data.table(DT[, table(XX, yy)])
+ans2 <- as.data.table(table(DT$XX, DT$yy))
+setnames(ans1, 'N', 'Freq')
+setnames(ans2, names(ans1))
+test(1240.1, ans1, setDT(as.data.frame(with(DT, table(XX, yy)), stringsAsFactors=FALSE)))
+test(1240.2, ans2, ans1)
+
+# Test for optimisation of 'order' to 'forder'
+set.seed(45L)
+DT <- data.table(x=sample(1e2, 1e6,TRUE), y=sample(1e2, 1e6,TRUE))
+# with optimisation -> order will be optimised to forder
+optim = getOption("datatable.optimize")
+options(datatable.optimize=Inf)
+t1 = unname(system.time(ans1 <- DT[order(x,-y)])['elapsed'])
+# without optimisation
+options(datatable.optimize=0L)
+t2 = unname(system.time(ans2 <- DT[order(x,-y)])['elapsed'])
+test(1241.1, ans1, ans2)
+test(1241.2, t1 < t2, TRUE) # with optimisation must be faster
+# restore optimisation
+options(datatable.optimize=optim)
+
+# check no warning yet for with=FALSE and :=. To be helpful warning, then helpful error.
+DT = data.table(a=1:3, b=4:6)
+myCol = "a"
+test(1242, DT[2,myCol:=6L,with=FALSE], data.table(a=INT(1,6,3), b=4:6))
+
+# consistency of output type of mult, #5378
+DT = data.table(id=rep(1:2,each=2), var=rnorm(4), key="id")
+test(1243, DT[.(1:2), list(var)][c(2,4)], DT[.(1:2), list(var), mult="last"])
+test(1244, DT[.(1:2), var], DT$var)
+test(1245, DT[.(1:2), var][c(2,4)], DT[.(1:2), var, mult="last"])
+
+#############################################
+# FR #5205 - fromLast argument to duplicated
+#############################################
+seed = as.integer(Sys.time())
+seedInfo = paste("forder decreasing argument test: seed = ", seed," ", sep="")
+set.seed(seed)
+DT <- data.table(w=sample(-5:5, 100, TRUE),
+x=as.numeric(sample(-5:5, 100, TRUE)),
+y=sample(paste("id", 1:10, sep=""), 100, TRUE),
+z=sample(c(TRUE, FALSE), 100, TRUE))
+
+colorder=sample(ncol(DT))
+setcolorder(DT, names(DT)[colorder])
+seedInfo = paste(seedInfo, "colorder = ", paste(colorder, collapse=","), sep="")
+
+test_no = 1246.0
+oldnfail = nfail
+for (i in seq_along(names(DT))) {
+ cc = combn(names(DT), i)
+ apply(cc, 2L, function(jj) {
+ test_no <<- signif(test_no+.01, 7) # first without key
+ test(test_no, duplicated(DT, by=jj, fromLast=TRUE), duplicated.data.frame(DT[, jj, with=FALSE], fromLast=TRUE))
+ test_no <<- signif(test_no+.01, 7)
+ setkeyv(DT, jj) # with key
+ test(test_no, duplicated(DT, by=jj, fromLast=TRUE), duplicated.data.frame(DT[, jj, with=FALSE], fromLast=TRUE))
+ })
+}
+if (nfail > oldnfail) cat(seedInfo, "\n") # to reproduce
+
+# with NA
+DT <- data.table(w=sample(c(-5:5,NA_integer_), 100, TRUE),
+x=as.numeric(sample(c(-5:5, NA), 100, TRUE)),
+y=sample(c(NA, paste("id", 1:10, sep="")), 100, TRUE),
+z=sample(c(NA, TRUE, FALSE), 100, TRUE))
+
+colorder=sample(ncol(DT))
+setcolorder(DT, names(DT)[colorder])
+seedInfo = paste(seedInfo, "colorder = ", paste(colorder, collapse=","), sep="")
+
+oldnfail = nfail
+for (i in seq_along(names(DT))) {
+ cc = combn(names(DT), i)
+ apply(cc, 2L, function(jj) {
+ test_no <<- signif(test_no+.01, 7) # first without key
+ test(test_no, duplicated(DT, by=jj, fromLast=TRUE), duplicated.data.frame(DT[, jj, with=FALSE], fromLast=TRUE))
+ test_no <<- signif(test_no+.01, 7)
+ setkeyv(DT, jj) # with key
+ test(test_no, duplicated(DT, by=jj, fromLast=TRUE), duplicated.data.frame(DT[, jj, with=FALSE], fromLast=TRUE))
+ })
+}
+if (nfail > oldnfail) cat(seedInfo, "\n") # to reproduce
+
+# FR #5172 - anyDuplicated.data.table
+set.seed(45L)
+dt <- data.table(x=sample(3,10,TRUE), y=sample(letters[1:3], 10,TRUE))
+test(1247.1, anyDuplicated(dt), anyDuplicated.data.frame(dt))
+test(1247.2, anyDuplicated(dt, fromLast=TRUE), anyDuplicated.data.frame(dt, fromLast=TRUE))
+test(1247.3, anyDuplicated(dt, by="y"), anyDuplicated.data.frame(dt[, "y", with=FALSE]))
+test(1247.4, anyDuplicated(dt, by="y", fromLast=TRUE), anyDuplicated.data.frame(dt[, "y", with=FALSE], fromLast=TRUE))
+
+# Fix for #5423 - j-expression y * eval(parse(..)) should work without needing "("
+DT <- data.table(x = seq(1,10,1), y = seq(2,20,2))
+test(1248.1, DT[, y := y * eval(parse(text="1*2"))], data.table(x=seq(1,10,1), y=seq(4,40,4)))
+# fix in 1248 was not complete. resurfaced again as bug #5527. Fixed now, test added here below:
+DT <- data.table(id=1:5, var=letters[1:5])
+ans <- copy(DT)
+id <- "va"
+test(1248.2, DT[, eval(parse(text=paste(id,"r",sep="")))], letters[1:5])
+test(1248.3, DT[, id2:=eval(parse(text=paste(id,"r",sep="")))], ans[, id2 := var])
+
+# test to make sure DT[order(...)] works fine when it's already sorted (forgot the case where forder returns integer(0) before)
+DT <- data.table(x=rep(1:4, each=5), y=1:20)
+test(1249.1, DT[order(x)], DT)
+test(1249.2, DT[order(y)], DT)
+test(1249.3, DT[order(x,y)], DT)
+
+# Fix for #5424 - duplicated 'by=FALSE' inconsistency
+set.seed(1L)
+DT <- data.table(x=sample(3,10,TRUE), y=sample(2,10,TRUE), key="x")
+test(1250.1, duplicated(DT, by=NULL), duplicated.data.frame(DT))
+test(1250.2, duplicated(DT, by=FALSE), error="Only NULL, column indices or column names are allowed in by")
+test(1250.3, duplicated(DT, by=TRUE), error="Only NULL, column indices or column names are allowed in by")
+
+# more tests for DT[order(...)] - now testing 'decreasing=FALSE/TRUE' argument
+set.seed(1L)
+DT <- data.table(x=sample(3,10,TRUE), y=sample(2,10,TRUE))
+test(1251.1, DT[order(x,y,decreasing=TRUE)], DT[order(-x,-y)])
+test(1251.2, DT[order(x,-y,decreasing=TRUE)], DT[order(-x,y)])
+# test in case of complex calls. check out the note in setkey.R under 'forder' for differences in forder and order for 'list' inputs. base is inconsistent I find.
+ix = with(DT, order(x+y))
+test(1251.3, DT[order(x+y)], DT[ix])
+ix = with(DT, order(-x-y))
+test(1251.4, DT[order(-x-y)], DT[ix])
+ix = with(DT, order(x+y, decreasing=TRUE))
+test(1251.5, DT[order(x+y, decreasing=TRUE)], DT[ix])
+ix = with(DT, order(4*x-5*y, decreasing=TRUE))
+test(1251.6, DT[order(4*x-5*y, decreasing=TRUE)], DT[ix])
+ix = with(DT, order(1-DT$x, decreasing=TRUE))
+test(1251.7, DT[order(1-DT$x, decreasing=TRUE)], DT[ix])
+test(1251.8, DT[order(x, list(-y), decreasing=TRUE)], error = "Column .* for ordering currently") # consistent with base (not the same error, but will error with "forder's" error instead)
+# more "edge cases" to ensure we're consistent with base
+test(1251.9, DT[order("a")], DT[1L])
+test(1251.10, DT[order("b", "a")], DT[1L])
+test(1251.11, DT[order(list("b", "a"))], error = "Column .* for ordering currently")
+test(1251.12, DT[order(list("b"), list("a"))], DT[1L])
+
+##############################################################
+# extensive tests for order optimisation within `[.data.table`
+##############################################################
+seed = as.integer(Sys.time())
+seedInfo = paste("forder decreasing argument test: seed = ", seed," ", sep="")
+set.seed(seed)
+# these variable try to simulate groups of length 1, 2, < 200, > 200 so as to cover all different internal implementations
+foo <- function(n) apply(matrix(sample(letters, n*8L, TRUE), ncol=8L), 1, paste, sep="")
+i1 = as.integer(sample(rep(c(-3:3, NA_integer_), c(1, 2, 190, 300, 7, 190, 210, 100))))
+i2 = as.integer(sample(rep(c(-2:2, -1e6, 1e6, NA_integer_), c(1, 2, 190, 300, 7, 190, 210, 100))))
+d1 = as.numeric(sample(rep(c(-2:2,Inf,-Inf, NA_real_, 5, -1e3), c(1, 190, 2, 300, 7, 50, 50, 100, 150, 150))))
+c1 = sample(rep(c(letters[1:5], NA_character_, "z"), c(1, 2, 190, 7, 300, 200, 300)))
+c2 = sample(c(foo(200), NA_character_), 1e3, TRUE)
+
+DT = data.table(i1, i2, d1, c1, c2)
+# randomise col order as well
+colorder=sample(ncol(DT))
+setcolorder(DT, names(DT)[colorder])
+seedInfo = paste(seedInfo, "colorder = ", paste(colorder, collapse=","), sep="")
+ans = vector("list", length(names(DT)))
+
+test_no = 1253.13
+oldnfail = nfail
+for (i in seq_along(names(DT))) {
+ cj = as.matrix(do.call(CJ, split(rep(c(1L,-1L), each=i), 1:i)))
+ ans[[i]] = combn(names(DT), i, function(x) {
+ tmp = apply(cj, 1, function(y) {
+ test_no <<- signif(test_no+.001, 7)
+ ll = as.call(c(as.name("order"),
+ lapply(seq_along(x), function(j) {
+ if (y[j] == 1L)
+ as.name(x[j])
+ else {
+ if (class(DT[[x[j]]]) =="character")
+ as.call(c(as.name("-"), as.call(list(as.name("xtfrm"), as.name(x[j])))))
+ else
+ as.call(list(as.name("-"), as.name(x[j])))
+ }
+ })
+ )
+ )
+ ans1 = forderv(DT, by=x, order=y, na.last=TRUE) # adding tests for both nalast=TRUE and nalast=NA
+ test(test_no, ans1, with(DT, eval(ll)))
+ test_no <<- signif(test_no+.001, 7)
+ ll <- as.call(c(as.list(ll), na.last=NA))
+ ans1 = forderv(DT, by=x, order=y, na.last=NA) # nalast=NA here.
+ test(test_no, ans1[ans1 != 0], with(DT, eval(ll)))
+ })
+ dim(tmp)=NULL
+ list(tmp)
+ })
+}
+ans = NULL
+if (nfail > oldnfail) cat(seedInfo, "\n") # to reproduce
+
+###############
+
+# turning off tolerance for UPCs (> 11 s.f. stored in numeric), #5369
+DT <- data.table(upc = c(301426027592, 301426027593, 314775802939, 314775802940, 314775803490, 314775803491, 314775815510, 314775815511, 314933000171, 314933000172),
+ year = 2006:2007)
+test(1253, DT[,.N,by=upc]$N, rep.int(2L,5L))
+setNumericRounding(0)
+test(1254, DT[,.N,by=upc], data.table(upc=DT$upc, N=1L))
+test(1255, unique(DT, by="upc"), DT)
+setNumericRounding(2)
+test(1256, DT[,.N,by=upc]$N, rep.int(2L,5L))
+DT = data.table(upc=rep(c(360734147771, 360734147770), each=3), year=rep(2009:2011, times=2))
+setNumericRounding(0)
+test(1257, DT[,.N,by=upc], data.table(upc=c(360734147771, 360734147770), N=3L))
+test(1258, DT[,.N,by=upc][order(upc)], data.table(upc=c(360734147770, 360734147771), N=3L))
+setNumericRounding(1)
+test(1259, DT[,.N,by=upc], data.table(upc=c(360734147771, 360734147770), N=3L))
+test(1260, DT[,.N,by=upc][order(upc)], data.table(upc=c(360734147770, 360734147771), N=3L))
+test(1261, getNumericRounding(), 1L)
+# the limit of double precision (16 s.f.) ...
+if (.Machine$sizeof.longdouble==16)
+ test(1262, length(unique(c(1.2345678901234560, 1.2345678901234561, 1.2345678901234562, 1.2345678901234563))), 2L)
+ # 2 not 4 is double precision limit which base::unique() relies on in this test
+ # valgrind will also return (3) instead of (2) here.. due to floating point precision limitation. changing the last two values to 1.2345678901234563 and 1.2345678901234564 returns 2.
+DT = data.table(id=c(1.234567890123450, 1.234567890123451, 1.234567890123452, 1.234567890123453)) # one less digit is limit
+test(1263, length(unique(DT$id)), 4L)
+test(1264, DT[,.N,by=id]$N, 4L) # 1 byte rounding isn't enough
+setNumericRounding(0)
+test(1265, DT[,.N,by=id]$N, INT(1,1,1,1))
+test(1266, getNumericRounding(), 0L)
+setNumericRounding(2)
+
+# fread reading NA in logical columns, #4766
+DF = data.frame(I=1:3, L=c(T,F,NA), R=3.14)
+write.csv(DF,f<-tempfile(),row.names=F)
+test(1267.1, fread(f)$L, c(TRUE, FALSE, NA))
+test(1267.2, fread(f), as.data.table(read.csv(f)))
+unlink(f)
+
+### FR #2722 test begins here ###
+#################################
+# FR #2722 optimise j=c(lapply(.SD,sum, ...)) - here any amount of such lapply(.SD, ...) can occur and in any order
+set.seed(45L)
+dt <- data.table(a=sample(2,10,TRUE), b=sample(3,10,TRUE), c=sample(4,10,TRUE), d=sample(5,10,TRUE))
+
+options(datatable.optimize=1L)
+ans2 <- dt[, c(lapply(.SD, mean), lapply(.SD, sum)), by=a]
+options(datatable.optimize=Inf)
+test(1268.1, dt[, c(lapply(.SD, mean), lapply(.SD, sum)), by=a, verbose=TRUE], ans2,
+ output="GForce optimized j to 'list(gmean(b), gmean(c), gmean(d), gsum(b), gsum(c), gsum(d))'")
+
+options(datatable.optimize=1L)
+ans2 <- dt[, c(lapply(.SD, mean), .N), by=a]
+options(datatable.optimize=Inf)
+test(1268.2, dt[, c(lapply(.SD, mean), .N), by=a, verbose=TRUE], ans2,
+ output = "lapply optimization changed j from 'c(lapply(.SD, mean), .N)' to 'list(mean(b), mean(c), mean(d), .N)'")
+
+options(datatable.optimize=1L)
+ans2 <- dt[, c(list(c), lapply(.SD, mean)), by=a]
+options(datatable.optimize=Inf)
+test(1268.3, dt[, c(list(c), lapply(.SD, mean)), by=a, verbose=TRUE], ans2,
+ output = "lapply optimization changed j from 'c(list(c), lapply(.SD, mean))' to 'list(c, mean(b), mean(c), mean(d))")
+
+test(1268.4, dt[, c(as.list(c), lapply(.SD, mean)), by=a],
+ error = "j doesn't evaluate to the same number of columns for each group")
+
+options(datatable.optimize=1L)
+ans2 <- dt[, c(sum(d), lapply(.SD, mean)), by=a]
+options(datatable.optimize=Inf)
+test(1268.5, dt[, c(sum(d), lapply(.SD, mean)), by=a, verbose=TRUE], ans2,
+ output = "GForce optimized j to 'list(gsum(d), gmean(b), gmean(c), gmean(d))'")
+
+options(datatable.optimize=1L)
+ans2 <- dt[, c(list(sum(d)), lapply(.SD, mean)), by=a]
+options(datatable.optimize=Inf)
+test(1268.6, dt[, c(list(sum(d)), lapply(.SD, mean)), by=a, verbose=TRUE], ans2,
+ output = "GForce optimized j to 'list(gsum(d), gmean(b), gmean(c), gmean(d))'")
+
+# newly added tests for #861
+# optimise, but no GForce
+options(datatable.optimize=1L)
+ans2 <- dt[, c(list(sum(d), .I), lapply(.SD, mean)), by=a]
+options(datatable.optimize=Inf)
+test(1268.7, dt[, c(list(sum(d), .I), lapply(.SD, mean)), by=a, verbose=TRUE], ans2,
+ output = "lapply optimization changed j from 'c(list(sum(d), .I), lapply(.SD, mean))' to 'list(sum(d), .I, mean(b), mean(c), mean(d))'")
+
+# don't optimise .I in c(...)
+options(datatable.optimize=1L)
+dt = data.table(x=c(1,1,1,2,2,2), y=1:6)
+ans2 <- dt[, c(.I, lapply(.SD, mean)), by=x]
+options(datatable.optimize=Inf)
+test(1268.8, dt[, c(.I, lapply(.SD, mean)), by=x, verbose=TRUE], ans2,
+ output = "lapply optimization is on, j unchanged as 'c(.I, lapply(.SD, mean))'")
+
+### FR #2722 tests end here ###
+
+# Wide range numeric and integer64, to test all bits
+x = sample( c(seq(-1e100, 1e100, length=1e5), c(seq(-1e-100,1e-100,length=1e5))) )
+setNumericRounding(0)
+test(1269, forderv(x), base::order(x))
+setNumericRounding(2) # not affected by rounding
+test(1270, forderv(x), base::order(x))
+if ("package:bit64" %in% search()) {
+ x = as.integer64(2)^(0:62)
+ x = sample(c(x,-x,0))
+ if (!inherits(try(bit64::order(x),silent=TRUE), "try-error")) # if for old version of bit64
+ test(1271, forderv(x), bit64::order(x)) # because GenomicRanges replaces this order
+ DT = data.table( a=as.integer64(2)^45 + 1:3, b=1:6 )
+ test(1272, DT[,sum(b),by=a], data.table(a=DT$a[1:3], V1=INT(5,7,9)))
+ test(1273, unique(DT, by="a"), DT[1:3])
+ test(1274, duplicated(DT, by="a"), rep(c(FALSE,TRUE),each=3))
+ setkey(DT,a)
+ test(1275, DT[.(as.integer64(35184372088834))], DT[3:4])
+ test(1276, unique(DT), DT[c(1,3,5)])
+ test(1277, duplicated(DT), rep(c(FALSE,TRUE),3))
+}
+
+# distinguishing small numbers from 0.0 as from v1.9.2, test from Rick
+# http://stackoverflow.com/questions/22290544/grouping-very-small-numbers-e-g-1e-28-and-0-0-in-data-table-v1-8-10-vs-v1-9-2
+test_no = 1278.001
+for (dround in c(0,2)) {
+ setNumericRounding(dround) # rounding should not affect the result here because although small, it's very accurace (1 s.f.)
+ for (i in c(-30:-1,1:30)) {
+ DT = data.table(c(1 * (10^i),2,9999,-1,0,1))
+ test(test_no, nrow(DT[, .N, by=V1]), 6)
+ test_no = test_no + 0.001
+ }
+}
+
+# rounding of milliseconds, workaround, TO DO: #5445
+# http://stackoverflow.com/questions/22356957/rounding-milliseconds-of-posixct-in-data-table-v1-9-2-ok-in-1-8-10
+DT = data.table(timestamp=as.POSIXct(
+ c("2013-01-01 17:51:00.707",
+ "2013-01-01 17:51:59.996",
+ "2013-01-01 17:52:00.059",
+ "2013-01-01 17:54:23.901",
+ "2013-01-01 17:54:23.913",
+ "2013-01-01 17:54:23.914")))
+setNumericRounding(2)
+test(1279, duplicated(DT), rep(c(FALSE,TRUE), c(4,2)))
+setNumericRounding(1)
+test(1280, duplicated(DT), rep(FALSE, 6))
+setNumericRounding(2)
+
+# FR #5465, keep.rownames argument for setDT, just for data.frames:
+DF <- data.frame(x=1:5, y=10:6)
+rownames(DF) <- letters[1:5]
+test(1281, setDT(DF, keep.rownames=TRUE), data.table(rn=letters[1:5], x=1:5, y=10:6))
+
+# Bug #5415 fix - BY doesn't retain names:
+DT <- data.table(fruit=c("apple","peach","pear"))
+test(1282, DT[, ans := .BY$fruit, by=fruit], data.table(fruit=DT$fruit, ans=DT$fruit))
+
+# bug #5443 - get() doesn't see i's columns, when i is a data.table:
+set.seed(1L)
+dt1 <- data.table(a=rep(1:2, each=2), c=sample(10,4))
+dt2 <- data.table(b=rep(2:3), c=sample(20,2), d=sample(20,2))
+setkey(dt1, a)
+setkey(dt2, b)
+
+# without by
+test(1283.1, dt1[dt2, list(a=a, c=get('c'), i.c=get('i.c'))], dt1[dt2, list(a=a, c=c, i.c=i.c)])
+test(1283.2, dt1[dt2, list(a=a, d=get('d'))], dt1[dt2, list(a=a, d=d)])
+# with by
+test(1283.3, dt1[dt2, list(a=a, c=get('c'), i.c=get('i.c')), by=.EACHI], dt1[dt2, list(a=a, c=c, i.c=i.c), by=.EACHI])
+test(1283.4, dt1[dt2, list(a=a, d=get('d')), by=.EACHI], dt1[dt2, list(a=a, d=d), by=.EACHI])
+
+# fix for bug #5583 - missed cases like dt[order(abs(x))].
+dt <- data.table(x=c(1L,-2L,3L))
+test(1284.1, dt[order(abs(x))], dt)
+test(1284.2, dt[order(-abs(x))], dt[3:1])
+
+# fix for bug #5582 - unique/duplicated on empty data.table returned NA
+dt <- data.table(x=numeric(0), y=character(0), key="x")
+test(1285.1, duplicated(dt), duplicated.data.frame(dt))
+test(1285.2, unique(dt), dt)
+
+# BUG #5672 fix
+a <- data.table(BOD, key="Time")
+b <- data.table(BOD, key="Time")[Time < 0] # zero row data.table
+ans <- merge(b, a, all=TRUE)
+test(1287, ans, data.table(Time=a$Time, demand.x=NA_real_, demand.y=a$demand, key="Time"))
+
+# more rbindlist tests - duplicate columns with "fill=TRUE"
+ll <- list(data.table(x=1, y=-1, x=-2), data.table(y=10, y=20, y=30, x=-10, a="a", b=Inf, c=factor(1)))
+test(1288.1, rbindlist(ll, use.names=TRUE, fill=FALSE), error = "Item 2 has 7 columns, inconsistent with item 1 which has 3 columns")
+# modified after fixing #725
+test(1288.2, rbindlist(ll, use.names=TRUE, fill=TRUE),
+ data.table(x=c(1,-10), y=c(-1,10), x=c(-2, NA), y=c(NA,20), y=c(NA,30), a=c(NA, "a"), b=c(NA, Inf), c=factor(c(NA, 1))))
+
+# check the name of output are consistent when binding two empty dts with one empy and other non-empty dt
+dt1 <- data.table(x=1:5, y=6:10)
+dt2 <- dt1[x > 5]
+setnames(dt3 <- copy(dt2), c("A", "B"))
+test(1288.3, names(rbindlist(list(dt2,dt3))), c("x", "y"))
+test(1288.4, names(rbindlist(list(dt3,dt2))), c("A", "B"))
+test(1288.5, names(rbindlist(list(dt1,dt3))), c("x", "y"))
+test(1288.6, names(rbindlist(list(dt3,dt1))), c("A", "B"))
+
+# check fix for bug #5612
+DT <- data.table(x=c(1,2,3))
+test(1288.7, rbind(DT, DT, data.table()), rbind(DT, data.table(), DT))
+
+# factor on fill=TRUE with NA column..
+DT1 = data.table(A=1:3,B=letters[1:3])
+DT2 = data.table(B=letters[4:5],C=factor(1:2))
+l = list(DT1,DT2)
+test(1288.8, rbindlist(l, use.names=TRUE, fill=TRUE), data.table(A=c(1:3,NA_integer_,NA_integer_), B=letters[1:5], C=factor(c(NA,NA,NA,1,2))))
+
+# adding more tests after modifying for better backwards compatibility:
+# rbindlist and rbind both work fine even when certain elements of list are not named at all, as long as fill = FALSE, but use.names=TRUE errors when all names are NULL
+# when fill=TRUE NO element of the list must have NULL names.
+ll <- list(list(1:3, 4:6), list(5:7, 8:10))
+test(1288.9, rbindlist(ll), data.table(V1=c(1:3, 5:7), V2=c(4:6, 8:10)))
+test(1288.10, rbindlist(ll, use.names=TRUE), error="use.names=TRUE but no item of input list has any names.")
+ll <- list(list(a=1:3, b=4:6), list(5:7, 8:10))
+test(1288.11, rbindlist(ll, use.names=TRUE), data.table(a=c(1:3, 5:7), b=c(4:6, 8:10)))
+ll <- list(list(1:3, 4:6), list(a=5:7, b=8:10))
+test(1288.12, rbindlist(ll, use.names=TRUE), data.table(a=c(1:3, 5:7), b=c(4:6, 8:10)))
+ll <- list(list(a=1:3, 4:6), list(5:7, b=8:10))
+test(1288.13, rbindlist(ll, use.names=TRUE), error="Answer requires 3 columns whereas one or more item(s) in the input list has only 2 columns. This could be because the items in the list may not")
+ll <- list(list(a=1:3, 4:6), list(5:7, b=8:10))
+test(1288.14, rbindlist(ll, fill=TRUE), data.table(a=c(1:3, rep(NA_integer_,3L)), V1=c(4:6,5:7), b=c(rep(NA_integer_, 3L), 8:10)))
+ll <- list(list(1:3, 4:6), list(5:7, 8:10))
+test(1288.15, rbindlist(ll, fill=TRUE), error="fill=TRUE, but names of input list at position 1")
+ll <- list(list(1:3, 4:6), list(a=5:7, b=8:10))
+test(1288.16, rbindlist(ll, fill=TRUE), error="fill=TRUE, but names of input list at position 1")
+
+# TO DO: TODO: think of and add more tests for rbindlist
+
+# fix for #5647
+dt <- data.table(x=1L, y=1:10)
+cp = copy(dt)
+test(1289.1, dt[,z := c(rep(NA, 5), y), by=x], cp[, z := c(rep(NA, 5), y[1:5])], warning="RHS 1 is length 15")
+dt<-data.table(x=c(1:2), y=1:10)
+cp = copy(dt)
+test(1289.2, dt[, z := c(rep(NA, 5),y), by=x], cp[, z := rep(NA_integer_, 10)], warning="RHS 1 is length 10")
+
+########################################
+# Extensve testing for "duplicate" names
+########################################
+# Rules: Basically, if index is directly given in 'j', just those columns are touched/operated on. But if 'column' names are given and there are more than one
+# occurrence of that column, then it's hard to decide which to keep and which to remove. So, to remove, all are removed, to keep, always the first is kept.
+# 1) when i,j,by are all absent (or) just 'i' is present then ALL duplicate columns are returned.
+# 2) When 'with=FALSE' and 'j' is a character and 'notj' is TRUE, all instances of the column to be removed will be removed.
+# 3) When 'with=FALSE' and 'j' is a character and 'notj' is FALSE, only the first column will be recognised in presence of duplicate columns.
+# 4) When 'with=FALSE' and 'j' is numeric and 'notj' is TRUE, just those indices will be removed.
+# 5) When 'with=FALSE' and 'j' is numeric and 'notj' is FALSE, all columns for indices given, if valid, are returned. (FIXES #5688)
+# 6) When .SD is in 'j', but '.SDcols' is not present, ALL columns are subset'd - FIXES BUG #5008.
+# 7) When .SD and .SDcols are present and .SDcols is numeric, columns corresponding to the given indices are returned.
+# 8) When .SD and .SDcols are present and .SDcols is character, duplicate column names will only return the first column, each time.
+# 9) When .SD and .SDcols are present and .SDcols is numeric, and it's -SDcols, then just those columns are removed.
+# 10) When .SD and .SDcols are present and .SDcols is character and -SDcols, then all occurrences of that object is removed.
+# 11) When no .SD and no .SDcols and no with=FALSE, only duplicate column names will return only the first column each time.
+# 12) With 'get("col")', it's the same as with all character types.
+# 13) A logical expression in 'j'.
+# 14) Finally, no tests but.. using 'by' with duplicate columns and aggregating may not return the intended result, as it may operate on column names in some cases.
+
+# All points are tested with this example:
+DT <- data.table(x=1:2, y=3:4, x=5:6, x=7:8, y=9:10, z=11:12)
+DT1 <- data.table(x=1L, y=3L, x=5L, x=7L, y=9L, z=11L)
+DT2 <- data.table(x=2L, y=4L, x=6L, x=8L, y=10L, z=12L)
+ll <- list(x=1:2, y=3:4, x=5:6, x=7:8, y=9:10, z=11:12)
+
+# case (1)
+test(1290.1, DT[1], DT1)
+test(1290.2, DT[], DT)
+test(1290.3, DT[(TRUE)], DT)
+# case (2)
+test(1290.4, DT[, !"x", with=FALSE], as.data.table(ll[c(2,5,6)]))
+test(1290.5, DT[, !"y", with=FALSE], as.data.table(ll[c(1,3,4,6)]))
+test(1290.6, DT[, !c("x", "x"), with=FALSE], as.data.table(ll[c(2,5,6)]))
+test(1290.7, DT[, !c("y", "y"), with=FALSE], as.data.table(ll[c(1,3,4,6)]))
+# case (3)
+test(1290.9, DT[, "x", with=FALSE], as.data.table(ll[1]))
+test(1290.10, DT[, "y", with=FALSE], as.data.table(ll[2]))
+test(1290.11, DT[, c("x", "x"), with=FALSE], as.data.table(ll[c(1,1)]))
+test(1290.12, DT[, c("y", "y"), with=FALSE], as.data.table(ll[c(2,2)]))
+# case (4)
+test(1290.13, DT[, !3, with=FALSE], as.data.table(ll[c(1,2,4,5,6)]))
+test(1290.14, DT[, !c(1,1,3,4), with=FALSE], as.data.table(ll[c(2,5,6)]))
+test(1290.15, DT[, !2, with=FALSE], as.data.table(ll[c(1,3,4,5,6)]))
+test(1290.16, DT[, !c(2,5,2), with=FALSE], as.data.table(ll[c(1,3,4,6)]))
+# case (5)
+test(1290.17, DT[, 3, with=FALSE], as.data.table(ll[3]))
+test(1290.18, DT[, c(1,1,3,4), with=FALSE], as.data.table(ll[c(1,1,3,4)]))
+test(1290.19, DT[, 2, with=FALSE], as.data.table(ll[2]))
+test(1290.20, DT[, c(2,5,2), with=FALSE], as.data.table(ll[c(2,5,2)]))
+# case (6)
+test(1290.21, DT[, .SD], as.data.table(ll))
+test(1290.22, DT[, .SD[1]], DT[1])
+test(1290.23, DT[, .SD[1, !3, with=FALSE]], as.data.table(DT[1, !3, with=FALSE]))
+# case (7)
+test(1290.24, DT[, .SD, .SDcols=c(1,1,3,4)], as.data.table(ll[c(1,1,3,4)]))
+# case (8)
+test(1290.25, DT[, .SD, .SDcols=c("x", "x", "y")], as.data.table(ll[c(1,1,2)]))
+# case (9)
+test(1290.26, DT[, .SD, .SDcols=-c(1,2)], as.data.table(ll[c(-(1:2))]))
+# case (10)
+test(1290.27, DT[, .SD, .SDcols=-c("x")], as.data.table(ll[c(2,6)]))
+# case (11)
+test(1290.28, DT[, x], ll[[1]])
+test(1290.29, DT[, list(x,x,y,y,y)], as.data.table(ll[c(1,1,2,2,2)]))
+test(1290.30, DT[, list(x,x,y)], as.data.table(ll[c(1,1,2)]))
+# cast (12)
+test(1290.31, DT[, get("x")], ll[[1]])
+test(1290.32, DT[, list(get("x"))], setnames(as.data.table(ll[1]), "V1"))
+test(1290.33, DT[, list(get("x"), get("y"))], setnames(as.data.table(ll[1:2]), c("V1", "V2")))
+# case (13)
+test(1290.34, DT[, names(DT) == "x", with=FALSE], as.data.table(ll[c(1,3,4)]))
+
+# Bug #5376.. DT[, bla ;= character(0), by=.] dint add new column when `DT is empty DT.
+dt1 = data.table(a=character(0),b=numeric(0))
+ans1 = data.table(a=character(0), b=numeric(0), c=numeric(0))
+ans2 = data.table(a=character(0), b=numeric(0), c=numeric(0), d=integer(0))
+test(1291.1, dt1[, c:=max(b), by='a'], ans1, warning="no non-missing arguments to max")
+test(1291.2, dt1[, d := integer(0), by=a], ans2)
+
+# Bug #5714
+test(1292.1, data.table(x=1:2, y=3:4)[, -(1:2), with=FALSE], null.data.table())
+test(1292.2, data.table(x=1:2)[, -1, with=FALSE], null.data.table())
+test(1292.3, data.table(x=1:2, y=3:4)[, !c("x","y"), with=FALSE], null.data.table())
+test(1292.4, data.table(x=1:2)[, !c("x"), with=FALSE], null.data.table())
+
+# Bug #5435 - print.data.table and digits option:
+DT <- structure(list(fisyr = 1995:1996, er = list(c(1, 3), c(1, 3)),
+ eg = c(0.0197315833926059, 0.0197315833926059), esal = list(
+ c(2329.89763779528, 2423.6811023622), c(2263.07456978967,
+ 2354.16826003824)), fr = list(c(4, 4), c(4, 4)), fg =
+c(0.039310363070415,
+ 0.039310363070415), fsal = list(c(2520.85433070866, 2520.85433070866
+ ), c(2448.55449330784, 2448.55449330784)), mr = list(c(5,
+ 30), c(5, 30)), mg = c(0.0197779376457164, 0.0197779376457164
+ ), msal = list(c(2571.70078740157, 4215.73622047244),
+c(2497.94263862333,
+ 4094.82600382409))), .Names = c("fisyr", "er", "eg", "esal",
+"fr", "fg", "fsal", "mr", "mg", "msal"), class = c("data.table",
+"data.frame"), row.names = c(NA, -2L))
+
+if (options()$width<80) options(width=80)
+ans1 = capture.output(print(DT, digits=4, row.names=FALSE))
+ans2 = c(" fisyr er eg esal fr fg fsal mr mg msal",
+ " 1995 1,3 0.01973 2330,2424 4,4 0.03931 2521,2521 5,30 0.01978 2572,4216",
+ " 1996 1,3 0.01973 2263,2354 4,4 0.03931 2449,2449 5,30 0.01978 2498,4095")
+test(1293, ans1, ans2)
+
+## Fixes bug #5442
+## Also improves upon bug fix #2551 to provide better warnings and at better places:
+dt <- data.table(a=1:3, b=c(7,8,9), c=c(TRUE, NA, FALSE), d=as.list(4:6), e=c("a", "b", "c"))
+
+test(1294.1, dt[, a := 1]$a, rep(1L, 3L))
+test(1294.2, dt[, a := 1.5]$a, rep(1L, 3L), warning="Coerced 'double' RHS to 'integer' to match the column's type")
+test(1294.3, dt[, a := NA]$a, rep(NA_integer_, 3L))
+test(1294.4, dt[, a := "a"]$a, rep(NA_integer_, 3L), warning="NAs introduced by coercion")
+test(1294.5, dt[, a := list(list(1))]$a, rep(1L, 3L), warning="Coerced 'list' RHS to 'integer' to match the column's type")
+test(1294.6, dt[, a := list(1L)]$a, rep(1L, 3L))
+test(1294.7, dt[, a := list(1)]$a, rep(1L, 3L))
+test(1294.8, dt[, a := TRUE]$a, rep(1L, 3L), warning="Coerced 'logical' RHS to 'integer' to match the column's type")
+test(1294.9, dt[, b := 1L]$b, rep(1,3))
+test(1294.10, dt[, b := NA]$b, rep(NA_real_,3))
+test(1294.11, dt[, b := "bla"]$b, rep(NA_real_, 3), warning="NAs introduced by coercion")
+test(1294.12, dt[, b := list(list(1))]$b, rep(1,3), warning="Coerced 'list' RHS to 'double' to match the column's type")
+test(1294.13, dt[, b := TRUE]$b, rep(1,3), warning="Coerced 'logical' RHS to 'double' to match the column's type")
+test(1294.14, dt[, b := list(1)]$b, rep(1,3))
+test(1294.15, dt[, c := 1]$c, rep(TRUE, 3), warning="Coerced 'double' RHS to 'logical' to match the column's type")
+test(1294.16, dt[, c := 1L]$c, rep(TRUE, 3), warning="Coerced 'integer' RHS to 'logical' to match the column's type")
+test(1294.17, dt[, c := NA]$c, rep(NA, 3))
+test(1294.18, dt[, c := list(1)]$c, rep(TRUE, 3), warning="Coerced 'double' RHS to 'logical' to match the column's type")
+test(1294.19, dt[, c := list(list(1))]$c, rep(TRUE, 3), warning="Coerced 'list' RHS to 'logical' to match the column's type")
+test(1294.20, dt[, c := "bla"]$c, rep(NA, 3), warning="Coerced 'character' RHS to 'logical' to match the column's type")
+test(1294.21, dt[, d := 1]$d, rep(list(1), 3), warning="Coerced 'double' RHS to 'list' to match the column's type")
+test(1294.22, dt[, d := 1L]$d, rep(list(1L), 3), warning="Coerced 'integer' RHS to 'list' to match the column's type")
+test(1294.23, dt[, d := TRUE]$d, rep(list(TRUE), 3), warning="Coerced 'logical' RHS to 'list' to match the column's type")
+test(1294.24, dt[, d := "bla"]$d, rep(list("bla"), 3), warning="Coerced 'character' RHS to 'list' to match the column's type")
+test(1294.25, dt[, d := list(list(1))]$d, rep(list(1), 3))
+test(1294.26, dt[, e := 1]$e, rep("1", 3), warning="Coerced 'double' RHS to 'character' to match the column's type")
+test(1294.27, dt[, e := 1L]$e, rep("1", 3), warning="Coerced 'integer' RHS to 'character' to match the column's type")
+test(1294.28, dt[, e := TRUE]$e, rep("TRUE", 3), warning="Coerced 'logical' RHS to 'character' to match the column's type")
+test(1294.29, dt[, e := list(list(1))]$e, rep("1", 3), warning="Coerced 'list' RHS to 'character' to match the column's type")
+test(1294.30, dt[, e := "bla"]$e, rep("bla", 3))
+test(1294.31, dt[, e := list("bla2")]$e, rep("bla2", 3))
+
+# FR #5357, when LHS evaluates to integer(0), provide warning and return dt, not an error.
+dt = data.table(a = 1:5, b1 = 1:5, b2 = 1:5)
+test(1295, dt[, grep("c", names(d)) := NULL], dt, warning="length(LHS) = 0, meaning no columns to delete or assign RHS to")
+
+# Updating logical column in one-row DT (corruption of new R 3.1 internal globals for TRUE, FALSE and NA)
+DT = data.table(a=1:6, b=c(TRUE,FALSE))
+test(1296, DT[,list(b,sum(b)),by=a], data.table(a=1:6, b=c(TRUE,FALSE), V2=c(1L,0L))) # was error "the ... list does not contain 2 elements"
+DT = DT[1L]
+set(DT,1L,"b",FALSE) # passing 1L as i here is needed to avoid column plonk, so changes the logical singleton in place
+test(1297, as.integer(TRUE[1]), 1L) # In R 3.1, TRUE[1] returns the global TRUE but TRUE doesn't yet (parses as new vector)
+test(1298, as.integer(TRUE), 1L)
+# orignal example, verbatim from James Sams :
+upc_table = data.table(upc=1:100000, upc_ver_uc=rep(c(1,2), times=50000), is_PL=rep(c(T, F, F, T), each=25000), product_module_code=rep(1:4, times=25000), ignore.column=2:100001)
+test(1299, upc_table[, .N, by=list(upc, upc_ver_uc)][,max(N)], 1L) # all size 1 groups
+test(1300, upc_table[, list(is_PL, product_module_code), keyby=list(upc, upc_ver_uc)][,upc[1:3]], 1:3L) # was warning "internal TRUE value has been modified"
+
+# Same test but for singleton small integers which r-devel also plan to globalise internally.
+DT = data.table(a=1:6, b=0:1)
+test(1301, DT[,list(b,sum(b)),by=a], data.table(a=1:6, b=c(0L,1L), V2=c(0L,1L)))
+DT = DT[1L]
+set(DT,1L,"b",3L)
+test(1302, 0L[1L], 3L-3L)
+test(1303, 0L, 3L-3L)
+
+# FR #5760. Test to just make sure that GForce and dogroups with .N are giving the same results.
+set.seed(2L)
+dt <- data.table(x=sample(rep(1:5e3, each=3)), y=sample(10))
+options(datatable.optimize = 1L)
+ans1 <- dt[, list(.N, sum(y)), by=x]
+options(datatable.optimize = 2L)
+ans2 <- dt[, list(.N, sum(y)), by=x]
+test(1304.1, ans1, ans2)
+
+dt <- data.table(x=sample(rep(1:5e3, each=3)), y=sample(10), key="x")
+options(datatable.optimize = 1L)
+ans1 <- dt[, list(.N, sum(y)), by=x]
+options(datatable.optimize = 2L)
+ans2 <- dt[, list(.N, sum(y)), by=x]
+test(1304.2, ans1, ans2)
+
+# FR #5528
+DT <- data.table(x=1:5, y=6:10)
+test(1305.1, setDF(DT), data.frame(x=1:5, y=6:10))
+# setDF should return if input is data.frame, not error.
+df <- data.frame(x=1:5, y=6:10)
+test(1305.2, setDF(df), df) # setDF works on data.frame
+# setDF also works on lists with equal lengths, #1132
+df <- list(a=1:5, b=6:10)
+test(1305.3, data.frame(df), setDF(df))
+df <- list(1:5, 6:10)
+test(1305.4, setDF(as.data.table(df)), setDF(df))
+test(1305.5, setDF(1:5), error="setDF only accepts")
+test(1305.6, setDF(list(1, 2:3)), error="All elements in argument")
+# Tests .7 - .13 for FR #1320: setDF accepts rownames argument
+dt <- data.table(a=1:5, b=6:10)
+df <- data.frame(a=1:5, b=6:10)
+lst <- list(a=1:5, b=6:10)
+df2 <- data.frame(a=1:5, b=6:10)
+rownames(df2) <- LETTERS[1:5]
+test(1305.7, setDF(dt, rownames=LETTERS[1:5]), df2)
+test(1305.8, setDF(df, rownames=LETTERS[1:5]), df2)
+test(1305.9, setDF(lst,rownames=LETTERS[1:5]), df2)
+# setDF returns an error for each type if rownames incorrect length
+dt <- data.table(a=1:5, b=6:10)
+df <- data.frame(a=1:5, b=6:10)
+lst <- list(a=1:5, b=6:10)
+test(1305.10, setDF(dt, rownames="a"), error='rownames incorrect length')
+test(1305.11, setDF(df, rownames="a"), error='rownames incorrect length')
+test(1305.12, setDF(lst,rownames="a"), error='rownames incorrect length')
+# setDF returns an error when rownames contains duplicates
+test(1305.13, setDF(dt, rownames=rep("a",5)), error='rownames contains duplicates')
+
+# .SD retains as much of head(key) as appropriate.
+# by= always keeps data appearance order, so it's which columns are grouped and selected that drive how much of key is retained
+DT = data.table(a=1:3,b=1:6,c=1:6,key="a,b")
+test(1306, DT[1:2,key(.SD)], c("a","b"))
+test(1307, DT[2:1,key(.SD)], NULL)
+test(1308, DT[,key(.SD),by=a], data.table(a=integer()))
+test(1309, DT[,key(.SD),by=b], data.table(b=DT$b, V1="a"))
+test(1310, DT[,key(.SD),by=c%%2L], data.table(c=c(1L,1L,0L,0L), V1=c("a","b","a","b")))
+test(1311, DT[,list(list(key(.SD))),by=a,.SDcols=1:2], data.table(a=1:3, V1=list(c("a","b")),key="a")) # .SDcols as Arun found
+
+# That setkey can't operate on locked tables such as .SD. Added in v1.9.3.
+DT = data.table(a=1:3,b=6:1)
+test(1312, DT[,setkey(.SD),by=a], error="Setting a physical key on .SD is reserved for possible future use")
+# was warning "Already keyed by this key but had invalid row order" due to the key not being cleared after the previous group. A solution could have been to put back the original key on populating .SD for each group. But instead we reserve it for future use and push the user towards doing it a different more efficient way (see Arun's speedups in the datatable-help thread).
+
+# gmin and gmax extensive testing (because there are tricky cases)
+DT <- data.table(x=rep(1:6, each=3), y=INT(4,-1,0, NA,4,10, 4,NA,10, 4,10,NA, -2147483647, -2147483647, -2147483647, 2147483647, 2147483647, 2147483647))
+# make sure GForce is running
+options(datatable.optimize=2L)
+
+# for integers
+test(1313.1, DT[, min(y), by=x], DT[, base:::min(y), by=x])
+test(1313.2, DT[, max(y), by=x], DT[, base:::max(y), by=x])
+test(1313.3, DT[, min(y, na.rm=TRUE), by=x], DT[, base:::min(y, na.rm=TRUE), by=x])
+test(1313.4, DT[, max(y, na.rm=TRUE), by=x], DT[, base:::max(y, na.rm=TRUE), by=x])
+# testing all NA - GForce automatically converts to numeric.. optimize=1L errors due to change from integer/numeric (like median)
+DT[x==6, y := INT(NA)]
+test(1313.5, DT[, min(y), by=x], DT[, base:::min(y), by=x])
+test(1313.6, DT[, max(y), by=x], DT[, base:::max(y), by=x])
+test(1313.7, DT[, min(y, na.rm=TRUE), by=x], data.table(x=1:6, V1=c(-1,4,4,4,-2147483647,Inf)), warning="No non-missing")
+test(1313.8, DT[, max(y, na.rm=TRUE), by=x], data.table(x=1:6, V1=c(4,10,10,10,-2147483647,-Inf)), warning="No non-missing")
+
+# for numeric
+DT <- data.table(x=rep(1:6, each=3), y=c(4,-1,0, NA,4,10, 4,NA,10, 4,10,NA, -Inf, NA, NA, Inf, NA, NA))
+test(1313.9, DT[, min(y), by=x], DT[, base:::min(y), by=x])
+test(1313.10, DT[, max(y), by=x], DT[, base:::max(y), by=x])
+test(1313.11, DT[, min(y, na.rm=TRUE), by=x], DT[, base:::min(y, na.rm=TRUE), by=x])
+test(1313.12, DT[, max(y, na.rm=TRUE), by=x], DT[, base:::max(y, na.rm=TRUE), by=x])
+# testing all NA - GForce automatically converts to numeric.. optimize=1L errors due to change from integer/numeric (like median)
+DT[x==6, y := NA_real_]
+test(1313.13, DT[, min(y), by=x], DT[, base:::min(y), by=x])
+test(1313.14, DT[, max(y), by=x], DT[, base:::max(y), by=x])
+test(1313.15, DT[, min(y, na.rm=TRUE), by=x], data.table(x=1:6, V1=c(-1,4,4,4,-Inf,Inf)), warning="No non-missing")
+test(1313.16, DT[, max(y, na.rm=TRUE), by=x], data.table(x=1:6, V1=c(4,10,10,10,-Inf,-Inf)), warning="No non-missing")
+
+# for date (attribute check.. especially after issues/689 !!!)
+DT <- data.table(x = rep(letters[1:2], each=5), y = as.POSIXct('2010-01-01', tz="UTC") + seq(0, 86400*9, 86400))
+test(1313.17, DT[, list(y=min(y)), by=x], DT[c(1,6)])
+test(1313.18, DT[, list(y=max(y)), by=x], DT[c(5,10)])
+DT[c(1,6), y := NA]
+test(1313.19, DT[, list(y=min(y)), by=x], DT[c(1,6)])
+test(1313.20, DT[, list(y=max(y)), by=x], DT[c(1,6)])
+test(1313.21, DT[, list(y=min(y, na.rm=TRUE)), by=x], DT[c(2,7)])
+test(1313.22, DT[, list(y=max(y, na.rm=TRUE)), by=x], DT[c(5,10)])
+
+# for character
+set.seed(1L)
+DT <- data.table(x=rep(1:6, each=3), y=sample(c("", letters[1:3], NA), 18, TRUE))
+test(1313.23, DT[, min(y), by=x], DT[, base:::min(y), by=x])
+test(1313.24, DT[, max(y), by=x], DT[, base:::max(y), by=x])
+test(1313.25, DT[, min(y, na.rm=TRUE), by=x], DT[, base:::min(y, na.rm=TRUE), by=x])
+test(1313.26, DT[, max(y, na.rm=TRUE), by=x], DT[, base:::max(y, na.rm=TRUE), by=x])
+DT[x==6, y := NA_character_]
+test(1313.27, DT[, min(y), by=x], DT[, base:::min(y), by=x])
+test(1313.28, DT[, max(y), by=x], DT[, base:::max(y), by=x])
+test(1313.29, DT[, min(y, na.rm=TRUE), by=x], data.table(x=1:6, V1=c("a","a","c","","a",NA)), warning="No non-missing")
+test(1313.30, DT[, max(y, na.rm=TRUE), by=x], data.table(x=1:6, V1=c("b","a","c","a","c",NA)), warning="No non-missing")
+
+# bug 700 - bmerge, roll=TRUE and nomatch=0L when i's key group occurs more than once
+dt1 <- data.table(structure(list(x = c(7L, 33L), y = structure(c(15912, 15912), class = "Date"), z = c(626550.35284, 7766.385)), .Names =
+c("x", "y", "z"), class = "data.frame", row.names = c(NA, -2L)), key = "x,y")
+dt2 <- data.table(structure(list(x = c(7L, 7L, 33L, 33L, 33L, 33L), y = structure(c(15884, 15917, 15884, 15884, 15917, 15917), class = "Date"), w = c(-0.118303, 0.141225, -0.03137, -0.02533, 0.045967, 0.043694)), .Names = c("x", "y", "w"), class = "data.frame", row.names = c(NA, -6L)), key = "x,y")
+test(1317.1, dt1[dt2, roll=TRUE, nomatch=0L], data.table(x=c(7L,33L,33L), y=as.Date(c("2013-07-31", "2013-07-31", "2013-07-31")), z=c(dt1$z[1:2], dt1$z[2]), w=c(dt2$w[2], dt2$w[5:6]), key="x,y"))
+
+# also test where 'i' is not sorted.
+set.seed(1L)
+dt2 <- dt2[sample(nrow(dt2))] # key should be gone
+test(1317.2, dt1[dt2, roll=TRUE, nomatch=0L], data.table(x=c(7L,33L,33L), y=as.Date(c("2013-07-31", "2013-07-31", "2013-07-31")), z=c(dt1$z[1:2], dt1$z[2]), w=c(dt2$w[1], dt2$w[c(2,6)])))
+
+# bug fix for #472 : "parse" in j
+set.seed(100)
+nrow <- 100L
+DT <- data.table(aa = sample(letters[1:5], nrow, replace = TRUE), bb = rnorm(nrow))
+sumExpr <- parse(text = "sum(bb, na.rm = TRUE)")
+meanExpr <- parse(text = "mean(bb, na.rm = TRUE)")
+test(1318.1, DT[, eval(sumExpr), by = aa], DT[, sum(bb, na.rm=TRUE), by=aa])
+test(1318.2, DT[, eval(meanExpr), by = aa], DT[, mean(bb, na.rm=TRUE), by=aa])
+test(1318.3, DT[, list(mySum = eval(sumExpr), myMean = eval(meanExpr)), by = aa], DT[, list(mySum=sum(bb, na.rm=TRUE), myMean=mean(bb, na.rm=TRUE)), by=aa])
+
+# get DT[order(.)] to be 100% consistent with base, even though the way base does some things is *utterly ridiculous*, inconsistent.
+# closes #696.
+DT <- data.table(a = 1:4, b = 8:5, c=letters[4:1])
+test(1319.1, DT[order(DT[, "b", with=FALSE])], DT[base:::order(DT[, "b", with=FALSE])])
+test(1319.2, DT[order(DT[, "c", with=FALSE])], DT[base:::order(DT[, "c", with=FALSE])])
+test(1319.3, DT[order(DT[, c("b","c"), with=FALSE])], DT[base:::order(DT[, c("b","c"), with=FALSE])])
+test(1319.4, DT[order(DT[, c("c","b"), with=FALSE])], DT[base:::order(DT[, c("c","b"), with=FALSE])])
+test(1319.5, DT[order(DT[, "b", with=FALSE], DT[, "a", with=FALSE])], DT[base:::order(DT[, "b", with=FALSE], DT[, "a", with=FALSE])])
+# test to make sure old things are not modified (ridiculous, but "consistency" demands it!)
+test(1319.6, DT[order(list(DT$a))], DT[1])
+test(1319.7, DT[order(list(DT$a), list(DT$b))], DT[1])
+test(1319.8, DT[order(list(DT$a, DT$b))], error="Column '1' is type 'list' which is not")
+
+# FR #703. Not so extensive testing because test 1223 already tests for everything else extensively. Only integer64 here.
+# this'll be the test for both DT[order(.)] and setorder(.) as both internally uses forder/forderv
+if ("package:bit64" %in% search()) {
+ set.seed(45L)
+ DT <- data.table(x=as.integer64(c(-50, 0, 50, 1e18, 1e-18)), y=sample(5))
+ ans1 <- forder(DT, x, na.last=TRUE, decreasing=FALSE)
+ ans2 <- forder(DT, x, na.last=FALSE, decreasing=FALSE)
+ ans3 <- forder(DT, x, na.last=TRUE, decreasing=TRUE)
+ ans4 <- forder(DT, x, na.last=FALSE, decreasing=TRUE)
+ test(1320.1, ans1, as.integer(c(1,2,5,3,4)))
+ test(1320.2, ans2, as.integer(c(1,2,5,3,4)))
+ test(1320.3, ans3, as.integer(c(4,3,2,5,1)))
+ test(1320.4, ans4, as.integer(c(4,3,2,5,1)))
+
+ set.seed(45L)
+ DT <- data.table(x=as.integer64(c(-50, 0, NA, 50, 1e18, NA, 1e-18)), y=sample(7))
+ ans1 <- forder(DT, x, na.last=TRUE, decreasing=FALSE)
+ ans2 <- forder(DT, x, na.last=FALSE, decreasing=FALSE)
+ ans3 <- forder(DT, x, na.last=TRUE, decreasing=TRUE)
+ ans4 <- forder(DT, x, na.last=FALSE, decreasing=TRUE)
+
+ test(1320.5, ans1, as.integer(c(1,2,7,4,5,3,6)))
+ test(1320.6, ans2, as.integer(c(3,6,1,2,7,4,5)))
+ test(1320.7, ans3, as.integer(c(5,4,2,7,1,3,6)))
+ test(1320.8, ans4, as.integer(c(3,6,5,4,2,7,1)))
+
+ # missed test - checking na.last=NA!
+ set.seed(45L)
+ DT <- data.table(x=as.integer64(c(-50, 0, NA, 50, 1e18, NA, 1e-18)), y=sample(7))
+ ans1 <- forder(DT, x, na.last=NA, decreasing=FALSE)
+ ans2 <- forder(DT, x, na.last=NA, decreasing=TRUE)
+
+ test(1320.9, ans1, as.integer(c(0,0,1,2,7,4,5)))
+ test(1320.10, ans2, as.integer(c(0,0,5,4,2,7,1)))
+}
+
+# fread newlines inside quoted fields
+test(1321, fread('A,B,C\n1,"foo\nbar",3\n4,baz,6'), data.table(A=c(1L,4L), B=c("foo\nbar","baz"), C=c(3L,6L)))
+test(1322, fread('A,B,C\n1,"foo
+bar",3\n4,baz,6'), data.table(A=c(1L,4L), B=c("foo\nbar","baz"), C=c(3L,6L)))
+# NB: don't remove the newline after foo in test 1322 above, that's what's being tested.
+test(1323, fread('col1,col2\n5,"4\n3"'), data.table(col1=5L, col2="4\n3"))
+test(1324, fread('A,B,C\n1,4,"foo"\n2,5,"bar'), data.table(A=1:2,B=4:5,C=c("foo", "\"bar")))
+test(1325, fread('A,B,C\n1,4,"foo"\n2,5,"bar"'), data.table(A=1:2,B=4:5,C=c("foo",'bar')))
+test(1326, fread('A,B,C\n1,4,"foo"\n2,5,bar"'), data.table(A=1:2,B=4:5,C=c("foo",'bar"')))
+test(1327, fread('A,B,C\n1,4,"foo"\n2,5,""bar""'), data.table(A=1:2,B=4:5,C=c("foo",'"bar"')))
+cat('A,B\n1,"Joe \\",Bloggs"', file = f<-tempfile())
+test(1328, fread(f), data.table(V1=1L, V2='Joe \\', V3='Bloggs"'), warning="Starting data input on line 2 and discarding line 1 because.*: A,B")
+unlink(f)
+test(1329, fread(), error="Input is either empty or fully whitespace after the skip or autostart")
+# add test that that escaped escapes at the end of a quoted field
+test(1330, fread('A,B\nfoo,1\nAnalyst\\,2\nbar,3'), data.table(A=c('foo','Analyst\\','bar'), B=1:3))
+test(1331.1, fread('A,B\nfoo,1\nAnalyst\\ ,2\nbar,3'), data.table(A=c('foo','Analyst\\','bar'), B=1:3)) # strip.white=TRUE
+test(1331.2, fread('A,B\nfoo,1\nAnalyst\\ ,2\nbar,3', strip.white=FALSE), data.table(A=c('foo','Analyst\\ ','bar'), B=1:3))
+test(1332, fread('A,B\nfoo,1\n"Analyst\\",2\nbar,3'), data.table(A=c('foo','Analyst\\','bar'), B=1:3))
+# double \\ in this file means one in the input, so the above " is escaped by a single '\' but still read ok
+test(1333.1, fread('A,B\nfoo,1\n"Analyst\\" ,2\nbar,3'), data.table(A = c("foo", "\"Analyst\\\"", "bar"), B = 1:3))
+test(1333.2, fread('A,B\nfoo,1\n"Analyst\\" ,2\nbar,3', strip.white=FALSE), data.table(A = c("foo", "\"Analyst\\\" ", "bar"), B = 1:3))
+test(1334, fread('A,B\nfoo,1\n"Analyst\\" ,",2\nbar,3'), data.table(A=c('foo', 'Analyst\\" ,', 'bar'), B=1:3))
+test(1335, fread('A,B\nfoo,1\n"Analyst\\\\",2\nbar,3'), data.table(A=c('foo','Analyst\\\\','bar'), B=1:3))
+
+# data from 12GB file in comments on http://stackoverflow.com/a/23858323/403310 ...
+# note that read.csv gets this wrong and puts jacoleman high school into the previous field, then fills the rest of the line silently.
+cat('A,B,C,D,E,F
+"12",0,"teacher private nfp\\\\\\\\"",""jacoleman high school","",""
+"TX",77406,"business analyst\\\\\\\\\\\\\\","the boeing co","",""
+"CA",94116,"na\\none","retired","",""
+', file = f<-tempfile()) # aside: notice the \\ before n of none as well
+test(1336.1, fread(f), data.table(A = c("12", "TX", "CA"), B = c(0L, 77406L, 94116L), C = c("teacher private nfp\\\\\\\\\"", "business analyst\\\\\\\\\\\\\\", "na\\none"), D = c("\"\"jacoleman high school\"", "the boeing co", "retired"), E = c("", "", ""), F = c("", "", "")))
+cat('A,B,C,D,E,F
+"12",0,"teacher private nfp\\\\\\\\"","jacoleman high school","",""
+"TX",77406,"business analyst\\\\\\\\\\\\\\","the boeing co","",""
+"CA",94116,"na\\none","retired","",""
+', file = f)
+test(1336.2, fread(f), data.table(A=c("12","TX","CA"), B=c(0L,77406L,94116L),C=c('teacher private nfp\\\\\\\\"','business analyst\\\\\\\\\\\\\\','na\\none'), D=c('jacoleman high school','the boeing co','retired'),E="",F=""))
+unlink(f)
+
+# file names ending with \ (quite common)
+# http://stackoverflow.com/questions/24375832/fread-and-column-with-a-trailing-backslash
+cat('file,size\n"windows\\user\\",123\n', file = f<-tempfile())
+test(1337, fread(f), data.table(file='windows\\user\\',size=123L))
+test(1338, fread(f), as.data.table(read.csv(f,stringsAsFactors=FALSE)))
+unlink(f)
+
+# TO DO, by checking for balanced embedded quotes
+# cat('http,size\n"www.blah?x="one",y="two","three"",123\n', file = f<-tempfile())
+# read.csv(f) -- unusually, seems to be a case it doesn't handle
+# test(1339, fread(f), data.table(http='www.blah?x="one",y="two","three"',size=123L))
+# unlink(f)
+
+# FR #706 - setorder and setorderv now has 'na.last=TRUE/FALSE' argument. It can't have value NA though, like `DT[order(.)]` as it reorders by reference, doesn't subset. Simple tests.
+set.seed(45L)
+DT <- data.table(x=sample(c(-2:2, NA_integer_), 20, TRUE), y=sample(c(-1:1, NA, Inf, -Inf, NaN), 20, TRUE))
+test(1340.1, setorder(copy(DT), x, na.last=TRUE ), DT[order( x, na.last=TRUE)])
+test(1340.2, setorder(copy(DT), x, na.last=FALSE), DT[order( x, na.last=FALSE)])
+test(1340.3, setorder(copy(DT), -x, na.last=TRUE ), DT[order(-x, na.last=TRUE)])
+test(1340.4, setorder(copy(DT), -x, na.last=FALSE), DT[order(-x, na.last=FALSE)])
+test(1340.5, setorder(copy(DT), y, na.last=TRUE ), DT[order( y, na.last=TRUE)])
+test(1340.6, setorder(copy(DT), y, na.last=FALSE), DT[order( y, na.last=FALSE)])
+test(1340.7, setorder(copy(DT), -y, na.last=TRUE ), DT[order(-y, na.last=TRUE)])
+test(1340.8, setorder(copy(DT), -y, na.last=FALSE), DT[order(-y, na.last=FALSE)])
+
+test(1340.9, setorderv(copy(DT), "x", 1L, na.last=TRUE ), DT[order( x, na.last=TRUE)])
+test(1340.10, setorderv(copy(DT), "x", 1L, na.last=FALSE), DT[order( x, na.last=FALSE)])
+test(1340.11, setorderv(copy(DT), "x", -1L, na.last=TRUE ), DT[order(-x, na.last=TRUE)])
+test(1340.12, setorderv(copy(DT), "x", -1L, na.last=FALSE), DT[order(-x, na.last=FALSE)])
+test(1340.13, setorderv(copy(DT), "y", 1L, na.last=TRUE ), DT[order( y, na.last=TRUE)])
+test(1340.14, setorderv(copy(DT), "y", 1L, na.last=FALSE), DT[order( y, na.last=FALSE)])
+test(1340.15, setorderv(copy(DT), "y", -1L, na.last=TRUE ), DT[order(-y, na.last=TRUE)])
+test(1340.16, setorderv(copy(DT), "y", -1L, na.last=FALSE), DT[order(-y, na.last=FALSE)])
+
+test(1340.17, setorder(copy(DT), x, na.last=NA), error="na.last must be logical TRUE/FALSE")
+test(1340.18, setorderv(copy(DT), "x", na.last=NA), error="na.last must be logical TRUE/FALSE")
+
+# bug #481 - DT[, list(list(.)), by=.] on R v3.1.0
+set.seed(1L)
+f <- function(x) list(x)
+DT <- data.table(x=sample(3,10,TRUE), y=as.numeric(sample(10)))
+test(1341.1, DT[, list(list(y)), by=x], data.table(x=unique(DT$x), V1=list(c(3,5,9), c(2,6,4,1), c(10,7,8))))
+test(1341.2, DT[, list(list(.I)), by=x], data.table(x=unique(DT$x), V1=list(c(1,5,10), c(2,3,8,9), c(4,6,7))))
+test(1341.3, DT[, list(f(y)), by=x], data.table(x=unique(DT$x), V1=list(c(3,5,9), c(2,6,4,1), c(10,7,8))))
+# test for list(list(.)) with :=
+test(1341.4, copy(DT)[, z := list(list(y)), by=x], copy(DT)[, z := list(list(copy(y))), by=x])
+test(1341.5, copy(DT)[, z := list(list(.I)), by=x], copy(DT)[, z := list(list(copy(.I))), by=x])
+test(1341.6, copy(DT)[, z := list(f(y)), by=x], copy(DT)[, z := list(f(copy(y))), by=x])
+
+# test regression on over-allocation (selfref) on unique() which uses new subsetDT()
+bla <- data.table(x=c(1,1,2,2), y=c(1,1,1,1))
+test(1342, unique(bla)[, bla := 2L], data.table(x=c(1,2),y=1,bla=2L))
+
+# blank and NA fields in logical columns
+test(1343, fread("A,B\n1,TRUE\n2,\n3,F"), data.table(A=1:3, B=c(TRUE,NA,FALSE)))
+test(1344, fread("A,B\n1,T\n2,NA\n3,"), data.table(A=1:3, B=c(TRUE,NA,NA)))
+
+# .N now available in i
+DT = data.table(a=1:3,b=1:6)
+test(1348, DT[.N], DT[6])
+test(1349, DT[.N-1:3], DT[5:3])
+test(1350, DT[.N+1], DT[NA])
+
+# Adding test to catch any future regressions - #734
+dt = data.table(id = rep(c('a','b'), each=2), val = rep(c(1,2,3), times=c(1,2,1)))
+setkey(dt, id, val)
+test(1351.1, dt[J("a"), val], c(1,2))
+test(1351.2, dt[J('a'), range(val)], c(1,2))
+
+# New feature: .() in j and .() in by
+DT = data.table(a=1:3, b=1:6, c=LETTERS[1:6])
+test(1352.1, DT[,.(b)], DT[,list(b)])
+test(1352.2, DT[,.(b,c)], DT[,c("b","c"),with=FALSE])
+test(1352.3, DT[,.(sum(b)),by=a], DT[,sum(b),by=a])
+test(1352.4, DT[,.(MySum=sum(b)), by=a], data.table(a=1:3, MySum=c(5L,7L,9L)))
+test(1352.5, DT[,sum(b),by=.(a)], DT[,sum(b),by=a])
+test(1352.6, DT[,sum(b),by=.(a%%2)], DT[,sum(b),by=a%%2])
+test(1352.7, DT[,sum(b),by=.(Grp=a%%2)], DT[,sum(b),by=list(Grp=a%%2)])
+test(1352.8, DT[,sum(b),by=.(a%%2,c)], DT[,sum(b),by=list(a%%2,c)])
+
+# that :=NULL together with i is now an error
+DT = data.table(a=1:3, b=1:6)
+test(1353.1, DT[2, b:=NULL], error="When deleting columns, i should not be provided")
+test(1353.2, DT[2, c("a","b"):=list(42, NULL)], error="When deleting columns, i should not be provided")
+
+# order optimisation caused trouble due to chaining because of 'substitute(x)' usage in [.data.table.
+set.seed(1L)
+X = data.table(id=1:10, val1=sample(3,10,TRUE))
+Y = data.table(val1=1:4, val2=8:5, key="val1")
+setkey(X, val1)
+test(1354, X[Y, val2 := i.val2, allow.cartesian=TRUE][, val1 := NULL][order(id)], data.table(id=1:10, val2=as.integer(c(8,7,7,6,8,6,6,7,7,8))))
+
+# Fix for #475, setDT(CO2) should error, as it's trying to modify the object whose binding is locked.
+# CO2 is not locked in R 2.14.1 but is in R >= 3.1.0. R NEWS isn't clear when that change happened, so just test there is an error when it is locked.
+if (bindingIsLocked("CO2",as.environment("package:datasets"))) {
+ test(1355, setDT(CO2), error="Can not convert 'CO2' to data.table by reference because binding is locked.")
+} else {
+ test(1355, setDT(CO2), CO2)
+}
+
+# Fix for #698. not join doesn't need to check for allow.cartesian=TRUE.
+DT1 <- data.table(x=rep(1:3, each=3L), y=1:9, key="x")
+DT2 <- data.table(x=rep(c(3L,1L), each=10), z=1L)
+test(1356, DT1[!DT2], data.table(x=2L, y=4:6, key="x"))
+
+# Fix for #745. as.data.table.matrix shouldn't convert character to factor
+m <- matrix(letters[1:4], ncol=2)
+test(1357, as.data.table(m), data.table(V1=letters[1:2], V2=letters[3:4]))
+
+# Fix for #471. A[A[A]] contains duplicate names in 1.9.3
+A <- data.table(foo = 1:2, bar = 3:4)
+setkey(A, foo)
+test(1358.1, names(A[A[A]]), c("foo", "bar", "i.bar", "i.bar.1"))
+test(1358.2, names(A[A[A[A]]]), c("foo", "bar", "i.bar", "i.bar.2", "i.bar.1"))
+
+# Fix for #743. 0 and -0 and the sign bit issue
+A <- data.table(x=c(0,0,-1,1,-1,0,-0,1,-1,1,0,1), y=1:12)
+test(1359.1, A[, .N, by=x], data.table(x=c(0,-1,1), N=c(5L,3L,4L)))
+dt1 <- data.table(x2 = 0L)
+dt2 <- data.table(x2 =-(11-11)/10)
+test(1359.2, as.integer(merge(dt2, dt1, by="x2")$x2), as.integer(merge(dt1, dt2, by="x2")$x2))
+
+# Fix for #744: X[Y, c(...), by=.EACHI] segfaults because of using 'i' as variable in for-loop that masked the original 'i' from input.
+dt <- data.table(id = c("A", "A", "B", "B", "C"), val1=1:5, val2=6:10, key = "id")
+sample <- c("A", "B")
+test(1360.1, dt[sample, c(.N), by = .EACHI], dt[sample, list(V1=.N), by=.EACHI])
+test(1360.2, copy(dt)[sample, N := c(.N), by = .EACHI], copy(dt)[sample, N := .N, by = .EACHI])
+
+# Fix for #500 - `lapply` call shouldn't redirect to `[.data.frame`.
+L <- list(data.table(BOD), data.table(BOD))
+test(1361, lapply(L, "[", Time==3L), list(L[[1L]][Time == 3L], L[[2L]][Time == 3L]))
+
+# Feature #735, first two cases: 1) .SD, and 2) DT[, c(.SD, lapply(.SD, ...)), by=...] optimisation:
+# Don't set options(datatable.verbose=TRUE) here because the "running test 1362.1 ..." messages cause output to scroll away errors on CRAN checks last 13 lines
+DT <- data.table(x=c(1,1,1,2,2), y=1:5, z=6:10)
+test(1362.1, DT[, .SD, by=x, verbose=TRUE],
+ output="lapply optimization changed j from '.SD' to 'list(y, z)'")
+test(1362.2, DT[, c(.SD), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(.SD)' to 'list(y, z)'")
+test(1362.3, DT[, c(.SD, lapply(.SD, sum)), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(.SD, lapply(.SD, sum))' to 'list(y, z, sum(y), sum(z))'")
+test(1362.4, DT[, c(lapply(.SD, sum), .SD), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(lapply(.SD, sum), .SD)' to 'list(sum(y), sum(z), y, z)'")
+test(1362.5, DT[, c(list(y), .SD, lapply(.SD, sum)), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(list(y), .SD, lapply(.SD, sum))' to 'list(y, y, z, sum(y), sum(z))'")
+# 3) .SD[1] and 4) .SD[1L]
+test(1362.6, DT[, c(.SD[1L]), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(.SD[1L])' to 'list(y[1L], z[1L])'")
+test(1362.7, DT[, c(.SD[1L], lapply(.SD, sum)), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(.SD[1L], lapply(.SD, sum))' to 'list(y[1L], z[1L], sum(y), sum(z))'")
+test(1362.8, DT[, c(.SD[.N]), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(.SD[.N])' to 'list(y[.N], z[.N])'")
+test(1362.9, DT[, .SD[1], by=x, verbose=TRUE],
+ output="lapply optimization changed j from '.SD[1]' to 'list(y[1], z[1])'")
+test(1362.11, DT[, c(.SD[1]), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(.SD[1])' to 'list(y[1], z[1])'")
+test(1362.12, DT[, c(.SD[1], lapply(.SD, sum)), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'c(.SD[1], lapply(.SD, sum))' to 'list(y[1], z[1], sum(y), sum(z))'")
+test(1362.13, DT[, head(.SD, 1), by=x, verbose=TRUE],
+ output="lapply optimization changed j from 'head(.SD, 1)' to 'list(head(y, 1), head(z, 1))'")
+# make sure .I is named as I when no name is given
+test(1362.14, names(DT[, c(list(.I, mean(y)), lapply(.SD, sum)), by=x]), c("x", "I", "V2", "y", "z"))
+# and if a name is given, it's retained
+test(1362.15, names(DT[, c(list(bla=.I, mean(y)), lapply(.SD, sum)), by=x]), c("x", "bla", "V2", "y", "z"))
+# Add test to ensure that mean() gets replaced with fastmean when GForce won't be used.
+test(1362.16, DT[, c(list(.I, mean(y)), lapply(.SD, mean)), by=x, verbose=TRUE],
+ output="Old mean optimization changed j from 'list(.I, mean(y), mean(y), mean(z))' to 'list(.I, .External(Cfastmean, y, FALSE), .External(Cfastmean, y, FALSE), .External(Cfastmean, z, FALSE))'")
+
+# setDT(DT), when input is already a data.table checks if selfrefok and if not, does alloc.col again.
+DT = list(data.frame(x=1:5, y=6:10))
+invisible(lapply(DT, setDT))
+DT = DT[[1L]]
+test(1363.1, selfrefok(DT), 1L)
+foo <- function(x) setDT(x)
+df = data.frame(x=1, y=2)
+foo(df)
+test(1363.2, selfrefok(df), 0L)
+setDT(df)
+test(1363.3, selfrefok(df), 1L)
+
+# setdiff, parly #547. internal as of now, and named setdiff_ because the name "set" can be confused with the set* functions.
+# maybe provide a %diff% operator that internally calls setdiff_?? Usage x %diff% y?
+X = data.table(a=c(1,1,1,1,3,3,2,2,2))[, `:=`(b=factor(a), c=as.character(a), d = as.integer(a), e=1:9)]
+Y = data.table(a=c(3,4), b=factor(3:4), c=c("3","4"), d=3:4, e=c(TRUE, FALSE), f=c(5L,7L))
+test(1364.1, setdiff_(X, Y, "a", "a"), data.table(a=c(1,2)))
+test(1364.2, setdiff_(X, Y, c("a", "e"), c("a", "f")), X[!5, list(a,e)])
+test(1364.3, setdiff_(X, Y, "a", "e"), error="When x's column ('a') is integer or numeric, the corresponding column in y ('e')")
+test(1364.4, setdiff_(X, Y, "b", "b"), data.table(b=factor(c(1,2), levels=c(1,2,3))))
+test(1364.5, setdiff_(X, Y, c("b", "e"), c("b", "f")), X[!5, list(b,e)])
+test(1364.6, setdiff_(X, Y, "b", "c"), data.table(b=factor(c(1,2), levels=c(1,2,3))))
+test(1364.7, setdiff_(X, Y, "c", "c"), data.table(c=as.character(c(1,2))))
+test(1364.8, setdiff_(X, Y, c("c", "e"), c("c", "f")), X[!5, list(c,e)])
+test(1364.9, setdiff_(X, Y, "c", "b"), data.table(c=c("1", "2")))
+test(1364.11, setdiff_(X, Y, "d", "d"), data.table(d=1:2))
+test(1364.12, setdiff_(X, Y, c("d", "e"), c("d", "f")), X[!5, list(d,e)])
+test(1364.13, setdiff_(X, Y, "d", "e"), error="When x's column ('d') is integer or numeric, the corresponding column in y ('e')")
+test(1364.14, setdiff_(X, Y, "b", "a"), error="When x's column ('b') is factor, the corresponding column in y ('a')")
+test(1364.15, setdiff_(X, Y, "c", "a"), error="When x's column ('c') is character, the corresponding column in y ('a') ")
+test(1364.16, setdiff_(X, Y), error="length(by.x) != length(by.y)")
+test(1364.17, setdiff_(X[, list(a)], Y[, list(a)]), data.table(a=c(1,2)))
+
+# not join along with by=.EACHI, #604
+DT <- data.table(A=c(1,1,1,2,2,2,2,3,3,4,5,5))[, `:=`(B=as.integer(A), C=c("c", "e", "a", "d"), D=factor(c("c", "e", "a", "d")), E=1:12)]
+setkey(DT, A)
+test(1365.1, DT[!J(c(2,5)), sum(E), by=.EACHI], DT[J(c(1,3,4)), sum(E), by=.EACHI])
+setkey(DT, B)
+test(1365.2, DT[!J(c(4:5)), list(.N, sum(E)), by=.EACHI], DT[J(1:3), list(.N, sum(E)), by=.EACHI])
+setkey(DT, C)
+test(1365.3, copy(DT)[!"c", f := .N, by=.EACHI], copy(DT)[c("a", "d", "e"), f := .N, by=.EACHI])
+setkey(DT, D)
+test(1365.4, DT[!J(factor("c")), .N, by=.EACHI], DT[J(factor(c("a", "d", "e"))), .N, by=.EACHI])
+test(1365.5, DT[!"c", lapply(.SD, sum), by=.EACHI, .SDcols=c("B", "E")], DT[c("a", "d", "e"), lapply(.SD, sum), by=.EACHI, .SDcols=c("B", "E")])
+
+# uniqlengths doesn't error on 0-length input
+test(1366, uniqlengths(integer(0), 0L), integer(0))
+
+# na.last=NA gets 0's for NAs not at the beginning when there are values so close to NA_integer_ for integers and -Inf for example for numerics. Moved logic to the end in forder.c so that we replace NAs with 0's after the ordering have been taken care of completely.
+x = c(-2147483000L, NA_integer_, 1L)
+test(1367.1, forderv(x, na.last=NA), c(0L,1L,3L))
+x = c(NA, Inf, 0, 1, -1, -Inf, NaN)
+test(1367.2, forderv(x, na.last=NA), c(0L, 0L, 6L, 5L, 3L, 4L, 2L))
+
+# Fix for integer overflow segfault in setRange
+x = c(-2147483647L, NA_integer_, 2L)
+test(1368.1, forderv(x), c(2L, 1L, 3L))
+x = c(2147483647L, NA_integer_, -2L)
+test(1368.2, forderv(x), c(2L, 3L, 1L))
+
+# tests for frankv. testing on vectors alone so that we can compare with base::rank
+# One difference is that NAs belong to the same group, unlike base::rank. So are NaNs.
+# So, they can't be compared to base::rank, won't be identical except for ties="first", and (ties="random", na.last=NA) - should document this.
+
+# no seed set on purpose
+dt = data.table(AA=sample(c(-2:2), 50, TRUE),
+ BB=sample(c(-2,-1,0,1,2,Inf,-Inf), 50, TRUE),
+ CC=sample(c(letters[1:5]), 50, TRUE),
+ DD=sample(c(-2:2), 50, TRUE),
+ EE=sample(as.logical(c(-2:2)), 50, TRUE))
+if ("package:bit64" %in% search()) dt[, DD := as.integer64(DD)]
+test_no = 1369.0
+for (i in seq_along(dt)) {
+ col = dt[[i]]
+ for (j in list(TRUE, FALSE, "keep")) {
+ for (k in c("average", "min", "max", "first")) {
+ if (k == "random") set.seed(45L)
+ if (class(col) == "integer64") {
+ r1 = rank(as.integer(col), ties.method=k, na.last=j)
+ r2 = rank(-xtfrm(as.integer(col)), ties.method=k, na.last=j)
+ }
+ else {
+ r1 = rank(col, ties.method=k, na.last=j)
+ r2 = rank(-xtfrm(col), ties.method=k, na.last=j)
+ }
+ if (k == "random") set.seed(45L)
+ r3 = frankv(col, ties.method=k, na.last=j)
+ r4 = frankv(col, order=-1L, ties.method=k, na.last=j)
+
+ test_no = signif(test_no+.01, 7)
+ test(test_no, r1, r3)
+ test_no = signif(test_no+.01, 7)
+ test(test_no, r2, r4)
+ }
+ }
+}
+# test na.last=NA here separately.
+dt = data.table(AA=sample(c(-2:2, NA), 50, TRUE),
+ BB=sample(c(-2,-1,0,1,2,Inf,-Inf, NA, NaN), 50, TRUE),
+ CC=sample(c(letters[1:5], NA), 50, TRUE),
+ DD=sample(c(-2:2, NA), 50, TRUE),
+ EE=sample(as.logical(c(-2:2, NA)), 50, TRUE))
+if ("package:bit64" %in% search()) dt[, DD := as.integer64(DD)]
+
+for (i in seq_along(dt)) {
+ col = dt[[i]]
+ for (k in c("average", "min", "max", "first")) {
+ if (k == "random") set.seed(45L)
+ if (class(col) == "integer64") {
+ r1 = rank(as.integer(col), ties.method=k, na.last=NA)
+ r2 = rank(-xtfrm(as.integer(col)), ties.method=k, na.last=NA)
+ }
+ else {
+ r1 = rank(col, ties.method=k, na.last=NA)
+ r2 = rank(-xtfrm(col), ties.method=k, na.last=NA)
+ }
+ if (k == "random") set.seed(45L)
+ r3 = frankv(col, ties.method=k, na.last=NA)
+ r4 = frankv(col, order=-1L, ties.method=k, na.last=NA)
+
+ test_no = signif(test_no+.01, 7)
+ test(test_no, r1, r3)
+ test_no = signif(test_no+.01, 7)
+ test(test_no, r2, r4)
+ }
+}
+
+
+# tests for is_na, which is equivalent of rowSums(is.na(dt)) > 0L
+# not exported yet, but we could!
+## UPDATE: also added tests for "any_na", internal version of anyNA
+## which also includes implementation for bit64::integer64, but the
+## real need is for merging factors correctly in joins, and we need
+## a fast check for NAs; can't rely on 3.1+ for anyNA.
+dt = list(AA=sample(c(NA,-2:2), 50, TRUE),
+ BB=sample(c(NA,-2,-1,0,NaN,1,2,Inf,-Inf), 50, TRUE),
+ CC=sample(c(NA,letters[1:5]), 50, TRUE),
+ DD=sample(c(NA,-2:2), 50, TRUE),
+ EE=sample(as.logical(c(NA,-2:2)), 50, TRUE))
+if ("package:bit64" %in% search()) dt[["DD"]] = as.integer64(dt[["DD"]])
+test_no = 1370.0
+ans = as.list(na.omit(as.data.table(dt)))
+for (i in seq_along(dt)) {
+ combn(names(dt), i, function(cols) {
+ test_no = signif(test_no+.01, 7)
+ ans1 = is_na(dt[cols])
+ ans2 = rowSums(is.na(as.data.table(dt[cols]))) > 0L
+ test(test_no, ans1, ans2)
+
+ # update: tests for any_na
+ test_no = signif(test_no+.01, 7)
+ test(test_no, any_na(dt[cols]), TRUE)
+ test_no = signif(test_no+.01, 7)
+ test(test_no, any_na(ans[cols]), FALSE)
+ TRUE
+ })
+}
+## The function is_na now gains a "by" argument where we can specify the columns. Tests have not been added for that yet.
+## However, I've added tests for 'na.omit.data.table' that uses this internally. So we don't have to add tests here again.
+## See tests 1394.*
+
+# extensive testing of overlap joins:
+
+# first test all argument check errors...
+x = data.table(chr=c("Chr1", "Chr1", "Chr2", "Chr2", "Chr2"), start=c(5,10, 1, 25, 50), end=c(11,20,4,52,60))
+y = data.table(chr=c("Chr1", "Chr1", "Chr2"), start=c(1, 15,1), end=c(4, 18, 55), val=1:3)
+# no by.x and by.y error
+test(1372.1, foverlaps(x, y, type="any"), error="'y' must be keyed (i.e., sorted, and, marked as sorted).")
+setkey(y, chr, end, start)
+test(1372.2, foverlaps(x, y, by.y=1:3, type="any"), error="The first 3 columns of y's key is not identical to the columns specified in by.y.")
+setkey(y, chr, start, end)
+setnames(y, c("chr", "pos1", "pos2", "val"))
+if ("package:GenomicRanges" %in% search()) {
+ setcolorder(y, c("chr", "val", "pos1", "pos2"))
+ ans1 = foverlaps(x, y, type="any", by.x=c("chr", "start", "end"), by.y=c("chr", "pos1", "pos2"), which=TRUE, nomatch=0L)
+ test(1372.3, foverlaps(x,y,by.x=1:3, nomatch=0L), data.table(chr=x$chr[2:5], y[c(2,3,3,3), -1, with=FALSE], x[2:5, 2:3, with=FALSE]))
+ gr <- function(x) {
+ GRanges(Rle(x[[1]]), IRanges(start=x[[2]], end=x[[3]]))
+ }
+ fo <- function(gr1, gr2, ...) {
+ olaps = findOverlaps(gr1, gr2, ...)
+ if (is.vector(olaps)) return(olaps)
+ ans = setDT(list(xid=queryHits(olaps), yid=subjectHits(olaps)))
+ setorder(ans)
+ ans
+ }
+ test(1372.4, setorder(ans1), fo(gr(x), gr(y[, c(1,3,4), with=FALSE]), type="any", select="all"))
+
+ runs = 3L # repeat 3 times..
+ types=c("any", "within", "start", "end")
+ mults=c("all", "first", "last")
+ maxgap=0L; minoverlap=1L
+ verbose=FALSE; which=TRUE
+ test_no <- 1372.4
+ for (run in seq_len(runs)) {
+ n1 = max(50L, sample(1e2L, 1, FALSE))
+ n2 = max(50L, sample(1e2L, 1, FALSE))
+ N = max(100L, sample(1e3L, 1, FALSE))
+
+ i1 = sample(N, n1, TRUE)
+ i2 = sample(N, n1, TRUE)
+ start = pmin(i1,i2)
+ end = pmax(i1,i2)
+ chr = sort(sample(paste("Chr", 1:2, sep=""), length(start), TRUE))
+ i = setDT(list(chr=chr, start=start, end=end))
+
+ i1 = sample(N, n2, TRUE)
+ i2 = sample(N, n2, TRUE)
+ start = pmin(i1,i2)
+ end = pmax(i1,i2)
+ chr = sort(sample(paste("Chr", 1:2, sep=""), length(start), TRUE))
+ x = setDT(list(chr=chr, start=start, end=end))
+ setkey(x); setkey(i)
+ for (type in types) {
+ for (mult in mults) {
+ # data.table overlap join
+ nomatch = ifelse(mult == "all", 0L, NA_integer_)
+ ans1 = foverlaps(i, x, mult=mult, type=type, nomatch=nomatch, which=which, verbose=verbose)
+ ans2 = fo(gr(i), gr(x), type=type, select=mult)
+ test_no = signif(test_no+.01, 7)
+ # cat("test =", test_no, ", i = ", run, ", type = ", type, ", mult = ", mult, "\n", sep="")
+ test(test_no, ans1, ans2)
+ }
+ }
+ }
+} else {
+ cat("Tests 1372.3+ not run. If required call library(GenomicRanges) first.\n")
+}
+
+# fix for bug in address - #824
+x = 1:10
+address(x) ## shouldn't increment NAM field
+out = capture.output(.Internal(inspect(x)))
+test(1373, grepl("NAM\\(1\\)", out), TRUE)
+
+# fix for bug #762 - key'd data.table with a non-existing column in 'by' is not handled properly.
+DT <- data.table(x=1:5, z=5:1, key="z")
+y <- c(1,3,2,3,2)
+test(1374.1, DT[, list(x=sum(x)), by=y], data.table(y=c(1,3,2), x=c(5L, 6L, 4L)))
+y <- c(1,2,2,3,3)
+test(1374.2, DT[, list(x=sum(x)), by=y], data.table(y=c(1,2,3), x=c(5L, 7L, 3L), key="y"))
+
+# order in i combined with := in j, updates those rows in that order
+# order in i without := in j, returns new object in that order, which is then updated
+# Similarly, subset in i with := in j, updates that subset
+DT = as.data.table(iris)
+DT[,Species:=as.character(Species)]
+test(1375.1, DT[,mean(Petal.Width),by=Species][order(-V1),Species:=toupper(Species)]$Species, c("SETOSA","VERSICOLOR","VIRGINICA"))
+test(1375.2, DT[,mean(Petal.Width),by=Species][order(-V1)][,Species:=toupper(Species)]$Species, c("VIRGINICA","VERSICOLOR","SETOSA"))
+test(1375.3, DT[,mean(Petal.Width),by=Species][V1>1,Species:=toupper(Species)]$Species, c("setosa","VERSICOLOR","VIRGINICA"))
+
+# Secondary keys a.k.a indexes ...
+DT = data.table(a=1:10,b=10:1)
+test(1376.1, key2(DT), NULL)
+test(1376.2, DT[b==7L,verbose=TRUE], DT[4L], output="Creating new index 'b'")
+test(1376.3, key2(DT), "b")
+test(1376.4, DT[b==8L,verbose=TRUE], DT[3L], output="Using existing index 'b'")
+test(1376.5, DT[a==7L,verbose=TRUE], DT[7L], output="Creating new index") # add 2nd secondary key
+test(1376.6, key2(DT), c("b","a")) # 2 secondary keys of single columns
+test(1376.7, DT[a==7L,verbose=TRUE], DT[7L], output="Using existing index 'a'")
+setkey(DT,b)
+test(1376.8, key2(DT), NULL)
+test(1376.9, list(DT[a==2L], key2(DT)), list(DT[9L],"a")) # create key2 for next test
+set2key(DT,NULL)
+test(1376.10, list(key(DT), key2(DT)), list("b", NULL))
+options(datatable.auto.index = FALSE)
+test(1376.11, list(DT[a==2L], key2(DT)), list(DT[9L],NULL))
+options(datatable.auto.index = TRUE)
+test(1376.12, list(DT[a==2L], key2(DT)), list(DT[9L],"a"))
+
+# When i is FALSE, it shouldn't matter if .SDcols is wrong. Package vardpoor relies on this in example(vardchanges).
+DT = data.table(a=1:3, b=4:6)
+test(1377.1, DT[FALSE, foo:=7], DT)
+test(1377.2, DT[0, foo:=7], DT)
+test(1377.3, DT[, foo := Reduce(function(x,y)paste(x,y,sep="__"), .SD), .SDcols=c("a","b")], data.table(a=1:3, b=4:6, foo=c("1__4","2__5","3__6")))
+test(1377.4, DT[, bar := Reduce(function(x,y)paste(x,y,sep="__"), .SD), .SDcols=c("a","zz")], error="Some items of .SDcols are not column names")
+test(1377.5, DT[FALSE, bar := Reduce(function(x,y)paste(x,y,sep="__"), .SD), .SDcols=c("a","zz")], DT)
+
+#====================================
+# fread issue with http download on Windows, thanks to Steve Miller for highlighting.
+# any file would do but this one is http://www.russell.com/common/indexes/csvs/russellmicrocapvalueindex_hist.csv
+# it happens to have a \r embedded in the first (quoted) column as well but that's not the issue
+# can't pass in the http: address directly because this runs on CRAN and any http: site might be unavailable
+# therefore, this doesn't actually test mode="wb" but close as we can get
+
+test(1378.1, dim(fread("russellCRLF.csv")), c(19,4))
+
+f = paste("file://",getwd(),"/russellCRLF.csv",sep="")
+# simulates a http:// request as far as file.download() and unlink() goes, without internet
+# download.file() in fread() changes the input data from \r\n to \n, on Windows.
+test(1378.2, dim(fread(f)), c(19,4))
+
+f = paste("file://",getwd(),"/russellCRCRLF.csv",sep="")
+# actually has 3 \r in the file, download.file() from file:// changes that to \r\r\n, so we can simulate download.file from http: in text mode.
+test(1378.3, fread(f), error="Line ending is .*r.*r.*n. R's download.file() appears to add the extra .*r in text mode on Windows. Please download again in binary mode (mode='wb') which might be faster too. Alternatively, pass the URL directly to fread and it will download the file in binary mode for you.")
+#====================================
+
+
+#====================================
+# Return to old bywithoutby behaviour. TO DO: delete these tests after Sep 2015
+
+options(datatable.old.bywithoutby=TRUE)
+deprecated_warn = "The data.table option 'datatable.old.bywithoutby' for grouping on join without providing `by` will be deprecated in the next release, use `by=.EACHI`."
+# Old tests from before commit: 0be720956fdd9c274e46133e154d4bbd5b2c7840
+# TO DO: address `allow.cartesian`. Some differences below...
+off = -1000
+TESTDT = data.table(a=as.integer(c(1,3,4,4,4,4,7)), b=as.integer(c(5,5,6,6,9,9,2)), v=1:7)
+setkey(TESTDT,a,b)
+test(off-8, TESTDT[SJ(c(-9,1,4,4,8),c(1,4,4,10,1)),v]$v, INT(NA,NA,NA,NA,NA), warning=deprecated_warn)
+test(off-9, TESTDT[SJ(c(-9,1,4,4,8),c(1,4,4,10,1)),v,roll=TRUE]$v, INT(NA,NA,NA,6,NA), warning=deprecated_warn)
+test(off-10, TESTDT[SJ(c(-9,1,4,4,8),c(1,4,4,10,1)),v,roll=TRUE,rollends=FALSE]$v, INT(NA,NA,NA,NA,NA), warning=deprecated_warn)
+test(off-16, TESTDT[SJ(c(4)),v][[2]], INT(3,4,5,6), warning=deprecated_warn)
+test(off-18, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",nomatch=0][[2]], INT(3:6), warning=deprecated_warn)
+test(off-185, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",nomatch=NA][[2]], INT(NA,NA,3:6,NA), warning=deprecated_warn)
+test(off-19, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",roll=TRUE,nomatch=0][[2]], INT(1,3:6,7), warning=deprecated_warn)
+test(off-186, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",roll=TRUE,nomatch=NA][[2]], INT(NA,1,3:6,7), warning=deprecated_warn)
+test(off-20, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=0][[2]], INT(1,3:6), warning=deprecated_warn)
+test(off-187, TESTDT[SJ(c(-3,2,4,8)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=NA][[2]], INT(NA,1,3:6,NA), warning=deprecated_warn)
+test(off-21, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",nomatch=0][[3]], INT(1,3:4), warning=deprecated_warn)
+test(off-188, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",nomatch=NA][[3]], INT(NA,1,NA,3:4,NA,NA,NA), warning=deprecated_warn)
+test(off-22, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,nomatch=0][[3]], INT(1,3:4,4,6), warning=deprecated_warn)
+test(off-189, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,nomatch=NA][[3]], INT(NA,1,NA,3:4,4,6,NA), warning=deprecated_warn)
+test(off-23, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=0][[3]], INT(1,3:4,4), warning=deprecated_warn)
+test(off-190, TESTDT[SJ(c(-9,1,4,4,4,4,8),c(1,5,5,6,7,10,3)),v,mult="all",roll=TRUE,rollends=FALSE,nomatch=NA][[3]], INT(NA,1,NA,3:4,4,NA,NA), warning=deprecated_warn)
+test(off-24, TESTDT[SJ(c(1,NA,4,NA,NA,4,4),c(5,5,6,6,7,9,10)),v,mult="all",roll=TRUE,nomatch=0][[3]], INT(1,3:4,5:6,6), warning=deprecated_warn)
+test(off-191, TESTDT[SJ(c(1,NA,4,NA,NA,4,4),c(5,5,6,6,7,9,10)),v,mult="all",roll=TRUE,nomatch=NA][[3]], INT(NA,NA,NA,1,3:4,5:6,6), warning=deprecated_warn)
+
+TESTDT[, a:=letters[a]]
+TESTDT[, b:=letters[b]]
+setkey(TESTDT,a,b)
+
+a = "d"
+# Variable Twister. a in this scope has same name as a inside DT scope.
+# Aug 2010 : As a result of bug 1005, and consistency with 'j' and 'by' we now allow self joins (test 183) in 'i'.
+test(off-70, TESTDT[eval(J(a)),v], data.table(a="d",v=3:6,key="a"), warning=deprecated_warn) # the eval() enabled you to use the 'a' in the calling scope, not 'a' in the TESTDT. TO DO: document this.
+test(off-71, TESTDT[eval(SJ(a)),v], data.table(a="d",v=3:6,key="a"), warning=deprecated_warn)
+test(off-72, TESTDT[eval(CJ(a)),v], data.table(a="d",v=3:6,key="a"), warning=deprecated_warn)
+
+DT = data.table(a=rep(1:3,each=2),b=c(TRUE,FALSE),v=1:6)
+setkey(DT,a,b)
+test(off-180, DT[J(2,FALSE),v]$v, 4L, warning=deprecated_warn)
+
+DT = data.table(A = c("o", "x"), B = 1:10, key = "A")
+test(off-183, DT[J(unique(A)), B]$B, DT$B, warning=deprecated_warn)
+
+# Tests of bug 1015 highlight by Harish
+# See thread "'by without by' now heeds nomatch=NA"
+# Tests 185-201 were added in above next to originals
+x <- data.table(a=c("a","b","d","e"),b=c("A","A","B","B"),d=c(1,2,3,4), key="a,b")
+y <- data.table(g=c("a","b","c","d"),h=c("A","A","A","A"))
+test(off-202, x[y], suppressWarnings(x[y,mult="all"]), warning=deprecated_warn)
+test(off-203, x[y,d]$d, c(1,2,NA,NA), warning=deprecated_warn)
+test(off-204, x[y,list(d)], suppressWarnings(x[y,d]), warning=deprecated_warn)
+test(off-205, x[y,list(d),mult="all"][,d], c(1,2,NA,NA), warning=deprecated_warn)
+
+DF = data.frame(a=LETTERS[1:10], b=1:10, stringsAsFactors=FALSE)
+DT = data.table(DF)
+setkey(DT,a) # used to complain about character
+test(off-215, DT["C",b]$b, 3L, warning=deprecated_warn)
+DT = data.table(DF,key="a")
+test(off-216, DT["C",b]$b, 3L, warning=deprecated_warn)
+DT = data.table(a=c(1,2,3),v=1:3,key="a")
+test(off-217, DT[J(2),v]$v, 2L, warning=deprecated_warn)
+DT = data.table(a=c(1,2.1,3),v=1:3,key="a")
+test(off-218, DT[J(2.1),v]$v, 2L, warning=deprecated_warn)
+
+DT = data.table(a=1:5,b=6:10,key="a")
+q = quote(a>3)
+test(off-220, DT[eval(q),b], 9:10)
+test(off-221, DT[eval(parse(text="a>4")),b], 10L)
+test(off-222, DT[eval(parse(text="J(2)")),b]$b, 7L, warning=deprecated_warn)
+
+# Join Inherited Scope, and X[Y] including Y's non-join columns
+X=data.table(a=rep(1:3,c(3,3,2)),foo=1:8,key="a")
+Y=data.table(a=2:3,bar=6:7)
+test(off-239, X[Y,sum(foo)], data.table(a=2:3,V1=c(15L,15L),key="a"), warning=deprecated_warn)
+test(off-240, X[Y,sum(foo*bar)], data.table(a=2:3,V1=c(90L,105L),key="a"), warning=deprecated_warn)
+test(off-241, X[Y], data.table(a=rep(2:3,3:2),foo=4:8,bar=rep(6:7,3:2),key="a"), warning=deprecated_warn)
+test(off-242, X[Y,list(foo,bar)][,sum(foo*bar)], 195L, warning=deprecated_warn)
+
+
+X=data.table(a=rep(LETTERS[1:2],2:3),b=1:5,v=10:14,key="a,b")
+test(off-246, X["A"], {tt=X[1:2];setkey(tt,a);tt}, warning=deprecated_warn) # key="a,b" is retained in 1.9.2 and 1.9.4; just old.bywithoutby=TRUE in 1.9.4 keeps "a" only, unfortunately.
+
+# Test .N==0 with nomatch=NA|0
+DT = data.table(a=1:2,b=1:6,key="a")
+test(off-349, DT[J(2:3),.N,nomatch=NA]$N, c(3L,0L), warning=deprecated_warn)
+test(off-350, DT[J(2:3),.N,nomatch=0]$N, c(3L), warning=deprecated_warn)
+# Test first .N==0 with nomatch=NA|0
+test(off-350.1, DT[J(4),.N]$N, 0L, warning=deprecated_warn)
+test(off-350.2, DT[J(0:4),.N]$N, c(0L,3L,3L,0L,0L), warning=deprecated_warn)
+
+# Test printing on nested data.table, bug #1803
+DT = data.table(x=letters[1:3],y=list(1:10,letters[1:4],data.table(a=1:3,b=4:6)))
+test(off-558, capture.output(print(DT)), c(" x y","1: a 1,2,3,4,5,6,","2: b a,b,c,d","3: c <data.table>"))
+test(off-559, setkey(DT,x)["a",y][[2]][[1]], 1:10, warning=deprecated_warn) # y is symbol representing list column, specially detected in dogroups
+
+# another test linked from #2162
+DT = data.table(x=rep(c("a","b","c"),each=3), y=c(1L,3L,6L), v=1:9, key="x")
+test(off-725, DT[c("a","b","d"),v][,list(v)], DT[J(c("a","b","d")),"v",with=FALSE], warning=deprecated_warn) # unfiled bug fix for NA matches; see NEWS 1.8.3
+
+DT = data.table(a=1:3,b=1:6,key="a")
+test(off-869, suppressWarnings(DT[J(2,42,84),print(.SD)]), output=" b1: 22: 5.*Empty data.table (0 rows) of 3 cols: a,V2,V3")
+
+rm(deprecated_warn)
+options(datatable.old.bywithoutby=FALSE)
+# End (test reverting to old bywithoutby behaviour) TO DO: delete these tests after Sep 2015
+#====================================
+
+
+oldv = options(datatable.fread.datatable = FALSE)
+test(1379.1, fread("A,B\n1,3\n2,4\n"), data.frame(A=1:2,B=3:4))
+test(1379.2, fread("A,B\n1,3\n2,4\n",data.table=TRUE), data.table(A=1:2,B=3:4))
+options(datatable.fread.datatable = TRUE)
+test(1379.3, fread("A,B\n1,3\n2,4\n",data.table=FALSE), data.frame(A=1:2,B=3:4))
+options(oldv)
+
+# That that RHS of == is coerced to x's type before bmerge in auto index. Package vardpoor does this in example(linqsr)
+DT = data.table(a=c(0,0,1,1,0,0), b=1:6) # 'a' type double here, as it is in vardpoor
+test(1380, DT[a==TRUE], DT[3:4])
+
+# Fix #847, as.data.table.list and character(0) issue
+x <- data.table(a=character(0), b=character(0), c=numeric(0))
+setkey(x, a, b)
+test(1381, x[J("foo", character(0)), nomatch=0L], x, warning="Item 2 is of size 0 but maximum size is 1,")
+
+# Fix for #813 and #758
+DT = data.table(x = 1:2)
+test(1382.1, DT[c(FALSE, FALSE), list(x, 3:4)], data.table(x=integer(0), V2=integer(0)))
+DT <- data.table(id = c("a", "a", "b", "b"), var = c(1.1, 2.5, 6.3, 4.5), key="id")
+test(1382.2, DT["c", list(id, check = any(var > 3)), nomatch=0L], data.table(id=character(0), check=logical(0), key="id"))
+test(1382.3, DT[c(FALSE), id], character(0))
+DT <- DT[1:3]; setkey(DT, id)
+test(1382.4, DT[c("c", "b"), list(id, check = any(var > 3)), nomatch=0L], data.table(id="b", check=TRUE, key="id"))
+
+# Fix for #742 - allow.cartesian should be ignored if `i` has no duplicates.
+DT <- data.table(id=rep(letters[1:2], 2), var = rnorm(4), key="id")
+test(1383.1, DT[letters[1:3], list(var)], DT[1:5, list(var)])
+# Fix for #800 - allow.cartesian should be ignored if jsub[1L] has `:=`. TODO: maybe still warn if `i` has duplicates?
+DT=data.table(id=c(1,1), date=c(1992,1991), value=c(4.1,4.5), key="id")
+test(1383.2, copy(DT)[DT, a:=1], DT[, a := 1])
+
+# Fix for #476 and #825
+if ("package:reshape" %in% search()) {
+ DT <- data.table(ID = c(611557L, 611557L, 611557L, 894125L, 894125L, 894125L, 894125L, 894125L, 898856L, 898856L, 898856L, 898856L, 898856L, 898856L, 898899L, 898899L, 898899L), DATUM = structure(c(16101, 16071, 16261, 16104, 16133, 16167, 16201, 16236, 16089, 16118, 16147, 16176, 16236, 16208, 16163, 16125, 16209), class = "Date"), N = c(25L, 9L, 23L, 29L, 26L, 26L, 27L, 28L, 39L, 39L, 38L, 36L, 40L, 39L, 19L, 20L, 19L), rank = c(2, 1, 3, 1, 2, 3, 4, 5, 1, 2, 3, 4, 6, 5, 2, 1, 3))
+ ans = cast(DT, ID ~ rank, value = "DATUM")
+ test(1383.3, names(DT), c("ID", "DATUM", "N", "rank"))
+} else {
+ cat("Tests 1383.3 not run. If required call library(reshape) first.\n")
+}
+
+if ("package:caret" %in% search()) {
+ DT <- data.table(x = rnorm(10), y = rnorm(10))
+ cv.ctrl <- trainControl(method = 'repeatedcv', number = 5, repeats = 1)
+ fit <- train(y ~ x, data = DT, 'lm', trControl = cv.ctrl)
+ test(1383.4, names(DT), c("x", "y"))
+} else {
+ cat("Tests 1383.4 not run. If required call library(caret) first.\n")
+}
+
+# Somehow DT[col==max(col)] was never tested, broken by auto-indexing new in v1.9.4, #858
+DT = data.table(a = c(1,1,1,2,2,2,3,3,3), b = rnorm(9))
+test(1384, DT[a == max(a)], DT[7:9])
+
+# Dups on RHS of == or %in%
+DT = data.table(id = paste("id",1:5,sep=""))
+id.sub = c("id1", "id2", "id3", "id3", "id4") # deliberate dup
+test(1385.1, DT[id %in% id.sub], DT[1:4])
+test(1385.2, DT[id == id.sub], DT[1:3])
+
+# reserved class attributes conflict with auto index names, #
+DT = data.table(class=c('a','b'), x=c(1,2))
+test(1386, DT[class=='a'], DT[1])
+
+# Fix for #774 - parsing a$b() in 'j'
+DT = data.table(x=1:5, y=6:10)
+ll = list(foo = function() 1L)
+test(1387.1, copy(DT)[, z := ll$foo()], copy(DT)[, z:=1L])
+test(1387.2, copy(DT)[, z := ll[[1L]]()], copy(DT)[, z:=1L])
+
+# Fix for #811 - ITime and negative integers formats wrong result.
+x = c(1L, -1L, -3700L)
+class(x) = "ITime"
+test(1388, as.character(x), c("00:00:01", "-00:00:01", "-01:01:40"))
+
+# Fix for #880. Another eval(parse(.)) issue.
+DT <- as.data.table(iris)
+DT[, foo := "Species"]
+test(1389, copy(DT)[,bar := eval(parse(text=foo[1]), envir=.SD)], copy(DT)[, bar := Species])
+
+# Fix for foverlaps() floating point interval (double) types. Should increment them by machine tolerance, not by 1L
+DT1 = data.table(start=c(0.88), end=c(0.88))
+DT2 = data.table(start=c(0.26, 0.5, 0.55, 0.7), end=c(0.61, 0.88, 0.88-.Machine$double.eps^0.5, 0.89))
+setkey(DT2)
+test(1390.1, foverlaps(DT1, DT2, which=TRUE), data.table(xid=1L, yid=c(2L, 4L)))
+DT1 = data.table(start=c(0.3,0.5), end=c(0.3,0.5))
+DT2 = data.table(start=c(0.4), end=c(0.4))
+setkey(DT2)
+test(1390.2, foverlaps(DT1, DT2, which=TRUE), data.table(xid=1:2, yid=as.integer(c(NA, NA))))
+tt = c( as.POSIXct('2011-10-11 07:49:36'), as.POSIXct('2011-10-11 07:49:37'))
+DT1 = data.table(start=tt, end=tt)
+DT2 = data.table(start=tt[1], end=tt[1])
+setkey(DT2)
+test(1390.3, foverlaps(DT1, DT2, which=TRUE), data.table(xid=1:2, yid=as.integer(c(1L, NA))))
+tt = c( as.POSIXct('2011-10-11 07:49:36.3'), as.POSIXct('2011-10-11 07:49:37.4'), as.POSIXct('2011-10-11 07:49:37.5'))
+DT1 = data.table(start=tt, end=tt)
+DT2 = data.table(start=tt[2], end=tt[2])
+setkey(DT2)
+test(1390.4, foverlaps(DT1, DT2, which=TRUE), data.table(xid=1:3, yid=as.integer(c(NA, 1L, NA))))
+tt = c( as.POSIXct('2011-10-11 07:49:36.0003'), as.POSIXct('2011-10-11 07:49:36.0199'), as.POSIXct('2011-10-11 07:49:36.0399'))
+DT1 = data.table(start=tt, end=tt)
+DT2 = data.table(start=tt[2], end=tt[2])
+setkey(DT2)
+test(1390.5, foverlaps(DT1, DT2, which=TRUE), data.table(xid=1:3, yid=as.integer(c(NA, 1, NA))))
+
+# Fix for #891. 'subset' and duplicate names.
+# duplicate column names rule - if column numbers, extract the right column. If names, extract always the first column
+DT = data.table(V1=1:5, V2=6:10, V3=11:15)
+setnames(DT, c("V1", "V2", "V1"))
+test(1391.1, subset(DT, select=c(3L,2L)), DT[, c(3L, 2L), with=FALSE])
+test(1391.2, subset(DT, select=c("V2", "V1")), DT[, c("V2", "V1"), with=FALSE])
+
+# Test faster version of na.omit() using is_na.
+DT = data.table(x=sample(c(1:2, NA), 30, TRUE), y=sample(c(1:5, NA, NaN), 30, TRUE))
+test(1392.1, na.omit(DT), DT[!is.na(x) & !is.na(y)])
+# added 'invert = ', a logical argument which when TRUE returns rows that has any NAs instead.
+test(1392.2, na.omit(DT, invert=TRUE), DT[is.na(x) | is.na(y)])
+
+# Fix for #899. Mix of ordered and normal factors where normal factors in more than 1 data.table has identical levels.
+DT1 = data.table(A = factor(INT(7,8,7,8,7)), B = factor(6:10), C = 0)
+DT2 = data.table(D = ordered(1:5), A = factor(INT(1:2,1:2,1L)), C = 0)
+DT3 = data.table(A = factor(INT(7:8)), C = 0)
+ans = data.table(A=factor(INT(7,8,7,8,7,1,2,1,2,1,7,8), levels=c("7", "8", "1", "2")), B=factor(INT(6:10, rep(NA,7))), C=0, D=ordered(INT(rep(NA,5), 1:5, rep(NA,2))))
+test(1393.1, rbindlist(list(DT1, DT2, DT3), fill = TRUE), ans)
+# test for #591 (R-Forge #2491)
+ans[, ID := rep(1:3, c(5,5,2))]
+setcolorder(ans, c("ID", LETTERS[1:4]))
+test(1393.2, rbindlist(list(DT1, DT2, DT3), fill = TRUE, idcol="ID"), ans)
+
+# Tests for na.omit.data.table (faster version + with a 'cols=' new argument)
+col = c(1:2, NA_integer_)
+DT = data.table(a=sample(col, 20, TRUE), b=as.numeric(sample(col,20,TRUE)), c=as.logical(sample(col,20,TRUE)), d=as.character(sample(col,20,TRUE)))
+# can't use complete.cases on bit64... will have to test integer64 separately.
+# if ("package:bit64" %in% search()) {
+# DT[, e := as.integer64(sample(col,20,TRUE))]
+# }
+test_no = 1394
+for (i in seq_along(DT)) {
+ combn(names(DT), i, function(cols) {
+ ans1 = na.omit(DT, cols=cols)
+ ans2 = DT[complete.cases(DT[, cols, with=FALSE])]
+ test_no <<- signif(test_no+.001, 7)
+ test(test_no, ans1, ans2)
+ 0L
+ })
+}
+
+# That data.table-unaware code in packages like knitr still work
+if ("package:knitr" %in% search()) {
+ DT = data.table(x=1, y=2)
+ test(1395, kable(DT), output="x.*y.*1.*2") # kable in knitr v1.6 calls DF[...] syntax
+} else {
+ cat("Test 1395 not run. If required call library(knitr) first.\n")
+}
+
+# dropping secondary keys on update or delete
+DT = data.table(a=1:3, b=4:6)
+test(1396, DT[a==2, verbose=TRUE], DT[2], output="Creating new index 'a'")
+test(1397, DT[b==6, verbose=TRUE], DT[3], output="Creating new index 'b'")
+test(1398, DT[b==6, verbose=TRUE], DT[3], output="Using existing index 'b'")
+test(1399, key2(DT), c("a","b"))
+test(1400, DT[2, a:=4L, verbose=TRUE], data.table(a=c(1L,4L,3L),b=4:6), output="Dropping index 'a' due to update on 'a' (column 1)")
+test(1401, key2(DT), "b")
+test(1402, DT[,b:=NULL,verbose=TRUE], data.table(a=c(1L,4L,3L)), output="Dropping index 'b' due to delete of 'b' (column 2)")
+test(1403, key2(DT), NULL)
+DT = data.table(x=1:5)
+test(1404, DT[, y := x <= 2L], data.table(x=1:5, y=c(TRUE,TRUE,FALSE,FALSE,FALSE)))
+test(1405, DT[y == TRUE, .N, verbose=TRUE], 2L, output="Creating new index")
+test(1406, DT[, y := x <= 3L, verbose=TRUE], data.table(x=1:5, y=c(TRUE,TRUE,TRUE,FALSE,FALSE)), output="Dropping index")
+test(1407, DT[y == TRUE, .N], 3L)
+DT = data.table(x=1:5, y=10:6)
+test(1408, DT[x==3,verbose=TRUE], DT[3], output="Creating")
+test(1409, key2(DT), "x")
+set(DT,1:3,1L,-10L)
+test(1410, key2(DT), NULL)
+test(1411, DT[x==5], DT[5])
+setorder(DT, y)
+test(1412, key2(DT), NULL)
+test(1413, DT[x==5], DT[1])
+DT = data.table(foo=1:3, bar=4:6, baz=9:7)
+set2key(DT,foo,bar,baz)
+test(1414, key2(DT), c("foo__bar__baz"))
+test(1415, DT[2,bar:=10L,verbose=TRUE], output="Dropping index 'foo__bar__baz' due to update on 'bar'") # test middle
+test(1416, key2(DT), NULL)
+set2key(DT,foo,bar,baz)
+test(1417, DT[2,baz:=10L,verbose=TRUE], output="Dropping index 'foo__bar__baz' due to update on 'baz'") # test last
+set2key(DT,bar,baz)
+test(1418, DT[2,c("foo","bar"):=10L,verbose=TRUE], output="Dropping index.* due to update on 'bar'") # test 2nd to 1st
+set2key(DT,bar,baz)
+test(1419, DT[2,c("foo","baz"):=10L,verbose=TRUE], output="Dropping index.* due to update on 'baz'") # test 2nd to 2nd
+
+# setnames updates secondary key
+DT = data.table(a=1:5,b=10:6)
+set2key(DT,b)
+test(1420, key2(DT), "b")
+setnames(DT,"b","foo")
+test(1421, key2(DT), "foo")
+test(1422, DT[foo==9, verbose=TRUE], DT[2], output="Using existing index 'foo'")
+set2key(DT,a,foo)
+test(1423, key2(DT), c("foo","a__foo")) # tests as well that order of attributes is retained although we don't use that property currently.
+test(1424, key2(setnames(DT,"foo","bar")), c("bar","a__bar"))
+test(1425, key2(setnames(DT,"a","baz")), c("bar","baz__bar"))
+test(1426, DT[baz==4L, verbose=TRUE], output="Creating new index 'baz'")
+test(1427, key2(DT), c("bar","baz__bar","baz"))
+test(1428, DT[bar==9L, verbose=TRUE], output="Using existing index 'bar'")
+test(1429, key2(setnames(DT,"bar","a")), c("baz", "a", "baz__a"))
+
+# Finalised == and %in% optimization in i
+DT = data.table(a=1:3,b=c(0,2,3,0,0,2))
+test(1430, DT[a==1:2], error="RHS of == is length 2 which is not 1 or nrow (6). For robustness, no recycling is allowed (other than of length 1 RHS). Consider %in% instead.")
+test(1431, DT[a %in% 1:2], DT[c(1,2,4,5)])
+test(1432, DT[a==b], DT[2:3])
+test(1433, DT[a %in% b], DT[c(2,3,5,6)])
+test(1434, DT[a==b+1], DT[c(1,4,6)])
+test(1435, DT[b==max(a)], DT[3])
+test(1436, DT[a==2,verbose=TRUE], DT[c(2,5)], output="Coercing double column i.'V1' to integer")
+DT[,a:=factor(letters[a])]
+test(1437, DT[a==factor("b"),verbose=TRUE], DT[c(2,5)], output="Creating new index 'a'")
+
+# fread dec=',' e.g. France
+test(1438, fread("A;B\n1;2,34\n", dec="12"), error="dec must be a single character")
+
+test(1439, (if (base::getRversion()<"3.3.0") suppressWarnings else identity)(fread("A;B\n1;2,34\n", dec="1")),
+ error="Unable to change to a locale which provides the desired dec") # this test runs on many machines so chose a dec for this test which is sure not to be valid in any locale
+test(1440, fread("A;B\n1;2,34\n", sep=".", dec="."), error="The two arguments to fread 'dec' and 'sep' are equal ('.')")
+if (.Platform$OS.type=="windows" ||
+ (!inherits(tt <- try(system("locale -a", intern=TRUE)), "try-error") && "fr_FR.utf8" %in% tt )) {
+ # e.g. on Matt's machine where I've installed fr_FR.utf8 which has dec=","
+ old = options(datatable.fread.dec.locale=if (.Platform$OS.type=="unix") "fr_FR.utf8" else "French_France.1252")
+ oldlocale = Sys.getlocale("LC_NUMERIC")
+ test(1441, fread("A;B\n1;2,34\n", dec=",", verbose=TRUE), data.table(A=1L, B=2.34), output="success!")
+ test(1442, Sys.getlocale("LC_NUMERIC"), oldlocale) # locale restored after success
+ test(1443.1, fread("A;B\n1;2,34\n", dec=",", sep=",", verbose=TRUE), error="'dec' and 'sep' are equal", output="success!")
+ test(1443.2, Sys.getlocale("LC_NUMERIC"), oldlocale) # locale restored after error. [ouput check in 1443.1 ensures it was changed]
+
+ # sep=".", issue #502
+ input = paste( paste("192.168.1.", 1:10, sep=""), collapse="\n")
+ test(1444.1, fread(input, sep=".", dec="*"), error="Unable to change to a locale which provides the desired dec")
+ test(1444.2, fread(input, sep="."), ans<-data.table(V1=192L,V2=168L,V3=1L,V4=1:10)) # by default, dec="," when sep="."
+ test(1444.3, fread(paste(paste("192. 168. 1. ", 1:10, sep = ""), collapse="\n"), sep="."), ans)
+ test(1444.4, fread(paste(paste("Hz.BB.GHG.", 1:10, sep = ""), collapse="\n"), sep="."), data.table(V1="Hz",V2="BB",V3="GHG",V4=1:10))
+
+ options(old) # return to default set it .onLoad
+} else {
+ cat("Tests 1441-1444 not run. If required install the 'fr_FR.utf8' locale.\n")
+}
+
+# doubled quote inside a quoted field followed by an embedded newline
+# This file is 36 rows to move that line outside the top, middle and bottom 5 test rows
+test(1445, fread("doublequote_newline.csv")[7:10], data.table(A=c(1L,1L,2L,1L), B=c("a","embedded \"\"field\"\"\nwith some embedded new\nlines as well","not this one","a")))
+# the example from #489 directly :
+test(1446, fread('A,B,C\n233,"AN ""EMBEDDED"" QUOTE FIELD",morechars\n'), data.table(A=233L, B='AN ""EMBEDDED"" QUOTE FIELD', C='morechars'))
+
+# # unescaped quoted subregion followed by newline
+# # commented this test for now as the logic now is to redirect to normal checks
+# test(1447, fread('A,B,C\n233,"an unescaped "embedded"
+# region followed by newline",morechars\n'))
+
+# when detecting types ...
+test(1448.1, fread('A,B\n1,"embedded""\nquote"\n2,should be ok\n'),
+ data.table(A=1:2,B=c('embedded""\nquote','should be ok')))
+test(1448.2, fread('A,B\n1,"embedded""
+quote"\n2,should be ok\n'),
+ data.table(A=1:2,B=c('embedded""
+quote','should be ok')))
+
+if ("package:bit64" %in% search()) {
+ # quoted multiline (scrambled data thanks to #810)
+ test(1449, fread("quoted_multiline.csv")[c(1,43:44),c(1,22:24),with=FALSE],
+ data.table(GPMLHTLN=as.integer64(c("3308386085360","3440245203140","1305220146734")),
+ BLYBZ = c(0L,4L,6L),
+ ZBJBLOAJAQI = c("LHCYS AYE ZLEMYA IFU HEI JG FEYE","",""),
+ JKCRUUBAVQ = c("",".\\YAPCNXJ\\004570_850034_757\\VWBZSS_848482_600874_487_PEKT-6-KQTVIL-7_30\\IRVQT\\HUZWLBSJYHZ\\XFWPXQ-WSPJHC-00-0770000855383.KKZ","")))
+}
+
+# Fix for #927
+DT = data.table(x=1L, y=2L)
+test(1450, DT[, set(.SD, j="x", value=10L)], error=".SD is locked. Updating .SD by reference using := or set")
+
+# Tests for shallow copy taking cols argument - not exported yet.
+DT = setDT(lapply(1:5, sample, 10, TRUE))
+ans1 = sapply(DT, address)
+fans2 = function(DT, cols=NULL) sapply(shallow(DT, cols), address)
+test(1451.1, ans1, fans2(DT)) # make sure default/old functionality is intact
+test(1451.2, ans1[3:4], fans2(DT, 3:4)) # using integer column numbers
+test(1451.3, ans1[c(5,2)], fans2(DT, c(5,2))) # using numeric column numbers
+test(1451.4, ans1[c(4,2,4)], fans2(DT,c(4,2,4))) # using duplicate column numbers
+test(1451.5, ans1[3:2], fans2(DT, c("V3", "V2"))) # using column names
+test(1451.6, ans1[c(3,3)], fans2(DT, c("V3", "V3"))) # using duplicate column names
+test(1451.7, shallow(DT, integer(0)), null.data.table()) # length-0 input work as intended as well.
+test(1451.8, shallow(DT, character(0)), null.data.table()) # length-0 input work as intended as well.
+
+test(1452, fread("notexist.csv"), error="File 'notexist.csv' does not exist. Include one or more spaces to consider the input a system command.")
+
+# Test for #802
+test(1453, fread("fread_line_error.csv"), error="Expecting 24 cols, but line 12 contains")
+
+# no-sep-found => sep="\n", use case for this in #738
+test(1454.1, fread('"Foo"`"Bar"\n1`2\n',sep="`"), data.table(Foo=1L,Bar=2L))
+test(1454.2, fread('"Foo"\n1\n',sep="`"), data.table(Foo=1L))
+
+# Fix for #958 - Don't create secondary keys on .SD
+DT <- data.table(a=c(1, 1, 1, 0, 0), b=c("A", "B", "A1", "A", "B"))
+test(1455, DT[, nrow(.SD[b == 'B']), by=.(a)], data.table(a=c(1,0), V1=1L))
+
+# Test for chmatch2 bug fix
+x1 = c("b", "a", "d", "a", "c", "a")
+x2 = c("a", "a", "a")
+x3 = c("d", "a", "a", "d", "a")
+table = rep(letters[1:3], each=2)
+test(1456.1, chmatch2(x1, table), as.integer(c(3,1,NA,2,5,NA)))
+test(1456.2, chmatch2(x2, table), as.integer(c(1,2,NA)))
+test(1456.3, chmatch2(x3, table), as.integer(c(NA,1,2,NA,NA)))
+
+# Add tests for which_
+x = sample(c(-5:5, NA), 25, TRUE)
+test(1458.1, which(x > 0), which_(x > 0)) # default is TRUE
+test(1458.2, which(x > 0), which_(x > 0, TRUE)) # test explicitly
+test(1458.3, which(!x > 0), which_(x > 0, FALSE))
+
+# Fix for #982. Testing subsetDT on complex/raw vectors, and added tests for other types.
+DT = data.table(a=c(1:3,NA_integer_), b=c(1,2,3,NA), c=as.complex(c(1:3,NA)), d=as.raw(1:4),
+ e=as.list(1:4), f=c(FALSE,FALSE,TRUE,NA), g=c("a", "b", "c", NA_character_))
+test(1459.1, .Call("CsubsetDT", DT, which(DT$a > 2), seq_along(DT)), setDT(as.data.frame(DT)[3, , drop=FALSE]))
+test(1459.2, .Call("CsubsetDT", DT, which(DT$b > 2), seq_along(DT)), setDT(as.data.frame(DT)[3, , drop=FALSE]))
+test(1459.3, .Call("CsubsetDT", DT, which(Re(DT$c) > 2), seq_along(DT)), setDT(as.data.frame(DT)[3, , drop=FALSE]))
+test(1459.4, .Call("CsubsetDT", DT, which(DT$d > 2), seq_along(DT)), setDT(as.data.frame(DT)[3:4, , drop=FALSE]))
+test(1459.5, .Call("CsubsetDT", DT, which(DT$f), seq_along(DT)), setDT(as.data.frame(DT)[3, , drop=FALSE]))
+test(1459.6, .Call("CsubsetDT", DT, which(DT$g == "c"), seq_along(DT)), setDT(as.data.frame(DT)[3, , drop=FALSE]))
+test(1459.7, .Call("CsubsetDT", DT, which(DT$a > 2 | is.na(DT$a)), seq_along(DT)), setDT(as.data.frame(DT)[3:4,]))
+test(1459.8, .Call("CsubsetDT", DT, which(DT$b > 2 | is.na(DT$b)), seq_along(DT)), setDT(as.data.frame(DT)[3:4,]))
+test(1459.9, .Call("CsubsetDT", DT, which(Re(DT$c) > 2 | is.na(DT$c)), seq_along(DT)), setDT(as.data.frame(DT)[3:4,]))
+test(1459.10, .Call("CsubsetDT", DT, which(DT$f | is.na(DT$f)), seq_along(DT)), setDT(as.data.frame(DT)[3:4,]))
+test(1459.11, .Call("CsubsetDT", DT, which(DT$g == "c" | is.na(DT$g)), seq_along(DT)), setDT(as.data.frame(DT)[3:4,]))
+test(1459.12, .Call("CsubsetDT", DT, 5L, seq_along(DT)), setDT(as.data.frame(DT)[5,]))
+
+# Test for na.omit with list, raw and complex types
+DT = data.table(x=c(1L,1L,NA), y=c(NA, NA, 1), z=as.raw(1:3), w=list(1,NA,2), v=c(1+5i, NA, NA))
+test(1460.1, na.omit(DT, cols="w"), DT)
+test(1460.2, na.omit(DT, cols="v"), DT[1])
+test(1460.3, na.omit(DT, cols=c("v", "y")), DT[0])
+test(1460.4, na.omit(DT, cols=c("z", "v")), DT[1])
+test(1460.5, na.omit(DT, cols=c("w", "v")), DT[1])
+
+# Fix for #985
+DT = data.table(x=c("a", "a", "b", "b"), v1=sample(4), v2=sample(4))
+test(1461.1, DT[, c(lapply(.SD, mean), lapply(.SD, sd)), by=x],
+ DT[, c(lapply(.SD, function(x) mean(x)), lapply(.SD, function(x) sd(x))), by = x])
+
+
+# Tests for #994
+DT = data.table(x=c("a", "a", "b", "b"), v1=sample(4), v2=sample(4))
+cols = c("v1", "v2")
+test(1462.1, DT[, mget(cols, as.environment(-1))], DT[, cols, with=FALSE]) # as.environment needed for testing on pre-R3.0.0 which we don't want to depend on yet
+test(1462.2, DT[, mget(cols[1], as.environment(-1))], DT[, cols[1], with=FALSE])
+test(1462.3, DT[, sum(unlist(mget(cols, as.environment(-1)))), by=x], DT[, sum(unlist(.SD)), by=x, .SDcol=cols])
+
+# test for 'shift'
+x=1:5
+y=factor(x)
+test(1463.1, shift(x,1L), as.integer(c(NA, 1:4)))
+test(1463.2, shift(x,1:2), list(as.integer(c(NA, 1:4)), as.integer(c(NA, NA, 1:3))))
+test(1463.3, shift(x,1L, 0L), as.integer(c(0L, 1:4)))
+test(1463.4, shift(x,1L, type="lead"), as.integer(c(2:5, NA)))
+test(1463.5, shift(x,1:2, type="lead"), list(as.integer(c(2:5, NA)), as.integer(c(3:5, NA, NA))))
+test(1463.6, shift(x,1L, 0L, type="lead"), as.integer(c(2:5, 0L)))
+test(1463.7, shift(y,1L), factor(c(NA,1:4), levels=1:5))
+test(1463.8, shift(y,1L, type="lead"), factor(c(2:5, NA), levels=1:5))
+
+x=as.numeric(x)
+test(1463.9, shift(x,1L), as.numeric(c(NA, 1:4)))
+test(1463.10, shift(x,1:2), list(as.numeric(c(NA, 1:4)), as.numeric(c(NA, NA, 1:3))))
+test(1463.11, shift(x,1L, 0L), as.numeric(c(0L, 1:4)))
+test(1463.12, shift(x,1L, type="lead"), as.numeric(c(2:5, NA)))
+test(1463.13, shift(x,1:2, type="lead"), list(as.numeric(c(2:5, NA)), as.numeric(c(3:5, NA, NA))))
+test(1463.14, shift(x,1L, 0L, type="lead"), as.numeric(c(2:5, 0L)))
+
+if ("package:bit64" %in% search()) {
+ x=as.integer64(x)
+ test(1463.15, shift(x,1L), as.integer64(c(NA, 1:4)))
+ test(1463.16, shift(x,1:2), list(as.integer64(c(NA, 1:4)), as.integer64(c(NA, NA, 1:3))))
+ test(1463.17, shift(x,1L, 0L), as.integer64(c(0L, 1:4)))
+ test(1463.18, shift(x,1L, type="lead"), as.integer64(c(2:5, NA)))
+ test(1463.19, shift(x,1:2, type="lead"), list(as.integer64(c(2:5, NA)), as.integer64(c(3:5, NA, NA))))
+ test(1463.20, shift(x,1L, 0L, type="lead"), as.integer64(c(2:5, 0L)))
+}
+
+x=as.character(x)
+test(1463.21, shift(x,1L), as.character(c(NA, 1:4)))
+test(1463.22, shift(x,1:2), list(as.character(c(NA, 1:4)), as.character(c(NA, NA, 1:3))))
+test(1463.23, shift(x,1L, 0L), as.character(c(0L, 1:4)))
+test(1463.24, shift(x,1L, type="lead"), as.character(c(2:5, NA)))
+test(1463.25, shift(x,1:2, type="lead"), list(as.character(c(2:5, NA)), as.character(c(3:5, NA, NA))))
+test(1463.26, shift(x,1L, 0L, type="lead"), as.character(c(2:5, 0L)))
+
+x=c(TRUE,FALSE,TRUE,FALSE,TRUE)
+test(1463.27, shift(x,1L), c(NA, x[-5L]))
+test(1463.28, shift(x,1:2), list(c(NA, x[-5L]), c(NA, NA, x[-(4:5)])))
+test(1463.29, shift(x,1L, 0L), c(FALSE, x[-5L]))
+test(1463.30, shift(x,1L, type="lead"), c(x[-1L], NA))
+test(1463.31, shift(x,1:2, type="lead"), list(c(x[-1L],NA), c(x[-(1:2)],NA,NA)))
+test(1463.32, shift(x,1L, 0L, type="lead"), c(x[-(1)], FALSE))
+
+# Fix for #1009 segfault in shift
+val = runif(1)
+test(1463.33, shift(val, 2L), NA_real_)
+test(1463.34, shift(val, 2L, type="lead"), NA_real_)
+
+test(1463.35, shift(1:5, -1L), error="n must be non-negative integer")
+test(1463.36, shift(1:5, 1L, fill=c(1:2)), error="fill must be a vector of length")
+
+# add tests for date and factor?
+
+# test for 'give.names=TRUE' on vectors
+x = 1:10
+nm = c("x_lag_1", "x_lag_2")
+ans = list(as.integer(c(NA, 1:9)), as.integer(c(NA, NA, 1:8)))
+setattr(ans, 'names', nm)
+test(1463.27, shift(x, 1:2, give.names=TRUE), ans)
+
+# FR #686
+DT = data.table(a=rep(c("A", "B", "C", "A", "B"), c(2,2,3,1,2)))
+DT[, b := as.integer(factor(a))][, c := as.numeric(factor(a))]
+test(1464.1, rleidv(DT, "a"), c(1L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 5L, 5L))
+test(1464.2, rleid(DT$a), c(1L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 5L, 5L))
+test(1464.3, rleidv(DT, "b"), c(1L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 5L, 5L))
+test(1464.4, rleid(DT$b), c(1L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 5L, 5L))
+test(1464.5, rleidv(DT, "c"), c(1L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 5L, 5L))
+test(1464.6, rleid(DT$c), c(1L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 5L, 5L))
+test(1464.7, rleid(as.complex(c(1,0+5i,0+5i,1))), error="Type 'complex' not supported")
+
+# data.table-xts conversion #882
+
+if ("package:xts" %in% search()) {
+ # Date index
+ dt = data.table(index = as.Date((as.Date("2014-12-12")-49):as.Date("2014-12-12"),origin="1970-01-01"),quantity = rep(c(1:5),10),value = rep(c(1:10)*100,5))
+ xt = as.xts(matrix(data = c(dt$quantity, dt$value),ncol = 2,dimnames = list(NULL,c("quantity","value"))),order.by = dt$index)
+ dt_xt = as.data.table(xt)
+ xt_dt = as.xts.data.table(dt)
+ test(1465.1, all.equal(dt, dt_xt, check.attributes = FALSE))
+ test(1465.2, xt, xt_dt)
+ # POSIXct index
+ dt <- data.table(index = as.POSIXct(as.Date((as.Date("2014-12-12")-49):as.Date("2014-12-12"),origin="1970-01-01"),origin="1970-01-01"),quantity = rep(c(1:5),10),value = rep(c(1:10)*100,5))
+ xt = as.xts(matrix(data = c(dt$quantity, dt$value),ncol = 2,dimnames = list(NULL,c("quantity","value"))),order.by = dt$index)
+ dt_xt = as.data.table(xt)
+ xt_dt = as.xts.data.table(dt)
+ test(1465.3, all.equal(dt, dt_xt, check.attributes = FALSE))
+ test(1465.4, xt, xt_dt)
+}
+
+# as.data.table.default #969
+ar <- array(NA, dim=c(10,4),dimnames = list(NULL,paste("col",1:4,sep="")))
+test(1466.1, as.data.table(as.data.frame(ar)), as.data.table(ar)) # array type
+x <- rep(Sys.time(),3)
+test(1466.2, as.data.table(as.data.frame(x)), as.data.table(x)) # posix type
+
+# fix for #1001
+options(datatable.auto.index=TRUE)
+DT <- data.table(a=1:2)
+test(1467.1, DT[a==3, b:=d+1], DT)
+# restore
+options(datatable.auto.index=FALSE)
+
+# fix for first bug reported in #1006 on 'foverlaps()'
+x <- c(-0.1, 0, 0.1)
+n <- length(x)
+dt.ref <- data.table(start=x[-n], end=x[-1], key=c("start", "end"))
+dt.query <- data.table(q1=c(-0.2, -0.05, 0.05, 0.15), q2=c(-0.2, -0.05, 0.05, 0.15), key=c("q1", "q2"))
+ans=cbind(dt.ref[, .(start,end)], dt.query[2:3, .(q1,q2)])
+setkey(ans, q1,q2)
+test(1468.1, foverlaps(dt.query, dt.ref, nomatch=0L), ans)
+# fix and additional tests for #1006 following OP's follow-up.
+dt1 = data.table(x=c(-6.36917800737546, -2.19964384651646),
+ y=c(-2.19964384651646, 4.07116428752538))
+dt2 = data.table(x= 2.91816502571793, y=2.91816502571793)
+setkey(dt1)
+setkey(dt2)
+test(1468.2, foverlaps(dt2, dt1, which=TRUE), data.table(xid=1L, yid=2L))
+dt1 = data.table(x=c(-6,-3), y=c(-3,4))
+dt2 = data.table(x=3,y=3)
+setkey(dt1)
+setkey(dt2)
+test(1468.3, foverlaps(dt2, dt1, which=TRUE), data.table(xid=1L, yid=2L))
+
+
+# Fix for #1010 (discovered while fixing #1007). Don't retain key if i had no key, but irows is sorted, and roll != FALSE... See example in #1010.
+DT = data.table(x=c(-5,5), y=1:2, key="x")
+test(1469.1, key(DT[J(c(2,0)), roll=TRUE]), NULL)
+test(1469.2, key(DT[J(c(2,0)), .(x,y), roll=TRUE]), NULL)
+test(1469.3, key(DT[J(c(2,0)), y, roll=TRUE, by=.EACHI]), NULL)
+test(1469.4, key(DT[J(c(2,0))]), NULL)
+test(1469.5, key(DT[SJ(c(2,0)), roll=TRUE]), "x")
+test(1469.6, key(DT[J(c(2,0)), roll="nearest"]), NULL)
+
+# 1007 fix, dealing with Inf and -Inf correctly in rolling joins.
+DT = data.table(x=c(-Inf, 3, Inf), y=1:3, key="x")
+test(1470.1, DT[J(c(2,-Inf,5,Inf)), roll=Inf], data.table(x=c(2,-Inf,5,Inf), y=c(1, 1, 2, 3)))
+test(1470.2, DT[J(c(2,-Inf,5,Inf)), roll=10], data.table(x=c(2,-Inf,5,Inf), y=INT(c(NA, 1, 2, 3))))
+test(1470.3, DT[SJ(c(2,-Inf,5,Inf)), roll=Inf], data.table(x=c(-Inf,2,5,Inf), y=c(1, 1, 2, 3), key="x"))
+
+# 1006, second bug with -Inf, now that #1007 is fixed.
+x <- c(-Inf, -0.1, 0, 0.1, Inf)
+n <- length(x)
+dt.ref <- data.table(start=x[-n], end=x[-1], key=c("start", "end"))
+dt.query <- data.table(q1=c(-0.2, -0.05, 0.05, 0.15), q2=c(-0.2, -0.05, 0.05, 0.15), key=c("q1", "q2"))
+test(1471, foverlaps(dt.query, dt.ref), data.table(dt.ref, dt.query, key=c("q1", "q2")))
+
+# #1014 (segfault) fix
+test(1472, shift(1, 1:2, NA, 'lag'), list(NA_real_, NA_real_))
+
+# #528, type=equal simple test
+# dt1 = data.table(x=1:5, y=6:10)
+# dt2 = data.table(x=3:7, y=8:12)
+# setkey(dt1)
+# setkey(dt2)
+# test(1473, foverlaps(dt1,dt2, which=TRUE, nomatch=0L, type="equal"),
+# data.table(xid=3:5, yid=1:3))
+
+# More tests for `frankv`, #760
+DT = data.table(x=c(4, 1, 4, NA, 1, NA, 4), y=c(1, 1, 1, 0, NA, 0, 2))
+test(1474.1, frankv(DT, "y", ties.method="dense"), frankv(DT$y, ties.method="dense"))
+test(1474.2, frank(DT, y, ties.method="dense"), frank(DT$y, ties.method="dense"))
+test(1474.3, frankv(DT, "y", order=-1L, ties.method="dense"), frankv(-DT$y, ties.method="dense"))
+test(1474.4, frank(DT, -y, ties.method="dense"), frank(-DT$y, ties.method="dense"))
+
+# uniqueN, #884, part of #756 and part of #1019
+DT <- data.table(A = rep(1:3, each=4), B = rep(1:4, each=3), C = rep(1:2, 6))
+test(1475.1, uniqueN(DT), 10L)
+test(1475.2, DT[, .(uN=uniqueN(.SD)), by=A], data.table(A=1:3, uN=c(3L,4L,3L)))
+
+# preserve class attribute in GForce mean (and sum)
+DT <- data.table(x = rep(1:3, each = 3), y = as.Date(seq(Sys.Date(), (Sys.Date() + 8), by = "day")))
+test(1476.1, DT[, .(y=mean(y)), x], setDT(aggregate(y ~ x, DT, mean)))
+
+# test for 'transpose' of a list, TODO: integer64 support.
+ll = lapply(1:12, function(x) {
+ if (x <= 3) sample(10, sample(5:10, 1L))
+ else if (x > 3 & x <= 6) as.numeric(sample(101:115, sample(7:12, 1L)))
+ else if (x > 7 & x <= 9) sample(c(TRUE, FALSE), sample(7:9, 1L), TRUE)
+ else sample(letters, sample(5:10, 1L))
+})
+ans1 = setDT(transpose(ll))
+ans2 = setDT(lapply(seq_along(ans1), function(x) sapply(ll, `[`, x)))
+test(1477.1, ans1, ans2)
+ans1 = setDT(transpose(ll[4:6]))
+ans2 = setDT(lapply(seq_along(ans1), function(x) sapply(ll[4:6], `[`, x)))
+test(1477.9, ans1, ans2)
+ans1 = setDT(transpose(ll[8:9]))
+ans2 = setDT(lapply(seq_along(ans1), function(x) sapply(ll[8:9], `[`, x)))
+test(1477.10, ans1, ans2)
+# class is preserved?
+dt = data.table(x=1:5, y=6:10)
+test(1477.2, transpose(dt), as.data.table(t(as.matrix(dt))))
+# factor column coerce to character
+ll = list(factor(letters[1:5]), factor(letters[6:8]))
+test(1477.3, transpose(ll), list(c("a", "f"), c("b", "g"), c("c", "h"), c("d", NA), c("e", NA)))
+# for data.frames
+test(1477.4, transpose(data.frame(x=1:2, y=3:4)), data.frame(V1=c(1L,3L), V2=c(2L,4L)))
+# test for `tstrsplit`
+ll = sapply(ll, paste, collapse=",")
+test(1477.5, transpose(strsplit(ll, ",", fixed=TRUE)), tstrsplit(ll, ",", fixed=TRUE))
+test(1477.6, transpose(1:5), error="l must be a list")
+test(1477.7, transpose(list(as.complex(c(1, 1+5i)))), error="Unsupported column type")
+test(1477.8, transpose(list(list(1:5))), error="Item 1 of list input is")
+
+# #480 `setDT` and 'lapply'
+ll = list(data.frame(a=1), data.frame(x=1, y=2), NULL, list())
+ll <- lapply(ll, setDT)
+test(1478.1, sapply(ll, truelength), rep(100L, 4L))
+test(1478.2, sapply(ll, length), INT(1,2,0,0))
+
+# rbindlist stack imbalance issue, #980.
+test(1479, rbindlist(replicate(4,rbindlist(replicate(47, NULL),
+ use.names=TRUE, fill=TRUE)), use.names=TRUE, fill=TRUE), null.data.table())
+
+# #936, assigning list column to a factor column by reference
+DT <- data.table(x = factor(c("a", "b c", "d e f")))
+test(1480, DT[, x := strsplit(as.character(x), " ")], data.table(x=list("a", letters[2:3], letters[4:6])))
+
+# #970, over-allocation issue
+a=data.frame(matrix(1,ncol=101L))
+options(datatable.alloccol=100L)
+ans1 = data.table(a)
+options(datatable.alloccol=101L)
+ans2 = data.table(a)
+test(1481, ans1, ans2)
+# Global option. so reset back, else test 1478 fails.
+options(datatable.alloccol=100L)
+
+# #479, check := assignment in environment (actual case is when loaded from disk, but we'll just simulate a scenario here).
+ee = new.env()
+ee$DT = data.frame(x=1L, y=1:3)
+setattr(ee$DT, 'class', c("data.table", "data.frame"))
+test(1482.1, truelength(ee$DT), 0L) # make sure that the simulated environment is right.
+test(1482.2, ee$DT[, z := 3:1], data.table(x=1L, y=1:3, z=3:1), warning="Invalid .internal.selfref detected and")
+test(1482.3, truelength(ee$DT) >= 100L, TRUE) # truelength restored?
+
+# Fix for #499 and #945
+x <- data.table(k=as.factor(c(NA,1,2)),v=c(0,1,2), key="k")
+y <- data.table(k=as.factor(c(NA,1,3)),v=c(0,1,3), key="k")
+test(1483.1, x[y], data.table(k=factor(c(NA,1,3)), v=c(0,1,NA), i.v=c(0,1,3), key="k"))
+test(1483.2, merge(x,y,all=TRUE), data.table(k=factor(c(NA,1,2,3)), v.x=c(0,1,2,NA), v.y=c(0,1,NA,3), key="k"))
+
+x <- data.table(country="US")
+y <- data.table(country=factor("USA"))
+test(1483.3, merge(x,y,by="country",all=T), data.table(country=factor(c("US", "USA")), key="country"))
+setkey(y)
+test(1483.4, y[x], data.table(country=factor("US"), key="country"))
+
+# Fix for #842
+SomeFunction <- function(x, setnull=1L) {
+ ans <- replicate(length(x), list("bla1", "bla2"), simplify=FALSE)
+ ans[setnull] <- list(NULL)
+ return(ans)
+}
+DT <- data.table(ID=1:3, key="ID")
+test(1484, DT[, SomeFunction(ID, setnull=1L)], DT[, SomeFunction(ID, setnull=2L)])
+
+# Fix for #868
+vals = c("setosa", "versicolor", "virginica")
+if (base::getRversion()>="3.1.0") {
+ # depends on bug fix to combn() in R 3.1.0
+ test(1485, as.data.table(combn(unique(iris$Species),2)), data.table(vals[1:2], vals[c(1,3)], vals[2:3]))
+}
+
+# Fix for #955
+DT <- data.table(Time=.POSIXct(0, tz="UTC")+0:1, Value=1:2)
+options(datatable.auto.index=FALSE) # Have to turn off to avoid error.
+ans1.1 = DT[Time==Time[1]]
+ans2.1 = DT[Time==.POSIXct(0, tz="UTC")]
+options(datatable.auto.index=TRUE)
+ans1.2 = DT[Time==Time[1]]
+ans2.2 = DT[Time==.POSIXct(0, tz="UTC")]
+test(1486.1, as.data.frame(ans1.1), as.data.frame(ans1.2))
+test(1486.2, as.data.frame(ans2.1), as.data.frame(ans2.1))
+
+# Fix for #832
+x <- matrix(1:9, ncol=3)
+setattr(x, "names", paste("V", seq_len(length(x)), sep = ""))
+test(1487.1, setattr(x, "class", c("data.table", "data.frame")), error="Internal structure doesn't seem to be a list")
+x <- matrix(1:9, ncol=3)
+class(x) = c("data.table", "data.frame")
+# not sure how to test this one, so using `tryCatch`
+test(1487.2, tryCatch(print(x), error=function(k) "bla"), "bla")
+
+# Fix for #1043
+DT = data.table(grp=LETTERS[1:2], categ=rep(c("X","Y"), each=2L), condition=rep(c("P","Q"), each=4L), value=sample(8))
+tbl = with(DT, table(grp, categ, condition))
+ans1 = setnames(setDF(data.table(tbl)), "N", "Freq")
+ans2 = data.frame(tbl)
+ans2[1:3] = lapply(ans2[1:3], as.character)
+test(1488, ans1, ans2)
+
+# joins where x is integer type and i is logical type
+DT = data.table(x=1:5, y=6:10, key="x")
+test(1489, DT[.(TRUE)], DT[1L])
+
+# Fix for #932
+DT <- data.table(v1 = c(1:3, NA), v2 = c(1,NA,2.5,NaN), v3=c(NA, FALSE, NA, TRUE), v4=c("a", NA, "b", "c"))
+options(datatable.auto.index = TRUE) # just to be sure
+set2key(DT, v1)
+test(1490.1, DT[v1==3], subset(DT, v1==3))
+test(1490.2, DT[!v1==3], subset(DT, !v1==3))
+test(1490.3, DT[v1==NA], subset(DT, v1==NA))
+test(1490.4, DT[!v1==NA], subset(DT, !v1==NA))
+
+set2key(DT, v2)
+test(1490.5, DT[v2==2.5], subset(DT, v2==2.5))
+test(1490.6, DT[!v2==2.5], subset(DT, !v2==2.5))
+test(1490.7, DT[v2==NA], subset(DT, v2==NA))
+test(1490.8, DT[!v2==NA], subset(DT, !v2==NA))
+test(1490.9, DT[v2==NaN], subset(DT, v2==NaN))
+test(1490.10, DT[!v2==NaN], subset(DT, !v2==NaN))
+
+set2key(DT, v3)
+test(1490.11, DT[v3==FALSE], subset(DT, v3==FALSE))
+test(1490.12, DT[!v3==FALSE], subset(DT, !v3==FALSE))
+test(1490.13, DT[v3==TRUE], subset(DT, v3==TRUE))
+test(1490.14, DT[!v3==TRUE], subset(DT, !v3==TRUE))
+test(1490.15, DT[v3==NA], subset(DT, v3==NA))
+test(1490.16, DT[!v3==NA], subset(DT, !v3==NA))
+test(1490.17, DT[(v3)], subset(DT, v3==TRUE))
+test(1490.18, DT[!(v3)], subset(DT, !v3==TRUE))
+
+set2key(DT, v4)
+test(1490.19, DT[v4=="b"], subset(DT, v4=="b"))
+test(1490.20, DT[!v4=="b"], subset(DT, !v4=="b"))
+test(1490.21, DT[v4==NA], subset(DT, v4==NA))
+test(1490.22, DT[!v4==NA], subset(DT, !v4==NA))
+
+# test for #957 test
+DT <- as.data.table(BOD)
+options(datatable.auto.index=FALSE)
+ans1 = DT[Time %in% c("1", "2")]
+options(datatable.auto.index=TRUE)
+ans2 = DT[Time %in% c("1", "2")]
+test(1490.23, ans1, ans2)
+
+# test for #961
+DT <- as.data.table(cars)
+options(datatable.auto.index=FALSE)
+ans1 = DT[speed %in% list(1, 4)]
+options(datatable.auto.index=TRUE)
+ans2 = DT[speed %in% list(1, 4)]
+test(1490.24, ans1, ans2)
+
+# replace "." with "list" in 'j'
+ee1 = quote(.(val = lm(x ~ .)))
+ee2 = quote(.(v1=.(.SD), v2=.(min(y)), v3=.(.(x)), v4=.(x)))
+ee3 = quote(.(v1=.(.SD), v2=.(lm(. ~ xx)), v3=.(.(x)), v4=.(x^2)))
+ee4 = quote(c("a", "b") := .(.SD))
+ee5 = quote(c("a", "b") := .(v1=x^2, v2 = .(.SD[[1L]])))
+ee6 = quote(.(v1=.(.SD), v2=.(lm(. ~ xx)), v3=list(.(x)), v4=.(x^2)))
+test(1491.1, replace_dot(ee1), quote(list(val = lm(x ~ .))))
+test(1491.2, replace_dot(ee2), quote(list(v1=list(.SD), v2=list(min(y)), v3=list(list(x)), v4=list(x))))
+test(1491.3, replace_dot(ee3), quote(list(v1=list(.SD), v2=list(lm(. ~ xx)), v3=list(list(x)), v4=list(x^2))))
+test(1491.4, replace_dot(ee4), quote(c("a", "b") := list(.SD)))
+test(1491.5, replace_dot(ee5), quote(c("a", "b") := list(v1=x^2, v2 = list(.SD[[1L]]))))
+test(1491.6, replace_dot(ee6), quote(list(v1=list(.SD), v2=list(lm(. ~ xx)), v3=list(list(x)), v4=list(x^2))))
+
+# Fix for #1050
+dt = data.table(x=1:5, y=6:10)
+options(datatable.auto.index=FALSE)
+ans1 <- dt[x == 2.5]
+options(datatable.auto.index=TRUE)
+ans2 <- dt[x == 2.5]
+test(1492, ans1, ans2)
+
+# Fix for #497
+dt = data.table(x=1:10, y=11:20)
+test(1493, dt[, .(x=sum(x)),by= x %% 2, verbose=TRUE], data.table(`x%%2`=c(1,0), x=c(25L,30L)), output="by-expression 'x%%2' is not named")
+
+# Fix for #705
+DT1 = data.table(date=as.POSIXct("2014-06-22", format="%Y-%m-%d", tz="GMT"))
+DT2 = data.table(date=as.Date("2014-06-23"))
+test(1494.1, rbind(DT1, DT2), error="Class attributes at column")
+test(1494.2, rbind(DT2, DT1), error="Class attributes at column")
+
+# test 1495 has been added to melt's test section (fix for #1055)
+
+# Fix for #1056
+DT = data.table(year=2010:2014, v1=runif(5), v2=1:5, v3=letters[1:5])
+test(1496, DT[, shift(v1, 1:2, NA, "lead", TRUE)], DT[, shift(.SD, 1:2, NA, "lead", TRUE), .SDcols=2L])
+
+# Fix for #1066
+DT = data.table(x=1, y=2, z=3, a=4, b=5, c=6)
+test(1497, DT[, .SD, .SDcols = !c("a", "c")], DT[, !c("a", "c"), with=FALSE])
+
+# Fix for #1060
+DT = data.table(x=1, y=2, z=3, a=4, b=5, c=6)
+test(1498.1, DT[, .SD, .SDcols=c(T,F)], DT[, c("x", "z", "b"), with=FALSE])
+test(1498.2, DT[, .SD, .SDcols=!c(T,F)], DT[, !c("x", "z", "b"), with=FALSE])
+
+# Fix for #1072
+dt <- data.table(group1 = "a", group2 = "z", value = 1)
+options(datatable.auto.index=FALSE)
+ans1 = dt[group1 %in% c("a", "b"), sum(value), group2]
+options(datatable.auto.index=TRUE)
+ans2 = dt[group1 %in% c("a", "b"), sum(value), group2]
+test(1499, ans1, ans2)
+
+# Fix for #488
+if ("package:bit64" %in% search()) {
+ test(1500.1, fread("x,y\n0,\n", colClasses = list(integer64 = "y")),
+ data.table(x=0L, y=as.integer64(NA)))
+ # more tests after new fix
+ test(1500.2, fread("x,y\n0,12345678901234\n0,\n0,\n0,\n0,\n,\n,\n,\n,\n,\n,\n,\n,\n,\n,\n,\n12345678901234,\n0,\n0,\n0,\n0,\n0,\n"),
+ data.table(x=as.integer64(c(rep(0L, 5L), rep(NA, 11), 12345678901234, rep(0L,5L))),
+ y=as.integer64(c(12345678901234, rep(NA,21)))))
+
+ x = c("12345678901234", rep("NA", 178), "a")
+ y = sample(letters, length(x), TRUE)
+ ll = paste(x,y, sep=",", collapse="\n")
+ test(1500.3, fread(ll),
+ data.table(V1=c("12345678901234", rep("", 178), "a"), V2=y), warning="Bumped column 1 to type character on data")
+
+ x = c("12345678901234", rep("NA", 178), "0.5")
+ y = sample(letters, length(x), TRUE)
+ ll = paste(x,y, sep=",", collapse="\n")
+ test(1500.4, fread(ll), data.table(V1=suppressWarnings(as.numeric(x)), V2=y))
+
+}
+
+# fix for #1082
+dt1 = data.table(x=rep(c("a","b","c"),each=3), y=c(1,3,6), v=1:9, key=c("x", "y"))
+dt2 = copy(dt1)
+test(1502.1, dt1["a", z := NULL], error="When deleting columns, i should not be provided")
+# this shouldn't segfault on 'dt1[...]'
+test(1502.2, dt1["a", z := 42L], dt2["a", z := 42L])
+
+# fix for #1080
+dt = data.table(col1 = c(1,2,3,2,5,3,2), col2 = c(0,9,8,9,6,5,4), key=c("col1"))
+test(1503.1, uniqueN(dt), 4L) # default on key columns
+test(1503.2, uniqueN(dt, by=NULL), 6L) # on all columns
+test(1503.3, uniqueN(dt$col1), 4L) # on just that column
+
+# .SDcols and with=FALSE understands colstart:colend syntax
+dt = setDT(lapply(1:10, function(x) sample(3, 10, TRUE)))
+# .SDcols
+test(1504.1, dt[, lapply(.SD, sum), by=V1, .SDcols=V8:V10],
+ dt[, lapply(.SD, sum), by=V1, .SDcols=8:10])
+test(1504.2, dt[, lapply(.SD, sum), by=V1, .SDcols=V10:V8],
+ dt[, lapply(.SD, sum), by=V1, .SDcols=10:8])
+test(1504.3, dt[, lapply(.SD, sum), by=V1, .SDcols=-(V8:V10)],
+ dt[, lapply(.SD, sum), by=V1, .SDcols=-(8:10)])
+test(1504.4, dt[, lapply(.SD, sum), by=V1, .SDcols=!(V8:V10)],
+ dt[, lapply(.SD, sum), by=V1, .SDcols=!(8:10)])
+# with=FALSE
+test(1504.5, dt[, V8:V10, with=FALSE], dt[, 8:10, with=FALSE])
+test(1504.6, dt[, V10:V8, with=FALSE], dt[, 10:8, with=FALSE])
+test(1504.7, dt[, -(V8:V10), with=FALSE], dt[, -(8:10), with=FALSE])
+test(1504.8, dt[, !(V8:V10), with=FALSE], dt[, !(8:10), with=FALSE])
+
+# Fix for #1083
+dt = data.table(x=1:4, y=c(TRUE,FALSE))
+test(1505.1, as.matrix(dt), as.matrix(as.data.frame(dt)))
+
+# setcolorder works with data.frames, #1018
+dt = data.table(x=1, y=2)
+test(1506, setcolorder(dt, c("y", "x")), data.table(y=2, x=1))
+
+# tstrsplit, #1094
+# factor to character
+x = factor(paste(letters[1:5], letters[6:10], sep="-"))
+test(1507.1, tstrsplit(x, "-"), list(letters[1:5], letters[6:10]))
+# type.convert
+x = paste(letters[1:5], 1:5, sep="-")
+test(1507.2, tstrsplit(x, "-"), list(letters[1:5], as.character(1:5)))
+test(1507.3, tstrsplit(x, "-", type.convert=TRUE), list(letters[1:5], 1:5))
+
+# implementing #575, keep.rownames can take a name
+x = matrix(1:6, ncol=2)
+rownames(x) = letters[3:1]
+test(1508.1, as.data.table(x, keep="bla"), data.table(bla=letters[3:1], x))
+x = as.data.frame(x)
+test(1508.2, as.data.table(x, keep="bla"), data.table(bla=letters[3:1], x))
+x = sample(10); setattr(x, 'names', letters[1:10])
+test(1508.3, as.data.table(x, keep="bla"), data.table(bla=letters[1:10], x=unname(x)))
+# also for setDT
+df = data.frame(x=1:5, y=6:10, row.names=letters[5:1])
+ans = data.table(foo=letters[5:1], df)
+test(1508.4, setDT(df, keep="foo"), ans)
+
+# #1509 test added for melt above.
+
+# #1510 transpose converts NULL to NAs
+ll = list(1:2, NULL, 3:4)
+test(1510.1, transpose(ll), list(c(1L, NA, 3L), c(2L, NA, 4L)))
+test(1510.2, transpose(ll, ignore=TRUE), list(c(1L, 3L), c(2L, 4L)))
+
+# setorder can reorder data.frames too, #1018
+DF = data.frame(x=sample(3,10,TRUE), y=sample(letters[1:2], 10, TRUE))
+rownames(DF) = sample(letters, 10)
+ans = DF[order(-xtfrm(DF$y), DF$x), ]
+test(1511, ans, setorder(DF, -y, x))
+
+# fix for #1108
+if ("package:bit64" %in% search()) {
+ dt <- data.table(id = as.integer64(1:3), a = c("a", "b", "c"), key = "id")
+ test(1512.1, dt[.(2)], dt[.(as.integer64(2))])
+ test(1512.2, dt[.(2L)], dt[.(as.integer64(2))])
+
+ dt <- data.table(id = as.numeric(1:3), a = c("a", "b", "c"), key = "id")
+ test(1512.3, dt[.(2L)], dt[.(2)])
+ test(1512.4, dt[.(as.integer64(2))], dt[.(2)])
+
+ dt <- data.table(id = 1:3, a = c("a", "b", "c"), key = "id")
+ test(1512.5, dt[.(2)], dt[.(2L)])
+ test(1512.6, dt[.(as.integer64(2))], dt[.(2L)])
+}
+
+# setDT gains key argument, #1121
+X = list(a = 4:1, b=runif(4))
+test(1513, setkey(as.data.table(X), a), setDT(X, key="a"))
+
+# Adding tests for `isReallyReal`
+x = as.numeric(sample(10))
+test(1514.1, isReallyReal(x), FALSE)
+x = as.numeric(sample(c(1:5, NA)))
+test(1514.2, isReallyReal(x), FALSE) # NAs are handled properly
+x = as.numeric(sample(c(1:2, NaN, NA)))
+test(1514.3, isReallyReal(x), TRUE)
+x = as.numeric(sample(c(1:2, Inf, NA)))
+test(1514.4, isReallyReal(x), TRUE)
+x = as.numeric(sample(c(1:2, -Inf, NA)))
+test(1514.5, isReallyReal(x), TRUE)
+x = as.numeric(runif(2))
+test(1514.6, isReallyReal(x), TRUE)
+x = numeric()
+test(1514.7, isReallyReal(x), FALSE)
+
+# #1091
+old.option = getOption("datatable.prettyprint.char")
+options(datatable.prettyprint.char = 5L)
+DT = data.table(x=1:2, y=c("abcdefghijk", "lmnopqrstuvwxyz"))
+test(1515.1, grep("abcde...", capture.output(print(DT))), 2L)
+options(datatable.prettyprint.char = old.option)
+
+# test 1516: chain setnames() - used while mapping source to target columns
+SRC = data.table(x=1:2, y=c("abcdefghij", "klmnopqrstuv"), z=rnorm(2))
+src_cols <- c("y","z")
+tgt_cols <- c("name","value")
+DT <- SRC[, src_cols, with=FALSE][, setnames(.SD, tgt_cols)]
+test(1516.1, names(SRC), c("x","y","z")) # src not altered by ref
+test(1516.2, names(DT), tgt_cols) # target expected
+test(1516.3, unname(unclass(DT[, tgt_cols, with=FALSE])), unname(unclass(SRC[,src_cols, with=FALSE]))) # content match
+
+# Fix for #1078 and #1128
+x = data.frame(x=1L, y=2L)
+setattr(x, 'class', c("foo", "data.frame"))
+test(1517.1, class(as.data.table(x)), c("data.table", "data.frame"))
+test(1517.2, class(setDT(x)), c("data.table", "data.frame"))
+x = data.table(x="a", y=2L)
+setattr(x, 'class', c("foo", "data.table", "data.frame"))
+test(1517.3, class(as.data.table(x)), c("data.table", "data.frame"))
+test(1517.4, class(setDT(x)), c("data.table", "data.frame"))
+# for plm package
+if ("package:plm" %in% search()) {
+ set.seed(45L)
+ x = data.table(V1=c(1L,2L), V2=LETTERS[1:3], V3=round(rnorm(4),4), V4=1:12)
+ px = pdata.frame(x, index=c("V2", "V4"), drop.index=FALSE, row.names=TRUE)
+ test(1517.5, class(as.data.table(px)), class(x))
+ test(1517.6, class(setDT(px)), class(x))
+}
+
+# Fix for setattr, #1142
+x = factor(rep(1:4, each=2L))
+ax = address(x)
+setattr(x, 'levels', c("a", "a", "b", "b"))
+test(1518.1, levels(x), c("a", "b"))
+test(1518.2, address(x), ax)
+
+# Fix for #1074 and #1092
+x = data.table(x=c(1,1,1,2), y=1:4, key="x")
+test(1519.1, x[.(2:3), .N, nomatch=0L], 1L)
+x = data.table(k = INT(0,2,3,7), o = "b", key = "k")
+y = data.table(k = 1:5, n = paste("n", 1:5, sep=""), key = "k")
+test(1519.2, x[y, o := n, nomatch = 0], data.table(k = INT(0,2,3,7), o = c("b","n2","n3","b"), key = "k"))
+
+# Fix for #1141 (thanks to @yvanrichard)
+x <- data.table(zxc = 1:3, vbn = 4:6)
+test(1520, x[, c('zxc', 'qwe', 'rty', 'vbn'), with = FALSE], error = "column(s) not found")
+
+# Fix for #1154 (unnecessary lock on .SD)
+x = data.table(a=c(1,1,2))[, unique(.SD)]
+test(1521, x[, b := 5], data.table(a=c(1,2), b=5))
+
+# Fix for #1160, fastmean retaining attributes
+x = data.table(a = c(2,2,1,1,2), b=setattr(1:5, 'class', c('bla', 'integer')))
+test(1522, class(x[, .(mean(b), all(b)), by=a]$V1), c('bla', 'integer'))
+
+# Fix for #1145, .N lock handled properly
+x = data.table(a=1:5)
+test(1523, x[, head(.SD, n=2)[1:.N]], data.table(a=1:2))
+
+# #637 add by.x and by.y to merge.data.table
+d1 <- data.table(x1=c(1,3,8), y1=rnorm(3), key="x1")
+d2 <- data.table(x2=c(3,8,10), y2=rnorm(3), key="x2")
+ans1 = merge(d1, d2, by.x = "x1", by.y = "x2")
+ans2 = setkey(setDT(merge.data.frame(d1, d2, by.x = key(d1), by.y = key(d2))), x1)
+test(1524, ans1, ans2)
+
+# 'unique =' argument for CJ, #1148
+x = c(1, 2, 1)
+y = c(5, 8, 8, 4)
+test(1525, CJ(x, y, unique=TRUE), CJ(c(1,2), c(4,5,8)))
+
+# `key` argument fix for `setDT` when input is already a `data.table`, #1169
+DT <- data.table(A = 1:4, B = 5:8)
+setDT(DT, key = "A")
+test(1526.1, key(DT), "A")
+test(1526.2, key(setDT(DT, key = NULL)), NULL)
+
+# #501, fread stringsAsFactors=FALSE
+dt = data.table(x=1:5, y = letters[1:5])
+text = "x,y\n1,a\n2,b\n3,c\n4,d\n5,e\n"
+test(1527.1, dt[, y := factor(y)], fread(text, stringsAsFactors=TRUE))
+set.seed(1L)
+dt = data.table(x=1:5, y = sample(letters[1:5]))
+text = "x,y\n1,b\n2,e\n3,d\n4,c\n5,a\n"
+test(1527.2, dt[, y := factor(y)], fread(text, stringsAsFactors=TRUE))
+set.seed(1L)
+dt = data.table(x=1:5, y = sample(letters[1:2], 5, TRUE))
+text = "x,y\n1,a\n2,a\n3,b\n4,b\n5,a\n"
+test(1527.3, dt[, y := factor(y)], fread(text, stringsAsFactors=TRUE))
+
+# #1027, check.names argument to fread
+nm1 = names(data.table(a=1:2, a=3:4))
+nm2 = names(fread("a,a\n1,2\n3,4", check.names=TRUE))
+nm3 = names(fread("a,a\n1,2\n3,4", check.names=FALSE))
+test(1528.1, make.unique(nm1), nm2)
+test(1528.2, nm1, nm3)
+
+# add tests for between
+x = sample(10, 20, TRUE)
+test(1529.1, between(x, 1L, 5L, TRUE), x >= 1L & x <= 5L)
+test(1529.2, x %between% c(1L, 5L), x >= 1L & x <= 5L)
+test(1529.3, between(x, 1L, 5L, FALSE), x > 1L & x < 5L)
+x = sample(c(1:10, NA), 20, TRUE)
+test(1529.4, between(x, 1L, 5L, TRUE), x >= 1L & x <= 5L)
+test(1529.5, x %between% c(1L, 5L), x >= 1L & x <= 5L)
+test(1529.6, between(x, 1L, 5L, FALSE), x > 1L & x < 5L)
+x = runif(15)
+test(1529.7, between(x, 0.25, 0.75, TRUE), x >= 0.25 & x <= 0.75)
+test(1529.8, x %between% c(0.25, 0.75), x >= 0.25 & x <= 0.75)
+test(1529.9, between(x, 0.25, 0.75, FALSE), x > 0.25 & x < 0.75)
+x = c(NA, runif(15), NA)
+test(1529.10, between(x, 0.25, 0.75, TRUE), x >= 0.25 & x <= 0.75)
+test(1529.11, x %between% c(0.25, 0.75), x >= 0.25 & x <= 0.75)
+test(1529.12, between(x, 0.25, 0.75, FALSE), x > 0.25 & x < 0.75)
+
+# add tests for which.first and which.last
+# which.first
+test(1530.1, which.first(sample(5, 20, TRUE)), error = "x not boolean")
+x <- sample(c(TRUE, FALSE), 20, TRUE)
+test(1530.2, which.first(x), which(x)[1L])
+# which.last
+test(1530.3, which.last(1:5), error = "x not boolean")
+test(1530.4, which.last(x), tail(which(x), 1L))
+
+# test xts's last()
+if ("package:xts" %in% search()) {
+ test(1531, xts::last(1:5), 5L)
+}
+
+# test for like, %like%
+set.seed(2L)
+x = apply(matrix(sample(letters, 12), nrow=2), 1, paste, collapse="")
+y = factor(sample(c(letters[1:5], x), 20, TRUE))
+xsub = substring(x, 1L, 1L)
+test(1532.1, y %like% xsub[1L], grepl(xsub[1L], y))
+test(1532.2, y %like% xsub[2L], grepl(xsub[2L], y))
+test(1532.3, like(y, xsub[1L]), grepl(xsub[1L], y))
+test(1532.4, like(y, xsub[2L]), grepl(xsub[2L], y))
+
+# coverage for setkey() to 100%
+dt1 = data.table(x=sample(5), y=1:5, key="y")
+dt2 = as.data.table(dt1); setattr(dt2, 'sorted', NULL)
+test(1533.1, setkeyv(dt1, character(0)), dt2, warning = "cols is a character vector")
+test(1533.2, setkeyv(dt1, "x", verbose=TRUE), setkey(dt2, x), output = "forder took")
+
+# coverage for %+% and trim
+test(1534, `%+%.default`(1:5, 6:10), "1,2,3,4,56,7,8,9,10")
+test(1535.1, trim(" abcde "), "abcde")
+test(1535.2, trim(" abcde"), "abcde")
+test(1535.3, trim("abcde "), "abcde")
+
+# remaining test for covering duplicated.data.table
+dt = data.table(x=1:5, y=6:10)
+test(1536, duplicated(dt, incomparables=TRUE), error = "argument 'incomparables != FALSE'")
+
+# test for covering melt 100%
+test(1537 , names(melt(dt, id=1L, variable.name = "x", value.name="x")), c("x", "x.1", "x.2"), output = "Duplicate column names")
+
+# test for tables()
+test(1538, tables(), output = "Total:")
+
+# uniqueN could supports list input #1224
+d1 <- data.table(a = 1:4, l = list(list(letters[1:2]),list(Sys.time()),list(1:10),list(letters[1:2])))
+test(1539, d1[,uniqueN(l)], 3L)
+
+# feature #1130 - joins without setting keys
+# can't test which=TRUE with DT1.copy's results..
+set.seed(45L)
+DT1 = data.table(x=sample(letters[1:3], 15, TRUE), y=sample(6:10, 15, TRUE),
+ a=sample(100, 15), b=runif(15))
+DT2 = CJ(x=letters[1:3], y=6:10)[, mul := sample(20, 15)][sample(15L, 5L)]
+DT3 = rbindlist(list(DT2, list(x="d", y=7L, mul=100L)))
+DT3 = DT3[sample(nrow(DT3))]
+
+# key on char column
+DT1.copy = copy(DT1)
+setkey(DT1.copy, x)
+test(1540.1, DT1[DT2, on=c(x="x")], DT1.copy[DT2])
+test(1540.33, DT1[DT2, on=c("x")], DT1.copy[DT2])
+test(1540.2, DT1[DT2, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="x"), .SDcols=c("a", "b")],
+ DT1.copy[DT2, lapply(.SD, function(x) x * mul),
+ by=.EACHI, .SDcols=c("a", "b")])
+test(1540.3, DT1[DT3, on=c(x="x")], DT1.copy[DT3])
+test(1540.4, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="x"), .SDcols=c("a", "b")],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, .SDcols=c("a", "b")])
+test(1540.5, DT1[DT3, on=c(x="x"), nomatch=0L], DT1.copy[DT3, nomatch=0L])
+test(1540.6, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="x"), .SDcols=c("a", "b"), nomatch=0L],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, .SDcols=c("a", "b"), nomatch=0L])
+test(1540.7, DT1[DT3, on=c(x="x"), roll=TRUE], DT1.copy[DT3, roll=TRUE])
+test(1540.8, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="x"), .SDcols=c("a", "b"), roll=TRUE],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, .SDcols=c("a", "b"), roll=TRUE])
+
+# key on integer col
+DT1.copy = copy(DT1)
+setkey(DT1.copy, y)
+test(1540.9, DT1[DT2, on=c(y="y")], DT1.copy[DT2[, c(2,1,3), with=FALSE]])
+test(1540.34, DT1[DT2, on=c("y")], DT1.copy[DT2[, c(2,1,3), with=FALSE]])
+test(1540.10, DT1[DT2, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(y="y"), .SDcols=c("a", "b")],
+ DT1.copy[DT2[, c(2,1,3), with=FALSE], lapply(.SD, function(x) x * mul),
+ by=.EACHI, .SDcols=c("a", "b")])
+test(1540.11, DT1[DT3, on=c(y="y")], DT1.copy[DT3[, c(2,1,3), with=FALSE]])
+test(1540.12, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(y="y"), .SDcols=c("a", "b")],
+ DT1.copy[DT3[, c(2,1,3), with=FALSE], lapply(.SD, function(x) x * mul),
+ by=.EACHI, .SDcols=c("a", "b")])
+test(1540.13, DT1[DT3, on=c(y="y"), nomatch=0L], DT1.copy[DT3[, c(2,1,3), with=FALSE], nomatch=0L])
+test(1540.14, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(y="y"), .SDcols=c("a", "b"), nomatch=0L],
+ DT1.copy[DT3[, c(2,1,3), with=FALSE], lapply(.SD, function(x) x * mul),
+ by=.EACHI, .SDcols=c("a", "b"), nomatch=0L])
+test(1540.15, DT1[DT3, on=c(y="y"), roll=TRUE], DT1.copy[DT3[, c(2,1,3), with=FALSE], roll=TRUE])
+test(1540.16, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(y="y"), .SDcols=c("a", "b"), roll=TRUE],
+ DT1.copy[DT3[, c(2,1,3), with=FALSE], lapply(.SD, function(x) x * mul),
+ by=.EACHI, .SDcols=c("a", "b"), roll=TRUE])
+
+# multiple keys
+DT1.copy = copy(DT1)
+setkey(DT1.copy, x, y)
+test(1540.17, DT1[DT2, on=c(x="x", y="y")], DT1.copy[DT2])
+test(1540.35, DT1[DT2, on=c("x", "y")], DT1.copy[DT2])
+test(1540.18, DT1[DT2, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="x", y="y")],
+ DT1.copy[DT2, lapply(.SD, function(x) x * mul), by=.EACHI])
+test(1540.19, DT1[DT3, on=c(x="x", y="y")], DT1.copy[DT3])
+test(1540.20, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="x", y="y")],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI])
+test(1540.21, DT1[DT3, on=c(x="x", y="y"), nomatch=0L], DT1.copy[DT3, nomatch=0L])
+test(1540.22, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="x", y="y"), nomatch=0L],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, nomatch=0L])
+test(1540.23, DT1[DT3, on=c(x="x", y="y"), roll=TRUE], DT1.copy[DT3, roll=TRUE])
+test(1540.24, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="x", y="y"), roll=TRUE],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, roll=TRUE])
+
+# multiple keys, non-identical names
+DT1.copy = copy(DT1)
+setkey(DT1.copy, x, y)
+setnames(DT2, c("q", "r", "mul"))
+setnames(DT3, names(DT2))
+test(1540.25, DT1[DT2, on=c(x="q", y="r")], DT1.copy[DT2])
+test(1540.26, DT1[DT2, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="q", y="r")],
+ DT1.copy[DT2, lapply(.SD, function(x) x * mul), by=.EACHI])
+test(1540.27, DT1[DT3, on=c(x="q", y="r")], DT1.copy[DT3])
+test(1540.28, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="q", y="r")],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI])
+test(1540.29, DT1[DT3, on=c(x="q", y="r"), nomatch=0L], DT1.copy[DT3, nomatch=0L])
+test(1540.30, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="q", y="r"), nomatch=0L],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, nomatch=0L])
+test(1540.31, DT1[DT3, on=c(x="q", y="r"), roll=TRUE], DT1.copy[DT3, roll=TRUE])
+test(1540.32, DT1[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, on=c(x="q", y="r"), roll=TRUE],
+ DT1.copy[DT3, lapply(.SD, function(x) x * mul),
+ by=.EACHI, roll=TRUE])
+
+# to do: add tests for :=
+
+# fix for #477, key not being retained on joins on factor columns
+set.seed(1)
+dtp <- data.table(pid = gl(3, 3, labels = c("du", "i", "nouana")),
+ year = gl(3, 1, 9, labels = c("2007", "2010", "2012")),
+ val = rnorm(9), key = c("pid", "year"))
+dtab <- data.table(pid = factor(c("i", "nouana")),
+ year = factor(c("2010", "2000")),
+ abn = sample(1:5, 2, replace = TRUE), key =
+ c("pid", "year"))
+test(1541, key(dtp[dtab]), c("pid", "year"))
+
+# fix DT[TRUE, :=] using too much working memory for i, #1249
+if (!inherits(try(Rprofmem(NULL), silent=TRUE), "try-error")) { # in case R not compiled with memory profiling enabled
+ f = tempfile()
+ N = 1000000 # or any large number of rows
+ DT = data.table(A=1:N, B=rnorm(N))
+ DT[TRUE, B := B * 2] # stabilize with initial dummy update
+ Rprofmem(f)
+ DT[TRUE, B := B * 2] # or some in-place update
+ Rprofmem(NULL)
+ test(1542, length(grep("000",readLines(f, warn=FALSE))), 1L) # one allocation for the RHS only
+ unlink(f)
+}
+
+# rest of #1130 - merge doesn't copy, instead uses joins without keys.
+set.seed(1L)
+d1 <- data.table(A = sample(letters[1:10]), X = 1:10, total = TRUE)
+d2 <- data.table(A = sample(letters[5:14]), Y = 1:10, total = FALSE)
+
+ans1 <- suppressWarnings(merge(setDF(d1), setDF(d2), by="A"))
+ans2 <- setDF(merge(setDT(d1), setDT(d2), by="A"))
+test(1543.1, ans1, ans2)
+ans1 <- suppressWarnings(merge(setDF(d1), setDF(d2), all=TRUE, by="A"))
+ans2 <- setDF(merge(setDT(d1), setDT(d2), all=TRUE, by="A"))
+test(1542.2, ans1, ans2)
+# test duplicate name cases
+setnames(d2, c("A", "Y"), c("B", "A"))
+ans1 <- suppressWarnings(merge(setDF(d1), setDF(d2), by.x="A", by.y="B"))
+ans2 <- setDF(merge(setDT(d1), setDT(d2), by.x="A", by.y="B"))
+test(1543.3, ans1, ans2)
+ans1 <- suppressWarnings(merge(setDF(d2), setDF(d1), by.x="B", by.y="A"))
+ans2 <- setDF(merge(setDT(d2), setDT(d1), by.x="B", by.y="A"))
+test(1543.4, ans1, ans2)
+ans1 <- suppressWarnings(merge(setDF(d2), setDF(d1), all=TRUE, by.x="B", by.y="A"))
+ans2 <- setDF(merge(setDT(d2), setDT(d1), all=TRUE, by.x="B", by.y="A"))
+test(1543.5, ans1, ans2)
+
+# test for sort=FALSE argument, #1282
+set.seed(1L)
+d1 <- data.table(A = sample(letters[1:10]), X = 1:10, total = TRUE)
+d2 <- data.table(A = sample(letters[5:14]), Y = 1:10, total = FALSE)
+test(1543.7, merge(setDT(d1), setDT(d2), by="A", sort=FALSE),
+ setDT(merge(setDF(d1), setDF(d2), by="A", sort=FALSE)))
+
+# thinko in merge dupnames handling
+dt1 = data.table(x=1:5, y1=2L, y2=3L)
+dt2 = data.table(a=4:6, y2=TRUE, y1 = FALSE)
+test(1543.6, setDF(merge(dt1, dt2, by.x="x", by.y="a")),
+ merge(as.data.frame(dt1), as.data.frame(dt2), by.x="x", by.y="a"))
+
+# fix #1290, restore colorder before setting names
+set.seed(1)
+dt1 <- data.table(sex = rep(1:2, 5), group = rep(letters[1:5], 2),V1 = sample(1:10))
+set.seed(2)
+dt2 <- data.table(group = rep(letters[1:5], 2),sex = rep(1:2, 5),V2 = sample(1:10))
+test(1543.7, setDF(merge(dt1, dt2, by = c("sex", "group"))),
+ merge(as.data.frame(dt1), as.data.frame(dt2), by=c("sex", "group")))
+by.x = c("sex.1", "group.1")
+by.y = c("sex.2", "group.2")
+setnames(dt1, 1:2, by.x)
+setnames(dt2, 1:2, rev(by.y))
+test(1543.8, setDF(merge(dt1, dt2, by.x=by.x, by.y=by.y)),
+ merge(as.data.frame(dt1), as.data.frame(dt2), by.x=by.x, by.y=by.y))
+
+# fix for #1258 (bug on .shallow - retains keys when it shouldn't)
+# nice catch and excellent report from @and3k
+x1 <- data.table(a1 = c('a', 'b', 'c'), a2 = c(1L, 3L, 2L))
+y <- data.table(a2 = 1:3)
+setkey(y, a2)
+setkey(x1, a1, a2)
+test(1544.1, setDF(merge(x1, y)), merge(as.data.frame(x1), as.data.frame(y)))
+test(1544.2, setDF(merge(x1, y, by="a2")), merge(as.data.frame(x1), as.data.frame(y), by="a2"))
+# also test shallow here so as to catch future regressions
+x1 <- data.table(a1 = c('a', 'b', 'c'), a2 = c(1L, 3L, 2L), key="a1,a2")
+test(1545.1, key(.shallow(x1, cols="a2")), NULL)
+test(1545.2, key(.shallow(x1, retain.key=FALSE)), NULL)
+test(1545.3, key(.shallow(x1, retain.key=TRUE)), key(x1))
+test(1545.4, key(.shallow(x1, cols="a1", retain.key=TRUE)), "a1")
+
+# test for #1234
+df1 = df2 = data.frame(cats = rep(c('', ' ', 'meow'), 5))
+df2[grep("^[ ]*$", df2$cats), "cats"] = NA_integer_
+test(1546, set(df1, grep("^[ ]*$", df1$cats), 1L, NA_integer_), df2)
+
+# Add test for getdots() function (although it doesn't seem to be used anywhere)
+foo <- function(x, y, ...) { getdots() }
+test(1547, foo(1L, 5L, a=2L, "c"), c("2", "c"))
+
+# Fix for encoding issues in windows, #563
+f="issue_563_fread.txt"
+ans1 <- fread(f, sep=",", header=TRUE)
+ans2 <- fread(f, sep=",", header=TRUE, encoding="UTF-8")
+test(1548.1, unique(unlist(lapply(ans1, Encoding))), "unknown")
+test(1548.2, unique(unlist(lapply(ans2, Encoding))), "UTF-8")
+
+# #1167 print.data.table row id in non-scientific notation
+DT <- data.table(a = rep(1:5,3*1e6), b = rep(letters[1:3],5*1e6))
+test(1549, capture.output(print(DT)), c(" a b", " 1: 1 a", " 2: 2 b", " 3: 3 c", " 4: 4 a", " 5: 5 b", " --- ", "14999996: 1 b", "14999997: 2 c", "14999998: 3 a", "14999999: 4 b", "15000000: 5 c"))
+rm(DT)
+
+# PR by @dselivanov
+# fixes #504 - handle nastring while reading (without coercion to character)
+# Note: this doesn't address cases like na.strings="-999" yet. See https://github.com/Rdatatable/data.table/pull/1236 for those examples.
+K = 10L
+nastrings = c('null', 'NULL', 'na', '_NA', 'NA', 'nan', 'Nan', 'NAN', 'NaN')
+DT = data.table(int = 1:K,
+ char = sample(letters, size = K, replace = T),
+ float = 1:K + 0.1,
+ bool = sample( c(T, F), K, replace = T))
+
+DT_NA = DT
+for (j in seq_len( ncol(DT) )) {
+ set(x = DT_NA, i = j, j = j, value = NA)
+}
+
+for(k in seq_along(nastrings)) {
+ dt0 = copy(DT)
+ for (j in seq_len( ncol(DT) )) {
+ set(x = dt0, i = NULL, j = j, value = as.character(dt0[[j]]))
+ set(x = dt0, i = j, j = j, value = nastrings[[k]])
+ }
+ str = do.call(paste, c(dt0, collapse="\n", sep=","))
+ str = paste(paste(names(dt0), collapse=","), str, sep="\n")
+ DT_fread = fread(str, na.strings = nastrings, verbose = FALSE)
+ test(1550 + k * 0.1, DT_fread, DT_NA)
+}
+
+# FR #568
+str = "a,b\n1.5,\"at the 5\" end of the gene.\""
+test(1551.1, fread(str), data.table(a = 1.5, b = "\"at the 5\" end of the gene.\""))
+#1256
+str = "x,y\nx1,\"oops\" y1\n"
+test(1551.2, fread(str), data.table(x = "x1", y = "\"oops\" y1"))
+#1077
+str = '2,3\n""foo,bar'
+test(1551.3, fread(str), data.table(V1 = c("2", "\"\"foo"), V2 = c("3", "bar")))
+#1079
+str = 'L1\tsome\tunquoted\tstuff\nL2\tsome\t"half" quoted\tstuff\nL3\tthis\t"should work"\tok thought'
+test(1551.4, fread(str), data.table(L1 = c("L2", "L3"), some = c("some", "this"), unquoted = c("\"half\" quoted", "should work"), stuff = c("stuff", "ok thought")))
+#1095
+rhs = read.table("issue_1095_fread.txt", sep=",", comment.char="", stringsAsFactors=FALSE, quote="", strip.white=TRUE)
+test(1551.5, fread("issue_1095_fread.txt"), setDT(rhs), warning="Bumped column 47 to type character on data row")
+
+# FR #1314 rest of na.strings issue
+str = "a,b,c,d\n#N/A,+1,5.5,FALSE\n#N/A,5,6.6,TRUE\n#N/A,+1,#N/A,-999\n#N/A,#N/A,-999,FALSE\n#N/A,1,NA,TRUE"
+read_table = function(str, ...) {
+ setDT(read.table(text=str, stringsAsFactors=FALSE, comment.char="", sep=",", header=TRUE, ...))[]
+}
+test(1552.1, fread(str, na.strings="#N/A"), read_table(str, na.strings="#N/A"))
+test(1552.2, fread(str, na.strings=c("#N/A", "-999")), read_table(str, na.strings=c("#N/A", "-999")))
+test(1552.3, fread(str, na.strings=c("#N/A", "-999", "+1")), read_table(str, na.strings=c("#N/A", "-999", "+1")))
+test(1552.4, fread(str, na.strings=c("#N/A", "-999", "+1", "1")), read_table(str, na.strings=c("#N/A", "-999", "+1", "1")))
+test(1552.5, fread(str, na.strings=c("#N/A", "-999", "FALSE")), read_table(str, na.strings=c("#N/A", "-999", "FALSE")))
+
+# FR #1177: 'quote' option of 'print.data.table'
+DT1 <- data.table(s1=paste(" ",LETTERS[1:5],sep=""),s2=LETTERS[1:5])
+ans1 <- c(" s1 s2","1: \" A\" \"A\"",
+ "2: \" B\" \"B\"","3: \" C\" \"C\"",
+ "4: \" D\" \"D\"","5: \" E\" \"E\"")
+ans2 <- c(" s1 s2","1: A A","2: B B",
+ "3: C C","4: D D","5: E E")
+test(1553.1, capture.output(print(DT1, quote = TRUE)), ans1)
+test(1553.2, capture.output(print(DT1)), ans2)
+
+# #826 - subset DT on single integer vector stored as matrix the same way as data.frame
+dt <- data.table(a=letters[1:10])
+idx <- c(2:4,7L,9:10)
+dim(idx) <- c(6L, 1L)
+dimnames(idx) <- list(NULL, "Resample1") # as in caret::createDataPartition
+test(1554.1, dt[idx], data.table(a=letters[idx]))
+test(1554.2, dt[-idx], data.table(a=letters[(1:10)[-idx]]))
+test(1554.3, dt[!idx], data.table(a=letters[(1:10)[-idx]]))
+test(1554.4, idx, structure(c(2L, 3L, 4L, 7L, 9L, 10L), .Dim = c(6L, 1L), .Dimnames = list(NULL, "Resample1")))
+
+# strip.white and other enhancements to 'fread()'
+# bug #1113
+ans1 <- fread("issue_1113_fread.txt")
+ans2 <- setDT(read.table("issue_1113_fread.txt", header=TRUE))
+setnames(ans2, names(ans1))
+test(1555.1, ans1, ans2)
+
+# bug #1035, take care of spaces automatically. Note that the columns are also read in proper types. Also with quotes when sep is not space.
+str1=" ITERATION THETA1 THETA2
+ 0 3.95527E+01 2.10651E+01"
+str2=" ITERATION, THETA1, THETA2
+ 0, 3.95527E+01, 2.10651E+01"
+str3=" ITERATION , THETA1 , THETA2
+ 0 , 3.95527E+01 , 2.10651E+01"
+str4=" ITERATION , THETA1 , \"THETA2\"
+ 0 , 3.95527E+01 , 2.10651E+01"
+str5=" ITERATION , THETA1 , THETA2
+ bla , 3.95527E+01 , 2.10651E+01"
+test(1555.2, fread(str1), data.table(ITERATION=0L, THETA1=39.5527, THETA2=21.0651))
+test(1555.3, fread(str2), data.table(ITERATION=0L, THETA1=39.5527, THETA2=21.0651))
+test(1555.4, fread(str3), data.table(ITERATION=0L, THETA1=39.5527, THETA2=21.0651))
+test(1555.5, fread(str4), data.table(ITERATION=0L, THETA1=39.5527, `"THETA2"`=21.0651))
+test(1555.6, fread(str5), data.table(ITERATION="bla", THETA1=39.5527, THETA2=21.0651))
+# without strip.white
+# header col spaces are dealt properly irrespective of strip.white
+test(1555.7, fread(str1, strip.white=FALSE), error="Expecting 4 cols, but line 2 contains")
+test(1555.8, names(fread(str2, strip.white=FALSE)), c("ITERATION","THETA1","THETA2 "))
+test(1555.9, names(fread(str3, strip.white=FALSE)), c("ITERATION ","THETA1 ","THETA2 "))
+test(1555.10, names(fread(str4, strip.white=FALSE)), c("ITERATION ","THETA1 ","\"THETA2\" "))
+
+# bug #1035, reply to the post from another user
+str1=" 22 4 6 4\n 34 22 34 5\n 6 2 1 4\n"
+str2="22 4 6 4\n34 22 34 5\n6 2 1 4\n"
+test(1555.11, fread(str1), fread(str2))
+
+# bug #785
+rhs <- setDT(read.table("issue_785_fread.txt", header=TRUE, stringsAsFactors=FALSE, sep="\t", strip.white=TRUE))
+test(1555.12, fread("issue_785_fread.txt"), rhs)
+
+# bug #529, http://stackoverflow.com/questions/22229109/r-data-table-fread-command-how-to-read-large-files-with-irregular-separators
+str1=" YYYY MM DD HH mm 19490 40790
+ 1991 10 1 1 0 1.046465E+00 1.568405E+00"
+str2="YYYY MM DD HH mm 19490 40790
+1991 10 1 1 0 1.046465E+00 1.568405E+00"
+test(1555.13, fread(str1), fread(str2))
+
+# fix for #1330
+test(1556.1, fread("issue_1330_fread.txt", nrow=2), data.table(a=1:2, b=1:2))
+test(1556.2, fread("issue_1330_fread.txt", nrow=4), data.table(a=1:2, b=1:2), warning="Stopped reading at empty line 4")
+
+# FR #768
+str="1,2\n3,4\n"
+test(1557.1, names(fread(str)), c("V1", "V2")) # autonamed
+test(1557.2, names(fread(str, col.names=letters[1:2])), letters[1:2])
+test(1557.3, names(fread(str, col.names=letters[1])), error="Can't assign 1 names to")
+test(1557.4, names(fread(str, col.names=letters[1:3])), error="Can't assign 3 names to")
+test(1557.5, names(fread(str, col.names=1:2)), error="Passed a vector of type")
+
+# Fix for #773
+f = "issue_773_fread.txt"
+ans = data.table(AAA=as.integer(c(4,7,rep(1,17),31,21)),
+ BBB=as.integer(c(5,8,rep(2,17),32,22)),
+ CCC=as.integer(c(6,9,rep(3,17),33,23)))
+test(1558, fread(f, nrow=21L), ans) # no warning
+
+# FR # 1338 -- check.names argument of setDT
+ans=data.table(X=1:3,"X.1"=1:3)
+dt1<-data.table(X=1:3,X=1:3)
+df1<-data.frame(X=1:3,X=1:3,check.names=FALSE)
+ls1<-list("X"=1:3,"X"=1:3)
+test(1559.1, setDT(dt1, check.names=TRUE), ans)
+test(1559.2, setDT(df1, check.names=TRUE), ans)
+test(1559.3, setDT(ls1, check.names=TRUE), ans)
+
+# Fix #1140
+test(1560.1, data.table(x=letters[1:5])[, 0, with=FALSE], null.data.table())
+test(1560.2, data.table(x=letters[1:5])[, c(0,FALSE), with=FALSE], null.data.table())
+
+# Fix for #1298
+d = data.table(a = 1)
+q = quote(.(a))
+test(1561, d[, 1, by = eval(q)], d[, 1, by = .(a)])
+
+# Fix for #1315
+d = as.IDate(seq(as.Date("2015-01-01"), as.Date("2015-01-15"), by='1 day'))
+test(1562.1, as.list(d), lapply(as.list(as.Date(d)), as.IDate))
+test(1562.2, sapply(d, identity), as.integer(sapply(as.Date(d), identity)))
+
+# Fix for #1216, .SDcols and with=FALSE should evaluate within frame of 'x' only when it's of the form a:b
+dt = data.table(index1=1:10, index2=10:1, index3=1, s=4, i=24)
+i = 2L
+test(1557.1, dt[, paste0("index", 1:i), with=FALSE], dt[, index1:index2, with=FALSE])
+test(1557.2, dt[, paste0("index", 1:i), with=FALSE], dt[, 1:2, with=FALSE])
+test(1557.3, dt[, 5:4, with=FALSE], dt[, i:s, with=FALSE])
+test(1557.4, dt[, .SD, .SDcols=paste0("index", 1:i)], dt[, .SD, .SDcols=index1:index2])
+
+##########################
+
+
+# TO DO: Add test for fixed bug #5519 - dcast returned error when a package imported data.table, but dint happen when "depends" on data.table. This is fixed (commit 1263 v1.9.3), but not sure how to add test.
+
+# TO DO: test and highlight in docs that negatives are fine and fast in forderv (ref R wish #15644)
+# TO DO: tests of freading classes like Date and the verbose messages there.
+# TO DO: Test mid read bump of logical T/F to character, collapse back to T and F.
+
+# TO DO: add examples of multiple LHS (name and position) and multiple RHS to example(":=")
+# TO DO: tests on double in add hoc by
+# TO DO: test on -i that retain key e.g. DT[-4] and DT[-4,sum(v),by=b] should both retain key
+# test on out of bound i subsets e.g. 6:10 when DT has 7 rows, and mixed negative and positive i integer is error.
+# test that ordered subsets when i is unkeyed now retain x's key (using is.sorted(f__))
+
+# TO DO: add FAQ that eval() is evaled in calling frame so don't need a, then update SO question of 14 March. See the test using variable name same as column name. Actually, is that true? Need "..J".
+# TO DO: why did SO answer using eval twice in j need .SD in lapply(f,eval,.SD) on 19 Apr
+# TO DO: change all 1 to 1L internally (done in data.table.R, other .R to do)
+
+# TO DO: check the "j is named list could be inefficient" message from verbose than Chris N showed recently to 15 May
+# TO DO: !make sure explicitly that unnamed lists are being executed by dogroups!
+# TO DO: Add to warning about a previous copy that class<-, levels<- can also copy whole vector. *Any* fun<- form basically.
+# TO DO: use looped := vs set test in example(":=") or example(setnames) to test overhead in [.data.table is tested to stay low in future.
+
+# TO DO: add tests on smaller examples with NAs for 'frankv', even though can't compare to base::rank.
+## See test-* for more tests
+
+##########################
+options(warn=0)
+options(oldbwb) # set at top of this file
+plat = paste("endian=",.Platform$endian,", sizeof(long double)==",.Machine$sizeof.longdouble,sep="")
+if (nfail > 0) {
+ if (nfail>1) {s1="s";s2="s: "} else {s1="";s2=" "}
+ cat("\r")
+ stop(nfail," error",s1," out of ",ntest, " (lastID=",lastnum,", ",plat, ") in inst/tests/tests.Rraw on ",date(),". Search tests.Rraw for test number",s2,paste(whichfail,collapse=", "),".")
+ # important to stop() here, so that 'R CMD check' fails
+}
+cat("\rAll ",ntest," tests (lastID=",lastnum,") in inst/tests/tests.Rraw completed ok in ",timetaken(started.at)," on ",date()," (",plat,")\n",sep="")
+# Reporting lastnum rather than ntest makes it easier to check user has the latest version, assuming
+# each release or patch has extra tests.
+# date() is included so we can tell when CRAN checks were run (in particular if they have been rerun since
+# an update to Rdevel itself; data.table doesn't have any other dependency) since there appears to be no other
+# way to see the timestamp that CRAN checks were run. Some CRAN machines lag by several days.
diff --git a/man/IDateTime.Rd b/man/IDateTime.Rd
new file mode 100644
index 0000000..5bca483
--- /dev/null
+++ b/man/IDateTime.Rd
@@ -0,0 +1,224 @@
+\name{IDateTime}
+\alias{IDate}
+\alias{as.IDate}
+\alias{ITime}
+\alias{as.ITime}
+\alias{IDateTime}
+\alias{as.character.ITime}
+\alias{as.chron.IDate}
+\alias{as.chron.ITime}
+\alias{as.Date.IDate}
+\alias{as.IDate.Date}
+\alias{as.IDate.default}
+\alias{as.ITime.character}
+\alias{as.ITime.default}
+\alias{as.ITime.POSIXlt}
+\alias{as.ITime.times}
+\alias{as.list.IDate}
+\alias{as.POSIXct.IDate}
+\alias{as.POSIXct.ITime}
+\alias{as.POSIXlt.ITime}
+\alias{c.IDate}
+\alias{cut.IDate}
+\alias{format.ITime}
+\alias{IDateTime.default}
+\alias{mean.IDate}
+\alias{print.ITime}
+\alias{rep.IDate}
+\alias{rep.ITime}
+\alias{round.IDate}
+\alias{seq.IDate}
+\alias{split.IDate}
+\alias{hour}
+\alias{yday}
+\alias{wday}
+\alias{mday}
+\alias{week}
+\alias{month}
+\alias{quarter}
+\alias{year}
+\alias{IDate-class}
+\alias{ITime-class}
+
+\title{ Integer based date class }
+\description{
+ Date and time classes with integer storage for fast sorting and
+ grouping. Still experimental!
+}
+\usage{
+as.IDate(x, ...)
+\method{as.IDate}{default}(x, \dots)
+\method{as.IDate}{Date}(x, \dots)
+\method{as.Date}{IDate}(x, \dots)
+\method{as.POSIXct}{IDate}(x, tz = "UTC", time = 0, \dots)
+\method{as.chron}{IDate}(x, time = NULL, \dots)
+\method{round}{IDate}(x, digits = c("weeks", "months", "quarters","years"), \ldots)
+
+as.ITime(x, ...)
+\method{as.ITime}{default}(x, \dots)
+\method{as.POSIXct}{ITime}(x, tz = "UTC", date = as.Date(Sys.time()), \dots)
+\method{as.chron}{ITime}(x, date = NULL, \dots)
+\method{as.character}{ITime}(x, \dots)
+\method{format}{ITime}(x, \dots)
+
+IDateTime(x, ...)
+\method{IDateTime}{default}(x, ...)
+
+hour(x)
+yday(x)
+wday(x)
+mday(x)
+week(x)
+month(x)
+quarter(x)
+year(x)
+
+}
+
+\arguments{
+ \item{x}{an object}
+ \item{\dots}{arguments to be passed to or from other methods. For
+ \code{as.IDate.default}, arguments are passed to \code{as.Date}. For
+ \code{as.ITime.default}, arguments are passed to \code{as.POSIXlt}.}
+ \item{tz}{time zone (see \code{strptime}).}
+ \item{date}{date object convertable with \code{as.IDate}.}
+ \item{time}{time-of-day object convertable with \code{as.ITime}.}
+ \item{digits}{really \code{units}; one of the units listed for rounding. May be abbreviated.}
+}
+\details{
+\code{IDate} is a date class derived from \code{Date}. It has the same
+internal representation as the \code{Date} class, except the storage
+mode is integer. \code{IDate} is a relatively simple wrapper, and it
+should work in almost all situations as a replacement for \code{Date}.
+
+Functions that use \code{Date} objects generally work for
+\code{IDate} objects. This package provides specific methods for
+\code{IDate} objects for \code{mean}, \code{cut}, \code{seq}, \code{c},
+\code{rep}, and \code{split} to return an \code{IDate} object.
+
+\code{ITime} is a time-of-day class stored as the integer number of
+seconds in the day. \code{as.ITime} does not allow days longer than 24
+hours. Because \code{ITime} is stored in seconds, you can add it to a
+\code{POSIXct} object, but you should not add it to a \code{Date}
+object.
+
+Conversions to and from \code{Date}, \code{POSIXct}, and \code{chron}
+formats are provided.
+
+\code{ITime} does not account for time zones. When converting
+\code{ITime} and \code{IDate} to POSIXct with \code{as.POSIXct}, a time
+zone may be specified.
+
+In \code{as.POSIXct} methods for \code{ITime} and \code{IDate}, the
+second argument is required to be \code{tz} based on the generic
+template, but to make converting easier, the second arguement is
+interpreted as a date instead of a time zone if it is of type
+\code{IDate} or \code{ITime}. Therefore, you can use either of the
+following: \code{as.POSIXct(time, date)} or \code{as.POSIXct(date,
+time)}.
+
+\code{IDateTime} takes a date-time input and returns a data table with
+columns \code{date} and \code{time}.
+
+Using integer storage allows dates and/or times to be used as data table
+keys. With positive integers with a range less than 100,000, grouping
+and sorting is fast because radix sorting can be used (see
+\code{sort.list}).
+
+Several convenience functions like \code{hour} and \code{quarter} are
+provided to group or extract by hour, month, and other date-time
+intervals. \code{as.POSIXlt} is also useful. For example,
+\code{as.POSIXlt(x)$mon} is the integer month. The R base convenience
+functions \code{weekdays}, \code{months}, and \code{quarters} can also
+be used, but these return character values, so they must be converted to
+factors for use with data.table.
+
+The \code{round} method for IDate's is useful for grouping and plotting. It can
+round to weeks, months, quarters, and years.
+
+}
+
+\value{
+ For \code{as.IDate}, a class of \code{IDate} and \code{Date} with the
+ date stored as the number of days since some origin.
+
+ For \code{as.ITime}, a class of \code{ITime}
+ stored as the number of seconds in the day.
+
+ For \code{IDateTime}, a data table with columns \code{idate} and
+ \code{itime} in \code{IDate} and \code{ITime} format.
+
+ \code{hour}, code{yday}, \code{wday}, \code{mday}, \code{week},
+ \code{month}, \code{quarter}, and \code{year} return integer values
+ for hour, day of year, day of week, day of month, week, month,
+ quarter, and year.
+
+}
+\references{
+
+ G. Grothendieck and T. Petzoldt, ``Date and Time Classes in R,''
+ R News, vol. 4, no. 1, June 2004.
+
+ H. Wickham, http://gist.github.com/10238.
+
+}
+
+\author{ Tom Short, t.short at ieee.org }
+
+\seealso{ \code{\link{as.Date}}, \code{\link{as.POSIXct}},
+ \code{\link{strptime}}, \code{\link{DateTimeClasses}}
+
+}
+
+\examples{
+
+# create IDate:
+(d <- as.IDate("2001-01-01"))
+
+# S4 coercion also works
+identical(as.IDate("2001-01-01"), as("2001-01-01", "IDate"))
+
+# create ITime:
+(t <- as.ITime("10:45"))
+
+# S4 coercion also works
+identical(as.ITime("10:45"), as("10:45", "ITime"))
+
+(t <- as.ITime("10:45:04"))
+
+(t <- as.ITime("10:45:04", format = "\%H:\%M:\%S"))
+
+as.POSIXct("2001-01-01") + as.ITime("10:45")
+
+datetime <- seq(as.POSIXct("2001-01-01"), as.POSIXct("2001-01-03"), by = "5 hour")
+(af <- data.table(IDateTime(datetime), a = rep(1:2, 5), key = "a,idate,itime"))
+
+af[, mean(a), by = "itime"]
+af[, mean(a), by = list(hour = hour(itime))]
+af[, mean(a), by = list(wday = factor(weekdays(idate)))]
+af[, mean(a), by = list(wday = wday(idate))]
+
+as.POSIXct(af$idate)
+as.POSIXct(af$idate, time = af$itime)
+as.POSIXct(af$idate, af$itime)
+as.POSIXct(af$idate, time = af$itime, tz = "GMT")
+
+as.POSIXct(af$itime, af$idate)
+as.POSIXct(af$itime) # uses today's date
+
+(seqdates <- seq(as.IDate("2001-01-01"), as.IDate("2001-08-03"), by = "3 weeks"))
+round(seqdates, "months")
+
+if (require(chron)) {
+ as.chron(as.IDate("2000-01-01"))
+ as.chron(as.ITime("10:45"))
+ as.chron(as.IDate("2000-01-01"), as.ITime("10:45"))
+ as.chron(as.ITime("10:45"), as.IDate("2000-01-01"))
+ as.ITime(chron(times = "11:01:01"))
+ IDateTime(chron("12/31/98","10:45:00"))
+}
+
+}
+\keyword{utilities}
+\keyword{chron}
+
diff --git a/man/J.Rd b/man/J.Rd
new file mode 100644
index 0000000..ea2a122
--- /dev/null
+++ b/man/J.Rd
@@ -0,0 +1,57 @@
+\name{J}
+\alias{J}
+\alias{CJ}
+\alias{SJ}
+\title{ Creates a Join data table }
+\description{
+ Creates a \code{data.table} to be passed in as the \code{i} to a \code{[.data.table} join.
+}
+
+\usage{
+# DT[J(...)] # J() only for use inside DT[...].
+SJ(...) # DT[SJ(...)]
+CJ(..., sorted = TRUE, unique = FALSE) # DT[CJ(...)]
+}
+
+\arguments{
+ \item{\dots}{ Each argument is a vector. Generally each vector is the same length but if they are not then usual silent repitition is applied. }
+ \item{sorted}{ logical. Should the input order be retained?}
+ \item{unique}{ logical. When \code{TRUE}, only unique values of each vectors are used (automatically). }
+}
+\details{
+ \code{SJ} and \code{CJ} are convenience functions for creating a data.table in the context of a data.table 'query' on \code{x}.
+
+ \code{x[data.table(id)]} is the same as \code{x[J(id)]} but the latter is more readable. Identical alternatives are \code{x[list(id)]} and \code{x[.(id)]}.
+
+ \code{x} must have a key when passing in a join table as the \code{i}. See \code{\link{[.data.table}}
+}
+\value{
+ \itemize{
+ \code{J} : the same result as calling list. J is a direct alias for list but results in clearer more readable code.
+
+ \code{SJ} : (S)orted (J)oin. The same value as J() but additionally setkey() is called on all the columns in the order they were passed in to SJ. For efficiency, to invoke a binary merge rather than a repeated binary full search for each row of \code{i}.
+
+ \code{CJ} : (C)ross (J)oin. A data.table is formed from the cross product of the vectors. For example, 10 ids, and 100 dates, CJ returns a 1000 row table containing all the dates for all the ids. It gains \code{sorted}, which by default is TRUE for backwards compatibility. FALSE retains input order.
+ }
+}
+\seealso{ \code{\link{data.table}}, \code{\link{test.data.table}} }
+\examples{
+DT = data.table(A=5:1,B=letters[5:1])
+setkey(DT,B) # re-orders table and marks it sorted.
+DT[J("b")] # returns the 2nd row
+DT[.("b")] # same. Style of package plyr.
+DT[list("b")] # same
+
+# CJ usage examples
+CJ(c(5,NA,1), c(1,3,2)) # sorted and keyed data.table
+do.call(CJ, list(c(5,NA,1), c(1,3,2))) # same as above
+CJ(c(5,NA,1), c(1,3,2), sorted=FALSE) # same order as input, unkeyed
+# use for 'unique=' argument
+x = c(1,1,2)
+y = c(4,6,4)
+CJ(x, y, unique=TRUE) # unique(x) and unique(y) are computed automatically
+
+}
+\keyword{ data }
+
+
diff --git a/man/address.Rd b/man/address.Rd
new file mode 100644
index 0000000..8f6786c
--- /dev/null
+++ b/man/address.Rd
@@ -0,0 +1,23 @@
+\name{address}
+\alias{address}
+\title{ Address in RAM of a variable }
+\description{
+ Returns the pointer address of its argument.
+}
+\usage{
+ address(x)
+}
+\arguments{
+ \item{x}{ Anything. }
+}
+\details{
+Sometimes useful in determining whether a value has been copied or not, programatically.
+}
+\value{
+ A character vector length 1.
+}
+\references{
+\url{http://stackoverflow.com/a/10913296/403310} (but implemented in C without using \code{.Internal(inspect())})
+}
+\keyword{ data }
+
diff --git a/man/all.equal.data.table.Rd b/man/all.equal.data.table.Rd
new file mode 100644
index 0000000..fdb157b
--- /dev/null
+++ b/man/all.equal.data.table.Rd
@@ -0,0 +1,50 @@
+\name{all.equal}
+\alias{all.equal}
+\alias{all.equal.data.table}
+\title{ Equality Test Between Two Data Tables }
+\description{
+ Performs some factor level ``stripping'' and other operations to allow
+ for a convenient test of data equality between \code{data.table} objects.
+}
+
+\usage{
+ \method{all.equal}{data.table}(target, current, trim.levels = TRUE, ...)
+}
+
+\arguments{
+ \item{target, current}{
+ \code{data.table}s to compare
+ }
+
+ \item{trim.levels}{
+ A logical indicating whether or not to remove all unused levels in columns
+ that are factors before running equality check.
+ }
+
+ \item{\dots}{
+ Passed down to internal call of \code{\link{all.equal.list}}
+ }
+}
+
+\details{
+ This function is used primarily to make life easy with a testing harness
+ built around \code{test_that}. A call to \code{test_that::(expect_equal|equal)}
+ will ultimately dispatch to this method when making an "equality" check.
+}
+
+\value{
+ Either \code{TRUE} or a vector of mode \code{"character"} describing the
+ differences between \code{target} and \code{current}.
+}
+
+\seealso{
+ \code{\link{all.equal.list}}
+}
+
+\examples{
+dt1 <- data.table(A = letters[1:10], X = 1:10, key = "A")
+dt2 <- data.table(A = letters[5:14], Y = 1:10, key = "A")
+identical(all.equal(dt1, dt1), TRUE)
+is.character(all.equal(dt1, dt2))
+}
+
diff --git a/man/as.data.table.xts.Rd b/man/as.data.table.xts.Rd
new file mode 100644
index 0000000..a38fe2b
--- /dev/null
+++ b/man/as.data.table.xts.Rd
@@ -0,0 +1,27 @@
+\name{as.data.table.xts}
+\alias{as.data.table.xts}
+\title{Efficient xts to as.data.table conversion}
+\description{
+ Efficient conversion xts to data.table.
+}
+\usage{
+\method{as.data.table}{xts}(x, keep.rownames = TRUE, ...)
+}
+\arguments{
+\item{x}{xts to convert to data.table}
+
+\item{keep.rownames}{keep xts index as \emph{index} column in result data.table}
+
+\item{\dots}{ignored, just for consistency with \code{as.data.table}}
+}
+\seealso{ \code{\link{as.xts.data.table}} }
+\examples{
+\dontrun{
+data(sample_matrix, package = "xts")
+sample.xts <- xts::as.xts(sample_matrix) # xts might not be attached on search path
+# print head of xts
+print(head(sample.xts))
+# print dt
+print(as.data.table(sample.xts))
+}
+}
diff --git a/man/as.xts.data.table.Rd b/man/as.xts.data.table.Rd
new file mode 100644
index 0000000..2f6b843
--- /dev/null
+++ b/man/as.xts.data.table.Rd
@@ -0,0 +1,24 @@
+\name{as.xts.data.table}
+\alias{as.xts.data.table}
+\title{Efficient data.table to xts conversion}
+\description{
+ Efficient conversion of data.table to xts, data.table must have \emph{POSIXct} or \emph{Date} type in first column.
+}
+\usage{
+as.xts.data.table(x)
+}
+\arguments{
+\item{x}{data.table to convert to xts, must have \emph{POSIXct} or \emph{Date} in the first column. All others non-numeric columns will be omitted with warning.}
+}
+\seealso{ \code{\link{as.data.table.xts}} }
+\examples{
+\dontrun{
+sample.dt <- data.table(date = as.Date((Sys.Date()-999):Sys.Date(),origin="1970-01-01"),
+ quantity = sample(10:50,1000,TRUE),
+ value = sample(100:1000,1000,TRUE))
+# print dt
+print(sample.dt)
+# print head of xts
+print(head(as.xts.data.table(sample.dt))) # xts might not be attached on search path
+}
+}
diff --git a/man/assign.Rd b/man/assign.Rd
new file mode 100644
index 0000000..0e361c8
--- /dev/null
+++ b/man/assign.Rd
@@ -0,0 +1,102 @@
+\name{:=}
+\alias{:=}
+\alias{set}
+\title{ Assignment by reference }
+\description{
+ Fast add, remove and modify subsets of columns, by reference.
+}
+\usage{
+# DT[i, LHS:=RHS, by=...]
+
+# DT[i, c("LHS1","LHS2") := list(RHS1, RHS2), by=...]
+
+# DT[i, `:=`(LHS1=RHS1,
+# LHS2=RHS2,
+# ...), by=...]
+
+ set(x, i=NULL, j, value)
+}
+\arguments{
+\item{LHS}{ A single column name. Or, when \code{with=FALSE}, a vector of column names or numeric positions (or a variable that evaluates as such). If the column doesn't exist, it is added, by reference. }
+\item{RHS}{ A vector of replacement values. It is recycled in the usual way to fill the number of rows satisfying \code{i}, if any. Or, when \code{with=FALSE}, a \code{list} of replacement vectors which are applied (the \code{list} is recycled if necessary) to each column of \code{LHS} . To remove a column use \code{NULL}. }
+\item{x}{ A \code{data.table}. Or, \code{set()} accepts \code{data.frame}, too. }
+\item{i}{ Optional. In set(), integer row numbers to be assigned \code{value}. \code{NULL} represents all rows more efficiently than creating a vector such as \code{1:nrow(x)}. }
+\item{j}{ In set(), integer column number to be assigned \code{value}. }
+\item{value}{ Value to assign by reference to \code{x[i,j]}. }
+}
+\details{
+\code{:=} is defined for use in \code{j} only. It \emph{updates} or \emph{adds} the column(s) by reference. It makes no copies of any part of memory at all. Typical usages are :
+\preformatted{
+ DT[i,colname:=value] # update (or add at the end if doesn't exist) a column called "colname" with value where i and (when new column) NA elsewhere
+ DT[i,"colname \%":=value] # same. column called "colname \%"
+ DT[i,(3:6):=value] # update existing columns 3:6 with value. Aside: parens are not required here since : already makes LHS a call rather than a symbol
+ DT[i,colnamevector:=value,with=FALSE] # old syntax. The contents of colnamevector in calling scope determine the column names or positions to update (or add)
+ DT[i,(colnamevector):=value] # same, shorthand. Now preferred. The parens are enough to stop the LHS being a symbol
+ DT[i,colC:=mean(colB),by=colA] # update (or add) column called "colC" by reference by group. A major feature of `:=`.
+ DT[,`:=`(new1=sum(colB), new2=sum(colC))] # multiple :=.
+}
+The following all result in a friendly error (by design) :
+\preformatted{
+ x := 1L # friendly error
+ DT[i,colname] := value # friendly error
+ DT[i]$colname := value # friendly error
+ DT[,{col1:=1L;col2:=2L}] # friendly error. Use `:=`() instead for multiple := (see above)
+}
+
+\code{:=} in \code{j} can be combined with all types of \code{i} (such as binary search), and all types of \code{by}. This a one reason why \code{:=} has been implemented in \code{j}. See FAQ 2.16 for analogies to SQL. \cr\cr % for some reason in this .Rd file (but not the others), newlines seem to be ignored.
+
+When \code{LHS} is a factor column and \code{RHS} is a character vector with items missing from the factor levels, the new level(s) are automatically added (by reference, efficiently), unlike base methods.\cr\cr
+
+Unlike \code{<-} for \code{data.frame}, the (potentially large) LHS is not coerced to match the type of the (often small) RHS. Instead the RHS is coerced to match the type of the LHS, if necessary. Where this involves double precision values being coerced to an integer column, a warning is given (whether or not fractional data is truncated). The motivation for this is efficiency. It is best to get the column types correct up front and stick to them. Changing a column type is possible but [...]
+
+\code{data.table}s are \emph{not} copied-on-change by \code{:=}, \code{setkey} or any of the other \code{set*} functions. See \code{\link{copy}}.\cr\cr
+
+Additional resources: search for "\code{:=}" in the \href{../doc/datatable-faq.pdf}{FAQs vignette} (3 FAQs mention \code{:=}), search Stack Overflow's \href{http://stackoverflow.com/search?q=\%5Bdata.table\%5D+reference}{data.table tag for "reference"} (6 questions).\cr\cr
+
+Advanced (internals) : sub assigning to existing columns is easy to see how that is done internally. Removing columns by reference is also straightforward by modifying the vector of column pointers only (using memmove in C). Adding columns is more tricky to see how that can be grown by reference: the list vector of column pointers is over-allocated, see \code{\link{truelength}}. By defining \code{:=} in \code{j} we believe update synax is natural, and scales, but also it bypasses \code{[ [...]
+
+Since \code{[.data.table} incurs overhead to check the existence and type of arguments (for example), \code{set()} provides direct (but less flexible) assignment by reference with low overhead, appropriate for use inside a \code{for} loop. See examples. \code{:=} is more flexible than \code{set()} because \code{:=} is intended to be combined with \code{i} and \code{by} in single queries on large datasets.
+
+}
+\value{
+ \code{DT} is modified by reference and the new value is returned. If you require a copy, take a copy first (using \code{DT2=copy(DT)}). Recall that this package is for large data (of mixed column types, with multi-column keys) where updates by reference can be many orders of magnitude faster than copying the entire table.
+}
+\seealso{ \code{\link{data.table}}, \code{\link{copy}}, \code{\link{alloc.col}}, \code{\link{truelength}}, \code{\link{set}}
+}
+\examples{
+DT = data.table(a=LETTERS[c(1,1:3)],b=4:7,key="a")
+DT[,c:=8] # add a numeric column, 8 for all rows
+DT[,d:=9L] # add an integer column, 9L for all rows
+DT[,c:=NULL] # remove column c
+DT[2,d:=10L] # subassign by reference to column d
+DT # DT changed by reference
+
+DT[b>4,b:=d*2L] # subassign to b using d, where b>4
+DT["A",b:=0L] # binary search for group "A" and set column b
+
+DT[,e:=mean(d),by=a] # add new column by group by reference
+DT["B",f:=mean(d)] # subassign to new column, NA initialized
+
+\dontrun{
+# Speed example ...
+
+m = matrix(1,nrow=100000,ncol=100)
+DF = as.data.frame(m)
+DT = as.data.table(m)
+
+system.time(for (i in 1:1000) DF[i,1] <- i)
+# 591 seconds
+system.time(for (i in 1:1000) DT[i,V1:=i])
+# 2.4 seconds ( 246 times faster, 2.4 is overhead in [.data.table )
+system.time(for (i in 1:1000) set(DT,i,1L,i))
+# 0.03 seconds ( 19700 times faster, overhead of [.data.table is avoided )
+
+# However, normally, we call [.data.table *once* on *large* data, not many times on small data.
+# The above is to demonstrate overhead, not to recommend looping in this way. But the option
+# of set() is there if you need it.
+}
+
+}
+\keyword{ data }
+
+
diff --git a/man/between.Rd b/man/between.Rd
new file mode 100644
index 0000000..d4d2cec
--- /dev/null
+++ b/man/between.Rd
@@ -0,0 +1,29 @@
+\name{between}
+\alias{between}
+\alias{\%between\%}
+\title{ Convenience function for range subset logic. }
+\description{
+ Intended for use in [.data.table i.
+}
+\usage{
+between(x,lower,upper,incbounds=TRUE)
+x %between% c(lower,upper)
+}
+\arguments{
+ \item{x}{ Any vector e.g. numeric, character, date, ... }
+ \item{lower}{ Lower range bound. }
+ \item{upper}{ Upper range bound. }
+ \item{incbounds}{ \code{TRUE} means inclusive bounds i.e. [lower,upper]. \code{FALSE} means exclusive bounds i.e. (lower,upper). }
+}
+% \details{
+% }
+\value{
+ Logical vector as the same length as \code{x} with value \code{TRUE} for those that lie within the range [lower,upper] or (lower,upper).
+}
+\note{ Current implementation does not make use of ordered keys. }
+\seealso{ \code{\link{data.table}}, \code{\link{like}} }
+\examples{
+DT = data.table(a=1:5, b=6:10)
+DT[b \%between\% c(7,9)]
+}
+\keyword{ data }
diff --git a/man/chmatch.Rd b/man/chmatch.Rd
new file mode 100644
index 0000000..62d02b0
--- /dev/null
+++ b/man/chmatch.Rd
@@ -0,0 +1,87 @@
+\name{chmatch}
+\alias{chmatch}
+\alias{\%chin\%}
+\alias{chorder}
+\alias{chgroup}
+\title{ Faster match of character vectors }
+\description{
+ \code{chmatch} returns a vector of the positions of (first) matches of its first argument in its second. Both arguments must be character vectors.
+
+ \code{\%chin\%} is like \code{\%in\%}, but for character vectors.
+}
+\usage{
+chmatch(x, table, nomatch=NA_integer_)
+x \%chin\% table
+chorder(x)
+chgroup(x)
+}
+\arguments{
+ \item{x}{ character vector: the values to be matched, or the values to be ordered or grouped }
+ \item{table}{ character vector: the values to be matched against. }
+ \item{nomatch}{ the value to be returned in the case when no match is found. Note that it is coerced to integer. }
+}
+\details{
+ Fast versions of \code{match}, \code{\%in\%} and \code{order}, optimised for character vectors. \code{chgroup} groups together duplicated values but retains the group order (according the first appearance order of each group), efficiently. They have been primarily developed for internal use by data.table, but have been exposed since that seemed appropriate.
+
+ Strings are already cached internally by R (\code{CHARSXP}) and that is utilised by these functions. No hash table is built or cached, so the first call is the same speed as subsequent calls. Essentially, a counting sort (similar to \code{base::sort.list(x,method="radix")}, see \code{\link{setkey}}) is implemented using the (almost) unused truelength of CHARSXP as the counter. \emph{Where} R \emph{has} used truelength of CHARSXP (where a character value is shared by a variable name), t [...]
+
+ Methods that do build and cache a hash table (such as the \href{http://www.rforge.net/fastmatch}{fastmatch package}) are \emph{much} faster on subsequent calls (almost instant) but a little slower on the first. Therefore \code{chmatch} may be particularly suitable for ephemeral vectors (such as local variables in functions) or tasks that are only done once. Much depends on the length of \code{x} and \code{table}, how many unique strings each contains, and whether the position of the fi [...]
+
+ It may be possible to speed up fastmatch's hash table build time by using the technique in \code{data.table}, and we have suggested this to its author. If successful, fastmatch would then be fastest in all cases.
+ }
+\value{
+ As \code{match} and \code{\%in\%}. \code{chorder} and \code{chgroup} return an integer index vector.
+}
+\seealso{ \code{\link[base]{match}}, \code{\link{\%in\%}}, \code{\link[fastmatch]{fmatch}}
+}
+\note{ The name \code{charmatch} was taken by \code{\link[base]{charmatch}}, hence \code{chmatch}.
+}
+\examples{
+# Please type 'example(chmatch)' to run this and see timings on your machine
+
+# N is set small here (1e5) because CRAN runs all examples and tests every night, to catch
+# any problems early as R itself changes and other packages run.
+# The comments here apply when N has been changed to 1e7.
+N = 1e5
+
+u = as.character(as.hexmode(1:10000))
+y = sample(u,N,replace=TRUE)
+x = sample(u)
+ # With N=1e7 ...
+system.time(a <- match(x,y)) # 4.8s
+system.time(b <- chmatch(x,y)) # 0.9s Faster than 1st fmatch
+identical(a,b)
+if (fastmatchloaded<-suppressWarnings(require(fastmatch))) {
+ print(system.time(c <- fmatch(x,y))) # 2.1s Builds and caches hash
+ print(system.time(c <- fmatch(x,y))) # 0.00s Uses hash
+ identical(a,c)
+}
+
+system.time(a <- x \%in\% y) # 4.8s
+system.time(b <- x \%chin\% y) # 0.9s
+identical(a,b)
+if (fastmatchloaded) {
+ match <- fmatch # fmatch is drop in replacement
+ print(system.time(c <- match(x,y))) # 0.00s
+ print(system.time(c <- x \%in\% y)) # 4.8s \%in\% still prefers base::match
+ # Anyone know how to get \%in\% to use fmatch (without masking \%in\% too)?
+ rm(match)
+ identical(a,c)
+}
+
+# Different example with more unique strings ...
+u = as.character(as.hexmode(1:(N/10)))
+y = sample(u,N,replace=TRUE)
+x = sample(u,N,replace=TRUE)
+system.time(a <- match(x,y)) # 34.0s
+system.time(b <- chmatch(x,y)) # 6.4s
+identical(a,b)
+if (fastmatchloaded) {
+ print(system.time(c <- fmatch(x,y))) # 7.9s
+ print(system.time(c <- fmatch(x,y))) # 4.0s
+ identical(a,c)
+}
+}
+\keyword{ data }
+
+
diff --git a/man/copy.Rd b/man/copy.Rd
new file mode 100644
index 0000000..ad71757
--- /dev/null
+++ b/man/copy.Rd
@@ -0,0 +1,41 @@
+\name{copy}
+\alias{copy}
+\title{ Copy an entire object }
+\description{
+ In \code{data.table} parlance, all \code{set*} functions change their input \emph{by reference}. That is, no copy is made at all, other than temporary working memory, which is as large as one column.. The only other \code{data.table} operator that modifies input by reference is \code{\link{:=}}. Check out the \code{See Also} section below for other \code{set*} function \code{data.table} provides.
+
+ \code{copy()} copies an entire object.
+}
+\usage{
+copy(x)
+}
+\arguments{
+ \item{x}{ A \code{data.table}. }
+}
+\details{
+\code{data.table} provides functions that operate on objects \emph{by reference} and minimise full object copies as much as possible. Still, it might be necessary in some situations to work on an object's copy which can be done using \code{DT.copy <- copy(DT)}. It may also be sometimes useful before \code{:=} (or \code{set}) is used to subassign to a column by reference.
+
+A \code{copy()} may be required when doing \code{dt_names = names(DT)}. Due to R's \emph{copy-on-modify}, \code{dt_names} still points to the same location in memory as \code{names(DT)}. Therefore modifying \code{DT} \emph{by reference} now, say by adding a new column, \code{dt_names} will also get updated. To avoid this, one has to \emph{explicitly} copy: \code{dt_names <- copy(names(DT))}.
+ }
+\value{
+ Returns a copy of the object.
+}
+\seealso{ \code{\link{data.table}}, \code{\link{setkey}}, \code{\link{setDT}}, \code{\link{setDF}}, \code{\link{set}} \code{\link{:=}}, \code{\link{setorder}}, \code{\link{setattr}}, \code{\link{setnames}}
+}
+\examples{
+# Type 'example(copy)' to run these at prompt and browse output
+
+DT = data.table(A=5:1,B=letters[5:1])
+DT2 = copy(DT) # explicit copy() needed to copy a data.table
+setkey(DT2,B) # now just changes DT2
+identical(DT,DT2) # FALSE. DT and DT2 are now different tables
+
+DT = data.table(A=5:1, B=letters[5:1])
+nm1 = names(DT)
+nm2 = copy(names(DT))
+DT[, C := 1L]
+identical(nm1, names(DT)) # TRUE, nm1 is also changed by reference
+identical(nm2, names(DT)) # FALSE, nm2 is a copy, different from names(DT)
+}
+\keyword{ data }
+
diff --git a/man/data.table-class.Rd b/man/data.table-class.Rd
new file mode 100644
index 0000000..27009e3
--- /dev/null
+++ b/man/data.table-class.Rd
@@ -0,0 +1,33 @@
+\name{data.table-class}
+\docType{class}
+
+\alias{class:data.table}
+\alias{data.table-class}
+
+\title{S4 Definition for data.table}
+\description{
+ A \code{data.table} can be used in S4 class definitions as either
+ a parent class (inside a \code{contains} argument of \code{setClass}),
+ or as an element of an S4 slot.
+}
+
+% \details{
+% }
+
+
+\author{ Steve Lianoglou }
+\seealso{
+ \code{\link{data.table}}
+}
+
+\examples{
+## Used in inheritence.
+setClass('SuperDataTable', contains='data.table')
+
+## Used in a slot
+setClass('Something', representation(x='character', dt='data.table'))
+x <- new("Something", x='check', dt=data.table(a=1:10, b=11:20))
+}
+
+\keyword{classes}
+\keyword{methods}
diff --git a/man/data.table.Rd b/man/data.table.Rd
new file mode 100644
index 0000000..85aad18
--- /dev/null
+++ b/man/data.table.Rd
@@ -0,0 +1,307 @@
+\name{data.table-package}
+\alias{data.table-package}
+\docType{package}
+\alias{data.table}
+\alias{as.data.table}
+\alias{is.data.table}
+\alias{Ops.data.table}
+\alias{is.na.data.table}
+\alias{[.data.table}
+\title{ Enhanced data.frame }
+\description{
+ \code{data.table} \emph{inherits} from \code{data.frame}. It offers fast subset, fast grouping, fast update, fast ordered joins and list columns in a short and flexible syntax, for faster development. It is inspired by \code{A[B]} syntax in \R where \code{A} is a matrix and \code{B} is a 2-column matrix. Since a \code{data.table} \emph{is} a \code{data.frame}, it is compatible with \R functions and packages that \emph{only} accept \code{data.frame}.
+
+ The 10 minute quick start guide to \code{data.table} may be a good place to start: \href{../doc/datatable-intro.pdf}{\code{vignette("datatable-intro")}}. Or, the first section of FAQs is intended to be read from start to finish and is considered core documentation: \href{../doc/datatable-faq.pdf}{\code{vignette("datatable-faq")}}. If you have read and searched these documents and the help page below, please feel free to ask questions on \href{http://r.789695.n4.nabble.com/datatable-he [...]
+
+ Please check the \href{http://datatable.r-forge.r-project.org/}{homepage} for up to the minute \href{https://github.com/Rdatatable/data.table/blob/master/README.md}{news}.
+
+ Tip: one of the quickest ways to learn the features is to type \code{example(data.table)} and study the output at the prompt.
+}
+\usage{
+data.table(..., keep.rownames=FALSE, check.names=FALSE, key=NULL)
+
+\method{[}{data.table}(x, i, j, by, keyby, with = TRUE,
+ nomatch = getOption("datatable.nomatch"), # default: NA_integer_
+ mult = "all",
+ roll = FALSE,
+ rollends = if (roll=="nearest") c(TRUE,TRUE)
+ else if (roll>=0) c(FALSE,TRUE)
+ else c(TRUE,FALSE),
+ which = FALSE,
+ .SDcols,
+ verbose = getOption("datatable.verbose"), # default: FALSE
+ allow.cartesian = getOption("datatable.allow.cartesian"), # default: FALSE
+ drop = NULL,
+ on = NULL # join without setting keys, new feature from v1.9.6+
+ )
+}
+\arguments{
+ \item{\dots}{ Just as \code{\dots} in \code{\link{data.frame}}. Usual recycling rules are applied to vectors of different lengths to create a list of equal length vectors.
+
+}
+ \item{keep.rownames}{ If \code{\dots} is a \code{matrix} or \code{data.frame}, \code{TRUE} will retain the rownames of that object in a column named \code{rn}.
+
+}
+ \item{check.names}{ Just as \code{check.names} in \code{\link{data.frame}}.
+
+}
+ \item{key}{ Character vector of one or more column names which is passed to \code{\link{setkey}}. It may be a single comma separated string such as \code{key="x,y,z"}, or a vector of names such as \code{key=c("x","y","z")}.
+
+}
+ \item{x}{ A \code{data.table}.
+
+}
+ \item{i}{ Integer, logical or character vector, single column numeric \code{matrix}, expression of column names, \code{list} or \code{data.table}.
+
+ integer and logical vectors work the same way they do in \code{\link{[.data.frame}}. Other than \code{NA}s in logical \code{i} are treated as \code{FALSE} and a single \code{NA} logical is not recycled to match the number of rows, as it is in \code{[.data.frame}.
+
+ character is matched to the first column of \code{x}'s key.
+
+ expression is evaluated within the frame of the \code{data.table} (i.e. it sees column names as if they are variables) and can evaluate to any of the other types.
+
+ When \code{i} is a \code{data.table}, \code{x} must have a key. \code{i} is \emph{joined} to \code{x} using \code{x}'s key and the rows in \code{x} that match are returned. An equi-join is performed between each column in \code{i} to each column in \code{x}'s key; i.e., column 1 of \code{i} is matched to the 1st column of \code{x}'s key, column 2 to the second, etc. The match is a binary search in compiled C in O(log n) time. If \code{i} has \emph{fewer} columns than \code{x}'s key the [...]
+
+ All types of `i` may be prefixed with \code{!}. This signals a \emph{not-join} or \emph{not-select} should be performed. Throughout \code{data.table} documentation, where we refer to the type of `i`, we mean the type of `i` \emph{after} the `!`, if present. See examples.
+
+ Advanced: When \code{i} is an expression of column names that evaluates to \code{data.table} or \code{list}, a join is performed. We call this a \emph{self join}.
+
+ Advanced: When \code{i} is a single variable name, it is not considered an expression of column names and is instead evaluated in calling scope.
+
+}
+ \item{j}{ A single column name, single expresson of column names, \code{list()} of expressions of column names, an expression or function call that evaluates to \code{list} (including \code{data.frame} and \code{data.table} which are \code{list}s, too), or (when \code{with=FALSE}) a vector of names or positions to select.
+
+ \code{j} is evaluated within the frame of the \code{data.table}; i.e., it sees column names as if they are variables. Use \code{j=list(...)} to return multiple columns and/or expressions of columns. A single column or single expression returns that type, usually a vector. See the examples.
+
+}
+ \item{by}{ A single unquoted column name, a \code{list()} of expressions of column names, a single character string containing comma separated column names (where spaces are significant since column names may contain spaces even at the start or end), or a character vector of column names.
+
+ The \code{list()} of expressions is evaluated within the frame of the \code{data.table} (i.e. it sees column names as if they are variables). The \code{data.table} is then grouped by the \code{by} and \code{j} is evaluated within each group. The order of the rows within each group is preserved, as is the order of the groups. \code{j=list(...)} may be omitted when there is just one expression, for convenience, typically a single expression such as \code{sum(colB)}; e.g., \code{DT[,sum(c [...]
+
+ When \code{by} contains the first \code{n} columns of \code{x}'s key, we call this a \emph{keyed by}. In a keyed by the groups appear contiguously in RAM and memory is copied in bulk internally, for extra speed. Otherwise, we call it an \emph{ad hoc by}. Ad hoc by is still many times faster than \code{tapply}, for example, but just not as fast as keyed by when datasets are very large, in particular when the size of \emph{each group} is large. Not to be confused with \code{keyby=} defin [...]
+
+ Advanced: When \code{i} is a \code{data.table}, \code{DT[i,j,by=.EACHI]} evaluates \code{j} for the groups in `DT` that each row in \code{i} joins to. That is, you can join (in \code{i}) and aggregate (in \code{j}) simultaneously. We call this \emph{grouping by each i}. It is particularly memory efficient as you don't have to materialise the join result only to aggregate later. Please refer to \href{http://stackoverflow.com/a/27004566/559784}{this Stackoverflow answer} for a more detai [...]
+
+ Advanced: When grouping, symbols \code{.SD}, \code{.BY}, \code{.N}, \code{.I} and \code{.GRP} may be used in the \code{j} expression, defined as follows.
+
+ \code{.SD} is a \code{data.table} containing the \bold{S}ubset of \code{x}'s \bold{D}ata for each group, excluding any columns used in \code{by} (or \code{keyby}).
+
+ \code{.BY} is a \code{list} containing a length 1 vector for each item in \code{by}. This can be useful when \code{by} is not known in advance. The \code{by} variables are also available to \code{j} directly by name; useful for example for titles of graphs if \code{j} is a plot command, or to branch with \code{if()} depending on the value of a group variable.
+
+ \code{.N} is an integer, length 1, containing the number of rows in the group. This may be useful when the column names are not known in advance and for convenience generally. When grouping by \code{i}, \code{.N} is the number of rows in \code{x} matched to, for each row of \code{i}, regardless of whether \code{nomatch} is \code{NA} or \code{0}. It is renamed to \code{N} (no dot) in the result (otherwise a column called \code{".N"} could conflict with the \code{.N} variable, see FAQ 4. [...]
+
+ \code{.I} is an integer vector equal to \code{seq_len(nrow(x))}. While grouping, it holds for each item in the group, it's row location in \code{x}. This is useful to subset in \code{j}; e.g. \code{DT[, .I[which.max(somecol)], by=grp]}.
+
+ \code{.GRP} is an integer, length 1, containing a simple group counter. 1 for the 1st group, 2 for the 2nd, etc.
+
+ \code{.SD}, \code{.BY}, \code{.N}, \code{.I} and \code{.GRP} are \emph{read only}. Their bindings are locked and attempting to assign to them will generate an error. If you wish to manipulate \code{.SD} before returning it, take a \code{copy(.SD)} first (see FAQ 4.5). Using \code{:=} in the \code{j} of \code{.SD} is reserved for future use as a (tortuously) flexible way to update \code{DT} by reference by group (even when groups are not contiguous in an ad hoc by).
+
+ Advanced: In the \code{X[Y,j]} form of grouping, the \code{j} expression sees variables in \code{X} first, then \code{Y}. We call this \emph{join inherited scope}. If the variable is not in \code{X} or \code{Y} then the calling frame is searched, its calling frame, and so on in the usual way up to and including the global environment.
+
+}
+ \item{keyby}{ An \emph{ad-hoc-by} or \emph{keyed-by} (just as \code{by=} defined above) but with an additional \code{setkey()} run on the \code{by} columns of the result afterwards, for convenience. It is common practice to use `keyby=` routinely when you wish the result to be sorted. Out loud we read \code{keyby=} as \emph{by= then setkey}. Otherwise, `by=` can be relied on to return the groups in the order they appear in the data.
+
+}
+ \item{with}{ By default \code{with=TRUE} and \code{j} is evaluated within the frame of \code{x}; column names can be used as variables. When \code{with=FALSE} \code{j} is a character vector of column names or a numeric vector of column positions to select, and the value returned is always a \code{data.table}. \code{with=FALSE} is often useful in \code{data.table} to select columns dynamically.
+
+}
+ \item{nomatch}{ Same as \code{nomatch} in \code{\link{match}}. When a row in \code{i} has no match to \code{x}'s key, \code{nomatch=NA} (default) means \code{NA} is returned for \code{x}'s non-join columns for that row of \code{i}. \code{0} means no rows will be returned for that row of \code{i}. The default value (used when \code{nomatch} is not supplied) can be changed from \code{NA} to \code{0} using \code{options(datatable.nomatch=0)}.
+
+}
+ \item{mult}{ When \emph{multiple} rows in \code{x} match to the row in \code{i}, \code{mult} controls which are returned: \code{"all"} (default), \code{"first"} or \code{"last"}.
+
+}
+ \item{roll}{ Applies to the last join column, generally a date but can be any ordered variable, irregular and including gaps. If \code{roll=TRUE} and \code{i}'s row matches to all but the last \code{x} join column, and its value in the last \code{i} join column falls in a gap (including after the last observation in \code{x} for that group), then the \emph{prevailing} value in \code{x} is \emph{rolled} forward. This operation is particularly fast using a modified binary search. The ope [...]
+ When \code{roll} is a positive number, this limits how far values are carried forward. \code{roll=TRUE} is equivalent to \code{roll=+Inf}.
+ When \code{roll} is a negative number, values are rolled backwards; i.e., next observation carried backwards (NOCB). Use \code{-Inf} for unlimited roll back.
+ When \code{roll} is \code{"nearest"}, the nearest value is joined to.
+
+}
+ \item{rollends}{ A logical vector length 2 (a single logical is recycled). When rolling forward (e.g. \code{roll=TRUE}) if a value is past the \emph{last} observation within each group defined by the join columns, \code{rollends[2]=TRUE} will roll the last value forwards. \code{rollends[1]=TRUE} will roll the first value backwards if the value is before it. If \code{rollends=FALSE} the value of \code{i} must fall in a gap in \code{x} but not after the end or before the beginning of the [...]
+
+}
+ \item{which}{ \code{TRUE} returns the row numbers of \code{x} that \code{i} matches to. \code{NA} returns the row numbers of \code{i} that have no match in \code{x}. By default \code{FALSE} and the rows in \code{x} that match are returned.
+
+}
+ \item{.SDcols}{ Advanced. Specifies the columns of \code{x} included in \code{.SD}. May be character column names or numeric positions. This is useful for speed when applying a function through a subset of (possible very many) columns; e.g., \code{DT[,lapply(.SD,sum),by="x,y",.SDcols=301:350]}.
+
+}
+ \item{verbose}{ \code{TRUE} turns on status and information messages to the console. Turn this on by default using \code{options(datatable.verbose=TRUE)}. The quantity and types of verbosity may be expanded in future.
+
+}
+ \item{allow.cartesian}{ \code{FALSE} prevents joins that would result in more than \code{nrow(x)+nrow(i)} rows. This is usually caused by duplicate values in \code{i}'s join columns, each of which join to the same group in `x` over and over again: a \emph{misspecified} join. Usually this was not intended and the join needs to be changed. The word 'cartesian' is used loosely in this context. The traditional cartesian join is (deliberately) difficult to achieve in \code{data.table}: wher [...]
+
+ \item{drop}{ Never used by \code{data.table}. Do not use. It needs to be here because \code{data.table} inherits from \code{data.frame}. See \code{vignette("datatable-faq")}.
+
+}
+ \item{on}{ A named atomic vector of column names indicating which columns in \code{i} should be joined to which columns in \code{x}. See \code{Examples}.}
+}
+\details{
+\code{data.table} builds on base \R functionality to reduce 2 types of time :
+\enumerate{
+\item programming time (easier to write, read, debug and maintain)
+\item compute time
+}
+
+It combines database like operations such as \code{\link{subset}}, \code{\link{with}} and \code{\link{by}} and provides similar joins that \code{\link{merge}} provides but faster. This is achieved by using R's column based ordered in-memory \code{data.frame} structure, \code{eval} within the environment of a \code{list}, the \code{[.data.table} mechanism to condense the features, and compiled C to make certain operations fast.
+
+The package can be used just for rapid programming (compact syntax). Largest compute time benefits are on 64bit platforms with plentiful RAM, or when smaller datasets are repeatedly queried within a loop, or when other methods use so much working memory that they fail with an out of memory error.
+
+As with \code{[.data.frame}, \emph{compound queries} can be concatenated on one line; e.g.,
+\preformatted{
+ DT[,sum(v),by=colA][V1<300][tail(order(V1))]
+ # sum(v) by colA then return the 6 largest which are under 300
+}
+The \code{j} expression does not have to return data; e.g.,
+\preformatted{
+ DT[,plot(colB,colC),by=colA]
+ # produce a set of plots (likely to pdf) returning no data
+}
+Multiple \code{data.table}s (e.g. \code{X}, \code{Y} and \code{Z}) can be joined in many ways; e.g.,
+\preformatted{
+ X[Y][Z]
+ X[Z][Y]
+ X[Y[Z]]
+ X[Z[Y]]
+}
+A \code{data.table} is a \code{list} of vectors, just like a \code{data.frame}. However :
+\enumerate{
+\item it never has rownames. Instead it may have one \emph{key} of one or more columns. This key can be used for row indexing instead of rownames.
+\item it has enhanced functionality in \code{[.data.table} for fast joins of keyed tables, fast aggregation, fast last observation carried forward (LOCF) and fast add/modify/delete of columns by reference with no copy at all.
+}
+
+Since a \code{list} \emph{is} a \code{vector}, \code{data.table} columns may be type \code{list}. Columns of type \code{list} can contain mixed types. Each item in a column of type \code{list} may be different lengths. This is true of \code{data.frame}, too.
+
+Several \emph{methods} are provided for \code{data.table}, including \code{is.na}, \code{na.omit},
+\code{t}, \code{rbind}, \code{cbind}, \code{merge} and others.
+}
+\references{
+\code{data.table} homepage: \url{http://datatable.r-forge.r-project.org/}\cr
+User reviews: \url{http://crantastic.org/packages/data-table}\cr
+\url{http://en.wikipedia.org/wiki/Binary_search}\cr
+\url{http://en.wikipedia.org/wiki/Radix_sort}
+}
+\note{ If \code{keep.rownames} or \code{check.names} are supplied they must be written in full because \R does not allow partial argument names after `\code{\dots}`. For example, \code{data.table(DF,keep=TRUE)} will create a
+column called \code{"keep"} containing \code{TRUE} and this is correct behaviour; \code{data.table(DF,keep.rownames=TRUE)} was intended.
+
+POSIXlt is not supported as a column type because it uses 40 bytes to store a single datetime. Unexpected errors may occur if you manage to create a column of type POSIXlt. Please see \href{http://r-forge.r-project.org/scm/viewvc.php/pkg/NEWS?view=markup&root=datatable}{NEWS} for 1.6.3, and \code{\link{IDateTime}} instead. IDateTime has methods to convert to and from POSIXlt.
+}
+\seealso{ \code{\link{data.frame}}, \code{\link{[.data.frame}} , \code{\link{setkey}}, \code{\link{J}}, \code{\link{SJ}}, \code{\link{CJ}}, \code{\link{merge.data.table}}, \code{\link{tables}}, \code{\link{test.data.table}}, \code{\link{IDateTime}}, \code{\link{unique.data.table}}, \code{\link{copy}}, \code{\link{:=}}, \code{\link{alloc.col}}, \code{\link{truelength}}, \code{\link{rbindlist}}, \code{\link{setNumericRounding}}
+}
+\examples{
+\dontrun{
+example(data.table) # to run these examples at the prompt}
+
+DF = data.frame(x=rep(c("a","b","c"),each=3), y=c(1,3,6), v=1:9)
+DT = data.table(x=rep(c("a","b","c"),each=3), y=c(1,3,6), v=1:9)
+DF
+DT
+identical(dim(DT),dim(DF)) # TRUE
+identical(DF$a, DT$a) # TRUE
+is.list(DF) # TRUE
+is.list(DT) # TRUE
+
+is.data.frame(DT) # TRUE
+
+tables()
+
+DT[2] # 2nd row
+DT[,v] # v column (as vector)
+DT[,list(v)] # v column (as data.table)
+DT[2:3,sum(v)] # sum(v) over rows 2 and 3
+DT[2:5,cat(v,"\n")] # just for j's side effect
+DT[c(FALSE,TRUE)] # even rows (usual recycling)
+
+DT[,2,with=FALSE] # 2nd column
+colNum = 2
+DT[,colNum,with=FALSE] # same
+
+setkey(DT,x) # set a 1-column key. No quotes, for convenience.
+setkeyv(DT,"x") # same (v in setkeyv stands for vector)
+v="x"
+setkeyv(DT,v) # same
+# key(DT)<-"x" # copies whole table, please use set* functions instead
+
+DT["a"] # binary search (fast)
+DT[x=="a"] # same; i.e. binary search (fast)
+
+DT[,sum(v),by=x] # keyed by
+DT[,sum(v),by=key(DT)] # same
+DT[,sum(v),by=y] # ad hoc by
+
+DT["a",sum(v)] # j for one group
+DT[c("a","b"),sum(v),by=.EACHI] # j for two groups
+
+X = data.table(c("b","c"),foo=c(4,2))
+X
+
+DT[X] # join
+DT[X,sum(v),by=.EACHI] # join and eval j for each row in i
+DT[X,mult="first"] # first row of each group
+DT[X,mult="last"] # last row of each group
+DT[X,sum(v)*foo,by=.EACHI] # join inherited scope
+
+setkey(DT,x,y) # 2-column key
+setkeyv(DT,c("x","y")) # same
+
+DT["a"] # join to 1st column of key
+DT[.("a")] # same, .() is an alias for list()
+DT[list("a")] # same
+DT[.("a",3)] # join to 2 columns
+DT[.("a",3:6)] # join 4 rows (2 missing)
+DT[.("a",3:6),nomatch=0] # remove missing
+DT[.("a",3:6),roll=TRUE] # rolling join (locf)
+
+DT[,sum(v),by=.(y\%\%2)] # by expression
+DT[,.SD[2],by=x] # 2nd row of each group
+DT[,tail(.SD,2),by=x] # last 2 rows of each group
+DT[,lapply(.SD,sum),by=x] # apply through columns by group
+
+DT[,list(MySum=sum(v),
+ MyMin=min(v),
+ MyMax=max(v)),
+ by=.(x,y\%\%2)] # by 2 expressions
+
+DT[,sum(v),x][V1<20] # compound query
+DT[,sum(v),x][order(-V1)] # ordering results
+
+print(DT[,z:=42L]) # add new column by reference
+print(DT[,z:=NULL]) # remove column by reference
+print(DT["a",v:=42L]) # subassign to existing v column by reference
+print(DT["b",v2:=84L]) # subassign to new column by reference (NA padded)
+
+DT[,m:=mean(v),by=x][] # add new column by reference by group
+ # NB: postfix [] is shortcut to print()
+
+DT[,.SD[which.min(v)],by=x][] # nested query by group
+
+DT[!.("a")] # not join
+DT[!"a"] # same
+DT[!2:4] # all rows other than 2:4
+DT[x!="b" | y!=3] # not yet optimized, currently vector scans
+DT[!.("b",3)] # same result but much faster
+
+
+# new feature: 'on' argument, from v1.9.6+
+DT1 = data.table(x=c("c", "a", "b", "a", "b"), a=1:5)
+DT2 = data.table(x=c("d", "c", "b"), mul=6:8)
+
+DT1[DT2, on=c(x="x")] # join on columns 'x'
+DT1[DT2, on="x"] # same as above
+DT1[DT2, .(sum(a) * mul), by=.EACHI, on="x"] # using by=.EACHI
+DT1[DT2, .(sum(a) * mul), by=.EACHI, on="x", nomatch=0L] # using by=.EACHI
+
+# Follow r-help posting guide, support is here (*not* r-help) :
+# http://stackoverflow.com/questions/tagged/data.table
+# or
+# datatable-help at lists.r-forge.r-project.org
+
+\dontrun{
+vignette("datatable-intro")
+vignette("datatable-faq")
+
+test.data.table() # over 1,300 low level tests
+
+update.packages() # keep up to date
+}}
+\keyword{ data }
+
diff --git a/man/dcast.data.table.Rd b/man/dcast.data.table.Rd
new file mode 100644
index 0000000..64b18ca
--- /dev/null
+++ b/man/dcast.data.table.Rd
@@ -0,0 +1,99 @@
+\name{dcast.data.table}
+\alias{dcast.data.table}
+\alias{dcast}
+\title{Fast dcast for data.table}
+\description{
+ \code{dcast.data.table} is a much faster version of \code{reshape2::dcast}, but for \code{data.table}s. More importantly, it's capable of handling very large data quite efficiently in terms of memory usage in comparison to \code{reshape2::dcast}.
+
+ From 1.9.6, \code{dcast} is a implemented as a S3 generic in \code{data.table}. To melt or cast data.tables, it is not necessary to load \code{reshape2} anymore. If you have to, then load \code{reshape2} package before loading \code{data.table}.
+
+ \bold{NEW}: \code{dcast.data.table} can now cast multiple \code{value.var} columns and also accepts multiple functions under \code{fun.aggregate} argument. See \code{examples} for more.
+}
+
+% \method{dcast}{data.table}
+\usage{
+\method{dcast}{data.table}(data, formula, fun.aggregate = NULL, sep = "_",
+ ..., margins = NULL, subset = NULL, fill = NULL,
+ drop = TRUE, value.var = guess(data),
+ verbose = getOption("datatable.verbose"))
+}
+\arguments{
+ \item{data}{ A \code{data.table}.}
+ \item{formula}{A formula of the form LHS ~ RHS to cast, see details.}
+ \item{fun.aggregate}{Should the data be aggregated before casting? If the formula doesn't identify single observation for each cell, then aggregation defaults to \code{length} with a message.
+
+ \bold{NEW}: it is possible to provide a list of functions to \code{fun.aggregate} argument. See \code{examples}.}
+ \item{sep}{Default is \code{_} for backwards compatibility. Character vector of length 1, indicating the separating character in variable names generated during casting.}
+ \item{...}{Any other arguments that maybe passed to the aggregating function.}
+ \item{margins}{Not implemented yet. Should take variable names to compute margins on. A value of \code{TRUE} would compute all margins.}
+ \item{subset}{Specified if casting should be done on subset of the data. Ex: subset = .(col1 <= 5) or subset = .(variable != "January").}
+ \item{fill}{Value to fill missing cells with. If \code{fun.aggregate} is present, takes the value by applying the function on 0-length vector.}
+ \item{drop}{\code{FALSE} will cast by including all missing combinations.}
+ \item{value.var}{Name of the column whose values will be filled to cast. Function `guess()` tries to, well, guess this column automatically, if none is provided.
+
+ \bold{NEW}: it is possible to cast multiple \code{value.var} columns simultaneously now. See \code{examples}.}
+ \item{verbose}{Not used yet. Maybe dropped in the future or used to provide information messages onto the console.}
+}
+\details{
+The cast formula takes the form \code{LHS ~ RHS} , ex: \code{var1 + var2 ~ var3}. The order of entries in the formula is essential. There are two special variables: \code{.} and \code{...}. Their functionality is identical to that of \code{reshape2::dcast}.
+
+\code{dcast} also allows \code{value.var} columns of type \code{list}.
+
+When variable combinations in \code{formula} doesn't identify a unique value in a cell, \code{fun.aggregate} will have to be specified, which defaults to \code{length} if unspecified. The aggregating function should take a vector as input and return a single value (or a list of length one) as output. In cases where \code{value.var} is a list, the function should be able to handle a list input and provide a single value or list of length one as output.
+
+If the formula's LHS contains the same column more than once, ex: \code{dcast(DT, x+x~ y)}, then the answer will have duplicate names. In those cases, the duplicate names are renamed using \code{make.unique} so that key can be set without issues.
+
+Names for columns that are being cast are generated in the same order (separated by an underscore, \code{_}) from the (unique) values in each column mentioned in the formula RHS.
+
+From \code{v1.9.4}, \code{dcast} tries to preserve attributes whereever possible.
+
+\bold{NEW}: From \code{v1.9.6}, it is possible to cast multiple \code{value.var} columns and also cast by providing multiple \code{fun.aggregate} functions. Multiple \code{fun.aggregate} functions should be provided as a \code{list}, for e.g., \code{list(mean, sum, function(x) paste(x, collapse="")}. \code{value.var} can be either a character vector or list of length=1, or a list of length equal to \code{length(fun.aggregate)}. When \code{value.var} is a character vector or a list of len [...]
+
+}
+\value{
+ A keyed \code{data.table} that has been cast. The key columns are equal to the variables in the \code{formula} LHS in the same order.
+}
+
+\examples{
+require(data.table)
+names(ChickWeight) <- tolower(names(ChickWeight))
+DT <- melt(as.data.table(ChickWeight), id=2:4) # calls melt.data.table
+
+# dcast is a S3 method in data.table from v1.9.6
+dcast(DT, time ~ variable, fun=mean)
+dcast(DT, diet ~ variable, fun=mean)
+dcast(DT, diet+chick ~ time, drop=FALSE)
+dcast(DT, diet+chick ~ time, drop=FALSE, fill=0)
+
+# using subset
+dcast(DT, chick ~ time, fun=mean, subset=.(time < 10 & chick < 20))
+
+\dontrun{
+# benchmark against reshape2's dcast, minimum of 3 runs
+set.seed(45)
+DT <- data.table(aa=sample(1e4, 1e6, TRUE),
+ bb=sample(1e3, 1e6, TRUE),
+ cc = sample(letters, 1e6, TRUE), dd=runif(1e6))
+system.time(dcast(DT, aa ~ cc, fun=sum)) # 0.12 seconds
+system.time(dcast(DT, bb ~ cc, fun=mean)) # 0.04 seconds
+# reshape2::dcast takes 31 seconds
+system.time(dcast(DT, aa + bb ~ cc, fun=sum)) # 1.2 seconds
+}
+
+# NEW FEATURE - multiple value.var and multiple fun.aggregate
+dt = data.table(x=sample(5,20,TRUE), y=sample(2,20,TRUE),
+ z=sample(letters[1:2], 20,TRUE), d1 = runif(20), d2=1L)
+# multiple value.var
+dcast(dt, x + y ~ z, fun=sum, value.var=c("d1","d2"))
+# multiple fun.aggregate
+dcast(dt, x + y ~ z, fun=list(sum, mean), value.var="d1")
+# multiple fun.agg and value.var (all combinations)
+dcast(dt, x + y ~ z, fun=list(sum, mean), value.var=c("d1", "d2"))
+# multiple fun.agg and value.var (one-to-one)
+dcast(dt, x + y ~ z, fun=list(sum, mean), value.var=list("d1", "d2"))
+}
+\seealso{
+ \code{\link{melt.data.table}}, \url{http://cran.r-project.org/package=reshape}
+}
+\keyword{data}
+
diff --git a/man/duplicated.Rd b/man/duplicated.Rd
new file mode 100644
index 0000000..9cd68f3
--- /dev/null
+++ b/man/duplicated.Rd
@@ -0,0 +1,106 @@
+\name{duplicated}
+\alias{duplicated}
+\alias{duplicated.data.table}
+\alias{unique}
+\alias{unique.data.table}
+\alias{anyDuplicated}
+\alias{anyDuplicated.data.table}
+\alias{uniqueN}
+\title{ Determine Duplicate Rows }
+\description{
+ \code{duplicated} returns a logical vector indicating which rows of a \code{data.table} (by
+ key columns or when no key all columns) are duplicates of a row with smaller subscripts.
+
+ \code{unique} returns a \code{data.table} with duplicated rows (by key) removed, or
+ (when no key) duplicated rows by all columns removed.
+
+ \code{anyDuplicated} returns the \emph{index} \code{i} of the first duplicated entry if there is one, and 0 otherwise.
+
+ \code{uniqueN} is equivalent to \code{length(unique(x))} but much faster for \code{atomic vectors}, \code{data.frames} and \code{data.tables}, for other types it dispatch to \code{length(unique(x))}. The number of unique rows are computed directly without materialising the intermediate unique data.table and is therefore memory efficient as well.
+
+}
+\usage{
+\method{duplicated}{data.table}(x, incomparables=FALSE, fromLast=FALSE, by=key(x), ...)
+
+\method{unique}{data.table}(x, incomparables=FALSE, fromLast=FALSE, by=key(x), ...)
+
+\method{anyDuplicated}{data.table}(x, incomparables=FALSE, fromLast=FALSE, by=key(x), ...)
+
+uniqueN(x, by=if (is.data.table(x)) key(x) else NULL)
+}
+\arguments{
+ \item{x}{ Atomic vectors, lists, data.frames or data.tables.}
+ \item{\dots}{ Not used at this time. }
+ \item{incomparables}{ Not used. Here for S3 method consistency. }
+ \item{fromLast}{ logical indicating if duplication should be considered from the reverse side, i.e., the last (or rightmost) of identical elements would correspond to \code{duplicated = FALSE}.}
+ \item{by}{
+ \code{character} or \code{integer} vector indicating which combinations of
+ columns form \code{x} to use for uniqueness checks. Defaults to
+ \code{key(x))} which, by default, only uses the keyed columns. \code{by=NULL}
+ uses all columns and acts like the analogous
+ \code{data.frame} methods.
+ }
+}
+\details{
+ Because data.tables are usually sorted by key, tests for duplication are especially quick when only the keyed columns are considered. Unlike \code{\link[base]{unique.data.frame}}, \code{paste} is not used to ensure equality of floating point data. It is instead accomplished directly (for speed) whilst avoiding unexpected behaviour due to floating point representation by rounding the last two bytes off the significand (default) as explained in \code{\link{setNumericRounding}}.
+
+ \code{v1.9.4} introduces \code{anyDuplicated} method for data.tables and is similar to base in functionality. It also implements the logical argument \code{fromLast} for all three functions, with default value \code{FALSE}.
+
+ Any combination of columns can be used to test for uniqueness (not just the
+ key columns) and are specified via the \code{by} parameter. To get
+ the analagous \code{data.frame} functionality, set \code{by} to \code{NULL}.
+}
+\value{
+ \code{duplicated} returns a logical vector of length \code{nrow(x)}
+ indicating which rows are duplicates.
+
+ \code{unique} returns a data table with duplicated rows removed.
+
+ \code{anyDuplicated} returns a integer value with the index of first duplicate. If none exists, 0L is returned.
+
+ \code{uniqueN} returns the number of unique elements in the vector, \code{data.frame} or \code{data.table}.
+
+}
+\seealso{ \code{\link{setNumericRounding}}, \code{\link{data.table}}, \code{\link{duplicated}}, \code{\link{unique}}, \code{\link{all.equal}} }
+\examples{
+DT <- data.table(A = rep(1:3, each=4), B = rep(1:4, each=3), C = rep(1:2, 6), key = "A,B")
+duplicated(DT)
+unique(DT)
+
+duplicated(DT, by="B")
+unique(DT, by="B")
+
+duplicated(DT, by=c("A", "C"))
+unique(DT, by=c("A", "C"))
+
+DT = data.table(a=c(2L,1L,2L), b=c(1L,2L,1L)) # no key
+unique(DT) # rows 1 and 2 (row 3 is a duplicate of row 1)
+
+DT = data.table(a=c(3.142, 4.2, 4.2, 3.142, 1.223, 1.223), b=rep(1,6))
+unique(DT) # rows 1,2 and 5
+
+DT = data.table(a=tan(pi*(1/4 + 1:10)), b=rep(1,10)) # example from ?all.equal
+length(unique(DT$a)) # 10 strictly unique floating point values
+all.equal(DT$a,rep(1,10)) # TRUE, all within tolerance of 1.0
+DT[,which.min(a)] # row 10, the strictly smallest floating point value
+identical(unique(DT),DT[1]) # TRUE, stable within tolerance
+identical(unique(DT),DT[10]) # FALSE
+
+# fromLast=TRUE
+DT <- data.table(A = rep(1:3, each=4), B = rep(1:4, each=3), C = rep(1:2, 6), key = "A,B")
+duplicated(DT, by="B", fromLast=TRUE)
+unique(DT, by="B", fromLast=TRUE)
+
+# anyDuplicated
+anyDuplicated(DT, by=c("A", "B")) # 3L
+any(duplicated(DT, by=c("A", "B"))) # TRUE
+
+# uniqueN, unique rows on key columns
+uniqueN(DT)
+# uniqueN, unique rows on all all columns
+uniqueN(DT, by=NULL)
+# uniqueN while grouped by "A"
+DT[, .(uN=uniqueN(.SD)), by=A]
+}
+\keyword{ data }
+
diff --git a/man/foverlaps.Rd b/man/foverlaps.Rd
new file mode 100644
index 0000000..30f009a
--- /dev/null
+++ b/man/foverlaps.Rd
@@ -0,0 +1,89 @@
+\name{foverlaps}
+\alias{foverlaps}
+\title{Fast overlap joins}
+\description{
+ A \emph{fast} binary-search based \emph{overlap join} of two \code{data.table}s. This is very much inspired by \code{findOverlaps} function from the bioconductor package \code{IRanges} (see link below under \code{See Also}).
+
+ Usually, \code{x} is a very large data.table with small interval ranges, and \code{y} is much smaller \emph{keyed} \code{data.table} with relatively larger interval spans. For an usage in \code{genomics}, see the examples section.
+
+ NOTE: This is still under development, meaning it's stable, but some features are yet to be implemented. Also, some arguments and/or the function name itself could be changed.
+}
+
+\usage{
+foverlaps(x, y, by.x = if (!is.null(key(x))) key(x) else key(y),
+ by.y = key(y), maxgap = 0L, minoverlap = 1L,
+ type = c("any", "within", "start", "end", "equal"),
+ mult = c("all", "first", "last"),
+ nomatch = getOption("datatable.nomatch"),
+ which = FALSE, verbose = getOption("datatable.verbose"))
+}
+\arguments{
+ \item{x, y}{ \code{data.table}s. \code{y} needs to be keyed, but not necessarily \code{x}. See examples. }
+ \item{by.x, by.y}{A vector of column names (or numbers) to compute the overlap joins. The last two columns in both \code{by.x} and \code{by.y} should each correspond to the \code{start} and \code{end} interval columns in \code{x} and \code{y} respectively. And the \code{start} column should always be <= \code{end} column. If \code{x} is keyed, \code{by.x} is equal to \code{key(x)}, else \code{key(y)}. \code{by.y} defaults to \code{key(y)}. }
+ \item{maxgap}{It should be a non-negative integer value, >= 0. Default is 0 (no gap). For intervals \code{[a,b]} and \code{[c,d]}, where \code{a<=b} and \code{c<=d}, when \code{c > b} or \code{d < a}, the two intervals don't overlap. If the gap between these two intervals is \code{<= maxgap}, these two intervals are considered as overlapping. Note: This is not yet implemented.}
+ \item{minoverlap}{ It should be a positive integer value, > 0. Default is 1. For intervals \code{[a,b]} and \code{[c,d]}, where \code{a<=b} and \code{c<=d}, when \code{c<=b} and \code{d>=a}, the two intervals overlap. If the length of overlap between these two intervals is \code{>= minoverlap}, then these two intervals are considered to be overlapping. Note: This is not yet implemented.}
+ \item{type}{ Default value is \code{any}. Allowed values are \code{any}, \code{within}, \code{start}, \code{end} and \code{equal}. Note: \code{equal} is not yet implemented. But this is just a normal join of the type \code{y[x, ...]}, unless you require also using \code{maxgap} and \code{minoverlap} arguments.
+
+ The types shown here are identical in functionality to the function \code{findOverlaps} in the bioconductor package \code{IRanges}. Let \code{[a,b]} and \code{[c,d]} be intervals in \code{x} and \code{y} with \code{a<=b} and \code{c<=d}. For \code{type="start"}, the intervals overlap iff \code{a == c}. For \code{type="end"}, the intervals overlap iff \code{b == d}. For \code{type="within"}, the intervals overlap iff \code{a>=c and b<=d}. For \code{type="equal"}, the intervals overl [...]
+
+ NB: \code{maxgap} argument, when > 0, is to be interpreted according to the type of the overlap. This will be updated once \code{maxgap} is implemented.}
+ \item{mult}{ When multiple rows in \code{y} match to the row in \code{x}, \code{mult=.} controls which values are returned - \code{"all"} (default), \code{"first"} or \code{"last"}.}
+ \item{nomatch}{ Same as \code{nomatch} in \code{\link{match}}. When a row (with interval say, \code{[a,b]}) in \code{x} has no match in \code{y}, \code{nomatch=NA} (default) means \code{NA} is returned for \code{y}'s non-\code{by.y} columns for that row of \code{x}. \code{nomatch=0} means no rows will be returned for that row of \code{x}. The default value (used when \code{nomatch} is not supplied) can be changed from \code{NA} to \code{0} using \code{options(datatable.nomatch=0)}. }
+ \item{which}{ When \code{TRUE}, if \code{mult="all"} returns a two column \code{data.table} with the first column corresponding to \code{x}'s row number and the second corresponding to \code{y}'s. when \code{nomatch=NA}, no matches return \code{NA} for \code{y}, and if \code{nomatch=0}, those rows where no match is found will be skipped; if \code{mult="first" or "last"}, a vector of length equal to the number of rows in \code{x} is returned, with no-match entries filled with \code{NA} [...]
+ \item{verbose}{ \code{TRUE} turns on status and information messages to the console. Turn this on by default using \code{options(datatable.verbose=TRUE)}. The quantity and types of verbosity may be expanded in future.}
+}
+\details{
+Very briefly, \code{foverlaps()} collapses the two-column interval in \code{y} to one-column of \emph{unique} values to generate a \code{lookup} table, and then performs the join depending on the type of \code{overlap}, using the already available \code{binary search} feature of \code{data.table}. The time (and space) required to generate the \code{lookup} is therefore proportional to the number of unique values present in the interval columns of \code{y} when combined together.
+
+Overlap joins takes advantage of the fact that \code{y} is sorted to speed-up finding overlaps. Therefore \code{y} has to be keyed (see \code{?setkey}) prior to running \code{foverlaps()}. A key on \code{x} is not necessary, although it \emph{might} speed things further. The columns in \code{by.x} argument should correspond to the columns specified in \code{by.y}. The last two columns should be the \emph{interval} columns in both \code{by.x} and \code{by.y}. The first interval column in [...]
+
+The \code{lookup} generation step could be quite time consuming if the number of unique values in \code{y} are too large (ex: in the order of tens of millions). There might be improvements possible by constructing lookup using RLE, which is a pending feature request. However most scenarios will not have too many unique values for \code{y}.
+
+Columns of \code{numeric} types (i.e., \code{double}) have their last two bytes rounded off while computing overlap joins, by defalult, to avoid any unexpected behaviour due to limitations in representing floating point numbers precisely. Have a look at \code{\link{setNumericRounding}} to learn more.
+}
+\value{
+ A new \code{data.table} by joining over the interval columns (along with other additional identifier columns) specified in \code{by.x} and \code{by.y}.
+
+ NB: When \code{which=TRUE}: \code{a)} \code{mult="first" or "last"} returns a \code{vector} of matching row numbers in \code{y}, and \code{b)} when \code{mult="all"} returns a data.table with two columns with the first containing row numbers of \code{x} and the second column with corresponding row numbers of \code{y}.
+
+ \code{nomatch=NA or 0} also influences whether non-matching rows are returned or not, as explained above.
+}
+
+\examples{
+require(data.table)
+## simple example:
+x = data.table(start=c(5,31,22,16), end=c(8,50,25,18), val2 = 7:10)
+y = data.table(start=c(10, 20, 30), end=c(15, 35, 45), val1 = 1:3)
+setkey(y, start, end)
+foverlaps(x, y, type="any", which=TRUE) ## return overlap indices
+foverlaps(x, y, type="any") ## return overlap join
+foverlaps(x, y, type="any", mult="first") ## returns only first match
+foverlaps(x, y, type="within") ## matches iff 'x' is within 'y'
+
+## with extra identifiers (ex: in genomics)
+x = data.table(chr=c("Chr1", "Chr1", "Chr2", "Chr2", "Chr2"),
+ start=c(5,10, 1, 25, 50), end=c(11,20,4,52,60))
+y = data.table(chr=c("Chr1", "Chr1", "Chr2"), start=c(1, 15,1),
+ end=c(4, 18, 55), geneid=letters[1:3])
+setkey(y, chr, start, end)
+foverlaps(x, y, type="any", which=TRUE)
+foverlaps(x, y, type="any")
+foverlaps(x, y, type="any", nomatch=0L)
+foverlaps(x, y, type="within", which=TRUE)
+foverlaps(x, y, type="within")
+foverlaps(x, y, type="start")
+
+## x and y have different column names - specify by.x
+x = data.table(seq=c("Chr1", "Chr1", "Chr2", "Chr2", "Chr2"),
+ start=c(5,10, 1, 25, 50), end=c(11,20,4,52,60))
+y = data.table(chr=c("Chr1", "Chr1", "Chr2"), start=c(1, 15,1),
+ end=c(4, 18, 55), geneid=letters[1:3])
+setkey(y, chr, start, end)
+foverlaps(x, y, by.x=c("seq", "start", "end"),
+ type="any", which=TRUE)
+}
+\seealso{
+ \code{\link{data.table}}, \url{http://www.bioconductor.org/packages/release/bioc/html/IRanges.html}, \code{\link{setNumericRounding}}
+}
+\keyword{ data }
+
diff --git a/man/frank.Rd b/man/frank.Rd
new file mode 100644
index 0000000..bcfaf25
--- /dev/null
+++ b/man/frank.Rd
@@ -0,0 +1,71 @@
+\name{frank}
+\alias{frank}
+\alias{frankv}
+\alias{rank}
+\title{Fast rank}
+\description{
+ Similar to \code{base::rank} but \emph{much faster}. And it accepts vectors, lists, data.frames or data.tables as input. In addition to the \code{ties.method} possibilities provided by \code{base::rank}, it also provides \code{ties.method="dense"}.
+
+ \code{bit64::integer64} type is also supported.
+}
+
+\usage{
+frank(x, ..., na.last=TRUE, ties.method=c("average",
+ "first", "random", "max", "min", "dense"))
+
+frankv(x, cols=seq_along(x), order=1L, na.last=TRUE,
+ ties.method=c("average", "first", "random",
+ "max", "min", "dense"))
+
+}
+\arguments{
+ \item{x}{ A vector, or list with all it's elements identical in length or data.frame or data.table. }
+ \item{...}{ Only for lists, data.frames and data.tables. The columns to calculate ranks based on. Do not quote column names. If ... is missing, all columns are considered by default. To sort by a column in descending order prefix a "-", e.g., frank(x, a, -b, c). The -b works when b is of type character as well.}
+ \item{cols}{ A character vector of column names (or numbers) of x, to which obtain ranks for. }
+ \item{order}{ An integer vector with only possible values of 1 and -1, corresponding to ascending and descending order. The length of order must be either 1 or equal to that of cols. If length(order) == 1, it's recycled to length(cols). }
+ \item{na.last}{ Control treatment of \code{NA}s. If \code{TRUE}, missing values in the data are put last; if \code{FALSE}, they are put first; if \code{NA}, they are removed; if \code{"keep"} they are kept with rank \code{NA}. }
+ \item{ties.method}{ A character string specifying how ties are treated, see \code{Details}. }
+}
+\details{
+ To be consistent with other \code{data.table} operations, \code{NA}s are considered identical to other \code{NA}s (and \code{NaN}s to other \code{NaN}s), unlike \code{base::rank}. Therefore, for \code{na.last=TRUE} and \code{na.last=FALSE}, \code{NA}s (and \code{NaN}s) are given identical ranks, unlike \code{\link[base]{rank}}.
+
+ \code{frank} is not limited to vectors. It accepts data.tables (and lists and data.frames) as well. It accepts unquoted column names (with names preceded with a \code{-} sign for descending order, even on character vectors), for e.g., \code{frank(DT, a, -b, c, ties.method="first")} where \code{a,b,c} are columns in \code{DT}. The equivalent in \code{frankv} is the \code{order} argument.
+
+ In addition to the \code{ties.method} values possible using base's \code{\link[base]{rank}}, it also provides another additional argument \emph{"dense"} which returns the ranks without any gaps in the ranking. See examples.
+}
+\value{
+ A numeric vector of length equal to \code{NROW(x)} (unless \code{na.last = NA}, when missing values are removed). The vector is of integer type unless \code{ties.method = "average"} when it is of double type (irrespective of ties).
+}
+
+\examples{
+# on vectors
+x = c(4, 1, 4, NA, 1, NA, 4)
+# NAs are considered identical (unlike base R)
+# default is average
+frankv(x) # na.last=TRUE
+frankv(x, na.last=FALSE)
+
+# ties.method = min
+frankv(x, ties.method="min")
+# ties.method = dense
+frankv(x, ties.method="dense")
+
+# on data.table
+DT = data.table(x, y=c(1, 1, 1, 0, NA, 0, 2))
+frankv(DT, cols="x") # same as frankv(x) from before
+frankv(DT, cols="x", na.last="keep")
+frankv(DT, cols="x", ties.method="dense", na.last=NA)
+frank(DT, x, ties.method="dense", na.last=NA) # equivalent of above using frank
+# on both columns
+frankv(DT, ties.method="first", na.last="keep")
+frank(DT, ties.method="first", na.last="keep") # equivalent of above using frank
+
+# order argument
+frank(DT, x, -y, ties.method="first")
+# equivalent of above using frankv
+frankv(DT, order=c(1L, -1L), ties.method="first")
+}
+\seealso{
+ \code{\link{data.table}}, \code{\link{setkey}}, \code{\link{setorder}}
+}
+\keyword{ data }
diff --git a/man/fread.Rd b/man/fread.Rd
new file mode 100644
index 0000000..e797dd0
--- /dev/null
+++ b/man/fread.Rd
@@ -0,0 +1,261 @@
+\name{fread}
+\alias{fread}
+\title{ Fast and friendly file finagler }
+\description{
+ Similar to \code{read.table} but faster and more convenient. All controls such as \code{sep}, \code{colClasses} and \code{nrows} are automatically detected. \code{bit64::integer64} types are also detected and read directly without needing to read as character before converting.
+
+ Dates are read as character currently. They can be converted afterwards using the excellent \code{fasttime} package or standard base functions.
+
+ `fread` is for \emph{regular} delimited files; i.e., where every row has the same number of columns. In future, secondary separator (\code{sep2}) may be specified \emph{within} each column. Such columns will be read as type \code{list} where each cell is itself a vector.
+}
+\usage{
+fread(input, sep="auto", sep2="auto", nrows=-1L, header="auto", na.strings="NA",
+stringsAsFactors=FALSE, verbose=getOption("datatable.verbose"), autostart=1L,
+skip=0L, select=NULL, drop=NULL, colClasses=NULL,
+integer64=getOption("datatable.integer64"), # default: "integer64"
+dec=if (sep!=".") "." else ",", col.names,
+check.names=FALSE, encoding="unknown", strip.white=TRUE,
+showProgress=getOption("datatable.showProgress"), # default: TRUE
+data.table=getOption("datatable.fread.datatable") # default: TRUE
+)
+}
+\arguments{
+ \item{input}{ Either the file name to read (containing no \\n character), a shell command that preprocesses the file (e.g. \code{fread("grep blah filename"))} or the input itself as a string (containing at least one \\n), see examples. In both cases, a length 1 character string. A filename input is passed through \code{\link[base]{path.expand}} for convenience and may be a URL starting http:// or file://. }
+ \item{sep}{ The separator between columns. Defaults to the first character in the set [\code{,\\t |;:}] that exists on line \code{autostart} outside quoted (\code{""}) regions, and separates the rows above \code{autostart} into a consistent number of fields, too. }
+ \item{sep2}{ The separator \emph{within} columns. A \code{list} column will be returned where each cell is a vector of values. This is much faster using less working memory than \code{strsplit} afterwards or similar techniques. For each column \code{sep2} can be different and is the first character in the same set above [\code{,\\t |;:}], other than \code{sep}, that exists inside each field outside quoted regions on line \code{autostart}. NB: \code{sep2} is not yet implemented. }
+ \item{nrows}{ The number of rows to read, by default -1 means all. Unlike \code{read.table}, it doesn't help speed to set this to the number of rows in the file (or an estimate), since the number of rows is automatically determined and is already fast. Only set \code{nrows} if you require the first 10 rows, for example. `nrows=0` is a special case that just returns the column names and types; e.g., a dry run for a large file or to quickly check format consistency of a set of files befo [...]
+ \item{header}{ Does the first data line contain column names? Defaults according to whether every non-empty field on the first data line is type character. If so, or TRUE is supplied, any empty column names are given a default name. }
+ \item{na.strings}{ A character vector of strings which are to be interpreted as \code{NA} values. By default \code{",,"} for columns read as type character is read as a blank string (\code{""}) and \code{",NA,"} is read as \code{NA}. Typical alternatives might be \code{na.strings=NULL} (no coercion to NA at all!) or perhaps \code{na.strings=c("NA","N/A","null")}. }
+ \item{stringsAsFactors}{ Convert all character columns to factors? }
+ \item{verbose}{ Be chatty and report timings? }
+ \item{autostart}{ Any line number within the region of machine readable delimited text, by default 30. If the file is shorter or this line is empty (e.g. short files with trailing blank lines) then the last non empty line (with a non empty line above that) is used. This line and the lines above it are used to auto detect \code{sep}, \code{sep2} and the number of fields. It's extremely unlikely that \code{autostart} should ever need to be changed, we hope. }
+ \item{skip}{ If -1 (default) use the procedure described below starting on line \code{autostart} to find the first data row. \code{skip>=0} means ignore \code{autostart} and take line \code{skip+1} as the first data row (or column names according to header="auto"|TRUE|FALSE as usual). \code{skip="string"} searches for \code{"string"} in the file (e.g. a substring of the column names row) and starts on that line (inspired by read.xls in package gdata). }
+ \item{select}{ Vector of column names or numbers to keep, drop the rest. }
+ \item{drop}{ Vector of column names or numbers to drop, keep the rest. }
+ \item{colClasses}{ A character vector of classes (named or unnamed), as read.csv. Or a named list of vectors of column names or numbers, see examples. colClasses in fread is intended for rare overrides, not for routine use. fread will only promote a column to a higher type if colClasses requests it. It won't downgrade a column to a lower type since NAs would result. You have to coerce such columns afterwards yourself, if you really require data loss. }
+ \item{integer64}{ "integer64" (default) reads columns detected as containing integers larger than 2^31 as type \code{bit64::integer64}. Alternatively, \code{"double"|"numeric"} reads as \code{base::read.csv} does; i.e., possibly with loss of precision and if so silently. Or, "character". }
+ \item{dec}{ The decimal separator as in \code{base::read.csv}. If not "." (default) then usually ",". See details. }
+ \item{col.names}{ A vector of optional names for the variables (columns). The default is to use the header column if present or detected, or if not "V" followed by the column number. }
+ \item{check.names}{ default is \code{FALSE}. If \code{TRUE}, it uses the base function \code{\link{make.unique}} to ensure that column names are all unique.}
+ \item{encoding}{ default is \code{"unknown"}. Other possible options are \code{"UTF-8"} and \code{"Latin-1"}. }
+ \item{strip.white}{ default is \code{TRUE}. Strips leading and trailing whitespaces of unquoted fields. If \code{FALSE}, only header trailing spaces are removed. }
+ \item{showProgress}{ TRUE displays progress on the console using \code{\\r}. It is produced in fread's C code where the very nice (but R level) txtProgressBar and tkProgressBar are not easily available. }
+ \item{data.table}{ TRUE returns a \code{data.table}. FALSE returns a \code{data.frame}. }
+}
+\details{
+
+Once the separator is found on line \code{autostart}, the number of columns is determined. Then the file is searched backwards from \code{autostart} until a row is found that doesn't have that number of columns. Thus, the first data row is found and any human readable banners are automatically skipped. This feature can be particularly useful for loading a set of files which may not all have consistently sized banners. Setting \code{skip>0} overrides this feature by setting \code{autostar [...]
+
+The first 5 rows, middle 5 rows and last 5 rows are then read to determine column types. The lowest type for each column is chosen from the ordered list \code{integer}, \code{integer64}, \code{double}, \code{character}. This enables \code{fread} to allocate exactly the right number of rows, with columns of the right type, up front once. The file may of course \emph{still} contain data of a different type in rows other than first, middle and last 5. In that case, the column types are bump [...]
+
+There is no line length limit, not even a very large one. Since we are encouraging \code{list} columns (i.e. \code{sep2}) this has the potential to encourage longer line lengths. So the approach of scanning each line into a buffer first and then rescanning that buffer is not used. There are no buffers used in \code{fread}'s C code at all. The field width limit is limited by R itself: the maximum width of a character string (currenly 2^31-1 bytes, 2GB).
+
+The filename extension (such as .csv) is irrelevant for "auto" \code{sep} and \code{sep2}. Separator detection is entirely driven by the file contents. This can be useful when loading a set of different files which may not be named consistently, or may not have the extension .csv despite being csv. Some datasets have been collected over many years, one file per day for example. Sometimes the file name format has changed at some point in the past or even the format of the file itself. So [...]
+
+If an empty line is encountered then reading stops there, with warning if any text exists after the empty line such as a footer. The first line of any text discarded is included in the warning message.
+
+\bold{Line endings:} All known line endings are detected automatically: \code{\\n} (*NIX including Mac), \code{\\r\\n} (Windows CRLF), \code{\\r} (old Mac) and \code{\\n\\r} (just in case). There is no need to convert input files first. \code{fread} running on any architecture will read a file from any architecture. Both \code{\\r} and \code{\\n} may be embedded in character strings (including column names) provided the field is quoted.
+
+\bold{Decimal separator and locale:} \code{fread(...,dec=",")} should just work. \code{fread} uses C function \code{strtod} to read numeric data; e.g., \code{1.23} or \code{1,23}. \code{strtod} retrieves the decimal separator (\code{.} or \code{,} usually) from the locale of the R session rather than as an argument passed to the \code{strtod} function. So for \code{fread(...,dec=",")} to work, \code{fread} changes this (and only this) R session's locale temporarily to a locale which prov [...]
+
+On Windows, "French_France.1252" is tried which should be available as standard (any locale with comma decimal separator would suffice) and on unix "fr_FR.utf8" (you may need to install this locale on unix). \code{fread()} is very careful to set the locale back again afterwards, even if the function fails with an error. The choice of locale is determined by \code{options()$datatable.fread.dec.locale}. This may be a \emph{vector} of locale names and if so they will be tried in turn until [...]
+
+\bold{Quotes:}
+
+ \itemize{
+ \item{Spaces and othe whitespace (other than \code{sep} and \code{\\n}) may appear in unquoted character fields, e.g., \code{...,2,Joe Bloggs,3.14,...}.}
+
+ \item{When \code{character} columns are \emph{quoted}, they must start and end with that quoting character immediately followed by \code{sep} or \code{\\n}, e.g., \code{...,2,"Joe Bloggs",3.14,...}.
+
+ In essence quoting character fields are \emph{required} only if \code{sep} or \code{\\n} appears in the string value. Quoting may be used to signify that numeric data should be read as text. Unescaped quotes may be present in a quoted field, e.g., \code{...,2,"Joe, "Bloggs"",3.14,...}, as well as escaped quotes, e.g., \code{...,2,"Joe \",Bloggs\"",3.14,...}.
+
+ If an embedded quote is followed by the separator inside a quoted field, the embedded quotes up to that point in that field must be balanced; e.g. \code{...,2,"www.blah?x="one",y="two"",3.14,...}.
+
+ Quoting may be used to signify that numeric data should be read as text.
+
+ On those fields that do not satisfy these conditions, e.g., fields with unbalanced quotes, \code{fread} re-attempts that field as if it isn't quoted. This is quite useful in reading files that contains fields with unbalanced quotes as well, automatically.}
+ }
+
+}
+\value{
+ A \code{data.table} by default. A \code{data.frame} when argument \code{data.table=FALSE}; e.g. \code{options(datatable.fread.datatable=FALSE)}.
+}
+\references{
+Background :\cr
+\url{http://cran.r-project.org/doc/manuals/R-data.html}\cr
+\url{http://stackoverflow.com/questions/1727772/quickly-reading-very-large-tables-as-dataframes-in-r}\cr
+\url{www.biostat.jhsph.edu/~rpeng/docs/R-large-tables.html}\cr
+\url{https://stat.ethz.ch/pipermail/r-help/2007-August/138315.html}\cr
+\url{http://www.cerebralmastication.com/2009/11/loading-big-data-into-r/}\cr
+\url{http://stackoverflow.com/questions/9061736/faster-than-scan-with-rcpp}\cr
+\url{http://stackoverflow.com/questions/415515/how-can-i-read-and-manipulate-csv-file-data-in-c}\cr
+\url{http://stackoverflow.com/questions/9352887/strategies-for-reading-in-csv-files-in-pieces}\cr
+\url{http://stackoverflow.com/questions/11782084/reading-in-large-text-files-in-r}\cr
+\url{http://stackoverflow.com/questions/45972/mmap-vs-reading-blocks}\cr
+\url{http://stackoverflow.com/questions/258091/when-should-i-use-mmap-for-file-access}\cr
+\url{http://stackoverflow.com/a/9818473/403310}\cr
+\url{http://stackoverflow.com/questions/9608950/reading-huge-files-using-memory-mapped-files}
+
+finagler = "to get or achieve by guile or manipulation" \url{http://dictionary.reference.com/browse/finagler}
+}
+\seealso{ \code{\link[utils]{read.csv}}, \code{\link[base]{url}}, \code{\link[base]{Sys.setlocale}}
+}
+\examples{
+\dontrun{
+
+# Demo speedup
+n=1e6
+DT = data.table( a=sample(1:1000,n,replace=TRUE),
+ b=sample(1:1000,n,replace=TRUE),
+ c=rnorm(n),
+ d=sample(c("foo","bar","baz","qux","quux"),n,replace=TRUE),
+ e=rnorm(n),
+ f=sample(1:1000,n,replace=TRUE) )
+DT[2,b:=NA_integer_]
+DT[4,c:=NA_real_]
+DT[3,d:=NA_character_]
+DT[5,d:=""]
+DT[2,e:=+Inf]
+DT[3,e:=-Inf]
+
+write.table(DT,"test.csv",sep=",",row.names=FALSE,quote=FALSE)
+cat("File size (MB):", round(file.info("test.csv")$size/1024^2),"\n")
+# 50 MB (1e6 rows x 6 columns)
+
+system.time(DF1 <-read.csv("test.csv",stringsAsFactors=FALSE))
+# 60 sec (first time in fresh R session)
+
+system.time(DF1 <- read.csv("test.csv",stringsAsFactors=FALSE))
+# 30 sec (immediate repeat is faster, varies)
+
+system.time(DF2 <- read.table("test.csv",header=TRUE,sep=",",quote="",
+ stringsAsFactors=FALSE,comment.char="",nrows=n,
+ colClasses=c("integer","integer","numeric",
+ "character","numeric","integer")))
+# 10 sec (consistently). All known tricks and known nrows, see references.
+
+require(data.table)
+system.time(DT <- fread("test.csv"))
+# 3 sec (faster and friendlier)
+
+require(sqldf)
+system.time(SQLDF <- read.csv.sql("test.csv",dbname=NULL))
+# 20 sec (friendly too, good defaults)
+
+require(ff)
+system.time(FFDF <- read.csv.ffdf(file="test.csv",nrows=n))
+# 20 sec (friendly too, good defaults)
+
+identical(DF1,DF2)
+all.equal(as.data.table(DF1), DT)
+identical(DF1,within(SQLDF,{b<-as.integer(b);c<-as.numeric(c)}))
+identical(DF1,within(as.data.frame(FFDF),d<-as.character(d)))
+
+# Scaling up ...
+l = vector("list",10)
+for (i in 1:10) l[[i]] = DT
+DTbig = rbindlist(l)
+tables()
+write.table(DTbig,"testbig.csv",sep=",",row.names=FALSE,quote=FALSE)
+# 500MB (10 million rows x 6 columns)
+
+system.time(DF <- read.table("testbig.csv",header=TRUE,sep=",",
+ quote="",stringsAsFactors=FALSE,comment.char="",nrows=1e7,
+ colClasses=c("integer","integer","numeric",
+ "character","numeric","integer")))
+# 100-200 sec (varies)
+
+system.time(DT <- fread("testbig.csv"))
+# 30-40 sec
+
+all(mapply(all.equal, DF, DT))
+
+
+# Real data example (Airline data)
+# http://stat-computing.org/dataexpo/2009/the-data.html
+
+download.file("http://stat-computing.org/dataexpo/2009/2008.csv.bz2",
+ destfile="2008.csv.bz2")
+# 109MB (compressed)
+
+system("bunzip2 2008.csv.bz2")
+# 658MB (7,009,728 rows x 29 columns)
+
+colClasses = sapply(read.csv("2008.csv",nrows=100),class)
+# 4 character, 24 integer, 1 logical. Incorrect.
+
+colClasses = sapply(read.csv("2008.csv",nrows=200),class)
+# 5 character, 24 integer. Correct. Might have missed data only using 100 rows
+# since read.table assumes colClasses is correct.
+
+system.time(DF <- read.table("2008.csv", header=TRUE, sep=",",
+ quote="",stringsAsFactors=FALSE,comment.char="",nrows=7009730,
+ colClasses=colClasses)
+# 360 secs
+
+system.time(DT <- fread("2008.csv"))
+# 40 secs
+
+table(sapply(DT,class))
+# 5 character and 24 integer columns. Correct without needing to worry about colClasses
+# issue above.
+
+
+# Reads URLs directly :
+fread("http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat")
+
+}
+
+# Reads text input directly :
+fread("A,B\n1,2\n3,4")
+
+# Reads pasted input directly :
+fread("A,B
+1,2
+3,4
+")
+
+# Finds the first data line automatically :
+fread("
+This is perhaps a banner line or two or ten.
+A,B
+1,2
+3,4
+")
+
+# Detects whether column names are present automatically :
+fread("
+1,2
+3,4
+")
+
+# Numerical precision :
+
+DT = fread("A\n1.010203040506070809010203040506\n") # silent loss of precision
+DT[,sprintf("\%.15E",A)] # stored accurately as far as double precision allows
+
+DT = fread("A\n1.46761e-313\n") # detailed warning about ERANGE; read as 'numeric'
+DT[,sprintf("\%.15E",A)] # beyond what double precision can store accurately to 15 digits
+
+# For greater accuracy use colClasses to read as character, then package Rmpfr.
+
+# colClasses
+data = "A,B,C,D\n1,3,5,7\n2,4,6,8\n"
+fread(data, colClasses=c(B="character",C="character",D="character")) # as read.csv
+fread(data, colClasses=list(character=c("B","C","D"))) # saves typing
+fread(data, colClasses=list(character=2:4)) # same using column numbers
+
+# drop
+fread(data, colClasses=c("B"="NULL","C"="NULL")) # as read.csv
+fread(data, colClasses=list(NULL=c("B","C"))) #
+fread(data, drop=c("B","C")) # same but less typing, easier to read
+fread(data, drop=2:3) # same using column numbers
+
+# select
+# (in read.csv you need to work out which to drop)
+fread(data, select=c("A","D")) # less typing, easier to read
+fread(data, select=c(1,4)) # same using column numbers
+
+}
+\keyword{ data }
+
diff --git a/man/last.Rd b/man/last.Rd
new file mode 100644
index 0000000..91dbd64
--- /dev/null
+++ b/man/last.Rd
@@ -0,0 +1,24 @@
+\name{last}
+\alias{last}
+\title{ Last item of an object }
+\description{
+ Returns the last item of a vector or list, or the last row of a data.frame or data.table.
+}
+\usage{
+last(x,...)
+}
+\arguments{
+ \item{x}{ A vector, list, data.frame or data.table. Otherwise the S3 method of \code{xts::last} is deployed. }
+ \item{...}{ Not applicable for \code{data.table::last}. Any arguments here are passed through to \code{xts::last}. }
+}
+% \details{
+% }
+\value{
+ If no other arguments are supplied it depends on the type of x. The last item of a vector or list. The last row of a \code{data.frame} or \code{data.table}. Otherwise, whatever \code{xts::last} returns (if package xts has been loaded, otherwise a helpful error).
+ If any argument is supplied in addition to \code{x} (such as \code{n} or \code{keep} in \code{xts::last}), regardless of \code{x}'s type, then \code{xts::last} is called if xts has been loaded, otherwise a helpful error.
+}
+\seealso{ \code{\link{NROW}}, \code{\link{head}}, \code{\link{tail}} }
+% \examples{
+% }
+\keyword{ data }
+
diff --git a/man/like.Rd b/man/like.Rd
new file mode 100644
index 0000000..1a17954
--- /dev/null
+++ b/man/like.Rd
@@ -0,0 +1,28 @@
+\name{like}
+\alias{like}
+\alias{\%like\%}
+\title{ Convenience function for calling regexpr. }
+\description{
+ Intended for use in [.data.table i.
+}
+\usage{
+like(vector,pattern)
+vector %like% pattern
+}
+\arguments{
+ \item{vector}{ Either a character vector or a factor. A factor is faster. }
+ \item{pattern}{ Passed on to \code{\link{grepl}}. }
+}
+% \details{
+% }
+\value{
+ Logical vector, \code{TRUE} for items that match \code{pattern}.
+}
+\note{ Current implementation does not make use of sorted keys. }
+\seealso{ \code{\link{data.table}}, \code{\link{grepl}} }
+\examples{
+DT = data.table(Name=c("Mary","George","Martha"), Salary=c(2,3,4))
+DT[Name \%like\% "^Mar"]
+}
+\keyword{ data }
+
diff --git a/man/melt.data.table.Rd b/man/melt.data.table.Rd
new file mode 100644
index 0000000..5fae5db
--- /dev/null
+++ b/man/melt.data.table.Rd
@@ -0,0 +1,104 @@
+\name{melt.data.table}
+\alias{melt.data.table}
+\alias{melt}
+\title{Fast melt for data.table}
+\description{
+ An S3 method for melting \code{data.table}s written entirely in C for speed. It also avoids any unnecessary copies by handling all arguments internally in a memory efficient manner.
+
+ From 1.9.6, to melt or cast data.tables, it is not necessary to load \code{reshape2} anymore. If you have to, then load \code{reshape2} package before loading \code{data.table}.
+
+ \bold{NEW}: \code{melt.data.table} now allows melting into multiple columns simultaneously. See the \code{details} and \code{examples} section.
+
+}
+\usage{
+## fast melt a data.table
+\method{melt}{data.table}(data, id.vars, measure.vars,
+ variable.name = "variable", value.name = "value",
+ ..., na.rm = FALSE, variable.factor = TRUE,
+ value.factor = FALSE,
+ verbose = getOption("datatable.verbose"))
+}
+\arguments{
+ \item{data}{ A \code{data.table} object to melt.}
+ \item{id.vars}{vector of id variables. Can be integer (corresponding id column numbers) or character (id column names) vector. If missing, all non-measure columns will be assigned to it.}
+ \item{measure.vars}{vector of measure variables. Can be integer (corresponding measure column numbers) or character (measure column names) vector. If missing, all non-id columns will be assigned to it.
+
+ \bold{NEW:} \code{measure.vars} also now accepts a list of character/integer vectors to melt into multiple columns - i.e., melt into more than one \code{value} columns simultaneously. Use the function \code{patterns} to provide multiple patterns conveniently. See the examples section. }
+ \item{variable.name}{name for the measured variable names column. The default name is 'variable'.}
+ \item{value.name}{name for the molten data values column. The default name is 'value'.}
+ \item{na.rm}{If \code{TRUE}, \code{NA} values will be removed from the molten data.}
+ \item{variable.factor}{If \code{TRUE}, the \code{variable} column will be converted to \code{factor}, else it will be a \code{character} column.}
+ \item{value.factor}{If \code{TRUE}, the \code{value} column will be converted to \code{factor}, else the molten value type is left unchanged.}
+ \item{verbose}{\code{TRUE} turns on status and information messages to the console. Turn this on by default using \code{options(datatable.verbose=TRUE)}. The quantity and types of verbosity may be expanded in future.}
+ \item{...}{any other arguments to be passed to/from other methods.}
+}
+\details{
+If \code{id.vars} and \code{measure.vars} are both missing, all non-\code{numeric/integer/logical} columns are assigned as id variables and the rest as measure variables. If only one of \code{id.vars} or \code{measure.vars} is supplied, the rest of the columns will be assigned to the other. Both \code{id.vars} and \code{measure.vars} can have the same column more than once and the same column can be both as id and measure variables.
+
+\code{melt.data.table} also accepts \code{list} columns for both id and measure variables.
+
+When all \code{measure.vars} are not of the same type, they'll be coerced according to the hierarchy \code{list} > \code{character} > \code{numeric > integer > logical}. For example, if any of the measure variables is a \code{list}, then entire value column will be coerced to a list. Note that, if the type of \code{value} column is a list, \code{na.rm = TRUE} will have no effect.
+
+From version \code{1.9.6}, \code{melt} gains a feature with \code{measure.vars} accepting a list of \code{character} or \code{integer} vectors as well to melt into multiple columns in a single function call efficiently. See the \code{examples} section for the usage.
+
+Attributes are preserved if all \code{value} columns are of the same type. By default, if any of the columns to be melted are of type \code{factor}, it'll be coerced to \code{character} type. This is to be compatible with \code{reshape2}'s \code{melt.data.frame}. To get a \code{factor} column, set \code{value.factor = TRUE}. \code{melt.data.table} also preserves \code{ordered} factors.
+}
+\value{
+ An unkeyed \code{data.table} containing the molten data.
+}
+
+\examples{
+set.seed(45)
+require(data.table)
+DT <- data.table(
+ i_1 = c(1:5, NA),
+ i_2 = c(NA,6,7,8,9,10),
+ f_1 = factor(sample(c(letters[1:3], NA), 6, TRUE)),
+ f_2 = factor(c("z", "a", "x", "c", "x", "x"), ordered=TRUE),
+ c_1 = sample(c(letters[1:3], NA), 6, TRUE),
+ d_1 = as.Date(c(1:3,NA,4:5), origin="2013-09-01"),
+ d_2 = as.Date(6:1, origin="2012-01-01"))
+# add a couple of list cols
+DT[, l_1 := DT[, list(c=list(rep(i_1, sample(5,1)))), by = i_1]$c]
+DT[, l_2 := DT[, list(c=list(rep(c_1, sample(5,1)))), by = i_1]$c]
+
+# id, measure as character/integer/numeric vectors
+melt(DT, id=1:2, measure="f_1")
+melt(DT, id=c("i_1", "i_2"), measure=3) # same as above
+melt(DT, id=1:2, measure=3L, value.factor=TRUE) # same, but 'value' is factor
+melt(DT, id=1:2, measure=3:4, value.factor=TRUE) # 'value' is *ordered* factor
+
+# preserves attribute when types are identical, ex: Date
+melt(DT, id=3:4, measure=c("d_1", "d_2"))
+melt(DT, id=3:4, measure=c("i_1", "d_1")) # attribute not preserved
+
+# on list
+melt(DT, id=1, measure=c("l_1", "l_2")) # value is a list
+melt(DT, id=1, measure=c("c_1", "l_1")) # c1 coerced to list
+
+# on character
+melt(DT, id=1, measure=c("c_1", "f_1")) # value is char
+melt(DT, id=1, measure=c("c_1", "i_2")) # i2 coerced to char
+
+# on na.rm=TRUE. NAs are removed efficiently, from within C
+melt(DT, id=1, measure=c("c_1", "i_2"), na.rm=TRUE) # remove NA
+
+# NEW FEATURE: measure.vars can be a list
+# melt "f_1,f_2" and "d_1,d_2" simultaneously, retain 'factor' attribute
+# convenient way using internal function patterns()
+melt(DT, id=1:2, measure=patterns("^f_", "^d_"), value.factor=TRUE)
+# same as above, but provide list of columns directly by column names or indices
+melt(DT, id=1:2, measure=list(3:4, c("d_1", "d_2")), value.factor=TRUE)
+
+# na.rm=TRUE removes rows with NAs in any 'value' columns
+melt(DT, id=1:2, measure=patterns("f_", "d_"), value.factor=TRUE, na.rm=TRUE)
+
+# return 'NA' for missing columns, 'na.rm=TRUE' ignored due to list column
+melt(DT, id=1:2, measure=patterns("l_", "c_"), na.rm=TRUE)
+
+}
+\seealso{
+ \code{\link{dcast}}, \url{http://had.co.nz/reshape/}
+}
+\keyword{ data }
+
diff --git a/man/merge.Rd b/man/merge.Rd
new file mode 100644
index 0000000..6f3bccd
--- /dev/null
+++ b/man/merge.Rd
@@ -0,0 +1,156 @@
+\name{merge}
+\alias{merge}
+\alias{merge.data.table}
+\title{ Merge Two Data Tables }
+\description{
+ Fast merge of two \code{data.table}s.
+
+ This \code{merge} method for \code{data.table} behaves very similarly to that
+ of \code{data.frame}s with one major exception: By default,
+ the columns used to merge the \code{data.table}s are the shared key columns
+ rather than the shared columns with the same names. Set the \code{by}, or \code{by.x},
+ \code{by.y} arguments explicitly to override this default.
+
+}
+
+\usage{
+\method{merge}{data.table}(x, y, by = NULL, by.x = NULL, by.y = NULL,
+all = FALSE, all.x = all, all.y = all, sort = TRUE, suffixes = c(".x", ".y"),
+allow.cartesian=getOption("datatable.allow.cartesian"), # default FALSE
+...)
+}
+
+\arguments{
+ \item{x, y}{
+ \code{data table}s. \code{y} is coerced to a \code{data.table} if
+ it isn't one already.
+ }
+
+ \item{by}{
+ A vector of shared column names in \code{x} and \code{y} to merge on.
+ This defaults to the shared key columns between the two tables.
+ If \code{y} has no key columns, this defaults to the key of \code{x}.
+ }
+
+ \item{by.x, by.y}{
+ Vectors of column names in \code{x} and \code{y} to merge on.
+ }
+
+ \item{all}{
+ logical; \code{all = TRUE} is shorthand to save setting both \code{all.x = TRUE} and
+ \code{all.y = TRUE}.
+ }
+
+ \item{all.x}{
+ logical; if \code{TRUE}, then extra rows will be added to the
+ output, one for each row in \code{x} that has no matching row in
+ \code{y}. These rows will have 'NA's in those columns that are
+ usually filled with values from \code{y}. The default is \code{FALSE},
+ so that only rows with data from both \code{x} and \code{y} are
+ included in the output.
+ }
+
+ \item{all.y}{
+ logical; analogous to \code{all.x} above.
+ }
+
+ \item{sort}{
+ logical. If \code{TRUE} (default), the merged \code{data.table} is sorted
+ by setting the key to the \code{by / by.x} columns. If \code{FALSE}, the
+ result is not sorted.
+ }
+
+ \item{suffixes}{
+ A \code{character(2)} specifying the suffixes to be used for making
+ non-\code{by} column names unique. The suffix behavior works in a similar
+ fashion as the \code{\link{merge.data.frame}} method does.
+ }
+
+ \item{allow.cartesian}{
+ See \code{allow.cartesian} in \code{\link{[.data.table}}.
+ }
+
+ \item{\dots}{
+ Not used at this time.
+ }
+}
+
+\details{
+ \code{\link{merge}} is a generic function in base R. It dispatches to either the
+ \code{merge.data.frame} method or \code{merge.data.table} method depending on the class of its first argument.
+
+ In versions \code{< v1.9.6}, if the specified columns in \code{by} was not the key (or head of the key) of \code{x} or \code{y}, then a \code{\link{copy}} is first rekeyed prior to performing the merge. This was less performant and memory inefficient.
+
+ In version \code{v1.9.4} secondary keys was implemented. In \code{v1.9.6}, the concept of secondary keys has been
+ extended to \code{merge}. No deep copies are made anymore and therefore very performant and memory efficient. Also there is better control for providing the columns to merge on with the help of newly implemented \code{by.x} and \code{by.y} arguments.
+
+ For a more \code{data.table}-centric way of merging two \code{data.table}s, see \code{\link{[.data.table}}; e.g., \code{x[y, ...]}. See FAQ 1.12 for a detailed comparison of \code{merge} and \code{x[y, ...]}.
+
+ Merges on numeric columns: Columns of numeric types (i.e., double) have their last two bytes rounded off while computing order, by defalult, to avoid any unexpected behaviour due to limitations in representing floating point numbers precisely. For large numbers (integers > 2^31), we recommend using \code{bit64::integer64}. Have a look at \code{\link{setNumericRounding}} to learn more.
+
+}
+
+\value{
+ A new \code{data.table} based on the merged \code{data table}s, sorted by the
+ columns set (or inferred for) the \code{by} argument.
+}
+
+\seealso{
+ \code{\link{data.table}}, \code{\link{[.data.table}},
+ \code{\link{merge.data.frame}}
+}
+
+\examples{
+(dt1 <- data.table(A = letters[1:10], X = 1:10, key = "A"))
+(dt2 <- data.table(A = letters[5:14], Y = 1:10, key = "A"))
+merge(dt1, dt2)
+merge(dt1, dt2, all = TRUE)
+
+(dt1 <- data.table(A = letters[rep(1:3, 2)], X = 1:6, key = "A"))
+(dt2 <- data.table(A = letters[rep(2:4, 2)], Y = 6:1, key = "A"))
+merge(dt1, dt2, allow.cartesian=TRUE)
+
+(dt1 <- data.table(A = c(rep(1L, 5), 2L), B = letters[rep(1:3, 2)], X = 1:6, key = "A,B"))
+(dt2 <- data.table(A = c(rep(1L, 5), 2L), B = letters[rep(2:4, 2)], Y = 6:1, key = "A,B"))
+merge(dt1, dt2)
+merge(dt1, dt2, by="B", allow.cartesian=TRUE)
+
+# test it more:
+d1 <- data.table(a=rep(1:2,each=3), b=1:6, key="a,b")
+d2 <- data.table(a=0:1, bb=10:11, key="a")
+d3 <- data.table(a=0:1, key="a")
+d4 <- data.table(a=0:1, b=0:1, key="a,b")
+
+merge(d1, d2)
+merge(d2, d1)
+merge(d1, d2, all=TRUE)
+merge(d2, d1, all=TRUE)
+
+merge(d3, d1)
+merge(d1, d3)
+merge(d1, d3, all=TRUE)
+merge(d3, d1, all=TRUE)
+
+merge(d1, d4)
+merge(d1, d4, by="a", suffixes=c(".d1", ".d4"))
+merge(d4, d1)
+merge(d1, d4, all=TRUE)
+merge(d4, d1, all=TRUE)
+
+# new feature, no need to set keys anymore
+set.seed(1L)
+d1 <- data.table(a=sample(rep(1:3,each=2)), z=1:6)
+d2 <- data.table(a=2:0, z=10:12)
+merge(d1, d2, by="a")
+merge(d1, d2, by="a", all=TRUE)
+
+# new feature, using by.x and by.y arguments
+setnames(d2, "a", "b")
+merge(d1, d2, by.x="a", by.y="b")
+merge(d1, d2, by.x="a", by.y="b", all=TRUE)
+merge(d2, d1, by.x="b", by.y="a")
+}
+
+\keyword{ data }
+
+
diff --git a/man/na.omit.data.table.Rd b/man/na.omit.data.table.Rd
new file mode 100644
index 0000000..55f2681
--- /dev/null
+++ b/man/na.omit.data.table.Rd
@@ -0,0 +1,52 @@
+\name{na.omit.data.table}
+\alias{na.omit.data.table}
+\alias{na.omit}
+\title{ Remove rows with missing values on columns specified }
+\description{
+ This is a \code{data.table} method for the S3 generic \code{stats::na.omit}. The internals are written in C for speed. See examples for benchmark timings.
+
+ \code{bit64::integer64} type is also supported.
+}
+
+\usage{
+\method{na.omit}{data.table}(object, cols=seq_along(object), invert=FALSE, ...)
+}
+\arguments{
+ \item{object}{ A \code{data.table}. }
+ \item{cols}{ A vector of column names (or numbers) on which to check for missing values. Default is all the columns. }
+ \item{invert}{ logical. If \code{FALSE} omits all rows with any missing values (default). \code{TRUE} returns just those rows with missing values instead. }
+ \item{\dots}{ Further arguments special methods could require. }
+}
+\details{
+The \code{data.table} method consists of an additional argument \code{cols}, which when specified looks for missing values in just those columns specified. The default value for \code{cols} is all the columns, to be consistent with the default behaviour of \code{stats::na.omit}.
+
+It does not add the attribute \code{na.action} as \code{stats::na.omit} does.
+}
+\value{
+A data.table with just the rows where the specified columns have no missing value in any of them.
+}
+\seealso{
+\code{\link{data.table}}
+}
+\examples{
+DT = data.table(x=c(1,NaN,NA,3), y=c(NA_integer_, 1:3), z=c("a", NA_character_, "b", "c"))
+# default behaviour
+na.omit(DT)
+# omit rows where 'x' has a missing value
+na.omit(DT, cols="x")
+# omit rows where either 'x' or 'y' have missing values
+na.omit(DT, cols=c("x", "y"))
+
+\dontrun{
+# Timings on relatively large data
+set.seed(1L)
+DT = data.table(x = sample(c(1:100, NA_integer_), 5e7L, TRUE),
+ y = sample(c(rnorm(100), NA), 5e7L, TRUE))
+system.time(ans1 <- na.omit(DT)) ## 2.6 seconds
+system.time(ans2 <- stats:::na.omit.data.frame(DT)) ## 29 seconds
+# identical? check each column separately, as ans2 will have additional attribute
+all(sapply(1:2, function(i) identical(ans1[[i]], ans2[[i]]))) ## TRUE
+}
+}
+\keyword{ data }
+
diff --git a/man/patterns.Rd b/man/patterns.Rd
new file mode 100644
index 0000000..0ff1c6e
--- /dev/null
+++ b/man/patterns.Rd
@@ -0,0 +1,24 @@
+\name{patterns}
+\alias{patterns}
+\title{Regex patterns to extract columns from data.table}
+\description{
+ From \code{v1.9.6}, \code{\link{melt.data.table}} has a new enhanced functionality in which \code{measure.vars} argument can accept a \emph{list of column names} and melt them into separate columns. See the \code{Efficient reshaping using data.tables} vignette linked below to learn more.
+
+ \code{patterns} is designed purely for convenience, to be used only within the \code{measure.vars} argument of \code{melt.data.table}. Column names corresponding to each pattern from the \code{data.table} is melted into a separate column.
+}
+\usage{
+patterns(...)
+}
+\arguments{
+ \item{...}{ A set of patterns. See example. }
+}
+\seealso{
+ \code{\link{melt}}, \url{https://github.com/Rdatatable/data.table/wiki/Getting-started}
+}
+\examples{
+# makes sense only in the context of melt at the moment
+dt = data.table(x1 = 1:5, x2 = 6:10, y1 = letters[1:5], y2 = letters[6:10])
+# melt all columns that begin with 'x' & 'y', respectively, into separate columns
+melt(dt, measure.vars = patterns("^x", "^y"))
+}
+\keyword{ data }
diff --git a/man/rbindlist.Rd b/man/rbindlist.Rd
new file mode 100644
index 0000000..eae4d1d
--- /dev/null
+++ b/man/rbindlist.Rd
@@ -0,0 +1,65 @@
+\name{rbindlist}
+\alias{rbindlist}
+\alias{rbind.data.table}
+\alias{rbind}
+\title{ Makes one data.table from a list of many }
+\description{
+ Same as \code{do.call("rbind", l)} on \code{data.frame}s, but much faster. See \code{DETAILS} for more.
+}
+\usage{
+rbindlist(l, use.names=fill, fill=FALSE, idcol=NULL)
+# rbind(..., use.names=TRUE, fill=FALSE, idcol=NULL)
+}
+\arguments{
+ \item{l}{ A list containing \code{data.table}, \code{data.frame} or \code{list} objects. At least one of the inputs should have column names set. \code{\dots} is the same but you pass the objects by name separately. }
+ \item{use.names}{If \code{TRUE} items will be bound by matching column names. By default \code{FALSE} for \code{rbindlist} (for backwards compatibility) and \code{TRUE} for \code{rbind} (consistency with base). Columns with duplicate names are bound in the order of occurrence, similar to base. When TRUE, at least one item of the input list has to have non-null column names.}
+ \item{fill}{If \code{TRUE} fills missing columns with NAs. By default \code{FALSE}. When \code{TRUE}, \code{use.names} has to be \code{TRUE}, and all items of the input list has to have non-null column names. }
+ \item{idcol}{Generates an index column. Default (\code{NULL}) is not to. If \code{idcol=TRUE} then the column is auto named \code{.id}. Alternatively the column name can be directly provided, e.g., \code{idcol = "id"}.
+
+ If input is a named list, ids are generated using them, else using integer vector from \code{1} to length of input list. See \code{examples}.}
+}
+\details{
+Each item of \code{l} can be a \code{data.table}, \code{data.frame} or \code{list}, including \code{NULL} (skipped) or an empty object (0 rows). \code{rbindlist} is most useful when there are a variable number of (potentially many) objects to stack, such as returned by \code{lapply(fileNames, fread)}. \code{rbind} however is most useful to stack two or three objects which you know in advance. \code{\dots} should contain at least one \code{data.table} for \code{rbind(...)} to call the fas [...]
+
+In versions \code{<= v1.9.2}, each item for \code{rbindlist} should have the same number of columns as the first non empty item. \code{rbind.data.table} gained a \code{fill} argument to fill missing columns with \code{NA} in \code{v1.9.2}, which allowed for \code{rbind(...)} binding unequal number of columns.
+
+In version \code{> v1.9.2}, these functionalities were extended to \code{rbindlist} (and written entirely in C for speed). \code{rbindlist} has \code{use.names} argument, which is set to \code{FALSE} by default for backwards compatibility. It also contains \code{fill} argument as well and can bind unequal columns when set to \code{TRUE}.
+
+With these changes, the only difference between \code{rbind(...)} and \code{rbindlist(l)} is their \emph{default argument} \code{use.names}.
+
+If column \code{i} of input items do not all have the same type; e.g, a \code{data.table} may be bound with a \code{list} or a column is \code{factor} while others are \code{character} types, they are coerced to the highest type (SEXPTYPE).
+
+Note that any additional attributes that might exist on individual items of the input list would not be preserved in the result.
+}
+\value{
+ An unkeyed \code{data.table} containing a concatenation of all the items passed in.
+}
+\seealso{ \code{\link{data.table}} }
+\examples{
+# default case
+DT1 = data.table(A=1:3,B=letters[1:3])
+DT2 = data.table(A=4:5,B=letters[4:5])
+l = list(DT1,DT2)
+rbindlist(l)
+
+# bind correctly by names
+DT1 = data.table(A=1:3,B=letters[1:3])
+DT2 = data.table(B=letters[4:5],A=4:5)
+l = list(DT1,DT2)
+rbindlist(l, use.names=TRUE)
+
+# fill missing columns, and match by col names
+DT1 = data.table(A=1:3,B=letters[1:3])
+DT2 = data.table(B=letters[4:5],C=factor(1:2))
+l = list(DT1,DT2)
+rbindlist(l, use.names=TRUE, fill=TRUE)
+
+# generate index column, auto generates indices
+rbindlist(l, use.names=TRUE, fill=TRUE, idcol=TRUE)
+# let's name the list
+setattr(l, 'names', c("a", "b"))
+rbindlist(l, use.names=TRUE, fill=TRUE, idcol="ID")
+
+}
+\keyword{ data }
+
diff --git a/man/rleid.Rd b/man/rleid.Rd
new file mode 100644
index 0000000..6a98dad
--- /dev/null
+++ b/man/rleid.Rd
@@ -0,0 +1,36 @@
+\name{rleid}
+\alias{rleid}
+\alias{rleidv}
+\title{ Generate run-length type group id}
+\description{
+ A convenience function for generating a \emph{run-length} type \emph{id} column to be used in grouping operations. It accepts atomic vectors, lists, data.frames or data.tables as input.
+}
+\usage{
+rleid(...)
+rleidv(x, cols=seq_along(x))
+}
+\arguments{
+ \item{x}{ A vector, list, data.frame or data.table. }
+ \item{...}{ A sequence of numeric, integer64, character or logical vectors, all of same length. For interactive use.}
+ \item{cols}{ Only meaningful for lists, data.frames or data.tables. A character vector of column names (or numbers) of x. }
+}
+\details{
+ At times aggregation (or grouping) operations need to be performed where consecutive runs of identical values should belong to the same group (See \code{\link[base]{rle}}). The use for such a function has come up repeatedly on StackOverflow, see the \code{See Also} section. This function allows to generate \emph{"run-length"} groups directly.
+
+ \code{rleid} is designed for interactive use and accepts a sequence of vectors as arguments. For programming, \code{rleidv} might be more useful.
+}
+\value{
+ An integer vector with same length as \code{NROW(x)}.
+}
+\examples{
+DT = data.table(grp=rep(c("A", "B", "C", "A", "B"), c(2,2,3,1,2)), value=1:10)
+rleid(DT$grp) # get run-length ids
+rleidv(DT, "grp") # same as above
+# get sum of value over run-length groups
+DT[, sum(value), by=.(grp, rleid(grp))]
+
+}
+\seealso{
+ \code{\link{data.table}}, \url{http://stackoverflow.com/q/21421047/559784}
+}
+\keyword{ data }
diff --git a/man/setDF.Rd b/man/setDF.Rd
new file mode 100644
index 0000000..68f7af7
--- /dev/null
+++ b/man/setDF.Rd
@@ -0,0 +1,42 @@
+\name{setDF}
+\alias{setDF}
+\title{Convert a data.table to data.frame by reference}
+\description{
+ In \code{data.table} parlance, all \code{set*} functions change their input \emph{by reference}. That is, no copy is made at all, other than temporary working memory, which is as large as one column.. The only other \code{data.table} operator that modifies input by reference is \code{\link{:=}}. Check out the \code{See Also} section below for other \code{set*} function \code{data.table} provides.
+
+ A helper function to convert a \code{data.table} or \code{list} of equal length to \code{data.frame} by reference.
+}
+\usage{
+setDF(x, rownames=NULL)
+}
+\arguments{
+ \item{x}{ A \code{data.table}, \code{data.frame} or \code{list} of equal length. }
+ \item{rownames}{ A \code{character} vector to assign as the row names of \code{x}. }
+}
+
+\details{
+ This feature request came up on the data.table mailing list: \url{http://bit.ly/1xkokNQ}. All \code{data.table} attributes including any keys of the input data.table are stripped off.
+
+ When using \code{rownames}, recall that the row names of a \code{data.frame} must be unique. By default, the assigned set of row names is simply the sequence 1, ..., \code{nrow(x)} (or \code{length(x)} for \code{list}s).
+}
+
+\value{
+ The input \code{data.table} is modified by reference to a \code{data.frame} and returned (invisibly). If you require a copy, take a copy first (using \code{DT2 = copy(DT)}). See \code{?copy}..
+}
+
+\seealso{ \code{\link{setkey}}, \code{\link{setcolorder}}, \code{\link{setattr}}, \code{\link{setnames}}, \code{\link{set}}, \code{\link{:=}}, \code{\link{setorder}}, \code{\link{copy}}, \code{\link{setDT}}
+}
+\examples{
+X = data.table(x=1:5, y=6:10)
+## convert 'X' to data.frame, without any copy.
+setDF(X)
+
+X = data.table(x=1:5, y=6:10)
+## idem, assigning row names
+setDF(X, rownames = LETTERS[1:5])
+
+X = list(x=1:5, y=6:10)
+# X is converted to a data.frame without any copy.
+setDF(X)
+}
+\keyword{ data }
\ No newline at end of file
diff --git a/man/setDT.Rd b/man/setDT.Rd
new file mode 100644
index 0000000..4f352e9
--- /dev/null
+++ b/man/setDT.Rd
@@ -0,0 +1,59 @@
+\name{setDT}
+\alias{setDT}
+\title{Convert lists and data.frames to data.table by reference}
+\description{
+ In \code{data.table} parlance, all \code{set*} functions change their input \emph{by reference}. That is, no copy is made at all, other than temporary working memory, which is as large as one column.. The only other \code{data.table} operator that modifies input by reference is \code{\link{:=}}. Check out the \code{See Also} section below for other \code{set*} function \code{data.table} provides.
+
+ \code{setDT} converts lists (both named and unnamed) and data.frames to data.tables \emph{by reference}. This feature was requested on \href{http://stackoverflow.com/questions/20345022/convert-a-data-frame-to-a-data-table-without-copy}{Stackoverflow}.
+
+}
+\usage{
+setDT(x, keep.rownames=FALSE, key=NULL, check.names=FALSE)
+}
+\arguments{
+ \item{x}{ A named or unnamed \code{list}, \code{data.frame} or \code{data.table}. }
+ \item{keep.rownames}{ For \code{data.frame}s, \code{TRUE} retains the \code{data.frame}'s row names under a new column \code{rn}. }
+ \item{key}{Character vector of one or more column names which is passed to \code{\link{setkeyv}}. It may be a single comma separated string such as \code{key="x,y,z"}, or a vector of names such as \code{key=c("x","y","z")}. }
+ \item{check.names}{ Just as \code{check.names} in \code{\link{data.frame}}. }
+}
+
+\details{
+ When working on large \code{lists} or \code{data.frames}, it might be both time and memory consuming to convert them to a \code{data.table} using \code{as.data.table(.)}, as this will make a complete copy of the input object before to convert it to a \code{data.table}. The \code{setDT} function takes care of this issue by allowing to convert \code{lists} - both named and unnamed lists and \code{data.frames} \emph{by reference} instead. That is, the input object is modified in place, no [...]
+}
+
+\value{
+ The input is modified by reference, and returned (invisibly) so it can be used in compound statements; e.g., \code{setDT(X)[, sum(B), by=A]}. If you require a copy, take a copy first (using \code{DT2 = copy(DT)}). See \code{?copy}.
+}
+
+\seealso{ \code{\link{setkey}}, \code{\link{setcolorder}}, \code{\link{setattr}}, \code{\link{setnames}}, \code{\link{set}}, \code{\link{:=}}, \code{\link{setorder}}, \code{\link{copy}}, \code{\link{setDF}}
+}
+\examples{
+
+set.seed(45L)
+X = data.frame(A=sample(3, 10, TRUE),
+ B=sample(letters[1:3], 10, TRUE),
+ C=sample(10), stringsAsFactors=FALSE)
+
+# Convert X to data.table by reference and
+# get the frequency of each "A,B" combination
+setDT(X)[, .N, by=.(A,B)]
+
+# convert list to data.table
+# autofill names
+X = list(1:4, letters[1:4])
+setDT(X)
+# don't provide names
+X = list(a=1:4, letters[1:4])
+setDT(X, FALSE)
+
+# setkey directly
+X = list(a = 4:1, b=runif(4))
+setDT(X, key="a")[]
+
+# check.names argument
+X = list(a=1:5, a=6:10)
+setDT(X, check.names=TRUE)[]
+
+}
+\keyword{ data }
+
diff --git a/man/setNumericRounding.Rd b/man/setNumericRounding.Rd
new file mode 100644
index 0000000..f437a51
--- /dev/null
+++ b/man/setNumericRounding.Rd
@@ -0,0 +1,59 @@
+\name{setNumericRounding}
+\alias{setNumericRounding}
+\alias{getNumericRounding}
+\title{ Change or turn off numeric rounding }
+\description{
+ Change rounding to 0, 1 or 2 bytes when joining, grouping or ordering numeric (i.e. double, POSIXct) columns.
+}
+\usage{
+setNumericRounding(x)
+getNumericRounding()
+}
+\arguments{
+ \item{x}{ integer or numeric vector: 2 (default), 1 or 0 byte rounding }
+}
+\details{
+ Computers cannot represent some floating point numbers (such as 0.6) precisely, using base 2. This leads to unexpected behaviour when
+ joining or grouping columns of type 'numeric'; i.e. 'double', see example below. To deal with this automatically for convenience,
+ when joining or grouping, data.table rounds such data to apx 11 s.f. which is plenty of digits for many cases. This is achieved by
+ rounding the last 2 bytes off the significand. Where this is not enough, \code{setNumericRounding} can be used to reduce to 1 byte
+ rounding, or no rounding (0 bytes rounded) for full precision.
+
+ It's bytes rather than bits because it's tied in with the radix sort algorithm for sorting numerics which sorts byte by byte. With the
+ default rounding of 2 bytes, at most 6 passes are needed. With no rounding, at most 8 passes are needed and hence may be slower. The
+ choice of default is not for speed however, but to avoid surprising results such as in the example below.
+
+ For large numbers (integers > 2^31), we recommend using \code{bit64::integer64} rather than setting rounding to \code{0}.
+
+ If you're using \code{POSIXct} type column with \emph{millisecond} (or lower) resolution, you might want to consider setting \code{setNumericRounding(1)} . This'll become the default for \code{POSIXct} types in the future, instead of the default \code{2}.
+}
+\value{
+ \code{setNumericRounding} returns no value; the new value is applied. \code{getNumericRounding} returns the current value: 0, 1 or 2.
+}
+\seealso{
+\url{http://en.wikipedia.org/wiki/Double-precision_floating-point_format}\cr
+\url{http://en.wikipedia.org/wiki/Floating_point}\cr
+\url{http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html}
+}
+\examples{
+DT = data.table(a=seq(0,1,by=0.2),b=1:2, key="a")
+DT
+setNumericRounding(0) # turn off rounding
+DT[.(0.4)] # works
+DT[.(0.6)] # no match, confusing since 0.6 is clearly there in DT
+
+setNumericRounding(2) # restore default
+DT[.(0.6)] # works as expected
+
+# using type 'numeric' for integers > 2^31 (typically ids)
+DT = data.table(id = c(1234567890123, 1234567890124, 1234567890125), val=1:3)
+print(DT, digits=15)
+DT[,.N,by=id] # 1 row
+setNumericRounding(0)
+DT[,.N,by=id] # 3 rows
+# better to use bit64::integer64 for such ids
+setNumericRounding(2)
+}
+\keyword{ data }
+
+
diff --git a/man/setattr.Rd b/man/setattr.Rd
new file mode 100644
index 0000000..a6522f0
--- /dev/null
+++ b/man/setattr.Rd
@@ -0,0 +1,68 @@
+\name{setattr}
+\alias{setattr}
+\alias{setnames}
+\title{ Set attributes of objects by reference }
+\description{
+ In \code{data.table}, all \code{set*} functions change their input \emph{by reference}. That is, no copy is made at all, other than temporary working memory which is as large as one column. The only other \code{data.table} operator that modifies input by reference is \code{\link{:=}}. Check out the \code{See Also} section below for other \code{set*} function that \code{data.table} provides.
+}
+\usage{
+setattr(x,name,value)
+setnames(x,old,new)
+}
+\arguments{
+ \item{x}{ \code{setnames} accepts \code{data.frame} and \code{data.table}. \code{setattr} accepts any input; e.g, list, columns of a \code{data.frame} or \code{data.table}. }
+ \item{name}{ The character attribute name. }
+ \item{value}{ The value to assign to the attribute or \code{NULL} removes the attribute, if present. }
+ \item{old}{ When \code{new} is provided, character names or numeric positions of column names to change. When \code{new} is not provided, the new column names, which must be the same length as the number of columns. See examples. }
+ \item{new}{ Optional. New column names, the same length as \code{old}. }
+}
+\details{
+
+ \code{setnames} operates on \code{data.table} and \code{data.frame} not other types like \code{list} and \code{vector}. It can be used to change names \emph{by name} with built-in checks and warnings (e.g., if any old names are missing or appear more than once).
+
+ \code{setattr} is a more general function that allows setting of any attribute to an object \emph{by reference}.
+
+ A very welcome change in R 3.1+ was that `names<-` and `colnames<-` no longer copy the \emph{entire} object as they used to (up to 4 times), see examples below. They now take a shallow copy. The `set*` functions in data.table are still useful because they don't even take a shallow copy. This allows changing names and attributes of a (usually very large) \code{data.table} in the global environment \emph{from within functions}. Like a database.
+
+ }
+\value{
+ The input is modified by reference, and returned (invisibly) so it can be used in compound statements; e.g., \code{setnames(DT,"V1", "Y")[, .N, by=Y]}. If you require a copy, take a copy first (using \code{DT2=copy(DT)}). See \code{?copy}.
+
+ Note that \code{setattr} is also in package \code{bit}. Both packages merely expose R's internal \code{setAttrib} function at C level but differ in return value. \code{bit::setattr} returns \code{NULL} (invisibly) to remind you the function is used for its side effect. \code{data.table::setattr} returns the changed object (invisibly) for use in compound statements.
+}
+\seealso{ \code{\link{data.table}}, \code{\link{setkey}}, \code{\link{setorder}}, \code{\link{setcolorder}}, \code{\link{set}}, \code{\link{:=}}, \code{\link{setDT}}, \code{\link{setDF}}, \code{\link{copy}}
+}
+\examples{
+
+DF = data.frame(a=1:2,b=3:4) # base data.frame to demo copies and syntax
+try(tracemem(DF)) # try() for R sessions opted out of memory profiling
+colnames(DF)[1] <- "A" # 4 shallow copies (R >= 3.1, was 4 deep copies before)
+names(DF)[1] <- "A" # 3 shallow copies
+names(DF) <- c("A", "b") # 1 shallow copy
+`names<-`(DF,c("A","b")) # 1 shallow copy
+
+DT = data.table(a=1:2,b=3:4,c=5:6) # compare to data.table
+try(tracemem(DT)) # by reference, no deep or shallow copies
+setnames(DT,"b","B") # by name, no match() needed (warning if "b" is missing)
+setnames(DT,3,"C") # by position with warning if 3 > ncol(DT)
+setnames(DT,2:3,c("D","E")) # multiple
+setnames(DT,c("a","E"),c("A","F")) # multiple by name (warning if either "a" or "E" is missing)
+setnames(DT,c("X","Y","Z")) # replace all (length of names must be == ncol(DT))
+
+DT = data.table(a=1:3, b=4:6)
+f = function(...) {
+ # ...
+ setattr(DT,"myFlag",TRUE) # by reference
+ # ...
+ localDT = copy(DT)
+ setattr(localDT,"myFlag2",TRUE)
+ # ...
+ invisible()
+}
+f()
+attr(DT,"myFlag") # TRUE
+attr(DT,"myFlag2") # NULL
+
+}
+\keyword{ data }
+
diff --git a/man/setcolorder.Rd b/man/setcolorder.Rd
new file mode 100644
index 0000000..48132c5
--- /dev/null
+++ b/man/setcolorder.Rd
@@ -0,0 +1,35 @@
+\name{setcolorder}
+\alias{setcolorder}
+
+\title{Fast column reordering of a data.table by reference}
+\description{
+ In \code{data.table} parlance, all \code{set*} functions change their input \emph{by reference}. That is, no copy is made at all, other than temporary working memory, which is as large as one column.. The only other \code{data.table} operator that modifies input by reference is \code{\link{:=}}. Check out the \code{See Also} section below for other \code{set*} function \code{data.table} provides.
+
+ \code{setcolorder} reorders the columns of data.table, \emph{by reference}, to the new order provided.
+}
+
+\usage{
+setcolorder(x, neworder)
+}
+\arguments{
+ \item{x}{ A \code{data.table}. }
+ \item{neworder}{ Character vector of the new column name ordering. May also be column numbers. }
+}
+\details{
+ To reorder \code{data.table} columns, the idiomatic way is to use \code{setcolorder(x, neworder)}, instead of doing \code{x <- x[, neworder, with=FALSE]}. This is because the latter makes an entire copy of the \code{data.table}, which maybe unnecessary in most situations. \code{setcolorder} also allows column numbers instead of names for \code{neworder} argument, although we recommend using names as a good programming practice.
+}
+\value{
+ The input is modified by reference, and returned (invisibly) so it can be used in compound statements. If you require a copy, take a copy first (using \code{DT2 = copy(DT)}). See \code{?copy}.
+}
+\seealso{ \code{\link{setkey}}, \code{\link{setorder}}, \code{\link{setattr}}, \code{\link{setnames}}, \code{\link{set}}, \code{\link{:=}}, \code{\link{setDT}}, \code{\link{setDF}}, \code{\link{copy}}, \code{\link{getNumericRounding}}, \code{\link{setNumericRounding}}
+}
+\examples{
+
+set.seed(45L)
+DT = data.table(A=sample(3, 10, TRUE),
+ B=sample(letters[1:3], 10, TRUE), C=sample(10))
+
+setcolorder(DT, c("C", "A", "B"))
+}
+\keyword{ data }
+
diff --git a/man/setkey.Rd b/man/setkey.Rd
new file mode 100644
index 0000000..857603a
--- /dev/null
+++ b/man/setkey.Rd
@@ -0,0 +1,92 @@
+\name{setkey}
+\alias{setkey}
+\alias{setkeyv}
+\alias{key}
+\alias{key<-}
+\alias{haskey}
+\alias{set2key}
+\alias{set2keyv}
+\alias{key2}
+\title{ Create key on a data table }
+\description{
+ In \code{data.table} parlance, all \code{set*} functions change their input \emph{by reference}. That is, no copy is made at all, other than temporary working memory, which is as large as one column.. The only other \code{data.table} operator that modifies input by reference is \code{\link{:=}}. Check out the \code{See Also} section below for other \code{set*} function \code{data.table} provides.
+
+ \code{setkey()} sorts a \code{data.table} and marks it as sorted (with an attribute \code{sorted}). The sorted columns are the key. The key can be any columns in any order. The columns are sorted in ascending order always. The table is changed \emph{by reference} and is therefore very memory efficient.
+
+ \code{key()} returns the \code{data.table}'s key if it exists, and \code{NULL} if none exist.
+
+ \code{haskey()} returns a logical \code{TRUE}/\code{FALSE} depending on whether the \code{data.table} has a key (or not).
+
+}
+\usage{
+setkey(x, ..., verbose=getOption("datatable.verbose"), physical = TRUE)
+setkeyv(x, cols, verbose=getOption("datatable.verbose"), physical = TRUE)
+set2key(...)
+set2keyv(...)
+key(x)
+key2(x)
+haskey(x)
+key(x) <- value # DEPRECATED, please use setkey or setkeyv instead.
+}
+\arguments{
+ \item{x}{ A \code{data.table}. }
+ \item{\dots}{ The columns to sort by. Do not quote the column names. If \code{\dots} is missing (i.e. \code{setkey(DT)}), all the columns are used. \code{NULL} removes the key. }
+ \item{cols}{ A character vector (only) of column names. }
+ \item{value}{ In (deprecated) \code{key<-}, a character vector (only) of column names.}
+ \item{verbose}{ Output status and information. }
+ \item{physical}{ TRUE changes the order of the data in RAM. FALSE adds a secondary key a.k.a. index. }
+}
+\details{
+ \code{setkey} reorders (or sorts) the rows of a data.table by the columns provided. In versions \code{1.9+}, for \code{integer} columns, a modified version of base's counting sort is implemented, which allows negative values as well. It is extremely fast, but is limited by the range of integer values being <= 1e5. If that fails, it falls back to a (fast) 4-pass radix sort for integers, implemented based on Pierre Terdiman's and Michael Herf's code (see links below). Similarly, a very f [...]
+
+ Note that columns of \code{numeric} types (i.e., \code{double}) have their last two bytes rounded off while computing order, by defalult, to avoid any unexpected behaviour due to limitations in representing floating point numbers precisely. Have a look at \code{\link{setNumericRounding}} to learn more.
+
+ The sort is \emph{stable}; i.e., the order of ties (if any) is preserved, in both versions - \code{<=1.8.10} and \code{>= 1.9.0}.
+
+ In \code{data.table} versions \code{<= 1.8.10}, for columns of type \code{integer}, the sort is attempted with the very fast \code{"radix"} method in \code{\link[base]{sort.list}}. If that fails, the sort reverts to the default method in \code{\link[base]{order}}. For character vectors, \code{data.table} takes advantage of R's internal global string cache and implements a very efficient order, also exported as \code{\link{chorder}}.
+
+ In v1.7.8, the \code{key<-} syntax was deprecated. The \code{<-} method copies the whole table and we know of no way to avoid that copy without a change in \R itself. Please use the \code{set}* functions instead, which make no copy at all. \code{setkey} accepts unquoted column names for convenience, whilst \code{setkeyv} accepts one vector of column names.
+
+ The problem (for \code{data.table}) with the copy by \code{key<-} (other than being slower) is that \R doesn't maintain the over allocated truelength, but it looks as though it has. Adding a column by reference using \code{:=} after a \code{key<-} was therefore a memory overwrite and eventually a segfault; the over allocated memory wasn't really there after \code{key<-}'s copy. \code{data.table}s now have an attribute \code{.internal.selfref} to catch and warn about such copies. This a [...]
+
+ For the same reason, please use the other \code{set*} functions which modify objects by reference, rather than using the \code{<-} operator which results in copying the entire object.
+
+ It isn't good programming practice, in general, to use column numbers rather than names. This is why \code{setkey} and \code{setkeyv} only accept column names. If you use column numbers then bugs (possibly silent) can more easily creep into your code as time progresses if changes are made elsewhere in your code; e.g., if you add, remove or reorder columns in a few months time, a \code{setkey} by column number will then refer to a different column, possibly returning incorrect results w [...]
+ }
+\value{
+ The input is modified by reference, and returned (invisibly) so it can be used in compound statements; e.g., \code{setkey(DT,a)[J("foo")]}. If you require a copy, take a copy first (using \code{DT2=copy(DT)}). \code{copy()} may also sometimes be useful before \code{:=} is used to subassign to a column by reference. See \code{?copy}.
+}
+\references{
+\url{http://en.wikipedia.org/wiki/Radix_sort}\cr
+\url{http://en.wikipedia.org/wiki/Counting_sort}\cr
+\url{http://cran.at.r-project.org/web/packages/bit/index.html}\cr
+\url{http://stereopsis.com/radix.html}
+}
+\note{ Despite its name, \code{base::sort.list(x,method="radix")} actually invokes a \emph{counting sort} in R, not a radix sort. See do_radixsort in src/main/sort.c. A counting sort, however, is particularly suitable for sorting integers and factors, and we like it. In fact we like it so much that \code{data.table} contains a counting sort algorithm for character vectors using R's internal global string cache. This is particularly fast for character vectors containing many duplicates, s [...]
+}
+\seealso{ \code{\link{data.table}}, \code{\link{tables}}, \code{\link{J}}, \code{\link[base]{sort.list}}, \code{\link{copy}}, \code{\link{setDT}}, \code{\link{setDF}}, \code{\link{set}} \code{\link{:=}}, \code{\link{setorder}}, \code{\link{setcolorder}}, \code{\link{setattr}}, \code{\link{setnames}}, \code{\link{chorder}}, \code{\link{setNumericRounding}}
+}
+\examples{
+# Type 'example(setkey)' to run these at prompt and browse output
+
+DT = data.table(A=5:1,B=letters[5:1])
+DT # before
+setkey(DT,B) # re-orders table and marks it sorted.
+DT # after
+tables() # KEY column reports the key'd columns
+key(DT)
+keycols = c("A","B")
+setkeyv(DT,keycols) # rather than key(DT)<-keycols (which copies entire table)
+
+DT = data.table(A=5:1,B=letters[5:1])
+DT2 = DT # does not copy
+setkey(DT2,B) # does not copy-on-write to DT2
+identical(DT,DT2) # TRUE. DT and DT2 are two names for the same keyed table
+
+DT = data.table(A=5:1,B=letters[5:1])
+DT2 = copy(DT) # explicit copy() needed to copy a data.table
+setkey(DT2,B) # now just changes DT2
+identical(DT,DT2) # FALSE. DT and DT2 are now different tables
+}
+\keyword{ data }
+
diff --git a/man/setorder.Rd b/man/setorder.Rd
new file mode 100644
index 0000000..b133919
--- /dev/null
+++ b/man/setorder.Rd
@@ -0,0 +1,61 @@
+\name{setorder}
+\alias{setorder}
+\alias{setorderv}
+\alias{order}
+\alias{fastorder}
+\alias{forder}
+
+\title{Fast row reordering of a data.table by reference}
+\description{
+ In \code{data.table} parlance, all \code{set*} functions change their input \emph{by reference}. That is, no copy is made at all, other than temporary working memory, which is as large as one column.. The only other \code{data.table} operator that modifies input by reference is \code{\link{:=}}. Check out the \code{See Also} section below for other \code{set*} function \code{data.table} provides.
+
+ \code{setorder} (and \code{setorderv}) reorders the rows of a \code{data.table} based on the columns (and column order) provided. It reorders the table \emph{by reference} and is therefore very memory efficient.
+
+ Also \code{x[order(.)]} is now optimised internally to use data.table's fast order by default. data.table always reorders in C-locale. To sort by session locale, use \code{x[base::order(.)]} instead.
+
+ \code{bit64::integer64} type is also supported for reordering rows of a \code{data.table}.
+}
+
+\usage{
+setorder(x, ..., na.last=FALSE)
+setorderv(x, cols, order=1L, na.last=FALSE)
+# optimised to use data.table's internal fast order
+# x[order(., na.last=TRUE)]
+}
+\arguments{
+ \item{x}{ A \code{data.table}. }
+ \item{...}{ The columns to sort by. Do not quote column names. If \code{...} is missing (ex: \code{setorder(x)}), \code{x} is rearranged based on all columns in ascending order by default. To sort by a column in descending order prefix a \code{"-"}, i.e., \code{setorder(x, a, -b, c)}. The \code{-b} works when \code{b} is of type \code{character} as well. }
+ \item{cols}{ A character vector of column names of \code{x}, to which to order by. Do not add \code{"-"} here. Use \code{order} argument instead.}
+ \item{order}{ An integer vector with only possible values of \code{1} and \code{-1}, corresponding to ascending and descending order. The length of \code{order} must be either \code{1} or equal to that of \code{cols}. If \code{length(order) == 1}, it's recycled to \code{length(cols)}. }
+ \item{na.last}{logical. If \code{TRUE}, missing values in the data are placed last; if \code{FALSE}, they are placed first; if \code{NA} they are removed. \code{na.last=NA} is valid only for \code{x[order(., na.last)]} and it's default is \code{TRUE}. \code{setorder} and \code{setorderv} only accept TRUE/FALSE with default \code{FALSE}.}
+}
+\details{
+ \code{data.table} implements fast radix based ordering. In versions <= 1.9.2, it was only capable of increasing order (ascending). From 1.9.4 on, the functionality has been extended to decreasing order (descending) as well. Columns of \code{numeric} types (i.e., \code{double}) have their last two bytes rounded off while computing order, by defalult, to avoid any unexpected behaviour due to limitations in representing floating point numbers precisely. Have a look at \code{\link{setNumer [...]
+
+ \code{setorder} accepts unquoted column names (with names preceded with a \code{-} sign for descending order) and reorders data.table rows \emph{by reference}, for e.g., \code{setorder(x, a, -b, c)}. Note that \code{-b} also works with columns of type \code{character} unlike \code{base::order}, which requires \code{-xtfrm(y)} instead (which is slow). \code{setorderv} in turn accepts a character vector of column names and an integer vector of column order separately.
+
+ Note that \code{\link{setkey}} still requires and will always sort only in ascending order, and is different from \code{setorder} in that it additionally sets the \code{sorted} attribute.
+
+ \code{na.last} argument, by default, is \code{FALSE} for \code{setorder} and \code{setorderv} to be consistent with \code{data.table}'s \code{setkey} and is \code{TRUE} for \code{x[order(.)]} to be consistent with \code{base::order}. Only \code{x[order(.)]} can have \code{na.last = NA} as it's a subset operation as opposed to \code{setorder} or \code{setorderv} which reorders the data.table by reference.
+
+ If \code{setorder} results in reordering of the rows of a keyed \code{data.table}, then it's key will be set to \code{NULL}.
+ }
+\value{
+ The input is modified by reference, and returned (invisibly) so it can be used in compound statements; e.g., \code{setorder(DT,a,-b)[, cumsum(c), by=list(a,b)]}. If you require a copy, take a copy first (using \code{DT2 = copy(DT)}). See \code{?copy}.
+}
+\seealso{ \code{\link{setkey}}, \code{\link{setcolorder}}, \code{\link{setattr}}, \code{\link{setnames}}, \code{\link{set}}, \code{\link{:=}}, \code{\link{setDT}}, \code{\link{setDF}}, \code{\link{copy}}, \code{\link{setNumericRounding}}
+}
+\examples{
+
+set.seed(45L)
+DT = data.table(A=sample(3, 10, TRUE),
+ B=sample(letters[1:3], 10, TRUE), C=sample(10))
+
+# setorder
+setorder(DT, A, -B)
+
+# same as above, but using setorderv
+setorderv(DT, c("A", "B"), c(1, -1))
+}
+\keyword{ data }
+
diff --git a/man/shift.Rd b/man/shift.Rd
new file mode 100644
index 0000000..31f37d0
--- /dev/null
+++ b/man/shift.Rd
@@ -0,0 +1,77 @@
+\name{shift}
+\alias{shift}
+\alias{lead}
+\alias{lag}
+\title{Fast lead/lag for vectors and lists}
+\description{
+ \code{lead} or \code{lag} vectors, lists, data.frames or data.tables implemented in C for speed.
+
+ \code{bit64::integer64} is also supported.
+}
+
+\usage{
+shift(x, n=1L, fill=NA, type=c("lag", "lead"), give.names=FALSE)
+}
+\arguments{
+ \item{x}{ A vector, list, data.frame or data.table. }
+ \item{n}{ Non-negative integer vector providing the periods to lead/lag by. To create multiple lead/lag vectors, provide multiple values to \code{n}. }
+ \item{fill}{ Value to pad by. }
+ \item{type}{ default is \code{"lag"}. The other possible value is \code{"lead"}. }
+ \item{give.names}{default is \code{FALSE} which returns an unnamed list. When \code{TRUE}, names are automatically generated corresponding to \code{type} and \code{n}. }
+}
+\details{
+ \code{shift} accepts vectors, lists, data.frames or data.tables. It always returns a list except when the input is a \code{vector} and \code{length(n) == 1} in which case a \code{vector} is returned, for convenience. This is so that it can be used conveniently within data.table's syntax. For example, \code{DT[, (cols) := shift(.SD, 1L), by=id]} would lag every column of \code{.SD} by 1 period for each group and \code{DT[, newcol := colA + shift(colB)]} would assign the sum of two \emph [...]
+
+ Argument \code{n} allows multiple values. For example, \code{DT[, (cols) := shift(.SD, 1:2), by=id]} would lag every column of \code{.SD} by \code{1} and \code{2} periods for each group. If \code{.SD} contained four columns, the first two elements of the list would correspond to \code{lag=1} and \code{lag=2} for the first column of \code{.SD}, the next two for second column of \code{.SD} and so on. Please see examples for more.
+
+ \code{shift} is designed mainly for use in data.tables along with \code{:=} or \code{set}. Therefore, it returns an unnamed list by default as assigning names for each group over and over can be quite time consuming with many groups. It may be useful to set names automatically in other cases, which can be done by setting \code{give.names} to \code{TRUE}.
+}
+\value{
+ A list containing the lead/lag of input \code{x}.
+}
+
+\examples{
+# on vectors, returns a vector as long as length(n) == 1, #1127
+x = 1:5
+# lag with period=1 and pad with NA (returns vector)
+shift(x, n=1, fill=NA, type="lag")
+# lag with period=1 and 2, and pad with 0 (returns list)
+shift(x, n=1:2, fill=0, type="lag")
+
+# on data.tables
+DT = data.table(year=2010:2014, v1=runif(5), v2=1:5, v3=letters[1:5])
+# lag columns 'v1,v2,v3' DT by 1 and fill with 0
+cols = c("v1","v2","v3")
+anscols = paste("lead", cols, sep="_")
+DT[, (anscols) := shift(.SD, 1, 0, "lead"), .SDcols=cols]
+
+# return a new data.table instead of updating
+# with names automatically set
+DT = data.table(year=2010:2014, v1=runif(5), v2=1:5, v3=letters[1:5])
+DT[, shift(.SD, 1:2, NA, "lead", TRUE), .SDcols=2:4]
+
+# lag/lead in the right order
+DT = data.table(year=2010:2014, v1=runif(5), v2=1:5, v3=letters[1:5])
+DT = DT[sample(nrow(DT))]
+# add lag=1 for columns 'v1,v2,v3' in increasing order of 'year'
+cols = c("v1","v2","v3")
+anscols = paste("lag", cols, sep="_")
+DT[order(year), (cols) := shift(.SD, 1, type="lag"), .SDcols=cols]
+DT[order(year)]
+
+# while grouping
+DT = data.table(year=rep(2010:2011, each=3), v1=1:6)
+DT[, c("lag1", "lag2") := shift(.SD, 1:2), by=year]
+
+# on lists
+ll = list(1:3, letters[4:1], runif(2))
+shift(ll, 1, type="lead")
+shift(ll, 1, type="lead", give.names=TRUE)
+shift(ll, 1:2, type="lead")
+
+}
+\seealso{
+ \code{\link{data.table}}
+}
+\keyword{ data }
+
diff --git a/man/subset.data.table.Rd b/man/subset.data.table.Rd
new file mode 100644
index 0000000..7c8ff80
--- /dev/null
+++ b/man/subset.data.table.Rd
@@ -0,0 +1,55 @@
+\name{subset.data.table}
+\alias{subset}
+\alias{subset.data.table}
+
+\title{ Subsetting data.tables }
+
+\description{
+ Returns subsets of a \code{data.table}.
+}
+
+\usage{
+ \method{subset}{data.table}(x, subset, select, \ldots)
+}
+
+\arguments{
+ \item{x}{
+ \code{data.table} to subset.
+ }
+ \item{subset}{
+ logical expression indicating elements or rows to keep
+ }
+ \item{select}{
+ expression indicating columns to select from \code{data.table}
+ }
+ \item{\ldots}{
+ further arguments to be passed to or from other methods
+ }
+}
+
+\details{
+ The \code{subset} argument works on the rows and will be evaluated
+ in the \code{data.table} so columns can be referred to (by name) as variables
+ in the expression.
+
+ The \code{data.table} that is returned will maintain the original keys
+ as long as they are not \code{select}-ed out.
+}
+
+\value{
+ A \code{data.table} containing the subset of rows and columns that are
+ selected.
+}
+\seealso{ \code{\link[base]{subset}} }
+\examples{
+
+dt <- data.table(a=sample(c('a', 'b', 'c'), 20, replace=TRUE),
+ b=sample(c('a', 'b', 'c'), 20, replace=TRUE),
+ c=sample(20), key=c('a', 'b'))
+
+sub <- subset(dt, a == 'a')
+all.equal(key(sub), key(dt))
+}
+\keyword{ data }
+
+
diff --git a/man/tables.Rd b/man/tables.Rd
new file mode 100644
index 0000000..1b9c11f
--- /dev/null
+++ b/man/tables.Rd
@@ -0,0 +1,30 @@
+\name{tables}
+\alias{tables}
+\title{Display all objects of class 'data.table' }
+\description{
+ Lists all data.table's in memory, including number of rows, column names and any keys.
+}
+\usage{
+tables(mb = TRUE, order.col = "NAME", width = 80, env=parent.frame(), silent=FALSE)
+}
+\arguments{
+ \item{mb}{ TRUE adds size of the data.table in MB to the output (slow
+ in older versions of R). }
+ \item{order.col}{ Quoted column name to sort the output by }
+ \item{width}{ Number of characters to truncate the COLS output }
+ \item{env}{ Usually tables() is executed at the prompt where parent.frame() returns .GlobalEnv. tables() may also be useful inside functions where parent.frame() is the local scope of the function, or set it to .GlobalEnv }
+ \item{silent}{ By default tables() is expected to be called at the prompt for its compact print output. silent=TRUE prints nothing. The data statistics are returned as a data.table, silently, whether silent is TRUE or FALSE }
+}
+% \details{
+% }
+\value{
+ A data.table containing the information printed.
+}
+\seealso{ \code{\link{data.table}}, \code{\link{setkey}}, \code{\link{ls}}, \code{\link{objects}}, \code{\link{object.size}} }
+\examples{
+DT = data.table(A=1:10,B=letters[1:10])
+DT2 = data.table(A=1:10000,ColB=10000:1)
+setkey(DT,B)
+tables()
+}
+\keyword{ data }
diff --git a/man/test.data.table.Rd b/man/test.data.table.Rd
new file mode 100644
index 0000000..9d383ef
--- /dev/null
+++ b/man/test.data.table.Rd
@@ -0,0 +1,26 @@
+\name{test.data.table}
+\alias{test.data.table}
+\title{ Runs a set of tests. }
+\description{
+ Runs a set of tests to check data.table is working correctly.
+}
+\usage{
+test.data.table(verbose=FALSE, pkg="pkg")
+}
+\arguments{
+\item{verbose}{ If TRUE sets datatable.verbose to TRUE for the duration of the tests. }
+\item{pkg}{Root directory name under which all package content (ex: DESCRIPTION, src/, R/, inst/ etc..) resides.}
+}
+\details{
+ Runs a series of tests. These can be used to see features and examples of usage, too. Running test.data.table will tell you the full location of the test file(s) to open.
+}
+\value{
+ TRUE if all tests were successful. FALSE otherwise.
+}
+\seealso{ \code{\link{data.table}} }
+\examples{
+\dontrun{
+test.data.table()
+}}
+\keyword{ data }
+
diff --git a/man/timetaken.Rd b/man/timetaken.Rd
new file mode 100644
index 0000000..0fa4b35
--- /dev/null
+++ b/man/timetaken.Rd
@@ -0,0 +1,24 @@
+\name{timetaken}
+\alias{timetaken}
+\title{ Pretty print of time taken }
+\description{
+ Pretty print of time taken since last started.at.
+}
+\usage{
+timetaken(started.at)
+}
+\arguments{
+ \item{started.at}{ The result of proc.time() taken some time earlier. }
+}
+% \details{
+% }
+\value{
+ A character vector of the form hh:mm:ss, or ss.mmm if under 60 seconds.
+}
+\examples{
+started.at=proc.time()
+Sys.sleep(1)
+cat("Finished in",timetaken(started.at),"\n")
+}
+\keyword{ data }
+
diff --git a/man/transform.data.table.Rd b/man/transform.data.table.Rd
new file mode 100644
index 0000000..b744b59
--- /dev/null
+++ b/man/transform.data.table.Rd
@@ -0,0 +1,69 @@
+\name{transform.data.table}
+\alias{transform}
+\alias{transform.data.table}
+\alias{within}
+\alias{within.data.table}
+\title{ Data table utilities }
+\description{
+ Utilities for \code{data.table} transformation.
+
+ \strong{\code{transform} by group is particularly slow. Please use \code{:=} by group instead.}
+
+ \code{within}, \code{transform} and other similar functions in \code{data.table} are not just provided for users who expect them to work, but for non-data.table-aware packages to retain keys, for example. Hopefully the (much) faster and more convenient \code{data.table} syntax will be used in time. See examples.
+}
+\usage{
+\method{transform}{data.table}(`_data`, \ldots)
+\method{within}{data.table}(data, expr, \ldots)
+}
+\arguments{
+ \item{data, _data}{ data.table to be transformed.}
+
+ \item{\ldots}{ for \code{transform}, Further arguments of the form
+ \code{tag=value}. Ignored for \code{within}.}
+
+ \item{expr}{ expression to be evaluated within the data.table.}
+}
+\details{
+\code{within} is like \code{with}, but modifications (columns changed,
+added, or removed) are updated in the returned data.table.
+
+Note that \code{transform} will keep the key of the
+\code{data.table} provided the \emph{targets} of the transform (i.e. the
+columns that appear in \ldots) are not in the key of the data.table.
+\code{within} also retains the key provided the key columns are not \emph{touched}.
+}
+\value{
+ The modified value of a copy of \code{data}.
+}
+\seealso{ \code{\link[base]{transform}}, \code{\link[base]{within}} and \code{\link{:=}} }
+\examples{
+DT <- data.table(a=rep(1:3, each=2), b=1:6)
+
+DT2 <- transform(DT, c = a^2)
+DT[, c:=a^2]
+identical(DT,DT2)
+
+DT2 <- within(DT, {
+ b <- rev(b)
+ c <- a*2
+ rm(a)
+})
+DT[,`:=`(b = rev(b),
+ c = a*2,
+ a = NULL)]
+identical(DT,DT2)
+
+DT$d = ave(DT$b, DT$c, FUN=max) # copies entire DT, even if it is 10GB in RAM
+DT = DT[, transform(.SD, d=max(b)), by="c"] # same, but even worse as .SD is copied for each group
+DT[, d:=max(b), by="c"] # same result, but much faster, shorter and scales
+
+# Multiple update by group. Convenient, fast, scales and easy to read.
+DT[, `:=`(minb = min(b),
+ meanb = mean(b),
+ bplusd = sum(b+d)), by=c\%/\%5]
+DT
+
+}
+\keyword{ data }
+
+
diff --git a/man/transpose.Rd b/man/transpose.Rd
new file mode 100644
index 0000000..8427f11
--- /dev/null
+++ b/man/transpose.Rd
@@ -0,0 +1,42 @@
+\name{transpose}
+\alias{transpose}
+\title{Efficient transpose of list}
+\description{
+ \code{transpose} is an efficient way to transpose \code{lists}, \code{data frames} or \code{data tables}.
+}
+
+\usage{
+transpose(l, fill=NA, ignore.empty=FALSE)
+}
+\arguments{
+ \item{l}{ A list, data.frame or data.table. }
+ \item{fill}{ Default is \code{NA}. It is used to fill shorter list elements so as to return each element of the transposed result of equal lengths. }
+ \item{ignore.empty}{Default is \code{FALSE}. \code{TRUE} will ignore length-0 list elements.}
+}
+\details{
+ The list elements (or columns of \code{data.frame}/\code{data.table}) should be all \code{atomic}. If list elements are of unequal lengths, the value provided in \code{fill} will be used so that the resulting list always has all elements of identical lengths. The class of input object is also preserved in the transposed result.
+
+ The \code{ignore.empty} argument can be used to skip or include length-0 elements.
+
+ This is particularly useful in tasks that require splitting a character column and assigning each part to a separate column. This operation is quite common enough that a function \code{\link{tstrsplit}} is exported.
+
+ \code{factor} columns are converted to \code{character} type. Attributes are not preserved at the moment. This may change in the future.
+
+}
+\value{
+ A transposed \code{list}, \code{data.frame} or \code{data.table}.
+}
+
+\examples{
+ll = list(1:5, 6:8)
+transpose(ll)
+setDT(transpose(ll, fill=0))[]
+
+dt = data.table(x=1:5, y=6:10)
+transpose(dt)
+}
+\seealso{
+ \code{\link{data.table}}, \code{\link{tstrsplit}}
+}
+\keyword{ data }
+
diff --git a/man/truelength.Rd b/man/truelength.Rd
new file mode 100644
index 0000000..04312d8
--- /dev/null
+++ b/man/truelength.Rd
@@ -0,0 +1,44 @@
+\name{truelength}
+\alias{truelength}
+\alias{alloc.col}
+\title{ Over-allocation access }
+\description{
+ These functions are experimental and somewhat advanced. By \emph{experimental} we mean their names might change and perhaps the syntax, argument names and types. So if you write a lot of code using them, you have been warned! They should work and be stable, though, so please report problems with them.
+}
+\usage{
+truelength(x)
+alloc.col(DT,
+ n = getOption("datatable.alloccol"), # default: quote(max(100L,ncol(DT)+64L))
+ verbose = getOption("datatable.verbose")) # default: FALSE
+}
+\arguments{
+\item{x}{ Any type of vector, including \code{data.table} which is a \code{list} vector of column pointers. }
+\item{DT}{ A \code{data.table}. }
+\item{n}{ The number of column pointer slots to reserve in memory, including existing columns. May be a numeric, or a quote()-ed expression (see default). If \code{DT} is a 10 column \code{data.table}, \code{n=1000} means grow the spare slots from 90 to 990, assuming the default of 100 has not been changed. }
+\item{verbose}{ Output status and information. }
+}
+\details{
+ When adding columns by reference using \code{:=}, we \emph{could} simply create a new column list vector (one longer) and memcpy over the old vector, with no copy of the column vectors themselves. That requires negligibe use of space and time, and is what v1.7.2 did. However, that copy of the list vector of column pointers only (but not the columns themselves), a \emph{shallow copy}, resulted in inconsistent behaviour in some circumstances. So, as from v1.7.3 data.table over allocat [...]
+
+ When the allocated column pointer slots are used up, to add a new column \code{data.table} must reallocate that vector. If two or more variables are bound to the same data.table this shallow copy may or may not be desirable, but we don't think this will be a problem very often (more discussion may be required on datatable-help). Setting \code{options(datatable.verbose=TRUE)} includes messages if and when a shallow copy is taken. To avoid shallow copies there are several options: use [...]
+
+ Please note : over allocation of the column pointer vector is not for efficiency per se. It's so that \code{:=} can add columns by reference without a shallow copy.
+}
+\value{
+ \code{truelength(x)} returns the length of the vector allocated in memory. \code{length(x)} of those items are in use. Currently, it's just the list vector of column pointers that is over-allocated (i.e. \code{truelength(DT)}), not the column vectors themselves, which would in future allow fast row \code{insert()}. For tables loaded from disk however, \code{truelength} is 0 in \R 2.14.0 and random in \R <= 2.13.2; i.e., in both cases perhaps unexpected. \code{data.table} detects this [...]
+
+ \code{alloc.col} \emph{reallocates} \code{DT} by reference. This may be useful for efficiency if you know you are about to going to add a lot of columns in a loop. It also returns the new \code{DT}, for convenience in compound queries.
+}
+\seealso{ \code{\link{copy}} }
+\examples{
+DT = data.table(a=1:3,b=4:6)
+length(DT) # 2 column pointer slots used
+truelength(DT) # 100 column pointer slots allocated
+alloc.col(DT,200)
+length(DT) # 2 used
+truelength(DT) # 200 allocated, 198 free
+DT[,c:=7L] # add new column by assigning to spare slot
+truelength(DT)-length(DT) # 197 slots spare
+}
+\keyword{ data }
+
diff --git a/man/tstrsplit.Rd b/man/tstrsplit.Rd
new file mode 100644
index 0000000..7944cc6
--- /dev/null
+++ b/man/tstrsplit.Rd
@@ -0,0 +1,39 @@
+\name{tstrsplit}
+\alias{tstrsplit}
+\alias{strsplit}
+\title{strsplit and transpose the resulting list efficiently}
+\description{
+ This is equivalent to \code{transpose(strsplit(...))}. This is a convenient wrapper function to split a column using \code{strsplit} and assign the transposed result to individual columns. See examples.
+
+}
+
+\usage{
+tstrsplit(x, ..., fill=NA, type.convert=FALSE)
+}
+\arguments{
+ \item{x}{The vector to split (and transpose).}
+ \item{...}{ All the arguments to be passed to \code{strsplit}. }
+ \item{fill}{ Default is \code{NA}. It is used to fill shorter list elements so as to return each element of the transposed result of equal lengths. }
+ \item{type.convert}{\code{TRUE} calls \code{\link{type.convert}} with \code{as.is=TRUE} on the columns.}
+}
+\details{
+ It internally calls \code{strsplit} first, and then \code{\link{transpose}} on the result.
+}
+\value{
+ A transposed list.
+}
+
+\examples{
+x = c("abcde", "ghij", "klmnopq")
+strsplit(x, "", fixed=TRUE)
+tstrsplit(x, "", fixed=TRUE)
+tstrsplit(x, "", fixed=TRUE, fill="<NA>")
+
+DT = data.table(x=c("A/B", "A", "B"), y=1:3)
+DT[, c("c1", "c2") := tstrsplit(x, "/", fixed=TRUE)][]
+}
+\seealso{
+ \code{\link{data.table}}, \code{\link{transpose}}
+}
+\keyword{ data }
+
diff --git a/src/Makevars b/src/Makevars
new file mode 100644
index 0000000..df3b51e
--- /dev/null
+++ b/src/Makevars
@@ -0,0 +1,5 @@
+
+all: $(SHLIB)
+ mv $(SHLIB) datatable$(SHLIB_EXT)
+
+
diff --git a/src/assign.c b/src/assign.c
new file mode 100644
index 0000000..c9688cf
--- /dev/null
+++ b/src/assign.c
@@ -0,0 +1,926 @@
+#include "data.table.h"
+#include <Rdefines.h>
+#include <Rmath.h>
+#include <Rversion.h>
+
+static SEXP *saveds=NULL;
+static R_len_t *savedtl=NULL, nalloc=0, nsaved=0;
+
+static void finalizer(SEXP p)
+{
+ SEXP x;
+ R_len_t n, l, tl;
+ if(!R_ExternalPtrAddr(p)) error("Internal error: finalizer hasn't received an ExternalPtr");
+ p = R_ExternalPtrTag(p);
+ if (!isString(p)) error("Internal error: finalizer's ExternalPtr doesn't see names in tag");
+ l = LENGTH(p);
+ tl = TRUELENGTH(p);
+ if (l<0 || tl<l) error("Internal error: finalizer sees l=%d, tl=%d",l,tl);
+ n = tl-l;
+ if (n==0) {
+ // gc's ReleaseLargeFreeVectors() will have reduced R_LargeVallocSize by the correct amount
+ // already, so nothing to do (but almost never the case).
+ return;
+ }
+ x = PROTECT(allocVector(INTSXP, 50)); // 50 so it's big enough to be on LargeVector heap. See NodeClassSize in memory.c:allocVector
+ // INTSXP rather than VECSXP so that GC doesn't inspect contents after LENGTH (thanks to Karl Miller, Jul 2015)
+ SETLENGTH(x,50+n*2*sizeof(void *)/4); // 1*n for the names, 1*n for the VECSXP itself (both are over allocated).
+ UNPROTECT(1);
+ return;
+}
+
+void setselfref(SEXP x) {
+ SEXP p;
+ // Store pointer to itself so we can detect if the object has been copied. See
+ // ?copy for why copies are not just inefficient but cause a problem for over-allocated data.tables.
+ // Called from C only, not R level, so returns void.
+ setAttrib(x, SelfRefSymbol, p=R_MakeExternalPtr(
+ R_NilValue, // for identical() to return TRUE. identical() doesn't look at tag and prot
+ getAttrib(x, R_NamesSymbol), // to detect if names has been replaced and its tl lost, e.g. setattr(DT,"names",...)
+ PROTECT(R_MakeExternalPtr( // to avoid an infinite loop in object.size(), if prot=x here
+ x, // to know if this data.table has been copied by key<-, attr<-, names<-, etc.
+ R_NilValue, // this tag and prot currently unused
+ R_NilValue
+ ))
+ ));
+ R_RegisterCFinalizerEx(p, finalizer, FALSE);
+ UNPROTECT(1); // The PROTECT above is needed by --enable-strict-barrier (it seems, iiuc)
+
+/* * base::identical doesn't check prot and tag of EXTPTR, just that the ptr itself is the
+ same in both objects. R_NilValue is always equal to R_NilValue. R_NilValue is a memory
+ location constant within an R session, but can vary from session to session. So, it
+ looks like a pointer to a user looking at attributes(DT), but they might wonder how it
+ works if they realise the selfref of all data.tables all point to the same address (rather
+ than to the table itself which would be reasonable to expect given the attribute's name).
+ * p=NULL rather than R_NilValue works too, other than we need something other than NULL
+ so we can detect tables loaded from disk (which set p to NULL, see 5.13 of R-exts).
+ * x is wrapped in another EXTPTR because of object.size (called by tables(), and by users).
+ If the prot (or tag) was x directly it sends object.size into an infinite loop and then
+ "segfault from C stack overflow" (object.size does count tag and prot, unlike identical,
+ but doesn't count what's pointed to).
+ * Could use weak reference possibly, but the fact that they can get be set to R_NilValue
+ by gc (?) didn't seem appropriate.
+ * If the .internal.selfref attribute is removed (e.g. by user code), nothing will break, but
+ an extra copy will just be taken on next :=, with warning, with a new selfref.
+ * object.size will count size of names twice, but that's ok as only small.
+ * Thanks to Steve L for suggesting ExtPtr for this, rather than the previous REALSXP
+ vector which required data.table to do a show/hide dance in a masked identical.
+*/
+}
+
+/* There are two reasons the finalizer doesn't restore the LENGTH to TRUELENGTH. i) The finalizer
+happens too late after GC has already released the memory, and ii) copies by base R (e.g.
+[<- in write.table just before test 894) allocate at length LENGTH but copy the TRUELENGTH over.
+If the finalizer sets LENGTH to TRUELENGTH, that's a fail as it wasn't really allocated at
+TRUELENGTH when R did the copy.
+Karl Miller suggested an ENVSXP so that restoring LENGTH in finalizer should work. This is the
+closest I got to getting it to pass all tests :
+
+ SEXP env = PROTECT(allocSExp(ENVSXP));
+ defineVar(SelfRefSymbol, x, env);
+ defineVar(R_NamesSymbol, getAttrib(x, R_NamesSymbol), env);
+ setAttrib(x, SelfRefSymbol, p = R_MakeExternalPtr(
+ R_NilValue, // for identical() to return TRUE. identical() doesn't look at tag and prot
+ R_NilValue, //getAttrib(x, R_NamesSymbol), // to detect if names has been replaced and its tl lost, e.g. setattr(DT,"names",...)
+ PROTECT( // needed when --enable-strict-barrier it seems, iiuc. TO DO: test under that flag and remove if not needed.
+ env // wrap x in env to avoid an infinite loop in object.size() if prot=x were here
+ )
+ ));
+ R_RegisterCFinalizerEx(p, finalizer, FALSE);
+ UNPROTECT(2);
+
+Then in finalizer:
+ SETLENGTH(names, tl)
+ SETLENGTH(dt, tl)
+
+and that finalizer indeed now happens before the GC releases memory (thanks to the env wrapper).
+
+However, we still have problem (ii) above and it didn't pass tests involving base R copies.
+
+We really need R itself to start setting TRUELENGTH to be the allocated length and then
+for GC to release TRUELENGTH not LENGTH. Would really tidy this up.
+*/
+
+static int _selfrefok(SEXP x, Rboolean checkNames, Rboolean verbose) {
+ SEXP v, p, tag, prot, names;
+ v = getAttrib(x, SelfRefSymbol);
+ if (v==R_NilValue || TYPEOF(v)!=EXTPTRSXP) {
+ // .internal.selfref missing is expected and normal for i) a pre v1.7.8 data.table loaded
+ // from disk, and ii) every time a new data.table is over-allocated for the first time.
+ // Not being an extptr is for when users contruct a data.table via structure() using dput, post
+ // a question, and find the extptr doesn't parse so put quotes around it (for example).
+ // In both cases the selfref is not ok.
+ return 0;
+ }
+ p = R_ExternalPtrAddr(v);
+ if (p==NULL) {
+ if (verbose) Rprintf(".internal.selfref ptr is NULL. This is expected and normal for a data.table loaded from disk. If not, please report to datatable-help.\n");
+ return -1;
+ }
+ if (!isNull(p)) error("Internal error: .internal.selfref ptr is not NULL or R_NilValue");
+ tag = R_ExternalPtrTag(v);
+ if (!(isNull(tag) || isString(tag))) error("Internal error: .internal.selfref tag isn't NULL or a character vector");
+ names = getAttrib(x, R_NamesSymbol);
+ if (names != tag && isString(names))
+ SET_TRUELENGTH(names, LENGTH(names));
+ // R copied this vector not data.table; it's not actually over-allocated. It looks over-allocated
+ // because R copies the original vector's tl over despite allocating length.
+ prot = R_ExternalPtrProtected(v);
+ if (TYPEOF(prot) != EXTPTRSXP) // Very rare. Was error(".internal.selfref prot is not itself an extptr").
+ return 0; // See http://stackoverflow.com/questions/15342227/getting-a-random-internal-selfref-error-in-data-table-for-r
+ if (x != R_ExternalPtrAddr(prot))
+ SET_TRUELENGTH(x, LENGTH(x)); // R copied this vector not data.table, it's not actually over-allocated
+ return checkNames ? names==tag : x==R_ExternalPtrAddr(prot);
+}
+
+static Rboolean selfrefok(SEXP x, Rboolean verbose) { // for readability
+ return(_selfrefok(x, FALSE, verbose)==1);
+}
+static Rboolean selfrefnamesok(SEXP x, Rboolean verbose) {
+ return(_selfrefok(x, TRUE, verbose)==1);
+}
+
+static SEXP shallow(SEXP dt, SEXP cols, R_len_t n)
+{
+ // NEW: cols argument to specify the columns to shallow copy on. If NULL, all columns.
+ // called from alloccol where n is checked carefully, or from shallow() at R level
+ // where n is set to truelength (i.e. a shallow copy only with no size change)
+ SEXP newdt, names, newnames;
+ R_len_t i,l;
+ int protecti=0;
+ PROTECT(newdt = allocVector(VECSXP, n)); // to do, use growVector here?
+ protecti++;
+ //copyMostAttrib(dt, newdt); // including class
+ DUPLICATE_ATTRIB(newdt, dt);
+ // TO DO: keepattr() would be faster, but can't because shallow isn't merely a shallow copy. It
+ // also increases truelength. Perhaps make that distinction, then, and split out, but marked
+ // so that the next change knows to duplicate.
+ // Does copyMostAttrib duplicate each attrib or does it point? It seems to point, hence DUPLICATE_ATTRIB
+ // for now otherwise example(merge.data.table) fails (since attr(d4,"sorted") gets written by setnames).
+ names = getAttrib(dt, R_NamesSymbol);
+ PROTECT(newnames = allocVector(STRSXP, n));
+ protecti++;
+ if (isNull(cols)) {
+ l = LENGTH(dt);
+ for (i=0; i<l; i++) SET_VECTOR_ELT(newdt, i, VECTOR_ELT(dt,i));
+ if (length(names)) {
+ if (length(names) < l) error("Internal error: length(names)>0 but <length(dt)");
+ for (i=0; i<l; i++) SET_STRING_ELT(newnames, i, STRING_ELT(names,i));
+ }
+ // else an unnamed data.table is valid e.g. unname(DT) done by ggplot2, and .SD may have its names cleared in dogroups, but shallow will always create names for data.table(NULL) which has 100 slots all empty so you can add to an empty data.table by reference ok.
+ } else {
+ l = length(cols);
+ for (i=0; i<l; i++) SET_VECTOR_ELT(newdt, i, VECTOR_ELT(dt,INTEGER(cols)[i]-1));
+ if (length(names)) {
+ // no need to check length(names) < l here. R-level checks if all value
+ // in 'cols' are valid - in the range of 1:length(names(x))
+ for (i=0; i<l; i++) SET_STRING_ELT( newnames, i, STRING_ELT(names,INTEGER(cols)[i]-1) );
+ }
+ }
+ setAttrib(newdt, R_NamesSymbol, newnames);
+ // setAttrib appears to change length and truelength, so need to do that first _then_ SET next,
+ // otherwise (if the SET were were first) the 100 tl is assigned to length.
+ SETLENGTH(newnames,l);
+ SET_TRUELENGTH(newnames,n);
+ SETLENGTH(newdt,l);
+ SET_TRUELENGTH(newdt,n);
+ setselfref(newdt);
+ // SET_NAMED(dt,1); // for some reason, R seems to set NAMED=2 via setAttrib? Need NAMED to be 1 for passing to assign via a .C dance before .Call (which sets NAMED to 2), and we can't use .C with DUP=FALSE on lists.
+ UNPROTECT(protecti);
+ return(newdt);
+}
+
+SEXP alloccol(SEXP dt, R_len_t n, Rboolean verbose)
+{
+ SEXP names, class;
+ R_len_t l, tl;
+ if (isNull(dt)) error("alloccol has been passed a NULL dt");
+ if (TYPEOF(dt) != VECSXP) error("dt passed to alloccol isn't type VECSXP");
+ class = getAttrib(dt, R_ClassSymbol);
+ if (isNull(class)) error("dt passed to alloccol has no class attribute. Please report result of traceback() to datatable-help.");
+ l = LENGTH(dt);
+ names = getAttrib(dt,R_NamesSymbol);
+ // names may be NULL when null.data.table() passes list() to alloccol for example.
+ // So, careful to use length() on names, not LENGTH().
+ if (length(names)!=l) error("Internal error: length of names (%d) is not length of dt (%d)",length(names),l);
+ if (!selfrefok(dt,verbose))
+ return shallow(dt,R_NilValue,(n>l) ? n : l); // e.g. test 848 and 851 in R > 3.0.2
+ // added (n>l) ? ... for #970, see test 1481.
+ // TO DO: test realloc names if selfrefnamesok (users can setattr(x,"name") themselves for example.
+ // if (TRUELENGTH(getAttrib(dt,R_NamesSymbol))!=tl)
+ // error("Internal error: tl of dt passes checks, but tl of names (%d) != tl of dt (%d)", tl, TRUELENGTH(getAttrib(dt,R_NamesSymbol)));
+
+ tl = TRUELENGTH(dt);
+ if (tl<0) error("Internal error, tl of class is marked but tl<0."); // R <= 2.13.2 and we didn't catch uninitialized tl somehow
+ if (tl>0 && tl<l) error("Internal error, please report (including result of sessionInfo()) to datatable-help: tl (%d) < l (%d) but tl of class is marked.", tl, l);
+ if (tl>l+1000) warning("tl (%d) is greater than 1000 items over-allocated (l = %d). If you didn't set the datatable.alloccol option to be very large, please report this to datatable-help including the result of sessionInfo().",tl,l);
+ if (n>tl) return(shallow(dt,R_NilValue,n)); // usual case (increasing alloc)
+ if (n<tl) warning("Attempt to reduce allocation from %d to %d ignored. Can only increase allocation via shallow copy.",tl,n);
+ // otherwise the finalizer can't clear up the Large Vector heap
+ return(dt);
+}
+
+SEXP alloccolwrapper(SEXP dt, SEXP newncol, SEXP verbose) {
+ if (!isInteger(newncol) || length(newncol)!=1) error("n must be integer length 1. Has datatable.alloccol somehow become unset?");
+ if (!isLogical(verbose) || length(verbose)!=1) error("verbose must be TRUE or FALSE");
+ return(alloccol(dt, INTEGER(newncol)[0], LOGICAL(verbose)[0]));
+}
+
+SEXP shallowwrapper(SEXP dt, SEXP cols) {
+ // selfref will be FALSE on manually created data.table, e.g., via dput() or structure()
+ if (!selfrefok(dt, FALSE)) {
+ int n = isNull(cols) ? length(dt) : length(cols);
+ return(shallow(dt, cols, n));
+ } else return(shallow(dt, cols, TRUELENGTH(dt)));
+}
+
+// is.data.table() C-function, extracted from assign.c
+// Check if "data.table" class exists somewhere in class (#5115)
+Rboolean isDatatable(SEXP x) {
+ SEXP class = getAttrib(x, R_ClassSymbol);
+ for (int i=0; i<length(class); i++) {
+ if (strcmp(CHAR(STRING_ELT(class, i)), "data.table") == 0) return(TRUE);
+ }
+ return (FALSE);
+}
+
+SEXP truelength(SEXP x) {
+ SEXP ans;
+ PROTECT(ans = allocVector(INTSXP, 1));
+ if (!isNull(x)) {
+ INTEGER(ans)[0] = TRUELENGTH(x);
+ } else {
+ INTEGER(ans)[0] = 0;
+ }
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP selfrefokwrapper(SEXP x, SEXP verbose) {
+ return ScalarInteger(_selfrefok(x,FALSE,LOGICAL(verbose)[0]));
+}
+
+
+void memrecycle(SEXP target, SEXP where, int r, int len, SEXP source);
+
+SEXP assign(SEXP dt, SEXP rows, SEXP cols, SEXP newcolnames, SEXP values, SEXP verb)
+{
+ // For internal use only by := in [.data.table, and set()
+ // newcolnames : add these columns (if any)
+ // cols : column names or numbers corresponding to the values to set
+ // rows : row numbers to assign
+ R_len_t i, j, nrow, targetlen, vlen, r, oldncol, oldtncol, coln, protecti=0, newcolnum;
+ SEXP targetcol, RHS, names, nullint, thisvalue, thisv, targetlevels, newcol, s, colnam, class, tmp, colorder, key, index, a;
+ SEXP bindingIsLocked = getAttrib(dt, install(".data.table.locked"));
+ Rboolean verbose = LOGICAL(verb)[0], anytodelete=FALSE, isDataTable=FALSE;
+ char *s1, *s2, *s3;
+ const char *c1, *c2, *tc1, *tc2;
+ int *buf, k=0;
+ size_t size; // must be size_t otherwise overflow later in memcpy
+ if (isNull(dt)) error("assign has been passed a NULL dt");
+ if (TYPEOF(dt) != VECSXP) error("dt passed to assign isn't type VECSXP");
+ if (length(bindingIsLocked) && LOGICAL(bindingIsLocked)[0])
+ error(".SD is locked. Updating .SD by reference using := or set are reserved for future use. Use := in j directly. Or use copy(.SD) as a (slow) last resort, until shallow() is exported.");
+
+ class = getAttrib(dt, R_ClassSymbol);
+ if (isNull(class)) error("Input passed to assign has no class attribute. Must be a data.table or data.frame.");
+ // Check if there is a class "data.table" somewhere (#5115).
+ // We allow set() on data.frame too; e.g. package Causata uses set() on a data.frame in tests/testTransformationReplay.R
+ // := is only allowed on a data.table. However, the ":=" = stop(...) message in data.table.R will have already
+ // detected use on a data.frame before getting to this point.
+ for (i=0; i<length(class); i++) { // There doesn't seem to be an R API interface to inherits(), but manually here isn't too bad.
+ if (strcmp(CHAR(STRING_ELT(class, i)), "data.table") == 0) break;
+ }
+ if (i<length(class))
+ isDataTable = TRUE;
+ else {
+ for (i=0; i<length(class); i++) {
+ if (strcmp(CHAR(STRING_ELT(class, i)), "data.frame") == 0) break;
+ }
+ if (i == length(class)) error("Input is not a data.table, data.frame or an object that inherits from either.");
+ isDataTable = FALSE; // meaning data.frame from now on. Can use set() on existing columns but not add new ones because DF aren't over-allocated.
+ }
+ oldncol = LENGTH(dt);
+ names = getAttrib(dt,R_NamesSymbol);
+ if (isNull(names)) error("dt passed to assign has no names");
+ if (length(names)!=oldncol)
+ error("Internal error in assign: length of names (%d) is not length of dt (%d)",length(names),oldncol);
+ if (oldncol<1) error("Cannot use := to add columns to a null data.table (no columns), currently. You can use := to add (empty) columns to a 0-row data.table (1 or more empty columns), though.");
+ nrow = length(VECTOR_ELT(dt,0));
+ if (isNull(rows)) {
+ targetlen = nrow;
+ // fast way to assign to whole column, without creating 1:nrow(x) vector up in R, or here in C
+ } else {
+ if (!LENGTH(rows)) return(dt);
+ if (isReal(rows)) {
+ rows = PROTECT(rows = coerceVector(rows, INTSXP));
+ protecti++;
+ warning("Coerced i from numeric to integer. Please pass integer for efficiency; e.g., 2L rather than 2");
+ }
+ if (!isInteger(rows))
+ error("i is type '%s'. Must be integer, or numeric is coerced with warning. If i is a logical subset, simply wrap with which(), and take the which() outside the loop if possible for efficiency.", type2char(TYPEOF(rows)));
+ targetlen = length(rows);
+ Rboolean anyToDo = FALSE;
+ for (i=0;i<targetlen;i++) {
+ if ((INTEGER(rows)[i]<0 && INTEGER(rows)[i]!=NA_INTEGER) || INTEGER(rows)[i]>nrow)
+ error("i[%d] is %d which is out of range [1,nrow=%d].",i+1,INTEGER(rows)[i],nrow);
+ if (INTEGER(rows)[i]>=1) anyToDo = TRUE;
+ }
+ if (!anyToDo) return(dt); // all items of rows either 0 or NA, nothing to do.
+ }
+ if (!length(cols)) {
+ warning("length(LHS) = 0, meaning no columns to delete or assign RHS to.");
+ return(dt);
+ }
+ // FR #2077 - set able to add new cols by reference
+ if (isString(cols)) {
+ PROTECT(tmp = chmatch(cols, names, 0, FALSE));
+ protecti++;
+ buf = (int *) R_alloc(length(cols), sizeof(int));
+ for (i=0; i<length(cols); i++) {
+ if (INTEGER(tmp)[i] == 0) buf[k++] = i;
+ }
+ if (k>0) {
+ if (!isDataTable) error("set() on a data.frame is for changing existing columns, not adding new ones. Please use a data.table for that. data.table's are over-allocated and don't shallow copy.");
+ PROTECT(newcolnames = allocVector(STRSXP, k));
+ protecti++;
+ for (i=0; i<k; i++) {
+ SET_STRING_ELT(newcolnames, i, STRING_ELT(cols, buf[i]));
+ INTEGER(tmp)[buf[i]] = oldncol+i+1;
+ }
+ }
+ cols = tmp;
+ } else {
+ if (isReal(cols)) {
+ cols = PROTECT(cols = coerceVector(cols, INTSXP));
+ protecti++;
+ warning("Coerced j from numeric to integer. Please pass integer for efficiency; e.g., 2L rather than 2");
+ }
+ if (!isInteger(cols))
+ error("j is type '%s'. Must be integer, character, or numeric is coerced with warning.", type2char(TYPEOF(cols)));
+ }
+ if (any_duplicated(cols,FALSE)) error("Can't assign to the same column twice in the same query (duplicates detected).");
+ if (!isNull(newcolnames) && !isString(newcolnames)) error("newcolnames is supplied but isn't a character vector");
+ if (isNull(values)) {
+ if (!length(cols)) {
+ warning("RHS is NULL, meaning delete columns(s). But, no columns in LHS to delete.");
+ return(dt);
+ }
+ } else {
+ if (TYPEOF(values)==VECSXP) {
+ if (length(cols)>1) {
+ if (length(values)==0) error("Supplied %d columns to be assigned an empty list (which may be an empty data.table or data.frame since they are lists too). To delete multiple columns use NULL instead. To add multiple empty list columns, use list(list()).", length(cols));
+ if (length(values)>length(cols))
+ warning("Supplied %d columns to be assigned a list (length %d) of values (%d unused)", length(cols), length(values), length(values)-length(cols));
+ else if (length(cols)%length(values) != 0)
+ warning("Supplied %d columns to be assigned a list (length %d) of values (recycled leaving remainder of %d items).",length(cols),length(values),length(cols)%length(values));
+ } // else it's a list() column being assigned to one column
+ }
+ }
+ // Check all inputs :
+ for (i=0; i<length(cols); i++) {
+ coln = INTEGER(cols)[i];
+ if (coln<1 || coln>oldncol+length(newcolnames)) {
+ if (!isDataTable) error("Item %d of column numbers in j is %d which is outside range [1,ncol=%d]. set() on a data.frame is for changing existing columns, not adding new ones. Please use a data.table for that.", i+1, coln, oldncol);
+ else error("Item %d of column numbers in j is %d which is outside range [1,ncol=%d]. Use column names instead in j to add new columns.", i+1, coln, oldncol);
+ }
+ coln--;
+ if (TYPEOF(values)==VECSXP && (length(cols)>1 || length(values)==1))
+ thisvalue = VECTOR_ELT(values,i%LENGTH(values));
+ else
+ thisvalue = values; // One vector applied to all columns, often NULL or NA for example
+ vlen = length(thisvalue);
+ if (coln+1 <= oldncol) colnam = STRING_ELT(names,coln);
+ else colnam = STRING_ELT(newcolnames,coln-length(names));
+ if (coln+1 <= oldncol && isNull(thisvalue)) continue; // delete existing column(s) afterwards, near end of this function
+ if (vlen<1 && nrow>0) {
+ if (coln+1 <= oldncol) {
+ error("RHS of assignment to existing column '%s' is zero length but not NULL. If you intend to delete the column use NULL. Otherwise, the RHS must have length > 0; e.g., NA_integer_. If you are trying to change the column type to be an empty list column then, as with all column type changes, provide a full length RHS vector such as vector('list',nrow(DT)); i.e., 'plonk' in the new column.", CHAR(STRING_ELT(names,coln)));
+ } else if (TYPEOF(thisvalue)!=VECSXP) { // list() is ok for new columns
+ newcolnum = coln-length(names);
+ if (newcolnum<0 || newcolnum>=length(newcolnames))
+ error("Internal logical error. length(newcolnames)=%d, length(names)=%d, coln=%d", length(newcolnames), length(names), coln);
+ if (isNull(thisvalue)) {
+ // fix for #1082
+ if (!isNull(rows)) error("When deleting columns, i should not be provided");
+ warning("Adding new column '%s' then assigning NULL (deleting it).",CHAR(STRING_ELT(newcolnames,newcolnum)));
+ continue;
+ }
+ error("RHS of assignment to new column '%s' is zero length but not empty list(). For new columns the RHS must either be empty list() to create an empty list column, or, have length > 0; e.g. NA_integer_, 0L, etc.", CHAR(STRING_ELT(newcolnames,newcolnum)));
+ }
+ }
+ if (!(isVectorAtomic(thisvalue) || isNewList(thisvalue))) // NULL had a continue earlier above
+ error("RHS of assignment is not NULL, not an an atomic vector (see ?is.atomic) and not a list column.");
+ if (isMatrix(thisvalue) && (j=INTEGER(getAttrib(thisvalue, R_DimSymbol))[1]) > 1) // matrix passes above (considered atomic vector)
+ warning("%d column matrix RHS of := will be treated as one vector", j);
+ if ((coln+1)<=oldncol && isFactor(VECTOR_ELT(dt,coln)) &&
+ !isString(thisvalue) && TYPEOF(thisvalue)!=INTSXP && !isReal(thisvalue) && !isNewList(thisvalue)) // !=INTSXP includes factor
+ error("Can't assign to column '%s' (type 'factor') a value of type '%s' (not character, factor, integer or numeric)", CHAR(STRING_ELT(names,coln)),type2char(TYPEOF(thisvalue)));
+ if (nrow>0) {
+ if (vlen>targetlen)
+ warning("Supplied %d items to be assigned to %d items of column '%s' (%d unused)", vlen, targetlen,CHAR(colnam),vlen-targetlen);
+ else if (vlen>0 && targetlen%vlen != 0)
+ warning("Supplied %d items to be assigned to %d items of column '%s' (recycled leaving remainder of %d items).",vlen,targetlen,CHAR(colnam),targetlen%vlen);
+ }
+ }
+ // having now checked the inputs, from this point there should be no errors,
+ // so we can now proceed to modify DT by reference.
+ if (length(newcolnames)) {
+ //if (length(rows)!=0) error("Attempt to add new column(s) and set subset of rows at the same time. Create the new column(s) first, and then you'll be able to assign to a subset. If i is set to 1:nrow(x) then please remove that (no need, it's faster without).");
+ oldtncol = TRUELENGTH(dt); // TO DO: oldtncol can be just called tl now, as we won't realloc here any more.
+
+ if (oldtncol<oldncol) error("Internal error, please report (including result of sessionInfo()) to datatable-help: oldtncol (%d) < oldncol (%d) but tl of class is marked.", oldtncol, oldncol);
+ if (oldtncol>oldncol+1000L) warning("truelength (%d) is greater than 1000 items over-allocated (length = %d). See ?truelength. If you didn't set the datatable.alloccol option very large, please report this to datatable-help including the result of sessionInfo().",oldtncol, oldncol);
+
+ if (oldtncol < oldncol+LENGTH(newcolnames))
+ error("Internal logical error. DT passed to assign has not been allocated enough column slots. l=%d, tl=%d, adding %d", oldncol, oldtncol, LENGTH(newcolnames));
+ if (!selfrefnamesok(dt,verbose))
+ error("It appears that at some earlier point, names of this data.table have been reassigned. Please ensure to use setnames() rather than names<- or colnames<-. Otherwise, please report to datatable-help.");
+ // Can growVector at this point easily enough, but it shouldn't happen in first place so leave it as
+ // strong error message for now.
+ else if (TRUELENGTH(names) != oldtncol)
+ error("selfrefnames is ok but tl names [%d] != tl [%d]", TRUELENGTH(names), oldtncol);
+ SETLENGTH(dt, oldncol+LENGTH(newcolnames));
+ SETLENGTH(names, oldncol+LENGTH(newcolnames));
+ for (i=0; i<LENGTH(newcolnames); i++)
+ SET_STRING_ELT(names,oldncol+i,STRING_ELT(newcolnames,i));
+ // truelengths of both already set by alloccol
+ }
+ for (i=0; i<length(cols); i++) {
+ coln = INTEGER(cols)[i]-1;
+ if (TYPEOF(values)==VECSXP && (LENGTH(cols)>1 || LENGTH(values)==1))
+ thisvalue = VECTOR_ELT(values,i%LENGTH(values));
+ else
+ thisvalue = values; // One vector applied to all columns, often NULL or NA for example
+ if (TYPEOF(thisvalue)==NILSXP) {
+ if (!isNull(rows)) error("When deleting columns, i should not be provided");
+ anytodelete = TRUE;
+ continue; // delete column(s) afterwards, below this loop
+ }
+ vlen = length(thisvalue);
+ if (length(rows)==0 && targetlen==vlen) {
+ if ( NAMED(thisvalue)==2 || // set() protects the NAMED of atomic vectors from .Call setting arguments to 2 by wrapping with list
+ (TYPEOF(values)==VECSXP && i>LENGTH(values)-1)) { // recycled RHS would have columns pointing to others, #2298.
+ if (verbose) {
+ if (NAMED(thisvalue)==2) Rprintf("RHS for item %d has been duplicated because NAMED is %d, but then is being plonked.\n",i+1, NAMED(thisvalue));
+ else Rprintf("RHS for item %d has been duplicated because the list of RHS values (length %d) is being recycled, but then is being plonked.\n", i+1, length(values));
+ }
+ thisvalue = duplicate(thisvalue); // PROTECT not needed as assigned as element to protected list below.
+ } else {
+ if (verbose) Rprintf("Direct plonk of unnamed RHS, no copy.\n"); // e.g. DT[,a:=as.character(a)] as tested by 754.3
+ }
+ SET_VECTOR_ELT(dt,coln,thisvalue); // plonk new column in as it's already the correct length
+ setAttrib(thisvalue, R_NamesSymbol, R_NilValue); // clear names such as DT[,a:=mapvector[a]]
+ setAttrib(thisvalue, R_DimSymbol, R_NilValue); // so that matrix is treated as vector
+ setAttrib(thisvalue, R_DimNamesSymbol, R_NilValue); // the 3rd of the 3 attribs not copied by copyMostAttrib, for consistency.
+ continue;
+ }
+ if (coln+1 > oldncol) { // new column
+ if (length(rows))
+ newcol = allocNAVector(TYPEOF(thisvalue),nrow); // fill with NAs first for where 'rows' (a subset) doesn't touch
+ else
+ newcol = allocVector(TYPEOF(thisvalue),nrow); // PROTECT not needed, protected by SET_VECTOR_ELT on next line
+ SET_VECTOR_ELT(dt,coln,newcol);
+ if (isVectorAtomic(thisvalue)) copyMostAttrib(thisvalue,newcol); // class etc but not names
+ // else for lists (such as data.frame and data.table) treat them as raw lists and drop attribs
+ if (vlen<1) continue; // e.g. DT[,newcol:=integer()] (adding new empty column)
+ targetcol = newcol;
+ RHS = thisvalue;
+ } else { // existing column
+ targetcol = VECTOR_ELT(dt,coln);
+ if (isFactor(targetcol)) {
+ // Coerce RHS to appropriate levels of LHS, adding new levels as necessary (unlike base)
+ // If it's the same RHS being assigned to several columns, we have to recoerce for each
+ // one because the levels of each target are likely different
+ if (isFactor(thisvalue)) {
+ PROTECT(thisvalue = asCharacterFactor(thisvalue));
+ protecti++;
+ }
+ targetlevels = getAttrib(targetcol, R_LevelsSymbol);
+ if (isNull(targetlevels)) error("somehow this factor column has no levels");
+ if (isString(thisvalue)) {
+ savetl_init(); // ** TO DO **: remove allocs that could fail between here and _end, or different way
+ for (j=0; j<length(thisvalue); j++) {
+ s = STRING_ELT(thisvalue,j);
+ if (TRUELENGTH(s)>0) {
+ savetl(s); // pre-2.14.0 this will save all the uninitialised truelengths
+ // so 2.14.0+ may be faster, but isn't required.
+ // as from v1.8.0 we assume R's internal hash is positive, so don't
+ // save the uninitialised truelengths that by chance are negative
+ }
+ SET_TRUELENGTH(s,0);
+ }
+ for (j=0; j<length(targetlevels); j++) {
+ s = STRING_ELT(targetlevels,j);
+ if (TRUELENGTH(s)>0) savetl(s);
+ SET_TRUELENGTH(s,j+1);
+ }
+ R_len_t addi = 0;
+ SEXP addlevels=NULL;
+ PROTECT(RHS = allocVector(INTSXP, length(thisvalue)));
+ protecti++;
+ for (j=0; j<length(thisvalue); j++) {
+ thisv = STRING_ELT(thisvalue,j);
+ if (TRUELENGTH(thisv)==0) {
+ if (addi==0) {
+ PROTECT(addlevels = allocVector(STRSXP, 100));
+ protecti++;
+ } else if (addi >= length(addlevels)) {
+ PROTECT(addlevels = growVector(addlevels, length(addlevels)+1000));
+ protecti++;
+ }
+ SET_STRING_ELT(addlevels,addi,thisv);
+ SET_TRUELENGTH(thisv,++addi+length(targetlevels));
+ }
+ INTEGER(RHS)[j] = TRUELENGTH(thisv);
+ }
+ if (addi > 0) {
+ R_len_t oldlen = length(targetlevels);
+ PROTECT(targetlevels = growVector(targetlevels, oldlen+addi));
+ protecti++;
+ for (j=0; j<addi; j++)
+ SET_STRING_ELT(targetlevels, oldlen+j, STRING_ELT(addlevels, j));
+ setAttrib(targetcol, R_LevelsSymbol, targetlevels);
+ }
+ for (j=0; j<length(targetlevels); j++) SET_TRUELENGTH(STRING_ELT(targetlevels,j),0); // important to reinstate 0 for countingcharacterorder and HASHPRI (if any) as done by savetl_end().
+ savetl_end();
+ } else {
+ // value is either integer or numeric vector
+ if (TYPEOF(thisvalue)!=INTSXP && !isReal(thisvalue))
+ error("Internal logical error. Up front checks (before starting to modify DT) didn't catch type of RHS ('%s') assigning to factor column '%s'. Please report to datatable-help.", type2char(TYPEOF(thisvalue)), CHAR(STRING_ELT(names,coln)));
+ if (isReal(thisvalue)) {
+ PROTECT(RHS = coerceVector(thisvalue,INTSXP));
+ protecti++;
+ warning("Coerced 'double' RHS to 'integer' to match the factor column's underlying type. Character columns are now recommended (can be in keys), or coerce RHS to integer or character first.");
+ } else RHS = thisvalue;
+ for (j=0; j<length(RHS); j++) {
+ if ( (INTEGER(RHS)[j]<1 || INTEGER(RHS)[j]>LENGTH(targetlevels))
+ && INTEGER(RHS)[j] != NA_INTEGER) {
+ warning("RHS contains %d which is outside the levels range ([1,%d]) of column %d, NAs generated", INTEGER(RHS)[j], LENGTH(targetlevels), i+1);
+ INTEGER(RHS)[j] = NA_INTEGER;
+ }
+ }
+ }
+ } else {
+ if (TYPEOF(targetcol) == TYPEOF(thisvalue))
+ RHS = thisvalue;
+ else {
+ // coerce the RHS to match the type of the column, unlike [<-.data.frame, for efficiency.
+ if (isString(targetcol) && isFactor(thisvalue)) {
+ PROTECT(RHS = asCharacterFactor(thisvalue));
+ protecti++;
+ if (verbose) Rprintf("Coerced factor to character to match the column's type (coercion is inefficient)\n"); // TO DO: datatable.pedantic would turn this into warning
+ } else {
+ PROTECT(RHS = coerceVector(thisvalue,TYPEOF(targetcol)));
+ protecti++;
+ // FR #2551, added test for equality between RHS and thisvalue to not provide the warning when length(thisvalue) == 1
+ if ( length(thisvalue) == 1 && TYPEOF(RHS) != VECSXP && TYPEOF(thisvalue) != VECSXP && (
+ (isReal(thisvalue) && isInteger(targetcol) && REAL(thisvalue)[0] == INTEGER(RHS)[0]) ||
+ (isLogical(thisvalue) && LOGICAL(thisvalue)[0] == NA_LOGICAL) ||
+ (isReal(RHS) && isInteger(thisvalue)) )) {
+ ;
+ } else {
+ s1 = (char *)type2char(TYPEOF(targetcol));
+ s2 = (char *)type2char(TYPEOF(thisvalue));
+ if (isReal(thisvalue)) s3="; may have truncated precision"; else s3="";
+ warning("Coerced '%s' RHS to '%s' to match the column's type%s. Either change the target column to '%s' first (by creating a new '%s' vector length %d (nrows of entire table) and assign that; i.e. 'replace' column), or coerce RHS to '%s' (e.g. 1L, NA_[real|integer]_, as.*, etc) to make your intent clear and for speed. Or, set the column type correctly up front when you create the table and stick to it, please.", s2, s1, s3, s2, s2, LENGTH(VECTOR_ELT(dt,0)), s1);
+ }
+ }
+ }
+ }
+ }
+ memrecycle(targetcol, rows, 0, length(rows) ? LENGTH(rows) : LENGTH(targetcol), RHS); // also called from dogroups
+ }
+ key = getAttrib(dt,install("sorted"));
+ if (length(key)) {
+ // if assigning to any key column, then drop the key. any() and subsetVector() don't seem to be
+ // exposed by R API at C level, so this is done here long hand.
+ PROTECT(tmp = allocVector(STRSXP, LENGTH(cols)));
+ protecti++;
+ for (i=0;i<LENGTH(cols);i++) SET_STRING_ELT(tmp,i,STRING_ELT(names,INTEGER(cols)[i]-1));
+ if (length(key)) {
+ PROTECT(tmp = chmatch(tmp, key, 0, TRUE));
+ protecti++;
+ for (i=0;i<LENGTH(tmp);i++) if (LOGICAL(tmp)[i]) {
+ // If a key column is being assigned to, clear the key, since it may change the row ordering.
+ // More likely that users will assign to non-key columns, though, most of the time.
+ setAttrib(dt, install("sorted"), R_NilValue);
+ break;
+ }
+ }
+ }
+ index = getAttrib(dt,install("index"));
+ if (index != R_NilValue) {
+ s = ATTRIB(index);
+ while(s != R_NilValue) {
+ a = TAG(s);
+ tc1 = c1 = CHAR(PRINTNAME(a)); // the index name; e.g. "__col1__col2"
+ s = CDR(s);
+ for (i=0; i<LENGTH(cols); i++) {
+ tc2 = c2 = CHAR(STRING_ELT(names, INTEGER(cols)[i]-1)); // the column name being updated; e.g. "col1"
+ while (*tc1) {
+ if (*tc1!='_' || *(tc1+1)!='_') error("Internal error: __ not found in index name");
+ tc1 += 2;
+ if (*tc1=='\0') error("Internal error: index name ends with trailing __");
+ while (*tc1 && *tc2 && *tc1 == *tc2) { tc1++; tc2++; }
+ if (*tc2=='\0' && (*tc1=='\0' || (*tc1=='_' && *(tc1+1)=='_'))) {
+ if (verbose) {
+ thisvalue = (TYPEOF(values)==VECSXP && LENGTH(values)>0) ? VECTOR_ELT(values,i%LENGTH(values)) : values;
+ Rprintf("Dropping index '%s' due to %s '%s' (column %d)\n", c1+2, isNull(thisvalue) ? "delete of" : "update on", c2, INTEGER(cols)[i]);
+ }
+ setAttrib(index, a, R_NilValue);
+ i = LENGTH(cols); // skip remaining cols, have already deleted this index
+ break; // next index
+ }
+ tc2 = c2; // back to start of column name being updated
+ while (*tc1 && (*tc1!='_' || *(tc1+1)!='_')) tc1++; // advance to next column in index name
+ }
+ tc1 = c1;
+ }
+ }
+ }
+ if (anytodelete) {
+ // Delete any columns assigned NULL (there was a 'continue' earlier in loop above)
+ // In reverse order to make repeated memmove easy. Otherwise cols would need to be updated as well after each delete.
+ PROTECT(colorder = duplicate(cols));
+ protecti++;
+ R_isort(INTEGER(colorder),LENGTH(cols));
+ PROTECT(colorder = match(cols, colorder, 0)); // actually matches colorder to cols (oddly, arguments are that way around)
+ protecti++;
+ // Can't find a visible R entry point to return ordering of cols, above is only way I could find.
+ // Need ordering (rather than just sorting) because the RHS corresponds in order to the LHS.
+
+ for (r=LENGTH(cols)-1; r>=0; r--) {
+ i = INTEGER(colorder)[r]-1;
+ coln = INTEGER(cols)[i]-1;
+ if (TYPEOF(values)==VECSXP && LENGTH(values)>0)
+ thisvalue = VECTOR_ELT(values,i%LENGTH(values));
+ else
+ thisvalue = values;
+ if (isNull(thisvalue)) {
+ // A new column being assigned NULL would have been warned above, added above, and now deleted (just easier
+ // to code it this way e.g. so that other columns may be added or removed ok by the same query).
+ size=sizeof(SEXP *);
+ memmove((char *)DATAPTR(dt)+coln*size,
+ (char *)DATAPTR(dt)+(coln+1)*size,
+ (LENGTH(dt)-coln-1)*size);
+ SET_VECTOR_ELT(dt, LENGTH(dt)-1, R_NilValue);
+ SETLENGTH(dt, LENGTH(dt)-1);
+ // adding using := by group relies on NULL here to know column slot is empty.
+ // good to tidy up the vector anyway.
+ memmove((char *)DATAPTR(names)+coln*size,
+ (char *)DATAPTR(names)+(coln+1)*size,
+ (LENGTH(names)-coln-1)*size);
+ SET_STRING_ELT(names, LENGTH(names)-1, NA_STRING); // no need really, just to be tidy.
+ SETLENGTH(names, LENGTH(names)-1);
+ if (LENGTH(names)==0) {
+ // That was last column deleted, leaving NULL data.table, so we need to reset .row_names, so that it really is the NULL data.table.
+ PROTECT(nullint=allocVector(INTSXP, 0));
+ protecti++;
+ setAttrib(dt, R_RowNamesSymbol, nullint); // i.e. .set_row_names(0)
+ //setAttrib(dt, R_NamesSymbol, R_NilValue);
+ }
+ }
+ }
+ }
+ UNPROTECT(protecti);
+ return(dt); // needed for `*tmp*` mechanism (when := isn't used), and to return the new object after a := for compound syntax.
+}
+
+void memrecycle(SEXP target, SEXP where, int start, int len, SEXP source)
+// like memcpy but recycles source and takes care of aging
+// 'where' a 1-based INTEGER vector subset of target to assign to, or NULL or integer()
+// assigns to target[start:start+len-1] or target[where[start:start+len-1]] where start is 0-based
+{
+ int r = 0, w;
+ if (len<1) return;
+ int slen = length(source) > len ? len : length(source); // fix for 5647. when length(source) > len, slen must be len.
+ if (slen<1) return;
+ if (TYPEOF(target) != TYPEOF(source)) error("Internal error: TYPEOF(target)['%s']!=TYPEOF(source)['%s']", type2char(TYPEOF(target)),type2char(TYPEOF(source)));
+ size_t size = SIZEOF(target);
+ if (!length(where)) {
+ switch (TYPEOF(target)) {
+ case INTSXP : case REALSXP : case LGLSXP :
+ break;
+ case STRSXP :
+ for (; r<slen; r++) // only one SET_STRING_ELT per RHS item is needed to set generations (overhead)
+ SET_STRING_ELT(target, start+r, STRING_ELT(source, r));
+ break;
+ case VECSXP :
+ for (; r<slen; r++)
+ SET_VECTOR_ELT(target, start+r, VECTOR_ELT(source, r));
+ // TO DO: if := in future could ever change a list item's contents by reference, would need to duplicate at that point
+ break;
+ default :
+ error("Unsupported type '%s'", type2char(TYPEOF(target)));
+ }
+ if (slen == 1) {
+ if (size==4) for (; r<len; r++)
+ INTEGER(target)[start+r] = INTEGER(source)[0]; // copies pointer on 32bit, sizes checked in init.c
+ else for (; r<len; r++)
+ REAL(target)[start+r] = REAL(source)[0]; // copies pointer on 64bit, sizes checked in init.c
+ } else if (slen<10) { // 10 is just a guess for when memcpy is faster. Certainly memcpy is slower when slen==1, but that's the most common case by far so not high priority to discover the optimum here. TO DO: revisit
+ if (size==4) for (; r<len; r++)
+ INTEGER(target)[start+r] = INTEGER(source)[r%slen];
+ else for (; r<len; r++)
+ REAL(target)[start+r] = REAL(source)[r%slen];
+ } else {
+ for (r=r>0?1:0; r<(len/slen); r++) { // if the first slen were done in the switch above, convert r=slen to r=1
+ memcpy((char *)DATAPTR(target) + (start+r*slen)*size,
+ (char *)DATAPTR(source),
+ slen * size);
+ }
+ memcpy((char *)DATAPTR(target) + (start+r*slen)*size,
+ (char *)DATAPTR(source),
+ (len%slen) * size);
+ }
+ } else {
+ switch (TYPEOF(target)) {
+ case INTSXP : case REALSXP : case LGLSXP :
+ break;
+ case STRSXP :
+ for (; r<slen; r++) {
+ w = INTEGER(where)[start+r]; if (w<1) continue; // 0 or NA
+ SET_STRING_ELT(target, w-1, STRING_ELT(source, r));
+ }
+ break;
+ case VECSXP :
+ for (; r<slen; r++) {
+ w = INTEGER(where)[start+r]; if (w<1) continue;
+ SET_VECTOR_ELT(target, w-1, VECTOR_ELT(source, r));
+ }
+ break;
+ default :
+ error("Unsupported type '%s'", type2char(TYPEOF(target)));
+ }
+ if (slen == 1) {
+ if (size==4) for (; r<len; r++) {
+ w = INTEGER(where)[start+r]; if (w<1) continue;
+ INTEGER(target)[ w-1 ] = INTEGER(source)[0];
+ } else for (; r<len; r++) {
+ w = INTEGER(where)[start+r]; if (w<1) continue;
+ REAL(target)[ w-1 ] = REAL(source)[0];
+ }
+ } else {
+ if (size==4) for (; r<len; r++) {
+ w = INTEGER(where)[start+r]; if (w<1) continue;
+ INTEGER(target)[ w-1 ] = INTEGER(source)[r%slen];
+ } else for (; r<len; r++) {
+ w = INTEGER(where)[start+r]; if (w<1) continue;
+ REAL(target)[ w-1 ] = REAL(source)[r%slen];
+ }
+ }
+ // if slen>10 it may be worth memcpy, but we'd need to first know if 'where' was a contiguous subset
+ }
+}
+
+SEXP allocNAVector(SEXPTYPE type, R_len_t n)
+{
+ // an allocVector following with initialization to NA since a subassign to a new column using :=
+ // routinely leaves untouched items (rather than 0 or "" as allocVector does with it's memset)
+ // We guess that author of allocVector would have liked to initialize with NA but was prevented since memset
+ // is restricted to one byte.
+ R_len_t i;
+ SEXP v;
+ PROTECT(v = allocVector(type, n));
+ switch(type) {
+ // NAs are special; no need to worry about generations.
+ case INTSXP :
+ case LGLSXP :
+ for (i=0; i<n; i++) INTEGER(v)[i] = NA_INTEGER;
+ break;
+ case REALSXP :
+ for (i=0; i<n; i++) REAL(v)[i] = NA_REAL;
+ break;
+ case STRSXP :
+ for (i=0; i<n; i++) ((SEXP *)DATAPTR(v))[i] = NA_STRING;
+ break;
+ case VECSXP :
+ // list columns already have each item initialized to NULL
+ break;
+ default :
+ error("Unsupported type '%s'", type2char(type));
+ }
+ UNPROTECT(1);
+ return(v);
+}
+
+void savetl_init() {
+ if (nsaved || nalloc || saveds || savedtl) error("Internal error: savetl_init checks failed (%d %d %p %p). Please report to datatable-help.", nsaved, nalloc, saveds, savedtl);
+ nsaved = 0;
+ nalloc = 100;
+ saveds = (SEXP *)malloc(nalloc * sizeof(SEXP));
+ if (saveds == NULL) error("Couldn't allocate saveds in savetl_init");
+ savedtl = (R_len_t *)malloc(nalloc * sizeof(R_len_t));
+ if (savedtl == NULL) {
+ free(saveds);
+ error("Couldn't allocate saveds in savetl_init");
+ }
+}
+
+void savetl(SEXP s)
+{
+ if (nsaved>=nalloc) {
+ nalloc *= 2;
+ char *tmp;
+ tmp = (char *)realloc(saveds, nalloc * sizeof(SEXP));
+ if (tmp == NULL) {
+ savetl_end();
+ error("Couldn't realloc saveds in savetl");
+ }
+ saveds = (SEXP *)tmp;
+ tmp = (char *)realloc(savedtl, nalloc * sizeof(R_len_t));
+ if (tmp == NULL) {
+ savetl_end();
+ error("Couldn't realloc savedtl in savetl");
+ }
+ savedtl = (R_len_t *)tmp;
+ }
+ saveds[nsaved] = s;
+ savedtl[nsaved] = TRUELENGTH(s);
+ nsaved++;
+}
+
+void savetl_end() {
+ // Can get called if nothing has been saved yet (nsaved==0), or even if _init() hasn't been called yet (pointers NULL). Such
+ // as to clear up before error. Also, it might be that nothing needed to be saved anyway.
+ for (int i=0; i<nsaved; i++) SET_TRUELENGTH(saveds[i],savedtl[i]);
+ free(saveds); // does nothing on NULL input
+ free(savedtl);
+ nsaved = nalloc = 0;
+ saveds = NULL;
+ savedtl = NULL;
+}
+
+SEXP setcharvec(SEXP x, SEXP which, SEXP new)
+{
+ int w;
+ if (!isString(x)) error("x must be a character vector");
+ if (!isInteger(which)) error("'which' must be an integer vector");
+ if (!isString(new)) error("'new' must be a character vector");
+ if (LENGTH(new)!=LENGTH(which)) error("'new' is length %d. Should be the same as length of 'which' (%d)",LENGTH(new),LENGTH(which));
+ for (int i=0; i<LENGTH(which); i++) {
+ w = INTEGER(which)[i];
+ if (w==NA_INTEGER || w<1 || w>LENGTH(x)) error("Item %d of 'which' is %d which is outside range of the length %d character vector", i+1,w,LENGTH(x));
+ SET_STRING_ELT(x, w-1, STRING_ELT(new, i));
+ }
+ return R_NilValue;
+}
+
+SEXP setcolorder(SEXP x, SEXP o)
+{
+ // checks have already been made at R level in setcolorder()
+ // reording columns by reference makes no difference to generations
+ // so there's no need to use SET_* api.
+ SEXP *tmp = Calloc(LENGTH(x),SEXP);
+ for (int i=0; i<LENGTH(x); i++)
+ tmp[i] = VECTOR_ELT(x, INTEGER(o)[i]-1);
+ memcpy((char *)DATAPTR(x),(char *)tmp,LENGTH(x)*sizeof(char *)); // sizeof is of type size_t (unsigned) - so no overflow here
+ SEXP names = getAttrib(x,R_NamesSymbol);
+ if (isNull(names)) error("dt passed to setcolorder has no names");
+ for (int i=0; i<LENGTH(x); i++)
+ tmp[i] = STRING_ELT(names, INTEGER(o)[i]-1);
+ memcpy((char *)DATAPTR(names),(char *)tmp,LENGTH(x)*sizeof(char *));
+ // No need to change key (if any); sorted attribute is column names not positions
+ Free(tmp);
+ return(R_NilValue);
+}
+
+SEXP pointWrapper(SEXP to, SEXP to_idx, SEXP from, SEXP from_idx) {
+
+ R_len_t i, fidx, tidx, l_to=length(to), l_from=length(from), l_idx=length(from_idx);
+ if (!isNewList(to) || !isNewList(from)) error("'to' and 'from' must be of type list");
+ if (length(from_idx) != length(to_idx) || l_idx == 0) error("'from_idx' and 'to_idx' must be non-empty integer vectors of same length.");
+ for (i=0; i<l_idx; i++) {
+ fidx = INTEGER(from_idx)[i]-1; // 1-based to 0-based
+ tidx = INTEGER(to_idx)[i]-1; // 1-based to 0-based
+ if (fidx < 0 || fidx > l_from-1) error("invalid from_idx[%d]=%d, falls outside 1 and length(from)=%d.", i+1, fidx, l_from);
+ if (tidx < 0 || tidx > l_to-1) error("invalid to_idx[%d]=%d, falls outside 1 and length(to)=%d.", i+1, tidx, l_to);
+ SET_VECTOR_ELT(to, tidx, VECTOR_ELT(from, fidx));
+ }
+ return(to);
+}
+
+/*
+SEXP pointer(SEXP x) {
+ SEXP ans;
+ PROTECT(ans = allocVector(REALSXP, 1));
+ REAL(ans)[0] = (double)x;
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP named(SEXP x) {
+ SEXP y = (SEXP)(REAL(x)[0]);
+ Rprintf("%d length = %d\n",NAMED(y), LENGTH(y));
+ return(R_NilValue);
+}
+
+void setnamed(double *x, int *v) { // call by .Call(,DUP=FALSE) only.
+ SEXP y = (SEXP)(*x);
+ Rprintf("%d length = %d\n",NAMED(y), LENGTH(y));
+ SET_NAMED(y,*v);
+}
+*/
+
+
diff --git a/src/bmerge.c b/src/bmerge.c
new file mode 100644
index 0000000..4fd1dc2
--- /dev/null
+++ b/src/bmerge.c
@@ -0,0 +1,324 @@
+#include "data.table.h"
+
+/*
+Implements binary search (a.k.a. divide and conquer).
+http://en.wikipedia.org/wiki/Binary_search
+http://www.tbray.org/ongoing/When/200x/2003/03/22/Binary
+http://googleresearch.blogspot.com/2006/06/extra-extra-read-all-about-it-nearly.html
+Differences over standard binary search (e.g. bsearch in stdlib.h) :
+ o list of vectors (key of many columns) of different types
+ o ties (groups)
+ o NA,NAN,-Inf,+Inf are distinct values and can be joined to
+ o type double is joined within tolerance (apx 11 s.f.)
+ o join to prevailing value (roll join a.k.a locf), forwards or backwards
+ o join to nearest
+ o roll the beginning and end optionally
+ o limit the roll distance to a user provided value
+*/
+
+#define ENC_KNOWN(x) (LEVELS(x) & 12)
+// 12 = LATIN1_MASK (1<<2) | UTF8_MASK (1<<3) // Would use these definitions from Defn.h, but that appears to be private to R. Hence 12.
+
+static SEXP i, x;
+static int ncol, *icols, *xcols, *o, *xo, *retFirst, *retLength, *allLen1, *rollends;
+static double roll, rollabs;
+static Rboolean nearest=FALSE, enc_warn=TRUE;
+#define XIND(i) (xo ? xo[(i)]-1 : i)
+
+void bmerge_r(int xlow, int xupp, int ilow, int iupp, int col, int lowmax, int uppmax);
+
+SEXP bmerge(SEXP iArg, SEXP xArg, SEXP icolsArg, SEXP xcolsArg, SEXP isorted, SEXP xoArg, SEXP rollarg, SEXP rollendsArg, SEXP nomatch, SEXP retFirstArg, SEXP retLengthArg, SEXP allLen1Arg)
+{
+ int xN, iN, protecti=0;
+ roll = 0.0;
+ nearest = FALSE;
+ enc_warn = TRUE;
+ if (isString(rollarg)) {
+ if (strcmp(CHAR(STRING_ELT(rollarg,0)),"nearest") != 0) error("roll is character but not 'nearest'");
+ roll=1.0; nearest=TRUE; // the 1.0 here is just any non-0.0, so roll!=0.0 can be used later
+ } else {
+ if (!isReal(rollarg)) error("Internal error: roll is not character or double");
+ roll = REAL(rollarg)[0]; // more common case (rolling forwards or backwards) or no roll when 0.0
+ }
+ rollabs = fabs(roll);
+
+ i = iArg; x = xArg; // set globals so bmerge_r can see them.
+ if (!isInteger(icolsArg)) error("Internal error: icols is not integer vector");
+ if (!isInteger(xcolsArg)) error("Internal error: xcols is not integer vector");
+ if (LENGTH(icolsArg) > LENGTH(xcolsArg)) error("Internal error: length(icols) [%d] > length(xcols) [%d]", LENGTH(icolsArg), LENGTH(xcolsArg));
+ icols = INTEGER(icolsArg);
+ xcols = INTEGER(xcolsArg);
+ xN = LENGTH(VECTOR_ELT(x,0));
+ iN = LENGTH(VECTOR_ELT(i,0));
+ ncol = LENGTH(icolsArg); // there may be more sorted columns in x than involved in the join
+ for(int col=0; col<ncol; col++) {
+ if (icols[col]==NA_INTEGER) error("Internal error. icols[%d] is NA", col);
+ if (xcols[col]==NA_INTEGER) error("Internal error. xcols[%d] is NA", col);
+ if (icols[col]>LENGTH(i) || icols[col]<1) error("icols[%d]=%d outside range [1,length(i)=%d]", col, icols[col], LENGTH(i));
+ if (xcols[col]>LENGTH(x) || xcols[col]<1) error("xcols[%d]=%d outside range [1,length(x)=%d]", col, xcols[col], LENGTH(x));
+ int it = TYPEOF(VECTOR_ELT(i, icols[col]-1));
+ int xt = TYPEOF(VECTOR_ELT(x, xcols[col]-1));
+ if (it != xt) error("typeof x.%s (%s) != typeof i.%s (%s)", CHAR(STRING_ELT(getAttrib(x,R_NamesSymbol),xcols[col]-1)), type2char(xt), CHAR(STRING_ELT(getAttrib(i,R_NamesSymbol),icols[col]-1)), type2char(it));
+ }
+ if (!isInteger(retFirstArg) || LENGTH(retFirstArg)!=iN) error("retFirst must be integer vector the same length as nrow(i)");
+ retFirst = INTEGER(retFirstArg);
+ if (!isInteger(retLengthArg) || LENGTH(retLengthArg)!=iN) error("retLength must be integer vector the same length as nrow(i)");
+ retLength = INTEGER(retLengthArg);
+ if (!isLogical(allLen1Arg) || LENGTH(allLen1Arg) != 1) error("allLen1 must be a length 1 logical vector");
+ allLen1 = LOGICAL(allLen1Arg);
+ if (!isLogical(rollendsArg) || LENGTH(rollendsArg) != 2) error("rollends must be a length 2 logical vector");
+ rollends = LOGICAL(rollendsArg);
+
+ if (nearest && TYPEOF(VECTOR_ELT(i, icols[ncol-1]-1))==STRSXP) error("roll='nearest' can't be applied to a character column, yet.");
+
+ for (int j=0; j<iN; j++) {
+ // defaults need to populated here as bmerge_r may well not touch many locations, say if the last row of i is before the first row of x.
+ retFirst[j] = INTEGER(nomatch)[0]; // default to no match for NA goto below
+ // retLength[j] = 0; // TO DO: do this to save the branch below and later branches at R level to set .N to 0
+ retLength[j] = INTEGER(nomatch)[0]==0 ? 0 : 1;
+ }
+ allLen1[0] = TRUE; // All-0 and All-NA are considered all length 1 according to R code currently. Really, it means any(length>1).
+
+ o = NULL;
+ if (!LOGICAL(isorted)[0]) {
+ SEXP order = PROTECT(vec_init(length(icolsArg), ScalarInteger(1))); // rep(1, length(icolsArg))
+ SEXP oSxp = PROTECT(forder(i, icolsArg, ScalarLogical(FALSE), ScalarLogical(TRUE), order, ScalarLogical(FALSE)));
+ protecti += 2;
+ if (!LENGTH(oSxp)) o = NULL; else o = INTEGER(oSxp);
+ }
+ xo = NULL;
+ if (length(xoArg)) {
+ if (!isInteger(xoArg)) error("Internal error: xoArg is not an integer vector");
+ xo = INTEGER(xoArg);
+ }
+
+ if (iN) bmerge_r(-1,xN,-1,iN,0,1,1);
+
+ UNPROTECT(protecti);
+ return(R_NilValue);
+}
+
+static union {
+ int i;
+ double d;
+ unsigned long long ll;
+ SEXP s;
+} ival, xval;
+
+static int mid, tmplow, tmpupp; // global to save them being added to recursive stack. Maybe optimizer would do this anyway.
+static SEXP ic, xc, class;
+
+void bmerge_r(int xlowIn, int xuppIn, int ilowIn, int iuppIn, int col, int lowmax, int uppmax)
+// col is >0 and <=ncol-1 if this range of [xlow,xupp] and [ilow,iupp] match up to but not including that column
+// lowmax=1 if xlowIn is the lower bound of this group (needed for roll)
+// uppmax=1 if xuppIn is the upper bound of this group (needed for roll)
+{
+ int xlow=xlowIn, xupp=xuppIn, ilow=ilowIn, iupp=iuppIn, j, k, ir, lir, tmp;
+ ir = lir = ilow + (iupp-ilow)/2; // lir = logical i row.
+ if (o) ir = o[lir]-1; // ir = the actual i row if i were ordered
+
+ ic = VECTOR_ELT(i,icols[col]-1); // ic = i column
+ xc = VECTOR_ELT(x,xcols[col]-1); // xc = x column
+ // it was checked in bmerge() that the types are equal
+
+ switch (TYPEOF(xc)) {
+ case LGLSXP : case INTSXP : // including factors
+ ival.i = INTEGER(ic)[ir];
+ while(xlow < xupp-1) {
+ mid = xlow + (xupp-xlow)/2; // Same as (xlow+xupp)/2 but without risk of overflow
+ xval.i = INTEGER(xc)[XIND(mid)];
+ if (xval.i<ival.i) { // relies on NA_INTEGER == INT_MIN, tested in init.c
+ xlow=mid;
+ } else if (xval.i>ival.i) { // TO DO: is *(&xlow, &xupp)[0|1]=mid more efficient than branch?
+ xupp=mid;
+ } else { // xval.i == ival.i including NA_INTEGER==NA_INTEGER
+ // branch mid to find start and end of this group in this column
+ // TO DO?: not if mult=first|last and col<ncol-1
+ tmplow = mid;
+ tmpupp = mid;
+ while(tmplow<xupp-1) {
+ mid = tmplow + (xupp-tmplow)/2;
+ xval.i = INTEGER(xc)[XIND(mid)];
+ if (xval.i == ival.i) tmplow=mid; else xupp=mid;
+ }
+ while(xlow<tmpupp-1) {
+ mid = xlow + (tmpupp-xlow)/2;
+ xval.i = INTEGER(xc)[XIND(mid)];
+ if (xval.i == ival.i) tmpupp=mid; else xlow=mid;
+ }
+ // xlow and xupp now surround the group in xc, we only need this range for the next column
+ break;
+ }
+ }
+ tmplow = lir;
+ tmpupp = lir;
+ while(tmplow<iupp-1) { // TO DO: could double up from lir rather than halving from iupp
+ mid = tmplow + (iupp-tmplow)/2;
+ xval.i = INTEGER(ic)[ o ? o[mid]-1 : mid ]; // reuse xval to search in i
+ if (xval.i == ival.i) tmplow=mid; else iupp=mid;
+ }
+ while(ilow<tmpupp-1) {
+ mid = ilow + (tmpupp-ilow)/2;
+ xval.i = INTEGER(ic)[ o ? o[mid]-1 : mid ];
+ if (xval.i == ival.i) tmpupp=mid; else ilow=mid;
+ }
+ // ilow and iupp now surround the group in ic, too
+ break;
+ case STRSXP :
+ ival.s = STRING_ELT(ic,ir);
+ while(xlow < xupp-1) {
+ mid = xlow + (xupp-xlow)/2;
+ xval.s = STRING_ELT(xc, XIND(mid));
+ if (enc_warn && (ENC_KNOWN(ival.s) || ENC_KNOWN(xval.s))) {
+ // The || is only done here to avoid the warning message being repeating in this code.
+ warning("A known encoding (latin1 or UTF-8) was detected in a join column. data.table compares the bytes currently, so doesn't support *mixed* encodings well; i.e., using both latin1 and UTF-8, or if any unknown encodings are non-ascii and some of those are marked known and others not. But if either latin1 or UTF-8 is used exclusively, and all unknown encodings are ascii, then the result should be ok. In future we will check for you and avoid this warning if everything is [...]
+ // TO DO: check and warn in forder whether any strings are non-ascii (>127) but unknown encoding
+ // check in forder whether both latin1 and UTF-8 have been used
+ // See bugs #5159 and #5266 and related #5295 to revisit
+ enc_warn = FALSE; // just warn once
+ }
+ tmp = StrCmp(xval.s, ival.s); // uses pointer equality first, NA_STRING are allowed and joined to, then uses strcmp on CHAR().
+ if (tmp == 0) { // TO DO: deal with mixed encodings and locale optionally
+ tmplow = mid;
+ tmpupp = mid;
+ while(tmplow<xupp-1) {
+ mid = tmplow + (xupp-tmplow)/2;
+ xval.s = STRING_ELT(xc, XIND(mid));
+ if (ival.s == xval.s) tmplow=mid; else xupp=mid; // the == here assumes (within this column) no mixing of latin1 and UTF-8, and no unknown non-ascii
+ } // TO DO: add checks to forder, see above.
+ while(xlow<tmpupp-1) {
+ mid = xlow + (tmpupp-xlow)/2;
+ xval.s = STRING_ELT(xc, XIND(mid));
+ if (ival.s == xval.s) tmpupp=mid; else xlow=mid; // see above re ==
+ }
+ break;
+ } else if (tmp < 0) {
+ xlow=mid;
+ } else {
+ xupp=mid;
+ }
+ }
+ tmplow = lir;
+ tmpupp = lir;
+ while(tmplow<iupp-1) {
+ mid = tmplow + (iupp-tmplow)/2;
+ xval.s = STRING_ELT(ic, o ? o[mid]-1 : mid);
+ if (xval.s == ival.s) tmplow=mid; else iupp=mid; // see above re ==
+ }
+ while(ilow<tmpupp-1) {
+ mid = ilow + (tmpupp-ilow)/2;
+ xval.s = STRING_ELT(ic, o ? o[mid]-1 : mid);
+ if (xval.s == ival.s) tmpupp=mid; else ilow=mid; // see above re ==
+ }
+ break;
+ case REALSXP :
+ class = getAttrib(xc, R_ClassSymbol);
+ twiddle = (isString(class) && STRING_ELT(class, 0)==char_integer64) ? &i64twiddle : &dtwiddle;
+ ival.ll = twiddle(DATAPTR(ic), ir, 1);
+ while(xlow < xupp-1) {
+ mid = xlow + (xupp-xlow)/2;
+ xval.ll = twiddle(DATAPTR(xc), XIND(mid), 1);
+ if (xval.ll<ival.ll) {
+ xlow=mid;
+ } else if (xval.ll>ival.ll) {
+ xupp=mid;
+ } else { // xval.ll == ival.ll)
+ tmplow = mid;
+ tmpupp = mid;
+ while(tmplow<xupp-1) {
+ mid = tmplow + (xupp-tmplow)/2;
+ xval.ll = twiddle(DATAPTR(xc), XIND(mid), 1);
+ if (xval.ll == ival.ll) tmplow=mid; else xupp=mid;
+ }
+ while(xlow<tmpupp-1) {
+ mid = xlow + (tmpupp-xlow)/2;
+ xval.ll = twiddle(DATAPTR(xc), XIND(mid), 1);
+ if (xval.ll == ival.ll) tmpupp=mid; else xlow=mid;
+ }
+ break;
+ }
+ }
+ tmplow = lir;
+ tmpupp = lir;
+ while(tmplow<iupp-1) {
+ mid = tmplow + (iupp-tmplow)/2;
+ xval.ll = twiddle(DATAPTR(ic), o ? o[mid]-1 : mid, 1 );
+ if (xval.ll == ival.ll) tmplow=mid; else iupp=mid;
+ }
+ while(ilow<tmpupp-1) {
+ mid = ilow + (tmpupp-ilow)/2;
+ xval.ll = twiddle(DATAPTR(ic), o ? o[mid]-1 : mid, 1 );
+ if (xval.ll == ival.ll) tmpupp=mid; else ilow=mid;
+ }
+ break;
+ default:
+ error("Type '%s' not supported as key column", type2char(TYPEOF(xc)));
+ }
+
+ if (xlow<xupp-1) { // if value found, low and upp surround it, unlike standard binary search where low falls on it
+ if (col<ncol-1) bmerge_r(xlow, xupp, ilow, iupp, col+1, 1, 1); // final two 1's are lowmax and uppmax
+ else {
+ int len = xupp-xlow-1;
+ if (len>1) allLen1[0] = FALSE;
+ for (j=ilow+1; j<iupp; j++) { // usually iterates once only for j=ir
+ k = o ? o[j]-1 : j;
+ retFirst[k] = xlow+2; // extra +1 for 1-based indexing at R level
+ retLength[k]= len;
+ }
+ }
+ }
+ else if (roll!=0.0 && col==ncol-1) {
+ // runs once per i row (not each search test), so not hugely time critical
+ if (xlow != xupp-1 || xlow<xlowIn || xupp>xuppIn) error("Internal error: xlow!=xupp-1 || xlow<xlowIn || xupp>xuppIn");
+ if (nearest) { // value of roll ignored currently when nearest
+ if ( (!lowmax || xlow>xlowIn) && (!uppmax || xupp<xuppIn) ) {
+ if ( ( TYPEOF(ic)==REALSXP && REAL(ic)[ir]-REAL(xc)[XIND(xlow)] <= REAL(xc)[XIND(xupp)]-REAL(ic)[ir] )
+ || ( TYPEOF(ic)<=INTSXP && INTEGER(ic)[ir]-INTEGER(xc)[XIND(xlow)] <= INTEGER(xc)[XIND(xupp)]-INTEGER(ic)[ir] )) {
+ retFirst[ir] = xlow+1;
+ retLength[ir] = 1;
+ } else {
+ retFirst[ir] = xupp+1;
+ retLength[ir] = 1;
+ }
+ } else if (uppmax && xupp==xuppIn && rollends[1]) {
+ retFirst[ir] = xlow+1;
+ retLength[ir] = 1;
+ } else if (lowmax && xlow==xlowIn && rollends[0]) {
+ retFirst[ir] = xupp+1;
+ retLength[ir] = 1;
+ }
+ } else {
+ if ( ( (roll>0.0 && (!lowmax || xlow>xlowIn) && (xupp<xuppIn || !uppmax || rollends[1]))
+ || (roll<0.0 && xupp==xuppIn && uppmax && rollends[1]) )
+ && ( (TYPEOF(ic)==REALSXP && (REAL(ic)[ir]-REAL(xc)[XIND(xlow)]-rollabs<1e-6 ||
+ REAL(ic)[ir]-REAL(xc)[XIND(xlow)] == rollabs)) // #1007 fix
+ || (TYPEOF(ic)<=INTSXP && (double)(INTEGER(ic)[ir]-INTEGER(xc)[XIND(xlow)])-rollabs<1e-6 )
+ || (TYPEOF(ic)==STRSXP) )) {
+ retFirst[ir] = xlow+1;
+ retLength[ir] = 1;
+ } else if
+ ( ( (roll<0.0 && (!uppmax || xupp<xuppIn) && (xlow>xlowIn || !lowmax || rollends[0]))
+ || (roll>0.0 && xlow==xlowIn && lowmax && rollends[0]) )
+ && ( (TYPEOF(ic)==REALSXP && (REAL(xc)[XIND(xupp)]-REAL(ic)[ir]-rollabs<1e-6 ||
+ REAL(xc)[XIND(xupp)]-REAL(ic)[ir] == rollabs)) // 1007 fix
+ || (TYPEOF(ic)<=INTSXP && (double)(INTEGER(xc)[XIND(xupp)]-INTEGER(ic)[ir])-rollabs<1e-6 )
+ || (TYPEOF(ic)==STRSXP) )) {
+ retFirst[ir] = xupp+1; // == xlow+2
+ retLength[ir] = 1;
+ }
+ }
+ if (iupp-ilow > 2 && retFirst[ir]!=NA_INTEGER) { // >=2 equal values in the last column being rolling to the same point.
+ for (j=ilow+1; j<iupp; j++) { // will rewrite retFirst[ir] to itself, but that's ok
+ if (o) k=o[j]-1; else k=j;
+ retFirst[k] = retFirst[ir];
+ retLength[k]= retLength[ir];
+ }
+ }
+ }
+ if (ilow>ilowIn && (xlow>xlowIn || (roll!=0.0 && col==ncol-1)))
+ bmerge_r(xlowIn, xlow+1, ilowIn, ilow+1, col, lowmax, uppmax && xlow+1==xuppIn);
+ if (iupp<iuppIn && (xupp<xuppIn || (roll!=0.0 && col==ncol-1)))
+ bmerge_r(xupp-1, xuppIn, iupp-1, iuppIn, col, lowmax && xupp-1==xlowIn, uppmax);
+}
+
+
diff --git a/src/chmatch.c b/src/chmatch.c
new file mode 100644
index 0000000..6e69e7b
--- /dev/null
+++ b/src/chmatch.c
@@ -0,0 +1,81 @@
+#include "data.table.h"
+
+#define ENC_KNOWN(x) (LEVELS(x) & 76)
+// LATIN1_MASK (1<<2) | UTF8_MASK (1<<3) | ASCII_MASK (1<<6)
+
+SEXP match_logical(SEXP table, SEXP x) {
+ R_len_t i;
+ SEXP ans, m;
+ ans = PROTECT(allocVector(LGLSXP, length(x)));
+ m = PROTECT(match(table, x, 0)); // nomatch=0
+ for (i=0; i<length(x); i++)
+ INTEGER(ans)[i] = INTEGER(m)[i] > 0;
+ UNPROTECT(2);
+ return(ans);
+}
+
+SEXP chmatch(SEXP x, SEXP table, R_len_t nomatch, Rboolean in) {
+ R_len_t i, m;
+ SEXP ans, s;
+ if (!isString(x) && !isNull(x)) error("x is type '%s' (must be 'character' or NULL)", type2char(TYPEOF(x)));
+ if (!isString(table) && !isNull(table)) error("table is type '%s' (must be 'character' or NULL)", type2char(TYPEOF(table)));
+ PROTECT(ans = allocVector(in ? LGLSXP : INTSXP,length(x))); // if fails, it fails before savetl
+ savetl_init();
+ for (i=0; i<length(x); i++) {
+ s = STRING_ELT(x,i);
+ if (s != NA_STRING && ENC_KNOWN(s) != 64) { // PREV: s != NA_STRING && !ENC_KNOWN(s) - changed to fix for bug #5159. The previous fix
+ // dealt with UNKNOWN encodings. But we could have the same string, where both are in different
+ // encodings than ASCII (ex: UTF8 and Latin1). To fix this, we'll to resort to 'match' if not ASCII.
+ // This takes care of all anomalies. It's unfortunate the 'chmatch' can't be used in these cases...
+ // // fix for the 2nd part of bug #5159
+
+ // explanation for PREV case: (still useful to keep it here)
+ // symbols (i.e. as.name() or as.symbol()) containing non-ascii characters (>127) seem to be 'unknown' encoding in R.
+ // The same string in different encodings (where unknown encoding is different to known, too) are different
+ // CHARSXP pointers; this chmatch relies on pointer equality only. We tried mkChar(CHAR(s)) [ what install() does ]
+ // but this impacted the fastest cases too much. Hence fall back to match() on the first unknown encoding detected
+ // since match() considers the same string in different encodings as equal (but slower). See #2538 and #4818.
+ savetl_end();
+ UNPROTECT(1);
+ return (in ? match_logical(table, x) : match(table, x, nomatch));
+ }
+ if (TRUELENGTH(s)>0) savetl(s);
+ // as from v1.8.0 we assume R's internal hash is positive. So in R < 2.14.0 we
+ // don't save the uninitialised truelengths that by chance are negative, but
+ // will save if positive. Hence R >= 2.14.0 may be faster and preferred now that R
+ // initializes truelength to 0 from R 2.14.0.
+ SET_TRUELENGTH(s,0);
+ }
+ for (i=length(table)-1; i>=0; i--) {
+ s = STRING_ELT(table,i);
+ if (s != NA_STRING && ENC_KNOWN(s) != 64) { // changed !ENC_KNOWN(s) to !ASCII(s) - check above for explanation
+ for (int j=i+1; j<LENGTH(table); j++) SET_TRUELENGTH(STRING_ELT(table,j),0); // reinstate 0 rather than leave the -i-1
+ savetl_end();
+ UNPROTECT(1);
+ return (in ? match_logical(table, x) : match(table, x, nomatch));
+ }
+ if (TRUELENGTH(s)>0) savetl(s);
+ SET_TRUELENGTH(s, -i-1);
+ }
+ if (in) {
+ for (i=0; i<length(x); i++) {
+ LOGICAL(ans)[i] = TRUELENGTH(STRING_ELT(x,i))<0;
+ // nomatch ignored for logical as base does I think
+ }
+ } else {
+ for (i=0; i<length(x); i++) {
+ m = TRUELENGTH(STRING_ELT(x,i));
+ INTEGER(ans)[i] = (m<0) ? -m : nomatch;
+ }
+ }
+ for (i=0; i<length(table); i++)
+ SET_TRUELENGTH(STRING_ELT(table,i),0); // reinstate 0 rather than leave the -i-1
+ savetl_end();
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP chmatchwrapper(SEXP x, SEXP table, SEXP nomatch, SEXP in) {
+ return(chmatch(x,table,INTEGER(nomatch)[0],LOGICAL(in)[0]));
+}
+
diff --git a/src/data.table.h b/src/data.table.h
new file mode 100644
index 0000000..c6cbc31
--- /dev/null
+++ b/src/data.table.h
@@ -0,0 +1,78 @@
+#include <R.h>
+#define USE_RINTERNALS
+#include <Rinternals.h>
+// #include <omp.h>
+// #include <signal.h> // the debugging machinery + breakpoint aidee
+// raise(SIGINT);
+
+#define SIZEOF(x) sizes[TYPEOF(x)]
+#ifdef MIN
+#undef MIN
+#endif
+#define MIN(a,b) (((a)<(b))?(a):(b))
+#define NAINT64 LLONG_MIN
+
+// init.c
+void setSizes();
+SEXP char_integer64;
+
+// dogroups.c
+SEXP keepattr(SEXP to, SEXP from);
+SEXP growVector(SEXP x, R_len_t newlen);
+size_t sizes[100]; // max appears to be FUNSXP = 99, see Rinternals.h
+SEXP SelfRefSymbol;
+
+// assign.c
+SEXP allocNAVector(SEXPTYPE type, R_len_t n);
+void savetl_init(), savetl(SEXP s), savetl_end();
+Rboolean isDatatable(SEXP x);
+
+// forder.c
+int StrCmp(SEXP x, SEXP y);
+unsigned long long dtwiddle(void *p, int i, int order);
+unsigned long long i64twiddle(void *p, int i, int order);
+unsigned long long (*twiddle)(void *, int, int);
+SEXP forder(SEXP DT, SEXP by, SEXP retGrp, SEXP sortStrArg, SEXP orderArg, SEXP naArg);
+
+// reorder.c
+SEXP reorder(SEXP x, SEXP order);
+
+// fcast.c
+SEXP vec_init(R_len_t n, SEXP val);
+
+// vecseq.c
+SEXP vecseq(SEXP x, SEXP len, SEXP clamp);
+
+// uniqlist.c
+SEXP uniqlist(SEXP l, SEXP order);
+SEXP uniqlengths(SEXP x, SEXP n);
+
+// chmatch.c
+SEXP chmatch(SEXP x, SEXP table, R_len_t nomatch, Rboolean in);
+
+SEXP isOrderedSubset(SEXP, SEXP);
+void setselfref(SEXP);
+
+// fmelt.c
+SEXP seq_int(int n, int start);
+SEXP set_diff(SEXP x, int n);
+SEXP which(SEXP x, Rboolean bool);
+
+// frank.c
+SEXP dt_na(SEXP x, SEXP cols);
+
+// assign.c
+SEXP alloccol(SEXP dt, R_len_t n, Rboolean verbose);
+void memrecycle(SEXP target, SEXP where, int r, int len, SEXP source);
+SEXP shallowwrapper(SEXP dt, SEXP cols);
+
+SEXP dogroups(SEXP dt, SEXP dtcols, SEXP groups, SEXP grpcols, SEXP jiscols, SEXP xjiscols, SEXP grporder, SEXP order, SEXP starts, SEXP lens, SEXP jexp, SEXP env, SEXP lhs, SEXP newnames, SEXP on, SEXP verbose);
+
+// bmerge.c
+SEXP bmerge(SEXP left, SEXP right, SEXP leftcols, SEXP rightcols, SEXP isorted, SEXP xoArg, SEXP rollarg, SEXP rollends, SEXP nomatch, SEXP retFirst, SEXP retLength, SEXP allLen1);
+
+// fcast.c
+SEXP coerce_to_char(SEXP s, SEXP env);
+
+// rbindlist.c
+SEXP combineFactorLevels(SEXP factorLevels, int * factorType, Rboolean * isRowOrdered);
diff --git a/src/dogroups.c b/src/dogroups.c
new file mode 100644
index 0000000..c23e41a
--- /dev/null
+++ b/src/dogroups.c
@@ -0,0 +1,585 @@
+#include "data.table.h"
+#include <Rdefines.h>
+//#include <sys/mman.h>
+#include <Rversion.h>
+#include <fcntl.h>
+#include <time.h>
+
+
+void setSizes() {
+ // called by init.c
+ int i;
+ for (i=0;i<100;i++) sizes[i]=0;
+ // only these types are currently allowed as column types :
+ sizes[INTSXP] = sizeof(int); // integer and factor
+ sizes[LGLSXP] = sizeof(int); // logical
+ sizes[REALSXP] = sizeof(double); // numeric
+ sizes[STRSXP] = sizeof(SEXP *); // character
+ sizes[VECSXP] = sizeof(SEXP *); // a column itself can be a list()
+ for (i=0;i<100;i++) {
+ if (sizes[i]>8) error("Type %d is sizeof() greater than 8 bytes on this machine. We haven't tested on any architecture greater than 64bit, yet.", i);
+ // One place we need the largest sizeof (assumed to be 8 bytes) is the working memory malloc in reorder.c
+ }
+ SelfRefSymbol = install(".internal.selfref");
+}
+
+SEXP dogroups(SEXP dt, SEXP dtcols, SEXP groups, SEXP grpcols, SEXP jiscols, SEXP xjiscols, SEXP grporder, SEXP order, SEXP starts, SEXP lens, SEXP jexp, SEXP env, SEXP lhs, SEXP newnames, SEXP on, SEXP verbose)
+{
+ R_len_t i, j, k, rownum, ngrp, njval=0, ngrpcols, ansloc=0, maxn, estn=-1, r, thisansloc, grpn, thislen, igrp, vlen, origIlen=0, origSDnrow=0;
+ int protecti=0;
+ SEXP names, names2, xknames, bynames, dtnames, ans=NULL, jval, thiscol, SD, BY, N, I, GRP, iSD, xSD, rownames, s, RHS, listwrap, target, source;
+ SEXP *nameSyms, *xknameSyms;
+ Rboolean wasvector, firstalloc=FALSE, NullWarnDone=FALSE, recycleWarn=TRUE;
+ #if defined(R_VERSION) && R_VERSION >= R_Version(3, 1, 0)
+ SEXP dupcol;
+ int named=0;
+ #endif
+ size_t size; // must be size_t, otherwise bug #5305 (integer overflow in memcpy)
+ clock_t tstart=0, tblock[10]={0}; int nblock[10]={0};
+
+ if (!isInteger(order)) error("Internal error: order not integer vector");
+ //if (TYPEOF(starts) != INTSXP) error("Internal error: starts not integer");
+ //if (TYPEOF(lens) != INTSXP) error("Internal error: lens not integer");
+ // starts can now be NA (<0): if (INTEGER(starts)[0]<0 || INTEGER(lens)[0]<0) error("starts[1]<0 or lens[1]<0");
+ if (!isNull(jiscols) && LENGTH(order) && !LOGICAL(on)[0]) error("Internal error: jiscols not NULL but o__ has length");
+ if (!isNull(xjiscols) && LENGTH(order) && !LOGICAL(on)[0]) error("Internal error: xjiscols not NULL but o__ has length");
+ if(!isEnvironment(env)) error("’env’ should be an environment");
+ ngrp = length(starts); // the number of groups (nrow(groups) will be larger when by)
+ ngrpcols = length(grpcols);
+ SD = findVar(install(".SD"), env);
+
+ defineVar(install(".BY"), BY = allocVector(VECSXP, ngrpcols), env);
+ bynames = PROTECT(allocVector(STRSXP, ngrpcols)); protecti++; // TO DO: do we really need bynames, can we assign names afterwards in one step?
+ for (i=0; i<ngrpcols; i++) {
+ j = INTEGER(grpcols)[i]-1;
+ SET_VECTOR_ELT(BY, i, allocVector(TYPEOF(VECTOR_ELT(groups, j)),
+ LENGTH(VECTOR_ELT(groups,0)) ? 1 : 0)); // This might be able to be 1 always; 0 when 'groups' are integer(0) seem sensible. #2440 was involved in the past.
+ // Fix for #5437, by cols with attributes when also used in `j` lost the attribute.
+ copyMostAttrib(VECTOR_ELT(groups, j), VECTOR_ELT(BY,i)); // not names, otherwise test 778 would fail
+ SET_STRING_ELT(bynames, i, STRING_ELT(getAttrib(groups,R_NamesSymbol), j));
+ defineVar(install(CHAR(STRING_ELT(bynames,i))), VECTOR_ELT(BY,i), env); // by vars can be used by name in j as well as via .BY
+ if (SIZEOF(VECTOR_ELT(BY,i))==0)
+ error("Unsupported type '%s' in column %d of 'by'", type2char(TYPEOF(VECTOR_ELT(BY, i))), i+1);
+ }
+ setAttrib(BY, R_NamesSymbol, bynames); // Fix for #5415 - BY doesn't retain names anymore
+ R_LockBinding(install(".BY"), env);
+ if (isNull(jiscols) && (length(bynames)!=length(groups) || length(bynames)!=length(grpcols))) error("!length(bynames)[%d]==length(groups)[%d]==length(grpcols)[%d]",length(bynames),length(groups),length(grpcols));
+ // TO DO: check this check above.
+
+ N = findVar(install(".N"), env);
+ GRP = findVar(install(".GRP"), env);
+ iSD = findVar(install(".iSD"), env); // 1-row and possibly no cols (if no i variables are used via JIS)
+ xSD = findVar(install(".xSD"), env);
+ I = findVar(install(".I"), env);
+
+ dtnames = getAttrib(dt, R_NamesSymbol); // added here to fix #4990 - `:=` did not issue recycling warning during "by"
+ // fetch rownames of .SD. rownames[1] is set to -thislen for each group, in case .SD is passed to
+ // non data.table aware package that uses rownames
+ for (s = ATTRIB(SD); s != R_NilValue && TAG(s)!=R_RowNamesSymbol; s = CDR(s));
+ // getAttrib0 basically but that's hidden in attrib.c
+ if (s==R_NilValue) error("row.names attribute of .SD not found");
+ rownames = CAR(s);
+ if (!isInteger(rownames) || LENGTH(rownames)!=2 || INTEGER(rownames)[0]!=NA_INTEGER) error("row.names of .SD isn't integer length 2 with NA as first item; i.e., .set_row_names(). [%s %d %d]",type2char(TYPEOF(rownames)),LENGTH(rownames),INTEGER(rownames)[0]);
+
+ // fetch names of .SD and prepare symbols. In case they are copied-on-write by user assigning to those variables
+ // using <- in j (which is valid, useful and tested), they are repointed to the .SD cols for each group.
+ names = getAttrib(SD, R_NamesSymbol);
+ if (length(names) != length(SD)) error("length(names)!=length(SD)");
+ nameSyms = Calloc(length(names), SEXP);
+ if (!nameSyms) error("Calloc failed to allocate %d nameSyms in dogroups",length(names));
+ for(i = 0; i < length(SD); i++) {
+ if (SIZEOF(VECTOR_ELT(SD, i))==0)
+ error("Type %d in .SD column %d", TYPEOF(VECTOR_ELT(SD, i)), i);
+ nameSyms[i] = install(CHAR(STRING_ELT(names, i)));
+ // fixes http://stackoverflow.com/questions/14753411/why-does-data-table-lose-class-definition-in-sd-after-group-by
+ copyMostAttrib(VECTOR_ELT(dt,INTEGER(dtcols)[i]-1), VECTOR_ELT(SD,i)); // not names, otherwise test 778 would fail
+ }
+
+ origIlen = length(I); // test 762 has length(I)==1 but nrow(SD)==0
+ if (length(SD)) origSDnrow = length(VECTOR_ELT(SD, 0));
+
+ xknames = getAttrib(xSD, R_NamesSymbol);
+ if (length(xknames) != length(xSD)) error("length(xknames)!=length(xSD)");
+ xknameSyms = Calloc(length(xknames), SEXP);
+ if (!xknameSyms) error("Calloc failed to allocate %d xknameSyms in dogroups",length(xknames));
+ for(i = 0; i < length(xSD); i++) {
+ if (SIZEOF(VECTOR_ELT(xSD, i))==0)
+ error("Type %d in .xSD column %d", TYPEOF(VECTOR_ELT(xSD, i)), i);
+ xknameSyms[i] = install(CHAR(STRING_ELT(xknames, i)));
+ }
+
+ if (length(iSD)!=length(jiscols)) error("length(iSD)[%d] != length(jiscols)[%d]",length(iSD),length(jiscols));
+ if (length(xSD)!=length(xjiscols)) error("length(xSD)[%d] != length(xjiscols)[%d]",length(xSD),length(xjiscols));
+
+
+ PROTECT(listwrap = allocVector(VECSXP, 1));
+ protecti++;
+ Rboolean jexpIsSymbolOtherThanSD = (isSymbol(jexp) && strcmp(CHAR(PRINTNAME(jexp)),".SD")!=0); // test 559
+
+ ansloc = 0;
+ for(i=0; i<ngrp; i++) { // even for an empty i table, ngroup is length 1 (starts is value 0), for consistency of empty cases
+ if (INTEGER(starts)[i] == 0 && i>0) continue; // replaced (i>0 || !isNull(lhs)) with i>0 to fix #5376
+ if (!isNull(lhs) &&
+ (INTEGER(starts)[i] == NA_INTEGER ||
+ (LENGTH(order) && INTEGER(order)[ INTEGER(starts)[i]-1 ]==NA_INTEGER)))
+ continue;
+ grpn = INTEGER(lens)[i];
+ INTEGER(N)[0] = INTEGER(starts)[i] == NA_INTEGER ? 0 : grpn;
+ // .N is number of rows matched to ( 0 even when nomatch is NA)
+ INTEGER(GRP)[0] = i+1; // group counter exposed as .GRP
+
+ for (j=0; j<length(iSD); j++) { // either this or the next for() will run, not both
+ size = SIZEOF(VECTOR_ELT(iSD,j));
+ memcpy((char *)DATAPTR(VECTOR_ELT(iSD,j)), // ok use of memcpy. Loop'd through columns not rows
+ (char *)DATAPTR(VECTOR_ELT(groups,INTEGER(jiscols)[j]-1))+i*size,
+ size);
+ }
+ igrp = (length(grporder) && !LOGICAL(on)[0]) ? INTEGER(grporder)[INTEGER(starts)[i]-1]-1 : (isNull(jiscols) ? INTEGER(starts)[i]-1 : i);
+ if (igrp >= 0) for (j=0; j<length(BY); j++) { // igrp can be -1 so 'if' is important, otherwise memcpy crash
+ size = SIZEOF(VECTOR_ELT(BY,j));
+ memcpy((char *)DATAPTR(VECTOR_ELT(BY,j)), // ok use of memcpy size 1. Loop'd through columns not rows
+ (char *)DATAPTR(VECTOR_ELT(groups,INTEGER(grpcols)[j]-1))+igrp*size,
+ size);
+ }
+ if (INTEGER(starts)[i] == NA_INTEGER || (LENGTH(order) && INTEGER(order)[ INTEGER(starts)[i]-1 ]==NA_INTEGER)) {
+ for (j=0; j<length(SD); j++) {
+ switch (TYPEOF(VECTOR_ELT(SD, j))) {
+ case LGLSXP :
+ LOGICAL(VECTOR_ELT(SD,j))[0] = NA_LOGICAL;
+ break;
+ case INTSXP :
+ INTEGER(VECTOR_ELT(SD,j))[0] = NA_INTEGER;
+ break;
+ case REALSXP :
+ REAL(VECTOR_ELT(SD,j))[0] = NA_REAL;
+ break;
+ case STRSXP :
+ SET_STRING_ELT(VECTOR_ELT(SD,j),0,NA_STRING);
+ break;
+ default:
+ error("Logical error. Type of column should have been checked by now");
+ }
+ }
+ grpn = 1; // TO DO: it is anyway?
+ INTEGER(I)[0] = 0;
+ for (j=0; j<length(xSD); j++) {
+ switch (TYPEOF(VECTOR_ELT(xSD, j))) {
+ case LGLSXP :
+ LOGICAL(VECTOR_ELT(xSD,j))[0] = NA_LOGICAL;
+ break;
+ case INTSXP :
+ INTEGER(VECTOR_ELT(xSD,j))[0] = NA_INTEGER;
+ break;
+ case REALSXP :
+ REAL(VECTOR_ELT(xSD,j))[0] = NA_REAL;
+ break;
+ case STRSXP :
+ SET_STRING_ELT(VECTOR_ELT(xSD,j),0,NA_STRING);
+ break;
+ default:
+ error("Logical error. Type of column should have been checked by now");
+ }
+ }
+ } else {
+ if (LOGICAL(verbose)[0]) tstart = clock();
+ if (LENGTH(order)==0) {
+ rownum = INTEGER(starts)[i]-1;
+ for (j=0; j<length(SD); j++) {
+ size = SIZEOF(VECTOR_ELT(SD,j));
+ memcpy((char *)DATAPTR(VECTOR_ELT(SD,j)), // direct memcpy best here, for usually large size groups. by= each row is slow and not recommended anyway, so we don't mind there's no switch here for grpn==1
+ (char *)DATAPTR(VECTOR_ELT(dt,INTEGER(dtcols)[j]-1))+rownum*size,
+ grpn*size);
+ // SD is our own alloc'd memory, and the source (DT) is protected throughout, so no need for SET_* overhead
+ }
+ for (j=0; j<grpn; j++) INTEGER(I)[j] = rownum+j+1;
+ for (j=0; j<length(xSD); j++) {
+ size = SIZEOF(VECTOR_ELT(xSD,j));
+ memcpy((char *)DATAPTR(VECTOR_ELT(xSD,j)), // ok use of memcpy. Loop'd through columns not rows
+ (char *)DATAPTR(VECTOR_ELT(dt,INTEGER(xjiscols)[j]-1))+rownum*size,
+ size);
+ }
+ if (LOGICAL(verbose)[0]) { tblock[0] += clock()-tstart; nblock[0]++; }
+ } else {
+ // Fairly happy with this block. No need for SET_* here. See comment above.
+ for (k=0; k<grpn; k++) INTEGER(I)[k] = INTEGER(order)[ INTEGER(starts)[i]-1 + k ];
+ for (j=0; j<length(SD); j++) {
+ size = SIZEOF(VECTOR_ELT(SD,j));
+ target = VECTOR_ELT(SD,j);
+ source = VECTOR_ELT(dt,INTEGER(dtcols)[j]-1);
+ if (size==4) {
+ for (k=0; k<grpn; k++) {
+ rownum = INTEGER(I)[k]-1;
+ INTEGER(target)[k] = INTEGER(source)[rownum]; // on 32bit, copies pointers too
+ }
+ } else { // size 8
+ for (k=0; k<grpn; k++) {
+ rownum = INTEGER(I)[k]-1;
+ REAL(target)[k] = REAL(source)[rownum]; // on 64bit, copies pointers too
+ }
+ }
+ }
+ if (LOGICAL(verbose)[0]) { tblock[1] += clock()-tstart; nblock[1]++; }
+ // The two blocks have separate timing statements to make sure which is running
+ }
+ }
+ INTEGER(rownames)[1] = -grpn; // the .set_row_names() of .SD. Not .N when nomatch=NA and this is a nomatch
+ for (j=0; j<length(SD); j++) {
+ SETLENGTH(VECTOR_ELT(SD,j), grpn);
+ defineVar(nameSyms[j], VECTOR_ELT(SD, j), env);
+ // In case user's j assigns to the columns names (env is static) (tests 387 and 388)
+ // nameSyms pre-stored to save repeated install() for efficiency.
+ }
+ for (j=0; j<length(xSD); j++) {
+ defineVar(xknameSyms[j], VECTOR_ELT(xSD, j), env);
+ }
+ SETLENGTH(I, grpn);
+
+ if (LOGICAL(verbose)[0]) tstart = clock(); // call to clock() is more expensive than an 'if'
+ PROTECT(jval = eval(jexp, env));
+
+ if (LOGICAL(verbose)[0]) { tblock[2] += clock()-tstart; nblock[2]++; }
+
+ if (isNull(jval)) {
+ // j may be a plot or other side-effect only
+ UNPROTECT(1);
+ continue;
+ }
+ if ((wasvector = (isVectorAtomic(jval) || jexpIsSymbolOtherThanSD))) { // see test 559
+ // Prior to 1.8.1 the wrap with list() was done up in R after the first group had tested.
+ // This wrap is done to make the code below easier to loop through. listwrap avoids copying jval.
+ SET_VECTOR_ELT(listwrap,0,jval);
+ jval = listwrap;
+ } else {
+ if (!isNewList(jval)) // isNewList tests for VECSXP. isList tests for (old) LISTSXP
+ error("j evaluates to type '%s'. Must evaluate to atomic vector or list.",type2char(TYPEOF(jval)));
+ if (!LENGTH(jval)) {
+ UNPROTECT(1);
+ continue;
+ }
+ for (j=0; j<LENGTH(jval); j++) {
+ thiscol = VECTOR_ELT(jval,j);
+ if (!isNull(thiscol) && (!isVector(thiscol) || isFrame(thiscol) || isArray(thiscol) ))
+ error("All items in j=list(...) should be atomic vectors or lists. If you are trying something like j=list(.SD,newcol=mean(colA)) then use := by group instead (much quicker), or cbind or merge afterwards.");
+ }
+ }
+ if (!isNull(lhs)) {
+ R_len_t origncol = LENGTH(dt);
+ for (j=0; j<length(lhs); j++) {
+ target = VECTOR_ELT(dt,INTEGER(lhs)[j]-1);
+ RHS = VECTOR_ELT(jval,j%LENGTH(jval));
+ if (isNull(target)) {
+ // first time adding to new column
+ if (isNull(RHS)) error("RHS is NULL when grouping :=. Makes no sense to delete a column by group. Perhaps use an empty vector instead.");
+ if (TRUELENGTH(dt) < INTEGER(lhs)[j]) error("Internal error: Trying to add new column by reference but tl is full; alloc.col should have run first at R level before getting to this point in dogroups");
+ SETLENGTH(dtnames, LENGTH(dtnames)+1);
+ SETLENGTH(dt, LENGTH(dt)+1);
+ SET_VECTOR_ELT(dt, INTEGER(lhs)[j]-1, allocNAVector(TYPEOF(RHS), LENGTH(VECTOR_ELT(dt,0))));
+ // Even if we could know reliably to switch from allocNAVector to allocVector for slight speedup, user code could still contain a switched halt, and in that case we'd want the groups not yet done to have NA rather than uninitialized or 0.
+ // dtnames = getAttrib(dt, R_NamesSymbol); // commented this here and added it on the beginning to fix #4990
+ SET_STRING_ELT(dtnames, INTEGER(lhs)[j]-1, STRING_ELT(newnames, INTEGER(lhs)[j]-origncol-1));
+ target = VECTOR_ELT(dt,INTEGER(lhs)[j]-1);
+ }
+ if (TYPEOF(target)!=TYPEOF(RHS)) error("Type of RHS ('%s') must match LHS ('%s'). To check and coerce would impact performance too much for the fastest cases. Either change the type of the target column, or coerce the RHS of := yourself (e.g. by using 1L instead of 1)", type2char(TYPEOF(RHS)), type2char(TYPEOF(target)));
+ size = SIZEOF(target);
+ vlen = length(RHS);
+ if (vlen==0) continue;
+ if (vlen>grpn && j<LENGTH(jval)) warning("RHS %d is length %d (greater than the size (%d) of group %d). The last %d element(s) will be discarded.", j+1, vlen, grpn, i+1, vlen-grpn);
+ // fix for #4990 - `:=` did not issue recycling warning during "by" operation.
+ if (vlen<grpn && vlen>0 && grpn%vlen != 0)
+ warning("Supplied %d items to be assigned to group %d of size %d in column '%s' (recycled leaving remainder of %d items).",vlen,i+1,grpn,CHAR(STRING_ELT(dtnames,INTEGER(lhs)[j]-1)),grpn%vlen);
+ // fix for issues/481 for := case
+ // missed it in commit: https://github.com/Rdatatable/data.table/commit/86276f48798491d328caa72f6ebcce4d51649440
+ // see that link (or scroll down for the non := version) for comments
+ #if defined(R_VERSION) && R_VERSION >= R_Version(3, 1, 0)
+ named=0;
+ if (isNewList(RHS) && NAMED(RHS) != 2) {
+ dupcol = VECTOR_ELT(RHS, 0);
+ named = NAMED(dupcol);
+ while(isNewList(dupcol)) {
+ if (named == 2) break;
+ else {
+ dupcol = VECTOR_ELT(dupcol, 0);
+ named = NAMED(dupcol);
+ }
+ }
+ if (named == 2) RHS = PROTECT(duplicate(RHS));
+ }
+ memrecycle(target, order, INTEGER(starts)[i]-1, grpn, RHS);
+ if (named == 2) UNPROTECT(1);
+ #else
+ memrecycle(target, order, INTEGER(starts)[i]-1, grpn, RHS);
+ #endif
+
+ // fixes bug #2531. Got to set the class back. See comment below for explanation. This is the new fix. Works great!
+ // Also fix for #5437 (bug due to regression in 1.9.2+)
+ copyMostAttrib(RHS, target); // not names, otherwise test 778 would fail
+ /* OLD FIX: commented now. The fix below resulted in segfault on factor columns because I dint set the "levels"
+ Instead of fixing that, I just removed setting class if it's factor. Not appropriate fix.
+ Correct fix of copying all attributes (except names) added above. Now, everything should be alright.
+ Test 1144 (#5104) will provide the right output now. Modified accordingly.
+ OUTDATED: if (!isFactor(RHS)) setAttrib(target, R_ClassSymbol, getAttrib(RHS, R_ClassSymbol));
+ OUTDATED: // added !isFactor(RHS) to fix #5104 (side-effect of fixing #2531) */
+ }
+ UNPROTECT(1);
+ continue;
+ }
+ maxn = 0;
+ if (njval==0) njval = LENGTH(jval); // for first group, then the rest (when non 0) must conform to the first >0 group
+ if (njval!=LENGTH(jval)) error("j doesn't evaluate to the same number of columns for each group"); // this would be a problem even if we unlisted afterwards. This way the user finds out earlier though so he can fix and rerun sooner.
+ for (j=0; j<njval; j++) {
+ k = length(VECTOR_ELT(jval,j)); // might be NULL, so length not LENGTH
+ maxn = k>maxn ? k : maxn;
+ }
+ if (ansloc + maxn > estn) {
+ if (estn == -1) {
+ // Given first group and j's result on it, make a good guess for size of result required.
+ // This was 'byretn' in R in v1.8.0, now here in C from v1.8.1 onwards.
+ if (grpn==0)
+ estn = maxn = 0; // empty case e.g. test 184. maxn is 1 here due to sum(integer()) == 0L
+ else if (maxn==1) // including when grpn==1 we default to assuming it's an aggregate
+ estn = LENGTH(starts);
+ // Common case 1 : j is a list of simple aggregates i.e. list of atoms only
+ else if (maxn >= grpn) {
+ estn = 0;
+ for (j=0;j<LENGTH(lens);j++) estn+=INTEGER(lens)[j];
+ // Common case 2 : j returns as many rows as there are in the group (maybe a join)
+ // TO DO: this might over allocate if first group has 1 row and j is actually a single row aggregate
+ // in cases when we're not sure could wait for the first few groups before deciding.
+ } else // maxn < grpn
+ estn = maxn * LENGTH(starts);
+ // Common case 3 : head or tail of .SD perhaps
+ if (estn<maxn) estn=maxn; // if the result for the first group is larger than the table itself(!) Unusual case where a join is being done in j via .SD and the 1-row table is an edge case of bigger picture.
+ PROTECT(ans = allocVector(VECSXP, ngrpcols + njval));
+ protecti++;
+ firstalloc=TRUE;
+ for(j=0; j<ngrpcols; j++) {
+ thiscol = VECTOR_ELT(groups, INTEGER(grpcols)[j]-1);
+ SET_VECTOR_ELT(ans, j, allocVector(TYPEOF(thiscol), estn));
+ copyMostAttrib(thiscol, VECTOR_ELT(ans,j)); // not names, otherwise test 778 would fail
+ }
+ for(j=0; j<njval; j++) {
+ thiscol = VECTOR_ELT(jval, j);
+ if (isNull(thiscol))
+ error("Column %d of j's result for the first group is NULL. We rely on the column types of the first result to decide the type expected for the remaining groups (and require consistency). NULL columns are acceptable for later groups (and those are replaced with NA of appropriate type and recycled) but not for the first. Please use a typed empty vector instead, such as integer() or numeric().", j+1);
+ if (LOGICAL(verbose)[0] && !isNull(getAttrib(thiscol, R_NamesSymbol))) {
+ if (wasvector) {
+ Rprintf("j appears to be a named vector. The same names will likely be created over and over again for each group and slow things down. Try and pass a named list (which data.table optimizes) or an unnamed list() instead.");
+ } else {
+ Rprintf("Column %d of j is a named vector (each item down the rows is named, somehow). Please remove those names for efficiency (to save creating them over and over for each group). They are ignored anyway.", j+1);
+ }
+ }
+ SET_VECTOR_ELT(ans, ngrpcols+j, allocVector(TYPEOF(thiscol), estn));
+ copyMostAttrib(thiscol, VECTOR_ELT(ans,ngrpcols+j)); // not names, otherwise test 276 would fail
+ }
+ names = getAttrib(jval, R_NamesSymbol);
+ if (!isNull(names)) {
+ if (LOGICAL(verbose)[0]) Rprintf("The result of j is a named list. It's very inefficient to create the same names over and over again for each group. When j=list(...), any names are detected, removed and put back after grouping has completed, for efficiency. Using j=transform(), for example, prevents that speedup (consider changing to :=). This message may be upgraded to warning in future.\n"); // e.g. test 104 has j=transform().
+ // names of result come from the first group and the names of remaining groups are ignored (all that matters for them is that the number of columns (and their types) match the first group.
+ names2 = PROTECT(allocVector(STRSXP,ngrpcols+njval));
+ protecti++;
+ // for (j=0; j<ngrpcols; j++) SET_STRING_ELT(names2, j, STRING_ELT(bynames,j)); // These get set back up in R
+ for (j=0; j<njval; j++) SET_STRING_ELT(names2, ngrpcols+j, STRING_ELT(names,j));
+ setAttrib(ans, R_NamesSymbol, names2);
+ // setAttrib(SD, R_NamesSymbol, R_NilValue); // so that lapply(.SD,mean) is unnamed from 2nd group on
+ }
+ } else {
+ estn = ((double)ngrp/i)*1.1*(ansloc+maxn);
+ if (LOGICAL(verbose)[0]) Rprintf("dogroups: growing from %d to %d rows\n", length(VECTOR_ELT(ans,0)), estn);
+ if (length(ans) != ngrpcols + njval) error("dogroups: length(ans)[%d]!=ngrpcols[%d]+njval[%d]",length(ans),ngrpcols,njval);
+ for (j=0; j<length(ans); j++) SET_VECTOR_ELT(ans, j, growVector(VECTOR_ELT(ans,j), estn));
+ }
+ }
+ // Now copy jval into ans ...
+ for (j=0; j<ngrpcols; j++) {
+ target = VECTOR_ELT(ans,j);
+ source = VECTOR_ELT(groups, INTEGER(grpcols)[j]-1); // target and source the same type by construction above
+ if (SIZEOF(target)==4) for (r=0; r<maxn; r++)
+ INTEGER(target)[ansloc+r] = INTEGER(source)[igrp];
+ else for (r=0; r<maxn; r++)
+ REAL(target)[ansloc+r] = REAL(source)[igrp];
+ // Shouldn't need SET_* to age objects here sice groups, TO DO revisit.
+ }
+ for (j=0; j<njval; j++) {
+ thisansloc = ansloc;
+ source = VECTOR_ELT(jval,j);
+ thislen = length(source);
+ target = VECTOR_ELT(ans, j+ngrpcols);
+ if (thislen == 0) {
+ // including NULL and typed empty vectors, fill with NA
+ // A NULL in the first group's jval isn't allowed; caught above after allocating ans
+ if (!NullWarnDone && maxn>1) { // maxn==1 in tests 172,280,281,282,403,405 and 406
+ warning("Item %d of j's result for group %d is zero length. This will be filled with %d NAs to match the longest column in this result. Later groups may have a similar problem but only the first is reported to save filling the warning buffer.", j+1, i+1, maxn);
+ NullWarnDone = TRUE;
+ }
+ switch (TYPEOF(target)) { // rarely called so no need to optimize this switch
+ case LGLSXP :
+ case INTSXP :
+ for (r=0; r<maxn; r++) INTEGER(target)[thisansloc+r] = NA_INTEGER;
+ break;
+ case REALSXP :
+ for (r=0; r<maxn; r++) REAL(target)[thisansloc+r] = NA_REAL;
+ break;
+ case STRSXP :
+ for (r=0; r<maxn; r++) SET_STRING_ELT(target,thisansloc+r,NA_STRING);
+ break;
+ default:
+ error("Logical error. Type of column should have been checked by now");
+ }
+ continue;
+ }
+ if (TYPEOF(source) != TYPEOF(target))
+ error("Column %d of result for group %d is type '%s' but expecting type '%s'. Column types must be consistent for each group.", j+1, i+1, type2char(TYPEOF(source)), type2char(TYPEOF(target)));
+ if (recycleWarn && maxn%thislen != 0) {
+ warning("Column %d of result for group %d is length %d but the longest column in this result is %d. Recycled leaving remainder of %d items. This warning is once only for the first group with this issue.",j+1,i+1,thislen,maxn,maxn%thislen);
+ recycleWarn = FALSE;
+ }
+ // fix for issues/481
+ #if defined(R_VERSION) && R_VERSION >= R_Version(3, 1, 0)
+ // added version because, for ex: DT[, list(list(unique(y))), by=x] gets duplicated
+ // because unique(y) returns NAMED(2). So, do it only if v>= 3.1.0. If <3.1.0,
+ // it gets duplicated anyway, so avoid copying twice!
+ named=0;
+ if (isNewList(source) && NAMED(source) != 2) {
+ // NAMED(source) != 2 prevents DT[, list(y), by=x] where 'y' is already a list
+ // or data.table and 99% of cases won't clear the if-statement above.
+ dupcol = VECTOR_ELT(source, 0);
+ named = NAMED(dupcol);
+ while(isNewList(dupcol)) {
+ // while loop basically peels each list() layer one by one until there's no
+ // list() wrapped anymore. Ex: consider DT[, list(list(list(sum(y)))), by=x] -
+ // here, we don't need to duplicate, but we won't know that until we reach
+ // 'sum(y)' and know that it's NAMED() != 2.
+ if (named == 2) break;
+ else {
+ dupcol = VECTOR_ELT(dupcol, 0);
+ named = NAMED(dupcol);
+ }
+ }
+ if (named == 2) source = PROTECT(duplicate(source));
+ }
+ memrecycle(target, R_NilValue, thisansloc, maxn, source);
+ if (named == 2) UNPROTECT(1);
+ #else
+ memrecycle(target, R_NilValue, thisansloc, maxn, source);
+ #endif
+ }
+ ansloc += maxn;
+ if (firstalloc) {
+ protecti++; // remember the first jval. If we UNPROTECTed now, we'd unprotect
+ firstalloc = FALSE; // ans. The first jval is needed to create the right size and type of ans.
+ // TO DO: could avoid this last 'if' by adding a dummy PROTECT after first alloc for this UNPROTECT(1) to do.
+ }
+ else UNPROTECT(1); // the jval. Don't want them to build up. The first jval can stay protected till the end ok.
+ }
+ if (isNull(lhs) && ans!=NULL) {
+ if (ansloc < LENGTH(VECTOR_ELT(ans,0))) {
+ if (LOGICAL(verbose)[0]) Rprintf("Wrote less rows (%d) than allocated (%d).\n",ansloc,LENGTH(VECTOR_ELT(ans,0)));
+ for (j=0; j<length(ans); j++) SET_VECTOR_ELT(ans, j, growVector(VECTOR_ELT(ans,j), ansloc));
+ // shrinks (misuse of word 'grow') back to the rows written, otherwise leak until ...
+ // ... TO DO: set truelength to LENGTH(VECTOR_ELT(ans,0)), length to ansloc and enhance finalizer to handle over-allocated rows.
+ }
+ } else ans = R_NilValue;
+ // Now reset length of .SD columns and .I to length of largest group, otherwise leak if the last group is smaller (often is).
+ for (j=0; j<length(SD); j++) SETLENGTH(VECTOR_ELT(SD,j), origSDnrow);
+ SETLENGTH(I, origIlen);
+ if (LOGICAL(verbose)[0]) {
+ if (nblock[0] && nblock[1]) error("Internal error: block 0 [%d] and block 1 [%d] have both run", nblock[0], nblock[1]);
+ int w = nblock[1]>0;
+ Rprintf("\n %s took %.3fs for %d groups\n", w ? "collecting discontiguous groups" : "memcpy contiguous groups",
+ 1.0*tblock[w]/CLOCKS_PER_SEC, nblock[w]);
+ Rprintf(" eval(j) took %.3fs for %d calls\n", 1.0*tblock[2]/CLOCKS_PER_SEC, nblock[2]);
+ }
+ UNPROTECT(protecti);
+ Free(nameSyms);
+ Free(xknameSyms);
+ return(ans);
+}
+
+SEXP keepattr(SEXP to, SEXP from)
+{
+ // Same as R_copyDFattr in src/main/attrib.c, but that seems not exposed in R's api
+ // Only difference is that we reverse from and to in the prototype, for easier calling above
+ SET_ATTRIB(to, ATTRIB(from));
+ IS_S4_OBJECT(from) ? SET_S4_OBJECT(to) : UNSET_S4_OBJECT(to);
+ SET_OBJECT(to, OBJECT(from));
+ return to;
+}
+
+SEXP growVector(SEXP x, R_len_t newlen)
+{
+ // Similar to EnlargeVector in src/main/subassign.c, with the following changes :
+ // * replaced switch and loops with one memcpy for INTEGER and REAL, but need to age CHAR and VEC.
+ // * no need to cater for names
+ // * much shorter and faster
+ SEXP newx;
+ R_len_t i, len = length(x);
+ if (isNull(x)) error("growVector passed NULL");
+ PROTECT(newx = allocVector(TYPEOF(x), newlen)); // TO DO: R_realloc(?) here?
+ if (newlen < len) len=newlen; // i.e. shrink
+ switch (TYPEOF(x)) {
+ case STRSXP :
+ for (i=0; i<len; i++)
+ SET_STRING_ELT(newx, i, STRING_ELT(x, i));
+ // TO DO. Using SET_ to ensure objects are aged, rather than memcpy. Perhaps theres a bulk/fast way to age CHECK_OLD_TO_NEW
+ break;
+ case VECSXP :
+ for (i=0; i<len; i++)
+ SET_VECTOR_ELT(newx, i, VECTOR_ELT(x, i));
+ // TO DO: Again, is there bulk op to avoid this loop, which still respects older generations
+ break;
+ default :
+ memcpy((char *)DATAPTR(newx), (char *)DATAPTR(x), len*SIZEOF(x)); // SIZEOF() returns size_t (just as sizeof()) so * shouldn't overflow
+ }
+ // if (verbose) Rprintf("Growing vector from %d to %d items of type '%s'\n", len, newlen, type2char(TYPEOF(x)));
+ // Would print for every column if here. Now just up in dogroups (one msg for each table grow).
+ keepattr(newx,x);
+ UNPROTECT(1);
+ return newx;
+}
+
+
+// benchmark timings for #481 fix:
+// old code - no changes, R v3.0.3
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 82.593 0.936 84.314
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 34.558 0.628 35.658
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 37.056 0.315 37.668
+//
+// All new changes in place, R v3.0.3
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 82.852 0.952 84.575
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 34.600 0.356 35.173
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 36.865 0.514 37.901
+
+// old code - no changes, R v3.1.0 --- BUT RESULTS ARE WRONG!
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 11.022 0.352 11.455
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 10.397 0.119 10.600
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 10.665 0.101 11.013
+
+// All new changes in place, R v3.1.0
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 83.279 1.057 89.856
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 30.569 0.633 31.452
+// > system.time(dt[, list(list(y)), by=x])
+// user system elapsed
+// 30.827 0.239 32.306
+
diff --git a/src/fastmean.c b/src/fastmean.c
new file mode 100644
index 0000000..177afc3
--- /dev/null
+++ b/src/fastmean.c
@@ -0,0 +1,141 @@
+#include "data.table.h"
+#include <Rdefines.h>
+#include <Rmath.h>
+
+/*
+Copied from src/main/summary.c with the following changes :
+i) Rather than mean.default doing x <- x[!is.na(x)] which incurs
+several vector allocations and scans, we just skip NA in the for
+loop in C.
+ii) Overhead of repeated calls to S3 dispatch of mean to mean.default
+is avoided by calling C directly. See wiki point 3 for the large
+difference this makes.
+Ordinarily we prefer not to duplicate base R stats functions in case
+we introduce a bug or create additonal maintenance burden. But,
+mean() is so slow, and so much slower than sum(), and so commonly used
+and benchmarked, that it warrants this fast mean.
+We can't call .Internal(mean(x)) because that's disallowed by QC.R, but
+anyway .Internal(mean(x)) doesn't respect na.rm=TRUE.
+We are careful to retain the double scan that summary.c does that
+adjusts for accumalated rounding errors in floating point.
+
+We explicitly test that fastmean returns the same result as
+base::mean in test.data.table(). For that we use exact equality under
+floating point, not all.equal. A stronger test. These
+tests run under R CMD check and run on all CRAN platforms daily to catch
+if we become out of line to base R (say if base R changed its mean).
+*/
+
+SEXP fastmean(SEXP args)
+{
+ long double s = 0., t = 0.;
+ R_len_t i, l = 0, n = 0;
+ SEXP x, ans, tmp;
+ Rboolean narm=FALSE;
+ x=CADR(args);
+ if (length(args)>2) {
+ tmp = CADDR(args);
+ if (!isLogical(tmp) || LENGTH(tmp)!=1 || LOGICAL(tmp)[0]==NA_LOGICAL)
+ error("narm should be TRUE or FALSE");
+ narm=LOGICAL(tmp)[0];
+ }
+ PROTECT(ans = allocNAVector(REALSXP, 1));
+ if (!isInteger(x) && !isReal(x) && !isLogical(x)) {
+ warning("argument is not numeric or logical: returning NA");
+ UNPROTECT(1);
+ return(ans);
+ }
+ l = LENGTH(x);
+ if (narm) {
+ switch(TYPEOF(x)) {
+ case LGLSXP:
+ case INTSXP:
+ for (i = 0; i<l; i++) {
+ if(INTEGER(x)[i] == NA_INTEGER) continue;
+ s += INTEGER(x)[i]; // no under/overflow here, s is long double not integer
+ n++;
+ }
+ if (n>0)
+ REAL(ans)[0] = (double) (s/n);
+ else
+ REAL(ans)[0] = R_NaN; // consistent with base: mean(NA,na.rm=TRUE)==NaN==mean(numeric(),na.rm=TRUE)
+ break;
+ case REALSXP:
+ for (i = 0; i<l; i++) {
+ if(ISNAN(REAL(x)[i])) continue; // TO DO: could drop this line and let NA propogate?
+ s += REAL(x)[i];
+ n++;
+ }
+ if (n==0) {
+ REAL(ans)[0] = R_NaN;
+ break;
+ }
+ s /= n;
+ if(R_FINITE((double)s)) {
+ for (i = 0; i<l; i++) {
+ if(ISNAN(REAL(x)[i])) continue;
+ t += (REAL(x)[i] - s);
+ }
+ s += t/n;
+ }
+ REAL(ans)[0] = (double) s;
+ break;
+ default:
+ error("Type '%s' not supported in fastmean", type2char(TYPEOF(x)));
+ }
+ } else { // narm==FALSE
+ switch(TYPEOF(x)) {
+ case LGLSXP:
+ case INTSXP:
+ for (i = 0; i<l; i++) {
+ if(INTEGER(x)[i] == NA_INTEGER) {UNPROTECT(1); return(ans);}
+ s += INTEGER(x)[i];
+ }
+ REAL(ans)[0] = (double) (s/l);
+ break;
+ case REALSXP:
+ for (i = 0; i<l; i++) {
+ if(ISNAN(REAL(x)[i])) {UNPROTECT(1); return(ans);}
+ s += REAL(x)[i];
+ }
+ s /= l;
+ if(R_FINITE((double)s)) {
+ for (i = 0; i<l; i++) {
+ // no NA if got this far
+ t += (REAL(x)[i] - s);
+ }
+ s += t/LENGTH(x);
+ }
+ REAL(ans)[0] = (double) s;
+ break;
+ default:
+ error("Type '%s' not supported in fastmean", type2char(TYPEOF(x)));
+ }
+ }
+ copyMostAttrib(x, ans);
+ UNPROTECT(1);
+ return(ans);
+}
+
+/*
+ case CPLXSXP:
+ PROTECT(ans = allocVector(CPLXSXP, 1));
+ for (i = 0; i < n; i++) {
+ s += COMPLEX(x)[i].r;
+ si += COMPLEX(x)[i].i;
+ }
+ s /= n; si /= n;
+ if( R_FINITE((double)s) && R_FINITE((double)si) ) {
+ for (i = 0; i < n; i++) {
+ t += COMPLEX(x)[i].r - s;
+ ti += COMPLEX(x)[i].i - si;
+ }
+ s += t/n; si += ti/n;
+ }
+ COMPLEX(ans)[0].r = (double) s;
+ COMPLEX(ans)[0].i = (double) si;
+ break;
+*/
+
+
+
diff --git a/src/fastradixdouble.c b/src/fastradixdouble.c
new file mode 100644
index 0000000..0d8720f
--- /dev/null
+++ b/src/fastradixdouble.c
@@ -0,0 +1,253 @@
+#include <R.h>
+#define USE_RINTERNALS
+#include <Rinternals.h>
+// #include <signal.h> // the debugging machinery + breakpoint aidee
+
+// Tested on:
+// R-3.0.2 osx 10.8.5 64-bit gcc 4.2.1,
+// R-2.15.2 debian 64-bit gcc-4.7.2,
+// R-2.15.3 osx 10.8.5 32-bit gcc-4.2.1,
+// R-2.15.3 osx 10.8.5 64-bit gcc-4.2.1
+
+// for tolerance
+extern SEXP fastradixint(SEXP vec, SEXP return_index);
+
+// adapted from Michael Herf's code - http://stereopsis.com/radix.html
+// TO IMPLEMENT (probably) - R's long vector support (R_xlen_t)
+// 1) logical argument 'return_index' = TRUE returns 'order' (indices) and FALSE returns sorted value directly instead of indices
+// 2) also allows ordering/sorting with 'tolerance' (another pass through length of input vector will happen + multiple integer radix sort calls).
+// The performance will depend on the number of groups that have to be sorted because under given tolerance they become identical. In theory,
+// most of the times the last pass shouldn't affect the performance at all.
+// the "decreasing=" feature has been removed. Use 'setrev' instead to get the reverse order if required (small performance improvement by doing this)
+
+// Hack for 32-bit and 64-bit versions of R (or architectures):
+// ------------------------------------------------------------
+// data.table requires that NA and NaN be sorted before all other numbers and that NA is before NaN:
+// 1) 'unsigned long' in 64-bit is 64-bit, but 32-bit in 32-bit. Therefore we've to use 'unsigned long long' which seems 64-bit in both 32-and 64-bit
+// 2) Inf=0x7ff0000000000000, -Inf=0xfff0000000000000, NA=0x7ff80000000007a2 and NaN=0x7ff8000000000000 in 32-bit unsigned long long
+// 3) Inf=0x7ff0000000000000, -Inf=0xfff0000000000000, NA=0x7ff00000000007a2 and NaN=0x7ff8000000000000 in 64-bit unsigned long long
+// Therefore in 32-bit, NA gets sorted before NaN and in 64-bit, NaN gets sorted before NA in 64 bit.
+// 4) also, sometimes NaN seems to be 0xfff8000000000000 instead of 0x7ff8000000000000.
+// As of now the solution is to 'set' the first 16 bits to 7ff8 if NA/NaN for both 32/64-bit
+// so that all these differences are nullified and then the usual checks are done.
+// At the end, we need NA to be 0x7ff80000000007a2 and NaN to be 0x7ff8000000000000 and that's what we'll make sure of.
+
+// HACK for NA/NaN sort before all the other numbers:
+// --------------------------------------------------
+// Once we set this to be NA/NaN, to make sure that NA/NaN gets sorted before
+// ALL the other numbers, we just flip the sign bit so that,
+// NA and NaN become 0xfff80000000007a2 and 0xfff8000000000000
+// NA now is smaller than NaN and will be sorted before any other number (including -Inf)
+
+#define FLIP_SIGN_BIT 0x8000000000000000
+#define RESET_NA_NAN 0x0000ffffffffffff
+#define SET_NA_NAN_COMP 0xfff8000000000000 // we can directly set complement in hack_na_nan
+
+unsigned long long flip_double(unsigned long long f) {
+ unsigned long long mask = -(long long)(f >> 63) | FLIP_SIGN_BIT;
+ return f ^ mask;
+}
+
+void flip_double_ref(unsigned long long *f) {
+ unsigned long long mask = -(long long)(*f >> 63) | FLIP_SIGN_BIT;
+ *f ^= mask;
+}
+
+unsigned long long invert_flip_double(unsigned long long f) {
+ unsigned long long mask = ((f >> 63) - 1) | FLIP_SIGN_BIT;
+ return f ^ mask;
+}
+
+void hack_na_nan(unsigned long long *f) {
+ // same as flip_double_ref but with bit twiddling first for NA/NaN
+ // removed mask setting as sign bit is always 0, so we just flip it
+ // since we've to flip the sign bit, why not directly set it in #define
+ *f &= RESET_NA_NAN;
+ *f |= SET_NA_NAN_COMP;
+}
+
+// utils for accessing 11-bit quantities
+#define _0(x) (x & 0x7FF)
+#define _1(x) (x >> 11 & 0x7FF)
+#define _2(x) (x >> 22 & 0x7FF)
+#define _3(x) (x >> 33 & 0x7FF)
+#define _4(x) (x >> 44 & 0x7FF)
+#define _5(x) (x >> 55)
+
+#define STACK_HIST 2048
+
+// x should be of type numeric
+SEXP fastradixdouble(SEXP x, SEXP tol, SEXP return_index) {
+ int i;
+ unsigned long long pos, fi, si, n;
+ unsigned long long sum0 = 0, sum1 = 0, sum2 = 0, sum3 = 0, sum4 = 0, sum5 = 0, tsum;
+ SEXP xtmp, order, ordertmp;
+
+ n = length(x);
+ if (!isReal(x) || n <= 0) error("List argument to 'fastradixdouble' must be non-empty and of type 'numeric'");
+ if (TYPEOF(return_index) != LGLSXP || length(return_index) != 1) error("Argument 'return_index' to 'fastradixdouble' must be logical TRUE/FALSE");
+ if (TYPEOF(tol) != REALSXP) error("Argument 'tol' to 'fastradixdouble' must be a numeric vector of length 1");
+
+
+ xtmp = PROTECT(allocVector(REALSXP, n));
+ ordertmp = PROTECT(allocVector(INTSXP, n));
+ order = PROTECT(allocVector(INTSXP, n));
+
+ unsigned long long *array = (unsigned long long*)REAL(x);
+ unsigned long long *sort = (unsigned long long*)REAL(xtmp);
+
+ // 6 histograms on the stack:
+ unsigned long long b0[STACK_HIST * 6];
+ unsigned long long *b1 = b0 + STACK_HIST;
+ unsigned long long *b2 = b1 + STACK_HIST;
+ unsigned long long *b3 = b2 + STACK_HIST;
+ unsigned long long *b4 = b3 + STACK_HIST;
+ unsigned long long *b5 = b4 + STACK_HIST;
+
+ // definitely faster on big data than a for-loop
+ memset(b0, 0, STACK_HIST*6*sizeof(unsigned long long));
+
+ // Step 1: parallel histogramming pass
+ for (i=0;i<n;i++) {
+ // flip NaN/NA sign bit so that they get sorted in the front (special for data.table)
+ if (ISNAN(REAL(x)[i])) hack_na_nan(&array[i]);
+ fi = flip_double((unsigned long long)array[i]);
+ b0[_0(fi)]++;
+ b1[_1(fi)]++;
+ b2[_2(fi)]++;
+ b3[_3(fi)]++;
+ b4[_4(fi)]++;
+ b5[_5(fi)]++;
+ }
+
+ // Step 2: Sum the histograms -- each histogram entry records the number of values preceding itself.
+ for (i=0;i<STACK_HIST;i++) {
+
+ tsum = b0[i] + sum0;
+ b0[i] = sum0 - 1;
+ sum0 = tsum;
+
+ tsum = b1[i] + sum1;
+ b1[i] = sum1 - 1;
+ sum1 = tsum;
+
+ tsum = b2[i] + sum2;
+ b2[i] = sum2 - 1;
+ sum2 = tsum;
+
+ tsum = b3[i] + sum3;
+ b3[i] = sum3 - 1;
+ sum3 = tsum;
+
+ tsum = b4[i] + sum4;
+ b4[i] = sum4 - 1;
+ sum4 = tsum;
+
+ tsum = b5[i] + sum5;
+ b5[i] = sum5 - 1;
+ sum5 = tsum;
+ }
+
+ for (i=0;i<n;i++) {
+ fi = array[i];
+ flip_double_ref(&fi);
+ pos = _0(fi);
+ sort[++b0[pos]] = fi;
+ INTEGER(ordertmp)[b0[pos]] = i;
+ }
+
+ for (i=0;i<n;i++) {
+ si = sort[i];
+ pos = _1(si);
+ array[++b1[pos]] = si;
+ INTEGER(order)[b1[pos]] = INTEGER(ordertmp)[i];
+ }
+
+ for (i=0;i<n;i++) {
+ fi = array[i];
+ pos = _2(fi);
+ sort[++b2[pos]] = fi;
+ INTEGER(ordertmp)[b2[pos]] = INTEGER(order)[i];
+ }
+
+ for (i=0;i<n;i++) {
+ si = sort[i];
+ pos = _3(si);
+ array[++b3[pos]] = si;
+ INTEGER(order)[b3[pos]] = INTEGER(ordertmp)[i];
+ }
+
+ for (i=0;i<n;i++) {
+ fi = array[i];
+ pos = _4(fi);
+ sort[++b4[pos]] = fi;
+ INTEGER(ordertmp)[b4[pos]] = INTEGER(order)[i];
+ }
+
+ for (i=0;i<n;i++) {
+ si = sort[i];
+ pos = _5(si);
+ array[++b5[pos]] = invert_flip_double(si);
+ INTEGER(order)[b5[pos]] = INTEGER(ordertmp)[i]+1;
+ }
+
+ // NOTE: that the result won't be 'exactly' identical to ordernumtol if there are too many values that are 'very close' to each other.
+ // However, I believe this is the correct version in those cases. Why? if you've 3 numbers that are sorted with no tolerance, and now under tolerance
+ // these numbers are equal, then all 3 indices *must* be sorted. That is ther right order. But in some close cases, 'ordernumtol' doesn't do this.
+ // To test, do: x <- rnorm(1e6); and run fastradixdouble and ordernumtol with same tolerance and compare results.
+
+ // check for tolerance and reorder wherever necessary
+ if (length(tol) > 0) {
+ i=1;
+ int j, start=0, end=0;
+ SEXP st,dst,rt,sq,ridx;
+ PROTECT(ridx = allocVector(LGLSXP, 1));
+ LOGICAL(ridx)[0] = TRUE;
+ while(i<n) {
+ if (!R_FINITE(REAL(x)[i]) || !R_FINITE(REAL(x)[i-1])) { i++; continue; }
+ // hack to skip checking Inf=Inf, -Inf=-Inf, NA=NA and NaN=NaN... using unsigned int
+ if (REAL(x)[i]-REAL(x)[i-1] > REAL(tol)[0] || array[i] == array[i-1]) { i++; continue; }
+ start = i-1;
+ i++;
+ while(i < n && REAL(x)[i] - REAL(x)[i-1] < REAL(tol)[0]) { i++; }
+ end = i-1;
+ i++;
+ if (end-start+1 == 1) continue;
+ PROTECT(st = allocVector(INTSXP, end-start+1));
+ PROTECT(sq = allocVector(REALSXP, end-start+1));
+ // To investigate: probably a simple bubble sort or shell sort may be quicker on groups with < 10 items than a 3-pass radix?
+ // Can't rely on R's base radix order because even if you've two items in group and one of them is 3 and the other is 1e6, then it won't work!
+ // Just doing this gives 4x speed-up under small group sizes. (from 37 to 6-9 seconds)
+ if (end-start+1 == 2) {
+ // avoid radix sort on 2 items
+ if (INTEGER(order)[start] > INTEGER(order)[end]) {
+ // then just swap
+ INTEGER(st)[0] = INTEGER(order)[start];
+ INTEGER(order)[start] = INTEGER(order)[end];
+ INTEGER(order)[end] = INTEGER(st)[0];
+
+ REAL(sq)[0] = REAL(x)[start];
+ REAL(x)[start] = REAL(x)[end];
+ REAL(x)[end] = REAL(sq)[0];
+ }
+ UNPROTECT(2); // st, sq
+ continue;
+ }
+ for (j=0; j<end-start+1; j++) {
+ INTEGER(st)[j] = INTEGER(order)[j+start];
+ REAL(sq)[j] = REAL(x)[j+start];
+ }
+ PROTECT(dst = duplicate(st));
+ PROTECT(rt = fastradixint(dst, ridx));
+ for (j=0; j<end-start+1; j++) {
+ INTEGER(order)[j+start] = INTEGER(st)[INTEGER(rt)[j]-1];
+ REAL(x)[j+start] = REAL(sq)[INTEGER(rt)[j]-1];
+ }
+ UNPROTECT(4); // st, dst, sq, rt
+ }
+ UNPROTECT(1); // ridx
+ }
+ UNPROTECT(3); // xtmp, order, ordertmp
+ if (LOGICAL(return_index)[0]) return(order);
+ return(x);
+}
diff --git a/src/fastradixint.c b/src/fastradixint.c
new file mode 100644
index 0000000..37b8936
--- /dev/null
+++ b/src/fastradixint.c
@@ -0,0 +1,100 @@
+#include <R.h>
+#define USE_RINTERNALS
+#include <Rinternals.h>
+
+// adapted from Michael Herf's code - http://stereopsis.com/radix.html
+// TO IMPLEMENT (probably) - R's long vector support
+// returns sort 'order' or 'value' depending on 'return_index' parameter (but not both currently)
+// allows for -ve integer values and NA (not possible in R's NA)
+// No restrictions imposed (stack size is small and is 3-pass) as opposed to base R's (max-min <= 1e5) to radix sort
+// better to replace regularorder1 (as base R seems to use 1-pass IIUC and therefore will be faster wherever applicable)
+#define _0(x) (x & 0x7FF)
+#define _1(x) (x >> 11 & 0x7FF)
+#define _2(x) (x >> 22)
+
+unsigned int flip_int(unsigned int f) {
+ return ((int)(f ^ 0x80000000));
+}
+
+void flip_int_ref(unsigned int *f) {
+ *f = ((int)(*f ^ 0x80000000));
+}
+unsigned int invert_flip_int(unsigned int f) {
+ return ((int)(f ^ 0x80000000));
+}
+
+#define STACK_HIST 2048
+
+SEXP fastradixint(SEXP x, SEXP return_index) {
+ int i;
+ unsigned int pos, fi, si, n;
+ unsigned int sum0 = 0, sum1 = 0, sum2 = 0, tsum;
+ SEXP ans, order, ordertmp;
+
+ n = length(x);
+ if (!isInteger(x) || n <= 0) error("Argument 'x' to 'fastradixint' must be non-empty and of type 'integer'");
+ if (TYPEOF(return_index) != LGLSXP || length(return_index) != 1) error("Argument 'return_index' to 'fastradixint' must be logical TRUE/FALSE");
+
+ ans = PROTECT(allocVector(INTSXP, n));
+ order = PROTECT(allocVector(INTSXP, n));
+ ordertmp = PROTECT(allocVector(INTSXP, n));
+
+ unsigned int *array = (unsigned int*)INTEGER(x);
+ unsigned int *sort = (unsigned int*)INTEGER(ans);
+
+ // 3 histograms on the stack:
+ unsigned int b0[STACK_HIST * 3];
+ unsigned int *b1 = b0 + STACK_HIST;
+ unsigned int *b2 = b1 + STACK_HIST;
+
+ // definitely faster on big data than a for-loop
+ memset(b0, 0, STACK_HIST*3*sizeof(unsigned int));
+
+ // Step 1: parallel histogramming pass
+ for (i=0;i<n;i++) {
+ fi = flip_int((unsigned int)array[i]);
+ b0[_0(fi)]++;
+ b1[_1(fi)]++;
+ b2[_2(fi)]++;
+ }
+
+ for (i=0;i<STACK_HIST;i++) {
+
+ tsum = b0[i] + sum0;
+ b0[i] = sum0 - 1;
+ sum0 = tsum;
+
+ tsum = b1[i] + sum1;
+ b1[i] = sum1 - 1;
+ sum1 = tsum;
+
+ tsum = b2[i] + sum2;
+ b2[i] = sum2 - 1;
+ sum2 = tsum;
+ }
+
+ for (i=0;i<n;i++) {
+ fi = array[i];
+ flip_int_ref(&fi);
+ pos = _0(fi);
+ sort[++b0[pos]] = fi;
+ INTEGER(order)[b0[pos]] = i;
+ }
+
+ for (i=0;i<n;i++) {
+ si = sort[i];
+ pos = _1(si);
+ array[++b1[pos]] = si;
+ INTEGER(ordertmp)[b1[pos]] = INTEGER(order)[i];
+ }
+
+ for (i=0;i<n;i++) {
+ fi = array[i];
+ pos = _2(fi);
+ sort[++b2[pos]] = invert_flip_int(fi);
+ INTEGER(order)[b2[pos]] = INTEGER(ordertmp)[i]+1;
+ }
+ UNPROTECT(3); // order, ordertmp, ans
+ if (LOGICAL(return_index)[0]) return(order);
+ return(ans);
+}
diff --git a/src/fcast.c b/src/fcast.c
new file mode 100644
index 0000000..0245311
--- /dev/null
+++ b/src/fcast.c
@@ -0,0 +1,419 @@
+#include "data.table.h"
+#include <Rdefines.h>
+// #include <signal.h> // the debugging machinery + breakpoint aidee
+// raise(SIGINT);
+
+static SEXP subsetVectorRaw(SEXP x, SEXP idx, int l, int tl)
+// Only for use by subsetDT() or subsetVector() below, hence static
+// l is the count of non-zero (including NAs) in idx i.e. the length of the result
+// tl is the amount to be allocated, tl>=l
+// TO DO: if no 0 or NA detected up front in subsetDT() below, could switch to a faster subsetVectorRawNo0orNA()
+{
+ int i, this, ansi=0, max=length(x);
+ if (tl<l) error("Internal error: tl<n passed to subsetVectorRaw");
+ SEXP ans = PROTECT(allocVector(TYPEOF(x), tl));
+ SETLENGTH(ans, l);
+ SET_TRUELENGTH(ans, tl);
+ switch(TYPEOF(x)) {
+ case INTSXP :
+ for (i=0; i<LENGTH(idx); i++) {
+ this = INTEGER(idx)[i];
+ if (this==0) continue;
+ INTEGER(ans)[ansi++] = (this==NA_INTEGER || this>max) ? NA_INTEGER : INTEGER(x)[this-1];
+ }
+ break;
+ case REALSXP :
+ for (i=0; i<LENGTH(idx); i++) {
+ this = INTEGER(idx)[i];
+ if (this==0) continue;
+ REAL(ans)[ansi++] = (this==NA_INTEGER || this>max) ? NA_REAL : REAL(x)[this-1];
+ }
+ break;
+ case LGLSXP :
+ for (i=0; i<LENGTH(idx); i++) {
+ this = INTEGER(idx)[i];
+ if (this==0) continue;
+ LOGICAL(ans)[ansi++] = (this==NA_INTEGER || this>max) ? NA_LOGICAL : LOGICAL(x)[this-1];
+ }
+ break;
+ case STRSXP :
+ for (i=0; i<LENGTH(idx); i++) {
+ this = INTEGER(idx)[i];
+ if (this==0) continue;
+ SET_STRING_ELT(ans, ansi++, (this==NA_INTEGER || this>max) ? NA_STRING : STRING_ELT(x, this-1));
+ }
+ break;
+ case VECSXP :
+ for (i=0; i<LENGTH(idx); i++) {
+ this = INTEGER(idx)[i];
+ if (this==0) continue;
+ SET_VECTOR_ELT(ans, ansi++, (this==NA_INTEGER || this>max) ? R_NilValue : VECTOR_ELT(x, this-1));
+ }
+ break;
+ // Fix for #982
+ // source: https://github.com/wch/r-source/blob/fbf5cdf29d923395b537a9893f46af1aa75e38f3/src/main/subset.c
+ case CPLXSXP :
+ for (i=0; i<LENGTH(idx); i++) {
+ this = INTEGER(idx)[i];
+ if (this == 0) continue;
+ if (this == NA_INTEGER || this>max) {
+ COMPLEX(ans)[ansi].r = NA_REAL;
+ COMPLEX(ans)[ansi].i = NA_REAL;
+ } else COMPLEX(ans)[ansi] = COMPLEX(x)[this-1];
+ ansi++;
+ }
+ break;
+ case RAWSXP :
+ for (i=0; i<LENGTH(idx); i++) {
+ this = INTEGER(idx)[i];
+ if (this == 0) continue;
+ RAW(ans)[ansi++] = (this == NA_INTEGER || this>max) ? (Rbyte) 0 : RAW(x)[this-1];
+ }
+ break;
+ default :
+ error("Unknown column type '%s'", type2char(TYPEOF(x)));
+ }
+ if (ansi != l) error("Internal error: ansi [%d] != l [%d] at the end of subsetVector", ansi, l);
+ copyMostAttrib(x, ans);
+ UNPROTECT(1);
+ return(ans);
+}
+
+static int check_idx(SEXP idx, int n)
+{
+ int i, this, ans=0;
+ if (!isInteger(idx)) error("Internal error. 'idx' is type '%s' not 'integer'", type2char(TYPEOF(idx)));
+ for (i=0; i<LENGTH(idx); i++) { // check idx once up front and count the non-0 so we know how long the answer will be
+ this = INTEGER(idx)[i];
+ if (this==0) continue;
+ if (this!=NA_INTEGER && this<0) error("Internal error: item %d of idx is %d. Negatives should have been dealt with earlier.", i+1, this);
+ // this>n is treated as NA for consistency with [.data.frame and things like cbind(DT[w],DT[w+1])
+ ans++;
+ }
+ return ans;
+}
+
+SEXP convertNegativeIdx(SEXP idx, SEXP maxArg)
+{
+ int this;
+ // + more precise and helpful error messages telling user exactly where the problem is (saving user debugging time)
+ // + a little more efficient than negativeSubscript in src/main/subscript.c (it's private to R so we can't call it anyway)
+
+ if (!isInteger(idx)) error("Internal error. 'idx' is type '%s' not 'integer'", type2char(TYPEOF(idx)));
+ if (!isInteger(maxArg) || length(maxArg)!=1) error("Internal error. 'maxArg' is type '%s' and length %d, should be an integer singleton", type2char(TYPEOF(maxArg)), length(maxArg));
+ int max = INTEGER(maxArg)[0];
+ if (max<0) error("Internal error. max is %d, must be >= 0.", max); // NA also an error which'll print as INT_MIN
+ int firstNegative = 0, firstPositive = 0, firstNA = 0, num0 = 0;
+ int i=0;
+ for (i=0; i<LENGTH(idx); i++) {
+ this = INTEGER(idx)[i];
+ if (this==NA_INTEGER) { if (firstNA==0) firstNA = i+1; continue; }
+ if (this==0) { num0++; continue; }
+ if (this>0) { if (firstPositive==0) firstPositive=i+1; continue; }
+ if (firstNegative==0) firstNegative=i+1;
+ }
+ if (firstNegative==0) return(idx); // 0's and NA can be mixed with positives, there are no negatives present, so we're done
+ if (firstPositive) error("Item %d of i is %d and item %d is %d. Cannot mix positives and negatives.",
+ firstNegative, INTEGER(idx)[firstNegative-1], firstPositive, INTEGER(idx)[firstPositive-1]);
+ if (firstNA) error("Item %d of i is %d and item %d is NA. Cannot mix negatives and NA.",
+ firstNegative, INTEGER(idx)[firstNegative-1], firstNA);
+
+ // idx is all negative without any NA but perhaps 0 present (num0) ...
+
+ char *tmp = Calloc(max, char); // 4 times less memory that INTSXP in src/main/subscript.c
+ int firstDup = 0, numDup = 0, firstBeyond = 0, numBeyond = 0;
+ for (i=0; i<LENGTH(idx); i++) {
+ this = -INTEGER(idx)[i];
+ if (this==0) continue;
+ if (this>max) {
+ numBeyond++;
+ if (firstBeyond==0) firstBeyond=i+1;
+ continue;
+ }
+ if (tmp[this-1]==1) {
+ numDup++;
+ if (firstDup==0) firstDup=i+1;
+ } else tmp[this-1] = 1;
+ }
+ if (numBeyond)
+ warning("Item %d of i is %d but there are only %d rows. Ignoring this and %d more like it out of %d.", firstBeyond, INTEGER(idx)[firstBeyond-1], max, numBeyond-1, LENGTH(idx));
+ if (numDup)
+ warning("Item %d of i is %d which has occurred before. Ignoring this and %d other duplicates out of %d.", firstDup, INTEGER(idx)[firstDup-1], numDup-1, LENGTH(idx));
+
+ SEXP ans = PROTECT(allocVector(INTSXP, max-LENGTH(idx)+num0+numDup+numBeyond));
+ int ansi = 0;
+ for (i=0; i<max; i++) {
+ if (tmp[i]==0) INTEGER(ans)[ansi++] = i+1;
+ }
+ Free(tmp);
+ UNPROTECT(1);
+ if (ansi != max-LENGTH(idx)+num0+numDup+numBeyond) error("Internal error: ansi[%d] != max[%d]-LENGTH(idx)[%d]+num0[%d]+numDup[%d]+numBeyond[%d] in convertNegativeIdx",ansi,max,LENGTH(idx),num0,numDup,numBeyond);
+ return(ans);
+}
+
+SEXP subsetDT(SEXP x, SEXP rows, SEXP cols) { // rows and cols are 1-based passed from R level
+
+// Originally for subsetting vectors in fcast and now the beginnings of [.data.table ported to C
+// Immediate need is for R 3.1 as lglVec[1] now returns R's global TRUE and we don't want := to change that global [think 1 row data.tables]
+// Could do it other ways but may as well go to C now as we were going to do that anyway
+
+ SEXP ans, tmp;
+ R_len_t i, j, ansn=0;
+ int this;
+ if (!isNewList(x)) error("Internal error. Argument 'x' to CsubsetDT is type '%s' not 'list'", type2char(TYPEOF(rows)));
+ if (!length(x)) return(x); // return empty list
+ ansn = check_idx(rows, length(VECTOR_ELT(x,0))); // check once up front before looping calls to subsetVectorRaw below
+ if (!isInteger(cols)) error("Internal error. Argument 'cols' to Csubset is type '%s' not 'integer'", type2char(TYPEOF(cols)));
+ for (i=0; i<LENGTH(cols); i++) {
+ this = INTEGER(cols)[i];
+ if (this<1 || this>LENGTH(x)) error("Item %d of 'cols' is %d which is outside 1-based range [1,ncol(x)=%d]", i+1, this, LENGTH(x));
+ }
+ ans = PROTECT(allocVector(VECSXP, LENGTH(cols)+64)); // just do alloc.col directly, eventually alloc.col can be deprecated.
+ copyMostAttrib(x, ans); // other than R_NamesSymbol, R_DimSymbol and R_DimNamesSymbol
+ // so includes row.names (oddly, given other dims aren't) and "sorted", dealt with below
+ SET_TRUELENGTH(ans, LENGTH(ans));
+ SETLENGTH(ans, LENGTH(cols));
+ for (i=0; i<LENGTH(cols); i++) {
+ SET_VECTOR_ELT(ans, i, subsetVectorRaw(VECTOR_ELT(x, INTEGER(cols)[i]-1), rows, ansn, ansn)); // column vectors aren't over allocated yet
+ }
+ setAttrib(ans, R_NamesSymbol, subsetVectorRaw( getAttrib(x, R_NamesSymbol), cols, LENGTH(cols), LENGTH(cols)+64 ));
+ tmp = PROTECT(allocVector(INTSXP, 2));
+ INTEGER(tmp)[0] = NA_INTEGER;
+ INTEGER(tmp)[1] = -ansn;
+ setAttrib(ans, R_RowNamesSymbol, tmp); // The contents of tmp must be set before being passed to setAttrib(). setAttrib looks at tmp value and copies it in the case of R_RowNamesSymbol. Caused hard to track bug around 28 Sep 2014.
+ // maintain key if ordered subset ...
+ SEXP key = getAttrib(x, install("sorted"));
+ if (length(key)) {
+ SEXP in = PROTECT(chmatch(key,getAttrib(ans,R_NamesSymbol), 0, TRUE)); // (nomatch ignored when in=TRUE)
+ i = 0; while(i<LENGTH(key) && LOGICAL(in)[i]) i++;
+ UNPROTECT(1);
+ // i is now the keylen that can be kept. 2 lines above much easier in C than R
+ if (i==0) {
+ setAttrib(ans, install("sorted"), R_NilValue);
+ // clear key that was copied over by copyMostAttrib() above
+ } else if (isOrderedSubset(rows, ScalarInteger(length(VECTOR_ELT(x,0))))) {
+ setAttrib(ans, install("sorted"), tmp=allocVector(STRSXP, i));
+ for (j=0; j<i; j++) SET_STRING_ELT(tmp, j, STRING_ELT(key, j));
+ }
+ }
+ setAttrib(ans, install(".data.table.locked"), R_NilValue);
+ setselfref(ans);
+ UNPROTECT(2);
+ return ans;
+}
+
+SEXP subsetVector(SEXP x, SEXP idx) { // idx is 1-based passed from R level
+ int n = check_idx(idx, length(x));
+ return subsetVectorRaw(x, idx, n, n);
+}
+
+// TO DO: margins
+SEXP fcast(SEXP lhs, SEXP val, SEXP nrowArg, SEXP ncolArg, SEXP idxArg, SEXP fill, SEXP fill_d, SEXP is_agg) {
+
+ int nrows=INTEGER(nrowArg)[0], ncols=INTEGER(ncolArg)[0];
+ int i,j,k, nlhs=length(lhs), nval=length(val), *idx = INTEGER(idxArg), thisidx;;
+ SEXP thiscol, target, ans, thisfill;
+ Rboolean isfill = TRUE, count;
+
+ ans = PROTECT(allocVector(VECSXP, nlhs + (nval * ncols)));
+ // set lhs cols
+ for (i=0; i < nlhs; i++) {
+ SET_VECTOR_ELT(ans, i, VECTOR_ELT(lhs, i));
+ }
+ // get val cols
+ for (i=0; i<nval; i++) {
+ thiscol = VECTOR_ELT(val, i);
+ thisfill = fill;
+ count = FALSE;
+ if (isNull(fill)) {
+ isfill = FALSE;
+ if (LOGICAL(is_agg)[0]) {
+ thisfill = PROTECT(allocNAVector(TYPEOF(thiscol), 1));
+ count = TRUE;
+ } else thisfill = VECTOR_ELT(fill_d, i);
+ }
+ if (isfill && TYPEOF(fill) != TYPEOF(thiscol)) {
+ thisfill = PROTECT(coerceVector(fill, TYPEOF(thiscol)));
+ count = TRUE;
+ }
+ switch (TYPEOF(thiscol)) {
+ case INTSXP:
+ for (j=0; j<ncols; j++) {
+ target = allocVector(TYPEOF(thiscol), nrows);
+ SET_VECTOR_ELT(ans, nlhs+j+i*ncols, target);
+ copyMostAttrib(thiscol, target);
+ for (k=0; k<nrows; k++) {
+ thisidx = idx[k*ncols + j];
+ INTEGER(target)[k] = (thisidx == NA_INTEGER) ? INTEGER(thisfill)[0] : INTEGER(thiscol)[thisidx-1];
+ }
+ }
+ break;
+ case REALSXP:
+ for (j=0; j<ncols; j++) {
+ target = allocVector(TYPEOF(thiscol), nrows);
+ SET_VECTOR_ELT(ans, nlhs+j+i*ncols, target);
+ copyMostAttrib(thiscol, target);
+ for (k=0; k<nrows; k++) {
+ thisidx = idx[k*ncols + j];
+ REAL(target)[k] = (thisidx == NA_INTEGER) ? REAL(thisfill)[0] : REAL(thiscol)[thisidx-1];
+ }
+ }
+ break;
+ case LGLSXP:
+ for (j=0; j<ncols; j++) {
+ target = allocVector(TYPEOF(thiscol), nrows);
+ SET_VECTOR_ELT(ans, nlhs+j+i*ncols, target);
+ copyMostAttrib(thiscol, target);
+ for (k=0; k<nrows; k++) {
+ thisidx = idx[k*ncols + j];
+ LOGICAL(target)[k] = (thisidx == NA_INTEGER) ? LOGICAL(thisfill)[0] : LOGICAL(thiscol)[thisidx-1];
+ }
+ }
+ break;
+ case STRSXP:
+ for (j=0; j<ncols; j++) {
+ target = allocVector(TYPEOF(thiscol), nrows);
+ SET_VECTOR_ELT(ans, nlhs+j+i*ncols, target);
+ copyMostAttrib(thiscol, target);
+ for (k=0; k<nrows; k++) {
+ thisidx = idx[k*ncols + j];
+ SET_STRING_ELT(target, k, (thisidx == NA_INTEGER) ? STRING_ELT(thisfill, 0) : STRING_ELT(thiscol, thisidx-1));
+ }
+ }
+ break;
+ case VECSXP:
+ for (j=0; j<ncols; j++) {
+ target = allocVector(TYPEOF(thiscol), nrows);
+ SET_VECTOR_ELT(ans, nlhs+j+i*ncols, target);
+ copyMostAttrib(thiscol, target);
+ for (k=0; k<nrows; k++) {
+ thisidx = idx[k*ncols + j];
+ SET_VECTOR_ELT(target, k, (thisidx == NA_INTEGER) ? VECTOR_ELT(thisfill, 0) : VECTOR_ELT(thiscol, thisidx-1));
+ }
+ }
+ break;
+ }
+ if (count) UNPROTECT(1);
+ }
+ UNPROTECT(1);
+ return(ans);
+}
+
+// internal functions that are not used anymore..
+
+// # nocov start
+// Note: all these functions below are internal functions and are designed specific to fcast.
+SEXP zero_init(R_len_t n) {
+ R_len_t i;
+ SEXP ans;
+ if (n < 0) error("Input argument 'n' to 'zero_init' must be >= 0");
+ ans = PROTECT(allocVector(INTSXP, n));
+ for (i=0; i<n; i++) INTEGER(ans)[i] = 0;
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP vec_init(R_len_t n, SEXP val) {
+
+ SEXP ans;
+ R_len_t i;
+ if (n < 0) error("Input argument 'n' to 'vec_init' must be >= 0");
+ ans = PROTECT(allocVector(TYPEOF(val), n));
+ switch(TYPEOF(val)) {
+ case INTSXP :
+ for (i=0; i<n; i++) INTEGER(ans)[i] = INTEGER(val)[0];
+ break;
+ case REALSXP :
+ for (i=0; i<n; i++) REAL(ans)[i] = REAL(val)[0];
+ break;
+ case LGLSXP :
+ for (i=0; i<n; i++) LOGICAL(ans)[i] = LOGICAL(val)[0];
+ break;
+ case STRSXP :
+ for (i=0; i<n; i++) SET_STRING_ELT(ans, i, STRING_ELT(val, 0));
+ break;
+ case VECSXP :
+ for (i=0; i<n; i++) SET_VECTOR_ELT(ans, i, VECTOR_ELT(val, 0));
+ default :
+ error("Unknown input type '%s'", type2char(TYPEOF(val)));
+ }
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP cast_order(SEXP v, SEXP env) {
+ R_len_t len;
+ SEXP call, ans;
+ if (TYPEOF(env) != ENVSXP) error("Argument 'env' to (data.table internals) 'cast_order' must be an environment");
+ if (TYPEOF(v) == VECSXP) len = length(VECTOR_ELT(v, 0));
+ else len = length(v);
+ PROTECT(call = lang2(install("forder"), v)); // TODO: save the 'eval' by calling directly the C-function.
+ ans = PROTECT(eval(call, env));
+ if (length(ans) == 0) { // forder returns integer(0) if already sorted
+ UNPROTECT(1); // ans
+ ans = PROTECT(seq_int(len, 1));
+ }
+ UNPROTECT(2);
+ return(ans);
+}
+
+SEXP cross_join(SEXP s, SEXP env) {
+ // Calling CJ is faster and don't have to worry about sorting or setting key.
+ SEXP call, r;
+ if (!isNewList(s) || isNull(s)) error("Argument 's' to 'cross_join' must be a list of length > 0");
+ PROTECT(call = lang3(install("do.call"), install("CJ"), s));
+ r = eval(call, env);
+ UNPROTECT(1);
+ return(r);
+}
+
+SEXP diff_int(SEXP x, R_len_t n) {
+
+ R_len_t i;
+ SEXP ans;
+ if (TYPEOF(x) != INTSXP) error("Argument 'x' to 'diff_int' must be an integer vector");
+ ans = PROTECT(allocVector(INTSXP, length(x)));
+ for (i=1; i<length(x); i++)
+ INTEGER(ans)[i-1] = INTEGER(x)[i] - INTEGER(x)[i-1];
+ INTEGER(ans)[length(x)-1] = n - INTEGER(x)[length(x)-1] + 1;
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP intrep(SEXP x, SEXP len) {
+
+ R_len_t i,j,l=0, k=0;
+ SEXP ans;
+ if (TYPEOF(x) != INTSXP || TYPEOF(len) != INTSXP) error("Arguments 'x' and 'len' to 'intrep' should both be integer vectors");
+ if (length(x) != length(len)) error("'x' and 'len' must be of same length");
+ // assuming both are of length >= 1
+ for (i=0; i<length(len); i++)
+ l += INTEGER(len)[i]; // assuming positive values for len. internal use - can't bother to check.
+ ans = PROTECT(allocVector(INTSXP, l));
+ for (i=0; i<length(len); i++) {
+ for (j=0; j<INTEGER(len)[i]; j++) {
+ INTEGER(ans)[k++] = INTEGER(x)[i];
+ }
+ }
+ UNPROTECT(1); // ans
+ return(ans);
+}
+
+// taken match_transform() from base:::unique.c and modified
+SEXP coerce_to_char(SEXP s, SEXP env)
+{
+ if(OBJECT(s)) {
+ if(inherits(s, "factor")) return asCharacterFactor(s);
+ else if(getAttrib(s, R_ClassSymbol) != R_NilValue) {
+ SEXP call, r;
+ PROTECT(call = lang2(install("as.character"), s));
+ r = eval(call, env);
+ UNPROTECT(1);
+ return r;
+ }
+ }
+ /* else */
+ return coerceVector(s, STRSXP);
+}
+
+// # nocov end
diff --git a/src/fmelt.c b/src/fmelt.c
new file mode 100644
index 0000000..ff6f11e
--- /dev/null
+++ b/src/fmelt.c
@@ -0,0 +1,701 @@
+#include "data.table.h"
+#include <Rdefines.h>
+// #include <signal.h> // the debugging machinery + breakpoint aidee
+// raise(SIGINT);
+
+// generate from 1 to n (a simple fun for melt, vecseq is convenient from R due to SEXP inputs)
+SEXP seq_int(int n, int start) {
+ SEXP ans = R_NilValue;
+ int i;
+ if (n <= 0) return(ans);
+ PROTECT(ans = allocVector(INTSXP, n));
+ for (i=0; i<n; i++) INTEGER(ans)[i] = start+i;
+ UNPROTECT(1);
+ return(ans);
+}
+
+// very specific "set_diff" for integers
+SEXP set_diff(SEXP x, int n) {
+ SEXP ans, xmatch;
+ int i, j = 0, *buf;
+ if (TYPEOF(x) != INTSXP) error("'x' must be an integer");
+ if (n <= 0) error("'n' must be a positive integer");
+ xmatch = match(x, seq_int(n, 1), 0); // took a while to realise: matches vec against x - thanks to comment from Matthew in assign.c!
+
+ buf = (int *) R_alloc(n, sizeof(int));
+ for (i=0; i<n; i++) {
+ if (INTEGER(xmatch)[i] == 0) {
+ buf[j++] = i+1;
+ }
+ }
+ n = j;
+ PROTECT(ans = allocVector(INTSXP, n));
+ memcpy(INTEGER(ans), buf, sizeof(int) * n); // sizeof is of type size_t - no integer overflow issues
+ UNPROTECT(1);
+ return(ans);
+}
+
+// plucked and modified from base (coerce.c and summary.c).
+// for melt's `na.rm=TRUE` option
+SEXP which_notNA(SEXP x) {
+ SEXP v, ans;
+ int i, j=0, n = length(x), *buf;
+
+ PROTECT(v = allocVector(LGLSXP, n));
+ switch (TYPEOF(x)) {
+ case LGLSXP:
+ for (i = 0; i < n; i++) LOGICAL(v)[i] = (LOGICAL(x)[i] != NA_LOGICAL);
+ break;
+ case INTSXP:
+ for (i = 0; i < n; i++) LOGICAL(v)[i] = (INTEGER(x)[i] != NA_INTEGER);
+ break;
+ case REALSXP:
+ for (i = 0; i < n; i++) LOGICAL(v)[i] = !ISNAN(REAL(x)[i]);
+ break;
+ case STRSXP:
+ for (i = 0; i < n; i++) LOGICAL(v)[i] = (STRING_ELT(x, i) != NA_STRING);
+ break;
+ default:
+ error("%s() applied to non-(list or vector) of type '%s'",
+ "which_notNA", type2char(TYPEOF(x)));
+ }
+
+ buf = (int *) R_alloc(n, sizeof(int));
+ for (i = 0; i < n; i++) {
+ if (LOGICAL(v)[i] == TRUE) {
+ buf[j] = i + 1;
+ j++;
+ }
+ }
+ n = j;
+ PROTECT(ans = allocVector(INTSXP, n));
+ memcpy(INTEGER(ans), buf, sizeof(int) * n);
+
+ UNPROTECT(2);
+ return(ans);
+}
+
+SEXP which(SEXP x, Rboolean bool) {
+
+ int i, j=0, n = length(x), *buf;
+ SEXP ans;
+ if (!isLogical(x)) error("Argument to 'which' must be logical");
+ buf = (int *) R_alloc(n, sizeof(int));
+ for (i = 0; i < n; i++) {
+ if (LOGICAL(x)[i] == bool) {
+ buf[j] = i + 1;
+ j++;
+ }
+ }
+ n = j;
+ PROTECT(ans = allocVector(INTSXP, n));
+ memcpy(INTEGER(ans), buf, sizeof(int) * n);
+
+ UNPROTECT(1);
+ return(ans);
+}
+
+// whichwrapper for R
+SEXP whichwrapper(SEXP x, SEXP bool) {
+ // if (LOGICAL(bool)[0] == NA_LOGICAL)
+ // error("bool should be logical TRUE/FALSE");
+ return which(x, LOGICAL(bool)[0]);
+}
+
+// hack by calling paste using eval. could change this to strcat, but not sure about buffer size for large data.tables... Any ideas Matthew?
+SEXP concat(SEXP vec, SEXP idx) {
+
+ SEXP s, t, v;
+ int i;
+
+ if (TYPEOF(vec) != STRSXP) error("concat: 'vec must be a character vector");
+ if (!isInteger(idx) || length(idx) < 0) error("concat: 'idx' must be an integer vector of length >= 0");
+ for (i=0; i<length(idx); i++) {
+ if (INTEGER(idx)[i] < 0 || INTEGER(idx)[i] > length(vec))
+ error("concat: 'idx' must take values between 0 and length(vec); 0 <= idx <= length(vec)");
+ }
+ PROTECT(v = allocVector(STRSXP, length(idx)));
+ for (i=0; i<length(idx); i++) {
+ SET_STRING_ELT(v, i, STRING_ELT(vec, INTEGER(idx)[i]-1));
+ }
+ PROTECT(t = s = allocList(3));
+ SET_TYPEOF(t, LANGSXP);
+ SETCAR(t, install("paste")); t = CDR(t);
+ SETCAR(t, v); t = CDR(t);
+ SETCAR(t, mkString(", "));
+ SET_TAG(t, install("collapse"));
+ UNPROTECT(2); // v, (t,s)
+ return(eval(s, R_GlobalEnv));
+}
+
+// deal with measure.vars of type VECSXP
+SEXP measurelist(SEXP measure, SEXP dtnames) {
+ int i, n=length(measure), protecti=0;
+ SEXP ans, tmp;
+ ans = PROTECT(allocVector(VECSXP, n)); protecti++;
+ for (i=0; i<n; i++) {
+ switch(TYPEOF(VECTOR_ELT(measure, i))) {
+ case STRSXP : tmp = PROTECT(chmatch(VECTOR_ELT(measure, i), dtnames, 0, FALSE)); protecti++; break;
+ case REALSXP : tmp = PROTECT(coerceVector(VECTOR_ELT(measure, i), INTSXP)); protecti++; break;
+ case INTSXP : tmp = VECTOR_ELT(measure, i); break;
+ default : error("Unknown 'measure.vars' type %s at index %d of list", type2char(TYPEOF(VECTOR_ELT(measure, i))), i+1);
+ }
+ SET_VECTOR_ELT(ans, i, tmp);
+ }
+ UNPROTECT(protecti);
+ return(ans);
+}
+
+// internal function just to unlist integer lists
+static SEXP unlist_(SEXP xint) {
+ int i,j,k=0,totn=0,n=length(xint);
+ SEXP ans, tmp;
+ for (i=0; i<n; i++)
+ totn += length(VECTOR_ELT(xint, i));
+ ans = PROTECT(allocVector(INTSXP, totn));
+ for (i=0; i<n; i++) {
+ tmp = VECTOR_ELT(xint, i);
+ for (j=0; j<length(tmp); j++)
+ INTEGER(ans)[k++] = INTEGER(tmp)[j];
+ }
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP checkVars(SEXP DT, SEXP id, SEXP measure, Rboolean verbose) {
+ int i, ncol=LENGTH(DT), targetcols=0, protecti=0, u=0, v=0;
+ SEXP thiscol, idcols = R_NilValue, valuecols = R_NilValue, tmp, tmp2, booltmp, unqtmp, ans;
+ SEXP dtnames = getAttrib(DT, R_NamesSymbol);
+
+ if (isNull(id) && isNull(measure)) {
+ for (i=0; i<ncol; i++) {
+ thiscol = VECTOR_ELT(DT, i);
+ if ((isInteger(thiscol) || isNumeric(thiscol) || isLogical(thiscol)) && !isFactor(thiscol)) targetcols++;
+ }
+ idcols = PROTECT(allocVector(INTSXP, ncol-targetcols)); protecti++;
+ tmp = PROTECT(allocVector(INTSXP, targetcols)); protecti++;
+ for (i=0; i<ncol; i++) {
+ thiscol = VECTOR_ELT(DT, i);
+ if ((isInteger(thiscol) || isNumeric(thiscol) || isLogical(thiscol)) && !isFactor(thiscol)) {
+ INTEGER(tmp)[u++] = i+1;
+ } else
+ INTEGER(idcols)[v++] = i+1;
+ }
+ valuecols = PROTECT(allocVector(VECSXP, 1)); protecti++;
+ SET_VECTOR_ELT(valuecols, 0, tmp);
+ warning("To be consistent with reshape2's melt, id.vars and measure.vars are internally guessed when both are 'NULL'. All non-numeric/integer/logical type columns are conisdered id.vars, which in this case are columns [%s]. Consider providing at least one of 'id' or 'measure' vars in future.", CHAR(STRING_ELT(concat(dtnames, idcols), 0)));
+ } else if (!isNull(id) && isNull(measure)) {
+ switch(TYPEOF(id)) {
+ case STRSXP : PROTECT(tmp = chmatch(id, dtnames, 0, FALSE)); protecti++; break;
+ case REALSXP : PROTECT(tmp = coerceVector(id, INTSXP)); protecti++; break;
+ case INTSXP : tmp = id; break;
+ default : error("Unknown 'id.vars' type %s, must be character or integer vector", type2char(TYPEOF(id)));
+ }
+ booltmp = PROTECT(duplicated(tmp, FALSE)); protecti++;
+ for (i=0; i<length(tmp); i++) {
+ if (INTEGER(tmp)[i] <= 0) error("Column '%s' not found in 'data'", CHAR(STRING_ELT(id, i)));
+ else if (INTEGER(tmp)[i] > ncol) error("id.vars value exceeds ncol(data)");
+ else if (!LOGICAL(booltmp)[i]) targetcols++;
+ else continue;
+ }
+ unqtmp = PROTECT(allocVector(INTSXP, targetcols)); protecti++;
+ u = 0;
+ for (i=0; i<length(booltmp); i++) {
+ if (!LOGICAL(booltmp)[i]) {
+ INTEGER(unqtmp)[u++] = INTEGER(tmp)[i];
+ }
+ }
+ tmp2 = PROTECT(set_diff(unqtmp, ncol)); protecti++;
+ valuecols = PROTECT(allocVector(VECSXP, 1)); protecti++;
+ SET_VECTOR_ELT(valuecols, 0, tmp2);
+ idcols = tmp;
+ if (verbose) {
+ Rprintf("'measure.vars' is missing. Assigning all columns other than 'id.vars' columns as 'measure.vars'.\n");
+ if (length(tmp2)) Rprintf("Assigned 'measure.vars' are [%s].\n", CHAR(STRING_ELT(concat(dtnames, tmp2), 0)));
+ }
+ } else if (isNull(id) && !isNull(measure)) {
+ switch(TYPEOF(measure)) {
+ case STRSXP : tmp2 = PROTECT(chmatch(measure, dtnames, 0, FALSE)); protecti++; break;
+ case REALSXP : tmp2 = PROTECT(coerceVector(measure, INTSXP)); protecti++; break;
+ case INTSXP : tmp2 = measure; break;
+ case VECSXP : tmp2 = PROTECT(measurelist(measure, dtnames)); protecti++; break;
+ default : error("Unknown 'measure.vars' type %s, must be character or integer vector/list", type2char(TYPEOF(measure)));
+ }
+ tmp = tmp2;
+ if (isNewList(measure)) {
+ tmp = PROTECT(unlist_(tmp2)); protecti++;
+ }
+ booltmp = PROTECT(duplicated(tmp, FALSE)); protecti++;
+ for (i=0; i<length(tmp); i++) {
+ if (INTEGER(tmp)[i] <= 0) error("Column '%s' not found in 'data'", CHAR(STRING_ELT(measure, i)));
+ else if (INTEGER(tmp)[i] > ncol) error("measure.vars value exceeds ncol(data)");
+ else if (!LOGICAL(booltmp)[i]) targetcols++;
+ else continue;
+ }
+ unqtmp = PROTECT(allocVector(INTSXP, targetcols)); protecti++;
+ u = 0;
+ for (i=0; i<length(booltmp); i++) {
+ if (!LOGICAL(booltmp)[i]) {
+ INTEGER(unqtmp)[u++] = INTEGER(tmp)[i];
+ }
+ }
+ idcols = PROTECT(set_diff(unqtmp, ncol)); protecti++;
+ if (isNewList(measure)) valuecols = tmp2;
+ else {
+ valuecols = PROTECT(allocVector(VECSXP, 1)); protecti++;
+ SET_VECTOR_ELT(valuecols, 0, tmp2);
+ }
+ if (verbose) {
+ Rprintf("'id.vars' is missing. Assigning all columns other than 'measure.vars' columns as 'id.vars'.\n");
+ if (length(idcols)) Rprintf("Assigned 'id.vars' are [%s].\n", CHAR(STRING_ELT(concat(dtnames, idcols), 0)));
+ }
+ } else if (!isNull(id) && !isNull(measure)) {
+ switch(TYPEOF(id)) {
+ case STRSXP : tmp = PROTECT(chmatch(id, dtnames, 0, FALSE)); protecti++; break;
+ case REALSXP : tmp = PROTECT(coerceVector(id, INTSXP)); protecti++; break;
+ case INTSXP : tmp = id; break;
+ default : error("Unknown 'id.vars' type %s, must be character or integer vector", type2char(TYPEOF(id)));
+ }
+ for (i=0; i<length(tmp); i++) {
+ if (INTEGER(tmp)[i] <= 0) error("Column '%s' not found in 'data'", CHAR(STRING_ELT(id, i)));
+ else if (INTEGER(tmp)[i] > ncol) error("measure.vars value exceeds ncol(data)");
+ }
+ idcols = PROTECT(tmp); protecti++;
+ switch(TYPEOF(measure)) {
+ case STRSXP : tmp2 = PROTECT(chmatch(measure, dtnames, 0, FALSE)); protecti++; break;
+ case REALSXP : tmp2 = PROTECT(coerceVector(measure, INTSXP)); protecti++; break;
+ case INTSXP : tmp2 = measure; break;
+ case VECSXP : tmp2 = PROTECT(measurelist(measure, dtnames)); protecti++; break;
+ default : error("Unknown 'measure.vars' type %s, must be character or integer vector", type2char(TYPEOF(measure)));
+ }
+ tmp = tmp2;
+ if (isNewList(measure)) {
+ tmp = PROTECT(unlist_(tmp2)); protecti++;
+ }
+ for (i=0; i<length(tmp); i++) {
+ if (INTEGER(tmp)[i] <= 0) error("Column '%s' not found in 'data'", CHAR(STRING_ELT(measure, i)));
+ else if (INTEGER(tmp)[i] > ncol) error("measure.vars value exceeds ncol(data)");
+ }
+ if (isNewList(measure)) valuecols = tmp2;
+ else {
+ valuecols = PROTECT(allocVector(VECSXP, 1)); protecti++;
+ SET_VECTOR_ELT(valuecols, 0, tmp2);
+ }
+ }
+ ans = PROTECT(allocVector(VECSXP, 2)); protecti++;
+ SET_VECTOR_ELT(ans, 0, idcols);
+ SET_VECTOR_ELT(ans, 1, valuecols);
+ UNPROTECT(protecti);
+ return(ans);
+}
+
+struct processData {
+ SEXP idcols, valuecols, naidx;
+ int lids, lvalues, lmax, lmin, protecti, totlen, nrow;
+ int *isfactor, *leach, *isidentical;
+ SEXPTYPE *maxtype;
+ Rboolean narm;
+};
+
+static void preprocess(SEXP DT, SEXP id, SEXP measure, SEXP varnames, SEXP valnames, Rboolean narm, Rboolean verbose, struct processData *data) {
+
+ SEXP vars,tmp,thiscol;
+ SEXPTYPE type;
+ int i,j;
+ data->lmax = 0; data->lmin = 0; data->protecti = 0, data->totlen = 0, data->nrow = length(VECTOR_ELT(DT, 0));
+ vars = checkVars(DT, id, measure, verbose);
+ data->idcols = PROTECT(VECTOR_ELT(vars, 0)); data->protecti++;
+ data->valuecols = PROTECT(VECTOR_ELT(vars, 1)); data->protecti++;
+ data->lids = length(data->idcols);
+ data->lvalues = length(data->valuecols);
+ data->narm = narm;
+ if (length(valnames) != data->lvalues) {
+ UNPROTECT(data->protecti);
+ if (isNewList(measure)) error("When 'measure.vars' is a list, 'value.name' must be a character vector of length =1 or =length(measure.vars).");
+ else error("When 'measure.vars' is either not specified or a character/integer vector, 'value.name' must be a character vector of length =1.");
+ }
+ if (length(varnames) != 1)
+ error("'variable.name' must be a character/integer vector of length=1.");
+ data->leach = malloc(sizeof(int) * data->lvalues);
+ data->isidentical = malloc(sizeof(int) * data->lvalues);
+ data->isfactor = calloc(sizeof(int), data->lvalues);
+ data->maxtype = calloc(sizeof(SEXPTYPE), data->lvalues);
+ for (i=0; i<data->lvalues; i++) {
+ tmp = VECTOR_ELT(data->valuecols, i);
+ data->leach[i] = length(tmp);
+ data->isidentical[i] = 1;
+ data->lmax = (data->lmax > data->leach[i]) ? data->lmax : data->leach[i];
+ data->lmin = (data->lmin < data->leach[i]) ? data->lmin : data->leach[i];
+ for (j=0; j<data->leach[i]; j++) {
+ thiscol = VECTOR_ELT(DT, INTEGER(tmp)[j]-1);
+ if (isFactor(thiscol)) {
+ data->isfactor[i] = (isOrdered(thiscol)) ? 2 : 1;
+ data->maxtype[i] = STRSXP;
+ } else {
+ type = TYPEOF(thiscol);
+ if (type > data->maxtype[i]) data->maxtype[i] = type;
+ }
+ }
+ for (j=0; j<data->leach[i]; j++) {
+ thiscol = VECTOR_ELT(DT, INTEGER(tmp)[j]-1);
+ if ( (!isFactor(thiscol) && data->maxtype[i] != TYPEOF(thiscol)) || (isFactor(thiscol) && data->maxtype[i] != STRSXP) ) {
+ data->isidentical[i] = 0;
+ break;
+ }
+ }
+ }
+ if (data->narm) {
+ data->naidx = PROTECT(allocVector(VECSXP, data->lmax));
+ data->protecti++;
+ }
+}
+
+SEXP getvaluecols(SEXP DT, SEXP dtnames, Rboolean valfactor, Rboolean verbose, struct processData *data) {
+
+ int i, j, k, protecti=0, counter=0, thislen=0;
+ SEXP tmp, seqcols, thiscol, thisvaluecols, target, ansvals, thisidx=R_NilValue, flevels, clevels;
+ Rboolean coerced=FALSE, thisfac=FALSE, *isordered, copyattr = FALSE, thisvalfactor;
+ size_t size;
+ for (i=0; i<data->lvalues; i++) {
+ thisvaluecols = VECTOR_ELT(data->valuecols, i);
+ if (!data->isidentical[i])
+ warning("'measure.vars' [%s] are not all of the same type. By order of hierarchy, the molten data value column will be of type '%s'. All measure variables not of type '%s' will be coerced to. Check DETAILS in ?melt.data.table for more on coercion.\n", CHAR(STRING_ELT(concat(dtnames, thisvaluecols), 0)), type2char(data->maxtype[i]), type2char(data->maxtype[i]));
+ if (data->maxtype[i] == VECSXP && data->narm) {
+ if (verbose) Rprintf("The molten data value type is a list at item %d. 'na.rm=TRUE' is ignored.\n", i+1);
+ data->narm = FALSE;
+ }
+ }
+ if (data->narm) {
+ seqcols = PROTECT(seq_int(data->lvalues, 1)); protecti++;
+ for (i=0; i<data->lmax; i++) {
+ tmp = PROTECT(allocVector(VECSXP, data->lvalues));
+ for (j=0; j<data->lvalues; j++) {
+ if (i < data->leach[j]) {
+ thisvaluecols = VECTOR_ELT(data->valuecols, j);
+ SET_VECTOR_ELT(tmp, j, VECTOR_ELT(DT, INTEGER(thisvaluecols)[i]-1));
+ } else {
+ SET_VECTOR_ELT(tmp, j, allocNAVector(data->maxtype[j], data->nrow));
+ }
+ }
+ tmp = PROTECT(dt_na(tmp, seqcols));
+ SET_VECTOR_ELT(data->naidx, i, which(tmp, FALSE));
+ UNPROTECT(2); // tmp
+ data->totlen += length(VECTOR_ELT(data->naidx, i));
+ }
+ } else data->totlen = data->nrow * data->lmax;
+ flevels = PROTECT(allocVector(VECSXP, data->lmax)); protecti++;
+ isordered = malloc(sizeof(Rboolean) * data->lmax);
+ ansvals = PROTECT(allocVector(VECSXP, data->lvalues)); protecti++;
+ for (i=0; i<data->lvalues; i++) {
+ thisvalfactor = (data->maxtype[i] == VECSXP) ? FALSE : valfactor;
+ target = allocVector(data->maxtype[i], data->totlen);
+ SET_VECTOR_ELT(ansvals, i, target);
+ thisvaluecols = VECTOR_ELT(data->valuecols, i);
+ counter = 0; copyattr = FALSE;
+ for (j=0; j<data->lmax; j++) {
+ thiscol = (j < data->leach[i]) ? VECTOR_ELT(DT, INTEGER(thisvaluecols)[j]-1)
+ : allocNAVector(data->maxtype[i], data->nrow);
+ if (!copyattr && data->isidentical[i] && !data->isfactor[i]) {
+ copyMostAttrib(thiscol, target);
+ copyattr = TRUE;
+ }
+ if (TYPEOF(thiscol) != TYPEOF(target) && (data->maxtype[i] == VECSXP || !isFactor(thiscol))) {
+ thiscol = PROTECT(coerceVector(thiscol, TYPEOF(target)));
+ coerced = TRUE;
+ }
+ if (data->narm) {
+ thisidx = VECTOR_ELT(data->naidx, j);
+ thislen = length(thisidx);
+ }
+ size = SIZEOF(thiscol);
+ switch (TYPEOF(target)) {
+ case VECSXP :
+ if (data->narm) {
+ for (k=0; k<thislen; k++)
+ SET_VECTOR_ELT(target, counter + k, VECTOR_ELT(thiscol, INTEGER(thisidx)[k]-1));
+ } else {
+ for (k=0; k<data->nrow; k++) SET_VECTOR_ELT(target, j*data->nrow + k, VECTOR_ELT(thiscol, k));
+ }
+ break;
+ case STRSXP :
+ if (data->isfactor[i]) {
+ if (isFactor(thiscol)) {
+ SET_VECTOR_ELT(flevels, j, getAttrib(thiscol, R_LevelsSymbol));
+ thiscol = PROTECT(asCharacterFactor(thiscol));
+ thisfac = TRUE;
+ isordered[j] = isOrdered(thiscol);
+ } else SET_VECTOR_ELT(flevels, j, thiscol);
+ }
+ if (data->narm) {
+ for (k=0; k<thislen; k++)
+ SET_STRING_ELT(target, counter + k, STRING_ELT(thiscol, INTEGER(thisidx)[k]-1));
+ } else {
+ for (k=0; k<data->nrow; k++) SET_STRING_ELT(target, j*data->nrow + k, STRING_ELT(thiscol, k));
+ }
+ break;
+ case REALSXP :
+ if (data->narm) {
+ for (k=0; k<thislen; k++)
+ REAL(target)[counter + k] = REAL(thiscol)[INTEGER(thisidx)[k]-1];
+ } else {
+ memcpy((char *)DATAPTR(target)+j*data->nrow*size, (char *)DATAPTR(thiscol), data->nrow*size);
+ }
+ break;
+ case INTSXP :
+ if (data->narm) {
+ for (k=0; k<thislen; k++)
+ INTEGER(target)[counter + k] = INTEGER(thiscol)[INTEGER(thisidx)[k]-1];
+ } else {
+ memcpy((char *)DATAPTR(target)+j*data->nrow*size, (char *)DATAPTR(thiscol), data->nrow*size);
+ }
+ break;
+ case LGLSXP :
+ if (data->narm) {
+ for (k=0; k<thislen; k++)
+ LOGICAL(target)[counter + k] = LOGICAL(thiscol)[INTEGER(thisidx)[k]-1];
+ } else {
+ memcpy((char *)DATAPTR(target)+j*data->nrow*size, (char *)DATAPTR(thiscol), data->nrow*size);
+ }
+ break;
+ default : error("Unknown column type '%s' for column '%s'.", type2char(TYPEOF(thiscol)), CHAR(STRING_ELT(dtnames, INTEGER(thisvaluecols)[i]-1)));
+ }
+ if (data->narm) counter += thislen;
+ if (coerced) {
+ UNPROTECT(1); coerced = FALSE;
+ }
+ if (thisfac) {
+ UNPROTECT(1); thisfac = FALSE;
+ }
+ }
+ if (thisvalfactor && data->isfactor[i] && TYPEOF(target) != VECSXP) {
+ clevels = combineFactorLevels(flevels, &(data->isfactor[i]), isordered);
+ SEXP factorLangSxp = PROTECT(lang3(install(data->isfactor[i] == 1 ? "factor" : "ordered"), target, clevels));
+ SET_VECTOR_ELT(ansvals, i, eval(factorLangSxp, R_GlobalEnv));
+ UNPROTECT(2); // clevels, factorLangSxp
+ }
+ }
+ UNPROTECT(protecti);
+ free(isordered);
+ return(ansvals);
+}
+
+SEXP getvarcols(SEXP DT, SEXP dtnames, Rboolean varfactor, Rboolean verbose, struct processData *data) {
+
+ int i,j,k,cnt=0,nrows=0, nlevels=0, protecti=0, thislen;
+ SEXP ansvars, thisvaluecols, levels, target, matchvals, thisnames;
+
+ ansvars = PROTECT(allocVector(VECSXP, 1)); protecti++;
+ target = allocVector(INTSXP, data->totlen);
+ SET_VECTOR_ELT(ansvars, 0, target);
+ if (data->lvalues == 1) {
+ thisvaluecols = VECTOR_ELT(data->valuecols, 0);
+ // tmp fix for #1055
+ thisnames = PROTECT(allocVector(STRSXP, length(thisvaluecols)));
+ for (i=0; i<length(thisvaluecols); i++) {
+ SET_STRING_ELT(thisnames, i, STRING_ELT(dtnames, INTEGER(thisvaluecols)[i]-1));
+ }
+ matchvals = PROTECT(match(thisnames, thisnames, 0));
+ if (data->narm) {
+ for (j=0; j<data->lmax; j++) {
+ thislen = length(VECTOR_ELT(data->naidx, j));
+ for (k=0; k<thislen; k++)
+ INTEGER(target)[nrows + k] = INTEGER(matchvals)[j];
+ nrows += thislen;
+ nlevels += (thislen != 0);
+ }
+ } else {
+ for (j=0; j<data->lmax; j++) {
+ for (k=0; k<data->nrow; k++)
+ INTEGER(target)[data->nrow*j + k] = INTEGER(matchvals)[j];
+ }
+ nlevels = data->lmax;
+ }
+ UNPROTECT(2); // matchvals, thisnames
+ } else {
+ if (data->narm) {
+ for (j=0; j<data->lmax; j++) {
+ thislen = length(VECTOR_ELT(data->naidx, j));
+ for (k=0; k<thislen; k++)
+ INTEGER(target)[nrows + k] = j+1;
+ nrows += thislen;
+ nlevels += (thislen != 0);
+ }
+ } else {
+ for (j=0; j<data->lmax; j++) {
+ for (k=0; k<data->nrow; k++)
+ INTEGER(target)[data->nrow*j + k] = j+1;
+ }
+ nlevels = data->lmax;
+ }
+ }
+ setAttrib(target, R_ClassSymbol, mkString("factor"));
+ cnt = 0;
+ if (data->lvalues == 1) {
+ levels = PROTECT(allocVector(STRSXP, nlevels));
+ thisvaluecols = VECTOR_ELT(data->valuecols, 0); // levels will be column names
+ for (i=0; i<data->lmax; i++) {
+ if (data->narm) {
+ if (length(VECTOR_ELT(data->naidx, i)) == 0) continue;
+ }
+ SET_STRING_ELT(levels, cnt++, STRING_ELT(dtnames, INTEGER(thisvaluecols)[i]-1));
+ }
+ } else levels = PROTECT(coerceVector(seq_int(nlevels, 1), STRSXP)); // generate levels = 1:nlevels
+ // base::unique is fast on vectors, and the levels on variable columns are usually small
+ SEXP uniqueLangSxp = PROTECT(lang2(install("unique"), levels));
+ setAttrib(target, R_LevelsSymbol, eval(uniqueLangSxp, R_GlobalEnv));
+ UNPROTECT(2); // levels, uniqueLangSxp
+ if (!varfactor) SET_VECTOR_ELT(ansvars, 0, asCharacterFactor(target));
+ UNPROTECT(protecti);
+ return(ansvars);
+}
+
+SEXP getidcols(SEXP DT, SEXP dtnames, Rboolean verbose, struct processData *data) {
+
+ int i,j,k, counter=0, thislen;
+ SEXP ansids, thiscol, target, thisidx;
+ size_t size;
+ ansids = PROTECT(allocVector(VECSXP, data->lids));
+ for (i=0; i<data->lids; i++) {
+ counter = 0;
+ thiscol = VECTOR_ELT(DT, INTEGER(data->idcols)[i]-1);
+ size = SIZEOF(thiscol);
+ target = allocVector(TYPEOF(thiscol), data->totlen);
+ SET_VECTOR_ELT(ansids, i, target);
+ copyMostAttrib(thiscol, target); // all but names,dim and dimnames. And if so, we want a copy here, not keepattr's SET_ATTRIB.
+ switch(TYPEOF(thiscol)) {
+ case REALSXP :
+ if (data->narm) {
+ for (j=0; j<data->lmax; j++) {
+ thisidx = VECTOR_ELT(data->naidx, j);
+ thislen = length(thisidx);
+ for (k=0; k<thislen; k++)
+ REAL(target)[counter + k] = REAL(thiscol)[INTEGER(thisidx)[k]-1];
+ counter += thislen;
+ }
+ } else {
+ for (j=0; j<data->lmax; j++)
+ memcpy((char *)DATAPTR(target)+j*data->nrow*size, (char *)DATAPTR(thiscol), data->nrow*size);
+ }
+ break;
+ case INTSXP :
+ if (data->narm) {
+ for (j=0; j<data->lmax; j++) {
+ thisidx = VECTOR_ELT(data->naidx, j);
+ thislen = length(thisidx);
+ for (k=0; k<thislen; k++)
+ INTEGER(target)[counter + k] = INTEGER(thiscol)[INTEGER(thisidx)[k]-1];
+ counter += thislen;
+ }
+ } else {
+ for (j=0; j<data->lmax; j++)
+ memcpy((char *)DATAPTR(target)+j*data->nrow*size, (char *)DATAPTR(thiscol), data->nrow*size);
+ }
+ break;
+ case LGLSXP :
+ if (data->narm) {
+ for (j=0; j<data->lmax; j++) {
+ thisidx = VECTOR_ELT(data->naidx, j);
+ thislen = length(thisidx);
+ for (k=0; k<thislen; k++)
+ LOGICAL(target)[counter + k] = LOGICAL(thiscol)[INTEGER(thisidx)[k]-1];
+ counter += thislen;
+ }
+ } else {
+ for (j=0; j<data->lmax; j++)
+ memcpy((char *)DATAPTR(target)+j*data->nrow*size, (char *)DATAPTR(thiscol), data->nrow*size);
+ }
+ break;
+ case STRSXP :
+ if (data->narm) {
+ for (j=0; j<data->lmax; j++) {
+ thisidx = VECTOR_ELT(data->naidx, j);
+ thislen = length(thisidx);
+ for (k=0; k<thislen; k++)
+ SET_STRING_ELT(target, counter + k, STRING_ELT(thiscol, INTEGER(thisidx)[k]-1));
+ counter += thislen;
+ }
+ } else {
+ // SET_STRING_ELT for j=0 and memcpy for j>0, WHY?
+ // From assign.c's memcrecycle - only one SET_STRING_ELT per RHS item is needed to set generations (overhead)
+ for (k=0; k<data->nrow; k++) SET_STRING_ELT(target, k, STRING_ELT(thiscol, k));
+ for (j=1; j<data->lmax; j++)
+ memcpy((char *)DATAPTR(target)+j*data->nrow*size, (char *)DATAPTR(target), data->nrow*size);
+ }
+ break;
+ case VECSXP :
+ for (j=0; j<data->lmax; j++) {
+ for (k=0; k<data->nrow; k++) {
+ SET_VECTOR_ELT(target, j*data->nrow + k, VECTOR_ELT(thiscol, k));
+ }
+ }
+ break;
+ default : error("Unknown column type '%s' for column '%s' in 'data'", type2char(TYPEOF(thiscol)), CHAR(STRING_ELT(dtnames, INTEGER(data->idcols)[i]-1)));
+ }
+ }
+ UNPROTECT(1);
+ return (ansids);
+}
+
+SEXP fmelt(SEXP DT, SEXP id, SEXP measure, SEXP varfactor, SEXP valfactor, SEXP varnames, SEXP valnames, SEXP narmArg, SEXP verboseArg) {
+
+ int i, ncol, protecti=0;
+ SEXP dtnames, ansvals, ansvars, ansids, ansnames, ans;
+ Rboolean narm=FALSE, verbose=FALSE;
+ struct processData data;
+
+ if (!isNewList(DT)) error("Input is not of type VECSXP, expected a data.table, data.frame or list");
+ if (!isLogical(valfactor)) error("Argument 'value.factor' should be logical TRUE/FALSE");
+ if (!isLogical(varfactor)) error("Argument 'variable.factor' should be logical TRUE/FALSE");
+ if (!isLogical(narmArg)) error("Argument 'na.rm' should be logical TRUE/FALSE.");
+ if (!isString(varnames)) error("Argument 'variable.name' must be a character vector");
+ if (!isString(valnames)) error("Argument 'value.name' must be a character vector");
+ if (!isLogical(verboseArg)) error("Argument 'verbose' should be logical TRUE/FALSE");
+ ncol = LENGTH(DT);
+ if (!ncol) {
+ if (verbose) Rprintf("ncol(data) is 0. Nothing to melt. Returning original data.table.");
+ return(DT);
+ }
+ dtnames = getAttrib(DT, R_NamesSymbol);
+ if (isNull(dtnames)) error("names(data) is NULL. Please report to data.table-help");
+ if (LOGICAL(narmArg)[0] == TRUE) narm = TRUE;
+ if (LOGICAL(verboseArg)[0] == TRUE) verbose = TRUE;
+
+ preprocess(DT, id, measure, varnames, valnames, narm, verbose, &data);
+ protecti = data.protecti;
+ // edge case no measure.vars
+ if (!data.lmax) {
+ ans = shallowwrapper(DT, data.idcols);
+ ans = PROTECT(duplicate(ans)); protecti++;
+ } else {
+ ansvals = PROTECT(getvaluecols(DT, dtnames, LOGICAL(valfactor)[0], verbose, &data)); protecti++;
+ ansvars = PROTECT(getvarcols(DT, dtnames, LOGICAL(varfactor)[0], verbose, &data)); protecti++;
+ ansids = PROTECT(getidcols(DT, dtnames, verbose, &data)); protecti++;
+
+ // populate 'ans'
+ ans = allocVector(VECSXP, data.lids+1+data.lvalues); // 1 is for variable column
+ for (i=0; i<data.lids; i++) {
+ SET_VECTOR_ELT(ans, i, VECTOR_ELT(ansids, i));
+ }
+ SET_VECTOR_ELT(ans, data.lids, VECTOR_ELT(ansvars, 0));
+ for (i=0; i<data.lvalues; i++) {
+ SET_VECTOR_ELT(ans, data.lids+1+i, VECTOR_ELT(ansvals, i));
+ }
+ // fill in 'ansnames'
+ ansnames = PROTECT(allocVector(STRSXP, data.lids+1+data.lvalues)); protecti++;
+ for (i=0; i<data.lids; i++) {
+ SET_STRING_ELT(ansnames, i, STRING_ELT(dtnames, INTEGER(data.idcols)[i]-1));
+ }
+ SET_STRING_ELT(ansnames, data.lids, STRING_ELT(varnames, 0));
+ for (i=0; i<data.lvalues; i++) {
+ SET_STRING_ELT(ansnames, data.lids+1+i, STRING_ELT(valnames, i));
+ }
+ setAttrib(ans, R_NamesSymbol, ansnames);
+ }
+ // should be 'free', not 'Free'. Fixes #1059
+ free(data.isfactor);
+ free(data.maxtype);
+ free(data.leach);
+ free(data.isidentical);
+ UNPROTECT(protecti);
+ return(ans);
+}
diff --git a/src/forder.c b/src/forder.c
new file mode 100644
index 0000000..a15b599
--- /dev/null
+++ b/src/forder.c
@@ -0,0 +1,1512 @@
+#include "data.table.h"
+// #define TIMING_ON
+
+/*
+ - Only forder() and *twiddle() functions are meant for use by other C code in data.table, hence all other functions here except forder and *twiddle are static.
+ - The static functions here make use of global variables, whose scope is limited to the same translation unit; therefore access from outside this file must be via forder and/or *twiddle only.
+ - The coding techniques deployed here are for efficiency; e.g. i) the static functions are recursive or called repetitively and we wish to minimise stack overhead, or ii) reach outside themselves to place results in the end result directly rather than returning small bits of memory.
+*/
+
+static int *gs[2] = {NULL}; // gs = groupsizes e.g. 23,12,87,2,1,34,...
+static int flip = 0; // two vectors flip flopped: flip and 1-flip
+static int gsalloc[2] = {0}; // allocated stack size
+static int gsngrp[2] = {0}; // used
+static int gsmax[2] = {0}; // max grpn so far
+static int gsmaxalloc = 0; // max size of stack, set by forder to nrows
+static Rboolean stackgrps = TRUE; // switched off for last column when not needed by setkey
+static Rboolean sortStr = TRUE; // TRUE for setkey, FALSE for by=
+static int *newo = NULL; // used by forder and [i|d|c]sort to reorder order. not needed if length(by)==1
+
+static int nalast = -1; // =1, 0, -1 for TRUE, NA, FALSE respectively. Value rewritten inside forder().
+ // note that na.last=NA (0) removes NAs, not retains them.
+static int order = 1; // =1, -1 for ascending and descending order respectively
+
+#define N_SMALL 200 // replaced n < 200 with n < N_SMALL. Easier to change later
+#define N_RANGE 100000 // range limit for counting sort. UPDATE: should be less than INT_MAX (see setRange for details)
+
+#define Error(...) do {savetl_end(); error(__VA_ARGS__);} while(0) // http://gcc.gnu.org/onlinedocs/cpp/Swallowing-the-Semicolon.html#Swallowing-the-Semicolon
+#undef warning
+#define warning(...) Do not use warning in this file // since it can be turned to error via warn=2
+/* use malloc/realloc (not Calloc/Realloc) so we can trap errors
+and call savetl_end() before the error(). */
+
+static void growstack(int newlen) {
+ if (newlen==0) newlen=100000; // no link to icount range restriction, just 100,000 seems a good minimum at 0.4MB.
+ if (newlen>gsmaxalloc) newlen=gsmaxalloc;
+ gs[flip] = realloc(gs[flip], newlen*sizeof(int));
+ if (gs[flip] == NULL) Error("Failed to realloc working memory stack to %d*4bytes (flip=%d)", newlen, flip);
+ gsalloc[flip] = newlen;
+}
+
+static void push(int x) {
+ if (!stackgrps || x==0) return;
+ if (gsalloc[flip] == gsngrp[flip]) growstack(gsngrp[flip]*2);
+ gs[flip][gsngrp[flip]++] = x;
+ if (x > gsmax[flip]) gsmax[flip] = x;
+}
+
+static void mpush(int x, int n) {
+ if (!stackgrps || x==0) return;
+ if (gsalloc[flip] < gsngrp[flip]+n) growstack((gsngrp[flip]+n)*2);
+ for (int i=0; i<n; i++) gs[flip][gsngrp[flip]++] = x;
+ if (x > gsmax[flip]) gsmax[flip] = x;
+}
+
+static void flipflop() {
+ flip = 1-flip;
+ gsngrp[flip] = 0;
+ gsmax[flip] = 0;
+ if (gsalloc[flip] < gsalloc[1-flip]) growstack(gsalloc[1-flip]*2);
+}
+
+static void gsfree() {
+ free(gs[0]); free(gs[1]);
+ gs[0] = NULL; gs[1]= NULL;
+ flip = 0;
+ gsalloc[0] = gsalloc[1] = 0;
+ gsngrp[0] = gsngrp[1] = 0;
+ gsmax[0] = gsmax[1] = 0;
+ gsmaxalloc = 0;
+}
+
+#ifdef TIMING_ON
+ // many calls to clock() can be expensive, hence compiled out rather than switch(verbose)
+ #include <time.h>
+ #define NBLOCK 20
+ static clock_t tblock[NBLOCK], tstart;
+ static int nblock[NBLOCK];
+ #define TBEG() tstart = clock();
+ #define TEND(i) tblock[i] += clock()-tstart; nblock[i]++; tstart = clock();
+#else
+ #define TBEG()
+ #define TEND(i)
+#endif
+
+
+/*
+ icount originally copied from do_radixsort in src/main/sort.c @ rev 51389. Then reworked here again in forder.c in v1.8.11
+ base::sort.list(method="radix") turns out not to be a radix sort, but a counting sort, and we like it.
+ See http://r.789695.n4.nabble.com/method-radix-in-sort-list-isn-t-actually-a-radix-sort-tp3309470p3309470.html
+ Main changes :
+ 1. Negatives are fine. Btw, wish raised for simple change to base R : https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15644
+ 2. Doesn't cummulate through 0's for speed in repeated calls of sparse counts by saving memset back to 0 of many 0
+ 3. Separated setRange so forder can redirect to iradix
+*/
+
+static int range, off; // used by both icount and forder
+static void setRange(int *x, int n)
+{
+ int i, tmp;
+ int xmin = NA_INTEGER, xmax = NA_INTEGER;
+ double overflow;
+
+ off = (nalast == 1) ? 0 : 1; // nalast^decreasing ? 0 : 1; // off=0 will store values starting from index 0. NAs will go last.
+ // off=1 will store values starting from index 1. NAs will be at 0th index.
+ i = 0;
+ while(i<n && x[i]==NA_INTEGER) i++;
+ if (i<n) xmax = xmin = x[i];
+ for(; i < n; i++) {
+ tmp = x[i];
+ if(tmp == NA_INTEGER) continue;
+ if (tmp > xmax) xmax = tmp;
+ else if (tmp < xmin) xmin = tmp;
+ }
+ if(xmin == NA_INTEGER) {range = NA_INTEGER; return;} // all NAs, nothing to do
+
+ overflow = (double)xmax - (double)xmin + 1; // ex: x=c(-2147483647L, NA_integer_, 1L) results in overflowing int range.
+ if (overflow > INT_MAX) {range = INT_MAX; return;} // detect and force iradix here, since icount is out of the picture
+ range = xmax-xmin+1;
+ off = order==1 ? -xmin+off : xmax+off; // so that off+order*x[i] (below in icount)
+ // => (x[i]-xmin)+0|1 or (xmax-x[i])+0|1
+ return;
+}
+
+// x*order results in integer overflow when -1*NA, so careful to avoid that here :
+static inline int icheck(int x) {
+ return ((nalast != 1) ? ((x != NA_INTEGER) ? x*order : x) : ((x != NA_INTEGER) ? (x*order)-1 : INT_MAX)); // if nalast==1, NAs must go last.
+}
+
+
+static void icount(int *x, int *o, int n)
+/* Counting sort:
+ 1. Places the ordering into o directly, overwriting whatever was there
+ 2. Doesn't change x
+ 3. Pushes group sizes onto stack
+*/
+{
+ int i=0, tmp;
+ int napos = (nalast == 1) ? range : 0; // increasing ? 0 : range // take care of 'nalast' argument
+ static unsigned int counts[N_RANGE+1] = {0}; // static is IMPORTANT, counting sort is called repetitively.
+ /* counts are set back to 0 at the end efficiently. 1e5 = 0.4MB i.e
+ tiny. We'll only use the front part of it, as large as range. So it's
+ just reserving space, not using it. Have defined N_RANGE to be 100000.*/
+ if (range > N_RANGE) Error("Internal error: range = %d; isorted can't handle range > %d", range, N_RANGE);
+ for(i=0; i<n; i++) {
+ if (x[i] == NA_INTEGER) counts[napos]++; // For nalast=NA case, we won't remove/skip NAs, rather set 'o' indices
+ else counts[off + order*x[i]]++; // to 0. subset will skip them. We can't know how many NAs to skip
+ } // beforehand - i.e. while allocating "ans" vector
+ // TO DO: at this point if the last count==n then it's all the same number and we can stop now.
+ // Idea from Terdiman, then improved on that by not needing to loop through counts.
+
+ tmp = 0; // *** BLOCK 4 ***
+ for (i=0; i<=range; i++)
+ /* no point in adding tmp<n && i<=range, since range includes max,
+ need to go to max, unlike 256 loops elsewhere in forder.c */
+ {
+ if (counts[i]) { // cumulate but not through 0's. Helps resetting zeros when n<range, below.
+ push(counts[i]);
+ counts[i] = (tmp += counts[i]);
+ }
+ }
+ for(i=n-1; i>=0; i--) {
+ o[--counts[(x[i] == NA_INTEGER) ? napos : off+order*x[i]]] = (int)(i+1); // This way na.last=TRUE/FALSE cases will have just a single if-check overhead.
+ }
+ if (nalast == 0) // nalast = 1, -1 are both taken care already.
+ for (i=0; i<n; i++) o[i] = (x[o[i]-1] == NA_INTEGER) ? 0 : o[i]; // nalast = 0 is dealt with separately as it just sets o to 0
+ // at those indices where x is NA. x[o[i]-1] because x is not modifed here.
+
+ /* counts were cumulated above so leaves non zero.
+ Faster to clear up now ready for next time. */
+ if (n < range) {
+ /* Many zeros in counts already. Loop through n instead,
+ doesn't matter if we set to 0 several times on any repeats */
+ counts[napos]=0;
+ for (i=0; i<n; i++) {
+ if (x[i]!=NA_INTEGER) counts[off + order*x[i]]=0;
+ }
+ } else {
+ memset(counts, 0, (range+1)*sizeof(int)); // *** BLOCK 6 ***
+ }
+ return;
+}
+
+static void iinsert(int *x, int *o, int n)
+/* orders both x and o by reference in-place. Fast for small vectors, low overhead.
+ don't be tempted to binsearch backwards here, have to shift anyway;
+ many memmove would have overhead and do the same thing. */
+/* when nalast == 0, iinsert will be called only from within iradix, where o[.] = 0
+ for x[.]=NA is already taken care of */
+{
+ int i, j, xtmp, otmp, tt;
+ for (i=1; i<n; i++) {
+ xtmp = x[i];
+ if (xtmp < x[i-1]) {
+ j = i-1;
+ otmp = o[i];
+ while (j>=0 && xtmp < x[j]) {
+ x[j+1] = x[j];
+ o[j+1] = o[j];
+ j--;
+ }
+ x[j+1] = xtmp;
+ o[j+1] = otmp;
+ }
+ }
+ tt = 0;
+ for (i=1; i<n; i++) if (x[i]==x[i-1]) tt++; else { push(tt+1); tt=0; }
+ push(tt+1);
+}
+
+/*
+ iradix is a counting sort performed forwards from MSB to LSB, with some tricks
+ and short circuits building on Terdiman and Herf.
+ http://codercorner.com/RadixSortRevisited.htm
+ http://stereopsis.com/radix.html
+
+ ~ Note they are LSD, but we do MSD here which is more complicated, for efficiency.
+ ~ NAs need no special treatment as NA is the most negative integer in R (checked in init.c once,
+ for efficiency) so NA naturally sort to the front.
+ ~ Using 4-pass 1-byte radix for the following reasons :
+ * 11-bit (Herf) reduces to 3-passes (3*11=33) yes, and LSD need random access
+ to o vector in each pass 1:n so reduction in passes
+ * is good, but ...
+ * ... Terdiman's idea to skip a radix if all values are equal occurs less the wider the
+ radix. A narrower radix benefits more from that.
+ * That's detected here using a single 'if', an improvement on Terdiman's exposition
+ of a single loop to find if any count==n
+ * The pass through counts bites when radix is wider, because we repetitively call this
+ iradix from fastorder forwards.
+ * Herf's parallel histogramming is neat. In 4-pass 1-byte it needs 4*256 storage, that's
+ tiny, and can be static. 4*256 << 3*2048
+ * 4-pass 1-byte is simpler and tighter code than 3-pass 11-bit, giving modern optimizers
+ and modern CPUs a better chance. We may get
+ * lucky anyway, if one or two of the 4-passes are skipped.
+
+ Recall: there are no comparisons at all in counting and radix, there is wide random access
+ in each LSD radix pass, though.
+*/
+
+static unsigned int radixcounts[8][257] = {{0}}; // 4 are used for iradix, 8 for dradix and i64radix
+static int skip[8];
+/* global because iradix and iradix_r interact and are called repetitively.
+ counts are set back to 0 after each use, to benefit from skipped radix. */
+static void *radix_xsub=NULL;
+static size_t radix_xsuballoc=0;
+
+static int *otmp=NULL, otmp_alloc=0;
+static void alloc_otmp(int n) {
+ if (otmp_alloc >= n) return;
+ otmp = (int *)realloc(otmp, n * sizeof(int));
+ if (otmp == NULL) Error("Failed to allocate working memory for otmp. Requested %d * %d bytes", n, sizeof(int));
+ otmp_alloc = n;
+}
+
+static void *xtmp=NULL; int xtmp_alloc=0; // TO DO: save xtmp if possible, see allocs in forder
+static void alloc_xtmp(int n) { // TO DO: currently always the largest type (double) but
+ // could be int if that's all that's needed
+ if (xtmp_alloc >= n) return;
+ xtmp = (double *)realloc(xtmp, n * sizeof(double));
+ if (xtmp == NULL) Error("Failed to allocate working memory for xtmp. Requested %d * %d bytes", n, sizeof(double));
+ xtmp_alloc = n;
+}
+
+static void iradix_r(int *xsub, int *osub, int n, int radix);
+
+static void iradix(int *x, int *o, int n)
+ /* As icount :
+ Places the ordering into o directly, overwriting whatever was there
+ Doesn't change x
+ Pushes group sizes onto stack */
+{
+ int i, j, radix, nextradix, itmp, thisgrpn, maxgrpn;
+ unsigned int thisx=0, shift, *thiscounts;
+
+ for (i=0;i<n;i++) {
+ /* parallel histogramming pass; i.e. count occurrences of
+ 0:255 in each byte. Sequential so almost negligible. */
+ thisx = (unsigned int)(icheck(x[i])) - INT_MIN; // relies on overflow behaviour. And shouldn't -INT_MIN be up in iradix?
+ radixcounts[0][thisx & 0xFF]++; // unrolled since inside n-loop
+ radixcounts[1][thisx >> 8 & 0xFF]++;
+ radixcounts[2][thisx >> 16 & 0xFF]++;
+ radixcounts[3][thisx >> 24 & 0xFF]++;
+ }
+ for (radix=0; radix<4; radix++) {
+ /* any(count == n) => all radix must have been that value =>
+ last x (still thisx) was that value */
+ i = thisx >> (radix*8) & 0xFF;
+ skip[radix] = radixcounts[radix][i] == n;
+ if (skip[radix]) radixcounts[radix][i] = 0; // clear it now, the other counts must be 0 already
+ }
+
+ radix = 3; // MSD
+ while (radix>=0 && skip[radix]) radix--;
+ if (radix==-1) { // All radix are skipped; i.e. one number repeated n times.
+ if (nalast == 0 && x[0] == NA_INTEGER) // all values are identical. return 0 if nalast=0 & all NA
+ for (i=0; i<n; i++) o[i] = 0; // because of 'return', have to take care of it here.
+ else for (i=0; i<n; i++) o[i] = (i+1);
+ push(n);
+ return;
+ }
+ for (i=radix-1; i>=0; i--) {
+ if (!skip[i]) memset(radixcounts[i], 0, 257*sizeof(unsigned int));
+ /* clear the counts as we only needed the parallel pass for skip[]
+ and we're going to use radixcounts again below. Can't use parallel
+ lower counts in MSD radix, unlike LSD. */
+ }
+ thiscounts = radixcounts[radix];
+ shift = radix * 8;
+
+ itmp = thiscounts[0];
+ maxgrpn = itmp;
+ for (i=1; itmp<n && i<256; i++) {
+ thisgrpn = thiscounts[i];
+ if (thisgrpn) { // don't cummulate through 0s, important below.
+ if (thisgrpn>maxgrpn) maxgrpn = thisgrpn;
+ thiscounts[i] = (itmp += thisgrpn);
+ }
+ }
+ for (i=n-1; i>=0; i--) {
+ thisx = ((unsigned int)(icheck(x[i])) - INT_MIN) >> shift & 0xFF;
+ o[--thiscounts[thisx]] = i+1;
+ }
+
+ if (radix_xsuballoc < maxgrpn) {
+ // The largest group according to the first non-skipped radix, so could be big (if radix is needed on first column)
+ // TO DO: could include extra bits to divide the first radix up more. Often the MSD has groups in just 0-4 out of 256.
+ // free'd at the end of forder once we're done calling iradix repetitively
+ radix_xsub = (int *)realloc(radix_xsub, maxgrpn*sizeof(double)); // realloc(NULL) == malloc
+ if (!radix_xsub) Error("Failed to realloc working memory %d*8bytes (xsub in iradix), radix=%d", maxgrpn, radix);
+ radix_xsuballoc = maxgrpn;
+ }
+
+ alloc_otmp(maxgrpn); // TO DO: can we leave this to forder and remove these calls??
+ alloc_xtmp(maxgrpn); // TO DO: doesn't need to be sizeof(double) always, see inside
+
+ nextradix = radix-1;
+ while (nextradix>=0 && skip[nextradix]) nextradix--;
+ if (thiscounts[0] != 0) Error("Internal error. thiscounts[0]=%d but should have been decremented to 0. dradix=%d", thiscounts[0], radix);
+ thiscounts[256] = n;
+ itmp = 0;
+ for (i=1; itmp<n && i<=256; i++) {
+ if (thiscounts[i] == 0) continue;
+ thisgrpn = thiscounts[i] - itmp; // undo cumulate; i.e. diff
+ if (thisgrpn == 1 || nextradix==-1) {
+ push(thisgrpn);
+ } else {
+ for (j=0; j<thisgrpn; j++)
+ ((int *)radix_xsub)[j] = icheck(x[o[itmp+j]-1]); // this is why this xsub here can't be the same memory as xsub in forder.
+ iradix_r(radix_xsub, o+itmp, thisgrpn, nextradix); // changes xsub and o by reference recursively.
+ }
+ itmp = thiscounts[i];
+ thiscounts[i] = 0;
+ }
+ if (nalast == 0) // nalast = 1, -1 are both taken care already.
+ for (i=0; i<n; i++) o[i] = (x[o[i]-1] == NA_INTEGER) ? 0 : o[i]; // nalast = 0 is dealt with separately as it just sets o to 0
+ // at those indices where x is NA. x[o[i]-1] because x is not
+ // modified by reference unlike iinsert or iradix_r
+}
+
+static void iradix_r(int *xsub, int *osub, int n, int radix)
+ // xsub is a recursive offset into xsub working memory above in iradix, reordered by reference.
+ // osub is a an offset into the main answer o, reordered by reference.
+ // radix iterates 3,2,1,0
+{
+ int i, j, itmp, thisx, thisgrpn, nextradix, shift;
+ unsigned int *thiscounts;
+
+ if (n < N_SMALL) { // N_SMALL=200 is guess based on limited testing. Needs calibrate().
+ // Was 50 based on sum(1:50)=1275 worst -vs- 256 cummulate + 256 memset + allowance since reverse order is unlikely.
+ iinsert(xsub, osub, n); // when nalast==0, iinsert will be called only from within iradix.
+ return;
+ }
+
+ shift = radix*8;
+ thiscounts = radixcounts[radix];
+
+ for (i=0; i<n; i++) {
+ thisx = (unsigned int)xsub[i] - INT_MIN; // sequential in xsub
+ thiscounts[thisx >> shift & 0xFF]++;
+ }
+ itmp = thiscounts[0];
+ for (i=1; itmp<n && i<256; i++)
+ if (thiscounts[i]) thiscounts[i] = (itmp += thiscounts[i]); // don't cummulate through 0s, important below
+ for (i=n-1; i>=0; i--) {
+ thisx = ((unsigned int)xsub[i] - INT_MIN) >> shift & 0xFF;
+ j = --thiscounts[thisx];
+ otmp[j] = osub[i];
+ ((int *)xtmp)[j] = xsub[i];
+ }
+ memcpy(osub, otmp, n*sizeof(int));
+ memcpy(xsub, xtmp, n*sizeof(int));
+
+ nextradix = radix-1;
+ while (nextradix>=0 && skip[nextradix]) nextradix--;
+ /* TO DO: If nextradix==-1 AND no further columns from forder AND !retGrp, we're
+ done. We have o. Remember to memset thiscounts before returning. */
+
+ if (thiscounts[0] != 0) Error("Logical error. thiscounts[0]=%d but should have been decremented to 0. radix=%d", thiscounts[0], radix);
+ thiscounts[256] = n;
+ itmp = 0;
+ for (i=1; itmp<n && i<=256; i++) {
+ if (thiscounts[i] == 0) continue;
+ thisgrpn = thiscounts[i] - itmp; // undo cummulate; i.e. diff
+ if (thisgrpn == 1 || nextradix==-1) {
+ push(thisgrpn);
+ } else {
+ iradix_r(xsub+itmp, osub+itmp, thisgrpn, nextradix);
+ }
+ itmp = thiscounts[i];
+ thiscounts[i] = 0;
+ }
+}
+
+// dradix from Arun's fastradixdouble.c
+// + changed to MSD and hooked into forder framework here.
+// + replaced tolerance with rounding s.f.
+
+static int dround = 2;
+static unsigned long long dmask1;
+static unsigned long long dmask2;
+
+SEXP setNumericRounding(SEXP droundArg)
+// init.c has initial call with default of 2
+{
+ if (!isInteger(droundArg) || LENGTH(droundArg)!=1) error("Must an integer or numeric vector length 1");
+ if (INTEGER(droundArg)[0] < 0 || INTEGER(droundArg)[0] > 2) error("Must be 2 (default) or 1 or 0");
+ dround = INTEGER(droundArg)[0];
+ dmask1 = dround ? 1 << (8*dround-1) : 0;
+ dmask2 = 0xffffffffffffffff << dround*8;
+ return R_NilValue;
+}
+
+SEXP getNumericRounding()
+{
+ return ScalarInteger(dround);
+}
+
+static union {double d;
+ unsigned long long ull;} u;
+ // int i;
+ // unsigned int ui;} u;
+
+unsigned long long dtwiddle(void *p, int i, int order)
+{
+ u.d = order*((double *)p)[i]; // take care of 'order' right at the beginning
+ if (R_FINITE(u.d)) {
+ u.ull = (u.d) ? u.ull + ((u.ull & dmask1) << 1) : 0; // handle 0, -0 case. Fix for issues/743.
+ // tested on vector length 100e6. was the fastest fix (see results at the bottom of page)
+ } else if (ISNAN(u.d)) {
+ /* 1. NA twiddled to all bits 0, sorts first. R's value 1954 cleared.
+ 2. NaN twiddled to set just bit 13, sorts immediately after NA. 13th bit to be
+ consistent with "quiet" na bit but any bit outside last 2 bytes would do.
+ (ref: http://r.789695.n4.nabble.com/Question-re-NA-NaNs-in-R-td4685014.html)
+ 3. This also normalises a difference between NA on 32bit R (bit 13 set) and 64bit R (bit 13 not set)
+ 4. -Inf twiddled to : 0 sign, exponent all 0, mantissa all 1, sorts after NaN
+ 5. +Inf twiddled to : 1 sign, exponent all 1, mantissa all 0, sorts last since finite
+ numbers are defined by not-all-1 in exponent */
+ u.ull = (ISNA(u.d) ? 0 : (1ULL << 51));
+ return (nalast == 1 ? ~u.ull : u.ull);
+ }
+ unsigned long long mask = (u.ull & 0x8000000000000000) ?
+ 0xffffffffffffffff : 0x8000000000000000; // always flip sign bit and if negative (sign bit was set) flip other bits too
+ return( (u.ull ^ mask) & dmask2 );
+}
+
+unsigned long long i64twiddle(void *p, int i, int order)
+// 'order' is in effect now - ascending and descending order implemented. Default
+// case (setkey) will not be affected much because nalast != 1 and order == 1 are
+// defaults.
+{
+ u.d = ((double *)p)[i];
+ u.ull ^= 0x8000000000000000;
+ if (nalast != 1) {
+ if (order != 1 && u.ull != 0) u.ull ^= 0xffffffffffffffff;
+ } else {
+ if ( (order == 1 && u.ull==0) || (order != 1) )
+ u.ull ^= 0xffffffffffffffff;
+ }
+ // another way - equivalent as above.
+ // if (order == 1) {// u.ull = 0 now means NA
+ // if (nalast == 1 && u.ull == 0) u.ull ^= 0xffffffffffffffff;
+ // } else {
+ // if (!(nalast != 1 && u.ull == 0)) u.ull ^= 0xffffffffffffffff;
+ // }
+ return u.ull;
+}
+
+Rboolean dnan(void *p, int i) {
+ u.d = ((double *)p)[i];
+ return (ISNAN(u.d));
+}
+
+Rboolean i64nan(void *p, int i) {
+ u.d = ((double *)p)[i];
+ return ((u.ull ^ 0x8000000000000000) == 0);
+}
+
+/*
+unsigned long long i32twiddle(void *p, int i)
+{
+ u.i = ((int *)p)[i]; // a cast of int to long would introduce 31 redundant bits after the sign bit (better to be packed together in 32)
+ unsigned int mask = -(int)(u.ui >> 31) | 0x80000000;
+ u.ui ^= mask;
+ return( u.ull );
+}
+*/
+
+unsigned long long (*twiddle)(void *, int, int);
+// integer64 has NA = 0x8000000000000000. And it gives TRUE for all ISNAN(.) when '.' is -ve number.
+// So, ISNAN(.) would just provide wrong results. This was particularly an issue while implementing
+// DT[order(., na.last=NA)] where '.' is an integer64 column. Therefore, 'is_nan'. This is basically
+// ISNAN(.) for double and (u.ull ^ 0x8000000000000000 == 0) for integer64.
+Rboolean (*is_nan)(void *, int);
+size_t colSize=8; // the size of the column type (4 or 8). Just 8 currently until iradix is merged in.
+
+static void dradix_r(unsigned char *xsub, int *osub, int n, int radix);
+
+#ifdef WORDS_BIGENDIAN
+#define RADIX_BYTE colSize-radix-1
+#else
+#define RADIX_BYTE radix
+#endif
+
+static void dradix(unsigned char *x, int *o, int n)
+{
+ int i, j, radix, nextradix, itmp, thisgrpn, maxgrpn;
+ unsigned int *thiscounts;
+ unsigned long long thisx=0;
+ // see comments in iradix for structure. This follows the same. TO DO: merge iradix in here (almost ready)
+ for (i=0;i<n;i++) {
+ thisx = twiddle(x,i,order);
+ for (radix=0; radix<colSize; radix++)
+ radixcounts[radix][((unsigned char *)&thisx)[RADIX_BYTE]]++;
+ // if dround==2 then radix 0 and 1 will be all 0 here and skipped.
+ /* on little endian, 0 is the least significant bits (the right)
+ / and 7 is the most including sign (the left); i.e. reversed. */
+ }
+ for (radix=0; radix<colSize; radix++) {
+ i = ((unsigned char *)&thisx)[RADIX_BYTE]; // thisx is the last x after loop above
+ skip[radix] = radixcounts[radix][i] == n;
+ if (skip[radix]) radixcounts[radix][i] = 0; // clear it now, the other counts must be 0 already
+ }
+ radix = colSize-1; // MSD
+ while (radix>=0 && skip[radix]) radix--;
+ if (radix==-1) { // All radix are skipped; i.e. one number repeated n times.
+ if (nalast == 0 && is_nan(x, 0)) // all values are identical. return 0 if nalast=0 & all NA
+ for (i=0; i<n; i++) o[i] = 0; // because of 'return', have to take care of it here.
+ else for (i=0; i<n; i++) o[i] = (i+1);
+ push(n);
+ return;
+ }
+ for (i=radix-1; i>=0; i--) { // clear the lower radix counts, we only did them to know skip. will be reused within each group
+ if (!skip[i]) memset(radixcounts[i], 0, 257*sizeof(unsigned int));
+ }
+ thiscounts = radixcounts[radix];
+ itmp = thiscounts[0];
+ maxgrpn = itmp;
+ for (i=1; itmp<n && i<256; i++) {
+ thisgrpn = thiscounts[i];
+ if (thisgrpn) { // don't cummulate through 0s, important below
+ if (thisgrpn>maxgrpn) maxgrpn = thisgrpn;
+ thiscounts[i] = (itmp += thisgrpn);
+ }
+ }
+ for (i=n-1; i>=0; i--) {
+ thisx = twiddle(x,i,order);
+ o[ --thiscounts[((unsigned char *)&thisx)[RADIX_BYTE]] ] = i+1;
+ }
+
+ if (radix_xsuballoc < maxgrpn) { // TO DO: centralize this alloc
+ // The largest group according to the first non-skipped radix, so could be big (if radix is needed on first column)
+ // TO DO: could include extra bits to divide the first radix up more. Often the MSD has groups in just 0-4 out of 256.
+ // free'd at the end of forder once we're done calling iradix repetitively
+ radix_xsub = (double *)realloc(radix_xsub, maxgrpn*sizeof(double)); // realloc(NULL) == malloc
+ if (!radix_xsub) Error("Failed to realloc working memory %d*8bytes (xsub in dradix), radix=%d", maxgrpn, radix);
+ radix_xsuballoc = maxgrpn;
+ }
+
+ alloc_otmp(maxgrpn); // TO DO: leave to forder and remove these calls?
+ alloc_xtmp(maxgrpn);
+
+ nextradix = radix-1;
+ while (nextradix>=0 && skip[nextradix]) nextradix--;
+ if (thiscounts[0] != 0) Error("Logical error. thiscounts[0]=%d but should have been decremented to 0. dradix=%d", thiscounts[0], radix);
+ thiscounts[256] = n;
+ itmp = 0;
+ for (i=1; itmp<n && i<=256; i++) {
+ if (thiscounts[i] == 0) continue;
+ thisgrpn = thiscounts[i] - itmp; // undo cummulate; i.e. diff
+ if (thisgrpn == 1 || nextradix==-1) {
+ push(thisgrpn);
+ } else {
+ if (colSize==4) { // ready for merging in iradix ...
+ error("Not yet used, still using iradix instead");
+ for (j=0; j<thisgrpn; j++) ((int *)radix_xsub)[j] = twiddle(x, o[itmp+j]-1, order);
+ } else for (j=0; j<thisgrpn; j++) ((unsigned long long *)radix_xsub)[j] = twiddle(x, o[itmp+j]-1, order); // this is why this xsub here can't be the same memory as xsub in forder
+ dradix_r(radix_xsub, o+itmp, thisgrpn, nextradix); // changes xsub and o by reference recursively.
+ }
+ itmp = thiscounts[i];
+ thiscounts[i] = 0;
+ }
+ if (nalast == 0) // nalast = 1, -1 are both taken care already.
+ for (i=0; i<n; i++) o[i] = is_nan(x, o[i]-1) ? 0 : o[i]; // nalast = 0 is dealt with separately as it just sets o to 0
+ // at those indices where x is NA. x[o[i]-1] because x is not
+ // modified by reference unlike iinsert or iradix_r
+
+}
+
+static void dinsert(unsigned long long *x, int *o, int n)
+// orders both x and o by reference in-place. Fast for small vectors, low overhead.
+// don't be tempted to binsearch backwards here, have to shift anyway; many memmove would have overhead and do the same thing
+// 'dinsert' will not be called when nalast = 0 and o[0] = -1.
+{
+ int i, j, otmp, tt;
+ unsigned long long xtmp;
+ for (i=1; i<n; i++) {
+ xtmp = x[i];
+ if (xtmp < x[i-1]) {
+ j = i-1;
+ otmp = o[i];
+ while (j>=0 && xtmp < x[j]) { x[j+1] = x[j]; o[j+1] = o[j]; j--; }
+ x[j+1] = xtmp;
+ o[j+1] = otmp;
+ }
+ }
+ tt = 0;
+ for (i=1; i<n; i++) if (x[i]==x[i-1]) tt++; else { push(tt+1); tt=0; }
+ push(tt+1);
+}
+
+static void dradix_r(unsigned char *xsub, int *osub, int n, int radix)
+ /* xsub is a recursive offset into xsub working memory above in dradix, reordered by reference.
+ osub is a an offset into the main answer o, reordered by reference.
+ dradix iterates 7,6,5,4,3,2,1,0 */
+{
+ int i, j, itmp, thisgrpn, nextradix;
+ unsigned int *thiscounts;
+ unsigned char *p;
+ if (n < 200) {
+ /* 200 is guess based on limited testing. Needs calibrate(). Was 50
+ based on sum(1:50)=1275 worst -vs- 256 cummulate + 256 memset +
+ allowance since reverse order is unlikely */
+ dinsert((void *)xsub, osub, n); // order=1 here because it's already taken care of in iradix
+ return;
+ }
+ thiscounts = radixcounts[radix];
+ p = xsub + RADIX_BYTE;
+ for (i=0; i<n; i++) {
+ thiscounts[*p]++;
+ p += colSize;
+ }
+ itmp = thiscounts[0];
+ for (i=1; itmp<n && i<256; i++)
+ if (thiscounts[i]) thiscounts[i] = (itmp += thiscounts[i]); // don't cummulate through 0s, important below
+ p = xsub + (n-1)*colSize;
+ if (colSize == 4) {
+ error("Not yet used, still using iradix instead");
+ for (i=n-1; i>=0; i--) {
+ j = --thiscounts[*(p+RADIX_BYTE)];
+ otmp[j] = osub[i];
+ ((int *)xtmp)[j] = *(int *)p;
+ p -= colSize;
+ }
+ } else {
+ for (i=n-1; i>=0; i--) {
+ j = --thiscounts[*(p+RADIX_BYTE)];
+ otmp[j] = osub[i];
+ ((unsigned long long *)xtmp)[j] = *(unsigned long long *)p;
+ p -= colSize;
+ }
+ }
+ memcpy(osub, otmp, n*sizeof(int));
+ memcpy(xsub, xtmp, n*colSize);
+
+ nextradix = radix-1;
+ while (nextradix>=0 && skip[nextradix]) nextradix--;
+ // TO DO: If nextradix==-1 and no further columns from forder, we're done. We have o. Remember to memset thiscounts before returning.
+
+ if (thiscounts[0] != 0) Error("Logical error. thiscounts[0]=%d but should have been decremented to 0. radix=%d", thiscounts[0], radix);
+ thiscounts[256] = n;
+ itmp = 0;
+ for (i=1; itmp<n && i<=256; i++) {
+ if (thiscounts[i] == 0) continue;
+ thisgrpn = thiscounts[i] - itmp; // undo cummulate; i.e. diff
+ if (thisgrpn == 1 || nextradix==-1) {
+ push(thisgrpn);
+ } else {
+ dradix_r(xsub + itmp*colSize, osub+itmp, thisgrpn, nextradix);
+ }
+ itmp = thiscounts[i];
+ thiscounts[i] = 0;
+ }
+}
+
+// TO DO?: dcount. Find step size, then range = (max-min)/step and proceed as icount. Many fixed precision floats (such as prices)
+// may be suitable. Fixed precision such as 1.10, 1.15, 1.20, 1.25, 1.30 ... do use all bits so dradix skipping may not help.
+
+static int *cradix_counts = NULL;
+static int cradix_counts_alloc = 0;
+static int maxlen = 1;
+static SEXP *cradix_xtmp = NULL;
+static int cradix_xtmp_alloc = 0;
+
+int StrCmp2(SEXP x, SEXP y) { // same as StrCmp but also takes into account 'na.last' argument.
+ if (x == y) return 0; // same cached pointer (including NA_STRING==NA_STRING)
+ if (x == NA_STRING) return nalast; // if x=NA, nalast=1 ? then x > y else x < y (Note: nalast == 0 is already taken care of in 'csorted', won't be 0 here)
+ if (y == NA_STRING) return -nalast; // if y=NA, nalast=1 ? then y > x
+ return order*strcmp(CHAR(x), CHAR(y)); // same as explanation in StrCmp
+}
+
+int StrCmp(SEXP x, SEXP y) // also used by bmerge and chmatch
+{
+ if (x == y) return 0; // same cached pointer (including NA_STRING==NA_STRING)
+ if (x == NA_STRING) return -1; // x<y
+ if (y == NA_STRING) return 1; // x>y
+ return strcmp(CHAR(x), CHAR(y)); // can return 0 here for the same string in known and unknown encodings, good if the unknown string is in that encoding but not if not
+} // ordering is ascii only (C locale). TO DO: revisit and allow user to change to strcoll, and take account of Encoding
+ // see comments in bmerge(). 10k calls of strcmp = 0.37s, 10k calls of strcoll = 4.7s. See ?Comparison, ?Encoding, Scollate in R internals.
+// TO DO: check that all unknown encodings are ascii; i.e. no non-ascii unknowns are present, and that either Latin1
+// or UTF-8 is used by user, not both. Then error if not. If ok, then can proceed with byte level. ascii is never marked known by R, but non-ascii (i.e. knowable encoding) could be marked unknown.
+// does R internals have is_ascii function exported? If not, simple enough.
+
+static void cradix_r(SEXP *xsub, int n, int radix)
+// xsub is a unique set of CHARSXP, to be ordered by reference
+// First time, radix==0, and xsub==x. Then recursively moves SEXP together for L1 cache efficiency.
+// Quite different to iradix because
+// 1) x is known to be unique so fits in cache (wide random access not an issue)
+// 2) they're variable length character strings
+// 3) no need to maintain o. Just simply reorder x. No grps or push.
+// Fortunately, UTF sorts in the same order if treated as ASCII, so we can simplify by doing it by bytes. TO DO: confirm
+// a forwards (MSD) radix for efficiency, although more complicated
+// This part has nothing to do with truelength. The truelength stuff is to do with finding the unique strings.
+// We may be able to improve CHARSXP derefencing by submitting patch to R to make R's string cache contiguous
+// but would likely be difficult. If we strxfrm, then it'll then be contiguous and compact then anyway.
+{
+ int i, j, itmp, *thiscounts, thisgrpn=0, thisx=0;
+ SEXP stmp;
+
+ // TO DO?: chmatch to existing sorted vector, then grow it.
+ // TO DO?: if (n<N_SMALL=200) insert sort, then loop through groups via ==
+ if (n<=1) return;
+ if (n==2) {
+ if (StrCmp(xsub[1],xsub[0])<0) { stmp=xsub[0]; xsub[0]=xsub[1]; xsub[1]=stmp; }
+ return;
+ }
+ // TO DO: if (n<50) cinsert (continuing from radix offset into CHAR) or using StrCmp. But 256 is narrow, so quick and not too much an issue.
+
+ thiscounts = cradix_counts + radix*256;
+ for (i=0; i<n; i++) {
+ thisx = xsub[i]==NA_STRING ? 0 : (radix<LENGTH(xsub[i]) ? (unsigned char)(CHAR(xsub[i])[radix]) : 1);
+ thiscounts[ thisx ]++; // 0 for NA, 1 for ""
+ }
+ if (thiscounts[thisx] == n && radix < maxlen-1) { // this also catches when subx has shorter strings than the rest, thiscounts[0]==n and we'll recurse very quickly through to the overall maxlen with no 256 overhead each time
+ cradix_r(xsub, n, radix+1);
+ thiscounts[thisx] = 0; // the rest must be 0 already, save the memset
+ return;
+ }
+ itmp = thiscounts[0];
+ for (i=1; i<256; i++)
+ if (thiscounts[i]) thiscounts[i] = (itmp += thiscounts[i]); // don't cummulate through 0s, important below
+ for (i=n-1; i>=0; i--) {
+ thisx = xsub[i]==NA_STRING ? 0 : (radix<LENGTH(xsub[i]) ? (unsigned char)(CHAR(xsub[i])[radix]) : 1);
+ j = --thiscounts[thisx];
+ cradix_xtmp[j] = xsub[i];
+ }
+ memcpy(xsub, cradix_xtmp, n*sizeof(SEXP));
+ if (radix == maxlen-1) {
+ memset(thiscounts, 0, 256*sizeof(int));
+ return;
+ }
+ if (thiscounts[0] != 0) Error("Logical error. counts[0]=%d in cradix but should have been decremented to 0. radix=%d", thiscounts[0], radix);
+ itmp = 0;
+ for (i=1;i<256;i++) {
+ if (thiscounts[i] == 0) continue;
+ thisgrpn = thiscounts[i] - itmp; // undo cummulate; i.e. diff
+ cradix_r(xsub+itmp, thisgrpn, radix+1);
+ itmp = thiscounts[i];
+ thiscounts[i] = 0; // set to 0 now since we're here, saves memset afterwards. Important to clear! Also more portable for machines where 0 isn't all bits 0 (?!)
+ }
+ if (itmp<n-1) cradix_r(xsub+itmp, n-itmp, radix+1); // final group
+}
+
+static SEXP *ustr = NULL;
+static int ustr_alloc = 0, ustr_n = 0;
+
+static void cgroup(SEXP *x, int *o, int n)
+// As icount :
+// Places the ordering into o directly, overwriting whatever was there
+// Doesn't change x
+// Pushes group sizes onto stack
+// Only run when sortStr==FALSE. Basically a counting sort, in first appearance order, directly.
+// Since it doesn't sort the strings, the name is cgroup.
+// there is no _pre for this. ustr created and cleared each time.
+{
+ SEXP s;
+ int i, k, cumsum;
+ // savetl_init() is called once at the start of forder
+ if (ustr_n != 0) Error("Internal error. ustr isn't empty when starting cgroup: ustr_n=%d, ustr_alloc=%d", ustr_n, ustr_alloc);
+ for(i=0; i<n; i++) {
+ s = x[i];
+ if (TRUELENGTH(s)<0) { // this case first as it's the most frequent
+ SET_TRUELENGTH(s, TRUELENGTH(s)-1); // use negative counts so as to detect R's own (positive) usage of tl on CHARSXP
+ continue;
+ }
+ if (TRUELENGTH(s)>0) { // Save any of R's own usage of tl (assumed positive, so we can both count and save in one scan), to restore
+ savetl(s); // afterwards. From R 2.14.0, tl is initialized to 0, prior to that it was random so this step saved too much.
+ SET_TRUELENGTH(s,0);
+ }
+ if (ustr_alloc<=ustr_n) {
+ ustr_alloc = (ustr_alloc == 0) ? 10000 : ustr_alloc*2; // 10000 = 78k of 8byte pointers. Small initial guess, negligible time to alloc.
+ if (ustr_alloc>n) ustr_alloc = n;
+ ustr = realloc(ustr, ustr_alloc * sizeof(SEXP));
+ if (ustr == NULL) Error("Unable to realloc %d * %d bytes in cgroup", ustr_alloc, sizeof(SEXP));
+ }
+ SET_TRUELENGTH(s, -1);
+ ustr[ustr_n++] = s;
+ }
+ // TO DO: the same string in different encodings will be considered different here. Sweep through ustr and merge counts where equal (sort needed therefore, unfortunately?, only if there are any marked encodings present)
+ cumsum = 0;
+ for(i=0; i<ustr_n; i++) { // 0.000
+ push(-TRUELENGTH(ustr[i]));
+ SET_TRUELENGTH(ustr[i], cumsum += -TRUELENGTH(ustr[i]));
+ }
+ int *target = (o[0] != -1) ? newo : o;
+ for(i=n-1; i>=0; i--) {
+ s = x[i]; // 0.400 (page fetches on string cache)
+ SET_TRUELENGTH(s, k = TRUELENGTH(s)-1);
+ target[k] = i+1; // 0.800 (random access to o)
+ }
+ for(i=0; i<ustr_n; i++) SET_TRUELENGTH(ustr[i],0); // The cummulate meant counts are left non zero, so reset for next time (0.00s).
+ ustr_n = 0;
+}
+
+static int *csort_otmp=NULL, csort_otmp_alloc=0;
+static void alloc_csort_otmp(int n) {
+ if (csort_otmp_alloc >= n) return;
+ csort_otmp = (int *)realloc(csort_otmp, n * sizeof(int));
+ if (csort_otmp == NULL) Error("Failed to allocate working memory for csort_otmp. Requested %d * %d bytes", n, sizeof(int));
+ csort_otmp_alloc = n;
+}
+
+static void csort(SEXP *x, int *o, int n)
+/*
+ As icount :
+ Places the ordering into o directly, overwriting whatever was there
+ Doesn't change x
+ Pushes group sizes onto stack
+ Requires csort_pre() to have created and sorted ustr already
+*/
+{
+ int i;
+ /* can't use otmp, since iradix might be called here and that uses otmp (and xtmp).
+ alloc_csort_otmp(n) is called from forder for either n=nrow if 1st column,
+ or n=maxgrpn if onwards columns */
+ for(i=0; i<n; i++) csort_otmp[i] = (x[i] == NA_STRING) ? NA_INTEGER : -TRUELENGTH(x[i]);
+ if (nalast == 0 && n == 2) { // special case for nalast==0. n==1 is handled inside forder. at least 1 will be NA here
+ if (o[0] == -1) for (i=0; i<n; i++) o[i] = i+1; // else use o from caller directly (not 1st column)
+ for (int i=0; i<n; i++) if (csort_otmp[i] == NA_INTEGER) o[i] = 0;
+ push(1); push(1);
+ return;
+ }
+ if (n < N_SMALL && nalast != 0) { // TO DO: calibrate() N_SMALL=200
+ if (o[0] == -1) for (i=0; i<n; i++) o[i] = i+1; // else use o from caller directly (not 1st column)
+ for (int i=0; i<n; i++) csort_otmp[i] = icheck(csort_otmp[i]);
+ iinsert(csort_otmp, o, n);
+ } else {
+ setRange(csort_otmp, n);
+ if (range == NA_INTEGER) Error("Internal error. csort's otmp contains all-NA");
+ int *target = (o[0] != -1) ? newo : o;
+ if (range <= N_RANGE) // && range<n) // TO DO: calibrate(). radix was faster (9.2s "range<=10000" instead of 11.6s
+ icount(csort_otmp, target, n); // "range<=N_RANGE && range<n") for run(7) where range=N_RANGE n=10000000
+ else iradix(csort_otmp, target, n);
+ }
+ // all i* push onto stack. Using their counts may be faster here than thrashing SEXP fetches over several passes as cgroup does
+ // (but cgroup needs that to keep orginal order, and cgroup saves the sort in csort_pre).
+}
+
+static void csort_pre(SEXP *x, int n)
+// Finds ustr and sorts it.
+// Runs once for each column (if sortStr==TRUE), then ustr is used by csort within each group
+// ustr is grown on each character column, to save sorting the same strings again if several columns contain the same strings
+{
+ SEXP s;
+ int i, old_un, new_un;
+ // savetl_init() is called once at the start of forder
+ old_un = ustr_n;
+ for(i=0; i<n; i++) {
+ s = x[i];
+ if (TRUELENGTH(s)<0) continue; // this case first as it's the most frequent. Already in ustr, this negative is its ordering.
+ if (TRUELENGTH(s)>0) { // Save any of R's own usage of tl (assumed positive, so we can both count and save in one scan), to restore
+ savetl(s); // afterwards. From R 2.14.0, tl is initialized to 0, prior to that it was random so this step saved too much.
+ SET_TRUELENGTH(s,0);
+ }
+ if (ustr_alloc<=ustr_n) {
+ ustr_alloc = (ustr_alloc == 0) ? 10000 : ustr_alloc*2; // 10000 = 78k of 8byte pointers. Small initial guess, negligible time to alloc.
+ if (ustr_alloc > old_un+n) ustr_alloc = old_un + n;
+ ustr = realloc(ustr, ustr_alloc * sizeof(SEXP));
+ if (ustr==NULL) Error("Failed to realloc ustr. Requested %d * %d bytes", ustr_alloc, sizeof(SEXP));
+ }
+ SET_TRUELENGTH(s, -1); // this -1 will become its ordering later below
+ ustr[ustr_n++] = s;
+ if (s!=NA_STRING && LENGTH(s)>maxlen) maxlen=LENGTH(s); // length on CHARSXP is the nchar of char * (excluding \0), and treats marked encodings as if ascii.
+ }
+ new_un = ustr_n;
+ if (new_un == old_un) return; // No new strings observed, seen them all before in previous column. ustr already sufficient.
+ // If we ever make ustr permanently held by data.table, we'll just need to make the final loop to set -i-1 before returning here.
+ // sort ustr. TO DO: just sort new ones and merge them in.
+ // These allocs are here, to save them being in the recursive cradix_r()
+ if (cradix_counts_alloc < maxlen) {
+ cradix_counts_alloc = maxlen + 10; // +10 to save too many reallocs
+ cradix_counts = (int *)realloc(cradix_counts, cradix_counts_alloc * 256 * sizeof(int) ); // stack of counts
+ if (!cradix_counts) Error("Failed to alloc cradix_counts");
+ memset(cradix_counts, 0, cradix_counts_alloc * 256 * sizeof(int));
+ }
+ if (cradix_xtmp_alloc < ustr_n) {
+ cradix_xtmp = (SEXP *)realloc( cradix_xtmp, ustr_n * sizeof(SEXP) ); // TO DO: Reuse the one we have in forder. Does it need to be n length?
+ if (!cradix_xtmp) Error("Failed to alloc cradix_tmp");
+ cradix_xtmp_alloc = ustr_n;
+ }
+ cradix_r(ustr, ustr_n, 0); // sorts ustr in-place by reference
+ for(i=0; i<ustr_n; i++) // save ordering in the CHARSXP. negative so as to distinguish with R's own usage.
+ SET_TRUELENGTH(ustr[i], -i-1);
+}
+
+// functions to test vectors for sortedness: isorted, dsorted and csorted
+// base:is.unsorted uses any(is.na(x)) at R level (inefficient), and returns NA in the presence of any NA.
+// Hence, here we deal with NAs in C and return true if NAs are all at the beginning (what we need in data.table).
+// We also return -1 if x is sorted in _strictly_ reverse order; a common case we optimize in forder.
+// If a vector is in decreasing order *with ties*, then an in-place reverse (no sort) would result in instability of ties (TO DO).
+// For use by forder only, which now returns NULL if already sorted (hence no need for separate is.sorted).
+// TO DO: test in big steps first to return faster if unsortedness is at the end (a common case of rbind'ing data to end)
+// These are all sequential access to x, so very quick and cache efficient.
+
+static int isorted(int *x, int n) // order = 1 is ascending and order=-1 is descending
+{ // also takes care of na.last argument with check through 'icheck'
+ // Relies on NA_INTEGER==INT_MIN, checked in init.c
+ int i=1,j=0;
+ if (nalast == 0) { // when nalast = NA,
+ for (int k=0; k<n; k++) if (x[k] != NA_INTEGER) j++;
+ if (j == 0) { push(n); return(-2); } // all NAs ? return special value to replace all o's values with '0'
+ if (j != n) return(0); // any NAs ? return 0 = unsorted and leave it to sort routines to replace o's with 0's
+ } // no NAs ? continue to check the rest of isorted - the same routine as usual
+ if (n<=1) { push(n); return(1); }
+ if (icheck(x[1]) < icheck(x[0])) {
+ i = 2;
+ while (i<n && icheck(x[i]) < icheck(x[i-1])) i++;
+ if (i==n) { mpush(1,n); return(-1);} // strictly opposite to expected 'order', no ties;
+ // e.g. no more than one NA at the beginning/end (for order=-1/1)
+ else return(0);
+ }
+ int old = gsngrp[flip];
+ int tt = 1;
+ for (i=1; i<n; i++) {
+ if (icheck(x[i]) < icheck(x[i-1])) { gsngrp[flip] = old; return(0); }
+ if (x[i]==x[i-1]) tt++; else { push(tt); tt=1; }
+ }
+ push(tt);
+ return(1); // same as 'order', NAs at the beginning for order=1, at end for order=-1, possibly with ties
+}
+
+static int dsorted(double *x, int n) // order=1 is ascending and -1 is descending
+{ // also accounts for nalast=0 (=NA), =1 (TRUE), -1 (FALSE) (in twiddle)
+ int i=1,j=0;
+ unsigned long long prev, this;
+ if (nalast == 0) { // when nalast = NA,
+ for (int k=0; k<n; k++) if (!is_nan(x, k)) j++;
+ if (j == 0) { push(n); return(-2); } // all NAs ? return special value to replace all o's values with '0'
+ if (j != n) return(0); // any NAs ? return 0 = unsorted and leave it to sort routines to replace o's with 0's
+ } // no NAs ? continue to check the rest of isorted - the same routine as usual
+ if (n<=1) { push(n); return(1); }
+ prev = twiddle(x,0,order);
+ this = twiddle(x,1,order);
+ if (this < prev) {
+ i = 2;
+ prev=this;
+ while (i<n && (this=twiddle(x,i,order)) < prev) {i++; prev=this; }
+ if (i==n) { mpush(1,n); return(-1);} // strictly opposite of expected 'order', no ties;
+ // e.g. no more than one NA at the beginning/end (for order=-1/1)
+ else return(0); // TO DO: improve to be stable for ties in reverse
+ }
+ int old = gsngrp[flip];
+ int tt = 1;
+ for (i=1; i<n; i++) {
+ this = twiddle(x,i,order); // TO DO: once we get past -Inf, NA and NaN at the bottom, and +Inf at the top,
+ // the middle only need be twiddled for tolerance (worth it?)
+ if (this < prev) { gsngrp[flip] = old; return(0); }
+ if (this==prev) tt++; else { push(tt); tt=1; }
+ prev = this;
+ }
+ push(tt);
+ return(1); // exactly as expected in 'order' (1=increasing, -1=decreasing), possibly with ties
+}
+
+static int csorted(SEXP *x, int n) // order=1 is ascending and -1 is descending
+{ // also accounts for nalast=0 (=NA), =1 (TRUE), -1 (FALSE)
+ int i=1, j=0, tmp;
+ if (nalast == 0) { // when nalast = NA,
+ for (int k=0; k<n; k++) if (x[k] != NA_STRING) j++;
+ if (j == 0) { push(n); return(-2); } // all NAs ? return special value to replace all o's values with '0'
+ if (j != n) return(0); // any NAs ? return 0 = unsorted and leave it to sort routines to replace o's with 0's
+ } // no NAs ? continue to check the rest of isorted - the same routine as usual
+ if (n<=1) { push(n); return(1); }
+ if (StrCmp2(x[1],x[0])<0) {
+ i = 2;
+ while (i<n && StrCmp2(x[i],x[i-1])<0) i++;
+ if (i==n) { mpush(1,n); return(-1);} // strictly opposite of expected 'order', no ties;
+ // e.g. no more than one NA at the beginning/end (for order=-1/1)
+ else return(0);
+ }
+ int old = gsngrp[flip];
+ int tt = 1;
+ for (i=1; i<n; i++) {
+ tmp = StrCmp2(x[i],x[i-1]);
+ if (tmp < 0) { gsngrp[flip] = old; return(0); }
+ if (tmp == 0) tt++; else { push(tt); tt=1; }
+ }
+ push(tt);
+ return(1); // exactly as expected in 'order', possibly with ties
+}
+
+static void isort(int *x, int *o, int n)
+{
+ if (n<=2) {
+ if (nalast == 0 && n == 2) { // nalast = 0 and n == 2 (check bottom of this file for explanation)
+ for (int i=0; i<n; i++) if (x[i] == NA_INTEGER) o[i] = 0;
+ push(1); push(1);
+ return;
+ } else Error("Internal error: isort received n=%d. isorted should have dealt with this (e.g. as a reverse sorted vector) already",n);
+ }
+ if (n<N_SMALL && o[0] != -1 && nalast != 0) { // see comment above in iradix_r on N_SMALL=200.
+ /* if not o[0] then can't just populate with 1:n here, since x is changed by ref too (so would need to be copied). */
+ /* pushes inside too. Changes x and o by reference, so not suitable in first column when o hasn't been populated yet
+ and x is the actual column in DT (hence check on o[0]). */
+ if (order != 1 || nalast != -1) // so that default case, i.e., order=1, nalast=FALSE will not be affected (ex: `setkey`)
+ for (int i=0; i<n; i++) x[i] = icheck(x[i]);
+ iinsert(x, o, n);
+ } else {
+ /* Tighter range (e.g. copes better with a few abormally large values in some groups), but also, when setRange was once at
+ colum level that caused an extra scan of (long) x first. 10,000 calls to setRange takes just 0.04s i.e. negligible. */
+ setRange(x, n);
+ if (range==NA_INTEGER) Error("Internal error: isort passed all-NA. isorted should have caught this before this point");
+ int *target = (o[0] != -1) ? newo : o;
+ if (range<=N_RANGE && range<=n) { // was range<10000 for subgroups, but 1e5 for the first column,
+ icount(x, target, n); // tried to generalise here. 1e4 rather than 1e5 here because iterated
+ } else { // was (thisgrpn < 200 || range > 20000) then radix
+ iradix(x, target, n); // a short vector with large range can bite icount when iterated (BLOCK 4 and 6)
+ }
+ }
+ // TO DO: add calibrate() to init.c
+}
+
+static void dsort(double *x, int *o, int n)
+{
+ if (n <= 2) { // nalast = 0 and n == 2 (check bottom of this file for explanation)
+ if (nalast == 0 && n == 2) { // don't have to twiddle here.. at least one will be NA and 'n' WILL BE 2.
+ for (int i=0; i<n; i++) if (is_nan(x, i)) o[i] = 0;
+ push(1); push(1);
+ return;
+ } Error("Internal error: dsort received n=%d. dsorted should have dealt with this (e.g. as a reverse sorted vector) already",n);
+ }
+ if (n<N_SMALL && o[0] != -1 && nalast != 0) { // see comment above in iradix_r re N_SMALL=200, and isort for o[0]
+ for (int i=0; i<n; i++) ((unsigned long long *)x)[i] = twiddle(x,i,order); // have to twiddle here anyways, can't speed up default case like in isort
+ dinsert((unsigned long long *)x, o, n);
+ } else {
+ dradix((unsigned char *)x, (o[0] != -1) ? newo : o, n);
+ }
+}
+
+SEXP forder(SEXP DT, SEXP by, SEXP retGrp, SEXP sortStrArg, SEXP orderArg, SEXP naArg)
+// sortStr TRUE from setkey, FALSE from by=
+{
+ int i, j, k, grp, ngrp, tmp, *osub, thisgrpn, n, col;
+ Rboolean isSorted = TRUE;
+ SEXP x, class;
+ void *xd;
+#ifdef TIMING_ON
+ memset(tblock, 0, NBLOCK*sizeof(clock_t));
+ memset(nblock, 0, NBLOCK*sizeof(int));
+ clock_t tstart; // local variable to mask the global tstart for ease when timing sub funs
+#endif
+ TBEG()
+
+ if (isNewList(DT)) {
+ if (!length(DT)) error("DT is an empty list() of 0 columns");
+ if (!isInteger(by) || !length(by)) error("DT has %d columns but 'by' is either not integer or length 0", length(DT)); // seq_along(x) at R level
+ for (i=0; i<LENGTH(by); i++) if (INTEGER(by)[i] < 1 || INTEGER(by)[i] > length(DT)) error("'by' value %d out of range [1,%d]", INTEGER(by)[i], length(DT));
+ n = length(VECTOR_ELT(DT,0));
+ x = VECTOR_ELT(DT,INTEGER(by)[0]-1);
+ } else {
+ if (!isNull(by)) error("Input is a single vector but 'by' is not NULL");
+ n = length(DT);
+ x = DT;
+ }
+ if (!isLogical(retGrp) || LENGTH(retGrp)!=1 || INTEGER(retGrp)[0]==NA_LOGICAL) error("retGrp must be TRUE or FALSE");
+ if (!isLogical(sortStrArg) || LENGTH(sortStrArg)!=1 || INTEGER(sortStrArg)[0]==NA_LOGICAL ) error("sortStr must be TRUE or FALSE");
+ if (!isLogical(naArg) || LENGTH(naArg) != 1) error("na.last must be logical TRUE, FALSE or NA of length 1");
+ sortStr = LOGICAL(sortStrArg)[0];
+ // static global set on top...
+ nalast = (LOGICAL(naArg)[0] == NA_LOGICAL) ? 0 : (LOGICAL(naArg)[0] == TRUE) ? 1 : -1; // 1=TRUE, -1=FALSE, 0=NA
+ gsmaxalloc = n; // upper limit for stack size (all size 1 groups). We'll detect and avoid that limit, but if just one non-1 group (say 2), that can't be avoided.
+
+ // TODO: check for 'orderArg'
+
+ SEXP ans = PROTECT(allocVector(INTSXP, n)); // once for the result, needs to be length n.
+ int *o = INTEGER(ans); // TO DO: save allocation if NULL is returned (isSorted==TRUE)
+ o[0] = -1; // so [i|c|d]sort know they can populate o directly with no working memory needed to reorder existing order
+ // had to repace this from '0' to '-1' because 'nalast = 0' replace 'o[.]' with 0 values.
+ xd = DATAPTR(x);
+
+ stackgrps = length(by)>1 || LOGICAL(retGrp)[0];
+ savetl_init(); // from now on use Error not error.
+
+ order = INTEGER(orderArg)[0];
+ switch(TYPEOF(x)) {
+ case INTSXP : case LGLSXP :
+ tmp = isorted(xd, n); break;
+ case REALSXP :
+ class = getAttrib(x, R_ClassSymbol);
+ if (isString(class) && STRING_ELT(class, 0) == char_integer64) {
+ twiddle = &i64twiddle;
+ is_nan = &i64nan; // see explanation under `is_nan` as to why we need this
+ } else {
+ twiddle = &dtwiddle;
+ is_nan = &dnan;
+ }
+ tmp = dsorted(xd, n); break;
+ case STRSXP :
+ tmp = csorted(xd, n); break;
+ default :
+ Error("First column being ordered is type '%s', not yet supported", type2char(TYPEOF(x)));
+ }
+ if (tmp) { // -1 or 1. NEW: or -2 in case of nalast == 0 and all NAs
+ if (tmp == 1) { // same as expected in 'order' (1 = increasing, -1 = decreasing)
+ isSorted = TRUE;
+ for (i=0; i<n; i++) o[i] = i+1; // TO DO: we don't need this if returning NULL? Save it? Unlikely huge gain, though.
+ } else if (tmp == -1) { // -1 (or -n for result of strcmp), strictly opposite to expected 'order'
+ isSorted = FALSE;
+ for (i=0; i<n; i++) o[i] = n-i;
+ } else if (nalast == 0 && tmp == -2) { // happens only when nalast=NA/0. Means all NAs, replace with 0's therefore!
+ isSorted = FALSE;
+ for (i=0; i<n; i++) o[i] = 0;
+ }
+ } else {
+ isSorted = FALSE;
+ switch(TYPEOF(x)) {
+ case INTSXP : case LGLSXP :
+ isort(xd, o, n); break;
+ case REALSXP :
+ dsort(xd, o, n); break;
+ case STRSXP :
+ if (sortStr) { csort_pre(xd, n); alloc_csort_otmp(n); csort(xd, o, n); }
+ else cgroup(xd, o, n);
+ break;
+ default :
+ Error("Internal error: previous default should have caught unsupported type");
+ }
+ }
+ TEND(0)
+
+ int maxgrpn = gsmax[flip]; // biggest group in the first column
+ void *xsub = NULL; // local to forder
+ int (*f)(); void (*g)();
+
+ if (length(by)>1 && gsngrp[flip]<n) {
+ xsub = (void *)malloc(maxgrpn * sizeof(double)); // double is the largest type, 8
+ if (xsub==NULL) Error("Couldn't allocate xsub in forder, requested %d * %d bytes.", maxgrpn, sizeof(double));
+ newo = (int *)malloc(maxgrpn * sizeof(int)); // global variable, used by isort, dsort, sort and cgroup
+ if (newo==NULL) Error("Couldn't allocate newo in forder, requested %d * %d bytes.", maxgrpn, sizeof(int));
+ }
+ TEND(1) // should be negligible time to malloc even large blocks, but time it anyway to be sure
+
+ for (col=2; col<=length(by); col++) {
+ x = VECTOR_ELT(DT,INTEGER(by)[col-1]-1);
+ xd = DATAPTR(x);
+ ngrp = gsngrp[flip];
+ if (ngrp == n && nalast != 0) break;
+ flipflop();
+ stackgrps = col!=LENGTH(by) || LOGICAL(retGrp)[0];
+ order = INTEGER(orderArg)[col-1];
+ switch(TYPEOF(x)) {
+ case INTSXP : case LGLSXP :
+ f = &isorted; g = &isort; break;
+ case REALSXP :
+ class = getAttrib(x, R_ClassSymbol);
+ if (isString(class) && STRING_ELT(class, 0) == char_integer64) {
+ twiddle = &i64twiddle;
+ is_nan = &i64nan;
+ } else {
+ twiddle = &dtwiddle;
+ is_nan = &dnan;
+ }
+ f = &dsorted; g = &dsort; break;
+ case STRSXP :
+ f = &csorted;
+ if (sortStr) { csort_pre(xd, n); alloc_csort_otmp(gsmax[1-flip]); g = &csort; }
+ else g = &cgroup; // no increasing/decreasing order required if sortStr = FALSE, just a dummy argument
+ break;
+ default:
+ Error("Column %d of 'by' (%d) is type '%s', not yet supported", col, INTEGER(by)[col-1], type2char(TYPEOF(x)));
+ }
+ size_t size = SIZEOF(x);
+ if (size!=4 && size!=8) Error("Column %d of 'by' is supported type '%s' but size (%d) isn't 4 or 8\n", col, type2char(TYPEOF(x)), size);
+ // sizes of int and double are checked to be 4 and 8 respectively in init.c
+ i = 0;
+ for (grp=0; grp<ngrp; grp++) {
+ thisgrpn = gs[1-flip][grp];
+ if (thisgrpn == 1) {
+ if (nalast==0) { // this edge case had to be taken care of here.. (see the bottom of this file for more explanation)
+ switch(TYPEOF(x)) {
+ case INTSXP :
+ if (INTEGER(x)[o[i]-1] == NA_INTEGER) { isSorted=FALSE; o[i] = 0; } break;
+ case LGLSXP :
+ if (LOGICAL(x)[o[i]-1] == NA_LOGICAL) { isSorted=FALSE; o[i] = 0; } break;
+ case REALSXP :
+ if (ISNAN(REAL(x)[o[i]-1])) { isSorted=FALSE; o[i] = 0; } break;
+ case STRSXP :
+ if (STRING_ELT(x, o[i]-1) == NA_STRING) { isSorted=FALSE; o[i] = 0; } break;
+ default :
+ Error("Internal error: previous default should have caught unsupported type");
+ }
+ }
+ i++; push(1); continue;
+ }
+ TBEG()
+ osub = o+i;
+ // ** TO DO **: if isSorted, we can just point xsub into x directly. If (*f)() returns 0, though, will have to copy x at that point
+ // When doing this, xsub could be allocated at that point for the first time.
+ if (size==4)
+ for (j=0; j<thisgrpn; j++) ((int *)xsub)[j] = ((int *)xd)[o[i++]-1];
+ else
+ for (j=0; j<thisgrpn; j++) ((double *)xsub)[j] = ((double *)xd)[o[i++]-1];
+ TEND(2)
+
+ // continue; // BASELINE short circuit timing point. Up to here is the cost of creating xsub.
+ tmp = (*f)(xsub, thisgrpn); // [i|d|c]sorted(); very low cost, sequential
+ TEND(3)
+
+ if (tmp) {
+ // *sorted will have already push()'d the groups
+ if (tmp==-1) {
+ isSorted = FALSE;
+ for (k=0;k<thisgrpn/2;k++) { // reverse the order in-place using no function call or working memory
+ tmp = osub[k]; // isorted only returns -1 for _strictly_ decreasing order, otherwise ties wouldn't be stable
+ osub[k] = osub[thisgrpn-1-k];
+ osub[thisgrpn-1-k] = tmp;
+ }
+ TEND(4)
+ } else if (nalast == 0 && tmp==-2) { // all NAs, replace osub[.] with 0s.
+ isSorted = FALSE;
+ for (k=0; k<thisgrpn; k++) osub[k] = 0;
+ }
+ continue;
+ }
+ isSorted = FALSE;
+ newo[0] = -1; // nalast=NA will result in newo[0] = 0. So had to change to -1.
+ (*g)(xsub, osub, thisgrpn); // may update osub directly, or if not will put the result in global newo
+
+ TEND(5)
+
+ if (newo[0] != -1) {
+ if (nalast != 0) for (j=0; j<thisgrpn; j++) ((int *)xsub)[j] = osub[ newo[j]-1 ]; // reuse xsub to reorder osub
+ else for (j=0; j<thisgrpn; j++) ((int *)xsub)[j] = (newo[j] == 0) ? 0 : osub[ newo[j]-1 ]; // final nalast case to handle!
+ memcpy(osub, xsub, thisgrpn*sizeof(int));
+ }
+ TEND(6)
+ }
+ }
+#ifdef TIMING_ON
+ for (i=0; i<NBLOCK; i++) {
+ Rprintf("Timing block %d = %8.3f %8d\n", i, 1.0*tblock[i]/CLOCKS_PER_SEC, nblock[i]);
+ if (i==12) Rprintf("\n");
+ }
+ Rprintf("Found %d groups and maxgrpn=%d\n", gsngrp[flip], gsmax[flip]);
+#endif
+
+ if (!sortStr && ustr_n!=0) Error("Internal error: at the end of forder sortStr==FALSE but ustr_n!=0 [%d]", ustr_n);
+ for(int i=0; i<ustr_n; i++)
+ SET_TRUELENGTH(ustr[i],0);
+ maxlen = 1; // reset global. Minimum needed to count "" and NA
+ ustr_n = 0;
+ savetl_end();
+ free(ustr); ustr=NULL; ustr_alloc=0;
+
+ if (isSorted) {
+ UNPROTECT(1); // The existing o vector, which we may save in future, if in future we only create when isSorted becomes FALSE
+ ans = PROTECT(allocVector(INTSXP, 0)); // Can't attach attributes to NULL
+ }
+ if (LOGICAL(retGrp)[0]) {
+ ngrp = gsngrp[flip];
+ setAttrib(ans, install("starts"), x = allocVector(INTSXP, ngrp));
+ //if (isSorted || LOGICAL(sort)[0])
+ for (INTEGER(x)[0]=1, i=1; i<ngrp; i++) INTEGER(x)[i] = INTEGER(x)[i-1] + gs[flip][i-1];
+ //else {
+ // it's not sorted already and we want to keep original group order
+ // cumsum = 0;
+ // for (i=0; i<ngrp; i++) { INTEGER(x)[i] = o[i+cumsum]; cumsum+=gs[flip][i]; }
+ // isort(INTEGER(x), ngrp);
+ //}
+ setAttrib(ans, install("maxgrpn"), ScalarInteger(gsmax[flip]));
+ }
+
+ gsfree();
+ free(radix_xsub); radix_xsub=NULL; radix_xsuballoc=0;
+ free(xsub); free(newo); xsub=newo=NULL;
+ free(xtmp); xtmp=NULL; xtmp_alloc=0;
+ free(otmp); otmp=NULL; otmp_alloc=0;
+ free(csort_otmp); csort_otmp=NULL; csort_otmp_alloc=0;
+
+ free(cradix_counts); cradix_counts=NULL; cradix_counts_alloc=0;
+ free(cradix_xtmp); cradix_xtmp=NULL; cradix_xtmp_alloc=0; // TO DO: use xtmp already got
+
+ UNPROTECT(1);
+ return( ans );
+}
+
+// TODO: implement 'order' argument to 'fsorted'
+// Not touching 'fsorted' for now for "decreasing order". Passing '1' as the value of 'order' argument (checks only ascending order as before).
+SEXP fsorted(SEXP x)
+{
+ // Just checks if ordered and returns FALSE early if not (and don't return ordering if so, unlike forder).
+ int tmp,n;
+ void *xd;
+ n = length(x);
+ if (n <= 1) return(ScalarLogical(TRUE));
+ if (!isVectorAtomic(x)) Error("is.sorted (R level) and fsorted (C level) only to be used on vectors. If needed on a list/data.table, you'll need the order anyway if not sorted, so use if (length(o<-forder(...))) for efficiency in one step, or equivalent at C level");
+ xd = DATAPTR(x);
+ stackgrps = FALSE;
+ order = 1;
+ switch(TYPEOF(x)) {
+ case INTSXP : case LGLSXP :
+ tmp = isorted(xd, n); break;
+ case REALSXP :
+ tmp = dsorted(xd, n); break;
+ case STRSXP :
+ tmp = csorted(xd, n); break;
+ default :
+ Error("type '%s' is not yet supported", type2char(TYPEOF(x)));
+ }
+ return(ScalarLogical( tmp==1 ? TRUE : FALSE ));
+}
+
+SEXP isOrderedSubset(SEXP x, SEXP nrow)
+// specialized for use in [.data.table only
+// Ignores 0s but heeds NAs and any out-of-range (which result in NA)
+{
+ int i=0, last, this;
+ if (!length(x)) return(ScalarLogical(TRUE));
+ if (!isInteger(x)) error("x has non-0 length but isn't an integer vector");
+ if (!isInteger(nrow) || LENGTH(nrow)!=1 || INTEGER(nrow)[0]<0) error("nrow must be integer vector length 1 and >=0");
+ if (LENGTH(x)<=1) return(ScalarLogical(TRUE));
+ while (i<LENGTH(x) && INTEGER(x)[i]==0) i++;
+ if (i==LENGTH(x)) return(ScalarLogical(TRUE));
+ last = INTEGER(x)[i]; // the first non-0
+ i++;
+ for (; i<LENGTH(x); i++) {
+ this = INTEGER(x)[i];
+ if (this == 0) continue;
+ if (this < last || this < 0 || this > INTEGER(nrow)[0])
+ return(ScalarLogical(FALSE));
+ last = this;
+ }
+ return(ScalarLogical(TRUE));
+}
+
+
+/*
+ // LSD approach ...
+
+ Rboolean firstTime = TRUE;
+ for (radix=0; radix<4; radix++) {
+ if (skip[radix]) continue;
+ tmp = ord; ord = ans; ans = tmp; // flip-flop
+ shift = radix * 8;
+ thiscounts = counts[radix];
+ for (i=1;i<256;i++) thiscounts[i] += thiscounts[i-1];
+ tt = clock();
+ if (firstTime) {
+ for(i = n-1; i >= 0; i--) {
+ thisx = x[i] >> shift & 0xFF;
+ ans[--thiscounts[thisx]] = i+1;
+ }
+ } else {
+ for(i = n-1; i >= 0; i--) {
+ thisx = x[ord[i]-1] >> shift & 0xFF;
+ ans[--thiscounts[thisx]] = ord[i];
+ }
+ }
+ memset(thiscounts, 0, 256*sizeof(unsigned int));
+ t[6+radix] += clock()-tt;
+ firstTime = FALSE;
+ }
+ if (firstTime) Error("Internal error: x is one repeated number so isorted should have skipped iradix, but iradix has been called and skipped all 4 radix");
+}
+*/
+
+SEXP isReallyReal(SEXP x) {
+ int n, i=0;
+ SEXP ans;
+ if (!isReal(x))
+ error("x must be of type double.");
+ n = length(x);
+ ans = PROTECT(allocVector(LGLSXP, 1));
+ while (i<n &&
+ ( ISNA(REAL(x)[i]) ||
+ ( R_FINITE(REAL(x)[i]) && REAL(x)[i] == (int)(REAL(x)[i])))) {
+ i++;
+ }
+ LOGICAL(ans)[0] = (i<n);
+ UNPROTECT(1);
+ return(ans);
+}
+
+void pbin(unsigned long long n)
+// trace utility for dev to be used in gdb, since couldn't get stdlib::atoi() to link
+{
+ int sofar = 0;
+ for(int shift=sizeof(long long)*8-1; shift>=0; shift--)
+ {
+ if (n >> shift & 1)
+ Rprintf("1");
+ else
+ Rprintf("0");
+ if (++sofar == 1 || sofar == 12) Rprintf(" ");
+ }
+ Rprintf("\n");
+}
+
+SEXP binary(SEXP x)
+// base::intToBits is close, but why does it print the result as "00 00 00 00" (raw) rather than ("0000") bits, seems odd.
+{
+ char buffer[69];
+ int j;
+ if (!isReal(x)) error("x must be type 'double'");
+ SEXP ans = PROTECT(allocVector(STRSXP, LENGTH(x)));
+ for (int i=0; i<LENGTH(x); i++) {
+ u.d = REAL(x)[i];
+ j = 0;
+ for(int bit=64; bit>=1; bit--)
+ {
+ buffer[j++] = '0' + (u.ull >> (bit-1) & 1);
+ if (bit==64 || bit==53 || bit==17 || bit==9) buffer[j++]=' ';
+ // ^sign ^exponent ^last 2 byte rounding
+ }
+ SET_STRING_ELT(ans, i, mkCharLen(buffer,68)); // 64 bits + 4 spaces
+ }
+ UNPROTECT(1);
+ return ans;
+}
+
+
+/*
+Note on challenges implementing `nalast=NA` so that things are under the same perspective when looked at later. To go to the point, there are four challenges in implementing `nalast=NA` with the current form of forder.c (which is excellent!):
+
+ 1) Handling the i|d|csorted routines for nalast=NA:
+ The current implementation of nalast=NA in *sorted routine is that if all values are NAs, then it returns -2, a different value, so that all values of o/osub can be replacedw with 0. If any values are NA, then we assume that this vector is unsorted and return 0 to be taken care of by the corresponding sort routine. Now, this may very well be questioned/improved. For ex: we could detect if the vector is sorted and also if the first value is NA, and if so, get the group size of thes [...]
+
+ 2) Handling cases where n==1 and n==2:
+ The current sorting routines don't take care of n<=2, and rightly so, as they can be dealt with directly. But it's not the case for nalast=NA because, when n==2 and all are NAs, point 1 will handle it. But when n==1 and it is NA and the column is not the first column to order upon, with the current implementation, "thisgrpn" is checked for "== 1" and if so, just pushes that group size and continues.. But we've to replace it with 0 in our case. So, nalast==0 is checked for inside t [...]
+
+ 3) o[0] and newo[0] = -1:
+ Since nalast already populates NAs with 0 values in o, we can't use the old value of 0 to check if it's already populated or not. Therefore this has been changed to -1.
+
+ 4) default cases are untouched as much as possible:
+ In order to not compromise in speed for default case (`setkey`), iinsert and dinsert are not touched at all, which means, we've to make sure that they are not run when n<200 and nalast==0, as they don't replace o[x=NA] with 0. And, 'icount', 'iradix', 'dradix' are modified to take care of nalast=NA, to my knowledge.
+
+Ends here.
+------------------------------------------
+This is just for my reference regarding the cases to take care of when checking for nalast=NA.
+n=1 is NA done.
+n=1 not NA done.
+n=2 both NAs done.
+n=2 any NA done.
+n=2 no NA done.
+n>2 all NAs done.
+n>2 any NA done.
+n>2 no NA done.
+
+*/
+
+/*
+Added 2nd August 2014 - Testing different fixes for #743
+When u.d = 0 or -0, we've to set it 0, so that the sign bit in -0 doesn't bite us later.
+
+# Vector to benchmark on:
+set.seed(45L)
+x = 1*sample(-1e5:1e5, 1e8, TRUE)
+require(data.table)
+
+# code to run the benchmark:
+system.time(data.table:::forderv(x))
+system.time(data.table:::forderv(x))
+system.time(data.table:::forderv(x))
+
+# A. Timing on code before fix:
+ user system elapsed
+ 11.992 1.063 13.113
+ 11.968 1.095 13.154
+ 11.943 1.106 13.141
+
+# B. Timing on fix using: u.ull <<= (!u.d); after the first line where we get 'u.d = order*...'
+system.time(data.table:::forderv(x))
+ 12.640 1.095 13.814
+ 12.629 1.129 13.836
+ 12.634 1.135 13.836
+
+# C. Timing on current solution:
+system.time(data.table:::forderv(x))
+ 12.208 1.134 13.426
+ 12.233 1.148 13.445
+ 12.223 1.154 13.548
+
+B is ~.7 sec slower whereas C is ~.3-.4 seconds slower. This should be okay on 100 million rows, I think!
+*/
diff --git a/src/frank.c b/src/frank.c
new file mode 100644
index 0000000..a8581e6
--- /dev/null
+++ b/src/frank.c
@@ -0,0 +1,193 @@
+#include "data.table.h"
+#include <Rdefines.h>
+// #include <signal.h> // the debugging machinery + breakpoint aidee
+// raise(SIGINT);
+
+extern SEXP char_integer64;
+
+static union {
+ double d;
+ unsigned long long ull;
+} u;
+
+SEXP dt_na(SEXP x, SEXP cols) {
+ int i, j, n=0, this;
+ double *dv;
+ SEXP v, ans, class;
+
+ if (!isNewList(x)) error("Internal error. Argument 'x' to Cdt_na is type '%s' not 'list'", type2char(TYPEOF(x)));
+ if (!isInteger(cols)) error("Internal error. Argument 'cols' to Cdt_na is type '%s' not 'integer'", type2char(TYPEOF(cols)));
+ for (i=0; i<LENGTH(cols); i++) {
+ this = INTEGER(cols)[i];
+ if (this<1 || this>LENGTH(x))
+ error("Item %d of 'cols' is %d which is outside 1-based range [1,ncol(x)=%d]", i+1, this, LENGTH(x));
+ if (!n) n = length(VECTOR_ELT(x, this-1));
+ }
+ ans = PROTECT(allocVector(LGLSXP, n));
+ for (i=0; i<n; i++) LOGICAL(ans)[i]=0;
+ for (i=0; i<LENGTH(cols); i++) {
+ v = VECTOR_ELT(x, INTEGER(cols)[i]-1);
+ if (!length(v) || isNewList(v) || isList(v)) continue; // like stats:::na.omit.data.frame, skip list/pairlist columns
+ if (n != length(v))
+ error("Column %d of input list x is length %d, inconsistent with first column of that item which is length %d.", i+1,length(v),n);
+ switch (TYPEOF(v)) {
+ case LGLSXP:
+ for (j=0; j<n; j++) LOGICAL(ans)[j] |= (LOGICAL(v)[j] == NA_LOGICAL);
+ break;
+ case INTSXP:
+ for (j=0; j<n; j++) LOGICAL(ans)[j] |= (INTEGER(v)[j] == NA_INTEGER);
+ break;
+ case STRSXP:
+ for (j=0; j<n; j++) LOGICAL(ans)[j] |= (STRING_ELT(v, j) == NA_STRING);
+ break;
+ case REALSXP:
+ class = getAttrib(v, R_ClassSymbol);
+ if (isString(class) && STRING_ELT(class, 0) == char_integer64) {
+ dv = (double *)REAL(v);
+ for (j=0; j<n; j++) {
+ u.d = dv[j];
+ LOGICAL(ans)[j] |= (u.ull == NAINT64);
+ }
+ } else {
+ for (j=0; j<n; j++) LOGICAL(ans)[j] |= ISNAN(REAL(v)[j]);
+ }
+ break;
+ case RAWSXP:
+ // no such thing as a raw NA
+ // vector already initialised to all 0's
+ break;
+ case CPLXSXP:
+ // taken from https://github.com/wch/r-source/blob/d75f39d532819ccc8251f93b8ab10d5b83aac89a/src/main/coerce.c
+ for (j=0; j<n; j++) LOGICAL(ans)[j] |= (ISNAN(COMPLEX(v)[j].r) || ISNAN(COMPLEX(v)[j].i));
+ break;
+ default:
+ error("Unknown column type '%s'", type2char(TYPEOF(v)));
+ }
+ }
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP frank(SEXP xorderArg, SEXP xstartArg, SEXP xlenArg, SEXP ties_method) {
+ int i=0, j=0, k=0, n;
+ int *xstart = INTEGER(xstartArg), *xlen = INTEGER(xlenArg), *xorder = INTEGER(xorderArg);
+ enum {MEAN, MAX, MIN, DENSE} ties = MEAN; // RUNLENGTH
+ SEXP ans;
+
+ if (!strcmp(CHAR(STRING_ELT(ties_method, 0)), "average")) ties = MEAN;
+ else if (!strcmp(CHAR(STRING_ELT(ties_method, 0)), "max")) ties = MAX;
+ else if (!strcmp(CHAR(STRING_ELT(ties_method, 0)), "min")) ties = MIN;
+ else if (!strcmp(CHAR(STRING_ELT(ties_method, 0)), "dense")) ties = DENSE;
+ // else if (!strcmp(CHAR(STRING_ELT(ties_method, 0)), "runlength")) ties = RUNLENGTH;
+ else error("Internal error: invalid ties.method for frankv(), should have been caught before. Please report to datatable-help");
+ n = length(xorderArg);
+ ans = (ties == MEAN) ? PROTECT(allocVector(REALSXP, n)) : PROTECT(allocVector(INTSXP, n));
+ if (n > 0) {
+ switch (ties) {
+ case MEAN :
+ for (i = 0; i < length(xstartArg); i++) {
+ for (j = xstart[i]-1; j < xstart[i]+xlen[i]-1; j++)
+ REAL(ans)[xorder[j]-1] = (2*xstart[i]+xlen[i]-1)/2.0;
+ }
+ break;
+ case MAX :
+ for (i = 0; i < length(xstartArg); i++) {
+ for (j = xstart[i]-1; j < xstart[i]+xlen[i]-1; j++)
+ INTEGER(ans)[xorder[j]-1] = xstart[i]+xlen[i]-1;
+ }
+ break;
+ case MIN :
+ for (i = 0; i < length(xstartArg); i++) {
+ for (j = xstart[i]-1; j < xstart[i]+xlen[i]-1; j++)
+ INTEGER(ans)[xorder[j]-1] = xstart[i];
+ }
+ break;
+ case DENSE :
+ k=1;
+ for (i = 0; i < length(xstartArg); i++) {
+ for (j = xstart[i]-1; j < xstart[i]+xlen[i]-1; j++)
+ INTEGER(ans)[xorder[j]-1] = k;
+ k++;
+ }
+ break;
+ // case RUNLENGTH :
+ // for (i = 0; i < length(xstartArg); i++) {
+ // k=1;
+ // for (j = xstart[i]-1; j < xstart[i]+xlen[i]-1; j++)
+ // INTEGER(ans)[xorder[j]-1] = k++;
+ // }
+ break;
+ }
+ }
+ UNPROTECT(1);
+ return(ans);
+}
+
+// internal version of anyNA for data.tables
+SEXP anyNA(SEXP x, SEXP cols) {
+ int i, j, n=0, this;
+ double *dv;
+ SEXP v, ans, class;
+
+ if (!isNewList(x)) error("Internal error. Argument 'x' to CanyNA is type '%s' not 'list'", type2char(TYPEOF(x)));
+ if (!isInteger(cols)) error("Internal error. Argument 'cols' to CanyNA is type '%s' not 'integer'", type2char(TYPEOF(cols)));
+ for (i=0; i<LENGTH(cols); i++) {
+ this = INTEGER(cols)[i];
+ if (this<1 || this>LENGTH(x))
+ error("Item %d of 'cols' is %d which is outside 1-based range [1,ncol(x)=%d]", i+1, this, LENGTH(x));
+ if (!n) n = length(VECTOR_ELT(x, this-1));
+ }
+ ans = PROTECT(allocVector(LGLSXP, 1));
+ LOGICAL(ans)[0]=0;
+ for (i=0; i<LENGTH(cols); i++) {
+ v = VECTOR_ELT(x, INTEGER(cols)[i]-1);
+ if (!length(v) || isNewList(v) || isList(v)) continue; // like stats:::na.omit.data.frame, skip list/pairlist columns
+ if (n != length(v))
+ error("Column %d of input list x is length %d, inconsistent with first column of that item which is length %d.", i+1,length(v),n);
+ j=0;
+ switch (TYPEOF(v)) {
+ case LGLSXP:
+ while(j < n && LOGICAL(v)[j] != NA_LOGICAL) j++;
+ if (j < n) LOGICAL(ans)[0] = 1;
+ break;
+ case INTSXP:
+ while(j < n && INTEGER(v)[j] != NA_INTEGER) j++;
+ if (j < n) LOGICAL(ans)[0] = 1;
+ break;
+ case STRSXP:
+ while (j < n && STRING_ELT(v, j) != NA_STRING) j++;
+ if (j < n) LOGICAL(ans)[0] = 1;
+ break;
+ case REALSXP:
+ class = getAttrib(v, R_ClassSymbol);
+ if (isString(class) && STRING_ELT(class, 0) == char_integer64) {
+ dv = (double *)REAL(v);
+ for (j=0; j<n; j++) {
+ u.d = dv[j];
+ if (u.ull == NAINT64) {
+ LOGICAL(ans)[0] = 1;
+ break;
+ }
+ }
+ } else {
+ while(j < n && !ISNAN(REAL(v)[j])) j++;
+ if (j < n) LOGICAL(ans)[0] = 1;
+ }
+ break;
+ case RAWSXP:
+ // no such thing as a raw NA
+ // vector already initialised to all 0's
+ break;
+ case CPLXSXP:
+ // taken from https://github.com/wch/r-source/blob/d75f39d532819ccc8251f93b8ab10d5b83aac89a/src/main/coerce.c
+ while (j < n && !ISNAN(COMPLEX(v)[j].r) && !ISNAN(COMPLEX(v)[j].i)) j++;
+ if (j < n) LOGICAL(ans)[0] = 1;
+ break;
+ default:
+ error("Unknown column type '%s'", type2char(TYPEOF(v)));
+ }
+ if (LOGICAL(ans)[0]) break;
+ }
+ UNPROTECT(1);
+ return(ans);
+}
diff --git a/src/fread.c b/src/fread.c
new file mode 100644
index 0000000..d5716ef
--- /dev/null
+++ b/src/fread.c
@@ -0,0 +1,1320 @@
+#include "data.table.h"
+#include <Rdefines.h>
+#include <ctype.h>
+#include <errno.h>
+#include <time.h>
+
+#ifdef WIN32 // means WIN64, too
+#include <windows.h>
+#include <stdio.h>
+#include <tchar.h>
+#else
+#include <sys/mman.h>
+#include <sys/stat.h>
+#include <fcntl.h> // for open()
+#include <unistd.h> // for close()
+#endif
+#include <signal.h> // the debugging machinery + breakpoint aidee
+
+/***** TO DO *****
+Restore test 1339 (balanced embedded quotes, see ?fread already updated).
+Confirm: http://stackoverflow.com/questions/23833294/data-tablefread-doesnt-like-missing-values-in-first-column
+construct test and investigate skip for completeness here: http://stackoverflow.com/questions/22086780/data-table-fread-error
+http://stackoverflow.com/questions/22229109/r-data-table-fread-command-how-to-read-large-files-with-irregular-separators
+http://r.789695.n4.nabble.com/Odd-problem-using-fread-to-read-in-a-csv-file-no-data-just-headers-tp4686302.html
+And even more diagnostics to verbose=TRUE so we can see where crashes are.
+colClasses shouldn't be ignored but rather respected and then warn if data accuracy is lost. See first NOTE in NEWS.
+Detect and coerce dates and times. By searching for - and :, and dateTtime etc, or R's own method or fasttime. POSIXct default, for microseconds? : http://stackoverflow.com/questions/14056370/cast-string-to-idatetime
+Fill in too-short lines : http://stackoverflow.com/questions/21124372/fread-doesnt-like-lines-with-less-fields-than-other-lines
+Allow to increase to top 500, middle 500 and bottom 500.
+madvise is too eager when reading just the top 10 rows.
+Add as.colClasses to fread.R after return from C level (e.g. for colClasses "Date", although as slow as read.csv via character)
+Allow comment char to ignore. Important in format detection. But require valid line data before comment character in the read loop? See http://stackoverflow.com/a/18922269/403310
+Jim Holtman requested column names be stipped of whitespace. On datatable-help 22 Jan 2014.
+Deal with row.names e.g. http://stackoverflow.com/questions/15448732/reading-csv-with-row-names-by-fread
+Test Garrett's two files again (wrap around ,,,,,, and different row lengths that the wc -l now fixes)
+Post from patricknik on 5 Jan re ""b"" in a field. And Aykut Firat on email.
+Warn about non whitespace (unprotected by comment.char) after the last column on any line (currently skipped silently)
+Warning about any imperfect number of columns
+Check tests exist for logical columns (currently read as character). T/True/TRUE/true are allowed in main/src/util.c
+A few TO DO inline in the code, including some speed fine tuning e.g. specialize Ispace and any other lib calls.
+Save repeated ch<eof checking in main read step. Last line might still be tricky if last line has no eol.
+test using at least "grep read.table ...Rtrunk/tests/
+---
+Secondary separator for list() columns, such as columns 11 and 12 in BED (no need for strsplit).
+If nrow is small, say 20, then it shouldn't mmap the entire file (that's probably *why* user just wants to look at head). Try MMAP_DONTNEED in that case to save needing to map the file in chunks.
+Add LaF comparison.
+as.read.table=TRUE/FALSE option. Or fread.table and fread.csv (see http://r.789695.n4.nabble.com/New-function-fread-in-v1-8-7-tp4653745p4654194.html).
+
+*****/
+
+static const char *ch, *eof;
+static char sep, eol, eol2; // sep2 TO DO
+static int eolLen, line, field;
+static Rboolean verbose, ERANGEwarning;
+static clock_t tCoerce, tCoerceAlloc;
+
+// Define our own fread type codes, different to R's SEXPTYPE :
+// i) INTEGER64 is not in R but an add on packages using REAL, we need to make a distinction here, without using class (for speed)
+// ii) 0:n codes makes it easier to bump through types in this order using ++.
+#define SXP_LGL 0 // LGLSXP String values T,F,TRUE,FALSE,True,False
+#define SXP_INT 1 // INTSXP
+#define SXP_INT64 2 // REALSXP
+#define SXP_REAL 3 // REALSXP
+#define SXP_STR 4 // STRSXP
+#define SXP_NULL 5 // NILSXP i.e. skip column (last so that all types can be bumped up to it by user)
+static const char TypeName[6][10] = {"LGL","INT","INT64","REAL","STR","NULL"}; // for messages and errors
+static int TypeSxp[6] = {LGLSXP,INTSXP,REALSXP,REALSXP,STRSXP,NILSXP};
+static union {double d; long long l; int b;} u; // b=boolean, can hold NA_LOGICAL
+static const char *fieldStart, *fieldEnd;
+static int fieldLen;
+#define NUT 8 // Number of User Types (just for colClasses where "numeric"/"double" are equivalent)
+static const char UserTypeName[NUT][10] = {"logical", "integer", "integer64", "numeric", "character", "NULL", "double", "CLASS" }; // important that first 6 correspond to TypeName. "CLASS" is the fall back to character then as.class at R level ("CLASS" string is just a placeholder).
+static int UserTypeNameMap[NUT] = { SXP_LGL, SXP_INT, SXP_INT64, SXP_REAL, SXP_STR, SXP_NULL, SXP_REAL, SXP_STR };
+// quote
+const char *quote;
+static int quoteStatus, stripWhite;
+
+const char *fnam=NULL, *mmp;
+size_t filesize;
+#ifdef WIN32
+HANDLE hFile=0;
+HANDLE hMap=0;
+void closeFile() {
+ if (fnam!=NULL) {
+ UnmapViewOfFile(mmp);
+ CloseHandle(hMap);
+ CloseHandle(hFile);
+ }
+}
+#else
+int fd=-1;
+void closeFile() {
+ if (fnam!=NULL) {
+ munmap((char *)mmp, filesize);
+ close(fd);
+ }
+}
+#endif
+// To solve: http://stackoverflow.com/questions/18597123/fread-data-table-locks-files
+void STOP(const char *format, ...) {
+ va_list args;
+ va_start(args, format);
+ char msg[2000];
+ vsnprintf(msg, 2000, format, args);
+ va_end(args);
+ closeFile(); // some errors point to data in the file, hence via msg buffer first
+ error(msg);
+}
+// ********************************************************************************************
+// NA handling.
+// algorithm is following
+// 1) Strto*() checks whether we can convert substring into given * type
+// 2) If not, we try to iteratively char-by-char starting from begining of the substring
+// look forward for maximum max(nchar(nastrings)) symbols:
+// ********************************************************************************************
+// max_na_nchar = max(nchar(nastrings))
+// lch = pointer to begining of substring we want to check
+// for (i in 0:max_na_nchar) {
+// ch = lch[i]
+// if( any of nastrings contain ch at position i)
+// continue
+// else return FALSE
+// }
+// return TRUE
+// ********************************************************************************************
+// for checking "if" condition we manage mask "na_mask" with length na_len = length(nastrings).
+// 1 on mask position i means that nastring[i] is still candidate for given substring.
+// 0 means this substring can't be casted into nastring[i], so nastring[i] is not candidate.
+int *NA_MASK;
+// means nastrings == 0; will do nothing with nastrings
+int FLAG_NA_STRINGS_NULL;
+const char **NA_STRINGS;
+int NA_MAX_NCHAR;
+int NASTRINGS_LEN;
+int *EACH_NA_STRING_LEN;
+// calculates maximum string length for a given R character vector
+int get_maxlen(SEXP char_vec) {
+ int maxlen = -1;
+ int cur_len;
+ for (int i=0; i< LENGTH(char_vec); i++) {
+ cur_len = strlen(CHAR(STRING_ELT(char_vec, i)));
+ maxlen = (cur_len > maxlen) ? cur_len : maxlen;
+ }
+ return maxlen;
+}
+// initialize mask. At the begining we assume any nastring can be candidate.
+static inline void init_mask() {
+ for(int i = 0; i < NASTRINGS_LEN; i++)
+ NA_MASK[i] = 1;
+}
+static inline int can_cast_to_na(const char* lch) {
+ const char *lch2 = lch;
+ // nastrings==NULL => do nothing
+ // TODO: move this out of this function and into strto* functions directly?
+ if(FLAG_NA_STRINGS_NULL) {
+ return 0;
+ }
+ init_mask();
+ // check whether mask contains any candidates which still potentially can be casted to NA
+ int non_zero_left = NASTRINGS_LEN;
+ //case when lch is empty string!
+ int na_found_flag = 1;
+ int pos = 0;
+ int j;
+ const char *nastring_iter;
+ // look for possible NA strings:
+ // max forward symbols = max length of the nastring template
+ while (pos < NA_MAX_NCHAR && lch2 != eof && *lch2 != sep && *lch2 != eol) {
+ j = 0;
+ na_found_flag = 0;
+ // check whether any of the nastrings template contains current (i-th forward) symbol at i-th forward positions
+ // iterate through nastrings
+ while( j < NASTRINGS_LEN && non_zero_left > 0 ) {
+ // not marked before
+ if( NA_MASK[j] != 0) {
+ nastring_iter = NA_STRINGS[j];
+ // nastring[j] candidate founded
+ if(EACH_NA_STRING_LEN[j] == pos + 1 && nastring_iter[pos] == *lch2) {
+ na_found_flag = 1;
+ }
+ // nastring[j] candidate has smaller length than we are looking
+ // or doesn't contain necessary symbol at position we are cheking
+ if(EACH_NA_STRING_LEN[j] < pos + 1 || nastring_iter[pos] != *lch2) {
+ NA_MASK[j] = 0;
+ non_zero_left--;
+ }
+ }
+ j++;
+ }
+ //all elements of mask == 0 means we can't convert tested substring to NA
+ if(non_zero_left == 0) {
+ return 0;
+ }
+ pos++;
+ lch2++;
+ }
+ // found delimiter after right NA candidate or empty string
+ if(na_found_flag && (lch2 == eof || *lch2 == sep || *lch2 == eol)) {
+ ch = lch2;
+ return 1;
+ }
+ else return 0;
+}
+// ********************************************************************************************
+
+static inline void skip_spaces() {
+ while(ch<eof && *ch==' ') ch++;
+}
+
+static inline void Field()
+{
+ quoteStatus=0;
+ if (*ch=='\"') { // protected, now look for the next ", so long as it doesn't leave unbalanced unquoted regions
+ quoteStatus=1;
+ fieldStart = ch+1;
+ int eolCount=0; // just >0 is used currently but may as well count
+ Rboolean noEmbeddedEOL=FALSE, quoteProblem=FALSE;
+ while(++ch<eof) {
+ if (*ch!='\"') {
+ if (noEmbeddedEOL && *ch==eol) { quoteProblem=TRUE; break; }
+ eolCount+=(*ch==eol);
+ continue; // fast return in most cases of characters
+ }
+ if (ch+1==eof || *(ch+1)==sep || *(ch+1)==eol) break;
+ // " followed by sep|eol|eof dominates a field ending with \" (for support of Windows style paths)
+
+ if (*(ch-1)!='\\') {
+ if (ch+1<eof && *(ch+1)=='\"') { ch++; continue; } // skip doubled-quote
+ // unescaped subregion
+ if (eolCount) {ch++; quoteProblem=TRUE; break;}
+ while (++ch<eof && (*ch!='\"' || *(ch-1)=='\\') && *ch!=eol);
+ if (ch==eof || *ch==eol) {quoteProblem=TRUE; break;}
+ noEmbeddedEOL = 1;
+ }
+ }
+ if (quoteProblem || ch==eof) {
+ // "..." logic has failed. Delegate to normal routine instead of erroring. Solves many cases, especially when files have both proper balanced, and imbalanced quotes. I think this is more towards fread's philosophy than having an explicit 'quote' argument..
+ quoteStatus=0;
+ ch = fieldStart-1;
+ } else {
+ fieldLen = (int)(ch-fieldStart);
+ ch++; // from closing quote to rest on sep|eol|eof
+ }
+ }
+ if (!quoteStatus) {
+ // either "" logic failed and we're here, or we're directly here.
+ // unprotected, look for next next [sep|eol]
+ if (sep==' ') {
+ fieldStart = ch;
+ while(ch<eof && *ch!=sep && *ch!=eol) ch++;
+ fieldLen = (int)(ch-fieldStart);
+ if (stripWhite) {
+ skip_spaces();
+ if (ch<eof && *ch!=eol) ch--; // leave on last space before new field's text
+ }
+ } else {
+ fieldStart=ch;
+ while(ch<eof && *ch!=sep && *ch!=eol) ch++;
+ if (stripWhite) {
+ fieldEnd=ch-1;
+ while(fieldEnd>=fieldStart && *fieldEnd==' ') fieldEnd--;
+ fieldLen = (int)(fieldEnd-fieldStart)+1;
+ } else fieldLen = (int)(ch-fieldStart);
+ }
+ }
+ // Rprintf("Processed field %.*s\n", (int)(ch-fieldStart), fieldStart);
+}
+
+static int countfields()
+{
+ int ncol=0;
+ if (ch==eof) return 0;
+ if (*ch==eol) { ch+=eolLen; return 0; }
+ while (1) {
+ ncol++;
+ skip_spaces();
+ Field();
+ if (ch==eof || *ch==eol) {
+ if (ch<eof) ch+=eolLen; // move ch to the start of the next line
+ return ncol;
+ }
+ if (*ch!=sep) STOP("Internal error: Field() has ended with '%c' not sep='%c'", *ch, sep);
+ ch++;
+ }
+}
+
+static inline Rboolean Strtoll()
+{
+ // Specialized strtoll that :
+ // i) skips leading isspace(), too, but other than field separator and eol (e.g. '\t' and ' \t' in FUT1206.txt)
+ // ii) has fewer branches for speed as no need for non decimal base
+ // iii) updates global ch directly saving arguments
+ // iv) safe for mmap which can't be \0 terminated on Windows (but can be on unix and mac)
+ // v) fails if whole field isn't consumed such as "3.14" (strtol consumes the 3 and stops)
+ // ... all without needing to read into a buffer at all (reads the mmap directly)
+ const char *lch=ch; int sign=1;
+ while (lch<eof && isspace(*lch) && *lch!=sep && *lch!=eol) lch++;
+ if (lch==eof || *lch==sep || *lch==eol) { // ',,' or ', ,' or '\t\t' or '\t \t' etc => NA
+ ch = lch; // advance global ch over empty field
+ return(TRUE); // caller already set u.l=NA_INTEGR or u.d=NA_REAL as appropriate
+ }
+ // moved this if-statement to the top for #1314
+ if(can_cast_to_na(lch)) {
+ // ch pointer already set to end of nastring by can_cast_to_na() function
+ return(TRUE);
+ }
+ if (*lch=='-') { sign=0; lch++; if (*lch<'0' || *lch>'9') return(FALSE); } // + or - symbols alone should be character
+ else if (*lch=='+') { sign=1; lch++; if (*lch<'0' || *lch>'9') return(FALSE); }
+ long long acc = 0;
+ while (lch<eof && '0'<=*lch && *lch<='9' && acc<(LLONG_MAX-10)/10) {
+ acc *= 10; // overflow avoided, thanks to BDR and CRAN's new ASAN checks, 25 Feb 2014
+ acc += *lch-'0'; // have assumed compiler will optimize the constant expression (LLONG_MAX-10)/10
+ lch++; // TO DO can remove lch<eof when last row is specialized in case of no final eol
+
+ }
+ // take care of leading spaces
+ while(lch<eof && *lch!=sep && *lch==' ') lch++;
+ //int len = lch-start;
+ //Rprintf("Strtoll field '%.*s' has len %d\n", lch-ch+1, ch, len);
+ if (lch==eof || *lch==sep || *lch==eol) { // only if field fully consumed (e.g. not ,123456A,)
+ ch = lch;
+ u.l = sign ? acc : -acc;
+ return(TRUE); // either int or int64 read ok, result in u.l. INT_MIN and INT_MAX checked by caller, as appropriate
+ }
+ return(FALSE); // invalid integer such as "3.14", "123ABC," or "12345678901234567890" (larger than even int64) => bump type.
+}
+
+static inline Rboolean Strtod()
+{
+ // Specialized strtod for same reasons as Strtoll (leading \t dealt with), but still uses stdlib:strtod (hard to do better, unlike strtoll).
+ // R's R_Strtod5 uses strlen() on input, so we can't use that here unless we copy field to a buffer (slow)
+ // Could fork glibc's strtod and change it to pass in dec rather than via locale but see :
+ // http://www.exploringbinary.com/how-glibc-strtod-works/
+ // i.e. feel more comfortable relying on glibc for speed and robustness (and more eyes have been on it)
+ // Since French_France.1252 is always available on Windows, should not be an issue there: fread() automates the locale change and revert.
+ // On unix, fr_FR.utf8 may need to be installed.
+ const char *lch=ch;
+ while (lch<eof && isspace(*lch) && *lch!=sep && *lch!=eol) lch++;
+ if (lch==eof || *lch==sep || *lch==eol) {u.d=NA_REAL; ch=lch; return(TRUE); } // e.g. ',,' or '\t\t'
+ // moved this if-loop to top for #1314
+ if(can_cast_to_na(lch)) {
+ u.d = NA_REAL;
+ // ch pointer already set to end of nastring by can_cast_to_na() function
+ return(TRUE);
+ }
+ const char *start=lch;
+ errno = 0;
+
+ u.d = strtod(start, (char **)&lch);
+ // take care of leading spaces
+ while(lch<eof && *lch!=sep && *lch==' ') lch++;
+ if (errno==0 && lch>start && (lch==eof || *lch==sep || *lch==eol)) {
+ ch = lch;
+ return(TRUE); // double read ok (result in u.d). Done. Most common case.
+ }
+ if (errno==ERANGE && lch>start) {
+ lch = start;
+ errno = 0;
+ u.d = (double)strtold(start, (char **)&lch);
+ if (errno==0 && lch>start && (lch==eof || *lch==sep || *lch==eol)) {
+ ch = lch;
+ if (ERANGEwarning) {
+ warning("C function strtod() returned ERANGE for one or more fields. The first was string input '%.*s'. It was read using (double)strtold() as numeric value %.16E (displayed here using %%.16E); loss of accuracy likely occurred. This message is designed to tell you exactly what has been done by fread's C code, so you can search yourself online for many references about double precision accuracy and these specific C functions. You may wish to use colClasses to read the colu [...]
+ ERANGEwarning = FALSE; // once only. Set to TRUE just before read data loop. FALSE initially when detecting types.
+ // This is carefully worded as an ERANGE warning because that's precisely what it is. Calling it a 'precision' warning
+ // might lead the user to think they'll get a precision warning on "1.23456789123456789123456789123456789" too, but they won't
+ // as that will be read fine by the first strtod() with silent loss of precision. IIUC.
+ }
+ return(TRUE);
+ }
+ }
+ return(FALSE); // invalid double, need to bump type.
+}
+
+static inline Rboolean Strtob()
+{
+ // String (T,F,True,False,TRUE or FALSE) to boolean. These usually come from R when it writes out.
+ const char *start=ch;
+ // moved this if-statement to top for #1314
+ if(can_cast_to_na(ch)) {
+ u.b = NA_LOGICAL;
+ // ch pointer already set to end of nastring by can_cast_to_na() function
+ return(TRUE);
+ } else if (*ch=='T') {
+ u.b = TRUE;
+ if (++ch==eof || *ch==sep || *ch==eol) return(TRUE);
+ if (*ch=='R' && *++ch=='U' && *++ch=='E' && (++ch==eof || *ch==sep || *ch==eol)) return(TRUE);
+ ch = start+1;
+ if (*ch=='r' && *++ch=='u' && *++ch=='e' && (++ch==eof || *ch==sep || *ch==eol)) return(TRUE);
+ }
+ else if (*ch=='F') {
+ u.b = FALSE;
+ if (++ch==eof || *ch==sep || *ch==eol) return(TRUE);
+ if (*ch=='A' && *++ch=='L' && *++ch=='S' && *++ch=='E' && (++ch==eof || *ch==sep || *ch==eol)) return(TRUE);
+ ch = start+1;
+ if (*ch=='a' && *++ch=='l' && *++ch=='s' && *++ch=='e' && (++ch==eof || *ch==sep || *ch==eol)) return(TRUE);
+ }
+ ch = start;
+ return(FALSE); // invalid boolean, need to bump type.
+}
+
+
+static int numDP(double *v, R_len_t n)
+{
+ // highly cut down version of formatReal() just for what we need, since formatReal is not in R's API and r-devel now prevents it
+ char buffer[10];
+ int maxdp=0, this;
+ double intpart; // not used or needed (only interested in dp after .)
+ for (R_len_t i=0; i<n && maxdp<6; i++) {
+ if (!R_FINITE(v[i])) continue;
+ snprintf(buffer, 9, "%.6f", modf(v[i],&intpart)); // 0.123456 => 6, 0.120000 => 2
+ this=6;
+ while (this>0 && buffer[this+1]=='0') this--;
+ if (this>maxdp) maxdp=this;
+ }
+ return(maxdp);
+}
+
+static SEXP coerceVectorSoFar(SEXP v, int oldtype, int newtype, R_len_t sofar, R_len_t col)
+{
+ // Like R's coerceVector() but :
+ // i) we only need to coerce elements up to the row read so far, for speed.
+ // ii) we can directly change type of vectors without an allocation when the size of the data type doesn't change
+ SEXP newv;
+ R_len_t i, protecti=0;
+ int dp;
+ clock_t tCoerce0 = clock();
+ const char *lch=ch;
+ while (lch!=eof && *lch!=sep && *lch!=eol) lch++; // lch now marks the end of field, used in verbose messages and errors
+ if (verbose) Rprintf("Bumping column %d from %s to %s on data row %d, field contains '%.*s'\n",
+ col+1, TypeName[oldtype], TypeName[newtype], sofar+1, lch-ch, ch);
+ if (sizes[TypeSxp[oldtype]]<4) STOP("Internal error: SIZEOF oldtype %d < 4", oldtype);
+ if (sizes[TypeSxp[newtype]]<4) STOP("Internal error: SIZEOF newtype %d < 4", newtype);
+ if (sizes[TypeSxp[oldtype]] == sizes[TypeSxp[newtype]] && newtype != SXP_STR) { // after && is quick fix. TO DO: revisit
+ TYPEOF(v) = TypeSxp[newtype];
+ newv=v;
+ } else {
+ clock_t tCoerceAlloc0 = clock();
+ PROTECT(newv = allocVector(TypeSxp[newtype], LENGTH(v)));
+ protecti++;
+ tCoerceAlloc += clock()-tCoerceAlloc0;
+ // This was 1.3s (all of tCoerce) when testing on 2008.csv; might have triggered a gc, included.
+ // Happily, mid read bumps are very rarely needed, due to testing types at the start, middle and end of the file, first.
+ }
+ setAttrib(newv, R_ClassSymbol, newtype==SXP_INT64 ? ScalarString(mkChar("integer64")) : R_NilValue);
+ switch(newtype) {
+ case SXP_INT :
+ switch(oldtype) {
+ case SXP_LGL :
+ for (i=0; i<sofar; i++) INTEGER(newv)[i] = INTEGER(v)[i];
+ break;
+ default :
+ STOP("Internal error: attempt to bump from type %d to type %d. Please report to datatable-help.", oldtype, newtype);
+ }
+ break;
+ case SXP_INT64:
+ switch(oldtype) {
+ case SXP_LGL : case SXP_INT :
+ for (i=0; i<sofar; i++) {
+ REAL(newv)[i] = (INTEGER(v)[i]==NA_INTEGER ? (u.l=NAINT64,u.d) : (u.l=(long long)INTEGER(v)[i],u.d));
+ }
+ break;
+ default :
+ STOP("Internal error: attempt to bump from type %d to type %d. Please report to datatable-help.", oldtype, newtype);
+ }
+ break;
+ case SXP_REAL:
+ switch(oldtype) {
+ case SXP_LGL : case SXP_INT :
+ for (i=0; i<sofar; i++) REAL(newv)[i] = (INTEGER(v)[i]==NA_INTEGER ? NA_REAL : (double)INTEGER(v)[i]);
+ break;
+ case SXP_INT64 :
+ for (i=0; i<sofar; i++) REAL(newv)[i] = ((*(long long *)&REAL(v)[i] == NAINT64) ? NA_REAL : (double)(*(long long *)&REAL(v)[i]));
+ break;
+ default :
+ STOP("Internal error: attempt to bump from type %d to type %d. Please report to datatable-help.", oldtype, newtype);
+ }
+ break;
+ case SXP_STR:
+ warning("Bumped column %d to type character on data row %d, field contains '%.*s'. Coercing previously read values in this column from logical, integer or numeric back to character which may not be lossless; e.g., if '00' and '000' occurred before they will now be just '0', and there may be inconsistencies with treatment of ',,' and ',NA,' too (if they occurred in this column before the bump). If this matters please rerun and set 'colClasses' to 'character' for this column. Pleas [...]
+ static char buffer[129]; // 25 to hold [+-]2^63, with spare space to be safe and snprintf too
+ switch(oldtype) {
+ case SXP_LGL : case SXP_INT :
+ // This for loop takes 0.000007 seconds if the bump occurs on line 179, for example, timing showed
+ for (i=0; i<sofar; i++) {
+ if (INTEGER(v)[i] == NA_INTEGER)
+ SET_STRING_ELT(newv,i,R_BlankString);
+ else {
+ snprintf(buffer,128,"%d",INTEGER(v)[i]);
+ SET_STRING_ELT(newv, i, mkChar(buffer));
+ }
+ }
+ break;
+ case SXP_INT64 :
+ for (i=0; i<sofar; i++) {
+ if (ISNA(REAL(v)[i]))
+ SET_STRING_ELT(newv,i,R_BlankString);
+ else {
+ #ifdef WIN32
+ snprintf(buffer,128,"%I64d",*(long long *)&REAL(v)[i]);
+ #else
+ snprintf(buffer,128,"%lld",*(long long *)&REAL(v)[i]);
+ #endif
+ SET_STRING_ELT(newv, i, mkChar(buffer));
+ }
+ }
+ break;
+ case SXP_REAL :
+ dp = numDP(REAL(v), sofar); // would use formatReal() but it's not in R's API and r-devel now prevents it
+ for (i=0; i<sofar; i++) {
+ if (ISNA(REAL(v)[i]))
+ SET_STRING_ELT(newv,i,R_BlankString);
+ else {
+ snprintf(buffer,128,"%.*f",dp,REAL(v)[i]);
+ SET_STRING_ELT(newv, i, mkChar(buffer));
+ }
+ }
+ break;
+ default :
+ STOP("Internal error: attempt to bump from type %d to type %d. Please report to datatable-help.", oldtype, newtype);
+ }
+ break;
+ default :
+ STOP("Internal error: attempt to bump from type %d to type %d. Please report to datatable-help.", oldtype, newtype);
+ }
+ UNPROTECT(protecti);
+ tCoerce += clock()-tCoerce0;
+ return(newv);
+}
+
+SEXP readfile(SEXP input, SEXP separg, SEXP nrowsarg, SEXP headerarg, SEXP nastrings, SEXP verbosearg, SEXP autostart, SEXP skip, SEXP select, SEXP drop, SEXP colClasses, SEXP integer64, SEXP dec, SEXP encoding, SEXP stripWhiteArg, SEXP showProgressArg)
+// can't be named fread here because that's already a C function (from which the R level fread function took its name)
+{
+ SEXP thiscol, ans, thisstr;
+ R_len_t i, resi, j, resj, k, protecti=0, nrow=0, ncol=0;
+ int thistype;
+ const char *pos, *ch2, *lineStart;
+ Rboolean header, allchar;
+ verbose=LOGICAL(verbosearg)[0];
+ clock_t t0 = clock();
+ ERANGEwarning = FALSE; // just while detecting types, then TRUE before the read data loop
+
+ // Encoding, #563: Borrowed from do_setencoding from base R
+ // https://github.com/wch/r-source/blob/ca5348f0b5e3f3c2b24851d7aff02de5217465eb/src/main/util.c#L1115
+ // Check for mkCharLenCE function to locate as to where where this is implemented.
+ cetype_t ienc;
+ if (!strcmp(CHAR(STRING_ELT(encoding, 0)), "Latin-1")) ienc = CE_LATIN1;
+ else if (!strcmp(CHAR(STRING_ELT(encoding, 0)), "UTF-8")) ienc = CE_UTF8;
+ else ienc = CE_NATIVE;
+
+ stripWhite = LOGICAL(stripWhiteArg)[0];
+
+ // Extra tracing for apparent 32bit Windows problem: https://github.com/Rdatatable/data.table/issues/1111
+ if (!isInteger(showProgressArg)) error("showProgress is not type integer but type '%s'. Please report.", type2char(TYPEOF(showProgressArg)));
+ if (LENGTH(showProgressArg)!=1) error("showProgress is not length 1 but length %d. Please report.", LENGTH(showProgressArg));
+ int showProgress = INTEGER(showProgressArg)[0];
+ if (showProgress!=0 && showProgress!=1)
+ error("showProgress is not 0 or 1 but %d. Please report.", showProgress);
+
+ if (!isString(dec) || LENGTH(dec)!=1 || strlen(CHAR(STRING_ELT(dec,0))) != 1)
+ error("dec must be a single character");
+ const char decChar = *CHAR(STRING_ELT(dec,0));
+
+ fnam = NULL; // reset global, so STOP() can call closeFile() which sees fnam
+
+ if (NA_INTEGER != INT_MIN) error("Internal error: NA_INTEGER (%d) != INT_MIN (%d).", NA_INTEGER, INT_MIN); // relied on by Stroll
+ if (sizeof(double) != 8) error("Internal error: sizeof(double) is %d bytes, not 8.", sizeof(double));
+ if (sizeof(long long) != 8) error("Internal error: sizeof(long long) is %d bytes, not 8.", sizeof(long long));
+
+ // raise(SIGINT);
+ // ********************************************************************************************
+ // Check inputs.
+ // ********************************************************************************************
+
+ if (!isLogical(headerarg) || LENGTH(headerarg)!=1) error("'header' must be 'auto', TRUE or FALSE"); // 'auto' was converted to NA at R level
+ header = LOGICAL(headerarg)[0];
+ if (!isNull(nastrings) && !isString(nastrings)) error("'na.strings' is type '%s'. Must be a character vector.", type2char(TYPEOF(nastrings)));
+ if (!isInteger(nrowsarg) || LENGTH(nrowsarg)!=1 || INTEGER(nrowsarg)[0]==NA_INTEGER) error("'nrows' must be a single non-NA number of type numeric or integer");
+ if (!isInteger(autostart) || LENGTH(autostart)!=1 || INTEGER(autostart)[0]<0) error("'autostart' must be a length 1 vector of type numeric or integer and >=0"); // NA_INTEGER is covered by <1
+ if (isNumeric(skip)) { skip = PROTECT(coerceVector(skip, INTSXP)); protecti++; }
+ if (!( (isInteger(skip) && LENGTH(skip)==1 && INTEGER(skip)[0]>=0) // NA_INTEGER is covered by >=0
+ ||(isString(skip) && LENGTH(skip)==1))) error("'skip' must be a length 1 vector of type numeric or integer >=0, or single character search string");
+ if (!isNull(separg)) {
+ if (!isString(separg) || LENGTH(separg)!=1 || strlen(CHAR(STRING_ELT(separg,0)))!=1) error("'sep' must be 'auto' or a single character");
+ if (*CHAR(STRING_ELT(separg,0))=='\"') error("sep = '%c' = quote, is not an allowed separator.",'\"');
+ if (*CHAR(STRING_ELT(separg,0)) == decChar) error("The two arguments to fread 'dec' and 'sep' are equal ('%c').", decChar);
+ }
+ if (!isString(integer64) || LENGTH(integer64)!=1) error("'integer64' must be a single character string");
+ if (strcmp(CHAR(STRING_ELT(integer64,0)), "integer64")!=0 &&
+ strcmp(CHAR(STRING_ELT(integer64,0)), "double")!=0 &&
+ strcmp(CHAR(STRING_ELT(integer64,0)), "numeric")!=0 &&
+ strcmp(CHAR(STRING_ELT(integer64,0)), "character")!=0)
+ error("integer64='%s' which isn't 'integer64'|'double'|'numeric'|'character'", CHAR(STRING_ELT(integer64,0)));
+ if (!isNull(select) && !isNull(drop)) error("Supply either 'select' or 'drop' but not both");
+ // ********************************************************************************************
+ // NA handling preparations
+ // ********************************************************************************************
+ if( ! isNull(nastrings)) {
+ FLAG_NA_STRINGS_NULL = 0;
+ NASTRINGS_LEN = LENGTH(nastrings);
+ NA_MASK = (int *)malloc(NASTRINGS_LEN * sizeof(int));
+ NA_MAX_NCHAR = get_maxlen(nastrings);
+ NA_STRINGS = malloc(NASTRINGS_LEN * sizeof(char *));
+ EACH_NA_STRING_LEN = malloc(NASTRINGS_LEN * sizeof(int));
+ for (int i = 0; i < NASTRINGS_LEN; i++) {
+ NA_STRINGS[i] = CHAR(STRING_ELT(nastrings, i));
+ EACH_NA_STRING_LEN[i] = strlen(CHAR(STRING_ELT(nastrings, i)));
+ }
+ } else {
+ FLAG_NA_STRINGS_NULL = 1;
+ }
+ // ********************************************************************************************
+ // Point to text input, or open and mmap file
+ // ********************************************************************************************
+ ch = ch2 = (const char *)CHAR(STRING_ELT(input,0));
+ while (*ch2!='\n' && *ch2) ch2++;
+ if (*ch2=='\n' || !*ch) {
+ if (verbose) Rprintf("Input contains a \\n (or is \"\"). Taking this to be text input (not a filename)\n");
+ filesize = strlen(ch);
+ mmp = ch;
+ eof = mmp+filesize;
+ if (*eof!='\0') error("Internal error: last byte of character input isn't \\0");
+ } else {
+ if (verbose) Rprintf("Input contains no \\n. Taking this to be a filename to open\n");
+ fnam = R_ExpandFileName(ch); // for convenience so user doesn't have to call path.expand() themselves
+#ifndef WIN32
+ fd = open(fnam, O_RDONLY);
+ if (fd==-1) error("file not found: %s",fnam);
+ struct stat stat_buf;
+ if (fstat(fd,&stat_buf) == -1) {close(fd); error("Opened file ok but couldn't obtain file size: %s", fnam);}
+ filesize = stat_buf.st_size;
+ if (filesize<=0) {close(fd); error("File is empty: %s", fnam);}
+ if (verbose) Rprintf("File opened, filesize is %.6f GB.\nMemory mapping ... ", 1.0*filesize/(1024*1024*1024));
+ // Would be nice to print 'Memory mapping' when not verbose, but then it would also print for small files
+ // which would be annoying. If we could estimate if the mmap was likely to take more than 2 seconds (and thus
+ // the % meter to kick in) then that'd be ideal. A simple size check isn't enough because it might already
+ // be cached from a previous run. Perhaps spawn an event, which is cancelled if mmap returns within 2 secs.
+#ifdef MAP_POPULATE
+ mmp = (const char *)mmap(NULL, filesize, PROT_READ, MAP_PRIVATE | MAP_POPULATE, fd, 0); // TO DO?: MAP_HUGETLB
+#else
+ mmp = (const char *)mmap(NULL, filesize, PROT_READ, MAP_PRIVATE, fd, 0); // for Mac
+#endif
+ if (mmp == MAP_FAILED) {
+ close(fd);
+#else
+ // Following: http://msdn.microsoft.com/en-gb/library/windows/desktop/aa366548(v=vs.85).aspx
+ hFile = INVALID_HANDLE_VALUE;
+ i = 0;
+ while(hFile==INVALID_HANDLE_VALUE && i<5) {
+ hFile = CreateFile(fnam, GENERIC_READ, FILE_SHARE_READ|FILE_SHARE_WRITE, NULL, OPEN_EXISTING, 0, NULL);
+ // FILE_SHARE_WRITE is required otherwise if the file is open in Excel, CreateFile fails. Should be ok now.
+ if (hFile==INVALID_HANDLE_VALUE) {
+ if (GetLastError()==ERROR_FILE_NOT_FOUND) error("File not found: %s",fnam);
+ if (i<4) Sleep(250); // 250ms
+ }
+ i++;
+ // Looped retry to avoid ephemeral locks by system utilities as recommended here : http://support.microsoft.com/kb/316609
+ }
+ if (hFile==INVALID_HANDLE_VALUE) error("Unable to open file after %d attempts (error %d): %s", i, GetLastError(), fnam);
+ LARGE_INTEGER liFileSize;
+ if (GetFileSizeEx(hFile,&liFileSize)==0) { CloseHandle(hFile); error("GetFileSizeEx failed (returned 0) on file: %s", fnam); }
+ filesize = (size_t)liFileSize.QuadPart;
+ if (filesize<=0) { CloseHandle(hFile); error("File is empty: %s", fnam); }
+ hMap=CreateFileMapping(hFile, NULL, PAGE_READONLY, 0, 0, NULL); // filesize+1 not allowed here, unlike mmap where +1 is zero'd
+ if (hMap==NULL) { CloseHandle(hFile); error("This is Windows, CreateFileMapping returned error %d for file %s", GetLastError(), fnam); }
+ if (verbose) Rprintf("File opened, filesize is %.6f GB.\nMemory mapping ... ", 1.0*filesize/(1024*1024*1024));
+ mmp = (const char *)MapViewOfFile(hMap,FILE_MAP_READ,0,0,filesize);
+ if (mmp == NULL) {
+ CloseHandle(hMap);
+ CloseHandle(hFile);
+#endif
+ if (sizeof(char *)==4)
+ error("Opened file ok, obtained its size on disk (%.1fMB) but couldn't memory map it. This is a 32bit machine. You don't need more RAM per se but this fread function is tuned for 64bit addressability at the expense of large file support on 32bit machines. You probably need more RAM to store the resulting data.table, anyway. And most speed benefits of data.table are on 64bit with large RAM, too. Please upgrade to 64bit.", filesize/(1024.0*1024));
+ // if we support this on 32bit, we may need to use stat64 instead, as R does
+ else if (sizeof(char *)==8)
+ error("Opened file ok, obtained its size on disk (%.1fMB), but couldn't memory map it. This is a 64bit machine so this is surprising. Please report to datatable-help.", filesize/1024^2);
+ else
+ error("Opened file ok, obtained its size on disk (%.1fMB), but couldn't memory map it. Size of pointer is %d on this machine. Probably failing because this is neither a 32bit or 64bit machine. Please report to datatable-help.", filesize/1024^2, sizeof(char *));
+ }
+#ifndef WIN32
+ // if (madvise((char *)mmp, filesize+1, MADV_SEQUENTIAL | MADV_WILLNEED) == -1) warning("Mapped file ok but madvise failed");
+ // TO DO: commented this out for the case of nrow=100, more testing to do.
+#endif
+ if (EOF > -1) error("Internal error. EOF is not -1 or less\n");
+ if (mmp[filesize-1] < 0) error("mmap'd region has EOF at the end");
+ eof = mmp+filesize; // byte after last byte of file. Never dereference eof as it's not mapped.
+ if (verbose) Rprintf("ok\n"); // to end 'Memory mapping ... '
+ }
+ clock_t tMap = clock();
+ // From now use STOP() wrapper instead of error(), for Windows to close file so as not to lock the file after an error.
+
+ // ********************************************************************************************
+ // Auto detect eol, first eol where there are two (i.e. CRLF)
+ // ********************************************************************************************
+ ch = mmp;
+ while (ch<eof && *ch!='\n' && *ch!='\r') {
+ if (*ch=='\"') while(++ch<eof && *ch!='\"') {}; // allows protection of \n and \r inside column names
+ ch++; // this 'if' needed in case opening protection is not closed before eof
+ }
+ if (ch>=eof) {
+ if (ch>eof) STOP("Internal error: ch>eof when detecting eol");
+ if (verbose) Rprintf("Input ends before any \\r or \\n observed. Input will be treated as a single data row.\n");
+ eol=eol2='\n'; eolLen=1;
+ } else {
+ eol=eol2=*ch; eolLen=1;
+ if (eol=='\r') {
+ if (ch+1<eof && *(ch+1)=='\n') {
+ if (verbose) Rprintf("Detected eol as \\r\\n (CRLF) in that order, the Windows standard.\n");
+ eol2='\n'; eolLen=2;
+ } else {
+ if (ch+1<eof && *(ch+1)=='\r')
+ STOP("Line ending is \\r\\r\\n. R's download.file() appears to add the extra \\r in text mode on Windows. Please download again in binary mode (mode='wb') which might be faster too. Alternatively, pass the URL directly to fread and it will download the file in binary mode for you.");
+ // NB: on Windows, download.file from file: seems to condense \r\r too. So
+ if (verbose) Rprintf("Detected eol as \\r only (no \\n or \\r afterwards). An old Mac 9 standard, discontinued in 2002 according to Wikipedia.\n");
+ }
+ } else if (eol=='\n') {
+ if (ch+1<eof && *(ch+1)=='\r') {
+ warning("Detected eol as \\n\\r, a highly unusual line ending. According to Wikipedia the Acorn BBC used this. If it is intended that the first column on the next row is a character column where the first character of the field value is \\r (why?) then the first column should start with a quote (i.e. 'protected'). Proceeding with attempt to read the file.\n");
+ eol2='\r'; eolLen=2;
+ } else if (verbose) Rprintf("Detected eol as \\n only (no \\r afterwards), the UNIX and Mac standard.\n");
+ } else
+ STOP("Internal error: if no \\r or \\n found then ch should be eof");
+ }
+
+ // ********************************************************************************************
+ // Position to line skip+1 or line containing skip="string" or line autostart
+ // ********************************************************************************************
+ line = 1; pos = mmp;
+ // line is for error and warning messages so considers embedded \n, just like wc -l, head -n and tail -n
+ if (isString(skip)) {
+ ch = strstr(mmp, CHAR(STRING_ELT(skip,0)));
+ if (!ch) STOP("skip='%s' not found in input (it is case sensitive and literal; i.e., no patterns, wildcards or regex)", CHAR(STRING_ELT(skip,0)));
+ while (ch>mmp && *(ch-1)!=eol2) ch--; // move to beginning of line
+ pos = ch;
+ ch = mmp;
+ while (ch<pos) line+=(*ch++==eol);
+ if (verbose) Rprintf("Found skip='%s' on line %d.\n", CHAR(STRING_ELT(skip,0)), line);
+ ch = pos;
+ } else {
+ ch = mmp;
+ int tmp = INTEGER(skip)[0];
+ tmp = tmp>0 ? tmp+1 : INTEGER(autostart)[0];
+ while (ch<eof && line<tmp) {
+ while (ch<eof && *ch!=eol) ch++;
+ if (ch<eof) ch+=eolLen;
+ line++;
+ }
+ pos = ch;
+ if (verbose) Rprintf("Positioned on line %d after skip or autostart\n", line);
+
+ while (ch<eof && isspace(*ch) && *ch!=eol) ch++;
+ Rboolean thisLineBlank = (ch==eof || *ch==eol);
+ ch = pos;
+ if (INTEGER(skip)[0]>0 && !thisLineBlank) {
+ if (verbose) Rprintf("This line isn't blank and skip>0 so we're done\n");
+ } else if (thisLineBlank) {
+ if (verbose) Rprintf("This line is blank. Moving to the next non-blank ... ");
+ while (ch<eof) {
+ while (ch<eof && isspace(*ch) && *ch!=eol) ch++;
+ if (ch<eof && *ch==eol) { line++; ch+=eolLen; pos=ch; }
+ else break;
+ }
+ if (ch==eof) STOP("Input is either empty or fully whitespace after the skip or autostart. Run again with verbose=TRUE.");
+ if (verbose) Rprintf("line %d\n", line);
+ } else {
+ if (verbose) Rprintf("This line is the autostart and not blank so searching up for the last non-blank ... ");
+ // 'autostart' = select-sub-table-using-line-within
+ line++;
+ while (ch>=mmp && !thisLineBlank) {
+ pos = ch;
+ line--;
+ ch -= eolLen+1;
+ i = 0;
+ while (ch>=mmp && *ch!=eol2) { i+=!isspace(*ch); ch--; }
+ ch++;
+ thisLineBlank = i==0;
+ }
+ ch = pos;
+ if (verbose) Rprintf("line %d\n", line);
+ }
+ }
+ if (pos>mmp && *(pos-1)!=eol2) STOP("Internal error. No eol2 immediately before line %d, '%.1s' instead", line, pos-1);
+
+
+ // ********************************************************************************************
+ // Auto detect separator, number of fields, and location of first row
+ // ********************************************************************************************
+ const char *seps;
+ if (isNull(separg)) {
+ seps=",\t |;:"; // separators, in order of preference. See ?fread. (colon last as it can appear in time fields)
+ if (verbose) Rprintf("Detecting sep ... ");
+ } else {
+ seps = (const char *)CHAR(STRING_ELT(separg,0)); // length 1 string of 1 character, checked above
+ if (verbose) Rprintf("Using supplied sep '%s' ... ", seps[0]=='\t'?"\\t":seps);
+ }
+ int nseps = strlen(seps);
+
+ const char *topStart=ch, *thisStart=ch;
+ char topSep=seps[0];
+ int topLine=0, topLen=0, topNcol=-1;
+ for (int s=0; s<nseps; s++) {
+ if (seps[s] == decChar) continue;
+ ch=pos; sep=seps[s];
+ i=0;
+ int thisLine=line, thisLen=0, thisNcol=-1; // this* = this run's starting *
+ while(ch<=eof && ++i<=30) {
+ lineStart = ch;
+ ncol = countfields();
+ if (ncol==-1) {
+ if (thisNcol==-1) break; // if first row has quote problem, move straight on to test a different sep
+ ncol=thisNcol; // skip the quote problem row for now (consider part of current run)
+ }
+ if (ncol==thisNcol) thisLen++;
+ if (ch==eof || i==30 || ncol!=thisNcol) {
+ // this* still refers to the previous run which has just finished
+ // Rprintf("\nRun: s='%c' thisLine=%d thisLen=%d thisNcol=%d", sep, thisLine, thisLen, thisNcol);
+ if (thisNcol>1 && (thisLen>topLen || // longest run wins
+ (thisLen==topLen && sep==topSep && thisNcol>topNcol))) { // if tied, the one that divides it more (test 1328, 2 rows)
+ topStart = thisStart;
+ topLine = thisLine;
+ topLen = thisLen;
+ topNcol = thisNcol;
+ topSep = sep;
+ }
+ if (lineStart==eof) break;
+ thisStart = lineStart;
+ thisLine = line+i-1;
+ thisLen = 1;
+ thisNcol = ncol;
+ }
+ }
+ }
+ if (topNcol<2) {
+ if (verbose) Rprintf("Deducing this is a single column input.\n");
+ sep=eol;
+ ch=pos;
+ line=1;
+ ncol=1;
+ } else {
+ sep=topSep;
+ ch=pos=topStart;
+ line=topLine;
+ ncol=topNcol;
+ if (verbose) {
+ if (isNull(separg)) { if (sep=='\t') Rprintf("'\\t'\n"); else Rprintf("'%c'\n", sep); }
+ else Rprintf("found ok\n");
+ }
+ }
+ if (verbose) {
+ if (sep!=eol) Rprintf("Detected %d columns. Longest stretch was from line %d to line %d\n",ncol,line,line+topLen-1);
+ ch2 = ch; while(++ch2<eof && *ch2!=eol && ch2-ch<10);
+ Rprintf("Starting data input on line %d (either column names or first row of data). First 10 characters: %.*s\n", line, (int)(ch2-ch), ch);
+ }
+ if (ch>mmp) {
+ if (*(ch-1)!=eol2) STOP("Internal error. No eol2 immediately before line %d after sep detection.", line);
+ ch2 = ch-eolLen-1;
+ i = 0;
+ while (ch2>=mmp && *ch2!=eol2) { i+=!isspace(*ch2); ch2--; }
+ ch2++;
+ if (i>0) {
+ if (line==2 && isInteger(skip) && INTEGER(skip)[0]==0) // warn about line 1, unless skip was provided
+ warning("Starting data input on line 2 and discarding line 1 because it has too few or too many items to be column names or data: %.*s", ch-ch2-eolLen, ch2);
+ else if (verbose)
+ Rprintf("The line before starting line %d is non-empty and will be ignored (it has too few or too many items to be column names or data): %.*s", line, ch-ch2-eolLen, ch2);
+ }
+ }
+ if (ch!=pos) STOP("Internal error. ch!=pos after sep detection");
+
+ // ********************************************************************************************
+ // Detect and assign column names (if present)
+ // ********************************************************************************************
+ SEXP names = PROTECT(allocVector(STRSXP, ncol));
+ protecti++;
+ allchar=TRUE;
+ for (i=0; i<ncol; i++) {
+ skip_spaces(); // remove trailing spaces
+ if (ch<eof && *ch=='\"') {
+ while(++ch<eof && (*ch!='\"' || (ch+1<eof && *(ch+1)!=sep && *(ch+1)!=eol))) {};
+ if (ch<eof && *ch++!='\"') STOP("Internal error: quoted field ends before EOF but not with \"sep");
+ } else { // if field reads as double ok then it's INT/INT64/REAL; i.e., not character (and so not a column name)
+ if (*ch!=sep && *ch!=eol && Strtod()) // blank column names (,,) considered character and will get default names
+ allchar=FALSE; // considered testing at least one isalpha, but we want 1E9 to be a value not a column name
+ else
+ while(ch<eof && *ch!=eol && *ch!=sep) ch++; // skip over unquoted character field
+ }
+ if (i<ncol-1) { // not the last column (doesn't have a separator after it)
+ if (ch<eof && *ch!=sep) STOP("Unexpected character ending field %d of line %d: %.*s", i+1, line, ch-pos+5, pos);
+ else if (ch<eof) ch++;
+ }
+ }
+ // discard any whitespace after last column name on first row before the eol (was a TODO)
+ skip_spaces();
+ if (ch<eof && *ch!=eol) STOP("Not positioned correctly after testing format of header row. ch='%c'",*ch);
+ if (verbose && header!=NA_LOGICAL) Rprintf("'header' changed by user from 'auto' to %s\n", header?"TRUE":"FALSE");
+ char buff[10]; // to construct default column names
+ if (header==FALSE || (header==NA_LOGICAL && !allchar)) {
+ if (verbose && header==NA_LOGICAL) Rprintf("Some fields on line %d are not type character (or are empty). Treating as a data row and using default column names.\n", line);
+ for (i=0; i<ncol; i++) {
+ sprintf(buff,"V%d",i+1);
+ SET_STRING_ELT(names, i, mkChar(buff));
+ }
+ ch = pos; // back to start of first row. Treat as first data row, no column names present.
+ } else {
+ if (verbose && header==NA_LOGICAL) Rprintf("All the fields on line %d are character fields. Treating as the column names.\n", line);
+ ch = pos;
+ line++;
+ for (i=0; i<ncol; i++) {
+ // Skip trailing spaces and call 'Field()' here as it's already designed to handle quote vs non-quote cases.
+ // Also Fiedl() it takes care of leading spaces. Easier to understand the logic.
+ skip_spaces(); Field();
+ if (fieldLen) {
+ SET_STRING_ELT(names, i, mkCharLen(fieldStart, fieldLen));
+ } else {
+ sprintf(buff,"V%d",i+1);
+ SET_STRING_ELT(names, i, mkChar(buff));
+ }
+ if (i<ncol-1) ch++; // move the beginning char of next field
+ }
+ while (ch<eof && *ch!=eol) ch++; // no need for skip_spaces() here
+ if (ch<eof && *ch==eol) ch+=eolLen; // now on first data row (row after column names)
+ pos = ch;
+ }
+ clock_t tLayout = clock();
+
+ // ********************************************************************************************
+ // Count number of rows
+ // ********************************************************************************************
+ i = INTEGER(nrowsarg)[0];
+ if (pos==eof || *pos==eol) {
+ nrow=0;
+ if (verbose) Rprintf("Byte after header row is eof or eol, 0 data rows present.\n");
+ } else if (i>-1) {
+ nrow = i;
+ if (verbose) Rprintf("nrow set to nrows passed in (%d)\n", nrow);
+ // Intended for nrow=10 to see top 10 rows quickly without touching remaining pages
+ } else {
+ long long neol=1, nsep=0, tmp;
+ while (ch<eof) {
+ // goal: quick row count with no branches while still coping with embedded sep and \n, below
+ neol+=(*ch==eol);
+ nsep+=(*ch++==sep);
+ }
+ if (ch!=eof) STOP("Internal error: ch!=eof after counting sep and eol");
+ i=0; int nblank=0;
+ while (i==0 && ch>pos) {
+ // count blank lines at the end
+ i=0; while (ch>pos && *--ch!=eol2) i += !isspace(*ch);
+ nblank += (i==0);
+ ch -= eolLen-1;
+ }
+ // if (nblank==0) There is non white after the last eol. Ok and dealt with. TO DO: reference test id here in comment
+ if (ncol==1) tmp = neol-nblank;
+ else tmp = MIN( nsep / (ncol-1), neol-nblank ); // good quick estimate with embedded sep and eol in mind
+ if (verbose || tmp>INT_MAX) {
+ Rprintf("Count of eol: %lld (including %d at the end)\n",neol,nblank);
+ Rprintf("Count of sep: %lld\n",nsep);
+ if (ncol==1) Rprintf("ncol==1 so sep count ignored\n");
+ else Rprintf("nrow = MIN( nsep [%lld] / ncol [%d] -1, neol [%lld] - nblank [%d] ) = %lld\n", nsep, ncol, neol, nblank, tmp);
+ if (tmp > INT_MAX) STOP("nrow larger than current 2^31 limit");
+ }
+ nrow = tmp;
+ // Advantages of exact count: i) no need to slightly over allocate (by 5%, say) so no need to clear up on heap during gc(),
+ // and ii) no need to implement realloc if estimate doesn't turn out to be large enough (e.g. if sample rows are wider than file average).
+ // TO DO: goes away if we allow over-allocated columns.
+ // The old estimate method based on size of first 10 rows :
+ // estn = (R_len_t)ceil(1.05 * 10 * (filesize-(pos-mmp)) / (pos2-pos1)) +5; // +5 for small files
+ // if (verbose) Rprintf("Estimated nrows: %d ( 1.05*%d*(%ld-(%ld-%ld))/(%ld-%ld) )\n",estn,10,filesize,pos,mmp,pos2,pos1);
+ }
+ clock_t tRowCount = clock();
+
+ // ********************************************************************************************
+ // Make best guess at column types using first 5 rows, middle 5 rows and last 5 rows
+ // ********************************************************************************************
+ int type[ncol]; for (i=0; i<ncol; i++) type[i]=0; // default type is lowest.
+ const char *str, *thispos;
+ for (j=0; j<(nrow>15?3:1); j++) {
+ switch(j) {
+ case 0: ch = pos; str=" first"; break; // str same width so the codes line up vertically
+ case 1: ch = pos + 1*(eof-pos)/3; str="+ middle"; break;
+ case 2: ch = pos + 2*(eof-pos)/3; str="+ last"; break; // 2/3 way through rather than end ... easier
+ }
+ if (j) {
+ // we may have landed inside quoted field containing embedded sep and/or embedded \n
+ // find next \n and see if 5 good lines follow. If not try next \n, and so on, until we find the real \n
+ // We don't know which line number this is because we jumped straight to it
+ int attempts=0;
+ while (ch<eof && attempts++<30) {
+ while (ch<eof && *ch!=eol) ch++;
+ if (ch<eof) ch+=eolLen;
+ thispos = ch;
+ i = 0;
+ // countfields() takes care of 'skip_spaces()'
+ while (ch<eof && i<5 && countfields()==ncol) i++;
+ if (i==5) {
+ ch=thispos;
+ break;
+ }
+ }
+ if (i<5) {
+ if (INTEGER(nrowsarg)[0]==-1)
+ warning("Unable to find 5 lines with expected number of columns (%s)\n", str);
+ continue;
+ }
+ }
+ i = 0;
+ while(i<5 && ch<eof && *ch!=eol) {
+ i++;
+ lineStart = ch;
+ for (field=0;field<ncol;field++) {
+ if (stripWhite) skip_spaces();
+ // Rprintf("Field %d: '%.20s'\n", field+1, ch);
+ fieldLen=0;
+ switch (type[field]) {
+ case SXP_LGL:
+ if (Strtob()) break;
+ type[field]++;
+ case SXP_INT:
+ ch2=ch;
+ u.l = NA_INTEGER; // see comments in the main read step lower down. Similar switch as here.
+ if (Strtoll() && INT_MIN<=u.l && u.l<=INT_MAX) break;
+ type[field]++; ch=ch2;
+ case SXP_INT64:
+ if (Strtoll()) break;
+ type[field]++;
+ case SXP_REAL:
+ if (Strtod()) break;
+ type[field]++;
+ case SXP_STR:
+ Field(); // don't do err=1 here because we don't know 'line' when j=1|2. Leave error to throw in data read step.
+ }
+ if (ch<eof && *ch==sep && field<ncol-1) {ch++; continue;} // done, next field
+ if (field<ncol-1) {
+ if (*ch>31) STOP("Expected sep ('%c') but '%c' ends field %d when detecting types (%s): %.*s", sep, *ch, field, str, ch-lineStart+1, lineStart);
+ else STOP("Expected sep ('%c') but new line, EOF (or other non printing character) ends field %d when detecting types (%s): %.*s", sep, field, str, ch-lineStart+1, lineStart);
+ }
+ }
+ while (ch<eof && *ch!=eol) ch++;
+ if (ch<eof && *ch==eol) ch+=eolLen;
+ }
+ if (verbose) { Rprintf("Type codes (%s 5 rows): ",str); for (i=0; i<ncol; i++) Rprintf("%d",type[i]); Rprintf("\n"); }
+ }
+ ch = pos;
+
+ // ********************************************************************************************
+ // Apply colClasses, select and integer64
+ // ********************************************************************************************
+ int numNULL = 0;
+ SEXP colTypeIndex, items, itemsInt, UserTypeNameSxp;
+ int tmp[ncol]; for (i=0; i<ncol; i++) tmp[i]=0; // used to detect ambiguities (dups) in user's input
+ if (length(colClasses)) {
+ UserTypeNameSxp = PROTECT(allocVector(STRSXP, NUT));
+ protecti++;
+ int thisType;
+ for (i=0; i<NUT; i++) SET_STRING_ELT(UserTypeNameSxp, i, mkChar(UserTypeName[i]));
+ if (isString(colClasses)) {
+ // this branch unusual for fread: column types for all columns in one long unamed character vector
+ if (length(getAttrib(colClasses, R_NamesSymbol))) STOP("Internal error: colClasses has names, but these should have been converted to list format at R level");
+ if (LENGTH(colClasses)!=1 && LENGTH(colClasses)!=ncol) STOP("colClasses is unnamed and length %d but there are %d columns. See ?data.table for colClasses usage.", LENGTH(colClasses), ncol);
+ colTypeIndex = PROTECT(chmatch(colClasses, UserTypeNameSxp, NUT, FALSE)); // if type not found then read as character then as. at R level
+ protecti++;
+ for (k=0; k<ncol; k++) {
+ thisType = UserTypeNameMap[ INTEGER(colTypeIndex)[ LENGTH(colClasses)==1 ? 0 : k] -1 ];
+ if (type[k]<thisType) {
+ if (verbose) Rprintf("Column %d ('%s') was detected as type '%s' but bumped to '%s' as requested by colClasses\n", k+1, CHAR(STRING_ELT(names,k)), UserTypeName[type[k]], UserTypeName[thisType] );
+ type[k]=thisType;
+ if (thisType == SXP_NULL) numNULL++;
+ } else if (verbose && type[k]>thisType) warning("Column %d ('%s') has been detected as type '%s'. Ignoring request from colClasses to read as '%s' (a lower type) since NAs (or loss of precision) may result.\n", k+1, CHAR(STRING_ELT(names,k)), UserTypeName[type[k]], UserTypeName[thisType]);
+ }
+ } else { // normal branch here
+ if (!isNewList(colClasses)) STOP("colClasses is not type list or character vector");
+ if (!length(getAttrib(colClasses, R_NamesSymbol))) STOP("colClasses is type list but has no names");
+ colTypeIndex = PROTECT(chmatch(getAttrib(colClasses, R_NamesSymbol), UserTypeNameSxp, NUT, FALSE));
+ protecti++;
+ for (i=0; i<LENGTH(colClasses); i++) {
+ thisType = UserTypeNameMap[INTEGER(colTypeIndex)[i]-1];
+ items = VECTOR_ELT(colClasses,i);
+ if (thisType == SXP_NULL) {
+ if (!isNull(drop) || !isNull(select)) STOP("Can't use NULL in colClasses when select or drop is used as well.");
+ drop = items;
+ continue;
+ }
+ if (isString(items)) itemsInt = PROTECT(chmatch(items, names, NA_INTEGER, FALSE));
+ else itemsInt = PROTECT(coerceVector(items, INTSXP));
+ protecti++;
+ for (j=0; j<LENGTH(items); j++) {
+ k = INTEGER(itemsInt)[j];
+ if (k==NA_INTEGER) {
+ if (isString(items)) STOP("Column name '%s' in colClasses[[%d]] not found", CHAR(STRING_ELT(items, j)),i+1);
+ else STOP("colClasses[[%d]][%d] is NA", i+1, j+1);
+ } else {
+ if (k<1 || k>ncol) STOP("Column number %d (colClasses[[%d]][%d]) is out of range [1,ncol=%d]",k,i+1,j+1,ncol);
+ k--;
+ if (tmp[k]++) STOP("Column '%s' appears more than once in colClasses", CHAR(STRING_ELT(names,k)));
+ if (type[k]<thisType) {
+ if (verbose) Rprintf("Column %d ('%s') was detected as type '%s' but bumped to '%s' as requested by colClasses[[%d]]\n", k+1, CHAR(STRING_ELT(names,k)), UserTypeName[type[k]], UserTypeName[thisType], i+1 );
+ type[k]=thisType;
+ if (thisType == SXP_NULL) numNULL++;
+ } else if (verbose && type[k]>thisType) Rprintf("Column %d ('%s') has been detected as type '%s'. Ignoring request from colClasses[[%d]] to read as '%s' (a lower type) since NAs would result.\n", k+1, CHAR(STRING_ELT(names,k)), UserTypeName[type[k]], i+1, UserTypeName[thisType]);
+ }
+ }
+ }
+ }
+ }
+ int readInt64As = SXP_INT64;
+ if (strcmp(CHAR(STRING_ELT(integer64,0)), "integer64")!=0) {
+ if (strcmp(CHAR(STRING_ELT(integer64,0)), "character")==0)
+ readInt64As = SXP_STR;
+ else // either 'double' or 'numeric' as checked above in input checks
+ readInt64As = SXP_REAL;
+ for (i=0; i<ncol; i++) if (type[i]==SXP_INT64) {
+ type[i] = readInt64As;
+ if (verbose) Rprintf("Column %d ('%s') has been detected as type 'integer64'. But reading this as '%s' according to the integer64 parameter.\n", i+1, CHAR(STRING_ELT(names,i)), CHAR(STRING_ELT(integer64,0)));
+ }
+ }
+ if (verbose) { Rprintf("Type codes: "); for (i=0; i<ncol; i++) Rprintf("%d",type[i]); Rprintf(" (after applying colClasses and integer64)\n"); }
+ if (length(drop)) {
+ if (any_duplicated(drop,FALSE)) STOP("Duplicates detected in drop");
+ if (isString(drop)) itemsInt = PROTECT(chmatch(drop, names, NA_INTEGER, FALSE));
+ else itemsInt = PROTECT(coerceVector(drop, INTSXP));
+ protecti++;
+ for (j=0; j<LENGTH(drop); j++) {
+ k = INTEGER(itemsInt)[j];
+ if (k==NA_INTEGER) {
+ if (isString(drop)) warning("Column name '%s' in 'drop' not found", CHAR(STRING_ELT(drop, j)));
+ else warning("drop[%d] is NA", j+1);
+ } else {
+ if (k<1 || k>ncol) warning("Column number %d (drop[%d]) is out of range [1,ncol=%d]",k,j+1,ncol);
+ else { type[k-1] = SXP_NULL; numNULL++; }
+ }
+ }
+ }
+ if (length(select)) {
+ if (any_duplicated(select,FALSE)) STOP("Duplicates detected in select");
+ if (isString(select)) {
+ itemsInt = PROTECT(chmatch(names, select, NA_INTEGER, FALSE)); protecti++;
+ for (i=0; i<ncol; i++) if (INTEGER(itemsInt)[i]==NA_INTEGER) { type[i]=SXP_NULL; numNULL++; }
+ } else {
+ itemsInt = PROTECT(coerceVector(select, INTSXP)); protecti++;
+ for (i=0; i<ncol; i++) tmp[i]=SXP_NULL;
+ for (i=0; i<LENGTH(itemsInt); i++) {
+ k = INTEGER(itemsInt)[i];
+ if (k<1 || k>ncol) STOP("Column number %d (select[%d]) is out of range [1,ncol=%d]",k,i+1,ncol);
+ tmp[k-1] = type[k-1];
+ }
+ for (i=0; i<ncol; i++) type[i] = tmp[i];
+ numNULL = ncol - LENGTH(itemsInt);
+ }
+ }
+ if (verbose) { Rprintf("Type codes: "); for (i=0; i<ncol; i++) Rprintf("%d",type[i]); Rprintf(" (after applying drop or select (if supplied)\n"); }
+ clock_t tColType = clock();
+
+ // ********************************************************************************************
+ // Allocate columns for known nrow
+ // ********************************************************************************************
+ if (verbose) Rprintf("Allocating %d column slots (%d - %d dropped)\n", ncol-numNULL, ncol, numNULL);
+ ans=PROTECT(allocVector(VECSXP,ncol-numNULL)); // safer to leave over allocation to alloc.col on return in fread.R
+ protecti++;
+ if (numNULL==0) {
+ setAttrib(ans,R_NamesSymbol,names);
+ } else {
+ SEXP resnames;
+ resnames = PROTECT(allocVector(STRSXP, ncol-numNULL)); protecti++;
+ for (i=0,resi=0; i<ncol; i++) if (type[i]!=SXP_NULL) {
+ SET_STRING_ELT(resnames,resi++,STRING_ELT(names,i));
+ }
+ setAttrib(ans, R_NamesSymbol, resnames);
+ }
+ for (i=0,resi=0; i<ncol; i++) {
+ if (type[i] == SXP_NULL) continue;
+ thistype = TypeSxp[ type[i] ];
+ thiscol = allocVector(thistype,nrow);
+ SET_VECTOR_ELT(ans,resi++,thiscol); // no need to PROTECT thiscol, see R-exts 5.9.1
+ if (type[i]==SXP_INT64) setAttrib(thiscol, R_ClassSymbol, ScalarString(mkChar("integer64")));
+ SET_TRUELENGTH(thiscol, nrow);
+ }
+ clock_t tAlloc = clock();
+
+ // ********************************************************************************************
+ // Read the data
+ // ********************************************************************************************
+ tCoerce = tCoerceAlloc = 0;
+ ch = pos; // back to start of first data row
+ ERANGEwarning = TRUE;
+ clock_t nexttime = t0+2*CLOCKS_PER_SEC; // start printing % done after a few seconds. If doesn't appear then you know mmap is taking a while.
+ // We don't want to be bothered by progress meter for quick tasks
+ int batchend; Rboolean hasPrinted=FALSE, whileBreak=FALSE;
+ i = 0;
+ while (i<nrow && ch<eof) {
+ if (showProgress==1 && clock()>nexttime) {
+ Rprintf("\rRead %.1f%% of %d rows", (100.0*i)/nrow, nrow); // prints straight away if the mmap above took a while, is the idea
+ R_FlushConsole(); // for Windows
+ nexttime = clock()+CLOCKS_PER_SEC;
+ hasPrinted = TRUE;
+ }
+ R_CheckUserInterrupt();
+ batchend = MIN(i+10000, nrow); // batched into 10k rows to save (expensive) calls to clock()
+ for (; i<batchend && ch<eof; i++) {
+ //Rprintf("Row %d : %.10s\n", i+1, ch);
+ if (*ch==eol) {
+ // blank line causes early stop. TO DO: allow blank line skips
+ whileBreak = TRUE; // break the enclosing while too, without changing i
+ break; // break this for
+ }
+ for (j=0,resj=0; j<ncol; resj+=(type[j]!=SXP_NULL),j++) {
+ //Rprintf("Field %d: '%.10s' as type %d\n", j+1, ch, type[j]);
+ if (stripWhite) skip_spaces();
+ thiscol = VECTOR_ELT(ans, resj);
+ switch (type[j]) {
+ case SXP_LGL:
+ if (Strtob()) { LOGICAL(thiscol)[i] = u.b; break; }
+ SET_VECTOR_ELT(ans, resj, thiscol = coerceVectorSoFar(thiscol, type[j]++, SXP_INT, i, j));
+ case SXP_INT:
+ ch2=ch; u.l=NA_INTEGER;
+ if (Strtoll() && INT_MIN<=u.l && u.l<=INT_MAX) { // relies on INT_MIN==NA.INTEGER, checked earlier
+ INTEGER(thiscol)[i] = (int)u.l;
+ break; // Most common case. Done with this field. Strtoll already moved ch for us to sit on next sep or eol.
+ }
+ ch=ch2; // moves ch back ready for type bump and reread of this field (an INT64 would have been read fine by Strtoll)
+ SET_VECTOR_ELT(ans, resj, thiscol = coerceVectorSoFar(thiscol, type[j], readInt64As, i, j));
+ type[j] = readInt64As;
+ if (readInt64As == SXP_REAL) goto case_SXP_REAL; // a goto here seems readable and reasonable to me
+ if (readInt64As == SXP_STR) goto case_SXP_STR;
+ case SXP_INT64:
+ // fix for #488. PREVIOSULY: u.d = NA_REAL;
+ u.l = NAINT64; // NAINT64 is defined in data.table.h, = LLONG_MIN
+ if (Strtoll()) { REAL(thiscol)[i] = u.d; break; }
+ SET_VECTOR_ELT(ans, resj, thiscol = coerceVectorSoFar(thiscol, type[j]++, SXP_REAL, i, j));
+ // A bump from INT to STR will bump through INT64 and then REAL before STR, coercing each time. Deliberately done this way. It's
+ // a small and very rare cost (see comments in coerceVectorSoFar), for better speed 99% of the time (saving deep branches).
+ // TO DO: avoid coercing several times and bump straight to the new type once, somehow.
+ case SXP_REAL: case_SXP_REAL:
+ if (Strtod()) { REAL(thiscol)[i] = u.d; break; }
+ SET_VECTOR_ELT(ans, resj, thiscol = coerceVectorSoFar(thiscol, type[j]++, SXP_STR, i, j));
+ case SXP_STR: case SXP_NULL: case_SXP_STR:
+ Field();
+ if (type[j]==SXP_STR) SET_STRING_ELT(thiscol, i, mkCharLenCE(fieldStart, fieldLen, ienc));
+ }
+ if (ch<eof && *ch==sep && j<ncol-1) {ch++; continue;} // done, next field
+ if (j<ncol-1) {
+ if (*ch>31) STOP("Expected sep ('%c') but '%c' ends field %d on line %d when reading data: %.*s", sep, *ch, j+1, line, ch-pos+1, pos);
+ else STOP("Expected sep ('%c') but new line or EOF ends field %d on line %d when reading data: %.*s", sep, j+1, line, ch-pos+1, pos);
+ // print whole line here because it's often something earlier in the line that messed up
+ }
+ }
+ if (stripWhite) skip_spaces();
+ //Rprintf("At end of line with i=%d and ch='%.10s'\n", i, ch);
+ if (ch<eof && *ch!=eol) {
+ // TODO: skip spaces here if strip.white=TRUE (arg to be added) and then check+warn
+ // TODO: warn about uncommented text here
+ error("Expecting %d cols, but line %d contains text after processing all cols. It is very likely that this is due to one or more fields having embedded sep='%c' and/or (unescaped) '\\n' characters within unbalanced unescaped quotes. fread cannot handle such ambiguous cases and those lines may not have been read in as expected. Please read the section on quotes in ?fread.", ncol, line, sep);
+ }
+ ch+=eolLen; // now that we error here, the if-statement isn't needed -> // if (ch<eof && *ch==eol) ch+=eolLen;
+ pos = ch; // start of line position only needed to include the whole line in any error message
+ line++;
+ }
+ if (whileBreak) break;
+ }
+ if (showProgress==1 && hasPrinted) {
+ j = 1+(clock()-t0)/CLOCKS_PER_SEC;
+ Rprintf("\rRead %d rows and %d (of %d) columns from %.3f GB file in %02d:%02d:%02d\n", i, ncol-numNULL, ncol, 1.0*filesize/(1024*1024*1024), j/3600, (j%3600)/60, j%60);
+ R_FlushConsole();
+ }
+ clock_t tRead = clock();
+
+ // Warn about any non-whitespace not read at the end
+ while (ch<eof && isspace(*ch)) ch++;
+ if (ch<eof) {
+ ch2 = ch;
+ while (ch2<eof && *ch2!=eol) ch2++;
+ if (INTEGER(nrowsarg)[0] == -1 || i < nrow) warning("Stopped reading at empty line %d but text exists afterwards (discarded): %.*s", line, ch2-ch, ch);
+ }
+ if (i<nrow) {
+ if (nrow-i > 100 && (double)i/nrow < 0.95)
+ warning("Read less rows (%d) than were allocated (%d). Run again with verbose=TRUE and please report.",i,nrow);
+ else if (verbose)
+ Rprintf("Read slightly fewer rows (%d) than were allocated (%d).\n", i, nrow);
+ nrow = i;
+ } else {
+ if (i!=nrow) STOP("Internal error: i [%d] > nrow [%d]", i, nrow);
+ if (verbose) Rprintf("Read %d rows. Exactly what was estimated and allocated up front\n", i);
+ }
+ for (j=0; j<ncol-numNULL; j++) SETLENGTH(VECTOR_ELT(ans,j), nrow);
+ // release memory from NA handling operations
+ if(!FLAG_NA_STRINGS_NULL) {
+ free(NA_MASK);
+ free(NA_STRINGS);
+ free(EACH_NA_STRING_LEN);
+ }
+ // ********************************************************************************************
+ // Convert na.strings to NA for character columns
+ // ********************************************************************************************
+ for (k=0; k<length(nastrings); k++) {
+ thisstr = STRING_ELT(nastrings,k);
+ for (j=0; j<ncol-numNULL; j++) {
+ thiscol = VECTOR_ELT(ans,j);
+ if (TYPEOF(thiscol)==STRSXP) {
+ for (i=0; i<nrow; i++)
+ if (STRING_ELT(thiscol,i)==thisstr) SET_STRING_ELT(thiscol, i, NA_STRING);
+ }
+ }
+ }
+ if (verbose) {
+ clock_t tn = clock(), tot=tn-t0;
+ if (tot<1) tot=1; // to avoid nan% output in some trivial tests where tot==0
+ Rprintf("%8.3fs (%3.0f%%) Memory map (rerun may be quicker)\n", 1.0*(tMap-t0)/CLOCKS_PER_SEC, 100.0*(tMap-t0)/tot);
+ Rprintf("%8.3fs (%3.0f%%) sep and header detection\n", 1.0*(tLayout-tMap)/CLOCKS_PER_SEC, 100.0*(tLayout-tMap)/tot);
+ Rprintf("%8.3fs (%3.0f%%) Count rows (wc -l)\n", 1.0*(tRowCount-tLayout)/CLOCKS_PER_SEC, 100.0*(tRowCount-tLayout)/tot);
+ Rprintf("%8.3fs (%3.0f%%) Column type detection (first, middle and last 5 rows)\n", 1.0*(tColType-tRowCount)/CLOCKS_PER_SEC, 100.0*(tColType-tRowCount)/tot);
+ Rprintf("%8.3fs (%3.0f%%) Allocation of %dx%d result (xMB) in RAM\n", 1.0*(tAlloc-tColType)/CLOCKS_PER_SEC, 100.0*(tAlloc-tColType)/tot, nrow, ncol);
+ Rprintf("%8.3fs (%3.0f%%) Reading data\n", 1.0*(tRead-tAlloc-tCoerce)/CLOCKS_PER_SEC, 100.0*(tRead-tAlloc-tCoerce)/tot);
+ Rprintf("%8.3fs (%3.0f%%) Allocation for type bumps (if any), including gc time if triggered\n", 1.0*tCoerceAlloc/CLOCKS_PER_SEC, 100.0*tCoerceAlloc/tot);
+ Rprintf("%8.3fs (%3.0f%%) Coercing data already read in type bumps (if any)\n", 1.0*(tCoerce-tCoerceAlloc)/CLOCKS_PER_SEC, 100.0*(tCoerce-tCoerceAlloc)/tot);
+ Rprintf("%8.3fs (%3.0f%%) Changing na.strings to NA\n", 1.0*(tn-tRead)/CLOCKS_PER_SEC, 100.0*(tn-tRead)/tot);
+ Rprintf("%8.3fs Total\n", 1.0*tot/CLOCKS_PER_SEC);
+ }
+ UNPROTECT(protecti);
+ closeFile();
+ return(ans);
+}
+
diff --git a/src/gsumm.c b/src/gsumm.c
new file mode 100644
index 0000000..83a1e6a
--- /dev/null
+++ b/src/gsumm.c
@@ -0,0 +1,443 @@
+#include "data.table.h"
+//#include <time.h>
+
+static int *grp = NULL; // the group of each x item, like a factor
+static int ngrp = 0; // number of groups
+static int *grpsize = NULL; // size of each group, used by gmean not gsum
+static int grpn = 0; // length of underlying x == length(grp)
+
+SEXP gstart(SEXP o, SEXP f, SEXP l) {
+ int i, j, g, *this;
+ // clock_t start = clock();
+ if (!isInteger(o)) error("o is not integer vector");
+ if (!isInteger(f)) error("f is not integer vector");
+ if (!isInteger(l)) error("l is not integer vector");
+ ngrp = LENGTH(l);
+ if (LENGTH(f) != ngrp) error("length(f)=%d != length(l)=%d", LENGTH(f), ngrp);
+ grpn=0;
+ grpsize = INTEGER(l); // l will be protected in calling R scope until gend(), too
+ for (i=0; i<ngrp; i++) grpn+=grpsize[i];
+ if (LENGTH(o) && LENGTH(o)!=grpn) error("o has length %d but sum(l)=%d", LENGTH(o), grpn);
+ grp = malloc(grpn * sizeof(int));
+ if (!grp) error("Unable to allocate %d * %d bytes in gstart", grpn, sizeof(int));
+ if (LENGTH(o)) {
+ for (g=0; g<ngrp; g++) {
+ this = INTEGER(o) + INTEGER(f)[g]-1;
+ for (j=0; j<grpsize[g]; j++) grp[ this[j]-1 ] = g;
+ }
+ } else {
+ for (g=0; g<ngrp; g++) {
+ this = grp + INTEGER(f)[g]-1;
+ for (j=0; j<grpsize[g]; j++) this[j] = g;
+ }
+ }
+ // Rprintf("gstart took %8.3f\n", 1.0*(clock()-start)/CLOCKS_PER_SEC);
+ return(R_NilValue);
+}
+
+SEXP gend() {
+ free(grp); grp = NULL; ngrp = 0;
+ return(R_NilValue);
+}
+
+// long double usage here results in test 648 being failed when running with valgrind
+// http://valgrind.org/docs/manual/manual-core.html#manual-core.limits
+SEXP gsum(SEXP x, SEXP narm)
+{
+ if (!isLogical(narm) || LENGTH(narm)!=1 || LOGICAL(narm)[0]==NA_LOGICAL) error("na.rm must be TRUE or FALSE");
+ if (!isVectorAtomic(x)) error("GForce sum can only be applied to columns, not .SD or similar. To sum all items in a list such as .SD, either add the prefix base::sum(.SD) or turn off GForce optimization using options(datatable.optimize=1). More likely, you may be looking for 'DT[,lapply(.SD,sum),by=,.SDcols=]'");
+ int i, thisgrp;
+ int n = LENGTH(x);
+ //clock_t start = clock();
+ SEXP ans;
+ if (grpn != length(x)) error("grpn [%d] != length(x) [%d] in gsum", grpn, length(x));
+ long double *s = malloc(ngrp * sizeof(long double));
+ if (!s) error("Unable to allocate %d * %d bytes for gsum", ngrp, sizeof(long double));
+ memset(s, 0, ngrp * sizeof(long double)); // all-0 bits == (long double)0, checked in init.c
+ switch(TYPEOF(x)) {
+ case LGLSXP: case INTSXP:
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if(INTEGER(x)[i] == NA_INTEGER) {
+ if (!LOGICAL(narm)[0]) s[thisgrp] = NA_REAL; // Let NA_REAL propogate from here. R_NaReal is IEEE.
+ continue;
+ }
+ s[thisgrp] += INTEGER(x)[i]; // no under/overflow here, s is long double (like base)
+ }
+ ans = PROTECT(allocVector(INTSXP, ngrp));
+ for (i=0; i<ngrp; i++) {
+ if (s[i] > INT_MAX || s[i] < INT_MIN) {
+ warning("Group %d summed to more than type 'integer' can hold so the result has been coerced to 'numeric' automatically, for convenience.", i+1);
+ UNPROTECT(1);
+ ans = PROTECT(allocVector(REALSXP, ngrp));
+ for (i=0; i<ngrp; i++) REAL(ans)[i] = (double)s[i];
+ break;
+ } else if (ISNA(s[i])) {
+ INTEGER(ans)[i] = NA_INTEGER;
+ } else {
+ INTEGER(ans)[i] = (int)s[i];
+ }
+ }
+ break;
+ case REALSXP:
+ ans = PROTECT(allocVector(REALSXP, ngrp));
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if(ISNAN(REAL(x)[i]) && LOGICAL(narm)[0]) continue; // else let NA_REAL propogate from here
+ s[thisgrp] += REAL(x)[i]; // done in long double, like base
+ }
+ for (i=0; i<ngrp; i++) {
+ if (s[i] > DBL_MAX) REAL(ans)[i] = R_PosInf;
+ else if (s[i] < -DBL_MAX) REAL(ans)[i] = R_NegInf;
+ else REAL(ans)[i] = (double)s[i];
+ }
+ break;
+ default:
+ free(s);
+ error("Type '%s' not supported by GForce sum (gsum). Either add the prefix base::sum(.) or turn off GForce optimization using options(datatable.optimize=1)", type2char(TYPEOF(x)));
+ }
+ free(s);
+ copyMostAttrib(x, ans);
+ UNPROTECT(1);
+ // Rprintf("this gsum took %8.3f\n", 1.0*(clock()-start)/CLOCKS_PER_SEC);
+ return(ans);
+}
+
+SEXP gmean(SEXP x, SEXP narm)
+{
+ SEXP ans;
+ int i, protecti=0, thisgrp, n;
+ //clock_t start = clock();
+ if (!isLogical(narm) || LENGTH(narm)!=1 || LOGICAL(narm)[0]==NA_LOGICAL) error("na.rm must be TRUE or FALSE");
+ if (!isVectorAtomic(x)) error("GForce mean can only be applied to columns, not .SD or similar. Likely you're looking for 'DT[,lapply(.SD,mean),by=,.SDcols=]'. See ?data.table.");
+ if (!LOGICAL(narm)[0]) {
+ ans = PROTECT(gsum(x,narm)); protecti++;
+ switch(TYPEOF(ans)) {
+ case LGLSXP: case INTSXP:
+ ans = PROTECT(coerceVector(ans, REALSXP)); protecti++;
+ case REALSXP:
+ for (i=0; i<ngrp; i++) REAL(ans)[i] /= grpsize[i]; // let NA propogate
+ break;
+ default :
+ error("Internal error: gsum returned type '%s'. typeof(x) is '%s'", type2char(TYPEOF(ans)), type2char(TYPEOF(x)));
+ }
+ UNPROTECT(protecti);
+ return(ans);
+ }
+ // na.rm=TRUE. Similar to gsum, but we need to count the non-NA as well for the divisor
+ n = LENGTH(x);
+ if (grpn != n) error("grpn [%d] != length(x) [%d] in gsum", grpn, length(x));
+
+ long double *s = malloc(ngrp * sizeof(long double));
+ if (!s) error("Unable to allocate %d * %d bytes for sum in gmean na.rm=TRUE", ngrp, sizeof(long double));
+ memset(s, 0, ngrp * sizeof(long double)); // all-0 bits == (long double)0, checked in init.c
+
+ int *c = malloc(ngrp * sizeof(int));
+ if (!c) error("Unable to allocate %d * %d bytes for counts in gmean na.rm=TRUE", ngrp, sizeof(int));
+ memset(c, 0, ngrp * sizeof(int)); // all-0 bits == (int)0, checked in init.c
+
+ switch(TYPEOF(x)) {
+ case LGLSXP: case INTSXP:
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if(INTEGER(x)[i] == NA_INTEGER) continue;
+ s[thisgrp] += INTEGER(x)[i]; // no under/overflow here, s is long double
+ c[thisgrp]++;
+ }
+ break;
+ case REALSXP:
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (ISNAN(REAL(x)[i])) continue;
+ s[thisgrp] += REAL(x)[i];
+ c[thisgrp]++;
+ }
+ break;
+ default:
+ free(s); free(c);
+ error("Type '%s' not supported by GForce mean (gmean) na.rm=TRUE. Either add the prefix base::mean(.) or turn off GForce optimization using options(datatable.optimize=1)", type2char(TYPEOF(x)));
+ }
+ ans = PROTECT(allocVector(REALSXP, ngrp));
+ for (i=0; i<ngrp; i++) {
+ if (c[i]==0) { REAL(ans)[i] = R_NaN; continue; } // NaN to follow base::mean
+ s[i] /= c[i];
+ if (s[i] > DBL_MAX) REAL(ans)[i] = R_PosInf;
+ else if (s[i] < -DBL_MAX) REAL(ans)[i] = R_NegInf;
+ else REAL(ans)[i] = (double)s[i];
+ }
+ free(s); free(c);
+ copyMostAttrib(x, ans);
+ UNPROTECT(1);
+ // Rprintf("this gmean na.rm=TRUE took %8.3f\n", 1.0*(clock()-start)/CLOCKS_PER_SEC);
+ return(ans);
+}
+
+// TO DO: gsd, gprod, gwhich.min, gwhich.max
+
+// gmin
+SEXP gmin(SEXP x, SEXP narm)
+{
+ if (!isLogical(narm) || LENGTH(narm)!=1 || LOGICAL(narm)[0]==NA_LOGICAL) error("na.rm must be TRUE or FALSE");
+ if (!isVectorAtomic(x)) error("GForce min can only be applied to columns, not .SD or similar. To find min of all items in a list such as .SD, either add the prefix base::min(.SD) or turn off GForce optimization using options(datatable.optimize=1). More likely, you may be looking for 'DT[,lapply(.SD,min),by=,.SDcols=]'");
+ R_len_t i, thisgrp=0;
+ int n = LENGTH(x);
+ //clock_t start = clock();
+ SEXP ans;
+ if (grpn != length(x)) error("grpn [%d] != length(x) [%d] in gmin", grpn, length(x));
+ char *update = Calloc(ngrp, char);
+ if (update == NULL) error("Unable to allocate %d * %d bytes for gmin", ngrp, sizeof(char));
+ switch(TYPEOF(x)) {
+ case LGLSXP: case INTSXP:
+ ans = PROTECT(allocVector(INTSXP, ngrp));
+ for (i=0; i<ngrp; i++) INTEGER(ans)[i] = 0;
+ if (!LOGICAL(narm)[0]) {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (INTEGER(x)[i] != NA_INTEGER && INTEGER(ans)[thisgrp] != NA_INTEGER) {
+ if ( update[thisgrp] != 1 || INTEGER(ans)[thisgrp] > INTEGER(x)[i] ) {
+ INTEGER(ans)[thisgrp] = INTEGER(x)[i];
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else INTEGER(ans)[thisgrp] = NA_INTEGER;
+ }
+ } else {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (INTEGER(x)[i] != NA_INTEGER) {
+ if ( update[thisgrp] != 1 || INTEGER(ans)[thisgrp] > INTEGER(x)[i] ) {
+ INTEGER(ans)[thisgrp] = INTEGER(x)[i];
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else {
+ if (update[thisgrp] != 1) {
+ INTEGER(ans)[thisgrp] = NA_INTEGER;
+ }
+ }
+ }
+ for (i=0; i<ngrp; i++) {
+ if (update[i] != 1) {// equivalent of INTEGER(ans)[thisgrp] == NA_INTEGER
+ warning("No non-missing values found in at least one group. Coercing to numeric type and returning 'Inf' for such groups to be consistent with base");
+ UNPROTECT(1);
+ ans = PROTECT(coerceVector(ans, REALSXP));
+ for (i=0; i<ngrp; i++) {
+ if (update[i] != 1) REAL(ans)[i] = R_PosInf;
+ }
+ break;
+ }
+ }
+ }
+ break;
+ case STRSXP:
+ ans = PROTECT(allocVector(STRSXP, ngrp));
+ for (i=0; i<ngrp; i++) SET_STRING_ELT(ans, i, mkChar(""));
+ if (!LOGICAL(narm)[0]) {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (STRING_ELT(x, i) != NA_STRING && STRING_ELT(ans, thisgrp) != NA_STRING) {
+ if ( update[thisgrp] != 1 || strcmp(CHAR(STRING_ELT(ans, thisgrp)), CHAR(STRING_ELT(x, i))) > 0 ) {
+ SET_STRING_ELT(ans, thisgrp, STRING_ELT(x, i));
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else SET_STRING_ELT(ans, thisgrp, NA_STRING);
+ }
+ } else {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (STRING_ELT(x, i) != NA_STRING) {
+ if ( update[thisgrp] != 1 || strcmp(CHAR(STRING_ELT(ans, thisgrp)), CHAR(STRING_ELT(x, i))) > 0 ) {
+ SET_STRING_ELT(ans, thisgrp, STRING_ELT(x, i));
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else {
+ if (update[thisgrp] != 1) {
+ SET_STRING_ELT(ans, thisgrp, NA_STRING);
+ }
+ }
+ }
+ for (i=0; i<ngrp; i++) {
+ if (update[i] != 1) {// equivalent of INTEGER(ans)[thisgrp] == NA_INTEGER
+ warning("No non-missing values found in at least one group. Returning 'NA' for such groups to be consistent with base");
+ break;
+ }
+ }
+ }
+ break;
+ case REALSXP:
+ ans = PROTECT(allocVector(REALSXP, ngrp));
+ for (i=0; i<ngrp; i++) REAL(ans)[i] = 0;
+ if (!LOGICAL(narm)[0]) {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if ( !ISNA(REAL(x)[i]) && !ISNA(REAL(ans)[thisgrp]) ) {
+ if ( update[thisgrp] != 1 || REAL(ans)[thisgrp] > REAL(x)[i] ) {
+ REAL(ans)[thisgrp] = REAL(x)[i];
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else REAL(ans)[thisgrp] = NA_REAL;
+ }
+ } else {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if ( !ISNA(REAL(x)[i]) ) {
+ if ( update[thisgrp] != 1 || REAL(ans)[thisgrp] > REAL(x)[i] ) {
+ REAL(ans)[thisgrp] = REAL(x)[i];
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else {
+ if (update[thisgrp] != 1) {
+ REAL(ans)[thisgrp] = R_PosInf;
+ }
+ }
+ }
+ // everything taken care of already. Just warn if all NA groups have occurred at least once
+ for (i=0; i<ngrp; i++) {
+ if (update[i] != 1) {// equivalent of REAL(ans)[thisgrp] == R_PosInf
+ warning("No non-missing values found in at least one group. Returning 'Inf' for such groups to be consistent with base");
+ break;
+ }
+ }
+ }
+ break;
+ default:
+ error("Type '%s' not supported by GForce min (gmin). Either add the prefix base::min(.) or turn off GForce optimization using options(datatable.optimize=1)", type2char(TYPEOF(x)));
+ }
+ copyMostAttrib(x, ans); // all but names,dim and dimnames. And if so, we want a copy here, not keepattr's SET_ATTRIB.
+ UNPROTECT(1);
+ Free(update);
+ // Rprintf("this gmin took %8.3f\n", 1.0*(clock()-start)/CLOCKS_PER_SEC);
+ return(ans);
+}
+
+// gmax
+SEXP gmax(SEXP x, SEXP narm)
+{
+ if (!isLogical(narm) || LENGTH(narm)!=1 || LOGICAL(narm)[0]==NA_LOGICAL) error("na.rm must be TRUE or FALSE");
+ if (!isVectorAtomic(x)) error("GForce max can only be applied to columns, not .SD or similar. To find max of all items in a list such as .SD, either add the prefix base::max(.SD) or turn off GForce optimization using options(datatable.optimize=1). More likely, you may be looking for 'DT[,lapply(.SD,max),by=,.SDcols=]'");
+ R_len_t i, thisgrp=0;
+ int n = LENGTH(x);
+ //clock_t start = clock();
+ SEXP ans;
+ if (grpn != length(x)) error("grpn [%d] != length(x) [%d] in gmax", grpn, length(x));
+ char *update = Calloc(ngrp, char);
+ if (update == NULL) error("Unable to allocate %d * %d bytes for gmax", ngrp, sizeof(char));
+ switch(TYPEOF(x)) {
+ case LGLSXP: case INTSXP:
+ ans = PROTECT(allocVector(INTSXP, ngrp));
+ for (i=0; i<ngrp; i++) INTEGER(ans)[i] = 0;
+ if (!LOGICAL(narm)[0]) { // simple case - deal in a straightforward manner first
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (INTEGER(x)[i] != NA_INTEGER && INTEGER(ans)[thisgrp] != NA_INTEGER) {
+ if ( update[thisgrp] != 1 || INTEGER(ans)[thisgrp] < INTEGER(x)[i] ) {
+ INTEGER(ans)[thisgrp] = INTEGER(x)[i];
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else INTEGER(ans)[thisgrp] = NA_INTEGER;
+ }
+ } else {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (INTEGER(x)[i] != NA_INTEGER) {
+ if ( update[thisgrp] != 1 || INTEGER(ans)[thisgrp] < INTEGER(x)[i] ) {
+ INTEGER(ans)[thisgrp] = INTEGER(x)[i];
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else {
+ if (update[thisgrp] != 1) {
+ INTEGER(ans)[thisgrp] = NA_INTEGER;
+ }
+ }
+ }
+ for (i=0; i<ngrp; i++) {
+ if (update[i] != 1) {// equivalent of INTEGER(ans)[thisgrp] == NA_INTEGER
+ warning("No non-missing values found in at least one group. Coercing to numeric type and returning 'Inf' for such groups to be consistent with base");
+ UNPROTECT(1);
+ ans = PROTECT(coerceVector(ans, REALSXP));
+ for (i=0; i<ngrp; i++) {
+ if (update[i] != 1) REAL(ans)[i] = -R_PosInf;
+ }
+ break;
+ }
+ }
+ }
+ break;
+ case STRSXP:
+ ans = PROTECT(allocVector(STRSXP, ngrp));
+ for (i=0; i<ngrp; i++) SET_STRING_ELT(ans, i, mkChar(""));
+ if (!LOGICAL(narm)[0]) { // simple case - deal in a straightforward manner first
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (STRING_ELT(x,i) != NA_STRING && STRING_ELT(ans, thisgrp) != NA_STRING) {
+ if ( update[thisgrp] != 1 || strcmp(CHAR(STRING_ELT(ans, thisgrp)), CHAR(STRING_ELT(x,i))) < 0 ) {
+ SET_STRING_ELT(ans, thisgrp, STRING_ELT(x, i));
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else SET_STRING_ELT(ans, thisgrp, NA_STRING);
+ }
+ } else {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if (STRING_ELT(x, i) != NA_STRING) {
+ if ( update[thisgrp] != 1 || strcmp(CHAR(STRING_ELT(ans, thisgrp)), CHAR(STRING_ELT(x, i))) < 0 ) {
+ SET_STRING_ELT(ans, thisgrp, STRING_ELT(x, i));
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else {
+ if (update[thisgrp] != 1) {
+ SET_STRING_ELT(ans, thisgrp, NA_STRING);
+ }
+ }
+ }
+ for (i=0; i<ngrp; i++) {
+ if (update[i] != 1) {// equivalent of INTEGER(ans)[thisgrp] == NA_INTEGER
+ warning("No non-missing values found in at least one group. Returning 'NA' for such groups to be consistent with base");
+ break;
+ }
+ }
+ }
+ break;
+ case REALSXP:
+ ans = PROTECT(allocVector(REALSXP, ngrp));
+ for (i=0; i<ngrp; i++) REAL(ans)[i] = 0;
+ if (!LOGICAL(narm)[0]) {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if ( !ISNA(REAL(x)[i]) && !ISNA(REAL(ans)[thisgrp]) ) {
+ if ( update[thisgrp] != 1 || REAL(ans)[thisgrp] < REAL(x)[i] ) {
+ REAL(ans)[thisgrp] = REAL(x)[i];
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else REAL(ans)[thisgrp] = NA_REAL;
+ }
+ } else {
+ for (i=0; i<n; i++) {
+ thisgrp = grp[i];
+ if ( !ISNA(REAL(x)[i]) ) {
+ if ( update[thisgrp] != 1 || REAL(ans)[thisgrp] < REAL(x)[i] ) {
+ REAL(ans)[thisgrp] = REAL(x)[i];
+ if (update[thisgrp] != 1) update[thisgrp] = 1;
+ }
+ } else {
+ if (update[thisgrp] != 1) {
+ REAL(ans)[thisgrp] = -R_PosInf;
+ }
+ }
+ }
+ // everything taken care of already. Just warn if all NA groups have occurred at least once
+ for (i=0; i<ngrp; i++) {
+ if (update[i] != 1) { // equivalent of REAL(ans)[thisgrp] == -R_PosInf
+ warning("No non-missing values found in at least one group. Returning '-Inf' for such groups to be consistent with base");
+ break;
+ }
+ }
+ }
+ break;
+ default:
+ error("Type '%s' not supported by GForce max (gmax). Either add the prefix base::max(.) or turn off GForce optimization using options(datatable.optimize=1)", type2char(TYPEOF(x)));
+ }
+ copyMostAttrib(x, ans); // all but names,dim and dimnames. And if so, we want a copy here, not keepattr's SET_ATTRIB.
+ UNPROTECT(1);
+ Free(update);
+ // Rprintf("this gmax took %8.3f\n", 1.0*(clock()-start)/CLOCKS_PER_SEC);
+ return(ans);
+}
diff --git a/src/ijoin.c b/src/ijoin.c
new file mode 100644
index 0000000..7d6f917
--- /dev/null
+++ b/src/ijoin.c
@@ -0,0 +1,691 @@
+#include "data.table.h"
+#include <Rdefines.h>
+#include <time.h>
+
+// TODO: implement 'lookup' for 'gaps' and 'overlaps' arguments
+SEXP lookup(SEXP ux, SEXP xlen, SEXP indices, SEXP gaps, SEXP overlaps, SEXP multArg, SEXP typeArg, SEXP verbose) {
+
+ SEXP vv, tt, lookup, type_lookup;
+ R_len_t i,j,k,*idx,*len1,*len2,xrows=INTEGER(xlen)[0],uxrows=LENGTH(VECTOR_ELT(ux, 0)),uxcols=LENGTH(ux);
+ int *from = (int *)INTEGER(VECTOR_ELT(indices, 0));
+ int *to = (int *)INTEGER(VECTOR_ELT(indices, 1));
+ clock_t pass1, pass2, pass3, start;
+ enum {ALL, FIRST, LAST} mult = ALL;
+ enum {ANY, WITHIN, START, END, EQUAL} type = ANY;
+
+ if (!strcmp(CHAR(STRING_ELT(multArg, 0)), "all")) mult = ALL;
+ else if (!strcmp(CHAR(STRING_ELT(multArg, 0)), "first")) mult = FIRST;
+ else if (!strcmp(CHAR(STRING_ELT(multArg, 0)), "last")) mult = LAST;
+ else error("Internal error: invalid value for 'mult'; this should have been caught before. Please report to datatable-help");
+
+ if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "any")) type = ANY;
+ else if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "within")) type = WITHIN;
+ else if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "start")) type = START;
+ else if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "end")) type = END;
+ else if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "equal")) type = EQUAL;
+ else error("Internal error: invalid value for 'type'; this should have been caught before. Please report to datatable-help");
+
+ // For reference: uxcols-1 = type_count, uxcols-2 = count, uxcols-3 = type_lookup, uxcols-4 = lookup
+ // first pass: calculate lengths first
+ start = clock();
+ len1 = (int *)INTEGER(VECTOR_ELT(ux, uxcols-2));
+ len2 = (int *)INTEGER(VECTOR_ELT(ux, uxcols-1));
+ switch (mult) {
+ case FIRST:
+ for (i=0; i<xrows; i++) {
+ for (j=from[i]; j<=to[i]; j++) {
+ len1[j-1]++;
+ }
+ }
+ if (type != WITHIN) {
+ for (i=0; i<uxrows; i++) // TODO: this allocation can be avoided if we take care of FIRST/LAST accordingly in 'overlaps'
+ if (len1[i]) len2[i] = 1;
+ }
+ break;
+
+ case LAST :
+ switch (type) {
+ case ANY:
+ for (i=0; i<xrows; i++) {
+ for (j=from[i]; j<=to[i]; j++) {
+ len1[j-1]++;
+ if (from[i]==j && !len2[j-1]) len2[j-1]++;
+ }
+ }
+ break;
+ case START: case END: case EQUAL: case WITHIN:
+ for (i=0; i<xrows; i++) {
+ for (j=from[i]; j<=to[i]; j++) {
+ len1[j-1]++;
+ }
+ }
+ if (type != WITHIN) {
+ for (i=0; i<uxrows; i++) // TODO: this allocation can be avoided if we take care of FIRST/LAST accordingly in 'overlaps'
+ if (len1[i]) len2[i] = 1;
+ }
+ break;
+ }
+ break;
+
+ case ALL :
+ switch (type) {
+ case START: case END:
+ for (i=0; i<xrows; i++) {
+ for (j=from[i]; j<=to[i]; j++) {
+ len1[j-1]++; len2[j-1]++; // alternatively, we could simply do with len2=len1 ?
+ }
+ }
+ break;
+ case EQUAL:
+ for (i=0; i<xrows; i++) {
+ len1[from[i]-1]++; len1[to[i]-1]++;
+ len2[from[i]-1]++; len2[to[i]-1]++;
+ }
+ break;
+ case ANY :
+ for (i=0; i<xrows; i++) {
+ k = from[i];
+ for (j=from[i]; j<=to[i]; j++) {
+ len1[j-1]++;
+ if (k==j) len2[j-1]++;
+ }
+ }
+ break;
+ case WITHIN :
+ for (i=0; i<xrows; i++) {
+ for (j=from[i]; j<=to[i]; j++) {
+ len1[j-1]++;
+ }
+ }
+ break;
+ }
+ break;
+ }
+ pass1 = clock() - start;
+ if (LOGICAL(verbose)[0])
+ Rprintf("First pass on calculating lengths in lookup ... done in %8.3f seconds\n", 1.0*(pass1)/CLOCKS_PER_SEC);
+ // second pass: allocate vectors
+ start = clock();
+ lookup = VECTOR_ELT(ux, uxcols-4);
+ type_lookup = VECTOR_ELT(ux, uxcols-3);
+ for (i=0; i<uxrows; i++) {
+ vv = allocVector(INTSXP, len1[i]);
+ SET_VECTOR_ELT(lookup, i, vv);
+ if (type != WITHIN) {
+ vv = allocVector(INTSXP, len2[i]);
+ SET_VECTOR_ELT(type_lookup, i, vv);
+ }
+ }
+ pass2 = clock() - start;
+ if (LOGICAL(verbose)[0])
+ Rprintf("Second pass on allocation in lookup ... done in %8.3f seconds\n", 1.0*(pass2)/CLOCKS_PER_SEC);
+ // generate lookup
+ start = clock();
+ idx = Calloc(uxrows, R_len_t); // resets bits, =0
+ switch (type) {
+ case ANY: case START: case END: case WITHIN:
+ for (i=0; i<xrows; i++) {
+ for (j=from[i]; j<=to[i]; j++) {
+ vv = VECTOR_ELT(lookup, j-1); // cache misses - memory efficiency? but 'lookups' are tiny - takes 0.036s on A.thaliana GFF for entire process)
+ INTEGER(vv)[idx[j-1]++] = i+1;
+ }
+ }
+ break;
+ case EQUAL:
+ for (i=0; i<xrows; i++) {
+ INTEGER(VECTOR_ELT(lookup, from[i]-1))[idx[from[i]-1]++] = i+1;
+ INTEGER(VECTOR_ELT(lookup, to[i]-1))[idx[to[i]-1]++] = i+1;
+ }
+ break;
+ }
+ Free(idx);
+ // generate type_lookup
+ if (type != WITHIN) {
+ switch (mult) {
+ case FIRST :
+ for (i=0; i<uxrows; i++) {
+ if (!len1[i]) continue;
+ vv = VECTOR_ELT(lookup, i);
+ tt = VECTOR_ELT(type_lookup, i);
+ INTEGER(tt)[0] = INTEGER(vv)[0];
+ }
+ break;
+
+ case LAST :
+ for (i=0; i<uxrows; i++) {
+ if (!len1[i]) continue;
+ vv = VECTOR_ELT(lookup, i);
+ tt = VECTOR_ELT(type_lookup, i);
+ INTEGER(tt)[0] = INTEGER(vv)[len1[i]-1];
+ }
+
+ case ALL :
+ switch (type) {
+ case START: case END: case EQUAL:
+ for (i=0; i<uxrows; i++)
+ SET_VECTOR_ELT(type_lookup, i, VECTOR_ELT(lookup, i));
+ break;
+
+ case ANY :
+ for (i=0; i<uxrows; i++) {
+ vv = VECTOR_ELT(lookup, i);
+ tt = VECTOR_ELT(type_lookup, i);
+ k=0;
+ for (j=len1[i]-len2[i]; j<len1[i]; j++)
+ INTEGER(tt)[k++] = INTEGER(vv)[j];
+ }
+ break;
+
+ case WITHIN :
+ // for (i=0; i<uxrows; i++) {
+ // vv = VECTOR_ELT(lookup, i);
+ // tt = VECTOR_ELT(type_lookup, i);
+ // for (j=0; j<len2[i]; j++)
+ // INTEGER(tt)[j] = INTEGER(vv)[j];
+ // }
+ break;
+ }
+ break;
+ }
+ }
+ pass3 = clock() - start;
+ if (LOGICAL(verbose)[0])
+ Rprintf("Final step in generating lookup ... done in %8.3f seconds\n", 1.0*(pass3)/CLOCKS_PER_SEC);
+ return(R_NilValue);
+}
+
+SEXP overlaps(SEXP ux, SEXP imatches, SEXP multArg, SEXP typeArg, SEXP nomatchArg, SEXP verbose) {
+
+ R_len_t i,j,k,m,uxcols=LENGTH(ux),rows=length(VECTOR_ELT(imatches,0));
+ int nomatch = INTEGER(nomatchArg)[0], totlen=0, len, thislen, wlen=0;
+ int *from = (int *)INTEGER(VECTOR_ELT(imatches, 0));
+ int *to = (int *)INTEGER(VECTOR_ELT(imatches, 1));
+ int *len1 = (int *)INTEGER(VECTOR_ELT(ux, uxcols-2));
+ int *len2 = (int *)INTEGER(VECTOR_ELT(ux, uxcols-1));
+ SEXP lookup = VECTOR_ELT(ux, uxcols-4);
+ SEXP type_lookup = VECTOR_ELT(ux, uxcols-3);
+ SEXP ans, f1__, f2__, tmp1, tmp2;
+ clock_t end1, end2, start;
+ enum {ALL, FIRST, LAST} mult = ALL;
+ enum {ANY, WITHIN, START, END, EQUAL} type = ANY;
+
+ if (!strcmp(CHAR(STRING_ELT(multArg, 0)), "all")) mult = ALL;
+ else if (!strcmp(CHAR(STRING_ELT(multArg, 0)), "first")) mult = FIRST;
+ else if (!strcmp(CHAR(STRING_ELT(multArg, 0)), "last")) mult = LAST;
+ else error("Internal error: invalid value for 'mult'; this should have been caught before. Please report to datatable-help");
+
+ if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "any")) type = ANY;
+ else if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "within")) type = WITHIN;
+ else if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "start")) type = START;
+ else if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "end")) type = END;
+ else if (!strcmp(CHAR(STRING_ELT(typeArg, 0)), "equal")) type = EQUAL;
+ else error("Internal error: invalid value for 'type'; this should have been caught before. Please report to datatable-help");
+
+ // As a first pass get the final length, so that we can allocate up-front and not deal with Calloc + Realloc + size calculation hassle
+ // Checked the time for this loop on realisitc data (81m reads) and took 0.27 seconds! No excuses ;).
+ start = clock();
+ if (mult == ALL) {
+ totlen=0;
+ switch (type) {
+ case START: case END:
+ for (i=0; i<rows; i++)
+ totlen += (from[i] > 0 && len2[from[i]-1]) ? len2[from[i]-1] : 1;
+ break;
+
+ case EQUAL:
+ for (i=0; i<rows; i++) {
+ len = totlen; wlen=0, j=0, m=0;
+ k = (from[i]>0) ? from[i] : 1;
+ if (k == to[i]) {
+ wlen = len1[k-1];
+ } else if (k < to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(type_lookup, to[i]-1);
+ while (j<len1[k-1] && m<len2[to[i]-1]) {
+ if ( INTEGER(tmp1)[j] == INTEGER(tmp2)[m] ) {
+ ++wlen; ++j; ++m;
+ } else if ( INTEGER(tmp1)[j] > INTEGER(tmp2)[m] ) {
+ break;
+ } else ++j;
+ }
+ }
+ totlen += wlen;
+ if (len == totlen)
+ ++totlen;
+ }
+ break;
+
+ case ANY:
+ for (i=0; i<rows; i++) {
+ len = totlen;
+ // k = (from[i] > 0) ? from[i] : 1;
+ k = from[i];
+ if (k<=to[i])
+ totlen += len1[k-1];
+ for (j=k+1; j<=to[i]; j++)
+ totlen += len2[j-1];
+ if (len == totlen)
+ ++totlen;
+ }
+ break;
+
+ case WITHIN:
+ for (i=0; i<rows; i++) {
+ len = totlen; j=0; m=0;
+ k = from[i];
+ if (k > 0) {
+ if (k == to[i]) {
+ totlen += len1[k-1];
+ } else if (k < to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(lookup, to[i]-1);
+ while (j<len1[k-1] && m<len1[to[i]-1]) {
+ if ( INTEGER(tmp1)[j] == INTEGER(tmp2)[m] ) {
+ ++totlen; ++j; ++m;
+ } else if ( INTEGER(tmp1)[j] > INTEGER(tmp2)[m] ) {
+ ++m;
+ } else ++j;
+ }
+ }
+ }
+ if (len == totlen)
+ ++totlen;
+ }
+ break;
+ }
+ } else totlen = rows;
+ end1 = clock() - start;
+ if (LOGICAL(verbose)[0])
+ Rprintf("First pass on calculating lengths in overlaps ... done in %8.3f seconds\n", 1.0*(end1)/CLOCKS_PER_SEC);
+
+ // ans[0] is the the position of 'query' and ans[1] is that of 'subject'
+ // allocate f1__ and f2__ and assign 'nomatch' to f2__
+ ans = PROTECT(allocVector(VECSXP, 2));
+ f1__ = allocVector(INTSXP, totlen);
+ SET_VECTOR_ELT(ans, 0, f1__);
+ f2__ = allocVector(INTSXP, totlen);
+ SET_VECTOR_ELT(ans, 1, f2__);
+ thislen=0;
+ start = clock();
+
+ // switching mult=ALL,FIRST,LAST separately to
+ // - enhance performance for special cases, and
+ // - easy to fix any bugs in the future
+ switch (mult) {
+ case ALL:
+ switch (type) {
+ case START : case END :
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ if (from[i] > 0) {
+ k = from[i];
+ tmp2 = VECTOR_ELT(type_lookup, k-1);
+ for (j=0; j<len2[k-1]; j++) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = INTEGER(tmp2)[j];
+ ++thislen;
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ case EQUAL :
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ if (from[i] > 0 && to[i] > 0) {
+ k = from[i];
+ if (k == to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(type_lookup, to[i]-1);
+ for (j=0; j<len1[k-1]; j++) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[j];
+ ++thislen;
+ }
+ } else if (k < to[i]) {
+ j=0; m=0;
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(type_lookup, to[i]-1);
+ while (j<len1[k-1] && m<len2[to[i]-1]) {
+ if ( INTEGER(tmp1)[j] == INTEGER(tmp2)[m] ) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[j];
+ ++thislen; ++j; ++m;
+ } else if ( INTEGER(tmp1)[j] > INTEGER(tmp2)[m] ) {
+ ++m;
+ } else ++j;
+ }
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ case ANY :
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ // k = (from[i]>0) ? from[i] : 1;
+ k = from[i];
+ if (k<=to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ for (m=0; m<len1[k-1]; m++) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[m];
+ ++thislen;
+ }
+ }
+ for (j=k+1; j<=to[i]; j++) {
+ tmp2 = VECTOR_ELT(type_lookup, j-1);
+ for (m=0; m<len2[j-1]; m++) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = INTEGER(tmp2)[m];
+ ++thislen;
+ }
+ }
+ // dint go through any loops above
+ if (len == thislen) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ case WITHIN :
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ k=from[i];
+ if (k > 0) {
+ if (k == to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ for (j=0; j<len1[k-1]; j++) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[j];
+ ++thislen;
+ }
+ } else if (k < to[i]) {
+ j=0; m=0;
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(lookup, to[i]-1);
+ while (j<len1[k-1] && m<len1[to[i]-1]) {
+ if ( INTEGER(tmp1)[j] == INTEGER(tmp2)[m] ) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[j];
+ ++thislen; ++j; ++m;
+ } else if ( INTEGER(tmp1)[j] > INTEGER(tmp2)[m] ) {
+ ++m;
+ } else ++j;
+ }
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f1__)[thislen] = i+1;
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+ }
+ break;
+
+ case FIRST:
+ switch (type) {
+ case START: case END:
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ INTEGER(f1__)[thislen] = i+1;
+ k = (from[i]>0) ? from[i] : 1;
+ if (k <= to[i]) { // len1[k-1] is equal to len2[k-1] and will always be >0, so no length check necessary.
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[0];
+ ++thislen;
+ }
+ if (len == thislen) {
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ case EQUAL :
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ INTEGER(f1__)[thislen] = i+1;
+ if (from[i] > 0 && to[i] > 0) {
+ k = from[i];
+ if (k == to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[0];
+ ++thislen;
+ } else if (k < to[i]) {
+ j=0; m=0;
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(type_lookup, to[i]-1);
+ while (j<len1[k-1] && m<len2[to[i]-1]) {
+ if ( INTEGER(tmp1)[j] == INTEGER(tmp2)[m] ) {
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[j];
+ ++thislen; ++j; ++m;
+ break;
+ } else if ( INTEGER(tmp1)[j] > INTEGER(tmp2)[m] ) {
+ ++m;
+ } else ++j;
+ }
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ case ANY:
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ INTEGER(f1__)[thislen] = i+1;
+ // k = (from[i]>0) ? from[i] : 1;
+ k = from[i];
+ for (j=k; j<=to[i]; j++) {
+ if (len2[j-1]) {
+ tmp2 = VECTOR_ELT(type_lookup, j-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp2)[0];
+ ++thislen;
+ break;
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ case WITHIN:
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ INTEGER(f1__)[thislen] = i+1;
+ k = from[i];
+ if (k > 0) {
+ if (k == to[i] && len1[k-1]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[0];
+ ++thislen;
+ } else if (k < to[i]) {
+ j=0; m=0;
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(lookup, to[i]-1);
+ while (j<len1[k-1] && m<len1[to[i]-1]) {
+ if ( INTEGER(tmp1)[j] == INTEGER(tmp2)[m] ) {
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[j];
+ ++thislen; ++j; ++m;
+ break;
+ } else if ( INTEGER(tmp1)[j] > INTEGER(tmp2)[m] ) {
+ ++m;;
+ } else ++j;
+ }
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+ }
+ break;
+
+ case LAST:
+ switch (type) {
+ case START: case END:
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ INTEGER(f1__)[thislen] = i+1;
+ k = (from[i]>0) ? from[i] : 1;
+ if (k <= to[i]) { // len1[k-1] is equal to len2[k-1] and will always be >0, so no length check necessary.
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[len1[k-1]-1];
+ ++thislen;
+ }
+ if (len == thislen) {
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ case EQUAL :
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ INTEGER(f1__)[thislen] = i+1;
+ if (from[i] > 0 && to[i] > 0) {
+ k = from[i];
+ if (k == to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[len1[k-1]-1];
+ ++thislen;
+ } else if (k < to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(type_lookup, to[i]-1);
+ j=len1[k-1]-1; m=len2[k-1]-1;
+ while (j>=0 && m>=0) {
+ if ( INTEGER(tmp1)[j] == INTEGER(tmp2)[m] ) {
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[j];
+ ++thislen; --j; --m;
+ break;
+ } else if ( INTEGER(tmp1)[j] < INTEGER(tmp2)[m] ) {
+ --m;
+ } else --j;
+ }
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ // OLD logic for 'any,last' which had to check for maximum for each 'i'. Better logic below.
+ // for 'first' we need to just get the minimum of first non-zero-length element, but not the same case for 'last'.
+ // We've to loop over from[i]:to[i] and get maximum of all tmp2 values (each is of length 1 already conveniently set uo) in that range
+ // case ANY:
+ // for (i=0; i<rows; i++) {
+ // len = thislen;
+ // INTEGER(f1__)[thislen] = i+1;
+ // INTEGER(f2__)[thislen] = 0;
+ // // k = (from[i]>0) ? from[i] : 1;
+ // k = from[i];
+ // for (j=k; j<=to[i]; j++) {
+ // if (len2[j-1]) {
+ // tmp2 = VECTOR_ELT(type_lookup, j-1);
+ // INTEGER(f2__)[thislen] = (INTEGER(f2__)[thislen] < INTEGER(tmp2)[len2[j-1]-1]) ? INTEGER(tmp2)[len2[j-1]-1] : INTEGER(f2__)[thislen];
+ // }
+ // }
+ // if (INTEGER(f2__)[thislen] == 0)
+ // INTEGER(f2__)[thislen] = nomatch;
+ // ++thislen;
+ // }
+ // break;
+
+ case ANY:
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ INTEGER(f1__)[thislen] = i+1;
+ // k = (from[i]>0) ? from[i] : 1;
+ k = from[i];
+ if (k <= to[i]) {
+ if (k==to[i] && len1[k-1]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[len1[k-1]-1];
+ ++thislen;
+ } else {
+ for (j=to[i]; j>k; j--) {
+ if (len2[j-1]) {
+ tmp2 = VECTOR_ELT(type_lookup, j-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp2)[0]; // tmp2 will be length 1
+ ++thislen; break;
+ }
+ }
+ if (len == thislen && len1[k-1]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[len1[k-1]-1];
+ ++thislen;
+ }
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+
+ case WITHIN:
+ for (i=0; i<rows; i++) {
+ len = thislen;
+ INTEGER(f1__)[thislen] = i+1;
+ k = from[i];
+ if (k > 0) {
+ if (k == to[i] && len1[k-1]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[len1[k-1]-1];
+ ++thislen;
+ } else if (k < to[i]) {
+ tmp1 = VECTOR_ELT(lookup, k-1);
+ tmp2 = VECTOR_ELT(lookup, to[i]-1);
+ j=len1[k-1]-1; m=len1[to[i]-1]-1;
+ while (j>=0 && m>=0) {
+ if ( INTEGER(tmp1)[j] == INTEGER(tmp2)[m] ) {
+ INTEGER(f2__)[thislen] = INTEGER(tmp1)[j];
+ ++thislen; --j; --m;
+ break;
+ } else if ( INTEGER(tmp1)[j] < INTEGER(tmp2)[m] ) {
+ --m;
+ } else --j;
+ }
+ }
+ }
+ if (len == thislen) {
+ INTEGER(f2__)[thislen] = nomatch;
+ ++thislen;
+ }
+ }
+ break;
+ }
+ break;
+ }
+ end2 = clock() - start;
+ if (LOGICAL(verbose)[0])
+ Rprintf("Final step, fetching indices in overlaps ... done in %8.3f seconds\n", 1.0*(end2)/CLOCKS_PER_SEC);
+ UNPROTECT(1);
+ return(ans);
+}
diff --git a/src/init.c b/src/init.c
new file mode 100644
index 0000000..3773f53
--- /dev/null
+++ b/src/init.c
@@ -0,0 +1,176 @@
+#include "data.table.h"
+#include <Rdefines.h>
+#include <R_ext/Rdynload.h>
+#include <R_ext/Visibility.h>
+
+// .Calls
+SEXP setattrib();
+SEXP bmerge();
+SEXP assign();
+SEXP dogroups();
+SEXP copy();
+SEXP shallowwrapper();
+SEXP alloccolwrapper();
+SEXP selfrefokwrapper();
+SEXP truelength();
+SEXP setcharvec();
+SEXP setcolorder();
+SEXP chmatchwrapper();
+SEXP readfile();
+SEXP reorder();
+SEXP rbindlist();
+SEXP vecseq();
+SEXP copyattr();
+SEXP setlistelt();
+SEXP setnamed();
+SEXP address();
+SEXP copyNamedInList();
+SEXP fmelt();
+SEXP fcast();
+SEXP uniqlist();
+SEXP uniqlengths();
+SEXP fastradixdouble();
+SEXP fastradixint();
+SEXP setrev();
+SEXP forder();
+SEXP fsorted();
+SEXP gstart();
+SEXP gend();
+SEXP gsum();
+SEXP gmean();
+SEXP gmin();
+SEXP gmax();
+SEXP isOrderedSubset();
+SEXP pointWrapper();
+SEXP setNumericRounding();
+SEXP getNumericRounding();
+SEXP binary();
+SEXP chmatch2();
+SEXP subsetDT();
+SEXP subsetVector();
+SEXP convertNegativeIdx();
+SEXP frank();
+SEXP dt_na();
+SEXP lookup();
+SEXP overlaps();
+SEXP whichwrapper();
+SEXP shift();
+SEXP transpose();
+SEXP anyNA();
+SEXP isReallyReal();
+SEXP setlevels();
+SEXP rleid();
+
+// .Externals
+SEXP fastmean();
+
+static const
+R_CallMethodDef callMethods[] = {
+{"Csetattrib", (DL_FUNC) &setattrib, -1},
+{"Cbmerge", (DL_FUNC) &bmerge, -1},
+{"Cassign", (DL_FUNC) &assign, -1},
+{"Cdogroups", (DL_FUNC) &dogroups, -1},
+{"Ccopy", (DL_FUNC) ©, -1},
+{"Cshallowwrapper", (DL_FUNC) &shallowwrapper, -1},
+{"Calloccolwrapper", (DL_FUNC) &alloccolwrapper, -1},
+{"Cselfrefokwrapper", (DL_FUNC) &selfrefokwrapper, -1},
+{"Ctruelength", (DL_FUNC) &truelength, -1},
+{"Csetcharvec", (DL_FUNC) &setcharvec, -1},
+{"Csetcolorder", (DL_FUNC) &setcolorder, -1},
+{"Cchmatchwrapper", (DL_FUNC) &chmatchwrapper, -1},
+{"Creadfile", (DL_FUNC) &readfile, -1},
+{"Creorder", (DL_FUNC) &reorder, -1},
+{"Crbindlist", (DL_FUNC) &rbindlist, -1},
+{"Cvecseq", (DL_FUNC) &vecseq, -1},
+{"Ccopyattr", (DL_FUNC) ©attr, -1},
+{"Csetlistelt", (DL_FUNC) &setlistelt, -1},
+{"Csetnamed", (DL_FUNC) &setnamed, -1},
+{"Caddress", (DL_FUNC) &address, -1},
+{"CcopyNamedInList", (DL_FUNC) ©NamedInList, -1},
+{"Cfmelt", (DL_FUNC) &fmelt, -1},
+{"Cfcast", (DL_FUNC) &fcast, -1},
+{"Cuniqlist", (DL_FUNC) &uniqlist, -1},
+{"Cuniqlengths", (DL_FUNC) &uniqlengths, -1},
+{"Cfastradixdouble", (DL_FUNC) &fastradixdouble, -1},
+{"Cfastradixint", (DL_FUNC) &fastradixint, -1},
+{"Csetrev", (DL_FUNC) &setrev, -1},
+{"Cforder", (DL_FUNC) &forder, -1},
+{"Cfsorted", (DL_FUNC) &fsorted, -1},
+{"Cgstart", (DL_FUNC) &gstart, -1},
+{"Cgend", (DL_FUNC) &gend, -1},
+{"Cgsum", (DL_FUNC) &gsum, -1},
+{"Cgmean", (DL_FUNC) &gmean, -1},
+{"Cgmin", (DL_FUNC) &gmin, -1},
+{"Cgmax", (DL_FUNC) &gmax, -1},
+{"CisOrderedSubset", (DL_FUNC) &isOrderedSubset, -1},
+{"CpointWrapper", (DL_FUNC) &pointWrapper, -1},
+{"CsetNumericRounding", (DL_FUNC) &setNumericRounding, -1},
+{"CgetNumericRounding", (DL_FUNC) &getNumericRounding, -1},
+{"Cbinary", (DL_FUNC) &binary, -1},
+{"Cchmatch2", (DL_FUNC) &chmatch2, -1},
+{"CsubsetDT", (DL_FUNC) &subsetDT, -1},
+{"CsubsetVector", (DL_FUNC) &subsetVector, -1},
+{"CconvertNegativeIdx", (DL_FUNC) &convertNegativeIdx, -1},
+{"Cfrank", (DL_FUNC) &frank, -1},
+{"Cdt_na", (DL_FUNC) &dt_na, -1},
+{"Clookup", (DL_FUNC) &lookup, -1},
+{"Coverlaps", (DL_FUNC) &overlaps, -1},
+{"Cwhichwrapper", (DL_FUNC) &whichwrapper, -1},
+{"Cshift", (DL_FUNC) &shift, -1},
+{"Ctranspose", (DL_FUNC) &transpose, -1},
+{"CanyNA", (DL_FUNC) &anyNA, -1},
+{"CisReallyReal", (DL_FUNC) &isReallyReal, -1},
+{"Csetlevels", (DL_FUNC) &setlevels, -1},
+{"Crleid", (DL_FUNC) &rleid, -1},
+
+{NULL, NULL, 0}
+};
+
+
+static const
+R_ExternalMethodDef externalMethods[] = {
+{"Cfastmean", (DL_FUNC) &fastmean, -1},
+{NULL, NULL, 0}
+};
+
+void attribute_visible R_init_datatable(DllInfo *info)
+// relies on pkg/src/Makevars to mv data.table.so to datatable.so
+{
+ R_registerRoutines(info, NULL, callMethods, NULL, externalMethods);
+ R_useDynamicSymbols(info, FALSE);
+ setSizes();
+ const char *msg = "... failed. Please forward this message to maintainer('data.table') or datatable-help.";
+ if (NA_INTEGER != INT_MIN) error("Checking NA_INTEGER [%d] == INT_MIN [%d] %s", NA_INTEGER, INT_MIN, msg);
+ if (NA_INTEGER != NA_LOGICAL) error("Checking NA_INTEGER [%d] == NA_LOGICAL [%d] %s", NA_INTEGER, NA_LOGICAL, msg);
+ if (sizeof(int) != 4) error("Checking sizeof(int) [%d] is 4 %s", sizeof(int), msg);
+ if (sizeof(double) != 8) error("Checking sizeof(double) [%d] is 8 %s", sizeof(double), msg); // 8 on both 32bit and 64bit.
+ if (sizeof(long long) != 8) error("Checking sizeof(long long) [%d] is 8 %s", sizeof(long long), msg);
+ if (sizeof(char *) != 4 && sizeof(char *) != 8) error("Checking sizeof(pointer) [%d] is 4 or 8 %s", sizeof(char *), msg);
+ if (sizeof(SEXP) != sizeof(char *)) error("Checking sizeof(SEXP) [%d] == sizeof(pointer) [%d] %s", sizeof(SEXP), sizeof(char *), msg);
+
+ SEXP tmp = PROTECT(allocVector(INTSXP,2));
+ if (LENGTH(tmp)!=2) error("Checking LENGTH(allocVector(INTSXP,2)) [%d] is 2 %s", LENGTH(tmp), msg);
+ if (TRUELENGTH(tmp)!=0) error("Checking TRUELENGTH(allocVector(INTSXP,2)) [%d] is 0 %s", TRUELENGTH(tmp), msg);
+ UNPROTECT(1);
+
+ // According to IEEE (http://en.wikipedia.org/wiki/IEEE_754-1985#Zero) we can rely on 0.0 being all 0 bits.
+ // But check here anyway just to be sure, just in case this answer is right (http://stackoverflow.com/a/2952680/403310).
+ int i = 314;
+ memset(&i, 0, sizeof(int));
+ if (i != 0) error("Checking memset(&i,0,sizeof(int)); i == (int)0 %s", msg);
+ unsigned int ui = 314;
+ memset(&ui, 0, sizeof(unsigned int));
+ if (ui != 0) error("Checking memset(&ui, 0, sizeof(unsigned int)); ui == (unsigned int)0 %s", msg);
+ double d = 3.14;
+ memset(&d, 0, sizeof(double));
+ if (d != 0.0) error("Checking memset(&d, 0, sizeof(double)); d == (double)0.0 %s", msg);
+ long double ld = 3.14;
+ memset(&ld, 0, sizeof(long double));
+ if (ld != 0.0) error("Checking memset(&ld, 0, sizeof(long double)); ld == (long double)0.0 %s", msg);
+
+ setNumericRounding(ScalarInteger(2));
+
+ char_integer64 = mkChar("integer64"); // for speed, similar to R_*Symbol.
+}
+
+
diff --git a/src/rbindlist.c b/src/rbindlist.c
new file mode 100644
index 0000000..f149ce7
--- /dev/null
+++ b/src/rbindlist.c
@@ -0,0 +1,926 @@
+#include "data.table.h"
+#include <Rdefines.h>
+#include <Rversion.h>
+#include <stdint.h>
+// #include <signal.h> // the debugging machinery + breakpoint aidee
+// raise(SIGINT);
+
+/* Eddi's hash setup for combining factor levels appropriately - untouched from previous state (except made combineFactorLevels static) */
+
+// Fixes #5150
+// a simple check for R version to decide if the type should be R_len_t or R_xlen_t
+// long vector support was added in R 3.0.0
+#if defined(R_VERSION) && R_VERSION >= R_Version(3, 0, 0)
+ typedef R_xlen_t RLEN;
+#else
+ typedef R_len_t RLEN;
+#endif
+
+// a simple linked list, will use this when finding global order for ordered factors
+// will keep two ints
+struct llist {
+ struct llist * next;
+ R_len_t i, j;
+};
+
+// hash table code copied from main/unique.c, specialized for our particular needs
+// as our table will just be strings
+// took out long vector ifdefs as that relied on too much base code
+// can revisit this later if there is need for more than ~1e9 length factor columns
+// UTF8 and Cache bools are not set correctly for now
+
+typedef size_t hlen;
+
+/* Hash function and equality test for keys */
+typedef struct _HashData HashData;
+
+struct _HashData {
+ int K;
+ hlen M;
+ RLEN nmax;
+ hlen (*hash)(SEXP, RLEN, HashData *);
+ int (*equal)(SEXP, RLEN, SEXP, RLEN);
+ struct llist ** HashTable;
+
+ int nomatch;
+ Rboolean useUTF8;
+ Rboolean useCache;
+};
+
+/*
+Integer keys are hashed via a random number generator
+based on Knuth's recommendations. The high order K bits
+are used as the hash code.
+
+NB: lots of this code relies on M being a power of two and
+on silent integer overflow mod 2^32.
+
+<FIXME> Integer keys are wasteful for logical and raw vectors, but
+the tables are small in that case. It would be much easier to
+implement long vectors, though.
+*/
+
+/* Currently the hash table is implemented as a (signed) integer
+array. So there are two 31-bit restrictions, the length of the
+array and the values. The values are initially NIL (-1). O-based
+indices are inserted by isDuplicated, and invalidated by setting
+to NA_INTEGER.
+*/
+
+static hlen scatter(unsigned int key, HashData *d)
+{
+ return 3141592653U * key >> (32 - d->K);
+}
+
+/* Hash CHARSXP by address. Hash values are int, For 64bit pointers,
+ * we do (upper ^ lower) */
+static hlen cshash(SEXP x, RLEN indx, HashData *d)
+{
+ intptr_t z = (intptr_t) STRING_ELT(x, indx);
+ unsigned int z1 = (unsigned int)(z & 0xffffffff), z2 = 0;
+#if SIZEOF_LONG == 8
+ z2 = (unsigned int)(z/0x100000000L);
+#endif
+ return scatter(z1 ^ z2, d);
+}
+
+static hlen shash(SEXP x, RLEN indx, HashData *d)
+{
+ unsigned int k;
+ const char *p;
+ const void *vmax = vmaxget();
+ if(!d->useUTF8 && d->useCache) return cshash(x, indx, d);
+ /* Not having d->useCache really should not happen anymore. */
+ p = translateCharUTF8(STRING_ELT(x, indx));
+ k = 0;
+ while (*p++)
+ k = 11 * k + (unsigned int) *p; /* was 8 but 11 isn't a power of 2 */
+ vmaxset(vmax); /* discard any memory used by translateChar */
+ return scatter(k, d);
+}
+
+static int sequal(SEXP x, RLEN i, SEXP y, RLEN j)
+{
+ // using our function instead of copying a lot more code from base
+ return !StrCmp(STRING_ELT(x, i), STRING_ELT(y, j));
+}
+
+/*
+Choose M to be the smallest power of 2
+not less than 2*n and set K = log2(M).
+Need K >= 1 and hence M >= 2, and 2^M < 2^31-1, hence n <= 2^29.
+
+Dec 2004: modified from 4*n to 2*n, since in the worst case we have
+a 50% full table, and that is still rather efficient -- see
+R. Sedgewick (1998) Algorithms in C++ 3rd edition p.606.
+*/
+static void MKsetup(HashData *d, RLEN n)
+{
+ if(n < 0 || n >= 1073741824) /* protect against overflow to -ve */
+ error("length %d is too large for hashing", n);
+
+ size_t n2 = 2U * (size_t) n;
+ d->M = 2;
+ d->K = 1;
+ while (d->M < n2) {
+ d->M *= 2;
+ d->K++;
+ }
+ d->nmax = n;
+}
+
+#define IMAX 4294967296L
+static void HashTableSetup(HashData *d, RLEN n)
+{
+ d->hash = shash;
+ d->equal = sequal;
+ MKsetup(d, n);
+ d->HashTable = malloc(sizeof(struct llist *) * (d->M));
+ if (d->HashTable == NULL) error("malloc failed in rbindlist.c. This part of the code will be reworked.");
+ for (RLEN i = 0; i < d->M; i++) d->HashTable[i] = NULL;
+}
+
+static void CleanHashTable(HashData *d)
+{
+ struct llist * root, * tmp;
+
+ for (RLEN i = 0; i < d->M; ++i) {
+ root = d->HashTable[i];
+ while (root != NULL) {
+ tmp = root->next;
+ free(root);
+ root = tmp;
+ }
+ }
+ free(d->HashTable);
+}
+
+// factorType is 1 for factor and 2 for ordered
+// will simply unique normal factors and attempt to find global order for ordered ones
+SEXP combineFactorLevels(SEXP factorLevels, int * factorType, Rboolean * isRowOrdered) {
+ // find total length
+ RLEN size = 0;
+ R_len_t len = LENGTH(factorLevels), n, i, j;
+ SEXP elem;
+ for (i = 0; i < len; ++i) {
+ elem = VECTOR_ELT(factorLevels, i);
+ n = LENGTH(elem);
+ size += n;
+ /* for (j = 0; j < n; ++j) { */
+ /* if(IS_BYTES(STRING_ELT(elem, j))) { */
+ /* data.useUTF8 = FALSE; break; */
+ /* } */
+ /* if(ENC_KNOWN(STRING_ELT(elem, j))) { */
+ /* data.useUTF8 = TRUE; */
+ /* } */
+ /* if(!IS_CACHED(STRING_ELT(elem, j))) { */
+ /* data.useCache = FALSE; break; */
+ /* } */
+ /* } */
+ }
+
+ // set up hash to put duplicates in
+ HashData data;
+ data.useUTF8 = FALSE;
+ data.useCache = TRUE;
+ HashTableSetup(&data, size);
+
+ struct llist **h = data.HashTable;
+ hlen idx;
+ struct llist * pl;
+ R_len_t uniqlen = 0;
+ // we insert in opposite order because it's more convenient later to choose first of the duplicates
+ for (i = len-1; i >= 0; --i) {
+ elem = VECTOR_ELT(factorLevels, i);
+ n = LENGTH(elem);
+ for (j = n-1; j >= 0; --j) {
+ idx = data.hash(elem, j, &data);
+ while (h[idx] != NULL) {
+ pl = h[idx];
+ if (data.equal(VECTOR_ELT(factorLevels, pl->i), pl->j, elem, j))
+ break;
+ // it's a collision, not a match, so iterate to a new spot
+ idx = (idx + 1) % data.M;
+ }
+ if (data.nmax-- < 0) error("hash table is full");
+
+ pl = malloc(sizeof(struct llist));
+ if (pl == NULL) error("malloc failed in rbindlist.c. This part of the code will be reworked.");
+ pl->next = NULL;
+ pl->i = i;
+ pl->j = j;
+ if (h[idx] != NULL) {
+ pl->next = h[idx];
+ } else {
+ ++uniqlen;
+ }
+ h[idx] = pl;
+ }
+ }
+
+ SEXP finalLevels = PROTECT(allocVector(STRSXP, uniqlen));
+ R_len_t counter = 0;
+ if (*factorType == 2) {
+ int * locs = malloc(sizeof(int) * len);
+ if (locs == NULL) error("malloc failed in rbindlist.c. This part of the code will be reworked.");
+ for (i = 0; i < len; ++i) locs[i] = 0;
+
+ R_len_t k;
+ SEXP tmp;
+ for (i = 0; i < len; ++i) {
+ if (!isRowOrdered[i]) continue;
+
+ elem = VECTOR_ELT(factorLevels, i);
+ n = LENGTH(elem);
+ for (j = locs[i]; j < n; ++j) {
+ idx = data.hash(elem, j, &data);
+ while (h[idx] != NULL) {
+ pl = h[idx];
+ if (data.equal(VECTOR_ELT(factorLevels, pl->i), pl->j, elem, j)) {
+ do {
+ if (!isRowOrdered[pl->i]) continue;
+
+ tmp = VECTOR_ELT(factorLevels, pl->i);
+ if (locs[pl->i] > pl->j) {
+ // failed to construct global order, need to break out of too many loops
+ // so will use goto :o
+ warning("ordered factor levels cannot be combined, going to convert to simple factor instead");
+ counter = 0;
+ *factorType = 1;
+ goto normalFactor;
+ }
+
+ for (k = locs[pl->i]; k < pl->j; ++k) {
+ SET_STRING_ELT(finalLevels, counter++, STRING_ELT(tmp, k));
+ }
+ locs[pl->i] = pl->j + 1;
+ } while ( (pl = pl->next) ); // added parenthesis to remove compiler warning 'suggest parentheses around assignment used as truth value'
+ SET_STRING_ELT(finalLevels, counter++, STRING_ELT(elem, j));
+ break;
+ }
+ // it's a collision, not a match, so iterate to a new spot
+ idx = (idx + 1) % data.M;
+ }
+ if (h[idx] == NULL) error("internal hash error, please report to datatable-help");
+ }
+ }
+
+ // fill in the rest of the unordered elements
+ Rboolean record;
+ for (i = 0; i < len; ++i) {
+ if (isRowOrdered[i]) continue;
+
+ elem = VECTOR_ELT(factorLevels, i);
+ n = LENGTH(elem);
+ for (j = 0; j < n; ++j) {
+ idx = data.hash(elem, j, &data);
+ while (h[idx] != NULL) {
+ pl = h[idx];
+ if (data.equal(VECTOR_ELT(factorLevels, pl->i), pl->j, elem, j)) {
+ // Fixes #899. "rest" can have identical levels in
+ // more than 1 data.table.
+ if (!(pl->i == i && pl->j == j)) break;
+ record = TRUE;
+ do {
+ // if this element was in an ordered list, it's been recorded already
+ if (isRowOrdered[pl->i]) {
+ record = FALSE;
+ break;
+ }
+ } while ( (pl = pl->next) ); // added parenthesis to remove compiler warning 'suggest parentheses around assignment used as truth value'
+ if (record)
+ SET_STRING_ELT(finalLevels, counter++, STRING_ELT(elem, j));
+
+ break;
+ }
+ // it's a collision, not a match, so iterate to a new spot
+ idx = (idx + 1) % data.M;
+ }
+ if (h[idx] == NULL) error("internal hash error, please report to datatable-help");
+ }
+ }
+ free (locs);
+ }
+
+ normalFactor:
+ if (*factorType == 1) {
+ for (i = 0; i < len; ++i) {
+ elem = VECTOR_ELT(factorLevels, i);
+ n = LENGTH(elem);
+ for (j = 0; j < n; ++j) {
+ idx = data.hash(elem, j, &data);
+ while (h[idx] != NULL) {
+ pl = h[idx];
+ if (data.equal(VECTOR_ELT(factorLevels, pl->i), pl->j, elem, j)) {
+ if (pl->i == i && pl->j == j) {
+ SET_STRING_ELT(finalLevels, counter++, STRING_ELT(elem, j));
+ }
+ break;
+ }
+ // it's a collision, not a match, so iterate to a new spot
+ idx = (idx + 1) % data.M;
+ }
+ if (h[idx] == NULL) error("internal hash error, please report to datatable-help");
+ }
+ }
+ }
+
+ CleanHashTable(&data);
+
+ return finalLevels;
+}
+
+
+/* Arun's addition and changes to incorporate 'usenames=T/F' and 'fill=T/F' arguments to rbindlist */
+
+/*
+ l = input list of data.tables/lists/data.frames
+ n = length(l)
+ ans = rbind'd result
+ i = an index over length of l
+ use.names = whether binding should check for names and bind them accordingly
+ fill = whether missing columns should be filled with NAs
+
+ ans_ptr = final_names - column names for 'ans' (list item 1)
+ ans_ptr = match_indices - when use.names=TRUE, for each element in l, what's the destination col index (in 'ans') for each of the cols (list item 2)
+ n_rows = total number of rows in 'ans'
+ n_cols = total number of cols in 'ans'
+ max_type = for each col in 'ans', what's the final SEXPTYPE? (for coercion if necessary)
+ is_factor = for each col in 'ans' mark which one's a factor (to convert to factor at the end)
+ is_ofactor = for each col in 'ans' mark which one's an ordered factor (to convert to ordered factor at the end)
+ fn_rows = the length of first column (rows) for each item in l.
+ mincol = get the minimum number of columns in an item from l. Used to check if 'fill=TRUE' is really necessary, even if set.
+*/
+
+struct preprocessData {
+ SEXP ans_ptr, colname;
+ size_t n_rows, n_cols;
+ int *fn_rows, *is_factor, first, lcount, mincol, protecti;
+ SEXPTYPE *max_type;
+};
+
+static SEXP unlist2(SEXP v) {
+
+ RLEN i, j, k=0, ni, n=0;
+ SEXP ans, vi, lnames, groups, runids;
+
+ for (i=0; i<length(v); i++) n += length(VECTOR_ELT(v, i));
+ ans = PROTECT(allocVector(VECSXP, 3));
+ lnames = PROTECT(allocVector(STRSXP, n));
+ groups = PROTECT(allocVector(INTSXP, n));
+ runids = PROTECT(allocVector(INTSXP, n));
+ for (i=0; i<length(v); i++) {
+ vi = VECTOR_ELT(v, i);
+ ni = length(vi);
+ for (j=0; j<ni; j++) {
+ SET_STRING_ELT(lnames, k + j, STRING_ELT(vi, j));
+ INTEGER(groups)[k + j] = i+1;
+ INTEGER(runids)[k + j] = j;
+ }
+ k+=j;
+ }
+ SET_VECTOR_ELT(ans, 0, lnames);
+ SET_VECTOR_ELT(ans, 1, groups);
+ SET_VECTOR_ELT(ans, 2, runids);
+ UNPROTECT(4);
+ return(ans);
+}
+
+// Don't use elsewhere. No checks are made on byArg and handleSorted
+// if handleSorted is 0, then it'll return integer(0) as such when
+// input is already sorted, like forder. if not, seq_len(nrow(dt)).
+static SEXP fast_order(SEXP dt, R_len_t byArg, R_len_t handleSorted) {
+
+ R_len_t i, protecti=0;
+ SEXP ans, by=R_NilValue, retGrp, sortStr, order, na, starts;
+
+ retGrp = PROTECT(allocVector(LGLSXP, 1)); LOGICAL(retGrp)[0] = TRUE;
+ sortStr = PROTECT(allocVector(LGLSXP, 1)); LOGICAL(sortStr)[0] = FALSE;
+ na = PROTECT(allocVector(LGLSXP, 1)); LOGICAL(na)[0] = FALSE;
+
+ if (byArg) {
+ by = PROTECT(allocVector(INTSXP, byArg));
+ order = PROTECT(allocVector(INTSXP, byArg));
+ for (i=0; i<byArg; i++) {
+ INTEGER(by)[i] = i+1;
+ INTEGER(order)[i] = 1;
+ }
+ UNPROTECT(5);
+ } else {
+ order = PROTECT(allocVector(INTSXP, 1)); INTEGER(order)[0] = 1;
+ UNPROTECT(4);
+ }
+ ans = PROTECT(forder(dt, by, retGrp, sortStr, order, na)); protecti++;
+ if (!length(ans) && handleSorted != 0) {
+ starts = PROTECT(getAttrib(ans, mkString("starts"))); protecti++;
+ // if cols are already sorted, 'forder' gives integer(0), got to replace it with 1:.N
+ ans = PROTECT(allocVector(INTSXP, length(VECTOR_ELT(dt, 0)))); protecti++;
+ for (i=0; i<length(ans); i++) INTEGER(ans)[i] = i+1;
+ setAttrib(ans, install("starts"), starts);
+ }
+ UNPROTECT(protecti); // ans
+ return(ans);
+}
+
+static SEXP uniq_lengths(SEXP v, R_len_t n) {
+
+ R_len_t i, nv=length(v);
+ SEXP ans = PROTECT(allocVector(INTSXP, nv));
+ for (i=1; i<nv; i++) {
+ INTEGER(ans)[i-1] = INTEGER(v)[i] - INTEGER(v)[i-1];
+ }
+ // last value
+ INTEGER(ans)[nv-1] = n - INTEGER(v)[nv-1] + 1;
+ UNPROTECT(1);
+ return(ans);
+}
+
+static SEXP match_names(SEXP v) {
+
+ R_len_t i, j, idx, ncols, protecti=0;
+ SEXP ans, dt, lnames, ti;
+ SEXP uorder, starts, ulens, index, firstofeachgroup, origorder;
+ SEXP fnames, findices, runid, grpid;
+
+ ans = PROTECT(allocVector(VECSXP, 2));
+ dt = PROTECT(unlist2(v)); protecti++;
+ lnames = VECTOR_ELT(dt, 0);
+ grpid = PROTECT(duplicate(VECTOR_ELT(dt, 1))); protecti++; // dt[1] will be reused, so backup
+ runid = VECTOR_ELT(dt, 2);
+
+ uorder = PROTECT(fast_order(dt, 2, 1)); protecti++; // byArg alone is set, everything else is set inside fast_order
+ starts = PROTECT(getAttrib(uorder, mkString("starts"))); protecti++;
+ ulens = PROTECT(uniq_lengths(starts, length(lnames))); protecti++;
+
+ // seq_len(.N) for each group
+ index = PROTECT(VECTOR_ELT(dt, 1)); protecti++; // reuse dt[1] (in 0-index coordinate), value already backed up above.
+ for (i=0; i<length(ulens); i++) {
+ for (j=0; j<INTEGER(ulens)[i]; j++)
+ INTEGER(index)[INTEGER(uorder)[INTEGER(starts)[i]-1+j]-1] = j;
+ }
+ // order again
+ uorder = PROTECT(fast_order(dt, 2, 1)); protecti++; // byArg alone is set, everything else is set inside fast_order
+ starts = PROTECT(getAttrib(uorder, mkString("starts"))); protecti++;
+ ulens = PROTECT(uniq_lengths(starts, length(lnames))); protecti++;
+ ncols = length(starts);
+ // check if order has to be changed (bysameorder = FALSE here by default - in `[.data.table` parlance)
+ firstofeachgroup = PROTECT(allocVector(INTSXP, length(starts)));
+ for (i=0; i<ncols; i++) INTEGER(firstofeachgroup)[i] = INTEGER(uorder)[INTEGER(starts)[i]-1];
+ origorder = PROTECT(fast_order(firstofeachgroup, 0, 0));
+ if (length(origorder)) {
+ reorder(starts, origorder);
+ reorder(ulens, origorder);
+ }
+ UNPROTECT(2);
+ // get fnames and findices
+ fnames = PROTECT(allocVector(STRSXP, ncols)); protecti++;
+ findices = PROTECT(allocVector(VECSXP, ncols)); protecti++;
+ for (i=0; i<ncols; i++) {
+ idx = INTEGER(uorder)[INTEGER(starts)[i]-1]-1;
+ SET_STRING_ELT(fnames, i, STRING_ELT(lnames, idx));
+ ti = PROTECT(allocVector(INTSXP, length(v)));
+ for (j=0;j<length(v);j++) INTEGER(ti)[j]=-1; // TODO: can we eliminate this?
+ for (j=0; j<INTEGER(ulens)[i]; j++) {
+ idx = INTEGER(uorder)[INTEGER(starts)[i]-1+j]-1;
+ INTEGER(ti)[INTEGER(grpid)[idx]-1] = INTEGER(runid)[idx];
+ }
+ UNPROTECT(1);
+ SET_VECTOR_ELT(findices, i, ti);
+ }
+ UNPROTECT(protecti);
+ SET_VECTOR_ELT(ans, 0, fnames);
+ SET_VECTOR_ELT(ans, 1, findices);
+ UNPROTECT(1); // ans
+ return(ans);
+}
+
+static void preprocess(SEXP l, Rboolean usenames, Rboolean fill, struct preprocessData *data) {
+
+ R_len_t i, j, idx;
+ SEXP li, lnames=R_NilValue, fnames, findices=R_NilValue, f_ind=R_NilValue, thiscol, col_name=R_NilValue, thisClass = R_NilValue;
+ SEXPTYPE type;
+
+ data->first = -1; data->lcount = 0; data->n_rows = 0; data->n_cols = 0; data->protecti = 0;
+ data->max_type = NULL; data->is_factor = NULL; data->ans_ptr = R_NilValue; data->mincol=0;
+ data->fn_rows = Calloc(LENGTH(l), int); data->colname = R_NilValue;
+
+ // get first non null name, 'rbind' was doing a 'match.names' for each item.. which is a bit more time consuming.
+ // And warning that it'll be matched by names is not necessary, I think, as that's the default for 'rbind'. We
+ // should instead document it.
+ for (i=0; i<LENGTH(l); i++) { // isNull is checked already in rbindlist
+ li = VECTOR_ELT(l, i);
+ if (isNull(li)) continue;
+ if (TYPEOF(li) != VECSXP) error("Item %d of list input is not a data.frame, data.table or list",i+1);
+ if (!LENGTH(li)) continue;
+ col_name = getAttrib(li, R_NamesSymbol);
+ if (!isNull(col_name)) break;
+ }
+ if (!isNull(col_name)) { data->colname = PROTECT(col_name); data->protecti++; }
+ if (usenames) { lnames = PROTECT(allocVector(VECSXP, LENGTH(l))); data->protecti++;}
+ for (i=0; i<LENGTH(l); i++) {
+ li = VECTOR_ELT(l, i);
+ if (isNull(li)) continue;
+ if (TYPEOF(li) != VECSXP) error("Item %d of list input is not a data.frame, data.table or list",i+1);
+ if (!LENGTH(li)) continue;
+ col_name = getAttrib(li, R_NamesSymbol);
+ if (fill && isNull(col_name))
+ error("fill=TRUE, but names of input list at position %d is NULL. All items of input list must have names set when fill=TRUE.", i+1);
+ data->lcount++;
+ data->fn_rows[i] = length(VECTOR_ELT(li, 0));
+ if (data->first == -1) {
+ data->first = i;
+ data->n_cols = LENGTH(li);
+ data->mincol = LENGTH(li);
+ if (!usenames) {
+ data->ans_ptr = PROTECT(allocVector(VECSXP, 2)); data->protecti++;
+ if (isNull(col_name)) SET_VECTOR_ELT(data->ans_ptr, 0, data->colname);
+ else SET_VECTOR_ELT(data->ans_ptr, 0, col_name);
+ } else {
+ if (isNull(col_name)) SET_VECTOR_ELT(lnames, i, data->colname);
+ else SET_VECTOR_ELT(lnames, i, col_name);
+ }
+ data->n_rows += data->fn_rows[i];
+ continue;
+ } else {
+ if (!fill && LENGTH(li) != data->n_cols)
+ if (LENGTH(li) != data->n_cols) error("Item %d has %d columns, inconsistent with item %d which has %d columns. If instead you need to fill missing columns, use set argument 'fill' to TRUE.",i+1, LENGTH(li), data->first+1, data->n_cols);
+ }
+ if (data->mincol > LENGTH(li)) data->mincol = LENGTH(li);
+ data->n_rows += data->fn_rows[i];
+ if (usenames) {
+ if (isNull(col_name)) SET_VECTOR_ELT(lnames, i, data->colname);
+ else SET_VECTOR_ELT(lnames, i, col_name);
+ }
+ }
+ if (usenames) {
+ data->ans_ptr = PROTECT(match_names(lnames)); data->protecti++;
+ fnames = VECTOR_ELT(data->ans_ptr, 0);
+ findices = VECTOR_ELT(data->ans_ptr, 1);
+ if (isNull(data->colname) && data->n_cols > 0)
+ error("use.names=TRUE but no item of input list has any names.\n");
+ if (!fill && length(fnames) != data->mincol) {
+ error("Answer requires %d columns whereas one or more item(s) in the input list has only %d columns. This could be because the items in the list may not all have identical column names or some of the items may have duplicate names. In either case, if you're aware of this and would like to fill those missing columns, set the argument 'fill=TRUE'.", length(fnames), data->mincol);
+ } else data->n_cols = length(fnames);
+ }
+
+ // decide type of each column
+ // initialize the max types - will possibly increment later
+ data->max_type = Calloc(data->n_cols, SEXPTYPE);
+ data->is_factor = Calloc(data->n_cols, int);
+ for (i = 0; i< data->n_cols; i++) {
+ thisClass = R_NilValue;
+ if (usenames) f_ind = VECTOR_ELT(findices, i);
+ for (j=data->first; j<LENGTH(l); j++) {
+ if (data->is_factor[i] == 2) break;
+ idx = (usenames) ? INTEGER(f_ind)[j] : i;
+ li = VECTOR_ELT(l, j);
+ if (isNull(li) || !LENGTH(li) || idx < 0) continue;
+ thiscol = VECTOR_ELT(li, idx);
+ // Fix for #705, check attributes
+ if (j == data->first)
+ thisClass = getAttrib(thiscol, R_ClassSymbol);
+ if (isFactor(thiscol)) {
+ data->is_factor[i] = (isOrdered(thiscol)) ? 2 : 1;
+ data->max_type[i] = STRSXP;
+ } else {
+ // Fix for #705, check attributes and error if non-factor class and not identical
+ if (!data->is_factor[i] &&
+ !R_compute_identical(thisClass, getAttrib(thiscol, R_ClassSymbol), 0)) {
+ error("Class attributes at column %d of input list at position %d does not match with column %d of input list at position %d. Coercion of objects of class 'factor' alone is handled internally by rbind/rbindlist at the moment.", i+1, j+1, i+1, data->first+1);
+ }
+ type = TYPEOF(thiscol);
+ if (type > data->max_type[i]) data->max_type[i] = type;
+ }
+ }
+ }
+}
+
+SEXP rbindlist(SEXP l, SEXP sexp_usenames, SEXP sexp_fill) {
+
+ R_len_t jj, ansloc, resi, i,j,r, idx, thislen;
+ struct preprocessData data;
+ Rboolean usenames, fill, to_copy = FALSE, coerced=FALSE;
+ SEXP fnames = R_NilValue, findices = R_NilValue, f_ind = R_NilValue, ans, lf, li, target, thiscol, levels;
+ SEXP factorLevels = R_NilValue, finalFactorLevels;
+ Rboolean *isRowOrdered = NULL;
+ R_len_t protecti;
+
+ // first level of error checks
+ if (!isLogical(sexp_usenames) || LENGTH(sexp_usenames)!=1 || LOGICAL(sexp_usenames)[0]==NA_LOGICAL)
+ error("use.names should be TRUE or FALSE");
+ if (!isLogical(sexp_fill) || LENGTH(sexp_fill) != 1 || LOGICAL(sexp_fill)[0] == NA_LOGICAL)
+ error("fill should be TRUE or FALSE");
+ if (!length(l)) return(l);
+ if (TYPEOF(l) != VECSXP) error("Input to rbindlist must be a list of data.tables");
+
+ usenames = LOGICAL(sexp_usenames)[0];
+ fill = LOGICAL(sexp_fill)[0];
+ if (fill && !usenames) {
+ // override default
+ warning("Resetting 'use.names' to TRUE. 'use.names' can not be FALSE when 'fill=TRUE'.\n");
+ usenames=TRUE;
+ }
+ // check for factor, get max types, and when usenames=TRUE get the answer 'names' and column indices for proper reordering.
+ preprocess(l, usenames, fill, &data);
+ fnames = VECTOR_ELT(data.ans_ptr, 0);
+ findices = VECTOR_ELT(data.ans_ptr, 1);
+ protecti = data.protecti;
+ if (data.n_rows == 0 && data.n_cols == 0) {
+ UNPROTECT(protecti);
+ return(R_NilValue);
+ }
+
+ factorLevels = PROTECT(allocVector(VECSXP, data.lcount));
+ isRowOrdered = Calloc(data.lcount, Rboolean);
+
+ ans = PROTECT(allocVector(VECSXP, data.n_cols)); protecti++;
+ setAttrib(ans, R_NamesSymbol, fnames);
+ lf = VECTOR_ELT(l, data.first);
+ for(j=0; j<data.n_cols; j++) {
+ if (fill) target = allocNAVector(data.max_type[j], data.n_rows);
+ else target = allocVector(data.max_type[j], data.n_rows);
+ SET_VECTOR_ELT(ans, j, target);
+
+ if (usenames) {
+ to_copy = TRUE;
+ f_ind = VECTOR_ELT(findices, j);
+ } else {
+ thiscol = VECTOR_ELT(lf, j);
+ if (!isFactor(thiscol)) copyMostAttrib(thiscol, target); // all but names,dim and dimnames. And if so, we want a copy here, not keepattr's SET_ATTRIB.
+ }
+ ansloc = 0;
+ jj = 0; // to increment factorLevels
+ resi = -1;
+ for (i=data.first; i<LENGTH(l); i++) {
+ li = VECTOR_ELT(l,i);
+ if (!length(li)) continue; // majority of time though, each item of l is populated
+ thislen = data.fn_rows[i];
+ idx = (usenames) ? INTEGER(f_ind)[i] : j;
+ if (idx < 0) {
+ ansloc += thislen;
+ resi++;
+ if (data.is_factor[j]) {
+ isRowOrdered[resi] = FALSE;
+ SET_VECTOR_ELT(factorLevels, jj, allocNAVector(data.max_type[j], 1)); // the only level here is NA.
+ jj++;
+ }
+ continue;
+ }
+ thiscol = VECTOR_ELT(li, idx);
+ if (thislen != length(thiscol)) error("Column %d of item %d is length %d, inconsistent with first column of that item which is length %d. rbind/rbindlist doesn't recycle as it already expects each item to be a uniform list, data.frame or data.table", j+1, i+1, length(thiscol), thislen);
+ // couldn't figure out a way to this outside this loop when fill = TRUE.
+ if (to_copy && !isFactor(thiscol)) {
+ copyMostAttrib(thiscol, target);
+ to_copy = FALSE;
+ }
+ resi++; // after the first, there might be NULL or empty which are skipped, resi increments up until lcount
+ if (TYPEOF(thiscol) != TYPEOF(target) && !isFactor(thiscol)) {
+ thiscol = PROTECT(coerceVector(thiscol, TYPEOF(target)));
+ coerced = TRUE;
+ // TO DO: options(datatable.pedantic=TRUE) to issue this warning :
+ // warning("Column %d of item %d is type '%s', inconsistent with column %d of item %d's type ('%s')",j+1,i+1,type2char(TYPEOF(thiscol)),j+1,first+1,type2char(TYPEOF(target)));
+ }
+ switch(TYPEOF(target)) {
+ case STRSXP :
+ isRowOrdered[resi] = FALSE;
+ if (isFactor(thiscol)) {
+ levels = getAttrib(thiscol, R_LevelsSymbol);
+ for (r=0; r<thislen; r++)
+ if (INTEGER(thiscol)[r]==NA_INTEGER)
+ SET_STRING_ELT(target, ansloc+r, NA_STRING);
+ else
+ SET_STRING_ELT(target, ansloc+r, STRING_ELT(levels,INTEGER(thiscol)[r]-1));
+
+ // add levels to factorLevels
+ // changed "i" to "jj" and increment 'jj' after so as to fill only non-empty tables with levels
+ SET_VECTOR_ELT(factorLevels, jj, levels); jj++;
+ if (isOrdered(thiscol)) isRowOrdered[resi] = TRUE;
+ } else {
+ if (TYPEOF(thiscol) != STRSXP) error("Internal logical error in rbindlist.c (not STRSXP), please report to datatable-help.");
+ for (r=0; r<thislen; r++) SET_STRING_ELT(target, ansloc+r, STRING_ELT(thiscol,r));
+
+ // if this column is going to be a factor, add column to factorLevels
+ // changed "i" to "jj" and increment 'jj' after so as to fill only non-empty tables with levels
+ if (data.is_factor[j]) {
+ SET_VECTOR_ELT(factorLevels, jj, thiscol);
+ jj++;
+ }
+ // removed 'coerced=FALSE; UNPROTECT(1)' as it resulted in a stack imbalance.
+ // anyways it's taken care of after the switch. So no need here.
+ }
+ break;
+ case VECSXP :
+ if (TYPEOF(thiscol) != VECSXP) error("Internal logical error in rbindlist.c (not VECSXP), please report to datatable-help.");
+ for (r=0; r<thislen; r++)
+ SET_VECTOR_ELT(target, ansloc+r, VECTOR_ELT(thiscol,r));
+ break;
+ case REALSXP:
+ case INTSXP:
+ case LGLSXP:
+ if (TYPEOF(thiscol) != TYPEOF(target)) error("Internal logical error in rbindlist.c, type of 'thiscol' should have already been coerced to 'target'. Please report to datatable-help.");
+ memcpy((char *)DATAPTR(target) + ansloc * SIZEOF(thiscol),
+ (char *)DATAPTR(thiscol),
+ thislen * SIZEOF(thiscol));
+ break;
+ default :
+ error("Unsupported column type '%s'", type2char(TYPEOF(target)));
+ }
+ ansloc += thislen;
+ if (coerced) {
+ UNPROTECT(1);
+ coerced = FALSE;
+ }
+ }
+ if (data.is_factor[j]) {
+ finalFactorLevels = combineFactorLevels(factorLevels, &(data.is_factor[j]), isRowOrdered);
+ SEXP factorLangSxp = PROTECT(lang3(install(data.is_factor[j] == 1 ? "factor" : "ordered"),
+ target, finalFactorLevels));
+ SET_VECTOR_ELT(ans, j, eval(factorLangSxp, R_GlobalEnv));
+ UNPROTECT(2); // finalFactorLevels, factorLangSxp
+ }
+ }
+ if (factorLevels != R_NilValue) UNPROTECT_PTR(factorLevels);
+
+ Free(data.max_type);
+ Free(data.is_factor);
+ Free(data.fn_rows);
+ Free(isRowOrdered);
+ UNPROTECT(protecti);
+ return(ans);
+}
+
+/*
+## The section below implements "chmatch2_old" and "chmatch2" (faster version of chmatch2_old).
+## It's basically 'pmatch' but without the partial matching part. These examples should
+## make it clearer.
+## Examples:
+## chmatch2_old(c("a", "a"), c("a", "a")) # 1,2 - the second 'a' in 'x' has a 2nd match in 'table'
+## chmatch2_old(c("a", "a"), c("a", "b")) # 1,NA - the second one doesn't 'see' the first 'a'
+## chmatch2_old(c("a", "a"), c("a", "a.1")) # 1,NA - differs from 'pmatch' output = 1,2
+##
+## The algorithm: given 'x' and 'y':
+## dt = data.table(val=c(x,y), grp1 = rep(1:2, c(length(x),length(y))), grp2=c(1:length(x), 1:length(y)))
+## dt[, grp1 := 0:(.N-1), by="val,grp1"]
+## dt[, grp2[2], by="val,grp1"]
+##
+## NOTE: This is FAST, but not AS FAST AS it could be. See chmatch2 for a faster implementation (and bottom
+## of this file for a benchmark). I've retained here for now. Ultimately, will've to discuss with Matt and
+## probably export it??
+*/
+SEXP chmatch2_old(SEXP x, SEXP table, SEXP nomatch) {
+
+ R_len_t i, j, k, nx, li, si, oi;
+ SEXP dt, l, ans, order, start, lens, grpid, index;
+ if (TYPEOF(nomatch) != INTSXP || length(nomatch) != 1) error("'nomatch' must be an integer of length 1");
+ if (!length(x) || isNull(x)) return(allocVector(INTSXP, 0));
+ if (TYPEOF(x) != STRSXP) error("'x' must be a character vector");
+ nx=length(x);
+ if (!length(table) || isNull(table)) {
+ ans = PROTECT(allocVector(INTSXP, nx));
+ for (i=0; i<nx; i++) INTEGER(ans)[i] = INTEGER(nomatch)[0];
+ UNPROTECT(1);
+ return(ans);
+ }
+ if (TYPEOF(table) != STRSXP) error("'table' must be a character vector");
+ // Done with special cases. On to the real deal.
+ l = PROTECT(allocVector(VECSXP, 2));
+ SET_VECTOR_ELT(l, 0, x);
+ SET_VECTOR_ELT(l, 1, table);
+
+ UNPROTECT(1); // l
+ dt = PROTECT(unlist2(l));
+
+ // order - first time
+ order = PROTECT(fast_order(dt, 2, 1));
+ start = PROTECT(getAttrib(order, mkString("starts")));
+ lens = PROTECT(uniq_lengths(start, length(order))); // length(order) = nrow(dt)
+ grpid = VECTOR_ELT(dt, 1);
+ index = VECTOR_ELT(dt, 2);
+
+ // replace dt[1], we don't need it anymore
+ k=0;
+ for (i=0; i<length(lens); i++) {
+ for (j=0; j<INTEGER(lens)[i]; j++) {
+ INTEGER(grpid)[INTEGER(order)[k+j]-1] = j;
+ }
+ k += j;
+ }
+ // order - again
+ UNPROTECT(3); // order, start, lens
+ order = PROTECT(fast_order(dt, 2, 1));
+ start = PROTECT(getAttrib(order, mkString("starts")));
+ lens = PROTECT(uniq_lengths(start, length(order)));
+
+ ans = PROTECT(allocVector(INTSXP, nx));
+ k = 0;
+ for (i=0; i<length(lens); i++) {
+ li = INTEGER(lens)[i];
+ si = INTEGER(start)[i]-1;
+ oi = INTEGER(order)[si]-1;
+ if (oi > nx-1) continue;
+ INTEGER(ans)[oi] = (li == 2) ? INTEGER(index)[INTEGER(order)[si+1]-1]+1 : INTEGER(nomatch)[0];
+ }
+ UNPROTECT(5); // order, start, lens, ans
+ return(ans);
+}
+
+// utility function used from within chmatch2
+static SEXP listlist(SEXP x) {
+
+ R_len_t i,j,k, nl;
+ SEXP lx, xo, xs, xl, tmp, ans, ans0, ans1;
+
+ lx = PROTECT(allocVector(VECSXP, 1));
+ SET_VECTOR_ELT(lx, 0, x);
+ xo = PROTECT(fast_order(lx, 1, 1));
+ xs = PROTECT(getAttrib(xo, mkString("starts")));
+ xl = PROTECT(uniq_lengths(xs, length(x)));
+
+ ans0 = PROTECT(allocVector(STRSXP, length(xs)));
+ ans1 = PROTECT(allocVector(VECSXP, length(xs)));
+ k=0;
+ for (i=0; i<length(xs); i++) {
+ SET_STRING_ELT(ans0, i, STRING_ELT(x, INTEGER(xo)[INTEGER(xs)[i]-1]-1));
+ nl = INTEGER(xl)[i];
+ tmp = allocVector(INTSXP, nl);
+ SET_VECTOR_ELT(ans1, i, tmp);
+ for (j=0; j<nl; j++) {
+ INTEGER(tmp)[j] = INTEGER(xo)[k+j];
+ }
+ k += j;
+ }
+ ans = PROTECT(allocVector(VECSXP, 2));
+ SET_VECTOR_ELT(ans, 0, ans0);
+ SET_VECTOR_ELT(ans, 1, ans1);
+ UNPROTECT(7);
+ return(ans);
+}
+
+/*
+## While chmatch2_old works great, I find it inefficient in terms of both memory (stores 2 indices over the
+## length of x+y) and speed (2 ordering and looping over unnecesssary amount of times). So, here's
+## another stab at a faster version of 'chmatch2_old', leveraging the power of 'chmatch' and data.table's
+## DT[ , list(list()), by=.] syntax.
+##
+## The algorithm:
+## x.agg = data.table(x)[, list(list(rep(x, .N))), by=x]
+## y.agg = data.table(y)[, list(list(rep(y, .N))), by=y]
+## mtch = chmatch(x.agg, y.agg, nomatch) ## here we look at only unique values!
+## Now, it's just a matter of filling corresponding matches from x.agg's indices with y.agg's indices.
+## BENCHMARKS ON THE BOTTOM OF THIS FILE
+*/
+SEXP chmatch2(SEXP x, SEXP y, SEXP nomatch) {
+
+ R_len_t i, j, k, nx, ix, iy;
+ SEXP xll, yll, xu, yu, ans, xl, yl, mx;
+ if (TYPEOF(nomatch) != INTSXP || length(nomatch) != 1) error("'nomatch' must be an integer of length 1");
+ if (!length(x) || isNull(x)) return(allocVector(INTSXP, 0));
+ if (TYPEOF(x) != STRSXP) error("'x' must be a character vector");
+ nx = length(x);
+ if (!length(y) || isNull(y)) {
+ ans = PROTECT(allocVector(INTSXP, nx));
+ for (i=0; i<nx; i++) INTEGER(ans)[i] = INTEGER(nomatch)[0];
+ UNPROTECT(1);
+ return(ans);
+ }
+ if (TYPEOF(y) != STRSXP) error("'table' must be a character vector");
+ // Done with special cases. On to the real deal.
+ xll = PROTECT(listlist(x));
+ yll = PROTECT(listlist(y));
+
+ xu = VECTOR_ELT(xll, 0);
+ yu = VECTOR_ELT(yll, 0);
+
+ mx = PROTECT(chmatch(xu, yu, 0, FALSE));
+ ans = PROTECT(allocVector(INTSXP, nx));
+ k=0;
+ for (i=0; i<length(mx); i++) {
+ xl = VECTOR_ELT(VECTOR_ELT(xll, 1), i);
+ ix = length(xl);
+ if (INTEGER(mx)[i] == 0) {
+ for (j=0; j<ix; j++)
+ INTEGER(ans)[INTEGER(xl)[j]-1] = INTEGER(nomatch)[0];
+ } else {
+ yl = VECTOR_ELT(VECTOR_ELT(yll, 1), INTEGER(mx)[i]-1);
+ iy = length(yl);
+ for (j=0; j < ix; j++)
+ INTEGER(ans)[INTEGER(xl)[j]-1] = (j < iy) ? INTEGER(yl)[j] : INTEGER(nomatch)[0];
+ k += ix;
+ }
+ }
+ UNPROTECT(4);
+ return(ans);
+
+}
+
+/*
+## Benchmark:
+set.seed(45L)
+x <- sample(letters, 1e6, TRUE)
+y <- sample(letters, 1e7, TRUE)
+system.time(ans1 <- .Call("Cchmatch2_old", x,y,0L)) # 2.405 seconds
+system.time(ans2 <- .Call("Cchmatch2", x,y,0L)) # 0.174 seconds
+identical(ans1, ans2) # [1] TRUE
+## Note: 'pmatch(x,y,0L)' dint finish still after about 5 minutes, so stopped.
+## Speed up of about ~14x!!! nice ;). (And this uses lesser memory as well).
+*/
diff --git a/src/reorder.c b/src/reorder.c
new file mode 100644
index 0000000..74008d8
--- /dev/null
+++ b/src/reorder.c
@@ -0,0 +1,143 @@
+#include "data.table.h"
+#include <Rdefines.h>
+
+// reverse a vector - equivalent of rev(x) in base, but implemented in C and about 12x faster (on 1e8)
+SEXP setrev(SEXP x) {
+ R_len_t j, n, len;
+ size_t size;
+ char *tmp, *xt;
+ if (TYPEOF(x) == VECSXP || isMatrix(x)) error("Input 'x' must be a vector");
+ len = length(x);
+ if (len <= 1) return(x);
+ size = SIZEOF(x);
+ if (!size) error("don't know how to reverse type '%s' of input 'x'.",type2char(TYPEOF(x)));
+ n = (int)(len/2);
+ xt = (char *)DATAPTR(x);
+ if (size==4) {
+ tmp = (char *)Calloc(1, int);
+ if (!tmp) error("unable to allocate temporary working memory for reordering x");
+ for (j=0;j<n;j++) {
+ *(int *)tmp = ((int *)xt)[j]; // just copies 4 bytes (pointers on 32bit too)
+ ((int *)xt)[j] = ((int *)xt)[len-1-j];
+ ((int *)xt)[len-1-j] = *(int *)tmp;
+ }
+ } else {
+ if (size!=8) error("Size of x isn't 4 or 8");
+ tmp = (char *)Calloc(1, double);
+ if (!tmp) error("unable to allocate temporary working memory for reordering x");
+ for (j=0;j<n;j++) {
+ *(double *)tmp = ((double *)xt)[j]; // just copies 8 bytes (pointers on 64bit too)
+ ((double *)xt)[j] = ((double *)xt)[len-1-j];
+ ((double *)xt)[len-1-j] = *(double *)tmp;
+ }
+ }
+ Free(tmp);
+ return(R_NilValue);
+}
+
+SEXP reorder(SEXP x, SEXP order)
+{
+ // For internal use only by setkey().
+ // Reordering a vector in-place doesn't change generations so we can skip SET_STRING_ELT overhead etc.
+ // Speed is dominated by page fetch when input is randomly ordered so we're at the software limit here (better bus etc should shine).
+ // 'order' must strictly be a permutation of 1:n (i.e. no repeats, zeros or NAs)
+ // If only a small subset is reordered, this is detected using start and end.
+ // x may be a vector, or a list of vectors e.g. data.table
+ char *tmp, *tmpp, *vd;
+ SEXP v;
+ R_len_t i, j, itmp, nrow, ncol, start, end;
+ size_t size; // must be size_t, otherwise bug #5305 (integer overflow in memcpy)
+
+ if (isNewList(x)) {
+ nrow = length(VECTOR_ELT(x,0));
+ ncol = length(x);
+ for (i=0;i<ncol;i++) {
+ v = VECTOR_ELT(x,i);
+ if (SIZEOF(v) == 0) error("Item %d of list is type '%s' which isn't yet supported", i+1, type2char(TYPEOF(v)));
+ if (length(v)!=nrow) error("Column %d is length %d which differs from length of column 1 (%d). Invalid data.table.", i+1, length(v), nrow);
+ }
+ } else {
+ if (SIZEOF(x) == 0) error("reorder accepts vectors but this non-VECSXP is type '%s' which isn't yet supported", type2char(TYPEOF(x)));
+ nrow = length(x);
+ ncol = 1;
+ }
+ if (!isInteger(order)) error("order must be an integer vector");
+ if (length(order) != nrow) error("nrow(x)[%d]!=length(order)[%d]",nrow,length(order));
+
+ start = 0;
+ while (start<nrow && INTEGER(order)[start] == start+1) start++;
+ if (start==nrow) return(R_NilValue); // input is 1:n, nothing to do
+ end = nrow-1;
+ while (INTEGER(order)[end] == end+1) end--;
+ for (i=start; i<=end; i++) { itmp=INTEGER(order)[i]-1; if (itmp<start || itmp>end) error("order is not a permutation of 1:nrow[%d]", nrow); }
+ // Creorder is for internal use (so we should get the input right!), but the check above seems sensible, otherwise
+ // would be segfault below. The for loop above should run in neglible time (sequential) and will also catch NAs.
+ // It won't catch duplicates in order, but that's ok. Checking that would be going too far given this is for internal use only.
+
+ tmp=(char *)malloc((end-start+1)*sizeof(double)); // Enough working space for one column of the largest type. setSizes() has a check too.
+ // So we can reorder a 10GB table in 16GB of RAM
+ if (!tmp) error("unable to allocate %d * %d bytes of working memory for reordering data.table", end-start+1, sizeof(double));
+ for (i=0; i<ncol; i++) {
+ v = isNewList(x) ? VECTOR_ELT(x,i) : x;
+ size = SIZEOF(v);
+ if (!size) error("don't know how to reorder type '%s' of column %d. Please send this message to datatable-help",type2char(TYPEOF(v)),i+1);
+ tmpp=tmp;
+ vd = (char *)DATAPTR(v);
+ if (size==4) {
+ for (j=start;j<=end;j++) {
+ *(int *)tmpp = ((int *)vd)[INTEGER(order)[j]-1]; // just copies 4 bytes (pointers on 32bit too)
+ tmpp += 4;
+ }
+ } else {
+ if (size!=8) error("Size of column %d's type isn't 4 or 8", i+1);
+ for (j=start;j<=end;j++) {
+ *(double *)tmpp = ((double *)vd)[INTEGER(order)[j]-1]; // just copies 8 bytes (pointers on 64bit too)
+ tmpp += 8;
+ }
+ }
+ memcpy(vd + start*size, tmp, (end-start+1) * size);
+ }
+ free(tmp);
+ return(R_NilValue);
+}
+
+
+/*
+used to be :
+for (j=0;j<nrow;j++) {
+ memcpy((char *)tmpp, (char *)DATAPTR(VECTOR_ELT(dt,i)) + ((size_t)(INTEGER(order)[j]-1))*size, size);
+ tmpp += size;
+}
+This added 5s in 4e8 calls (see below) [-O3 on]. That 5s is insignificant vs page fetch, though, unless already
+ordered when page fetch goes away. Perhaps memcpy(dest, src, 4) and memcpy(dest, src, 8) would be optimized to remove
+call overhead but memcpy(dest, src, size) isn't since optimizer doesn't know value of 'size' variable up front.
+
+DT = setDT(lapply(1:4, function(x){sample(1e5,1e8,replace=TRUE)}))
+o = fastorder(DT, 1L)
+none = 1:1e8
+
+# worst case 5% faster, thoroughly random # before after
+system.time(.Call(Creorder,DT,o)) # 102.301 97.082
+system.time(.Call(Creorder,DT,o)) # 102.602 97.001
+
+# best case 50% faster, already ordered # before after
+system.time(.Call(Creorder,DT,none)) # 9.310 4.187
+system.time(.Call(Creorder,DT,none)) # 9.295 4.077
+
+# Somewhere inbetween 5%-50% would be e.g. grouped data where we're reordering the blocks.
+# But, likely the worst case speedup most of the time. On my slow netbook anyway, with slow RAM.
+
+# However, on decent laptop (faster RAM/bus etc) it looks better at 40% speedup consistently ...
+
+# thoroughly random # before after
+system.time(.Call("Creorder",DT,o)) # 33.216 19.184
+system.time(.Call("Creorder",DT,o)) # 30.556 18.667
+
+# already ordered # before after
+system.time(.Call("Creorder",DT,none)) # 4.172 2.384
+system.time(.Call("Creorder",DT,none)) # 4.076 2.356
+
+*/
+
+
+
diff --git a/src/shift.c b/src/shift.c
new file mode 100644
index 0000000..df23917
--- /dev/null
+++ b/src/shift.c
@@ -0,0 +1,206 @@
+#include "data.table.h"
+#include <Rdefines.h>
+#include <time.h>
+
+SEXP shift(SEXP obj, SEXP k, SEXP fill, SEXP type) {
+
+ size_t size;
+ R_len_t i=0, j, m, nx, nk, xrows, thisk;
+ SEXP x, tmp=R_NilValue, this, ans, thisfill, class;
+ unsigned long long *dthisfill;
+ enum {LAG, LEAD} stype = LAG;
+ if (!length(obj)) return(obj); // NULL, list()
+ if (isVectorAtomic(obj)) {
+ x = allocVector(VECSXP, 1);
+ SET_VECTOR_ELT(x, 0, obj);
+ } else x = obj;
+ if (!isNewList(x))
+ error("x must be a list, data.frame or data.table");
+ if (!isInteger(k))
+ error("Internal error: n must be integer");
+ if (length(fill) != 1)
+ error("fill must be a vector of length 1");
+ if (!isString(type) || length(type) != 1)
+ error("type must be a character vector of length 1");
+
+ if (!strcmp(CHAR(STRING_ELT(type, 0)), "lag")) stype = LAG;
+ else if (!strcmp(CHAR(STRING_ELT(type, 0)), "lead")) stype = LEAD;
+ else error("Internal error: invalid type for shift(), should have been caught before. Please report to datatable-help");
+
+ nx = length(x); nk = length(k);
+ i=0;
+ while(i < nk && INTEGER(k)[i] >= 0) i++;
+ if (i != nk)
+ error("n must be non-negative integer values (>= 0)");
+ ans = PROTECT(allocVector(VECSXP, nk * nx));
+ if (stype == LAG) {
+ for (i=0; i<nx; i++) {
+ this = VECTOR_ELT(x, i);
+ size = SIZEOF(this);
+ xrows = length(this);
+ switch (TYPEOF(this)) {
+ case INTSXP :
+ thisfill = PROTECT(coerceVector(fill, INTSXP));
+ for (j=0; j<nk; j++) {
+ thisk = (xrows >= INTEGER(k)[j]) ? INTEGER(k)[j] : xrows;
+ tmp = allocVector(INTSXP, xrows);
+ SET_VECTOR_ELT(ans, i*nk+j, tmp);
+ if (xrows - INTEGER(k)[j] > 0)
+ memcpy((char *)DATAPTR(tmp)+(INTEGER(k)[j]*size),
+ (char *)DATAPTR(this),
+ (xrows-INTEGER(k)[j])*size);
+ for (m=0; m<thisk; m++)
+ INTEGER(tmp)[m] = INTEGER(thisfill)[0];
+ copyMostAttrib(this, tmp);
+ if (isFactor(this))
+ setAttrib(tmp, R_LevelsSymbol, getAttrib(this, R_LevelsSymbol));
+ }
+ break;
+
+ case REALSXP :
+ class = getAttrib(this, R_ClassSymbol);
+ if (isString(class) && STRING_ELT(class, 0) == char_integer64) {
+ thisfill = PROTECT(allocVector(REALSXP, 1));
+ dthisfill = (unsigned long long *)REAL(thisfill);
+ if (INTEGER(fill)[0] == NA_INTEGER)
+ dthisfill[0] = NAINT64;
+ else dthisfill[0] = (unsigned long long)INTEGER(fill)[0];
+ } else {
+ thisfill = PROTECT(coerceVector(fill, REALSXP));
+ }
+ for (j=0; j<nk; j++) {
+ thisk = (xrows >= INTEGER(k)[j]) ? INTEGER(k)[j] : xrows;
+ tmp = allocVector(REALSXP, xrows);
+ SET_VECTOR_ELT(ans, i*nk+j, tmp);
+ if (xrows - INTEGER(k)[j] > 0) {
+ memcpy((char *)DATAPTR(tmp)+(INTEGER(k)[j]*size),
+ (char *)DATAPTR(this),
+ (xrows-INTEGER(k)[j])*size);
+ }
+ for (m=0; m<thisk; m++) {
+ REAL(tmp)[m] = REAL(thisfill)[0];
+ }
+ copyMostAttrib(this, tmp);
+ }
+ break;
+
+ case LGLSXP :
+ thisfill = PROTECT(coerceVector(fill, LGLSXP));
+ for (j=0; j<nk; j++) {
+ thisk = (xrows >= INTEGER(k)[j]) ? INTEGER(k)[j] : xrows;
+ tmp = allocVector(LGLSXP, xrows);
+ SET_VECTOR_ELT(ans, i*nk+j, tmp);
+ if (xrows - INTEGER(k)[j] > 0)
+ memcpy((char *)DATAPTR(tmp)+(INTEGER(k)[j]*size),
+ (char *)DATAPTR(this),
+ (xrows-INTEGER(k)[j])*size);
+ for (m=0; m<thisk; m++)
+ LOGICAL(tmp)[m] = LOGICAL(thisfill)[0];
+ copyMostAttrib(this, tmp);
+ }
+ break;
+
+ case STRSXP :
+ thisfill = PROTECT(coerceVector(fill, STRSXP));
+ for (j=0; j<nk; j++) {
+ tmp = allocVector(STRSXP, xrows);
+ SET_VECTOR_ELT(ans, i*nk+j, tmp);
+ for (m=0; m<xrows; m++)
+ SET_STRING_ELT(tmp, m, (m < INTEGER(k)[j]) ? STRING_ELT(thisfill, 0) : STRING_ELT(this, m - INTEGER(k)[j]));
+ copyMostAttrib(this, tmp);
+ }
+ break;
+ default :
+ error("Unsupported type '%s'", type2char(TYPEOF(this)));
+ }
+ copyMostAttrib(this, tmp);
+ if (isFactor(this))
+ setAttrib(tmp, R_LevelsSymbol, getAttrib(this, R_LevelsSymbol));
+ UNPROTECT(1);
+ }
+ } else if (stype == LEAD) {
+ for (i=0; i<nx; i++) {
+ this = VECTOR_ELT(x, i);
+ size = SIZEOF(this);
+ xrows = length(this);
+ switch (TYPEOF(this)) {
+ case INTSXP :
+ thisfill = PROTECT(coerceVector(fill, INTSXP));
+ for (j=0; j<nk; j++) {
+ thisk = (xrows >= INTEGER(k)[j]) ? INTEGER(k)[j] : xrows;
+ tmp = allocVector(INTSXP, xrows);
+ SET_VECTOR_ELT(ans, i*nk+j, tmp);
+ if (xrows - INTEGER(k)[j] > 0)
+ memcpy((char *)DATAPTR(tmp),
+ (char *)DATAPTR(this)+(INTEGER(k)[j]*size),
+ (xrows-INTEGER(k)[j])*size);
+ for (m=xrows-thisk; m<xrows; m++)
+ INTEGER(tmp)[m] = INTEGER(thisfill)[0];
+ copyMostAttrib(this, tmp);
+ if (isFactor(this))
+ setAttrib(tmp, R_LevelsSymbol, getAttrib(this, R_LevelsSymbol));
+ }
+ break;
+
+ case REALSXP :
+ class = getAttrib(this, R_ClassSymbol);
+ if (isString(class) && STRING_ELT(class, 0) == char_integer64) {
+ thisfill = PROTECT(allocVector(REALSXP, 1));
+ dthisfill = (unsigned long long *)REAL(thisfill);
+ if (INTEGER(fill)[0] == NA_INTEGER)
+ dthisfill[0] = NAINT64;
+ else dthisfill[0] = (unsigned long long)INTEGER(fill)[0];
+ } else {
+ thisfill = PROTECT(coerceVector(fill, REALSXP));
+ }
+ for (j=0; j<nk; j++) {
+ thisk = (xrows >= INTEGER(k)[j]) ? INTEGER(k)[j] : xrows;
+ tmp = allocVector(REALSXP, xrows);
+ SET_VECTOR_ELT(ans, i*nk+j, tmp);
+ if (xrows - INTEGER(k)[j] > 0)
+ memcpy((char *)DATAPTR(tmp),
+ (char *)DATAPTR(this)+(INTEGER(k)[j]*size),
+ (xrows-INTEGER(k)[j])*size);
+ for (m=xrows-thisk; m<xrows; m++)
+ REAL(tmp)[m] = REAL(thisfill)[0];
+ copyMostAttrib(this, tmp);
+ }
+ break;
+
+ case LGLSXP :
+ thisfill = PROTECT(coerceVector(fill, LGLSXP));
+ for (j=0; j<nk; j++) {
+ thisk = (xrows >= INTEGER(k)[j]) ? INTEGER(k)[j] : xrows;
+ tmp = allocVector(LGLSXP, xrows);
+ SET_VECTOR_ELT(ans, i*nk+j, tmp);
+ if (xrows - INTEGER(k)[j] > 0)
+ memcpy((char *)DATAPTR(tmp),
+ (char *)DATAPTR(this)+(INTEGER(k)[j]*size),
+ (xrows-INTEGER(k)[j])*size);
+ for (m=xrows-thisk; m<xrows; m++)
+ LOGICAL(tmp)[m] = LOGICAL(thisfill)[0];
+ copyMostAttrib(this, tmp);
+ }
+ break;
+
+ case STRSXP :
+ thisfill = PROTECT(coerceVector(fill, STRSXP));
+ for (j=0; j<nk; j++) {
+ tmp = allocVector(STRSXP, xrows);
+ SET_VECTOR_ELT(ans, i*nk+j, tmp);
+ for (m=0; m<xrows; m++)
+ SET_STRING_ELT(tmp, m, (xrows-m <= INTEGER(k)[j]) ? STRING_ELT(thisfill, 0) : STRING_ELT(this, m + INTEGER(k)[j]));
+ copyMostAttrib(this, tmp);
+ }
+ break;
+ default :
+ error("Unsupported type '%s'", type2char(TYPEOF(this)));
+ }
+ UNPROTECT(1);
+ }
+ }
+ UNPROTECT(1);
+ if (isVectorAtomic(obj) && length(ans) == 1)
+ return (VECTOR_ELT(ans, 0));
+ return(ans);
+}
diff --git a/src/transpose.c b/src/transpose.c
new file mode 100644
index 0000000..3bfded7
--- /dev/null
+++ b/src/transpose.c
@@ -0,0 +1,98 @@
+#include "data.table.h"
+#include <Rdefines.h>
+#include <time.h>
+
+SEXP transpose(SEXP l, SEXP fill, SEXP ignoreArg) {
+
+ R_len_t i, j, k=0, ln, *len, maxlen=0, zerolen=0, anslen;
+ SEXP li, thisi, ans;
+ SEXPTYPE type, maxtype=0;
+ Rboolean ignore, coerce = FALSE;
+
+ if (!isNewList(l))
+ error("l must be a list.");
+ if (!length(l))
+ return(duplicate(l));
+ if (!isLogical(ignoreArg) || LOGICAL(ignoreArg)[0] == NA_LOGICAL)
+ error("ignore.empty should be logical TRUE/FALSE.");
+ if (length(fill) != 1)
+ error("fill must be NULL or length=1 vector.");
+ ln = LENGTH(l);
+ ignore = LOGICAL(ignoreArg)[0];
+
+ // preprocessing
+ len = Calloc(ln, R_len_t);
+ for (i=0; i<ln; i++) {
+ li = VECTOR_ELT(l, i);
+ if (!isVectorAtomic(li) && !isNull(li))
+ error("Item %d of list input is not an atomic vector", i+1);
+ len[i] = length(li);
+ if (len[i] > maxlen)
+ maxlen = len[i];
+ zerolen += (len[i] == 0);
+ if (isFactor(li)) {
+ maxtype = STRSXP;
+ } else {
+ type = TYPEOF(li);
+ if (type > maxtype)
+ maxtype = type;
+ }
+ }
+ // coerce fill to maxtype
+ fill = PROTECT(coerceVector(fill, maxtype));
+
+ // allocate 'ans'
+ ans = PROTECT(allocVector(VECSXP, maxlen));
+ anslen = (!ignore) ? ln : (ln - zerolen);
+ for (i=0; i<maxlen; i++) {
+ thisi = allocVector(maxtype, anslen);
+ SET_VECTOR_ELT(ans, i, thisi);
+ }
+
+ // transpose
+ for (i=0; i<ln; i++) {
+ if (ignore && !len[i]) continue;
+ li = VECTOR_ELT(l, i);
+ if (TYPEOF(li) != maxtype) {
+ coerce = TRUE;
+ if (!isFactor(li)) li = PROTECT(coerceVector(li, maxtype));
+ else li = PROTECT(asCharacterFactor(li));
+ }
+ switch (maxtype) {
+ case INTSXP :
+ for (j=0; j<maxlen; j++) {
+ thisi = VECTOR_ELT(ans, j);
+ INTEGER(thisi)[k] = (j < len[i]) ? INTEGER(li)[j] : INTEGER(fill)[0];
+ }
+ break;
+ case LGLSXP :
+ for (j=0; j<maxlen; j++) {
+ thisi = VECTOR_ELT(ans, j);
+ LOGICAL(thisi)[k] = (j < len[i]) ? LOGICAL(li)[j] : LOGICAL(fill)[0];
+ }
+ break;
+ case REALSXP :
+ for (j=0; j<maxlen; j++) {
+ thisi = VECTOR_ELT(ans, j);
+ REAL(thisi)[k] = (j < len[i]) ? REAL(li)[j] : REAL(fill)[0];
+ }
+ break;
+ case STRSXP :
+ for (j=0; j<maxlen; j++) {
+ thisi = VECTOR_ELT(ans, j);
+ SET_STRING_ELT(thisi, k, (j < len[i]) ? STRING_ELT(li, j) : STRING_ELT(fill, 0));
+ }
+ break;
+ default :
+ error("Unsupported column type '%s'", type2char(maxtype));
+ }
+ if (coerce) {
+ coerce = FALSE;
+ UNPROTECT(1);
+ }
+ k++;
+ }
+ Free(len);
+ UNPROTECT(2);
+ return(ans);
+}
diff --git a/src/uniqlist.c b/src/uniqlist.c
new file mode 100644
index 0000000..bca42e3
--- /dev/null
+++ b/src/uniqlist.c
@@ -0,0 +1,135 @@
+#include "data.table.h"
+
+// DONE: return 'uniqlist' as a vector (same as duplist) and write a separate function to get group sizes
+// Also improvements for numeric type with a hack of checking unsigned int (to overcome NA/NaN/Inf/-Inf comparisons) (> 2x speed-up)
+SEXP uniqlist(SEXP l, SEXP order)
+{
+ // This works like UNIX uniq as referred to by ?base::unique; i.e., it
+ // drops immediately repeated rows but doesn't drop duplicates of any
+ // previous row. Unless, order is provided, then it also drops any previous
+ // row. l must be a list of same length vectors ans is allocated first
+ // (maximum length the number of rows) and the length returned in anslen.
+ // DONE: ans is now grown
+ Rboolean b, byorder;
+ unsigned long long *ulv; // for numeric check speed-up
+ SEXP v, ans, class;
+ R_len_t i, j, nrow, ncol, len, thisi, previ, isize=1000;
+
+ int *iidx = Calloc(isize, int); // for 'idx'
+ int *n_iidx; // to catch allocation errors using Realloc!
+ if (NA_INTEGER != NA_LOGICAL || sizeof(NA_INTEGER)!=sizeof(NA_LOGICAL))
+ error("Have assumed NA_INTEGER == NA_LOGICAL (currently R_NaInt). If R changes this in future (seems unlikely), an extra case is required; a simple change.");
+ ncol = length(l);
+ nrow = length(VECTOR_ELT(l,0));
+ len = 1;
+ iidx[0] = 1; // first row is always the first of the first group
+ byorder = INTEGER(order)[0] != -1;
+ // Using MISSING() does not seem stable under windows. Always having arguments passed in seems a good idea anyway.
+ thisi = byorder ? INTEGER(order)[0]-1 : 0;
+ for (i=1; i<nrow; i++) {
+ previ = thisi;
+ thisi = byorder ? INTEGER(order)[i]-1 : i;
+ j = ncol; // the last column varies the most frequently so check that first and work backwards
+ b = TRUE;
+ while (--j>=0 && b) {
+ v=VECTOR_ELT(l,j);
+ switch (TYPEOF(v)) {
+ case INTSXP : case LGLSXP :
+ b=INTEGER(v)[thisi]==INTEGER(v)[previ]; break;
+ case STRSXP :
+ b=STRING_ELT(v,thisi)==STRING_ELT(v,previ); break; // forder checks no non-ascii unknown, and either UTF-8 or Latin1 but not both. So == pointers is ok given that check.
+ case REALSXP :
+ ulv = (unsigned long long *)REAL(v);
+ b = ulv[thisi] == ulv[previ]; // (gives >=2x speedup)
+ if (!b) {
+ class = getAttrib(v, R_ClassSymbol);
+ twiddle = (isString(class) && STRING_ELT(class, 0)==char_integer64) ? &i64twiddle : &dtwiddle;
+ b = twiddle(ulv, thisi, 1) == twiddle(ulv, previ, 1);
+ }
+ break;
+ // TO DO: store previ twiddle call, but it'll need to be vector since this is in a loop through columns. Hopefully the first == will short circuit most often
+ default :
+ error("Type '%s' not supported", type2char(TYPEOF(v)));
+ }
+ }
+ if (!b) iidx[len++] = i+1;
+ if (len >= isize) {
+ isize = 1.1*isize*nrow/i;
+ n_iidx = Realloc(iidx, isize, int);
+ if (n_iidx != NULL) iidx = n_iidx; else error("Error in reallocating memory in 'uniqlist'\n");
+ }
+ }
+ PROTECT(ans = allocVector(INTSXP, len));
+ memcpy(INTEGER(ans), iidx, sizeof(int)*len); // sizeof is of type size_t - no integer overflow issues
+ Free(iidx);
+ UNPROTECT(1);
+ return(ans);
+}
+
+SEXP uniqlengths(SEXP x, SEXP n) {
+ SEXP ans;
+ R_len_t i, len;
+ if (TYPEOF(x) != INTSXP || length(x) < 0) error("Input argument 'x' to 'uniqlengths' must be an integer vector of length >= 0");
+ if (TYPEOF(n) != INTSXP || length(n) != 1) error("Input argument 'n' to 'uniqlengths' must be an integer vector of length 1");
+ PROTECT(ans = allocVector(INTSXP, length(x)));
+ len = length(x);
+ for (i=1; i<len; i++) {
+ INTEGER(ans)[i-1] = INTEGER(x)[i] - INTEGER(x)[i-1];
+ }
+ INTEGER(ans)[len-1] = INTEGER(n)[0] - INTEGER(x)[len-1] + 1;
+ UNPROTECT(1);
+ return(ans);
+}
+
+// we could compute `uniqlist` and `uniqlengths` and then construct the result
+// but that seems unnecessary waste of memory and roundabout..
+// so, we'll do it directly here.. 'order()' is implemented in C-api, but not used
+// in R side yet, but if need be, it can be added easily.
+SEXP rleid(SEXP l, SEXP order)
+{
+ Rboolean b, byorder;
+ unsigned long long *ulv; // for numeric check speed-up
+ SEXP v, ans, class;
+ R_len_t nrow = length(VECTOR_ELT(l,0)), ncol = length(l);
+ R_len_t i, j, len = 1, thisi, previ;
+
+ if (!nrow || !ncol) return (allocVector(INTSXP, 0));
+ ans = PROTECT(allocVector(INTSXP, nrow));
+ if (NA_INTEGER != NA_LOGICAL || sizeof(NA_INTEGER)!=sizeof(NA_LOGICAL))
+ error("Have assumed NA_INTEGER == NA_LOGICAL (currently R_NaInt). If R changes this in future (seems unlikely), an extra case is required; a simple change.");
+
+ INTEGER(ans)[0] = 1; // first row is always the first of first group
+ byorder = INTEGER(order)[0] != -1;
+ thisi = byorder ? INTEGER(order)[0]-1 : 0;
+ for (i=1; i<nrow; i++) {
+ previ = thisi;
+ thisi = byorder ? INTEGER(order)[i]-1 : i;
+ j = ncol; // the last column varies the most frequently so check that first and work backwards
+ b = TRUE;
+ while (--j>=0 && b) {
+ v=VECTOR_ELT(l,j);
+ switch (TYPEOF(v)) {
+ case INTSXP : case LGLSXP :
+ b=INTEGER(v)[thisi]==INTEGER(v)[previ]; break;
+ case STRSXP :
+ b=STRING_ELT(v,thisi)==STRING_ELT(v,previ); break; // forder checks no non-ascii unknown, and either UTF-8 or Latin1 but not both. So == pointers is ok given that check.
+ case REALSXP :
+ ulv = (unsigned long long *)REAL(v);
+ b = ulv[thisi] == ulv[previ]; // (gives >=2x speedup)
+ if (!b) {
+ class = getAttrib(v, R_ClassSymbol);
+ twiddle = (isString(class) && STRING_ELT(class, 0)==char_integer64) ? &i64twiddle : &dtwiddle;
+ b = twiddle(ulv, thisi, 1) == twiddle(ulv, previ, 1);
+ }
+ break;
+ // TO DO: store previ twiddle call, but it'll need to be vector since this is in a loop through columns. Hopefully the first == will short circuit most often
+ default :
+ error("Type '%s' not supported", type2char(TYPEOF(v)));
+ }
+ }
+ INTEGER(ans)[i] = b ? len : ++len;
+ }
+ UNPROTECT(1);
+ return(ans);
+}
+
diff --git a/src/vecseq.c b/src/vecseq.c
new file mode 100644
index 0000000..2aa37a0
--- /dev/null
+++ b/src/vecseq.c
@@ -0,0 +1,42 @@
+#include "data.table.h"
+
+SEXP vecseq(SEXP x, SEXP len, SEXP clamp)
+{
+ // Name taken from bit::vecseq, but,
+ // * implemented in C rather than R
+ // * takes len rather than y endpoints
+ // * no concat=FALSE or eval arguments as not needed.
+ // * Adds clamp to prevent very large vectors being created (unless allow.cartesian=TRUE)
+ // Specially for use by [.data.table after binary search. Now so specialized that for general use
+ // bit::vecseq is recommended (Jens has coded it in C now).
+ R_len_t reslen,i,j,k, thisx;
+ double limit;
+ SEXP ans;
+
+ if (!isInteger(x)) error("x must be an integer vector");
+ if (!isInteger(len)) error("len must be an integer vector");
+ if (LENGTH(x) != LENGTH(len)) error("x and len must be the same length");
+ reslen = 0;
+ for (i=0; i<LENGTH(len); i++) {
+ if (INT_MAX-reslen < INTEGER(len)[i])
+ error("Join results in more than 2^31 rows (internal vecseq reached physical limit). Very likely misspecified join. Check for duplicate key values in i each of which join to the same group in x over and over again. If that's ok, try by=.EACHI to run j for each group to avoid the large allocation. Otherwise, please search for this error message in the FAQ, Wiki, Stack Overflow and datatable-help for advice.");
+ reslen += INTEGER(len)[i];
+ }
+ if (!isNull(clamp)) {
+ if (!isNumeric(clamp) || LENGTH(clamp)!=1) error("clamp must be a double vector length 1");
+ limit = REAL(clamp)[0];
+ if (limit<0) error("clamp must be positive");
+ if (reslen>limit) error("Join results in %d rows; more than %d = nrow(x)+nrow(i). Check for duplicate key values in i each of which join to the same group in x over and over again. If that's ok, try by=.EACHI to run j for each group to avoid the large allocation. If you are sure you wish to proceed, rerun with allow.cartesian=TRUE. Otherwise, please search for this error message in the FAQ, Wiki, Stack Overflow and datatable-help for advice.", reslen, (int)limit);
+ }
+ ans = PROTECT(allocVector(INTSXP, reslen));
+ k = 0;
+ for (i=0; i<LENGTH(len); i++) {
+ thisx = INTEGER(x)[i];
+ for (j=0; j<INTEGER(len)[i]; j++) {
+ INTEGER(ans)[k++] = thisx++;
+ }
+ }
+ UNPROTECT(1);
+ return(ans);
+}
+
diff --git a/src/wrappers.c b/src/wrappers.c
new file mode 100644
index 0000000..52b58b7
--- /dev/null
+++ b/src/wrappers.c
@@ -0,0 +1,98 @@
+#include "data.table.h"
+#include <Rdefines.h>
+
+// Wrappers for R internal functions. We can't rely on calling
+// Rf_setAttrib and Rf_duplicate directly from .Call in R on
+// all platforms, as we found out when v1.6.5 went to CRAN on
+// 25 Aug 2011, see Professor Ripley's response that day.
+
+SEXP setattrib(SEXP x, SEXP name, SEXP value)
+{
+ if (TYPEOF(name) != STRSXP) error("Attribute name must be of type character");
+ if ( !isNewList(x) &&
+ strcmp(CHAR(STRING_ELT(name, 0)), "class") == 0 &&
+ isString(value) && (strcmp(CHAR(STRING_ELT(value, 0)), "data.table") == 0 ||
+ strcmp(CHAR(STRING_ELT(value, 0)), "data.frame") == 0) )
+ error("Internal structure doesn't seem to be a list. Can't set class to be 'data.table' or 'data.frame'. Use 'as.data.table()' or 'as.data.frame()' methods instead.");
+ setAttrib(x, name,
+ NAMED(value) ? duplicate(value) : value);
+ // duplicate is temp fix to restore R behaviour prior to R-devel change on 10 Jan 2014 (r64724).
+ // TO DO: revisit. Enough to reproduce is: DT=data.table(a=1:3); DT[2]; DT[,b:=2]
+ // ... Error: selfrefnames is ok but tl names [1] != tl [100]
+ return(R_NilValue);
+}
+
+// fix for #1142 - duplicated levels for factors
+SEXP setlevels(SEXP x, SEXP levels, SEXP ulevels) {
+
+ R_len_t nx = length(x), i;
+ SEXP xchar, newx;
+ xchar = PROTECT(allocVector(STRSXP, nx));
+ for (i=0; i<nx; i++)
+ SET_STRING_ELT(xchar, i, STRING_ELT(levels, INTEGER(x)[i]-1));
+ newx = PROTECT(chmatch(xchar, ulevels, NA_INTEGER, FALSE));
+ for (i=0; i<nx; i++) INTEGER(x)[i] = INTEGER(newx)[i];
+ setAttrib(x, R_LevelsSymbol, ulevels);
+ UNPROTECT(2);
+ return(x);
+}
+
+SEXP copy(SEXP x)
+{
+ return(duplicate(x));
+}
+
+SEXP copyattr(SEXP from, SEXP to)
+{
+ // for use by [.data.table to retain attribs such as "comments" when subsetting and j is missing
+ copyMostAttrib(from, to);
+ return(R_NilValue);
+}
+
+SEXP setlistelt(SEXP l, SEXP i, SEXP value)
+{
+ R_len_t i2;
+ // Internal use only. So that := can update elements of a list of data.table, #2204. Just needed to overallocate/grow the VECSXP.
+ if (!isNewList(l)) error("First argument to setlistelt must be a list()");
+ if (!isInteger(i) || LENGTH(i)!=1) error("Second argument to setlistelt must a length 1 integer vector");
+ i2 = INTEGER(i)[0];
+ if (LENGTH(l) < i2 || i2<1) error("i (%d) is outside the range of items [1,%d]",i2,LENGTH(l));
+ SET_VECTOR_ELT(l, i2-1, value);
+ return(R_NilValue);
+}
+
+SEXP setnamed(SEXP x, SEXP value)
+{
+ if (!isInteger(value) || LENGTH(value)!=1) error("Second argument to setnamed must a length 1 integer vector");
+ SET_NAMED(x,INTEGER(value)[0]);
+ return(x);
+}
+
+SEXP address(SEXP x)
+{
+ // A better way than : http://stackoverflow.com/a/10913296/403310
+ char buffer[32];
+ snprintf(buffer, 32, "%p", (void *)x);
+ return(ScalarString(mkChar(buffer)));
+}
+
+SEXP copyNamedInList(SEXP x)
+{
+ // As from R 3.1.0 list() no longer copies NAMED inputs
+ // Since data.table allows subassignment by reference, we need a way to copy NAMED inputs, still.
+ // But for many other applications (such as in j and elsewhere internally) the new non-copying list() in R 3.1.0 is very welcome.
+
+ // This is intended to be called just after list(...) in data.table(). It isn't for use on a single data.table, as
+ // member columns of a list aren't marked as NAMED when the VECSXP is.
+
+ // For now, this makes the old behaviour of list() in R<3.1.0 available for use, where we need it.
+
+ if (TYPEOF(x) != VECSXP) error("x isn't a VECSXP");
+ for (int i=0; i<LENGTH(x); i++) {
+ if (NAMED(VECTOR_ELT(x, i))) {
+ SET_VECTOR_ELT(x, i, duplicate(VECTOR_ELT(x,i)));
+ }
+ }
+ return R_NilValue;
+}
+
diff --git a/tests/autoprint.R b/tests/autoprint.R
new file mode 100644
index 0000000..c98eba0
--- /dev/null
+++ b/tests/autoprint.R
@@ -0,0 +1,46 @@
+require(data.table)
+# Tests the suppression of := output
+# Since this tests autoprinting at the console, it needs to use the .Rout.save mechanism in R CMD check
+DT = data.table(a=1:2) # Should print at console?
+DT # yes
+DT[1] # yes
+DT[2,a:=3L] # no
+DT # yes
+DT[FALSE,a:=3L] # no
+DT[a==4L,a:=5L] # no
+DT[a %in% 4:8, a:=5L] # no
+DT # yes
+print(DT[2,a:=4L]) # no
+print(DT) # yes
+if (TRUE) DT[2,a:=5L] # no. used to print before v1.9.5
+if (TRUE) if (TRUE) DT[2,a:=6L] # no. used to print before v1.9.5
+(function(){DT[2,a:=5L];NULL})() # print NULL
+DT # no (from v1.9.5+). := suppresses next auto print (can't distinguish just "DT" symbol alone at the prompt)
+DT # yes. 2nd time needed, or solutions below
+(function(){DT[2,a:=5L];NULL})() # print NULL
+DT[] # yes. guaranteed print
+(function(){DT[2,a:=5L];NULL})() # print NULL
+print(DT) # no. only DT[] is guaranteed print from v1.9.6 and R 3.2.0
+(function(){DT[2,a:=5L][];NULL})() # print NULL
+DT # yes. i) function needs to add [] after last one, so that "DT" alone is guaranteed anyway
+(function(){DT[2,a:=5L];DT[];NULL})() # print NULL
+DT # yes. ii) or as a separate DT[] after the last := inside the function
+DT2 = data.table(b=3:4) # no
+(function(){DT[2,a:=6L];DT2[1,b:=7L];NULL})()
+DT # yes. last := was on DT2 not DT
+{DT[2,a:=6L];invisible()} # no
+print(DT) # no
+(function(){print(DT[2,a:=7L]);print(DT);invisible()})() # yes*2
+{print(DT[2,a:=8L]);print(DT);invisible()} # yes*1 Not within function so as at prompt
+DT[1][,a:=9L] # no (was too tricky to detect that DT[1] is a new object). Simple rule is that := always doesn't print
+DT[2,a:=10L][1] # yes
+DT[1,a:=10L][1,a:=10L] # no
+DT[,a:=as.integer(a)] # no
+DT[1,a:=as.integer(a)] # no
+DT[1,a:=10L][] # yes. ...[] == oops, forgot print(...)
+
+# Test that error in := doesn't suppress next valid print, bug #2376
+try(DT[,foo:=ColumnNameTypo]) # error: not found.
+DT # yes
+DT # yes
+
diff --git a/tests/autoprint.Rout.save b/tests/autoprint.Rout.save
new file mode 100644
index 0000000..13e2f6d
--- /dev/null
+++ b/tests/autoprint.Rout.save
@@ -0,0 +1,124 @@
+
+R version 3.1.1 (2014-07-10) -- "Sock it to Me"
+Copyright (C) 2014 The R Foundation for Statistical Computing
+Platform: x86_64-pc-linux-gnu (64-bit)
+
+R is free software and comes with ABSOLUTELY NO WARRANTY.
+You are welcome to redistribute it under certain conditions.
+Type 'license()' or 'licence()' for distribution details.
+
+R is a collaborative project with many contributors.
+Type 'contributors()' for more information and
+'citation()' on how to cite R or R packages in publications.
+
+Type 'demo()' for some demos, 'help()' for on-line help, or
+'help.start()' for an HTML browser interface to help.
+Type 'q()' to quit R.
+
+> require(data.table)
+Loading required package: data.table
+> # Tests the suppression of := output
+> # Since this tests autoprinting at the console, it needs to use the .Rout.save mechanism in R CMD check
+> DT = data.table(a=1:2) # Should print at console?
+> DT # yes
+ a
+1: 1
+2: 2
+> DT[1] # yes
+ a
+1: 1
+> DT[2,a:=3L] # no
+> DT # yes
+ a
+1: 1
+2: 3
+> DT[FALSE,a:=3L] # no
+> DT[a==4L,a:=5L] # no
+> DT[a %in% 4:8, a:=5L] # no
+> DT # yes
+ a
+1: 1
+2: 3
+> print(DT[2,a:=4L]) # no
+> print(DT) # yes
+ a
+1: 1
+2: 4
+> if (TRUE) DT[2,a:=5L] # no. used to print before v1.9.5
+> if (TRUE) if (TRUE) DT[2,a:=6L] # no. used to print before v1.9.5
+> (function(){DT[2,a:=5L];NULL})() # print NULL
+NULL
+> DT # no (from v1.9.5+). := suppresses next auto print (can't distinguish just "DT" symbol alone at the prompt)
+> DT # yes. 2nd time needed, or solutions below
+ a
+1: 1
+2: 5
+> (function(){DT[2,a:=5L];NULL})() # print NULL
+NULL
+> DT[] # yes. guaranteed print
+ a
+1: 1
+2: 5
+> (function(){DT[2,a:=5L];NULL})() # print NULL
+NULL
+> print(DT) # no. only DT[] is guaranteed print from v1.9.6 and R 3.2.0
+> (function(){DT[2,a:=5L][];NULL})() # print NULL
+NULL
+> DT # yes. i) function needs to add [] after last one, so that "DT" alone is guaranteed anyway
+ a
+1: 1
+2: 5
+> (function(){DT[2,a:=5L];DT[];NULL})() # print NULL
+NULL
+> DT # yes. ii) or as a separate DT[] after the last := inside the function
+ a
+1: 1
+2: 5
+> DT2 = data.table(b=3:4) # no
+> (function(){DT[2,a:=6L];DT2[1,b:=7L];NULL})()
+NULL
+> DT # yes. last := was on DT2 not DT
+ a
+1: 1
+2: 6
+> {DT[2,a:=6L];invisible()} # no
+> print(DT) # no
+> (function(){print(DT[2,a:=7L]);print(DT);invisible()})() # yes*2
+ a
+1: 1
+2: 7
+ a
+1: 1
+2: 7
+> {print(DT[2,a:=8L]);print(DT);invisible()} # yes*1 Not within function so as at prompt
+ a
+1: 1
+2: 8
+> DT[1][,a:=9L] # no (was too tricky to detect that DT[1] is a new object). Simple rule is that := always doesn't print
+> DT[2,a:=10L][1] # yes
+ a
+1: 1
+> DT[1,a:=10L][1,a:=10L] # no
+> DT[,a:=as.integer(a)] # no
+> DT[1,a:=as.integer(a)] # no
+> DT[1,a:=10L][] # yes. ...[] == oops, forgot print(...)
+ a
+1: 10
+2: 10
+>
+> # Test that error in := doesn't suppress next valid print, bug #2376
+> try(DT[,foo:=ColumnNameTypo]) # error: not found.
+Error in eval(expr, envir, enclos) : object 'ColumnNameTypo' not found
+> DT # yes
+ a
+1: 10
+2: 10
+> DT # yes
+ a
+1: 10
+2: 10
+>
+>
+> proc.time()
+ user system elapsed
+ 3.14 0.10 3.22
diff --git a/tests/knitr.R b/tests/knitr.R
new file mode 100644
index 0000000..e70a79a
--- /dev/null
+++ b/tests/knitr.R
@@ -0,0 +1,4 @@
+require(knitr)
+knit("knitr.Rmd", quiet=TRUE)
+cat(readLines("knitr.md"),sep="\n")
+
diff --git a/tests/knitr.Rmd b/tests/knitr.Rmd
new file mode 100644
index 0000000..fbaada2
--- /dev/null
+++ b/tests/knitr.Rmd
@@ -0,0 +1,11 @@
+```{r test_id, message=FALSE, results="show", echo=TRUE, warning=FALSE}
+require(data.table) # print?
+DT = data.table(x=1:3, y=4:6) # no
+DT # yes
+DT[, z := 7:9] # no
+print(DT[, z := 10:12]) # yes
+if (1 < 2) DT[, a := 1L] # no
+DT # yes
+```
+Some text.
+
diff --git a/tests/knitr.Rout.save b/tests/knitr.Rout.save
new file mode 100644
index 0000000..8aa8004
--- /dev/null
+++ b/tests/knitr.Rout.save
@@ -0,0 +1,67 @@
+
+R version 3.1.1 (2014-07-10) -- "Sock it to Me"
+Copyright (C) 2014 The R Foundation for Statistical Computing
+Platform: x86_64-pc-linux-gnu (64-bit)
+
+R is free software and comes with ABSOLUTELY NO WARRANTY.
+You are welcome to redistribute it under certain conditions.
+Type 'license()' or 'licence()' for distribution details.
+
+R is a collaborative project with many contributors.
+Type 'contributors()' for more information and
+'citation()' on how to cite R or R packages in publications.
+
+Type 'demo()' for some demos, 'help()' for on-line help, or
+'help.start()' for an HTML browser interface to help.
+Type 'q()' to quit R.
+
+> require(knitr)
+Loading required package: knitr
+> knit("knitr.Rmd", quiet=TRUE)
+Loading required package: data.table
+[1] "knitr.md"
+> cat(readLines("knitr.md"),sep="\n")
+
+```r
+require(data.table) # print?
+DT = data.table(x=1:3, y=4:6) # no
+DT # yes
+```
+
+```
+## x y
+## 1: 1 4
+## 2: 2 5
+## 3: 3 6
+```
+
+```r
+DT[, z := 7:9] # no
+print(DT[, z := 10:12]) # yes
+```
+
+```
+## x y z
+## 1: 1 4 10
+## 2: 2 5 11
+## 3: 3 6 12
+```
+
+```r
+if (1 < 2) DT[, a := 1L] # no
+DT # yes
+```
+
+```
+## x y z a
+## 1: 1 4 10 1
+## 2: 2 5 11 1
+## 3: 3 6 12 1
+```
+Some text.
+
+>
+>
+> proc.time()
+ user system elapsed
+ 3.116 0.128 3.257
diff --git a/tests/test-all.R b/tests/test-all.R
new file mode 100644
index 0000000..8772cc0
--- /dev/null
+++ b/tests/test-all.R
@@ -0,0 +1,4 @@
+library(testthat)
+library(data.table)
+
+test_package("data.table")
diff --git a/tests/tests.R b/tests/tests.R
new file mode 100644
index 0000000..0aac7d2
--- /dev/null
+++ b/tests/tests.R
@@ -0,0 +1,10 @@
+require(data.table)
+test.data.table()
+
+# Turn off verbose repeat to save time (particularly Travis, but also CRAN)
+# test.data.table(verbose=TRUE)
+# Calling it again in the past revealed some memory bugs but also verbose mode checks the verbose messages run ok
+# TO DO: check we test each verbose message at least once, instead of a full repeat of all tests
+
+
+
diff --git a/vignettes/Makefile b/vignettes/Makefile
new file mode 100644
index 0000000..333ec94
--- /dev/null
+++ b/vignettes/Makefile
@@ -0,0 +1,15 @@
+# Makefile to use knitr for package vignettes
+
+# put all PDF targets here, separated by spaces
+PDFS= datatable-faq.pdf datatable-intro.pdf datatable-intro-vignette.html
+
+all: $(PDFS)
+
+clean:
+ rm -rf *.tex *.bbl *.blg *.aux *.out *.toc *.log *.spl *tikzDictionary *.md figure/
+
+%.pdf: %.Rnw
+ $(R_HOME)/bin/Rscript -e "tools::texi2pdf('$*.tex')"
+
+%.html: %.Rmd
+ $(R_HOME)/bin/Rscript -e "if (getRversion() < '3.0.0') knitr::knit2html('$*.Rmd')"
diff --git a/vignettes/css/bootstrap.css b/vignettes/css/bootstrap.css
new file mode 100644
index 0000000..1453f27
--- /dev/null
+++ b/vignettes/css/bootstrap.css
@@ -0,0 +1,118 @@
+code,
+kbd,
+pre,
+samp {
+ font-family: Source Code Pro, Inconsolata, Monaco, Consolas, Menlo, Courier New, monospace;
+}
+
+code {
+ padding: 0px 2px;
+ font-size: 90%;
+ color: #c7254e;
+ white-space: nowrap;
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diff --git a/vignettes/datatable-faq.Rnw b/vignettes/datatable-faq.Rnw
new file mode 100644
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--- /dev/null
+++ b/vignettes/datatable-faq.Rnw
@@ -0,0 +1,585 @@
+\documentclass[a4paper]{article}
+
+\usepackage[margin=3cm]{geometry}
+%%\usepackage[round]{natbib}
+\usepackage[colorlinks=true,urlcolor=blue]{hyperref}
+
+%%\newcommand{\acronym}[1]{\textsc{#1}}
+%%\newcommand{\class}[1]{\mbox{\textsf{#1}}}
+\newcommand{\code}[1]{\mbox{\texttt{#1}}}
+\newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}}
+\newcommand{\proglang}[1]{\textsf{#1}}
+\SweaveOpts{keep.source=TRUE}
+%% \VignetteIndexEntry{Frequently asked questions}
+
+<<echo=FALSE,results=hide>>=
+if (!exists("data.table",.GlobalEnv)) library(data.table) # see Intro.Rnw for comments on these two lines
+rm(list=as.character(tables()$NAME),envir=.GlobalEnv)
+options(width=70) # so lines wrap round
+@
+
+\begin{document}
+\title{FAQs about the \pkg{data.table} package in \proglang{R}}
+\date{Revised: \today\\(A later revision may be available on the \href{https://github.com/Rdatatable/data.table/wiki}{homepage})}
+\maketitle
+
+The first section, Beginner FAQs, is intended to be read in order, from start to finish.
+
+\tableofcontents
+\section{Beginner FAQs}
+
+\subsection{Why does \code{DT[,5]} return \code{5}?}
+Because by default, unlike a \code{data.frame}, the 2nd argument is an \emph{expression}
+which is evaluated within the scope of \code{DT}. 5 evaluates to 5. It is generally bad practice to refer
+to columns by number rather than name. If someone else comes along and reads your code later, they
+may have to hunt around to find out which column is number 5. Furthermore, if you or someone else
+changes the column ordering of \code{DT} higher up in your \proglang{R} program, you might get bugs if you forget to
+change all the places in your code which refer to column number 5.
+
+Say column 5 is called ''region'', just do \code{DT[,region]} instead. Notice there are no quotes around
+the column name. This is what we mean by j being evaluated within the scope of the \code{data.table}. That
+scope consists of an environment where the column names are variables.
+
+You can place \emph{any} \proglang{R} expression in \code{j}; e.g., \code{DT[,colA*colB/2]}. Further, \code{j} may be a set of \proglang{R} expressions (including calls to any \proglang{R} package) wrapped with \code{list()}, \code{.()} or an anonymous code block wrapped with braces \code{{}}. A simple example is \code{DT[,fitdistr(d1-d1,"normal")]}.
+
+Having said this, there are some circumstances where referring to a column by number is ok, such as
+a sequence of columns. In these situations just do \code{DT[,5:10,with=FALSE]} or \newline \code{DT[,c(1,4,10),with=FALSE]}.
+See \code{?data.table} for an explanation of the \code{with} argument. It lets you use \code{data.table} the same way as \code{data.frame}, when you need to.
+
+Note that \code{with()} has been a base function for a long time. That's one reason we say \code{data.table} builds
+upon base functionality. There is little new here really, \code{data.table} is just making use of \code{with()}
+and building it into the syntax.
+
+\subsection{Why does \code{DT[,"region"]} return \code{"region"}?}
+See answer to 1.1 above. Try \code{DT[,region]} instead. Or \code{DT[,"region",with=FALSE]}.
+
+
+\subsection{Why does \code{DT[,region]} return a vector? I'd like a 1-column \code{data.table}. There is no \code{drop} argument like I'm used to in \code{data.frame}.}
+Try \code{DT[,.(region)]} instead. \code{.()} is an alias for \code{list()} and ensures a \code{data.table} is returned.
+
+\subsection{Why does \code{DT[,x,y,z]} not work? I wanted the 3 columns \code{x},\code{y} and \code{z}.}
+The \code{j} expression is the 2nd argument. The correct way to do this is \code{DT[,.(x,y,z)]}.
+
+\subsection{I assigned a variable \code{mycol="x"} but then \code{DT[,mycol]} returns \code{"x"}. How do I get it to look up the column name contained in the \code{mycol} variable?}
+This is what we mean when we say the \code{j} expression 'sees' objects in the calling scope. The variable \code{mycol} does not exist as a column name of
+\code{DT} so \proglang{R} then looked in the calling scope and found \code{mycol} there and returned its value \code{"x"}. This is correct behaviour. Had \code{mycol} been a column name,
+then that column's data would have been returned. What you probably meant was \code{DT[,mycol,with=FALSE]}, which will return the \code{x} column's
+data as you wanted. Alternatively, since a \code{data.table} \emph{is} a \code{list}, too, you can write \code{DT[["x"]]} or \code{DT[[mycol]]}.
+
+\subsection{Ok but I don't know the expressions in advance. How do I programatically pass them in?}
+To create expressions use the \code{quote()} function. We refer to these as \emph{quote()-ed} expressions to
+save confusion with the double quotes used to create a character vector such as \code{c("x")}. The simplest
+quote()-ed expression is just one column name :
+
+\code{q = quote(x)}
+
+\code{DT[,eval(q)] \# returns the column x as a vector}
+
+\code{q = quote(list(x))}
+
+\code{DT[,eval(q)] \# returns the column x as a 1-column data.table}
+\newline
+Since these are \emph{expressions}, we are not restricted to column names only :
+
+\code{q = quote(mean(x))}
+
+\code{DT[,eval(q)] \# identical to DT[,mean(x)]}
+
+\code{q = quote(list(x,sd(y),mean(y*z)))}
+
+\code{DT[,eval(q)] \# identical to DT[,list(x,sd(y),mean(y*z))]}
+\newline
+However, if it's just simply a vector of column names you need, it may be simpler to pass a character vector to \code{j} and use \code{with=FALSE}.
+
+To pass an expression into your own function, one idiom is as follows :
+<<>>=
+DT = as.data.table(iris)
+setkey(DT,Species)
+myfunction = function(dt, expr) {
+ e = substitute(expr)
+ dt[,eval(e),by=Species]
+}
+myfunction(DT,sum(Sepal.Width))
+@
+
+\code{quote()} and \code{eval()} are like macros in other languages. Instead of \code{j=myfunction()} (which won't work without laboriously passing in all the arguments) it's \code{j=eval(mymacro)}. This can be more efficient than a function call, and convenient. When data.table sees \code{j=eval(mymacro)} it knows to find \code{mymacro} in calling scope so as not to be tripped up if a column name happens to be called \code{mymacro}, too.
+
+For example, let's make sure that exactly the same \code{j} is run for a set of different grouping criteria :
+
+<<>>=
+DT = as.data.table(iris)
+whatToRun = quote( .(AvgWidth = mean(Sepal.Width),
+ MaxLength = max(Sepal.Length)) )
+DT[, eval(whatToRun), by=Species]
+DT[, eval(whatToRun), by=.(FirstLetter=substring(Species,1,1))]
+DT[, eval(whatToRun), by=.(Petal.Width=round(Petal.Width,0))]
+@
+
+\subsection{What are the benefits of being able to use column names as if they are variables inside \code{DT[...]}?}
+\code{j} doesn't have to be just column names. You can write any \proglang{R} \emph{expression} of column names directly as the \code{j}; e.g.,
+\code{DT[,mean(x*y/z)]}. The same applies
+to \code{i}; e.g., \code{DT[x>1000, sum(y*z)]}.
+This runs the \code{j} expression on the set of rows where the \code{i} expression is true. You don't even need to return data; e.g., \code{DT[x>1000, plot(y,z)]}. Finally, you can do \code{j} by group by adding \code{by=}; e.g., \code{DT[x>1000, sum(y*z), by=w]}. This runs \code{j} for each group in column \code{w} but just over the rows where \code{x>1000}. By placing the 3 parts of the query (where, select and group by) inside the square brackets, \code{data.table} sees this query as [...]
+
+\subsection{OK, I'm starting to see what \code{data.table} is about, but why didn't you enhance \code{data.frame} in \proglang{R}? Why does it have to be a new package?}
+As FAQ 1.1 highlights, \code{j} in \code{[.data.table} is fundamentally different from \code{j} in \code{[.data.frame}. Even something as simple as \code{DF[,1]} would break existing code in many packages and user code. This is by design. We want it to work this way for more complicated syntax to work. There are other differences, too (see FAQ \ref{faq:SmallerDiffs}).
+
+Furthermore, \code{data.table} \emph{inherits} from \code{data.frame}. It \emph{is} a \code{data.frame}, too. A \code{data.table} can be passed to any package that only accepts \code{data.frame} and that package can use \code{[.data.frame} syntax on the \code{data.table}.
+
+We \emph{have} proposed enhancements to \proglang{R} wherever possible, too. One of these was accepted as a new feature in \proglang{R} 2.12.0 :
+\begin{quotation}unique() and match() are now faster on character vectors where
+ all elements are in the global CHARSXP cache and have unmarked
+ encoding (ASCII). Thanks to Matt Dowle for suggesting
+ improvements to the way the hash code is generated in unique.c.\end{quotation}
+
+A second proposal was to use memcpy in duplicate.c, which is much faster than a for loop in C. This would improve the \emph{way} that \proglang{R} copies data internally (on some measures by 13 times). The thread on r-devel is here : \url{http://tolstoy.newcastle.edu.au/R/e10/devel/10/04/0148.html}.
+
+\subsection{Why are the defaults the way they are? Why does it work the way it does?}
+The simple answer is because the main author originally designed it for his own use. He wanted it that way. He finds it a more natural, faster way to
+write code, which also executes more quickly.
+
+\subsection{Isn't this already done by \code{with()} and \code{subset()} in base?}
+Some of the features discussed so far are, yes. The package builds upon base functionality. It does the same sorts of things but with
+less code required and executes many times faster if used correctly.
+
+\subsection{Why does \code{X[Y]} return all the columns from \code{Y} too? Shouldn't it return a subset of \code{X}?}
+This was changed in v1.5.3. \code{X[Y]} now includes \code{Y}'s non-join columns. We refer to this feature as \emph{join inherited scope} because not only are \code{X} columns available to the j expression, so are \code{Y} columns. The downside is that \code{X[Y]} is less efficient since every item of \code{Y}'s non-join columns are duplicated to match the (likely large) number of rows in \code{X} that match. We therefore strongly encourage \code{X[Y,j]} instead of \code{X[Y]}. See next FAQ.
+
+\subsection{What is the difference between \code{X[Y]} and \code{merge(X,Y)}?}
+\code{X[Y]} is a join, looking up \code{X}'s rows using \code{Y} (or \code{Y}'s key if it has one) as an index.\newline
+\code{Y[X]} is a join, looking up \code{Y}'s rows using \code{X} (or \code{X}'s key if it has one) as an index.\newline
+\code{merge(X,Y)}\footnote{Here we mean either the \code{merge} \emph{method} for \code{data.table} or the \code{merge} method for \code{data.frame} since both methods work in the same way in this respect. See \code{?merge.data.table} and FAQ 2.24 for more information about method dispatch.} does both ways at the same time. The number of rows of \code{X[Y]} and \code{Y[X]} usually differ; whereas the number of rows returned by \code{merge(X,Y)} and \code{merge(Y,X)} is the same.
+
+\emph{BUT} that misses the main point. Most tasks require something to be done on the data after a join or merge. Why merge all the columns of data, only to use a small subset of them afterwards? You may suggest \code{merge(X[,ColsNeeded1],Y[,ColsNeeded2])}, but that takes copies of the subsets of data and it requires the programmer to work out which columns are needed. \code{X[Y,j]} in data.table does all that in one step for you. When you write \code{X[Y,sum(foo*bar)]}, \code{data.tabl [...]
+
+\subsection{Anything else about \code{X[Y,sum(foo*bar)]}?}
+This behaviour changed in v1.9.4 (Sep 2014). It now does the \code{X[Y]} join and then runs \code{sum(foo*bar)} over all the rows; i.e., \code{X[Y][,sum(foo*bar)]}. It used to run \code{j} for each \emph{group} of \code{X} that each row of \code{Y} matches to. That can still be done as it's very useful but you now need to be explicit and specify \code{by=.EACHI}; i.e., \code{X[Y,sum(foo*bar),by=.EACHI]}. We call this \emph{grouping by each i}.
+For example, and making it complicated by using \emph{join inherited scope}, too :
+<<>>=
+X = data.table(grp=c("a","a","b","b","b","c","c"), foo=1:7)
+setkey(X,grp)
+Y = data.table(c("b","c"), bar=c(4,2))
+X
+Y
+X[Y,sum(foo*bar)]
+X[Y,sum(foo*bar),by=.EACHI]
+@
+
+\subsection{That's nice. How did you manage to change it?}
+The request to change came from users. The feeling was that if a query is doing grouping then an explicit `by=` should be present for code readability reasons. An option is provided to return the old behaviour: \code{options(datatable.old.bywithoutby)}, by default FALSE. This enables upgrading to test the other new features / bug fixes in v1.9.4, with later migration of any by-without-by queries when ready (by adding \code{by=.EACHI} to them). We retained 47 pre-change tests and added th [...]
+
+Of the 66 packages on CRAN or Bioconductor that depend or import data.table at the time of releasing v1.9.4, only one was affected by the change. That could be because many packages don't have comprehensive tests, or just that grouping by each row in i wasn't being used much by downstream packages. We always test the new version with all dependent packages before release and coordinate any changes with those maintainers. So this release was quite straightforward in that regard.
+
+Another compelling reason to make the change was that previously, there was no efficient way to achieve what \code{X[Y,sum(foo*bar)]} does now. You had to write \code{X[Y][,sum(foo*bar)]}. That was suboptimal because \code{X[Y]} joined all the columns and passed them all to the second compound query without knowing that only foo and bar are needed. To solve that efficiency problem, extra programming effort was required: \code{X[Y,list(foo,bar)][,sum(foo*bar)]}. The change to \code{by=.E [...]
+
+\section{General syntax}
+
+\subsection{How can I avoid writing a really long \code{j} expression? You've said I should use the column \emph{names}, but I've got a lot of columns.}
+When grouping, the \code{j} expression can use column names as variables, as you know, but it can also use a reserved symbol \code{.SD} which refers to the {\bf S}ubset of the \code{{\bf D}ata.table} for each group (excluding the grouping columns). So to sum up all your columns it's just \code{DT[,lapply(.SD,sum),by=grp]}. It might seem tricky, but it's fast to write and fast to run. Notice you don't have to create an anonymous \code{function}. The \code{.SD} object is efficiently implem [...]
+So please don't do this, for example, \code{DT[,sum(.SD[["sales"]]),by=grp]}. That works but is inefficient and inelegant. This is what was intended: \code{DT[,sum(sales),by=grp]} and could be 100's of times faster. If you do use all the data in \code{.SD} for each group (such as in \code{DT[,lapply(.SD,sum),by=grp]}) then that's very good usage of \code{.SD}. Also see \code{?data.table} for the \code{.SDcols} argument which allows you to specify a subset of columns for \code{.SD}.
+
+\subsection{Why is the default for \code{mult} now \code{"all"}?}
+In v1.5.3 the default was changed to \code{"all"}. When \code{i} (or \code{i}'s key if it has one) has fewer columns than \code{x}'s key, \code{mult} was already set to \code{"all"} automatically. Changing the default makes this clearer and easier for users as it came up quite often.
+
+In versions up to v1.3, \code{"all"} was slower. Internally, \code{"all"} was implemented by joining using \code{"first"}, then again from scratch using \code{"last"}, after which a diff between them was performed to work out the span of the matches in \code{x} for each row in \code{i}. Most often we join to single rows, though, where \code{"first"},\code{"last"} and \code{"all"} return the same result. We preferred maximum performance for the majority of situations so the default chosen [...]
+
+In v1.4 the binary search in C was changed to branch at the deepest level to find first and last. That branch will likely occur within the same final pages of RAM so there should no longer be a speed disadvantage in defaulting \code{mult} to \code{"all"}. We warned that the default might change and made the change in v1.5.3.
+
+A future version of \code{data.table} may allow a distinction between a key and a \emph{unique key}. Internally \code{mult="all"} would perform more like \code{mult="first"} when all \code{x}'s key columns were joined to and \code{x}'s key was a unique key. \code{data.table} would need checks on insert and update to make sure a unique key is maintained. An advantage of specifying a unique key would be that \code{data.table} would ensure no duplicates could be inserted, in addition to per [...]
+
+
+\subsection{I'm using \code{c()} in the \code{j} and getting strange results.}
+This is a common source of confusion. In \code{data.frame} you are used to, for example:
+<<>>=
+DF = data.frame(x=1:3,y=4:6,z=7:9)
+DF
+DF[,c("y","z")]
+@
+which returns the two columns. In \code{data.table} you know you can use the column names directly and might try :
+<<>>=
+DT = data.table(DF)
+DT[,c(y,z)]
+@
+but this returns one vector. Remember that the \code{j} expression is evaluated within the environment of \code{DT} and \code{c()} returns a vector. If 2 or more columns are required, use \code{list()} or \code{.()} instead:
+<<>>=
+DT[,.(y,z)]
+@
+\code{c()} can be useful in a \code{data.table} too, but its behaviour is different from that in \code{[.data.frame}.
+
+\subsection{I have built up a complex table with many columns. I want to use it as a template for a new table; i.e., create a new table with no rows, but with the column names and types copied from my table. Can I do that easily?}
+Yes. If your complex table is called \code{DT}, try \code{NEWDT = DT[0]}.
+
+\subsection{Is a null data.table the same as \code{DT[0]}?}
+No. By "null data.table" we mean the result of \code{data.table(NULL)} or \code{as.data.table(NULL)}; i.e.,
+<<>>=
+data.table(NULL)
+data.frame(NULL)
+as.data.table(NULL)
+as.data.frame(NULL)
+is.null(data.table(NULL))
+is.null(data.frame(NULL))
+@
+The null \code{data.table|frame} is \code{NULL} with some attributes attached, making it not NULL anymore. In R only pure \code{NULL} is \code{NULL} as tested by \code{is.null()}. When referring to the "null data.table" we use lower case null to help distinguish from upper case \code{NULL}. To test for the null data.table, use \code{length(DT)==0} or \code{ncol(DT)==0} (\code{length} is slightly faster as it's a primitive function).
+An \emph{empty} data.table (\code{DT[0]}) has one or more columns, all of which are empty. Those empty columns still have names and types.
+<<>>=
+DT = data.table(a=1:3,b=c(4,5,6),d=c(7L,8L,9L))
+DT[0]
+sapply(DT[0],class)
+@
+
+\subsection{Why has the \code{DT()} alias been removed?}\label{faq:DTremove1}
+\code{DT} was introduced originally as a wrapper for a list of \code{j} expressions. Since \code{DT} was an alias for \code{data.table}, this was a convenient way to take care of silent recycling in cases where each item of the \code{j} list evaluated to different lengths. The alias was one reason grouping was slow, though.
+As of v1.3, \code{list()} or \code{.()} should be passed instead to the \code{j} argument. These are much faster, especially when there are many groups. Internally, this was a nontrivial change. Vector recycling is now done internally, along with several other speed enhancements for grouping.
+
+\subsection{But my code uses \code{j=DT(...)} and it works. The previous FAQ says that \code{DT()} has been removed.}\label{faq:DTremove2}
+Then you are using a version prior to 1.5.3. Prior to 1.5.3 \code{[.data.table} detected use of \code{DT()} in the \code{j} and automatically replaced it with a call to \code{list()}. This was to help the transition for existing users.
+
+\subsection{What are the scoping rules for \code{j} expressions?}
+Think of the subset as an environment where all the column names are variables. When a variable \code{foo} is used in the \code{j} of a query such as \code{X[Y,sum(foo)]}, \code{foo} is looked for in the following order :
+\begin{enumerate}
+\item The scope of \code{X}'s subset; i.e., \code{X}'s column names.
+\item The scope of each row of \code{Y}; i.e., \code{Y}'s column names (\emph{join inherited scope})
+\item The scope of the calling frame; e.g., the line that appears before the \code{data.table} query.
+\item Exercise for reader: does it then ripple up the calling frames, or go straight to \code{globalenv()}?
+\item The global environment
+\end{enumerate}
+This is \emph{lexical scoping} as explained in \href{http://cran.r-project.org/doc/FAQ/R-FAQ.html#Lexical-scoping}{R FAQ 3.3.1}.
+The environment in which the function was created is not relevant, though, because there is \emph{no function}. No anonymous \emph{function}
+is passed to the \code{j}. Instead, an anonymous \emph{body} is passed to the \code{j}; for example,
+<<>>=
+DT = data.table(x=rep(c("a","b"),c(2,3)),y=1:5)
+DT
+DT[,{z=sum(y);z+3},by=x]
+@
+Some programming languages call this a \emph{lambda}.
+
+\subsection{Can I trace the \code{j} expression as it runs through the groups?}
+Try something like this:
+<<>>=
+DT[,{
+ cat("Objects:",paste(objects(),collapse=","),"\n")
+ cat("Trace: x=",as.character(x)," y=",y,"\n")
+ sum(y)
+},by=x]
+@
+
+\subsection{Inside each group, why are the group variables length 1?}
+In the previous FAQ, \code{x} is a grouping variable
+and (as from v1.6.1) has length 1 (if inspected or used in \code{j}). It's for efficiency and convenience. Therefore, there is no difference between the following two statements:
+<<>>=
+DT[,.(g=1,h=2,i=3,j=4,repeatgroupname=x,sum(y)),by=x]
+DT[,.(g=1,h=2,i=3,j=4,repeatgroupname=x[1],sum(y)),by=x]
+@
+If you need the size of the current group, use \code{.N} rather than calling \code{length()} on any column.
+
+\subsection{Only the first 10 rows are printed, how do I print more?}
+There are two things happening here. First, if the number of rows in a \code{data.table} are large (\code{> 100} by default), then a summary of the \code{data.table} is printed to the console by default. Second, the summary of a large \code{data.table} is printed by takingthe top and bottom \code{n} rows of the \code{data.table} and only printing those. Both of these parameters (when to trigger a summary and how much of a table to use as a summary) are configurable by \proglang{R}'s \cod [...]
+
+For instance, to enforce the summary of a \code{data.table} to only happen when a \code{data.table} is greater than 50 rows, you could \code{options(datatable.print.nrows=50)}. To disable the summary-by-default completely, you could \code{options(datatable.print.nrows=Inf)}. You could also call \code{print} directly, as in \code{print(your.data.table, nrows=Inf)}.
+
+If you want to show more than just the top (and bottom) 10 rows of a \code{data.table} summary (say you like 20), set \code{options(datatable.print.topn=20)}, for example. Again, you could also just call \code{print} directly, as in \code{print(your.data.table, topn=20)}
+
+\subsection{With an \code{X[Y]} join, what if \code{X} contains a column called \code{"Y"}?}
+When \code{i} is a single name such as \code{Y} it is evaluated in the calling frame. In all other cases such as calls to \code{.()} or other expressions, \code{i} is evaluated within the scope of \code{X}. This facilitates easy \emph{self joins} such as \code{X[J(unique(colA)),mult="first"]}.
+
+\subsection{\code{X[Z[Y]]} is failing because \code{X} contains a column \code{"Y"}. I'd like it to use the table \code{Y} in calling scope.}
+The \code{Z[Y]} part is not a single name so that is evaluated within the frame of \code{X} and the problem occurs. Try \code{tmp=Z[Y];X[tmp]}. This is robust to \code{X} containing a column \code{"tmp"} because \code{tmp} is a single name. If you often encounter conflics of this type, one simple solution may be to name all tables in uppercase and all column names in lowercase, or some similar scheme.
+
+\subsection{Can you explain further why \code{data.table} is inspired by \code{A[B]} syntax in base?}
+Consider \code{A[B]} syntax using an example matrix \code{A} :
+<<>>=
+A = matrix(1:12,nrow=4)
+A
+@
+To obtain cells (1,2)=5 and (3,3)=11 many users (we believe) may try this first :
+<<>>=
+A[c(1,3),c(2,3)]
+@
+That returns the union of those rows and columns, though. To reference the cells, a 2-column matrix is required. \code{?Extract} says :
+\begin{quotation}
+When indexing arrays by [ a single argument i can be a matrix with as many columns as there are dimensions of x; the result is then a vector with elements corresponding to the sets of indices in each row of i.
+\end{quotation}
+Let's try again.
+<<>>=
+B = cbind(c(1,3),c(2,3))
+B
+A[B]
+@
+A matrix is a 2-dimension structure with row names and column names. Can we do the same with names?
+<<>>=
+rownames(A) = letters[1:4]
+colnames(A) = LETTERS[1:3]
+A
+B = cbind(c("a","c"),c("B","C"))
+A[B]
+@
+So, yes we can. Can we do the same with \code{data.frame}?
+<<>>=
+A = data.frame(A=1:4,B=letters[11:14],C=pi*1:4)
+rownames(A) = letters[1:4]
+A
+B
+A[B]
+@
+But, notice that the result was coerced to character. \proglang{R} coerced \code{A} to matrix first so that the syntax could work, but the result isn't ideal. Let's try making \code{B} a \code{data.frame}.
+<<>>=
+B = data.frame(c("a","c"),c("B","C"))
+cat(try(A[B],silent=TRUE))
+@
+So we can't subset a \code{data.frame} by a \code{data.frame} in base R. What if we want row names and column names that aren't character but integer or float? What if we want more than 2 dimensions of mixed types? Enter \code{data.table}.
+
+Furthermore, matrices, especially sparse matrices, are often stored in a 3 column tuple: (i,j,value). This can be thought of as a key-value pair where \code{i} and \code{j} form a 2-column key. If we have more than one value, perhaps of different types it might look like (i,j,val1,val2,val3,...). This looks very much like a \code{data.frame}. Hence \code{data.table} extends \code{data.frame} so that a \code{data.frame X} can be subset by a \code{data.frame Y}, leading to the \code{X[Y]} syntax.
+
+\subsection{Can base be changed to do this then, rather than a new package?}
+\code{data.frame} is used \emph{everywhere} and so it is very difficult to make \emph{any} changes to it.
+\code{data.table} \emph{inherits} from \code{data.frame}. It \emph{is} a \code{data.frame}, too. A \code{data.table} \emph{can} be passed to any package that \emph{only} accepts \code{data.frame}. When that package uses \code{[.data.frame} syntax on the \code{data.table}, it works. It works because \code{[.data.table} looks to see where it was called from. If it was called from such a package, \code{[.data.table} diverts to \code{[.data.frame}.
+
+\subsection{I've heard that \code{data.table} syntax is analogous to SQL.}
+Yes :
+\begin{itemize}
+\item{\code{i} <==> where}
+\item{\code{j} <==> select}
+\item{\code{:=} <==> update}
+\item{\code{by} <==> group by}
+\item{\code{i} <==> order by (in compound syntax)}
+\item{\code{i} <==> having (in compound syntax)}
+\item{\code{nomatch=NA} <==> outer join}
+\item{\code{nomatch=0} <==> inner join}
+\item{\code{mult="first"|"last"} <==> N/A because SQL is inherently unordered}
+\item{\code{roll=TRUE} <==> N/A because SQL is inherently unordered}
+\end{itemize}
+The general form is : \newline
+\code{\hspace*{2cm}DT[where,select|update,group by][order by][...] ... [...]}
+\newline\newline
+A key advantage of column vectors in \proglang{R} is that they are \emph{ordered}, unlike SQL\footnote{It may be a surprise to learn that \code{select top 10 * from ...} does \emph{not} reliably return the same rows over time in SQL. You do need to include an \code{order by} clause, or use a clustered index to guarantee row order; i.e., SQL is inherently unordered.}. We can use ordered functions in \code{data.table} queries such as \code{diff()} and we can use \emph{any} \proglang{R} fun [...]
+
+
+\subsection{What are the smaller syntax differences between \code{data.frame} and \code{data.table}?}\label{faq:SmallerDiffs}
+\begin{itemize}
+\item{\code{DT[3]} refers to the 3rd row, but \code{DF[3]} refers to the 3rd column}
+\item{\code{DT[3,]} == \code{DT[3]}, but \code{DF[,3]} == \code{DF[3]} (somewhat confusingly)}
+\item{For this reason we say the comma is \emph{optional} in \code{DT}, but not optional in \code{DF}}
+\item{\code{DT[[3]]} == \code{DF[3]} == \code{DF[[3]]}}
+\item{\code{DT[i,]} where \code{i} is a single integer returns a single row, just like \code{DF[i,]}, but unlike a matrix single row subset which returns a vector.}
+\item{\code{DT[,j,with=FALSE]} where \code{j} is a single integer returns a one column \code{data.table}, unlike \code{DF[,j]} which returns a vector by default}
+\item{\code{DT[,"colA",with=FALSE][[1]]} == \code{DF[,"colA"]}.}
+\item{\code{DT[,colA]} == \code{DF[,"colA"]}}
+\item{\code{DT[,list(colA)]} == \code{DF[,"colA",drop=FALSE]}}
+\item{\code{DT[NA]} returns 1 row of \code{NA}, but \code{DF[NA]} returns a copy of \code{DF} containing
+ \code{NA} throughout. The symbol \code{NA} is type logical in \proglang{R} and
+ is therefore recycled by \code{[.data.frame}. Intention was probably \code{DF[NA\_integer\_]}.
+ \code{[.data.table} does this automatically for convenience.}
+\item{\code{DT[c(TRUE,NA,FALSE)]} treats the \code{NA} as \code{FALSE}, but \code{DF[c(TRUE,NA,FALSE)]} returns
+ \code{NA} rows for each \code{NA}}
+\item{\code{DT[ColA==ColB]} is simpler than \code{DF[!is.na(ColA) \& !is.na(ColB) \& ColA==ColB,]}}
+\item{\code{data.frame(list(1:2,"k",1:4))} creates 3 columns, \code{data.table} creates one \code{list} column.}
+\item{\code{check.names} is by default \code{TRUE} in \code{data.frame} but \code{FALSE} in \code{data.table}, for convenience.}
+\item{\code{stringsAsFactors} is by default \code{TRUE} in \code{data.frame} but \code{FALSE} in \code{data.table}, for efficiency. Since a global string cache was added to R, characters items are a pointer to the single cached string and there is no longer a performance benefit of coverting to factor.}
+\item{Atomic vectors in \code{list} columns are collapsed when printed using ", " in \code{data.frame}, but "," in \code{data.table} with a trailing comma after the 6th item to avoid accidental printing of large embedded objects.}
+\end{itemize}
+In \code{[.data.frame} we very often set \code{drop=FALSE}. When we forget, bugs can arise in edge cases where single columns are selected and all of a sudden a vector is returned rather than a single column \code{data.frame}. In \code{[.data.table} we took the opportunity to make it consistent and drop \code{drop}.
+\newline\newline
+When a \code{data.table} is passed to a \code{data.table}-unaware package, that package it not concerned with any of these differences; it just works.
+
+\subsection{I'm using \code{j} for its side effect only, but I'm still getting data returned. How do I stop that?}
+In this case \code{j} can be wrapped with \code{invisible()}; e.g., \code{DT[,invisible(hist(colB)),by=colA]}\footnote{\code{hist()} returns the breakpoints in addition to plotting to the graphics device}.
+
+\subsection{Why does \code{[.data.table} now have a \code{drop} argument from v1.5?}
+So that \code{data.table} can inherit from \code{data.frame} without using \code{\dots}. If we used \code{\dots} then invalid argument names would not be caught.
+
+The \code{drop} argument is never used by \code{[.data.table}. It is a placeholder for non \code{data.table} aware packages when they use the \code{[.data.frame} syntax directly on a \code{data.table}.
+
+\subsection{Rolling joins are cool and very fast! Was that hard to program?}
+The prevailing row on or before the \code{i} row is the final row the binary search tests anyway. So \code{roll=TRUE} is essentially just a switch in the binary search C code to return that row.
+
+\subsection{Why does \code{DT[i,col:=value]} return the whole of \code{DT}? I expected either no visible value (consistent with \code{<-}), or a message or return value containing how many rows were updated. It isn't obvious that the data has indeed been updated by reference.}
+This has changed in v1.8.3 to meet your expectations. Please upgrade.
+The whole of \code{DT} is returned (now invisibly) so that compound syntax can work; e.g., \code{DT[i,done:=TRUE][,sum(done)]}. The number of rows updated is returned when verbosity is on, either on a per query basis or globally using \code{options(datatable.verbose=TRUE)}.
+
+\subsection{Ok, thanks. What was so difficult about the result of \code{DT[i,col:=value]} being returned invisibly?}
+\proglang{R} internally forces visibility on for \code{[}. The value of FunTab's eval column (see src/main/names.c) for \code{[} is 0 meaning force \code{R\_Visible} on (see R-Internals section 1.6). Therefore, when we tried \code{invisible()} or setting \code{R\_Visible} to 0 directly ourselves, \code{eval} in src/main/eval.c would force it on again.
+
+To solve this problem, the key was to stop trying to stop the print method running after a \code{:=}. Instead, inside \code{:=} we now (from v1.8.3) set a global flag which the print method uses to know whether to actually print or not.
+
+\subsection{I've noticed that \code{base::cbind.data.frame} (and \code{base::rbind.data.frame}) appear to be changed by \code{data.table}. How is this possible? Why?}
+It is a temporary, last resort solution until we discover a better way to solve the problems listed below. Essentially, the issue is that \code{data.table} inherits from \code{data.frame}, \emph{and}, \code{base::cbind} and \code{base::rbind} (uniquely) do their own S3 dispatch internally as documented by \code{?cbind}. The change is adding one \code{for} loop to the start of each function directly in base; e.g.,
+<<>>=
+base::cbind.data.frame
+@
+That modification is made dynamically; i.e., the base definition of \code{cbind.data.frame} is fetched, the \code{for} loop added to the beginning and then assigned back to base. This solution is intended to be robust to different definitions of \code{base::cbind.data.frame} in different versions of \proglang{R}, including unknown future changes. Again, it is a last resort until a better solution is known or made available. The competing requirements are :
+\begin{itemize}
+\item \code{cbind(DT,DF)} needs to work. Defining \code{cbind.data.table} doesn't work because \code{base::cbind} does its own S3 dispatch and requires that the \emph{first} \code{cbind} method for each object it is passed is \emph{identical}. This is not true in \code{cbind(DT,DF)} because the first method for \code{DT} is \code{cbind.data.table} but the first method for \code{DF} is \code{cbind.data.frame}. \code{base::cbind} then falls through to its internal \code{bind} code which ap [...]
+\item This naturally leads to trying to mask \code{cbind.data.frame} instead. Since a \code{data.table} is a \code{data.frame}, \code{cbind} would find the same method for both \code{DT} and \code{DF}. However, this doesn't work either because \code{base::cbind} appears to find methods in \code{base} first; i.e., \code{base::cbind.data.frame} isn't maskable. This is reproducible as follows :
+\end{itemize}
+<<>>=
+foo = data.frame(a=1:3)
+cbind.data.frame = function(...)cat("Not printed\n")
+cbind(foo)
+@
+<<echo=FALSE>>=
+rm("cbind.data.frame")
+@
+\begin{itemize}
+\item Finally, we tried masking \code{cbind} itself (v1.6.5 and v1.6.6). This allowed \code{cbind(DT,DF)} to work, but introduced compatibility issues with package IRanges, since IRanges also masks \code{cbind}. It worked if IRanges was lower on the search() path than \code{data.table}, but if IRanges was higher then \code{data.table}'s \code{cbind} would never be called and the strange looking matrix output occurs again (FAQ \ref{faq:cbinderror}).
+\end{itemize}
+If you know of a better solution, that still solves all the issues above, then please let us know and we'll gladly change it.
+
+\subsection{I've read about method dispatch (e.g. \code{merge} may or may not dispatch to \code{merge.data.table}) but \emph{how} does R know how to dispatch? Are dots significant or special? How on earth does R know which function to dispatch and when?}
+This comes up quite a lot, but it's really earth shatteringly simple. A function such as \code{merge} is \emph{generic} if it consists of a call to \code{UseMethod}. When you see people talking about whether or not functions are \emph{generic} functions they are merely typing the function, without \code{()} afterwards, looking at the program code inside it and if they see a call to \code{UseMethod} then it is \emph{generic}. What does \code{UseMethod} do? It literally slaps the function [...]
+
+You might now ask: where is this documented in R? Answer: it's quite clear, but, you need to first know to look in \code{?UseMethod} and \emph{that} help file contains :
+
+ "When a function calling UseMethod('fun') is applied to an object with class attribute c('first', 'second'), the system searches for a function called fun.first and, if it finds it, applies it to the object. If no such function is found a function called fun.second is tried. If no class name produces a suitable function, the function fun.default is used, if it exists, or an error results."
+
+Happily, an internet search for "How does R method dispatch work" (at the time of writing) returns the \code{?UseMethod} help page as the top link. Admittedly, other links rapidly descend into the intracies of S3 vs S4, internal generics and so on.
+
+However, features like basic S3 dispatch (pasting the function name together with the class name) is why some R folk love R. It's so simple. No complicated registration or signature is required. There isn't much needed to learn. To create the \code{merge} method for \code{data.table} all that was required, literally, was to merely create a function called \code{merge.data.table}.
+
+\section{Questions relating to compute time}
+
+\subsection{I have 20 columns and a large number of rows. Why is an expression of one column so quick?}
+Several reasons:
+\begin{itemize}
+\item Only that column is grouped, the other 19 are ignored because \code{data.table} inspects the \code{j} expression and realises it doesn't use the other columns.
+\item One memory allocation is made for the largest group only, then that memory is re-used for the other groups. There is very little garbage to collect.
+\item \proglang{R} is an in-memory column store; i.e., the columns are contiguous in RAM. Page fetches from RAM into L2 cache are minimised.
+\end{itemize}
+
+\subsection{I don't have a key on a large table, but grouping is still really quick. Why is that?}
+\code{data.table} uses radix sorting. This is significantly faster than other sort algorithms. See our presentations on our homepage for more information.
+
+This is also one reason why \code{setkey()} is quick.
+
+When no key is set, or we group in a different order from that of the key, we call it an \emph{ad hoc by}.
+
+\subsection{Why is grouping by columns in the key faster than an ad hoc by?}
+Because each group is contiguous in RAM, thereby minimising page fetches and memory can be
+copied in bulk (memcpy in C) rather than looping in C.
+
+\section{Error messages}
+\subsection{\code{Could not find function "DT"}}
+See FAQ \ref{faq:DTremove1} and FAQ \ref{faq:DTremove2}.
+
+\subsection{\code{unused argument(s) (MySum = sum(v))}}
+This error is generated by \code{DT[,MySum=sum(v)]}. \code{DT[,.(MySum=sum(v))]} was intended, or \code{DT[,j=.(MySum=sum(v))]}.
+
+\subsection{\code{'translateCharUTF8' must be called on a CHARSXP}}
+This error (and similar; e.g., \code{'getCharCE' must be called on a CHARSXP}) may be nothing do with character data or locale. Instead, this can be a symptom of an earlier memory corruption. To date these have been reproducible and fixed (quickly). Please report it to datatable-help.
+
+\subsection{\code{cbind(DT,DF) returns a strange format e.g. 'Integer,5'}} \label{faq:cbinderror}
+This occurs prior to v1.6.5, for \code{rbind(DT,DF)} too. Please upgrade to v1.6.7 or later.
+
+\subsection{\code{cannot change value of locked binding for '.SD'}}
+\code{.SD} is locked by design. See \code{?data.table}. If you'd like to manipulate \code{.SD} before using it, or returning it, and don't wish to modify \code{DT} using \code{:=}, then take a copy first (see \code{?copy}); e.g.,
+<<>>=
+DT = data.table(a=rep(1:3,1:3),b=1:6,c=7:12)
+DT
+DT[,{ mySD = copy(.SD)
+ mySD[1,b:=99L]
+ mySD },
+ by=a]
+@
+
+\subsection{\code{cannot change value of locked binding for '.N'}}
+Please upgrade to v1.8.1 or later. From this version, if \code{.N} is returned by \code{j} it is renamed to \code{N} to avoid any abiguity in any subsequent grouping between the \code{.N} special variable and a column called \code{".N"}.
+The old behaviour can be reproduced by forcing \code{.N} to be called \code{.N}, like this :
+<<>>=
+DT = data.table(a=c(1,1,2,2,2),b=c(1,2,2,2,1))
+DT
+DT[,list(.N=.N),list(a,b)] # show intermediate result for exposition
+cat(try(
+ DT[,list(.N=.N),by=list(a,b)][,unique(.N),by=a] # compound query more typical
+,silent=TRUE))
+@
+If you are already running v1.8.1 or later then the error message is now more helpful than the \code{cannot change value of locked binding} error. As you can see above, since this vignette was produced using v1.8.1 or later.
+The more natural syntax now works :
+<<>>=
+if (packageVersion("data.table") >= "1.8.1") {
+ DT[,.N,by=list(a,b)][,unique(N),by=a]
+}
+if (packageVersion("data.table") >= "1.9.3") {
+ DT[,.N,by=.(a,b)][,unique(N),by=a] # same
+}
+@
+
+\section{Warning messages}
+\subsection{\code{The following object(s) are masked from 'package:base': cbind, rbind}}
+This warning was present in v1.6.5 and v.1.6.6 only, when loading the package. The motivation was to allow \code{cbind(DT,DF)} to work, but as it transpired, broke (full) compatibility with package IRanges. Please upgrade to v1.6.7 or later.
+
+\subsection{\code{Coerced numeric RHS to integer to match the column's type}}
+Hopefully, this is self explanatory. The full message is :\newline
+
+\code{Coerced numeric RHS to integer to match the column's type; may have truncated}\newline
+\code{precision. Either change the column to numeric first by creating a new numeric}\newline
+\code{vector length 5 (nrows of entire table) yourself and assigning that (i.e. }\newline
+\code{'replace' column), or coerce RHS to integer yourself (e.g. 1L or as.integer)}\newline
+\code{to make your intent clear (and for speed). Or, set the column type correctly}\newline
+\code{up front when you create the table and stick to it, please.}\newline
+
+To generate it, try :
+<<>>=
+DT = data.table(a=1:5,b=1:5)
+suppressWarnings(
+DT[2,b:=6] # works (slower) with warning
+)
+class(6) # numeric not integer
+DT[2,b:=7L] # works (faster) without warning
+class(7L) # L makes it an integer
+DT[,b:=rnorm(5)] # 'replace' integer column with a numeric column
+@
+
+\section{General questions about the package}
+
+\subsection{v1.3 appears to be missing from the CRAN archive?}
+That is correct. v1.3 was available on R-Forge only. There were several large
+changes internally and these took some time to test in development.
+
+\subsection{Is \code{data.table} compatible with S-plus?}
+Not currently.
+\begin{itemize}
+\item A few core parts of the package are written in C and use internal \proglang{R} functions and \proglang{R} structures.
+\item The package uses lexical scoping which is one of the differences between \proglang{R} and \proglang{S-plus} explained by
+\href{http://cran.r-project.org/doc/FAQ/R-FAQ.html#Lexical-scoping}{R FAQ 3.3.1}.
+\end{itemize}
+
+\subsection{Is it available for Linux, Mac and Windows?}
+Yes, for both 32-bit and 64-bit on all platforms. Thanks to CRAN. There are no special or OS-specific libraries used.
+
+\subsection{I think it's great. What can I do?}
+Please file suggestions, bug reports and enhancement requests on \href{https://github.com/Rdatatable/data.table/issues}{GitHub}.
+This helps make the package better.
+
+Please do vote for the package on \href{http://crantastic.org/packages/data-table}{Crantastic}. This helps encourage the developers and helps other \proglang{R} users find the package. If you have time to write a comment too, that can help others in the community. Just simply clicking that you use the package, though, is much appreciated.
+
+You can submit pull requests to change the code and/or documentation yourself.
+
+\subsection{I think it's not great. How do I warn others about my experience?}
+Please put your vote and comments on \href{http://crantastic.org/packages/data-table}{Crantastic}. Please make it constructive so we have a chance to improve.
+
+\subsection{I have a question. I know the r-help posting guide tells me to contact the maintainer (not r-help), but is there a larger group of people I can ask?}
+Yes, there are two options. You can post to \href{mailto:datatable-help at lists.r-forge.r-project.org}{datatable-help}. It's like r-help, but just for this package. Or the \href{http://stackoverflow.com/questions/tagged/data.table}{\code{data.table} tag on Stack Overflow}. Feel free to answer questions in those places, too.
+
+\subsection{Where are the datatable-help archives?}
+The \href{https://github.com/Rdatatable/data.table/wiki}{homepage} contains links to the archives in several formats.
+
+\subsection{I'd prefer not to contact datatable-help, can I mail just one or two people privately?}
+Sure. You're more likely to get a faster answer from datatable-help or Stack Overflow, though. Asking publicly in those places helps build the knowledge base.
+
+\subsection{I have created a package that depends on \code{data.table}. How do I ensure my package is \code{data.table}-aware so that inheritance from \code{data.frame} works?}
+Either i) include \code{data.table} in the \code{Depends:} field of your DESCRIPTION file, or ii) include \code{data.table} in the \code{Imports:} field of your DESCRIPTION file AND \code{import(data.table)} in your NAMESPACE file.
+
+\subsection{Why is this FAQ in pdf format? Can it moved to HTML?}
+Yes we'd like to move it to a HTML vignette. Just haven't got to that yet.
+The benefits of vignettes (rather than a wiki) including the following:
+\begin{itemize}
+\item We include \proglang{R} code in the vignettes. This code is \emph{actually run} when the file is created, not copy and pasted.
+\item This document is \emph{reproducible}. Grab the .Rnw and you can run it yourself.
+\item CRAN checks the package (including running vignettes) every night on Linux, Mac and Windows, both 32bit and 64bit. Results are posted to \url{http://cran.r-project.org/web/checks/check_results_data.table.html}. Included there are results from r-devel; i.e., not yet released R. That serves as a very useful early warning system for any potential future issues as \proglang{R} itself develops.
+\item This file is bound into each version of the package. The package is not accepted on CRAN unless this file passes checks. Each version of the package will have its own FAQ file which will be relevant for that version. Contrast this to a single website, which can be ambiguous if the answer depends on the version.
+\item You can open it offline at your \proglang{R} prompt using \code{vignette()}.
+\item You can extract the code from the document and play with it using\newline \code{edit(vignette("datatable-faq"))}.
+\end{itemize}
+
+\end{document}
+
+
diff --git a/vignettes/datatable-intro-vignette.Rmd b/vignettes/datatable-intro-vignette.Rmd
new file mode 100644
index 0000000..7e2335f
--- /dev/null
+++ b/vignettes/datatable-intro-vignette.Rmd
@@ -0,0 +1,716 @@
+---
+title: "Introduction to data.table"
+date: "`r Sys.Date()`"
+output:
+ rmarkdown::html_document:
+ theme: spacelab
+ highlight: pygments
+ css : css/bootstrap.css
+vignette: >
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r, echo = FALSE, message = FALSE}
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+# options(datatable.auto.index=FALSE)
+```
+This vignette introduces the *data.table* syntax, its general form, how to *subset* rows, *select and compute* on columns and perform aggregations *by group*. Familiarity with *data.frame* data structure from base R is useful, but not essential to follow this vignette.
+
+***
+
+## Data analysis using data.table
+
+Data manipulation operations such as *subset*, *group*, *update*, *join* etc., are all inherently related. Keeping these *related operations together* allows for:
+
+* *concise* and *consistent* syntax irrespective of the set of operations you would like to perform to achieve your end goal.
+
+* performing analysis *fluidly* without the cognitive burden of having to map each operation to a particular function from a set of functions available before to perform the analysis.
+
+* *automatically* optimising operations internally, and very effectively, by knowing precisely the data required for each operation and therefore very fast and memory efficient.
+
+Briefly, if you are interested in reducing *programming* and *compute* time tremendously, then this package is for you. The philosophy that *data.table* adheres to makes this possible. Our goal is to illustrate it through this series of vignettes.
+
+
+## Data {#data}
+
+In this vignette, we will use [NYC-flights14](https://github.com/arunsrinivasan/flights/wiki/NYC-Flights-2014-data) data. It contains On-Time flights data from the [Bureau of Transporation Statistics](http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236) for all the flights that departed from New York City airports in 2014 (inspired by [nycflights13](https://github.com/hadley/nycflights13)). The data is available only for Jan-Oct'14.
+
+We can use *data.table's* fast file reader `fread` to load *flights* directly as follows:
+
+```{r echo=FALSE}
+options(width=100)
+```
+
+```{r}
+flights <- fread("flights14.csv")
+flights
+dim(flights)
+```
+
+Aside: `fread` accepts `http` and `https` URLs directly as well as operating system commands such as `sed` and `awk` output. See `?fread` for examples.
+
+## Introduction
+
+In this vignette, we will
+
+1. start with basics - what is a *data.table*, its general form, how to *subset* rows, *select and compute* on columns
+
+2. and then we will look at performing data aggregations by group,
+
+## 1. Basics {#basics-1}
+
+### a) What is data.table? {#what-is-datatable-1a}
+
+*data.table* is an R package that provides **an enhanced version** of *data.frames*. In the [Data](#data) section, we already created a *data.table* using `fread()`. We can also create one using the `data.table()` function. Here is an example:
+
+```{r}
+DT = data.table(ID = c("b","b","b","a","a","c"), a = 1:6, b = 7:12, c=13:18)
+DT
+class(DT$ID)
+```
+
+You can also convert existing objects to a *data.table* using `as.data.table()`.
+
+#### Note that: {.bs-callout .bs-callout-info}
+
+* Unlike *data.frames*, columns of `character` type are *never* converted to `factors` by default.
+
+* Row numbers are printed with a `:` in order to visually separate the row number from the first column.
+
+* When the number of rows to print exceeds the global option `datatable.print.nrows` (default = `r getOption("datatable.print.nrows")`), it automatically prints only the top 5 and bottom 5 rows (as can be seen in the [Data](#data) section).
+
+ ```{.r}
+ getOption("datatable.print.nrows")
+ ```
+
+* *data.table* doesn't set or use *row names*, ever. We will see as to why in *"Keys and fast binary search based subset"* vignette.
+
+### b) General form - in what way is a data.table *enhanced*? {#enhanced-1b}
+
+In contrast to a *data.frame*, you can do *a lot more* than just subsetting rows and selecting columns within the frame of a *data.table*, i.e., within `[ ... ]`. To understand it we will have to first look at the *general form* of *data.table* syntax, as shown below:
+
+```{r eval = FALSE}
+DT[i, j, by]
+
+## R: i j by
+## SQL: where select | update group by
+```
+
+Users who have a SQL background might perhaps immediately relate to this syntax.
+
+#### The way to read it (out loud) is: {.bs-callout .bs-callout-info}
+
+Take `DT`, subset rows using `i`, then calculate `j`, grouped by `by`.
+
+#
+
+Let's begin by looking at `i` and `j` first - subsetting rows and operating on columns.
+
+### c) Subset rows in `i` {#subset-i-1c}
+
+#### -- Get all the flights with "JFK" as the origin airport in the month of June.
+
+```{r}
+ans <- flights[origin == "JFK" & month == 6L]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Within the frame of a *data.table*, columns can be referred to *as if they are variables*. Therefore, we simply refer to `dest` and `month` as if they are variables. We do not need to add the prefix `flights$` each time. However using `flights$dest` and `flights$month` would work just fine.
+
+* The *row indices* that satisfies the condition `origin == "JFK" & month == 6L` are computed, and since there is nothing else left to do, a *data.table* all columns from `flights` corresponding to those *row indices* are simply returned.
+
+* A comma after the condition is also not required in `i`. But `flights[dest == "JFK" & month == 6L, ]` would work just fine. In *data.frames* however, the comma is necessary.
+
+#### -- Get the first two rows from `flights`. {#subset-rows-integer}
+
+```{r}
+ans <- flights[1:2]
+ans
+```
+#### {.bs-callout .bs-callout-info}
+
+* In this case, there is no condition. The row indices are already provided in `i`. We therefore return a *data.table* with all columns from `flight` for those *row indices*.
+
+#### -- Sort `flights` first by column `origin` in *ascending* order, and then by `dest` in *descending* order:
+
+We can use the base R function `order()` to accomplish this.
+
+```{r}
+ans <- flights[order(origin, -dest)]
+head(ans)
+```
+#### `order()` is internally optimised {.bs-callout .bs-callout-info}
+
+* We can use "-" on a *character* columns within the frame of a *data.table* to sort in decreasing order.
+
+* In addition, `order(...)` within the frame of a *data.table* uses *data.table*'s internal fast radix order `forder()`, which is much faster than `base::order`. Here's a small example to highlight the difference.
+
+ ```{r}
+ odt = data.table(col=sample(1e7))
+ (t1 <- system.time(ans1 <- odt[base::order(col)])) ## uses order from base R
+ (t2 <- system.time(ans2 <- odt[order(col)])) ## uses data.table's forder
+ (identical(ans1, ans2))
+ ````
+
+The speedup here is **~`r round(t1[3]/t2[3])`x**. We will discuss *data.table*'s fast order in more detail in the *data.table internals* vignette.
+
+* This is so that you can improve performance tremendously while using already familiar functions.
+
+#
+```{r echo = FALSE}
+rm(odt); rm(ans1); rm(ans2); rm(t1); rm(t2)
+```
+
+### d) Select column(s) in `j` {#select-j-1d}
+
+#### -- Select `arr_delay` column, but return it as a *vector*.
+
+```{r}
+ans <- flights[, arr_delay]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Since columns can be referred to as if they are variables within the frame of data.tables, we directly refer to the *variable* we want to subset. Since we want *all the rows*, we simply skip `i`.
+
+* It returns *all* the rows for the column `arr_delay`.
+
+#### -- Select `arr_delay` column, but return as a *data.table* instead.
+
+```{r}
+ans <- flights[, list(arr_delay)]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We wrap the *variables* (column names) within `list()`, which ensures that a *data.table* is returned. In case of a single column name, not wrapping with `list()` returns a vector instead, as seen in the [previous example](#select-j-1d).
+
+* *data.table* also allows using `.()` to wrap columns with. It is an *alias* to `list()`; they both mean the same. Feel free to use whichever you prefer.
+
+ We will continue to use `.()` from here on.
+
+#
+
+*data.tables* (and *data.frames*) are internally *lists* as well, but with all its columns of equal length and with a *class* attribute. Allowing `j` to return a *list* enables converting and returning a *data.table* very efficiently.
+
+#### Tip: {.bs-callout .bs-callout-warning #tip-1}
+
+As long as `j-expression` returns a *list*, each element of the list will be converted to a column in the resulting *data.table*. This makes `j` quite powerful, as we will see shortly.
+
+#### -- Select both `arr_delay` and `dep_delay` columns.
+
+```{r}
+ans <- flights[, .(arr_delay, dep_delay)]
+head(ans)
+
+## alternatively
+# ans <- flights[, list(arr_delay, dep_delay)]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Wrap both columns within `.()`, or `list()`. That's it.
+
+#
+
+#### -- Select both `arr_delay` and `dep_delay` columns *and* rename them to `delay_arr` and `delay_dep`.
+
+Since `.()` is just an alias for `list()`, we can name columns as we would while creating a *list*.
+
+```{r}
+ans <- flights[, .(delay_arr = arr_delay, delay_dep = dep_delay)]
+head(ans)
+```
+
+That's it.
+
+### e) Compute or *do* in `j`
+
+#### -- How many trips have had total delay < 0?
+
+```{r}
+ans <- flights[, sum((arr_delay + dep_delay) < 0)]
+ans
+```
+
+#### What's happening here? {.bs-callout .bs-callout-info}
+
+* *data.table*'s `j` can handle more than just *selecting columns* - it can handle *expressions*, i.e., *compute on columns*. This shouldn't be surprising, as columns can be referred to as if they are variables. Then we should be able to *compute* by calling functions on those variables. And that's what precisely happens here.
+
+### f) Subset in `i` *and* do in `j`
+
+#### -- Calculate the average arrival and departure delay for all flights with "JFK" as the origin airport in the month of June.
+
+```{r}
+ans <- flights[origin == "JFK" & month == 6L,
+ .(m_arr=mean(arr_delay), m_dep=mean(dep_delay))]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We first subset in `i` to find matching *row indices* where `origin` airport equals *"JFK"*, and `month` equals *6*. At this point, we *do not* subset the entire *data.table* corresponding to those rows.
+
+* Now, we look at `j` and find that it uses only *two columns*. And what we have to do is to compute their `mean()`. Therefore we subset just those columns corresponding to the matching rows, and compute their `mean()`.
+
+Because the three main components of the query (`i`, `j` and `by`) are *together* inside `[...]`, *data.table* can see all three and optimise the query altogether *before evaluation*, not each separately. We are able to therefore avoid the entire subset, for both speed and memory efficiency.
+
+#### -- How many trips have been made in 2014 from "JFK" airport in the month of June?
+
+```{r}
+ans <- flights[origin == "JFK" & month == 6L, length(dest)]
+ans
+```
+
+The function `length()` requires an input argument. We just needed to compute the number of rows in the subset. We could have used any other column as input argument to `length()` really.
+
+This type of operation occurs quite frequently, especially while grouping as we will see in the next section, that *data.table* provides a *special symbol* `.N` for it.
+
+#### Special symbol `.N`: {.bs-callout .bs-callout-info #special-N}
+
+`.N` is a special in-built variable that holds the number of observations in the current group. It is particularly useful when combined with `by` as we'll see in the next section. In the absence of group by operations, it simply returns the number of rows in the subset.
+
+#
+So we can now accomplish the same task by using `.N` as follows:
+
+```{r}
+ans <- flights[origin == "JFK" & month == 6L, .N]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Once again, we subset in `i` to get the *row indices* where `origin` airport equals *"JFK"*, and `month` equals *6*.
+
+* We see that `j` uses only `.N` and no other columns. Therefore the entire subset is not materialised. We simply return the number of rows in the subset (which is just the length of row indices).
+
+* Note that we did not wrap `.N` with `list()` or `.()`. Therefore a vector is returned.
+
+We could have accomplished the same operation by doing `nrow(flights[origin == "JFK" & month == 6L])`. However, it would have to subset the entire *data.table* first corresponding to the *row indices* in `i` *and then* return the rows using `nrow()`, which is unnecessary and inefficient. We will cover this and other optimisation aspects in detail under the *data.table design* vignette.
+
+### g) Great! But how can I refer to columns by names in `j` (like in a *data.frame*)?
+
+You can refer to column names the *data.frame* way using `with = FALSE`.
+
+#### -- Select both `arr_delay` and `dep_delay` columns the *data.frame* way.
+
+```{r}
+ans <- flights[, c("arr_delay", "dep_delay"), with=FALSE]
+head(ans)
+```
+
+The argument is named `with` after the R function `with()` because of similar functionality. Suppose you've a *data.frame* `DF` and you'd like to subset all rows where `x > 1`.
+
+```{r}
+DF = data.frame(x = c(1,1,1,2,2,3,3,3), y = 1:8)
+
+## (1) normal way
+DF[DF$x > 1, ] # data.frame needs that ',' as well
+
+## (2) using with
+DF[with(DF, x > 1), ]
+```
+
+#### {.bs-callout .bs-callout-info #with_false}
+
+* Using `with()` in (2) allows using `DF`'s column `x` as if it were a variable.
+
+ Hence the argument name `with` in *data.table*. Setting `with=FALSE` disables the ability to refer to columns as if they are variables, thereby restoring the "*data.frame* mode".
+
+* We can also *deselect* columns using `-` or `!`. For example:
+
+ ```{r eval=FALSE}
+ ## not run
+
+ # returns all columns except arr_delay and dep_delay
+ ans <- flights[, !c("arr_delay", "dep_delay"), with=FALSE]
+ # or
+ ans <- flights[, -c("arr_delay", "dep_delay"), with=FALSE]
+ ```
+
+* From `v1.9.5+`, we can also select by specifying start and end column names, for e.g, `year:day` to select the first three columns.
+
+ ```{r eval=FALSE}
+ ## not run
+
+ # returns year,month and day
+ ans <- flights[, year:day, with=FALSE]
+ # returns day, month and year
+ ans <- flights[, day:year, with=FALSE]
+ # returns all columns except year, month and day
+ ans <- flights[, -(year:day), with=FALSE]
+ ans <- flights[, !(year:day), with=FALSE]
+ ```
+
+ This is particularly handy while working interactively.
+
+#
+
+`with = TRUE` is default in *data.table* because we can do much more by allowing `j` to handle expressions - especially when combined with `by` as we'll see in a moment.
+
+## 2. Aggregations
+
+We've already seen `i` and `j` from *data.table*'s general form in the previous section. In this section, we'll see how they can be combined together with `by` to perform operations *by group*. Let's look at some examples.
+
+### a) Grouping using `by`
+
+#### -- How can we get the number of trips corresponding to each origin airport?
+
+```{r}
+ans <- flights[, .(.N), by=.(origin)]
+ans
+
+## or equivalently using a character vector in 'by'
+# ans <- flights[, .(.N), by="origin"]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We know `.N` [is a special variable](#special-N) that holds the number of rows in the current group. Grouping by `origin` obtains the number of rows, `.N`, for each group.
+
+* By doing `head(flights)` you can see that the origin airports occur in the order *"JFK"*, *"LGA"* and *"EWR"*. The original order of grouping variables is preserved in the result.
+
+* Since we did not provide a name for the column returned in `j`, it was named `N`automatically by recognising the special symbol `.N`.
+
+* `by` also accepts character vector of column names. It is particularly useful to program with, for e.g., designing a function with the columns to be group by as a function argument.
+
+* When there's only one column or expression to refer to in `j` and `by`, we can drop the `.()` notation. This is purely for convenience. We could instead do:
+
+ ```{r}
+ ans <- flights[, .N, by=origin]
+ ans
+ ```
+
+ We'll use this convenient form wherever applicable hereafter.
+
+#
+
+#### -- How can we calculate the number of trips for each origin airport for carrier code *"AA"*? {#origin-.N}
+
+The unique carrier code *"AA"* corresponds to *American Airlines Inc.*
+
+```{r}
+ans <- flights[carrier == "AA", .N, by=origin]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We first obtain the row indices for the expression `carrier == "AA"` from `i`.
+
+* Using those *row indices*, we obtain the number of rows while grouped by `origin`. Once again no columns are actually materialised here, because the `j-expression` does not require any columns to be actually subsetted and is therefore fast and memory efficient.
+
+#### -- How can we get the total number of trips for each `origin, dest` pair for carrier code *"AA"*? {#origin-dest-.N}
+
+```{r}
+ans <- flights[carrier == "AA", .N, by=.(origin,dest)]
+head(ans)
+
+## or equivalently using a character vector in 'by'
+# ans <- flights[carrier == "AA", .N, by=c("origin", "dest")]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `by` accepts multiple columns. We just provide all the columns by which to group by.
+
+#### -- How can we get the average arrival and departure delay for each `orig,dest` pair for each month for carrier code *"AA"*? {#origin-dest-month}
+
+```{r}
+ans <- flights[carrier == "AA",
+ .(mean(arr_delay), mean(dep_delay)),
+ by=.(origin, dest, month)]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We did not provide column names for expressions in `j`, they were automatically generated (`V1`, `V2`).
+
+* Once again, note that the input order of grouping columns is preserved in the result.
+
+#
+
+Now what if we would like to order the result by those grouping columns `origin`, `dest` and `month`?
+
+### b) keyby
+
+*data.table* retaining the original order of groups is intentional and by design. There are cases when preserving the original order is essential. But at times we would like to automatically sort by the variables we grouped by.
+
+#### -- So how can we directly order by all the grouping variables?
+
+```{r}
+ans <- flights[carrier == "AA",
+ .(mean(arr_delay), mean(dep_delay)),
+ keyby=.(origin, dest, month)]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* All we did was to change `by` to `keyby`. This automatically orders the result by the grouping variables in increasing order. Note that `keyby()` is applied after performing the operation, i.e., on the computed result.
+
+**Keys:** Actually `keyby` does a little more than *just ordering*. It also *sets a key* after ordering by setting an *attribute* called `sorted`. But we'll learn more about `keys` in the next vignette.
+
+For now, all you've to know is you can use `keyby` to automatically order by the columns specified in `by`.
+
+### c) Chaining
+
+Let's reconsider the task of [getting the total number of trips for each `origin, dest` pair for carrier *"AA"*](#origin-dest-.N).
+
+```{r}
+ans <- flights[carrier == "AA", .N, by = .(origin, dest)]
+```
+
+#### -- How can we order `ans` using the columns `origin` in ascending order, and `dest` in descending order?
+
+We can store the intermediate result in a variable, and then use `order(origin, -dest)` on that variable. It seems fairly straightforward.
+
+```{r}
+ans <- ans[order(origin, -dest)]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Recall that we can use "-" on a *character* column in `order()` within the frame of a *data.table*. This is possible to due *data.table*'s internal query optimisation.
+
+* Also recall that `order(...)` with the frame of a *data.table* is *automatically optimised* to use *data.table*'s internal fast radix order `forder()` for speed. So you can keep using the already *familiar* base R functions without compromising in speed or memory efficiency that *data.table* offers. We will cover this in more detail in the *data.table internals* vignette.
+
+#
+
+But this requires having to assign the intermediate result and then overwriting that result. We can do one better and avoid this intermediate assignment on to a variable altogther by `chaining` expressions.
+
+```{r}
+ans <- flights[carrier == "AA", .N, by=.(origin, dest)][order(origin, -dest)]
+head(ans, 10)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We can tack expressions one after another, *forming a chain* of operations, i.e., `DT[ ... ][ ... ][ ... ]`.
+
+* Or you can also chain them vertically:
+
+ ```{r eval=FALSE}
+ DT[ ...
+ ][ ...
+ ][ ...
+ ]
+ ```
+
+### d) Expressions in `by`
+
+#### -- Can `by` accept *expressions* as well or just take columns?
+
+Yes it does. As an example, if we would like to find out how many flights started late but arrived early (or on time), started and arrived late etc...
+
+```{r}
+ans <- flights[, .N, .(dep_delay>0, arr_delay>0)]
+ans
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* The last row corresponds to `dep_delay > 0 = TRUE` and `arr_delay > 0 = FALSE`. We can see that `r flights[!is.na(arr_delay) & !is.na(dep_delay), .N, .(dep_delay>0, arr_delay>0)][, N[4L]]` flights started late but arrived early (or on time).
+
+* Note that we did not provide any names to `by-expression`. And names have been automatically assigned in the result.
+
+* You can provide other columns along with expressions, for example: `DT[, .N, by=.(a, b>0)]`.
+
+### e) Multiple columns in `j` - `.SD`
+
+#### -- Do we have to compute `mean()` for each column individually?
+
+It is of course not practical to have to type `mean(myCol)` for every column one by one. What if you had a 100 columns to compute `mean()` of?
+
+How can we do this efficiently? To get there, refresh on [this tip](#tip-1) - *"As long as j-expression returns a list, each element of the list will be converted to a column in the resulting data.table"*. Suppose we can refer to the *data subset for each group* as a variable *while grouping*, then we can loop through all the columns of that variable using the already familiar base function `lapply()`. We don't have to learn any new function.
+
+#### Special symbol `.SD`: {.bs-callout .bs-callout-info #special-SD}
+
+*data.table* provides a *special* symbol, called `.SD`. It stands for **S**ubset of **D**ata. It by itself is a *data.table* that holds the data for *the current group* defined using `by`.
+
+Recall that a *data.table* is internally a list as well with all its columns of equal length.
+
+#
+
+Let's use the [*data.table* `DT` from before](#what-is-datatable-1a) to get a glimpse of what `.SD` looks like.
+
+```{r}
+DT
+
+DT[, print(.SD), by=ID]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `.SD` contains all the columns *except the grouping columns* by default.
+
+* It is also generated by preserving the original order - data corresponding to `ID = "b"`, then `ID = "a"`, and then `ID = "c"`.
+
+#
+
+To compute on (multiple) columns, we can then simply use the base R function `lapply()`.
+
+```{r}
+DT[, lapply(.SD, mean), by=ID]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `.SD` holds the rows corresponding to columns *a*, *b* and *c* for that group. We compute the `mean()` on each of these columns using the already familiar base function `lapply()`.
+
+* Each group returns a list of three elements containing the mean value which will become the columns of the resulting `data.table`.
+
+* Since `lapply()` returns a *list*, there is no need to wrap it with an additional `.()` (if necessary, refer to [this tip](#tip-1)).
+
+#
+
+We are almost there. There is one little thing left to address. In our `flights` *data.table*, we only wanted to calculate the `mean()` of two columns `arr_delay` and `dep_delay`. But `.SD` would contain all the columns other than the grouping variables by default.
+
+#### -- How can we specify just the columns we would like to compute the `mean()` on?
+
+#### .SDcols {.bs-callout .bs-callout-info}
+
+Using the argument `.SDcols`. It accepts either column names or column indices. For example, `.SDcols = c("arr_delay", "dep_delay")` ensures that `.SD` contains only these two columns for each group.
+
+Similar to the [with = FALSE section](#with_false), you can also provide the columns to remove instead of columns to keep using `-` or `!` sign as well as select consecutive columns as `colA:colB` and deselect consecutive columns as `!(colA:colB) or `-(colA:colB)`.
+
+#
+Now let us try to use `.SD` along with `.SDcols` to get the `mean()` of `arr_delay` and `dep_delay` columns grouped by `origin`, `dest` and `month`.
+
+```{r}
+flights[carrier == "AA", ## Only on trips with carrier "AA"
+ lapply(.SD, mean), ## compute the mean
+ by=.(origin, dest, month), ## for every 'origin,dest,month'
+ .SDcols=c("arr_delay", "dep_delay")] ## for just those specified in .SDcols
+```
+
+### f) Subset `.SD` for each group:
+
+#### -- How can we return the first two rows for each `month`?
+
+```{r}
+ans <- flights[, head(.SD, 2), by=month]
+head(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `.SD` is a *data.table* that holds all the rows for *that group*. We simply subset the first two rows as we have seen [here](#subset-rows-integer) already.
+
+* For each group, `head(.SD, 2)` returns the first two rows as a *data.table* which is also a list. So we do not have to wrap it with `.()`.
+
+### g) Why keep `j` so flexible?
+
+So that we have a consistent syntax and keep using already existing (and familiar) base functions instead of learning new functions. To illustrate, let us use the *data.table* `DT` we created at the very beginning under [What is a data.table?](#what-is-datatable-1a) section.
+
+#### -- How can we concatenate columns `a` and `b` for each group in `ID`?
+
+```{r}
+DT[, .(val = c(a,b)), by=ID]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* That's it. There is no special syntax required. All we need to know is the base function `c()` which concatenates vectors and [the tip from before](#tip-1).
+
+#### -- What if we would like to have all the values of column `a` and `b` concatenated, but returned as a list column?
+
+```{r}
+DT[, .(val = list(c(a,b))), by=ID]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Here, we first concatenate the values with `c(a,b)` for each group, and wrap that with `list()`. So for each group, we return a list of all concatenated values.
+
+* Note those commas are for display only. A list column can contain any object in each cell, and in this example, each cell is itself a vector and some cells contain longer vectors than others.
+
+#
+Once you start internalising usage in `j`, you will realise how powerful the syntax can be. A very useful way to understand it is by playing around, with the help of `print()`.
+
+For example:
+
+```{r}
+## (1) look at the difference between
+DT[, print(c(a,b)), by=ID]
+
+## (2) and
+DT[, print(list(c(a,b))), by=ID]
+```
+
+In (1), for each group, a vector is returned, with length = 6,4,2 here. However (2) returns a list of length 1 for each group, with its first element holding vectors of length 6,4,2. Therefore (1) results in a length of ` 6+4+2 = `r 6+4+2``, whereas (2) returns `1+1+1=`r 1+1+1``.
+
+## Summary
+
+The general form of *data.table* syntax is:
+
+```{r eval=FALSE}
+DT[i, j, by]
+```
+
+We have seen so far that,
+
+#### Using `i`: {.bs-callout .bs-callout-info}
+
+* We can subset rows similar to a *data.frame* - except you don't have to use `DT$` repetitively since columns within the frame of a *data.table* are seen as if they are *variables*.
+
+* We can also sort a *data.table* using `order()`, which internally uses *data.table*'s fast order for performance.
+
+We can do much more in `i` by keying a *data.table*, which allows blazing fast subsets and joins. We will see this in the *"Keys and fast binary search based subsets"* and *"Joins and rolling joins"* vignette.
+
+#### Using `j`: {.bs-callout .bs-callout-info}
+
+1. Select columns the *data.table* way: `DT[, .(colA, colB)]`.
+
+2. Select columns the *data.frame* way: `DT[, c("colA", "colB"), with=FALSE]`.
+
+3. Compute on columns: `DT[, .(sum(colA), mean(colB))]`.
+
+4. Provide names if necessary: `DT[, .(sA =sum(colA), mB = mean(colB))]`.
+
+5. Combine with `i`: `DT[colA > value, sum(colB)]`.
+
+#
+
+#### Using `by`: {.bs-callout .bs-callout-info}
+
+* Using `by`, we can group by columns by specifying a *list of columns* or a *character vector of column names* or even *expressions*. The flexibility of `j`, combined with `by` and `i` makes for a very powerful syntax.
+
+* `by` can handle multiple columns and also *expressions*.
+
+* We can `keyby` grouping columns to automatically sort the grouped result.
+
+* We can use `.SD` and `.SDcols` in `j` to operate on multiple columns using already familiar base functions. Here are some examples:
+
+ 1. `DT[, lapply(.SD, fun), by=., .SDcols=...]` - applies `fun` to all columns specified in `.SDcols` while grouping by the columns specified in `by`.
+
+ 2. `DT[, head(.SD, 2), by=.]` - return the first two rows for each group.
+
+ 3. `DT[col > val, head(.SD, 1), by=.]` - combine `i` along with `j` and `by`.
+
+#
+
+#### And remember the tip: {.bs-callout .bs-callout-warning}
+
+As long as `j` returns a *list*, each element of the list will become a column in the resulting *data.table*.
+
+#
+
+We will see how to *add/update/delete* columns *by reference* and how to combine them with `i` and `by` in the next vignette.
+
+***
+
diff --git a/vignettes/datatable-intro.Rnw b/vignettes/datatable-intro.Rnw
new file mode 100644
index 0000000..49627cb
--- /dev/null
+++ b/vignettes/datatable-intro.Rnw
@@ -0,0 +1,293 @@
+\documentclass[a4paper]{article}
+
+\usepackage[margin=3cm]{geometry}
+%%\usepackage[round]{natbib}
+\usepackage[colorlinks=true,urlcolor=blue]{hyperref}
+
+%%\newcommand{\acronym}[1]{\textsc{#1}}
+%%\newcommand{\class}[1]{\mbox{\textsf{#1}}}
+\newcommand{\code}[1]{\mbox{\texttt{#1}}}
+\newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}}
+\newcommand{\proglang}[1]{\textsf{#1}}
+\SweaveOpts{keep.source=TRUE, strip.white=all}
+%% \VignetteIndexEntry{Quick introduction}
+
+<<echo=FALSE,results=hide>>=
+if (!exists("data.table",.GlobalEnv)) library(data.table)
+# In devel won't call library, but R CMD build/check will.
+rm(list=as.character(tables()$NAME),envir=.GlobalEnv)
+# for development when we Sweave this file repeatedly. Otherwise first tables() shows tables from last run
+@
+
+\begin{document}
+\title{Introduction to the \pkg{data.table} package in \proglang{R}}
+\date{Revised: \today\\(A later revision may be available on the \href{https://github.com/Rdatatable/data.table/wiki}{homepage})}
+\maketitle
+
+\section*{Introduction}
+
+This vignette is aimed at those who are already familiar with creating and subsetting \code{data.frame} in \proglang{R}. We aim for this quick introduction
+to be readable in {\bf 10 minutes}, briefly covering a few features:
+1.\,Keys; 2.\,Fast Grouping; and 3.\,Fast \emph{ordered} join.
+
+\section*{Creation}
+
+Recall that we create a \code{data.frame} using the function \code{data.frame()}:
+<<>>=
+DF = data.frame(x=c("b","b","b","a","a"),v=rnorm(5))
+DF
+@
+A \code{data.table} is created in exactly the same way:
+<<>>=
+DT = data.table(x=c("b","b","b","a","a"),v=rnorm(5))
+DT
+@
+Observe that a \code{data.table} prints the row numbers with a colon so as to visually separate the row number from the first column.
+We can easily convert existing \code{data.frame} objects to \code{data.table}.
+<<>>=
+CARS = data.table(cars)
+head(CARS)
+@
+We have just created two \code{data.table}s: \code{DT} and \code{CARS}. It is often useful to see a list of all
+\code{data.table}s in memory:
+<<>>=
+tables()
+@
+
+The MB column is useful to quickly assess memory use and to spot if any redundant tables can be
+removed to free up memory. Just like \code{data.frame}s, \code{data.table}s must fit inside RAM.
+
+Some users regularly work with 20 or more tables in memory, rather like a database.
+The result of \code{tables()} is itself a \code{data.table}, returned silently, so that \code{tables()}
+can be used in programs. \code{tables()} is unrelated to the base function \code{table()}.
+
+To see the column types :
+
+<<>>=
+sapply(DT,class)
+@
+
+You may have noticed the empty column KEY in the result of \code{tables()} above. This is the subject of the next section.
+
+
+\section*{1. Keys}
+
+Let's start by considering \code{data.frame}, specifically \code{rownames}. We know that each row has exactly one row name. However, a person (for example) has at least two names, a first name and a second name. It's useful to organise a telephone directory sorted by surname then first name.
+
+In \code{data.table}, a \emph{key} consists of one \emph{or more} columns. These columns may be integer, factor or numeric as well as character. Furthermore, the rows are sorted by the key. Therefore, a \code{data.table} can have at most one key because it cannot be sorted in more than one way. We can think of a key as like super-charged row names; i.e., mult-column and multi-type.
+
+Uniqueness is not enforced; i.e., duplicate key values are allowed. Since
+the rows are sorted by the key, any duplicates in the key will appear consecutively.
+
+Let's remind ourselves of our tables:
+<<>>=
+tables()
+DT
+@
+
+No keys have been set yet.
+
+<<>>=
+DT[2,] # select row 2
+DT[x=="b",] # select rows where column x == "b"
+@
+
+Aside: notice that we did not need to prefix \code{x} with \code{DT\$x}. In \code{data.table} queries, we can use column names as if they are variables directly.
+
+But since there are no rownames, the following does not work:
+<<>>=
+cat(try(DT["b",],silent=TRUE))
+@
+
+The error message tells us we need to use \code{setkey()}:
+<<>>=
+setkey(DT,x)
+DT
+@
+
+Notice that the rows in \code{DT} have now been re-ordered according to the values of \code{x}.
+The two \code{"a"} rows have moved to the top.
+We can confirm that \code{DT} does indeed have a key using \code{haskey()}, \code{key()},
+\code{attributes()}, or just running \code{tables()}.
+
+<<>>=
+tables()
+@
+
+Now that we are sure \code{DT} has a key, let's try again:
+
+<<>>=
+DT["b",]
+@
+
+By default all the rows in the group are returned\footnote{In contrast to a \code{data.frame} where only the first rowname is returned when the rownames contain duplicates.}. The \code{mult} argument (short for \emph{multiple}) allows the first or last row of the group to be returned instead.
+
+<<>>=
+DT["b",mult="first"]
+DT["b",mult="last"]
+@
+
+Also, the comma is optional.
+
+<<>>=
+DT["b"]
+@
+
+Let's now create a new \code{data.frame}. We will make it large enough to demonstrate the
+difference between a \emph{vector scan} and a \emph{binary search}.
+<<print=TRUE>>=
+grpsize = ceiling(1e7/26^2) # 10 million rows, 676 groups
+tt=system.time( DF <- data.frame(
+ x=rep(LETTERS,each=26*grpsize),
+ y=rep(letters,each=grpsize),
+ v=runif(grpsize*26^2),
+ stringsAsFactors=FALSE)
+)
+head(DF,3)
+tail(DF,3)
+dim(DF)
+@
+
+We might say that \proglang{R} has created a 3 column table and \emph{inserted}
+\Sexpr{format(nrow(DF),big.mark=",",scientific=FALSE)} rows. It took \Sexpr{format(tt[3],nsmall=3)} secs, so it inserted
+\Sexpr{format(as.integer(nrow(DF)/tt[3]),big.mark=",",scientific=FALSE)} rows per second. This is normal in base \proglang{R}. Notice that we set \code{stringsAsFactors=FALSE}. This makes it a little faster for a fairer comparison, but feel free to experiment.
+
+Let's extract an arbitrary group from \code{DF}:
+
+<<print=TRUE>>=
+tt=system.time(ans1 <- DF[DF$x=="R" & DF$y=="h",]) # 'vector scan'
+head(ans1,3)
+dim(ans1)
+@
+
+Now convert to a \code{data.table} and extract the same group:
+
+<<>>=
+DT = as.data.table(DF) # but normally use fread() or data.table() directly, originally
+system.time(setkey(DT,x,y)) # one-off cost, usually
+@
+<<print=TRUE>>=
+ss=system.time(ans2 <- DT[list("R","h")]) # binary search
+head(ans2,3)
+dim(ans2)
+identical(ans1$v, ans2$v)
+@
+<<echo=FALSE>>=
+if(!identical(ans1$v, ans2$v)) stop("vector scan vs binary search not equal")
+@
+
+At \Sexpr{format(ss[3],nsmall=3)} seconds, this was {\bf\Sexpr{as.integer(tt[3]/ss[3])}} times faster than \Sexpr{format(tt[3],nsmall=3)} seconds,
+and produced precisely the same result. If you are thinking that a few seconds is not much to save, it's the relative speedup that's important. The
+vector scan is linear, but the binary search is O(log n). It scales. If a task taking 10 hours is sped up by 100 times to 6 minutes, that is
+significant\footnote{We wonder how many people are deploying parallel techniques to code that is vector scanning}.
+
+We can do vector scans in \code{data.table}, too. In other words we can use data.table \emph{badly}.
+
+<<>>=
+system.time(ans1 <- DT[x=="R" & y=="h",]) # works but is using data.table badly
+system.time(ans2 <- DF[DF$x=="R" & DF$y=="h",]) # the data.frame way
+mapply(identical,ans1,ans2)
+@
+
+
+If the phone book analogy helped, the {\bf\Sexpr{as.integer(tt[3]/ss[3])}} times speedup should not be surprising. We use the key to take advantage of the fact
+that the table is sorted and use binary search to find the matching rows. We didn't vector scan; we didn't use \code{==}.
+
+When we used \code{x=="R"} we \emph{scanned} the entire column x, testing each and every value to see if it equalled "R". We did
+it again in the y column, testing for "h". Then \code{\&} combined the two logical results to create a single logical vector which was
+passed to the \code{[} method, which in turn searched it for \code{TRUE} and returned those rows. These were \emph{vectorized} operations. They
+occurred internally in R and were very fast, but they were scans. \emph{We} did those scans because \emph{we} wrote that R code.
+
+
+When \code{i} is a \code{list} (and \code{data.table} is a \code{list} too), we say that we are \emph{joining}. In this case, we are joining DT to the 1 row, 2 column table returned by \code{list("R","h")}. Since we do this a lot, there is an alias for \code{list}: \code{.()}.
+
+<<>>=
+identical( DT[list("R","h"),],
+ DT[.("R","h"),])
+@
+<<echo=FALSE>>=
+if(!identical(DT[list("R","h"),],DT[.("R","h"),])) stop("list != . check")
+@
+
+Both vector scanning and binary search are available in \code{data.table}, but one way of using \code{data.table} is much better than the other.
+
+The join syntax is a short, fast to write and easy to maintain. Passing a \code{data.table} into a \code{data.table} subset is analogous to \code{A[B]} syntax in base \proglang{R} where \code{A} is a matrix and \code{B} is a 2-column matrix\footnote{Subsetting a keyed \code{data.table} by a n-column
+\code{data.table} is consistent with subsetting a n-dimension array by a n-column matrix in base R}. In fact, the \code{A[B]} syntax in base R inspired the \code{data.table} package. There are
+other types of ordered joins and further arguments which are beyond the scope of this quick introduction.
+
+The merge method of \code{data.table} is very similar to \code{X[Y]}, but there are some differences. See FAQ 1.12.
+
+This first section has been about the first argument inside \code{DT[...]}, namely \code{i}. The next section is about the 2nd and 3rd arguments: \code{j} and \code{by}.
+
+
+\section*{2. Fast grouping}
+
+
+The second argument to \code{DT[...]} is \code{j} and may consist of
+one or more expressions whose arguments are (unquoted) column names, as if the column names were variables. Just as we saw earlier in \code{i} as well.
+
+<<>>=
+DT[,sum(v)]
+@
+
+When we supply a \code{j} expression and a 'by' expression, the \code{j} expression is repeated for each 'by' group.
+
+<<>>=
+DT[,sum(v),by=x]
+@
+
+The \code{by} in \code{data.table} is fast. Let's compare it to \code{tapply}.
+
+<<>>=
+ttt=system.time(tt <- tapply(DT$v,DT$x,sum)); ttt
+sss=system.time(ss <- DT[,sum(v),by=x]); sss
+head(tt)
+head(ss)
+identical(as.vector(tt), ss$V1)
+@
+<<echo=FALSE>>=
+if(!identical(as.vector(tt), ss$V1)) stop("by check failed")
+@
+
+At \Sexpr{sprintf("%0.3f",sss[3])} sec, this was {\bf\Sexpr{as.integer(ttt[3]/sss[3])}} times faster than
+\Sexpr{sprintf("%0.3f",ttt[3])} sec, and produced precisely the same result.
+
+Next, let's group by two columns:
+
+<<>>=
+ttt=system.time(tt <- tapply(DT$v,list(DT$x,DT$y),sum)); ttt
+sss=system.time(ss <- DT[,sum(v),by="x,y"]); sss
+tt[1:5,1:5]
+head(ss)
+identical(as.vector(t(tt)), ss$V1)
+@
+<<echo=FALSE>>=
+if(!identical(as.vector(t(tt)), ss$V1)) stop("group check failed")
+@
+
+This was {\bf\Sexpr{as.integer(ttt[3]/sss[3])}} times faster, and the syntax is a little simpler and easier to read.
+\newline
+
+
+\section*{3. Fast ordered joins}
+
+This is also known as last observation carried forward (LOCF) or a \emph{rolling join}.
+
+Recall that \code{X[Y]} is a join between \code{data.table} \code{X} and \code{data.table} \code{Y}. If \code{Y} has 2 columns, the first column is matched
+to the first column of the key of \code{X} and the 2nd column to the 2nd. An equi-join is performed by default, meaning that the values must be equal.
+
+Instead of an equi-join, a rolling join is :
+
+\code{X[Y,roll=TRUE]}
+
+As before the first column of \code{Y} is matched to \code{X} where the values are equal. The last join column in \code{Y} though, the 2nd one in
+this example, is treated specially. If no match is found, then the row before is returned, provided the first column still matches.
+
+Further controls are rolling forwards, backwards, nearest and limited staleness.
+
+For examples type \code{example(data.table)} and follow the output at the prompt.
+
+
+\end{document}
+
+
diff --git a/vignettes/datatable-keys-fast-subset.Rmd b/vignettes/datatable-keys-fast-subset.Rmd
new file mode 100644
index 0000000..b9ebd7c
--- /dev/null
+++ b/vignettes/datatable-keys-fast-subset.Rmd
@@ -0,0 +1,534 @@
+---
+title: "Keys and fast binary search based subset"
+date: "`r Sys.Date()`"
+output:
+ rmarkdown::html_document:
+ theme: spacelab
+ highlight: pygments
+ css : css/bootstrap.css
+vignette: >
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r, echo = FALSE, message = FALSE}
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+options(datatable.auto.index=FALSE)
+```
+
+This vignette is aimed at those who are already familiar with *data.table* syntax, its general form, how to subset rows in `i`, select and compute on columns, add/modify/delete columns *by reference* in `j` and group by using `by`. If you're not familar with these concepts, please read the *"Introduction to data.table"* and *"data.table reference semantics"* vignettes first.
+
+***
+
+## Data {#data}
+
+We will use the same `flights` data as in the *"Introduction to data.table"* vignette.
+
+```{r echo=FALSE}
+options(width=100)
+```
+
+```{r}
+flights <- fread("flights14.csv")
+head(flights)
+dim(flights)
+```
+
+## Introduction
+
+In this vignette, we will
+
+* first introduce the concept of `key` in *data.table*, and set and use keys to perform *fast binary search* based subsets in `i`,
+
+* see that we can combine key based subsets along with `j` and `by` in the exact same way as before,
+
+* look at other additional useful arguments - `mult` and `nomatch`,
+
+* and finally conclude by looking at the advantage of setting keys - perform *fast binary search based subsets* and compare with the traditional vector scan approach.
+
+## 1. Keys
+
+### a) What is a *key*?
+
+In the *"Introduction to data.table"* vignette, we saw how to subset rows in `i` using logical expressions, row numbers and using `order()`. In this section, we will look at another way of subsetting incredibly fast - using *keys*.
+
+But first, let's start by looking at *data.frames*. All *data.frames* have a row names attribute. Consider the *data.frame* `DF` below.
+
+```{r}
+set.seed(1L)
+DF = data.frame(ID1 = sample(letters[1:2], 10, TRUE),
+ ID2 = sample(1:3, 10, TRUE),
+ val = sample(10),
+ stringsAsFactors = FALSE,
+ row.names = sample(LETTERS[1:10]))
+DF
+
+rownames(DF)
+```
+
+We can *subset* a particular row using its row name as shown below:
+
+```{r}
+DF["C", ]
+```
+
+i.e., row names are more or less *an index* to rows of a *data.frame*. However,
+
+1. Each row is limited to *exactly one* row name.
+
+ But, a person (for example) has at least two names - a *first* and a *second* name. It is useful to organise a telephone directory by *surname* then *first name*.
+
+2. And row names should be *unique*.
+
+ ```{r eval=FALSE}
+ rownames(DF) = sample(LETTERS[1:5], 10, TRUE)
+ # Warning: non-unique values when setting 'row.names': 'C', 'D'
+ # Error in `row.names<-.data.frame`(`*tmp*`, value = value): duplicate 'row.names' are not allowed
+ ```
+
+Now let's convert it to a *data.table*.
+
+```{r}
+DT = as.data.table(DF)
+DT
+
+rownames(DT)
+```
+
+* Note that row names have been reset.
+
+* *data.tables* never uses row names. Since *data.tables* **inherit** from *data.frames*, it still has the row names attribute. But it never uses them. We'll see in a moment as to why.
+
+ If you would like to preserve the row names, use `keep.rownames = TRUE` in `as.data.table()` - this will create a new column called `rn` and assign row names to this column.
+
+Instead, in *data.tables* we set and use `keys`. Think of a `key` as **supercharged rownames**.
+
+#### Keys and their properties {.bs-callout .bs-callout-info #key-properties}
+
+1. We can set keys on *multiple columns* and the column can be of *different types* -- *integer*, *numeric*, *character*, *factor*, *integer64* etc. *list* and *complex* types are not supported yet.
+
+2. Uniqueness is not enforced, i.e., duplicate key values are allowed. Since rows are sorted by key, any duplicates in the key columns will appear consecutively.
+
+3. Setting a `key` *does two things*:
+
+ a. reorders the rows of the *data.table* by the column(s) provided *by reference*, always in *increasing* order.
+
+ b. marks those columns as *key* columns by setting an attribute called `sorted` to the *data.table*.
+
+ Since the rows are reordered, a *data.table* can have at most one key because it can not be sorted in more than one way.
+
+#
+
+For the rest of the vignette, we will work with `flights` data set.
+
+### b) Set, get and use keys on a *data.table*
+
+#### -- How can we set the column `origin` as key in the *data.table* `flights`?
+
+```{r}
+setkey(flights, origin)
+head(flights)
+
+## alternatively we can provide character vectors to the function 'setkeyv()'
+# setkeyv(flights, "origin") # useful to program with
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* You can use the function `setkey()` and provide the column names (without quoting them). This is helpful during interactive use.
+
+* Alternatively you can pass a character vector of column names to the function `setkeyv()`. This is particularly useful while designing functions to pass columns to set key on as function arguments.
+
+* Note that we did not have to assign the result back to a variable. This is because like the `:=` function we saw in the *"Introduction to data.table"* vignette, `setkey()` and `setkeyv()` modify the input *data.table* *by reference*. They return the result invisibly.
+
+* The *data.table* is now reordered (or sorted) by the column we provided - `origin`. Since we reorder by reference, we only require additional memory of one column of length equal to the number of rows in the *data.table*, and is therefore very memory efficient.
+
+* You can also set keys directly when creating *data.tables* using the `data.table()` function using `key=` argument. It takes a character vector of column names.
+
+#### set* and `:=`: {.bs-callout .bs-callout-info}
+
+In *data.table*, the `:=` operator and all the `set*` (e.g., `setkey`, `setorder`, `setnames` etc..) functions are the only ones which modify the input object *by reference*.
+
+#
+
+Once you *key* a *data.table* by certain columns, you can subset by querying those key columns using the `.()` notation in `i`. Recall that `.()` is an *alias to* `list()`.
+
+#### -- Use the key column `origin` to subset all rows where the origin airport matches *"JFK"*
+
+```{r}
+flights[.("JFK")]
+
+## alternatively
+# flights[J("JFK")] (or) flights[list("JFK")]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* The *key* column has already been set to `origin`. So it is sufficient to provide the value, here *"JFK"*, directly. The `.()` syntax helps identify that the task requires looking up the value *"JFK"* in the key column of *data.table* (here column `origin` of `flights` *data.table*).
+
+* The *row indices* corresponding to the value *"JFK"* in `origin` is obtained first. And since there is no expression in `j`, all columns corresponding to those row indices are returned.
+
+* On single column key of *character* type, you can drop the `.()` notation and use the values directly when subsetting, like subset using row names on *data.frames*.
+
+ ```{r eval=FALSE}
+ flights["JFK"] ## same as flights[.("JFK")]
+ ```
+
+* We can subset any amount of values as required
+
+ ```{r eval = FALSE}
+ flights[c("JFK", "LGA")] ## same as flights[.(c("JFK", "LGA"))]
+ ```
+
+ This returns all columns corresponding to those rows where `origin` column matches either *"JFK"* or *"LGA"*.
+
+#### -- How can we get the column(s) a *data.table* is keyed by?
+
+Using the function `key()`.
+
+```{r}
+key(flights)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* It returns a character vector of all the key columns.
+
+* If no key is set, it returns `NULL`.
+
+### c) Keys and multiple columns
+
+To refresh, *keys* are like *supercharged* rownames. We can set key on multiple columns and they can be of multiple types.
+
+#### -- How can I set keys on both `origin` *and* `dest` columns?
+
+```{r}
+setkey(flights, origin, dest)
+head(flights)
+
+## or alternatively
+# setkeyv(flights, c("origin", "dest")) # provide a character vector of column names
+
+key(flights)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* It sorts the *data.table* first by the column `origin` and then by `dest` *by reference*.
+
+#### -- Subset all rows using key columns where first key column `origin` matches *"JFK"* and second key column `dest` matches *"MIA"*
+
+```{r}
+flights[.("JFK", "MIA")]
+```
+
+#### How does the subset work here? {.bs-callout .bs-callout-info #multiple-key-point}
+
+* It is important to undertand how this works internally. *"JFK"* is first matched against the first key column `origin`. And *within those matching rows*, *"MIA"* is matched against the second key column `dest` to obtain *row indices* where both `origin` and `dest` match the given values.
+
+* Since no `j` is provided, we simply return *all columns* corresponding to those row indices.
+
+#
+
+#### -- Subset all rows where just the first key column `origin` matches *"JFK"*
+
+```{r}
+key(flights)
+
+flights[.("JFK")] ## or in this case simply flights["JFK"], for convenience
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Since we did not provide any values for the second key column `dest`, it just matches *"JFK"* against the first key column `origin` and returns all the matched rows.
+
+#### -- Subset all rows where just the second key column `dest` matches *"MIA"*
+
+```{r}
+flights[.(unique(origin), "MIA")]
+```
+
+#### What's happening here? {.bs-callout .bs-callout-info}
+
+* Read [this](#multiple-key-point) again. The value provided for the second key column *"MIA"* has to find the matching vlaues in `dest` key column *on the matching rows provided by the first key column `origin`*. We can not skip the values of key columns *before*. Therfore we provide *all* unique values from key column `origin`.
+
+* *"MIA"* is automatically recycled to fit the length of `unique(origin)` which is *3*.
+
+## 2) Combining keys with `j` and `by`
+
+All we have seen so far is the same concept -- obtaining *row indices* in `i`, but just using a different method -- using `keys`. It shouldn't be surprising that we can do exactly the same things in `j` and `by` as seen from the previous vignettes. We will highlight this with a few examples.
+
+### a) Select in `j`
+
+#### -- Return `arr_delay` column as a *data.table* corresponding to `origin = "LGA"` and `dest = "TPA"`.
+
+```{r}
+key(flights)
+flights[.("LGA", "TPA"), .(arr_delay)]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* The *row indices* corresponding to `origin == "LGA" and `dest == "TPA"` are obtained using *key based subset*.
+
+* Once we have the row indices, we look at `j` which requires only the `arr_delay` column. So we simply select the column `arr_delay` for those *row indices* in the exact same way as we have seen in *Introduction to data.table* vignette.
+
+* We could have returned the result by using `with = FALSE` as well.
+
+ ```{r eval = FALSE}
+ flights[.("LGA", "TPA"), "arr_delay", with=FALSE]
+ ```
+
+### b) Chaining
+
+#### -- On the result obtained above, use chaining to order the column in decreasing order.
+
+```{r}
+flights[.("LGA", "TPA"), .(arr_delay)][order(-arr_delay)]
+```
+
+### c) Compute or *do* in `j`
+
+#### -- Find the maximum arrival delay correspondong to `origin = "LGA"` and `dest = "TPA"`.
+
+```{r}
+flights[.("LGA", "TPA"), max(arr_delay)]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We can verify that the result is identical to first value (486) from the previous example.
+
+### d) *sub-assign* by reference using `:=` in `j`
+
+We have seen this example already in the *Reference semantics* vignette. Let's take a look at all the `hours` available in the `flights` *data.table*:
+
+```{r}
+# get all 'hours' in flights
+flights[, sort(unique(hour))]
+```
+
+We see that there are totally `25` unique values in the data. Both *0* and *24* hours seem to be present. Let's go ahead and replace *24* with *0*, but this time using *key*.
+
+```{r}
+setkey(flights, hour)
+key(flights)
+flights[.(24), hour := 0L]
+key(flights)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We first set `key` to *hour*. This reorders `flights` by the column *hour* and marks that column as the `key` column.
+
+* Now we can subset on *hour* by using the `.()` notation. We subset for the value *24* and obtain the corresponding *row indices*.
+
+* And on those row indices, we replace the `key` column with the value `0`.
+
+* Since we have replaced values on the *key* column, the *data.table* `flights` isn't sorted by `hour` anymore. Therefore, the key has been automatically removed by setting to NULL.
+
+#
+Now, there shouldn't be any *24* in the *hour* column.
+
+```{r}
+flights[, sort(unique(hour))]
+```
+
+### e) Aggregation using `by`
+
+Let's set the key back to `origin, dest` first.
+
+```{r}
+setkey(flights, origin, dest)
+key(flights)
+```
+
+#### -- Get the maximum departure delay for each `month` corresponding to `origin = "JFK"`. Order the result by `month`
+
+```{r}
+ans <- flights["JFK", max(dep_delay), keyby=month]
+head(ans)
+key(ans)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We subset on the `key` column *origin* to obtain the *row indices* corresponding to *"JFK"*.
+
+* Once we obtain the row indices, we only need two columns - `month` to group by and `dep_delay` to obtain `max()` for each group. *data.table's* query optimisation therefore subsets just those two columns corresponding to the *row indices* obtained in `i`, for speed and memory efficiency.
+
+* And on that subset, we group by *month* and compute `max(dep_delay)`.
+
+* We use `keyby` to automatically key that result by *month*. Now we understand what that means. In addition to ordering, it also sets *month* as the `key` column.
+
+## 3) Additional arguments - `mult` and `nomatch`
+
+### a) The *mult* argument
+
+We can choose, for each query, if *"all"* the matching rows should be returned, or just the *"first"* or *"last"* using the `mult` argument. The default value is *"all"* - what we've seen so far.
+
+#### -- Subset only the first matching row from all rows where `origin` matches *"JFK"* and `dest` matches *"MIA"*
+
+```{r}
+flights[.("JFK", "MIA"), mult="first"]
+```
+
+#### -- Subset only the last matching row of all the rows where `origin` matches *"LGA", "JFK", "EWR"* and `dest` matches *"XNA"*
+
+```{r}
+flights[.(c("LGA", "JFK", "EWR"), "XNA"), mult="last"]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* The query *"JFK", "XNA"* doesn't match any rows in `flights` and therefore returns `NA`.
+
+* Once again, the query for second key column `dest`, *"XNA"*, is recycled to fit the length of the query for first key column `origin`, which is of length 3.
+
+### The *nomatch* argument
+
+We can choose if queries that do not match should return `NA` or be skipped altogether using the `nomatch` argument.
+
+#### -- From the previous example, Subset all rows only if there's a match
+
+```{r}
+flights[.(c("LGA", "JFK", "EWR"), "XNA"), mult="last", nomatch = 0L]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Default value for `nomatch` is `NA`. Setting `nomatch = 0L` skips queries with no matches.
+
+* The query “JFK”, “XNA” doesn’t match any rows in flights and therefore is skipped.
+
+## 4) binary search vs vector scans
+
+We have seen so far how we can set and use keys to subset. But what's the advantage? For example, instead of doing:
+
+```{r eval=FALSE}
+# key by origin,dest columns
+flights[.("JFK", "MIA")]
+```
+
+we could have done:
+
+```{r eval=FALSE}
+flights[origin == "JFK" & dest == "MIA"]
+```
+
+One advantage very likely is shorter syntax. But even more than that, *binary search based subsets* are **incredibly fast**.
+
+### a) Performance of binary search approach
+
+To illustrate, let's create a sample *data.table* with 20 million rows and three columns and key it by columns `x` and `y`.
+
+```{r}
+set.seed(2L)
+N = 2e7L
+DT = data.table(x = sample(letters, N, TRUE),
+ y = sample(1000L, N, TRUE),
+ val=runif(N), key = c("x", "y"))
+print(object.size(DT), units="Mb")
+
+key(DT)
+```
+
+`DT` is ~380MB. It is not really huge, but this will do to illustrate the point.
+
+From what we have seen in the Introduction to data.table section, we can subset those rows where columns `x = "g"` and `y = 877` as follows:
+
+```{r}
+## (1) Usual way of subsetting - vector scan approach
+t1 <- system.time(ans1 <- DT[x == "g" & y == 877L])
+t1
+head(ans1)
+dim(ans1)
+```
+
+Now let's try to subset by using keys.
+
+```{r}
+## (2) Subsetting using keys
+t2 <- system.time(ans2 <- DT[.("g", 877L)])
+t2
+head(ans2)
+dim(ans2)
+
+identical(ans1$val, ans2$val)
+```
+
+* The speedup is **~`r round(t1[3]/t2[3])`x**!
+
+### b) Why does keying a *data.table* result in blazing fast susbets?
+
+To understand that, let's first look at what *vector scan approach* (method 1) does.
+
+#### Vector scan approach: {.bs-callout .bs-callout-info}
+
+* The column `x` is searched for the value *"g"* row by row, on all 20 million of them. This results in a *logical vector* of size 20 million, with values `TRUE, FALSE or NA` corresponding to `x`'s value.
+
+* Similarly, the column `y` is searched for `877` on all 20 million rows one by one, and stored in another logical vector.
+
+* Element wise `&` operations are performed on the intermediate logical vectors and all the rows where the expression evaluates to `TRUE` are returned.
+
+This is what we call a *vector scan approach*. And this is quite inefficient, especially on larger tables and when one needs repeated subsetting, because it has to scan through all the rows each time.
+
+#
+
+Now let us look at binary search approach (method 2). Recall from [Properties of key](#key-properties) - *setting keys reorders the data.table by key columns*. Since the data is sorted, we don't have to *scan through the entire length of the column*! We can instead use *binary search* to search a value in `O(log n)` as opposed to `O(n)` in case of *vector scan approach*, where `n` is the number of rows in the *data.table*.
+
+#### Binary search approach: {.bs-callout .bs-callout-info}
+
+Here's a very simple illustration. Let's consider the (sorted) numbers shown below:
+
+```{r eval=FALSE}
+1, 5, 10, 19, 22, 23, 30
+```
+
+Suppose we'd like to find the matching position of the value *1*, using binary search, this is how we would proceed - because we know that the data is *sorted*.
+
+* Start with the middle value = 19. Is 1 == 19? No. 1 < 19.
+
+* Since the value we're looking for is smaller than 19, it should be somewhere before 19. So we can discard the rest of the half that are >= 19.
+
+* Our set is now reduced to *1, 5, 10*. Grab the middle value once again = 5. Is 1 == 5? No. 1 < 5.
+
+* Our set is reduced to *1*. Is 1 == 1? Yes. The corresponding index is also 1. And that's the only match.
+
+A vector scan approach on the other hand would have to scan through all the values (here, 7).
+
+#
+
+It can be seen that with every search we reduce the number of searches by half. This is why *binary search* based subsets are **incredibly fast**. Since rows of each column of *data.tables* have contiguous locations in memory, the operations are performed in a very cache efficient manner (also contributes to *speed*).
+
+In addition, since we obtain the matching row indices directly without having to create those huge logical vectors (equal to the number of rows in a *data.table*), it is quite **memory efficient** as well.
+
+## Summary
+
+In this vignette, we have learnt another method to subset rows in `i` by keying a *data.table*. Setting keys allows us to perform blazing fast subsets by using *binary search*. In particular, we have seen how to
+
+#### {.bs-callout .bs-callout-info}
+
+* set key and subset using the key on a *data.table*.
+
+* subset using keys which fetches *row indices* in `i`, but much faster.
+
+* combine key based subsets with `j` and `by`. Note that the `j` and `by` operations are exactly the same as before.
+
+Key based subsets are **incredibly fast** and are particularly useful when the task involves *repeated subsetting*.
+
+#
+
+We don't have to set and use keys for aggregation operations in general, unless the data is extremely large and/or the task requires repeated subsetting where key based subsets will be noticeably performant.
+
+However, keying *data.tables* are essential to *join* two *data.tables* which is the subject of discussion in the next vignette *"Joins and rolling joins"*. We will extend the concept of key based subsets to joining two *data.tables* based on `key` columns.
+
+***
+
diff --git a/vignettes/datatable-reference-semantics.Rmd b/vignettes/datatable-reference-semantics.Rmd
new file mode 100644
index 0000000..2a66bc6
--- /dev/null
+++ b/vignettes/datatable-reference-semantics.Rmd
@@ -0,0 +1,368 @@
+---
+title: "Reference semantics"
+date: "`r Sys.Date()`"
+output:
+ rmarkdown::html_document:
+ theme: spacelab
+ highlight: pygments
+ css : css/bootstrap.css
+vignette: >
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r, echo = FALSE, message = FALSE}
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+# options(datatable.auto.index=FALSE)
+```
+This vignette discusses *data.table*'s reference semantics which allows to *add/update/delete* columns of a *data.table by reference*, and also combine them with `i` and `by`. It is aimed at those who are already familiar with *data.table* syntax, its general form, how to subset rows in `i`, select and compute on columns, and perform aggregations by group. If you're not familar with these concepts, please read the *"Introduction to data.table"* vignette first.
+
+***
+
+## Data {#data}
+
+We will use the same `flights` data as in the *"Introduction to data.table"* vignette.
+
+```{r echo=FALSE}
+options(width=100)
+```
+
+```{r}
+flights <- fread("flights14.csv")
+flights
+dim(flights)
+```
+
+## Introduction
+
+In this vignette, we will
+
+1. first discuss reference semantics briefly and look at the two different forms in which the `:=` operator can be used
+
+2. then see how we can *add/update/delete* columns *by reference* in `j` using the `:=` operator and how to combine with `i` and `by`.
+
+3. and finally we will look at using `:=` for its *side-effect* and how we can avoid the side effects using `copy()`.
+
+## 1. Reference semantics
+
+All the operations we have seen so far in the previous vignette resulted in a new data set. We will see how to *add* new column(s), *update* or *delete* existing column(s) on the original data.
+
+### a) Background
+
+Before we look at *reference semantics*, consider the *data.frame* shown below:
+
+```{r}
+DF = data.frame(ID = c("b","b","b","a","a","c"), a = 1:6, b = 7:12, c=13:18)
+DF
+```
+
+When we did:
+
+```{r eval=FALSE}
+DF$c <- 18:13 # (1) -- replace entire column
+# or
+DF$c[DF$ID == "b"] <- 15:13 # (2) -- subassign in column 'c'
+```
+
+both (1) and (2) resulted in [*deep* copy of the entire *data.frame*](http://r.789695.n4.nabble.com/speeding-up-perception-td3640920.html#a3646694) in versions of `R` versions `< 3.1`. [It copied more than once](http://stackoverflow.com/q/23898969/559784). To improve performance by avoiding these redundant copies, *data.table* utilised the [available but unused `:=` operator in R](http://stackoverflow.com/q/7033106/559784).
+
+Great performance improvements were made in `R v3.1` as a result of which only a *shallow* copy is made for (1) and not *deep* copy. However, for (2) still, the entire column is *deep* copied even in `R v3.1+`. This means the more columns one subassigns to in the *same query*, the more *deep* copies R does.
+
+#### *shallow* vs *deep* copy {.bs-callout .bs-callout-info}
+
+A *shallow* copy is just a copy of the vector of column pointers (corresponding to the columns in a *data.frame* or *data.table*). The actual data is not physically copied in memory.
+
+A *deep* copy on the other hand copies the entire data to another location in memory.
+
+#
+With *data.table's* `:=` operator, absolutely no copies are made in *both* (1) and (2), irrespective of R version you are using. This is because `:=` operator updates *data.table* columns *in-place* (by reference).
+
+### b) The `:=` operator
+
+It can be used in `j` in two ways:
+
+(a) The `LHS := RHS` form
+
+ ```{r eval=FALSE}
+ DT[, c("colA", "colB", ...) := list(valA, valB, ...)]
+
+ # when you have only one column to assign to you
+ # can drop the quotes and list(), for convenience
+ DT[, colA := valA]
+ ```
+
+(b) The functional form
+
+ ```{r eval=FALSE}
+ DT[, `:=`(colA = valA, # valA is assigned to colA
+ colB = valB, # valB is assigned to colB
+ ...
+ )]
+ ```
+
+#### {.bs-callout .bs-callout-warning}
+
+Note that the code above explains how `:=` can be used. They are not working examples. We will start using them on `flights` *data.table* from the next section.
+
+#
+
+#### {.bs-callout .bs-callout-info}
+
+* Form (a) is usually easy to program with and is particularly useful when you don't know the columns to assign values to in advance.
+
+* On the other hand, form (b) is handy if you would like to jot some comments down for later.
+
+* The result is returned *invisibly*.
+
+* Since `:=` is available in `j`, we can combine it with `i` and `by` operations just like the aggregation operations we saw in the previous vignette.
+
+#
+
+In the two forms of `:=` shown above, note that we don't assign the result back to a variable. Because we don't need to. The input *data.table* is modified by reference. Let's go through examples to understand what we mean by this.
+
+For the rest of the vignette, we will work with `flights` *data.table*.
+
+## 2. Add/update/delete columns *by reference*
+
+### a) Add columns by reference {#ref-j}
+
+#### -- How can we add columns *speed* and *total delay* of each flight to `flights` *data.table*?
+
+```{r}
+flights[, `:=`(speed = distance / (air_time/60), # speed in km/hr
+ delay = arr_delay + dep_delay)] # delay in minutes
+head(flights)
+
+## alternatively, using the 'LHS := RHS' form
+# flights[, c("speed", "delay") := list(distance/(air_time/60), arr_delay + dep_delay)]
+```
+
+#### Note that {.bs-callout .bs-callout-info}
+
+* We did not have to assign the result back to `flights`.
+
+* The `flights` *data.table* now contains the two newly added columns. This is what we mean by *added by reference*.
+
+* We used the functional form so that we could add comments on the side to explain what the computation does. You can also see the `LHS := RHS` form (commented).
+
+### b) Update some rows of columns by reference - *sub-assign* by reference {#ref-i-j}
+
+Let's take a look at all the `hours` available in the `flights` *data.table*:
+
+```{r}
+# get all 'hours' in flights
+flights[, sort(unique(hour))]
+```
+
+We see that there are totally `25` unique values in the data. Both *0* and *24* hours seem to be present. Let's go ahead and replace *24* with *0*.
+
+#### -- Replace those rows where `hour == 24` with the value `0`
+
+```{r}
+# subassign by reference
+flights[hour == 24L, hour := 0L]
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We can use `i` along with `:=` in `j` the very same way as we have already seen in the *"Introduction to data.table"* vignette.
+
+* Column `hour` is replaced with `0` only on those *row indices* where the condition `hour == 24L` specified in `i` evaluates to `TRUE`.
+
+* `:=` returns the result invisibly. Sometimes it might be necessary to see the result after the assignment. We can accomplish that by adding an empty `[]` at the end of the query as shown below:
+
+ ```{r}
+ flights[hour == 24L, hour := 0L][]
+ ```
+
+#
+Let's look at all the `hours` to verify.
+
+```{r}
+# check again for '24'
+flights[, sort(unique(hour))]
+```
+
+### c) Delete column by reference
+
+#### -- Remove `delay` column
+
+```{r}
+flights[, c("delay") := NULL]
+head(flights)
+
+## or using the functional form
+# flights[, `:=`(delay = NULL)]
+```
+
+#### {.bs-callout .bs-callout-info #delete-convenience}
+
+* Assigning `NULL` to a column *deletes* that column. And it happens *instantly*.
+
+* We can also pass column numbers instead of names in the `LHS`, although it is good programming practice to use column names.
+
+* When there is just one column to delete, we can drop the `c()` and double quotes and just use the column name *unquoted*, for convenience. That is:
+
+ ```{r eval=FALSE}
+ flights[, delay := NULL]
+ ```
+
+ is equivalent to the code above.
+
+### d) `:=` along with grouping using `by` {#ref-j-by}
+
+We have already seen the use of `i` along with `:=` in [Section 2b](#ref-i-j). Let's now see how we can use `:=` along with `by`.
+
+#### -- How can we add a new column which contains for each `orig,dest` pair the maximum speed?
+
+```{r}
+flights[, max_speed := max(speed), by=.(origin, dest)]
+head(flights)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* We add a new column `max_speed` using the `:=` operator by reference.
+
+* We provide the columns to group by the same way as shown in the *Introduction to data.table* vignette. For each group, `max(speed)` is computed, which returns a single value. That value is recycled to fit the length of the group. Once again, no copies are being made at all. `flights` *data.table* is modified *in-place*.
+
+* We could have also provided `by` with a *character vector* as we saw in the *Introduction to data.table* vignette, e.g., `by = c("origin", "dest")`.
+
+#
+
+### e) Multiple columns and `:=`
+
+#### -- How can we add two more columns computing `max()` of `dep_delay` and `arr_delay` for each month, using `.SD`?
+
+```{r}
+in_cols = c("dep_delay", "arr_delay")
+out_cols = c("max_dep_delay", "max_arr_delay")
+flights[, c(out_cols) := lapply(.SD, max), by = month, .SDcols = in_cols]
+head(flights)
+```
+#### {.bs-callout .bs-callout-info}
+
+* We use the `LHS := RHS` form. We store the input column names and the new columns to add in separate variables and provide them to `.SDcols` and for `LHS` (for better readability).
+
+* Note that since we allow assignment by reference without quoting column names when there is only one column as explained in [Section 2c](#delete-convenience), we can not do `out_cols := lapply(.SD, max)`. That would result in adding one new column named `out_col`. Instead we should do either `c(out_cols)` or simply `(out_cols)`. Wrapping the variable name with `(` is enough to differentiate between the two cases.
+
+* The `LHS := RHS` form allows us to operate on multiple columns. In the RHS, to compute the `max` on columns specified in `.SDcols`, we make use of the base function `lapply()` along with `.SD` in the same way as we have seen before in the *"Introduction to data.table"* vignette. It returns a list of two elements, containing the maximum value corresponding to `dep_delay` and `arr_delay` for each group.
+
+#
+Before moving on to the next section, let's clean up the newly created columns `speed`, `max_speed`, `max_dep_delay` and `max_arr_delay`.
+
+```{r}
+# RHS gets automatically recycled to length of LHS
+flights[, c("speed", "max_speed", "max_dep_delay", "max_arr_delay") := NULL]
+head(flights)
+```
+
+## 3) `:=` and `copy()`
+
+`:=` modifies the input object by reference. Apart from the features we have discussed already, sometimes we might want to use the update by reference feature for its side effect. And at other times it may not be desirable to modify the original object, in which case we can use `copy()` function, as we will see in a moment.
+
+### a) `:=` for its side effect
+
+Let's say we would like to create a function that would return the *maximum speed* for each month. But at the same time, we would also like to add the column `speed` to *flights*. We could write a simple function as follows:
+
+```{r}
+foo <- function(DT) {
+ DT[, speed := distance / (air_time/60)]
+ DT[, .(max_speed = max(speed)), by=month]
+}
+ans = foo(flights)
+head(flights)
+head(ans)
+```
+#### {.bs-callout .bs-callout-info}
+
+* Note that the new column `speed` has been added to `flights` *data.table*. This is because `:=` performs operations by reference. Since `DT` (the function argument) and `flights` refer to the same object in memory, modifying `DT` also reflects on `flights`.
+
+* And `ans` contains the maximum speed for each month.
+
+### b) The `copy()` function
+
+In the previous section, we used `:=` for its side effect. But of course this may not be always desirable. Sometimes, we would like to pass a *data.table* object to a function, and might want to use the `:=` operator, but *wouldn't* want to update the original object. We can accomplish this using the function `copy()`.
+
+#### {.bs-callout .bs-callout-info}
+
+The `copy()` function *deep* copies the input object and therefore any subsequent update by reference operations performed on the copied object will not affect the original object.
+
+#
+
+There are two particular places where `copy()` function is essential:
+
+1. Contrary to the situation we have seen in the previous point, we may not want the input data.table to a function to be modified *by reference*. As an example, let's consider the task in the previous section, except we don't want to modify `flghts` by reference.
+
+ Let's first delete the `speed` column we generated in the previous section.
+
+ ```{r}
+ flights[, speed := NULL]
+ ```
+ Now, we could accomplish the task as follows:
+
+ ```{r}
+ foo <- function(DT) {
+ DT <- copy(DT) ## deep copy
+ DT[, speed := distance / (air_time/60)] ## doesn't affect 'flights'
+ DT[, .(max_speed = max(speed)), by=month]
+ }
+ ans <- foo(flights)
+ head(flights)
+ head(ans)
+ ```
+
+#### {.bs-callout .bs-callout-info}
+
+* Using `copy()` function did not update `flights` *data.table* by reference. It doesn't contain the column `speed`.
+
+* And `ans` contains the maximum speed corresponding to each month.
+
+However we could improve this functionality further by *shallow* copying instead of *deep* copying. In fact, we would very much like to [provide this functionality for `v1.9.8`](https://github.com/Rdatatable/data.table/issues/617). We will touch up on this again in the *data.table design* vignette.
+
+#
+
+2. When we store the column names on to a variable, e.g., `DT_n = names(DT)`, and then *add/update/delete* column(s) *by reference*. It would also modify `DT_n`, unless we do `copy(names(DT))`.
+
+ ```{r}
+ DT = data.table(x=1, y=2)
+ DT_n = names(DT)
+ DT_n
+
+ ## add a new column by reference
+ DT[, z := 3]
+
+ ## DT_n also gets updated
+ DT_n
+
+ ## use `copy()`
+ DT_n = copy(names(DT))
+ DT[, w := 4]
+
+ ## DT_n doesn't get updated
+ DT_n
+ ```
+
+## Summary
+
+#### The `:=` operator {.bs-callout .bs-callout-info}
+
+* It is used to *add/update/delete* columns by reference.
+
+* We have also seen how to use `:=` along with `i` and `by` the same way as we have seen in the *Introduction to data.table* vignette. We can in the same way use `keyby`, chain operations together, and pass expressions to `by` as well all in the same way. The syntax is *consistent*.
+
+* We can use `:=` for its side effect or use `copy()` to not modify the original object while updating by reference.
+
+#
+
+So far we have seen a whole lot in `j`, and how to combine it with `by` and little of `i`. Let's turn our attention back to `i` in the next vignette *"Keys and fast binary search based subset"* to peform *blazing fast subsets* by *keying data.tables*.
+
+***
+
diff --git a/vignettes/datatable-reshape.Rmd b/vignettes/datatable-reshape.Rmd
new file mode 100644
index 0000000..bfde7d4
--- /dev/null
+++ b/vignettes/datatable-reshape.Rmd
@@ -0,0 +1,254 @@
+---
+title: "Efficient reshaping using data.tables"
+date: "`r Sys.Date()`"
+output:
+ rmarkdown::html_document:
+ theme: spacelab
+ highlight: pygments
+ css : css/bootstrap.css
+vignette: >
+ %\VignetteIndexEntry{Vignette Title}
+ %\VignetteEngine{knitr::rmarkdown}
+ \usepackage[utf8]{inputenc}
+---
+
+```{r, echo = FALSE, message = FALSE}
+require(data.table)
+knitr::opts_chunk$set(
+ comment = "#",
+ error = FALSE,
+ tidy = FALSE,
+ cache = FALSE,
+ collapse=TRUE)
+```
+
+This vignette discusses the default usage of reshaping functions `melt` (wide to long) and `dcast` (long to wide) for *data.tables* as well as the **new extended functionalities** of melting and casting on *multiple columns* available from `v1.9.6`.
+
+***
+
+```{r echo=FALSE}
+options(width=100)
+```
+
+## Data
+
+We will load the data sets directly within sections.
+
+## Introduction
+
+The `melt` and `dcast` functions for *data.tables* are extensions of the corresponding functions from the [reshape2](http://cran.r-project.org/package=reshape2) package.
+
+In this vignette, we will
+
+1. first briefly look at the default *melting* and *casting* of *data.tables* to convert them from *wide* to *long* format and vice versa,
+
+2. then look at scenarios where the current functionalities becomes cumbersome and inefficient,
+
+3. and finally look at the new improvements to both `melt` and `dcast` methods for *data.tables* to handle multiple columns simultaneously.
+
+The extended functionalities are in line with *data.table's* philosophy of performing operations efficiently and in a straightforward manner.
+
+#### Note: {.bs-callout .bs-callout-info}
+
+From `v1.9.6` on, you don't have to load `reshape2` package to use these functions for *data.tables*. You just need to load `data.table`. If you've to load `reshape2` for melting or casting matrices and/or data.frames, then make sure to load it *before* loading `data.table`.
+
+
+## 1. Default functionality
+
+### a) `melt`ing *data.tables* (wide to long)
+
+Suppose we have a `data.table` (artificial data) as shown below:
+
+```{r}
+DT = fread("melt_default.csv")
+DT
+## dob stands for date of birth.
+
+str(DT)
+```
+
+
+
+#
+
+#### - Convert `DT` to *long* form where each `dob` is a separate observation.
+
+We could accomplish this using `melt()` by specifying `id.vars` and `measure.vars` arguments as follows:
+
+```{r}
+DT.m1 = melt(DT, id.vars = c("family_id", "age_mother"),
+ measure.vars = c("dob_child1", "dob_child2", "dob_child3"))
+DT.m1
+str(DT.m1)
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `measure.vars` specify the set of columns we would like to collapse (or combine) together.
+
+* We can also specify column *indices* instead of *names*.
+
+* By default, `variable` column is of type `factor`. Set `variable.factor` argument to `FALSE` if you'd like to return a *character* vector instead. `variable.factor` argument is only available in `melt` from `data.table` and not in the [`reshape2` package](http://github.com/hadley/reshape).
+
+* By default, the molten columns are automatically named `variable` and `value`.
+
+* `melt` preserves column attributes in result.
+
+#
+
+#### - Name the `variable` and `value` columns to `child` and `dob` respectively
+
+
+```{r}
+DT.m1 = melt(DT, measure.vars = c("dob_child1", "dob_child2", "dob_child3"),
+ variable.name = "child", value.name = "dob")
+DT.m1
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* By default, when one of `id.vars` or `measure.vars` is missing, the rest of the columns are *automatically assigned* to the missing argument.
+
+* When neither `id.vars` nor `measure.vars` are specified, as mentioned under `?melt`, all *non*-`numeric`, `integer`, `logical` columns will be assigned to `id.vars`.
+
+ In addition, a warning message is issued highlighting the columns that are automatically considered to be `id.vars`.
+
+### b) `Cast`ing *data.tables* (long to wide)
+
+In the previous section, we saw how to get from wide form to long form. Let's see the reverse operation in this section.
+
+#### - How can we get back to the original data table `DT` from `DT.m`?
+
+That is, we'd like to collect all *child* observations corresponding to each `family_id, age_mother` together under the same row. We can accomplish it using `dcast` as follows:
+
+```{r}
+dcast(DT.m1, family_id + age_mother ~ child, value.var = "dob")
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* `dcast` uses *formula* interface. The variables on the *LHS* of formula represents the *id* vars and *RHS* the *measure* vars.
+
+* `value.var` denotes the column to be filled in with while casting to wide format.
+
+* `dcast` also tries to preserve attributes in result wherever possible.
+
+#
+
+#### - Starting from `DT.m`, how can we get the number of children in each family?
+
+You can also pass a function to aggregate by in `dcast` with the argument `fun.aggregate`. This is particularly essential when the formula provided does not identify single observation for each cell.
+
+```{r}
+dcast(DT.m1, family_id ~ ., fun.agg = function(x) sum(!is.na(x)), value.var = "dob")
+```
+
+Check `?dcast` for other useful arguments and additional examples.
+
+## 2. Limitations in current `melt/dcast` approaches
+
+So far we've seen features of `melt` and `dcast` that are based on `reshape2` package, but implemented efficiently for *data.table*s, using internal `data.table` machinery (*fast radix ordering*, *binary search* etc..).
+
+However, there are situations we might run into where the desired operation is not expressed in a straightforward manner. For example, consider the *data.table* shown below:
+
+```{r}
+DT = fread("melt_enhanced.csv")
+DT
+## 1 = female, 2 = male
+```
+
+And you'd like to combine (melt) all the `dob` columns together, and `gender` columns together. Using the current functionalty, we can do something like this:
+
+```{r}
+DT.m1 = melt(DT, id = c("family_id", "age_mother"))
+DT.m1[, c("variable", "child") := tstrsplit(variable, "_", fixed=TRUE)]
+DT.c1 = dcast(DT.m1, family_id + age_mother + child ~ variable, value.var = "value")
+DT.c1
+
+str(DT.c1) ## gender column is character type now!
+```
+
+#### Issues {.bs-callout .bs-callout-info}
+
+1. What we wanted to do was to combine all the `dob` and `gender` type columns together respectively. Instead we are combining *everything* together, and then splitting them again. I think it's easy to see that it's quite roundabout (and inefficient).
+
+ As an analogy, imagine you've a closet with four shelves of clothes and you'd like to put together the clothes from shelves 1 and 2 together (in 1), and 3 and 4 together (in 3). What we are doing is more or less to combine all the clothes together, and then split them back on to shelves 1 and 3!
+
+2. The columns to *melt* may be of different types, as in this case (character and integer types). By *melting* them all together, the columns will be coerced in result, as explained by the warning message above and shown from output of `str(DT.c1)`, where `gender` has been converted to *character* type.
+
+3. We are generating an additional column by splitting the `variable` column into two columns, whose purpose is quite cryptic. We do it because we need it for *casting* in the next step.
+
+4. Finally, we cast the data set. But the issue is it's a much more computationally involved operation than *melt*. Specifically, it requires computing the order of the variables in formula, and that's costly.
+
+#
+
+In fact, `base::reshape` is capable of performing this operation in a very straightforward manner. It is an extremely useful and often underrated function. You should definitely give it a try!
+
+## 3. Enhanced (new) functionality
+
+### a) Enhanced `melt`
+
+Since we'd like for *data.tables* to perform this operation straightforward and efficient using the same interface, we went ahead and implemented an *additional functionality*, where we can `melt` to multiple columns *simultaneously*.
+
+#### - `melt` multiple columns simultaneously
+
+The idea is quite simple. We pass a list of columns to `measure.vars`, where each element of the list contains the columns that should be combined together.
+
+```{r}
+colA = paste("dob_child", 1:3, sep="")
+colB = paste("gender_child", 1:3, sep="")
+DT.m2 = melt(DT, measure = list(colA, colB), value.name = c("dob", "gender"))
+DT.m2
+
+str(DT.m2) ## col type is preserved
+```
+
+#### - Using `patterns()`
+
+Usually in these problems, the columns we'd like to melt can be distinguished by a common pattern. We can use the function `patterns()`, implemented for convenience, to provide regular expressions for the columns to be combined together. The above operation can be rewritten as:
+
+```{r}
+DT.m2 = melt(DT, measure = patterns("^dob", "^gender"), value.name = c("dob", "gender"))
+DT.m2
+```
+
+That's it!
+
+#### {.bs-callout .bs-callout-info}
+
+* We can remove the `variable` column if necessary.
+
+* The functionality is implemented entirely in C, and is therefore both *fast* and *memory efficient* in addition to being *straightforward*.
+
+### b) Enhanced `dcast`
+
+Okay great! We can now melt into multiple columns simultaneously. Now given the data set `DT.m2` as shown above, how can we get back to the same format as the original data we started with?
+
+If we use the current functionality of `dcast`, then we'd have to cast twice and bind the results together. But that's once again verbose, not straightforward and is also inefficient.
+
+#### - Casting multiple `value.var`s simultaneously
+
+We can now provide **multiple `value.var` columns** to `dcast` for *data.tables* directly so that the operations are taken care of internally and efficiently.
+
+```{r}
+## new 'cast' functionality - multiple value.vars
+DT.c2 = dcast(DT.m2, family_id + age_mother ~ variable, value.var = c("dob", "gender"))
+DT.c2
+```
+
+#### {.bs-callout .bs-callout-info}
+
+* Attributes are preserved in result wherever possible.
+
+* Everything is taken care of internally, and efficiently. In addition to being fast, it is also very memory efficient.
+
+#
+
+#### Multiple functions to `fun.aggregate`: {.bs-callout .bs-callout-info}
+
+You can also provide *multiple functions* to `fun.aggregate` to `dcast` for *data.tables*. Check the examples in `?dcast` which illustrates this functionality.
+
+#
+
+***
+
diff --git a/vignettes/flights14.csv b/vignettes/flights14.csv
new file mode 100644
index 0000000..30cde7a
--- /dev/null
+++ b/vignettes/flights14.csv
@@ -0,0 +1,253317 @@
+year,month,day,dep_delay,arr_delay,carrier,origin,dest,air_time,distance,hour
+2014,1,1,14,13,AA,JFK,LAX,359,2475,9
+2014,1,1,-3,13,AA,JFK,LAX,363,2475,11
+2014,1,1,2,9,AA,JFK,LAX,351,2475,19
+2014,1,1,-8,-26,AA,LGA,PBI,157,1035,7
+2014,1,1,2,1,AA,JFK,LAX,350,2475,13
+2014,1,1,4,0,AA,EWR,LAX,339,2454,18
+2014,1,1,-2,-18,AA,JFK,LAX,338,2475,21
+2014,1,1,-3,-14,AA,JFK,LAX,356,2475,15
+2014,1,1,-1,-17,AA,JFK,MIA,161,1089,15
+2014,1,1,-2,-14,AA,JFK,SEA,349,2422,18
+2014,1,1,-5,-17,AA,EWR,MIA,161,1085,16
+2014,1,1,7,-5,AA,JFK,SFO,365,2586,17
+2014,1,1,3,1,AA,JFK,BOS,39,187,12
+2014,1,1,142,133,AA,JFK,LAX,345,2475,19
+2014,1,1,-5,-26,AA,JFK,BOS,35,187,17
+2014,1,1,18,69,AA,JFK,ORD,155,740,17
+2014,1,1,25,36,AA,JFK,IAH,234,1417,16
+2014,1,1,-1,1,AA,JFK,AUS,232,1521,17
+2014,1,1,191,185,AA,EWR,DFW,214,1372,16
+2014,1,1,-7,-6,AA,LGA,ORD,142,733,5
+2014,1,1,-7,0,AA,LGA,ORD,143,733,6
+2014,1,1,-8,-17,AA,LGA,ORD,139,733,6
+2014,1,1,-2,15,AA,LGA,ORD,145,733,7
+2014,1,1,-3,1,AA,LGA,ORD,139,733,8
+2014,1,1,44,42,AA,LGA,ORD,141,733,10
+2014,1,1,-1,-2,AA,EWR,DFW,214,1372,14
+2014,1,1,3,12,AA,LGA,ORD,137,733,10
+2014,1,1,-5,24,AA,LGA,ORD,145,733,11
+2014,1,1,26,46,AA,LGA,ORD,132,733,13
+2014,1,1,52,91,AA,LGA,ORD,136,733,16
+2014,1,1,55,84,AA,LGA,ORD,133,733,17
+2014,1,1,36,59,AA,LGA,ORD,138,733,18
+2014,1,1,0,-27,AA,JFK,STT,197,1623,7
+2014,1,1,11,44,AA,LGA,ORD,136,733,19
+2014,1,1,-5,-17,AA,JFK,SJU,198,1598,18
+2014,1,1,-10,-19,AA,LGA,MIA,164,1096,11
+2014,1,1,-6,-4,AA,LGA,MIA,168,1096,13
+2014,1,1,7,-8,AA,JFK,MIA,166,1089,9
+2014,1,1,51,29,AA,LGA,DFW,210,1389,17
+2014,1,1,-2,-3,AA,LGA,MIA,165,1096,5
+2014,1,1,17,7,AA,LGA,DFW,224,1389,17
+2014,1,1,-3,-10,AA,EWR,MIA,154,1085,6
+2014,1,1,4,-17,AA,EWR,DFW,215,1372,9
+2014,1,1,0,-8,AA,LGA,DFW,230,1389,7
+2014,1,1,-5,-14,AA,LGA,DFW,209,1389,8
+2014,1,1,-4,-21,AA,LGA,DFW,214,1389,10
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+2014,10,31,167,138,UA,LGA,ORD,102,733,19
+2014,10,31,54,143,UA,LGA,ORD,109,733,18
+2014,10,31,-5,-25,UA,EWR,MCO,132,937,5
+2014,10,31,-6,-38,UA,JFK,LAX,323,2475,11
+2014,10,31,-6,-5,UA,LGA,ORD,114,733,6
+2014,10,31,-5,-12,UA,EWR,BOS,35,200,17
+2014,10,31,-3,-35,UA,EWR,PHX,282,2133,8
+2014,10,31,11,-5,UA,EWR,BOS,32,200,21
+2014,10,31,-1,-19,UA,EWR,IAH,193,1400,5
+2014,10,31,1,-25,UA,LGA,IAH,189,1416,10
+2014,10,31,0,-32,UA,EWR,IAH,185,1400,18
+2014,10,31,0,-27,UA,EWR,SAN,324,2425,17
+2014,10,31,-1,-7,UA,EWR,BOS,38,200,7
+2014,10,31,4,16,UA,EWR,SFO,347,2565,7
+2014,10,31,13,-25,UA,EWR,SFO,340,2565,19
+2014,10,31,-1,-27,UA,EWR,SFO,338,2565,20
+2014,10,31,65,44,UA,EWR,CLE,57,404,19
+2014,10,31,9,-21,UA,EWR,LAX,323,2454,13
+2014,10,31,13,5,UA,EWR,FLL,151,1065,17
+2014,10,31,-1,-23,UA,EWR,RSW,152,1068,21
+2014,10,31,72,64,UA,EWR,BQN,208,1585,22
+2014,10,31,4,-30,UA,EWR,SAT,209,1569,18
+2014,10,31,5,-22,UA,EWR,PDX,323,2434,17
+2014,10,31,9,-11,UA,EWR,DEN,217,1605,14
+2014,10,31,15,6,UA,EWR,MCO,137,937,17
+2014,10,31,1,-37,UA,EWR,LAS,286,2227,13
+2014,10,31,18,-40,UA,EWR,SNA,314,2434,18
+2014,10,31,1,4,UA,EWR,SFO,346,2565,15
+2014,10,31,0,-29,UA,EWR,SFO,333,2565,17
+2014,10,31,-1,-9,UA,EWR,BOS,36,200,8
+2014,10,31,14,22,UA,EWR,BOS,37,200,15
+2014,10,31,-4,-45,UA,EWR,SEA,321,2402,13
+2014,10,31,-3,-7,UA,EWR,FLL,153,1065,14
+2014,10,31,11,-10,UA,EWR,ATL,109,746,8
+2014,10,31,40,14,UA,EWR,LAX,320,2454,18
+2014,10,31,316,289,UA,EWR,ORD,106,719,22
+2014,10,31,8,-1,UA,EWR,IAH,191,1400,16
+2014,10,31,0,-11,UA,EWR,MCO,136,937,11
+2014,10,31,4,-6,UA,EWR,FLL,153,1065,9
+2014,10,31,104,74,UA,EWR,SEA,334,2402,10
+2014,10,31,2,-15,UA,EWR,SJU,200,1608,7
+2014,10,31,3,-7,UA,EWR,TPA,147,997,15
+2014,10,31,271,236,UA,LGA,IAH,195,1416,23
+2014,10,31,10,-5,UA,EWR,ORD,110,719,6
+2014,10,31,21,6,UA,EWR,SFO,340,2565,16
+2014,10,31,-9,-43,UA,EWR,SFO,334,2565,8
+2014,10,31,49,48,UA,EWR,SFO,336,2565,14
+2014,10,31,0,-10,UA,EWR,RSW,156,1068,6
+2014,10,31,9,4,UA,EWR,MIA,160,1085,10
+2014,10,31,3,-32,UA,EWR,LAX,320,2454,20
+2014,10,31,-6,-31,UA,EWR,MSY,161,1167,6
+2014,10,31,5,2,UA,EWR,ORD,132,719,10
+2014,10,31,46,41,UA,EWR,FLL,149,1065,19
+2014,10,31,-1,-18,UA,EWR,CLE,61,404,7
+2014,10,31,14,-17,UA,LGA,IAH,200,1416,17
+2014,10,31,7,-11,UA,EWR,FLL,151,1065,21
+2014,10,31,-4,-1,UA,EWR,MIA,155,1085,16
+2014,10,31,1,-15,UA,EWR,FLL,151,1065,6
+2014,10,31,-3,-26,UA,EWR,IAH,198,1400,14
+2014,10,31,3,-14,UA,EWR,IAH,195,1400,17
+2014,10,31,24,-5,UA,LGA,IAH,198,1416,6
+2014,10,31,14,11,UA,EWR,MIA,151,1085,19
+2014,10,31,41,19,UA,EWR,SFO,344,2565,12
+2014,10,31,427,393,UA,EWR,ORD,100,719,21
+2014,10,31,10,-27,UA,EWR,LAX,326,2454,10
+2014,10,31,18,-14,UA,EWR,LAS,291,2227,16
+2014,10,31,1,-30,UA,LGA,IAH,201,1416,14
+2014,10,31,-5,-14,UA,EWR,IAH,189,1400,8
+2014,10,31,-8,16,MQ,LGA,RDU,83,431,11
+2014,10,31,-4,15,MQ,LGA,DTW,75,502,11
+2014,10,31,-5,1,MQ,LGA,SDF,110,659,8
diff --git a/vignettes/melt_default.csv b/vignettes/melt_default.csv
new file mode 100644
index 0000000..3289307
--- /dev/null
+++ b/vignettes/melt_default.csv
@@ -0,0 +1,6 @@
+family_id,age_mother,dob_child1,dob_child2,dob_child3
+1,30,1998-11-26,2000-01-29,NA
+2,27,1996-06-22,NA,NA
+3,26,2002-07-11,2004-04-05,2007-09-02
+4,32,2004-10-10,2009-08-27,2012-07-21
+5,29,2000-12-05,2005-02-28,NA
diff --git a/vignettes/melt_enhanced.csv b/vignettes/melt_enhanced.csv
new file mode 100644
index 0000000..8b173da
--- /dev/null
+++ b/vignettes/melt_enhanced.csv
@@ -0,0 +1,6 @@
+family_id,age_mother,dob_child1,dob_child2,dob_child3,gender_child1,gender_child2,gender_child3
+1,30,1998-11-26,2000-01-29,NA,1,2,NA
+2,27,1996-06-22,NA,NA,2,NA,NA
+3,26,2002-07-11,2004-04-05,2007-09-02,2,2,1
+4,32,2004-10-10,2009-08-27,2012-07-21,1,1,1
+5,29,2000-12-05,2005-02-28,NA,2,1,NA
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-med/r-cran-data.table.git
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