| data.table-package {data.table} | R Documentation |
Enhanced data.frame
Description
data.table inherits from data.frame. It offers fast and memory efficient: file reader and writer, aggregations, updates, equi, non-equi, rolling, range and interval joins, in a short and flexible syntax, for faster development.
It is inspired by A[B] syntax in R where A is a matrix and B is a 2-column matrix. Since a data.table is a data.frame, it is compatible with R functions and packages that accept only data.frames.
Type vignette(package="data.table") to get started. The Introduction to data.table vignette introduces data.table's x[i, j, by] syntax and is a good place to start. If you have read the vignettes and the help page below, please read the data.table support guide.
Please check the homepage for up to the minute live NEWS.
Tip: one of the quickest ways to learn the features is to type example(data.table) and study the output at the prompt.
Usage
data.table(..., keep.rownames=FALSE, check.names=FALSE, key=NULL, stringsAsFactors=FALSE)
## S3 method for class 'data.table'
x[i, j, by, keyby, with = TRUE,
nomatch = NA,
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, env = NULL]
Arguments
... |
Just as |
keep.rownames |
If |
check.names |
Just as |
key |
Character vector of one or more column names which is passed to |
stringsAsFactors |
Logical (default is |
x |
A |
i |
Integer, logical or character vector, single column numeric
If
Using When the binary operator Support for non-equi join was recently implemented, which allows for other binary operators See Advanced: When |
j |
When As long as The expression '.()' is a shorthand alias to When Advanced: Advanced: When Advanced: Columns of See |
by |
Column names are seen as if they are variables (as in
Advanced: When Advanced: In the |
keyby |
Same as |
with |
By default When |
nomatch |
When a row in |
mult |
When |
roll |
When
Rolling joins apply to the last join column, generally a date but can be any variable. It is particularly fast using a modified binary search. A common idiom is to select a contemporaneous regular time series ( |
rollends |
A logical vector length 2 (a single logical is recycled) indicating whether values falling before the first value or after the last value for a group should be rolled as well.
When |
which |
|
.SDcols |
Specifies the columns of For convenient interactive use, the form Inversion (column dropping instead of keeping) can be accomplished be prepending the argument with Finally, you can filter columns to include in |
verbose |
|
allow.cartesian |
|
drop |
Never used by |
on |
Indicate which columns in
See examples as well as |
env |
List or an environment, passed to |
Details
data.table builds on base R functionality to reduce 2 types of time:
programming time (easier to write, read, debug and maintain), and
compute time (fast and memory efficient).
The general form of data.table syntax is:
DT[ i, j, by ] # + extra arguments
| | |
| | -------> grouped by what?
| -------> what to do?
---> on which rows?
The way to read this out loud is: "Take DT, subset rows by i, then compute j grouped by by. Here are some basic usage examples expanding on this definition. See the vignette (and examples) for working examples.
X[, a] # return col 'a' from X as vector. If not found, search in parent frame.
X[, .(a)] # same as above, but return as a data.table.
X[, sum(a)] # return sum(a) as a vector (with same scoping rules as above)
X[, .(sum(a)), by=c] # get sum(a) grouped by 'c'.
X[, sum(a), by=c] # same as above, .() can be omitted in j and by on single expression for convenience
X[, sum(a), by=c:f] # get sum(a) grouped by all columns in between 'c' and 'f' (both inclusive)
X[, sum(a), keyby=b] # get sum(a) grouped by 'b', and sort that result by the grouping column 'b'
X[, sum(a), by=b, keyby=TRUE] # same order as above, but using sorting flag
X[, sum(a), by=b][order(b)] # same order as above, but by chaining compound expressions
X[c>1, sum(a), by=c] # get rows where c>1 is TRUE, and on those rows, get sum(a) grouped by 'c'
X[Y, .(a, b), on="c"] # get rows where Y$c == X$c, and select columns 'X$a' and 'X$b' for those rows
X[Y, .(a, i.a), on="c"] # get rows where Y$c == X$c, and then select 'X$a' and 'Y$a' (=i.a)
X[Y, sum(a*i.a), on="c", by=.EACHI] # for *each* 'Y$c', get sum(a*i.a) on matching rows in 'X$c'
X[, plot(a, b), by=c] # j accepts any expression, generates plot for each group and returns no data
# see ?assign to add/update/delete columns by reference using the same consistent interface
A data.table query may be invoked on a data.frame using functional form DT(...), see examples. The class of the input is retained.
A data.table is a list of vectors, just like a data.frame. However :
it never has or uses rownames. Rownames based indexing can be done by setting a key of one or more columns or done ad-hoc using the
onargument (now preferred).it has enhanced functionality in
[.data.tablefor 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.
See the see also section for the several other methods that are available for operating on data.tables efficiently.
Note
If keep.rownames or check.names are supplied they must be written in full because R does not allow partial argument names after .... For example, data.table(DF, keep=TRUE) will create a
column called keep containing TRUE and this is correct behaviour; 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. They are implicitly converted to POSIXct type with warning. You may also be interested in IDateTime instead; it has methods to convert to and from POSIXlt.
References
https://r-datatable.com (data.table homepage)
https://en.wikipedia.org/wiki/Binary_search
See Also
special-symbols, data.frame, [.data.frame, as.data.table, setkey, setorder, setDT, setDF, J, SJ, CJ, merge.data.table, tables, test.data.table, IDateTime, unique.data.table, copy, :=, setalloccol, truelength, rbindlist, setNumericRounding, datatable-optimize, fsetdiff, funion, fintersect, fsetequal, anyDuplicated, uniqueN, rowid, rleid, na.omit, frank
Examples
## Not run:
example(data.table) # to run these examples yourself
## End(Not run)
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()
# 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
## Not run:
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
}
## End(Not run)