```
if (FALSE) {
example(data.table) # to run these examples yourself
}
DF = data.frame(x=rep(c("b","a","c"),each=3), y=c(1,3,6), v=1:9)
DT = data.table(x=rep(c("b","a","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()
# basic row subset operations
DT[2] # 2nd row
DT[3:2] # 3rd and 2nd row
DT[order(x)] # no need for order(DT$x)
DT[order(x), ] # same as above. The ',' is optional
DT[y>2] # all rows where DT$y > 2
DT[y>2 & v>5] # compound logical expressions
DT[!2:4] # all rows other than 2:4
DT[-(2:4)] # same
# select|compute columns data.table way
DT[, v] # v column (as vector)
DT[, list(v)] # v column (as data.table)
DT[, .(v)] # same as above, .() is a shorthand alias to list()
DT[, sum(v)] # sum of column v, returned as vector
DT[, .(sum(v))] # same, but return data.table (column autonamed V1)
DT[, .(sv=sum(v))] # same, but column named "sv"
DT[, .(v, v*2)] # return two column data.table, v and v*2
# subset rows and select|compute data.table way
DT[2:3, sum(v)] # sum(v) over rows 2 and 3, return vector
DT[2:3, .(sum(v))] # same, but return data.table with column V1
DT[2:3, .(sv=sum(v))] # same, but return data.table with column sv
DT[2:5, cat(v, "\n")] # just for j's side effect
# select columns the data.frame way
DT[, 2] # 2nd column, returns a data.table always
colNum = 2
DT[, ..colNum] # same, .. prefix conveys one-level-up in calling scope
DT[["v"]] # same as DT[, v] but faster if called in a loop
# grouping operations - j and by
DT[, sum(v), by=x] # ad hoc by, order of groups preserved in result
DT[, sum(v), keyby=x] # same, but order the result on by cols
DT[, sum(v), by=x, keyby=TRUE] # same, but using sorting flag
DT[, sum(v), by=x][order(x)] # same but by chaining expressions together
# fast ad hoc row subsets (subsets as joins)
DT["a", on="x"] # same as x == "a" but uses binary search (fast)
DT["a", on=.(x)] # same, for convenience, no need to quote every column
DT[.("a"), on="x"] # same
DT[x=="a"] # same, single "==" internally optimised to use binary search (fast)
DT[x!="b" | y!=3] # not yet optimized, currently vector scan subset
DT[.("b", 3), on=c("x", "y")] # join on columns x,y of DT; uses binary search (fast)
DT[.("b", 3), on=.(x, y)] # same, but using on=.()
DT[.("b", 1:2), on=c("x", "y")] # no match returns NA
DT[.("b", 1:2), on=.(x, y), nomatch=NULL] # no match row is not returned
DT[.("b", 1:2), on=c("x", "y"), roll=Inf] # locf, nomatch row gets rolled by previous row
DT[.("b", 1:2), on=.(x, y), roll=-Inf] # nocb, nomatch row gets rolled by next row
DT["b", sum(v*y), on="x"] # on rows where DT$x=="b", calculate sum(v*y)
# all together now
DT[x!="a", sum(v), by=x] # get sum(v) by "x" for each i != "a"
DT[!"a", sum(v), by=.EACHI, on="x"] # same, but using subsets-as-joins
DT[c("b","c"), sum(v), by=.EACHI, on="x"] # same
DT[c("b","c"), sum(v), by=.EACHI, on=.(x)] # same, using on=.()
# joins as subsets
X = data.table(x=c("c","b"), v=8:7, foo=c(4,2))
X
DT[X, on="x"] # right join
X[DT, on="x"] # left join
DT[X, on="x", nomatch=NULL] # inner join
DT[!X, on="x"] # not join
DT[X, on=c(y="v")] # join using column "y" of DT with column "v" of X
DT[X, on="y==v"] # same as above (v1.9.8+)
DT[X, on=.(y<=foo)] # NEW non-equi join (v1.9.8+)
DT[X, on="y<=foo"] # same as above
DT[X, on=c("y<=foo")] # same as above
DT[X, on=.(y>=foo)] # NEW non-equi join (v1.9.8+)
DT[X, on=.(x, y<=foo)] # NEW non-equi join (v1.9.8+)
DT[X, .(x,y,x.y,v), on=.(x, y>=foo)] # Select x's join columns as well
DT[X, on="x", mult="first"] # first row of each group
DT[X, on="x", mult="last"] # last row of each group
DT[X, sum(v), by=.EACHI, on="x"] # join and eval j for each row in i
DT[X, sum(v)*foo, by=.EACHI, on="x"] # join inherited scope
DT[X, sum(v)*i.v, by=.EACHI, on="x"] # 'i,v' refers to X's v column
DT[X, on=.(x, v>=v), sum(y)*foo, by=.EACHI] # NEW non-equi join with by=.EACHI (v1.9.8+)
# setting keys
kDT = copy(DT) # (deep) copy DT to kDT to work with it.
setkey(kDT,x) # set a 1-column key. No quotes, for convenience.
setkeyv(kDT,"x") # same (v in setkeyv stands for vector)
v="x"
setkeyv(kDT,v) # same
haskey(kDT) # TRUE
key(kDT) # "x"
# fast *keyed* subsets
kDT["a"] # subset-as-join on *key* column 'x'
kDT["a", on="x"] # same, being explicit using 'on=' (preferred)
# all together
kDT[!"a", sum(v), by=.EACHI] # get sum(v) for each i != "a"
# multi-column key
setkey(kDT,x,y) # 2-column key
setkeyv(kDT,c("x","y")) # same
# fast *keyed* subsets on multi-column key
kDT["a"] # join to 1st column of key
kDT["a", on="x"] # on= is optional, but is preferred
kDT[.("a")] # same, .() is an alias for list()
kDT[list("a")] # same
kDT[.("a", 3)] # join to 2 columns
kDT[.("a", 3:6)] # join 4 rows (2 missing)
kDT[.("a", 3:6), nomatch=NULL] # remove missing
kDT[.("a", 3:6), roll=TRUE] # locf rolling join
kDT[.("a", 3:6), roll=Inf] # same as above
kDT[.("a", 3:6), roll=-Inf] # nocb rolling join
kDT[!.("a")] # not join
kDT[!"a"] # same
# more on special symbols, see also ?"special-symbols"
DT[.N] # last row
DT[, .N] # total number of rows in DT
DT[, .N, by=x] # number of rows in each group
DT[, .SD, .SDcols=x:y] # select columns 'x' through 'y'
DT[ , .SD, .SDcols = !x:y] # drop columns 'x' through 'y'
DT[ , .SD, .SDcols = patterns('^[xv]')] # select columns matching '^x' or '^v'
DT[, .SD[1]] # first row of all columns
DT[, .SD[1], by=x] # first row of 'y' and 'v' for each group in 'x'
DT[, c(.N, lapply(.SD, sum)), by=x] # get rows *and* sum columns 'v' and 'y' by group
DT[, .I[1], by=x] # row number in DT corresponding to each group
DT[, grp := .GRP, by=x] # add a group counter column
DT[ , dput(.BY), by=.(x,y)] # .BY is a list of singletons for each group
X[, DT[.BY, y, on="x"], by=x] # join within each group
DT[, {
# write each group to a different file
fwrite(.SD, file.path(tempdir(), paste0('x=', .BY$x, '.csv')))
}, by=x]
dir(tempdir())
# add/update/delete by reference (see ?assign)
print(DT[, z:=42L]) # add new column by reference
print(DT[, z:=NULL]) # remove column by reference
print(DT["a", v:=42L, on="x"]) # subassign to existing v column by reference
print(DT["b", v2:=84L, on="x"]) # 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()
# advanced usage
DT = data.table(x=rep(c("b","a","c"),each=3), v=c(1,1,1,2,2,1,1,2,2), y=c(1,3,6), a=1:9, b=9:1)
DT[, sum(v), by=.(y%%2)] # expressions in by
DT[, sum(v), by=.(bool = y%%2)] # same, using a named list to change by column name
DT[, .SD[2], by=x] # get 2nd row of each group
DT[, tail(.SD,2), by=x] # last 2 rows of each group
DT[, lapply(.SD, sum), by=x] # sum of all (other) columns for each group
DT[, .SD[which.min(v)], by=x] # nested query by group
DT[, list(MySum=sum(v),
MyMin=min(v),
MyMax=max(v)),
by=.(x, y%%2)] # by 2 expressions
DT[, .(a = .(a), b = .(b)), by=x] # list columns
DT[, .(seq = min(a):max(b)), by=x] # j is not limited to just aggregations
DT[, sum(v), by=x][V1<20] # compound query
DT[, sum(v), by=x][order(-V1)] # ordering results
DT[, c(.N, lapply(.SD,sum)), by=x] # get number of observations and sum per group
DT[, {tmp <- mean(y);
.(a = a-tmp, b = b-tmp)
}, by=x] # anonymous lambda in 'j', j accepts any valid
# expression. TO REMEMBER: every element of
# the list becomes a column in result.
pdf("new.pdf")
DT[, plot(a,b), by=x] # can also plot in 'j'
dev.off()
file.remove("new.pdf")
# using rleid, get max(y) and min of all cols in .SDcols for each consecutive run of 'v'
DT[, c(.(y=max(y)), lapply(.SD, min)), by=rleid(v), .SDcols=v:b]
# Support guide and links:
# https://github.com/Rdatatable/data.table/wiki/Support
if (FALSE) {
if (interactive()) {
vignette(package="data.table") # 9 vignettes
test.data.table() # 6,000 tests
# keep up to date with latest stable version on CRAN
update.packages()
# get the latest devel version that has passed all tests
update_dev_pkg()
# read more at:
# https://github.com/Rdatatable/data.table/wiki/Installation
}
}
```

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