Package 'data.table'

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, 
  showProgress = getOption("datatable.showProgress", interactive())]

Arguments

Details

data.table builds on base R functionality to reduce 2 types of time:

1. programming time (easier to write, read, debug and maintain), and

2. 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 :

1. 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 on argument (now preferred).

2. it has enhanced functionality in [.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.

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, rowwiseDT

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)

Description

Fast add, remove and update subsets of columns, by reference. := operator can be used in two ways: LHS := RHS form, and Functional form. See Usage.

set is a low-overhead loop-able version of :=. It is particularly useful for repetitively updating rows of certain columns by reference (using a for-loop). See Examples. It can not perform grouping operations.

let is an alias for the functional form and behaves exactly like `:=`.

Usage

# 1. LHS := RHS form
# DT[i, LHS := RHS, by = ...]
# DT[i, c("LHS1", "LHS2") := list(RHS1, RHS2), by = ...]

# 2a. Functional form with `:=`
# DT[i, `:=`(LHS1 = RHS1,
#            LHS2 = RHS2,
#            ...), by = ...]

# 2b. Functional form with let
# DT[i, let(LHS1 = RHS1,
#            LHS2 = RHS2,
#            ...), by = ...]

# 3. Multiple columns in place
# DT[i, names(.SD) := lapply(.SD, fx), by = ..., .SDcols = ...]

set(x, i = NULL, j, value)

Arguments

Details

:= is defined for use in j only. It adds or updates or removes column(s) by reference. It makes no copies of any part of memory at all. Please read vignette("datatable-reference-semantics") and follow with examples. Some typical usages are:

    DT[, col := val]                              # update (or add at the end if doesn't exist) a column called "col" with value "val" (recycled if necessary).
    DT[i, col := val]                             # same as above, but only for those rows specified in i and (for new columns) NA elsewhere.
    DT[i, "col a" := val]                         # same. column is called "col a"
    DT[i, (3:6) := val]                           # 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, colvector := val, with = FALSE]         # OLD syntax. The contents of "colvector" in calling scope determine the column(s).
    DT[i, (colvector) := val]                     # same (NOW PREFERRED) shorthand syntax. The parens are enough to stop the LHS being a symbol; same as c(colvector).
    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))] # Functional form
    DT[, let(new1 = sum(colB), new2 = sum(colC))] # New alias for functional form.

The .Last.updated variable contains the number of rows updated by the most recent := or set calls, which may be useful, for example, in production settings for testing assumptions about the number of rows affected by a statement; see .Last.updated for details.

Note that for efficiency no check is performed for duplicate assignments, i.e. if multiple values are passed for assignment to the same index, assignment to this index will occur repeatedly and sequentially; for a given use case, consider whether it makes sense to create your own test for duplicates, e.g. in production code.

All of the following result in a friendly error (by design) :

    x := 1L
    DT[i, col] := val
    DT[i]$col := val
    DT[, {col1 := 1L; col2 := 2L}]                # Use the functional form, `:=`(), instead (see above).

For additional resources, please read vignette("datatable-faq"). Also have a look at StackOverflow's data.table tag.

:= in j can be combined with all types of i (such as binary search), and all types of by. This a one reason why := has been implemented in j. Please see vignette("datatable-reference-semantics") and also FAQ 2.16 for analogies to SQL.

When LHS is a factor column and 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.

Unlike <- for 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 when fractional data is truncated. It is best to get the column types correct up front and stick to them. Changing a column type is possible but deliberately harder: provide a whole column as the RHS. This RHS is then plonked into that column slot and we call this plonk syntax, or replace column syntax if you prefer. By needing to construct a full length vector of a new type, you as the user are more aware of what is happening and it is clearer to readers of your code that you really do intend to change the column type; e.g., DT[, colA:=as.integer(colA)]. A plonk occurs whenever you provide a RHS value to ':=' which is nrow long. When a column is plonked, the original column is not updated by reference because that would entail updating every single element of that column whereas the plonk is just one column pointer update.

data.tables are not copied-on-change by :=, setkey or any of the other set* functions. See copy.

Value

DT is modified by reference and returned invisibly. If you require a copy, take a copy first (using DT2 = copy(DT)).

Advanced (internals):

It is easy to see how sub-assigning to existing columns is done internally. Removing columns by reference is also straightforward by modifying the vector of column pointers only (using memmove in C). However adding (new) columns is more tricky as to how the data.table can be grown by reference: the list vector of column pointers is over-allocated, see truelength. By defining := in j we believe update syntax is natural, and scales, but it also bypasses [<- dispatch and allows := to update by reference with no copies of any part of memory at all.

Since [.data.table incurs overhead to check the existence and type of arguments (for example), set() provides direct (but less flexible) assignment by reference with low overhead, appropriate for use inside a for loop. See examples. := is more powerful and flexible than set() because := is intended to be combined with i and by in single queries on large datasets.

Note

DT[a > 4, b := c] is different from DT[a > 4][, b := c]. The first expression updates (or adds) column b with the value c on those rows where a > 4 evaluates to TRUE. X is updated by reference, therefore no assignment needed. Note that this does not apply when 'i' is missing, i.e. DT[].

The second expression on the other hand updates a new data.table that's returned by the subset operation. Since the subsetted data.table is ephemeral (it is not assigned to a symbol), the result would be lost; unless the result is assigned, for example, as follows: ans <- DT[a > 4][, b := c].

See Also

data.table, copy, setalloccol, truelength, set, .Last.updated

Examples

DT = data.table(a = LETTERS[c(3L,1:3)], b = 4:7)
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 := -8L]             # subassign by reference to d; 2nd row is -8L now
DT                          # DT changed by reference
DT[2, d := 10L][]           # shorthand for update and print

DT[b > 4, b := d * 2L]      # subassign to b with d*2L on those rows where b > 4 is TRUE
DT[b > 4][, b := d * 2L]    # different from above. [, := ] is performed on the subset
                            # which is an new (ephemeral) data.table. Result needs to be
                            # assigned to a variable (using `<-`).

DT[, e := mean(d), by = a]  # add new column by group by reference
DT["A", b := 0L, on = "a"]  # ad-hoc update of column b for group "A" using
			    # joins-as-subsets with binary search and 'on='
# same as above but using keys
setkey(DT, a)
DT["A", b := 0L]            # binary search for group "A" and set column b using keys
DT["B", f := mean(d)]       # subassign to new column, NA initialized

# Adding multiple columns
## by name
DT[ , c('sin_d', 'log_e', 'cos_d') :=
   .(sin(d), log(e), cos(d))]
## by patterned name
DT[ , paste(c('sin', 'cos'), 'b', sep = '_') :=
   .(sin(b), cos(b))]
## using lapply & .SD
DT[ , paste0('tan_', c('b', 'd', 'e')) :=
   lapply(.SD, tan), .SDcols = c('b', 'd', 'e')]
## using forced evaluation to disambiguate a vector of names
##   and overwrite existing columns with their squares
sq_cols = c('b', 'd', 'e')
DT[ , (sq_cols) := lapply(.SD, `^`, 2L), .SDcols = sq_cols]
## by integer (NB: for robustness, it is not recommended
##   to use explicit integers to update/define columns)
DT[ , c(2L, 3L, 4L) := .(sqrt(b), sqrt(d), sqrt(e))]
## by implicit integer
DT[ , grep('a$', names(DT)) := tolower(a)]
## by implicit integer, using forced evaluation
sq_col_idx = grep('d$', names(DT))
DT[ , (sq_col_idx) := lapply(.SD, dnorm),
   .SDcols = sq_col_idx]

# Examples using `set` function
## Set value for single cell
set(DT, 1L, "b", 10L)
## Set values for multiple columns in a specific row
set(DT, 2L, c("b", "d"), list(20L, 30L))
## Set values by column indices
set(DT, 3L, c(2L, 4L), list(40L, 50L))
## Set value for an entire column without specifying rows
set(DT, j = "b", value = 100L)
set(DT, NULL, "b", 100L) # equivalent
## Set values for multiple columns without specifying rows
set(DT, j = c("b", "d"), value = list(200L, 300L))
## Set values for multiple columns with multiple specified rows.
set(DT, c(1L, 3L), c("b", "d"), value = list(500L, 800L))

## Not run: 
# Speed example:

m = matrix(1, nrow = 2e6L, ncol = 100L)
DF = as.data.frame(m)
DT = as.data.table(m)

system.time(for (i in 1:1000) DF[i, 1] = i)
# 15.856 seconds
system.time(for (i in 1:1000) DT[i, V1 := i])
# 0.279 seconds  (57 times faster)
system.time(for (i in 1:1000) set(DT, i, 1L, i))
# 0.002 seconds  (7930 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.

## End(Not run)

Description

Returns number of rows affected by last := or set().

Usage

.Last.updated

Details

Be aware that in the case of duplicate indices, multiple updates occur (duplicates are overwritten); .Last.updated will include all of the updates performed, including duplicated ones. See examples.

Value

Integer.

See Also

:=

Examples

d = data.table(a=1:4, b=2:5)
d[2:3, z:=5L]
.Last.updated

# updated count takes duplicates into account #2837
DT = data.table(a = 1L)
DT[c(1L, 1L), a := 2:3]
.Last.updated

Description

Returns the pointer address of its argument.

Usage

address(x)

Arguments

Details

Sometimes useful in determining whether a value has been copied or not, programmatically.

Value

A character vector length 1.

References

https://stackoverflow.com/a/10913296/403310 (but implemented in C without using .Internal(inspect()))

See Also

copy

Examples

x=1
address(x)

Description

Convenient test of data equality between data.table objects. Performs some factor level stripping.

Usage

## S3 method for class 'data.table'
all.equal(target, current, trim.levels=TRUE, check.attributes=TRUE,
    ignore.col.order=FALSE, ignore.row.order=FALSE, tolerance=sqrt(.Machine$double.eps),
    ...)

Arguments

Details

For efficiency data.table method will exit on detected non-equality issues, unlike most all.equal methods which process equality checks further. Besides that fact it also handles the most time consuming case of ignore.row.order = TRUE very efficiently.

Value

Either TRUE or a vector of mode "character" describing the differences between target and current.

See Also

all.equal

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")
isTRUE(all.equal(dt1, dt1))
is.character(all.equal(dt1, dt2))

# ignore.col.order
x <- copy(dt1)
y <- dt1[, .(X, A)]
all.equal(x, y)
all.equal(x, y, ignore.col.order = TRUE)

# ignore.row.order
x <- setkeyv(copy(dt1), NULL)
y <- dt1[sample(nrow(dt1))]
all.equal(x, y)
all.equal(x, y, ignore.row.order = TRUE)

# check.attributes
x = copy(dt1)
y = setkeyv(copy(dt1), NULL)
all.equal(x, y)
all.equal(x, y, check.attributes = FALSE)
x = data.table(1L)
y = 1L
all.equal(x, y)
all.equal(x, y, check.attributes = FALSE)

# trim.levels
x <- data.table(A = factor(letters[1:10])[1:4]) # 10 levels
y <- data.table(A = factor(letters[1:5])[1:4]) # 5 levels
all.equal(x, y, trim.levels = FALSE)
all.equal(x, y, trim.levels = FALSE, check.attributes = FALSE)
all.equal(x, y)

Description

Functions to check if an object is data.table, or coerce it if possible.

Usage

as.data.table(x, keep.rownames=FALSE, ...)

## S3 method for class 'data.table'
as.data.table(x, ...)

## S3 method for class 'array'
as.data.table(x, keep.rownames=FALSE, key=NULL, sorted=TRUE,
              value.name="value", na.rm=TRUE, ...)

is.data.table(x)

Arguments

Details

as.data.table is a generic function with many methods, and other packages can supply further methods.

If a list is supplied, each element is converted to a column in the data.table with shorter elements recycled automatically. Similarly, each column of a matrix is converted separately.

character objects are not converted to factor types unlike as.data.frame.

If a data.frame is supplied, all classes preceding "data.frame" are stripped. Similarly, for data.table as input, all classes preceding "data.table" are stripped. as.data.table methods returns a copy of original data. To modify by reference see setDT and setDF.

keep.rownames argument can be used to preserve the (row)names attribute in the resulting data.table.

See Also

data.table, setDT, setDF, copy, setkey, J, SJ, CJ, merge.data.table, :=, setalloccol, truelength, rbindlist, setNumericRounding, datatable-optimize

Examples

nn = c(a=0.1, b=0.2, c=0.3, d=0.4)
as.data.table(nn)
as.data.table(nn, keep.rownames=TRUE)
as.data.table(nn, keep.rownames="rownames")

# char object not converted to factor
cc = c(X="a", Y="b", Z="c")
as.data.table(cc)
as.data.table(cc, keep.rownames=TRUE)
as.data.table(cc, keep.rownames="rownames")

mm = matrix(1:4, ncol=2, dimnames=list(c("r1", "r2"), c("c1", "c2")))
as.data.table(mm)
as.data.table(mm, keep.rownames=TRUE)
as.data.table(mm, keep.rownames="rownames")
as.data.table(mm, key="c1")

ll = list(a=1:2, b=3:4)
as.data.table(ll)
as.data.table(ll, keep.rownames=TRUE)
as.data.table(ll, keep.rownames="rownames")

DF = data.frame(x=rep(c("x","y","z"),each=2), y=c(1,3,6), row.names=LETTERS[1:6])
as.data.table(DF)
as.data.table(DF, keep.rownames=TRUE)
as.data.table(DF, keep.rownames="rownames")

DT = data.table(x=rep(c("x","y","z"),each=2), y=c(1:6))
as.data.table(DT)
as.data.table(DT, key='x')

ar = rnorm(27)
ar[sample(27, 15)] = NA
dim(ar) = c(3L,3L,3L)
as.data.table(ar)

Description

Efficient conversion xts to data.table.

Usage

## S3 method for class 'xts'
as.data.table(x, keep.rownames = TRUE, key=NULL, ...)

Arguments

See Also

as.xts.data.table

Examples

if (requireNamespace("xts", quietly = TRUE)) {
  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 data.table
  print(as.data.table(sample.xts))
}

Description

Converts a data.table into a matrix, optionally using one of the columns in the data.table as the matrix rownames.

Usage

## S3 method for class 'data.table'
as.matrix(x, rownames=NULL, rownames.value=NULL, ...)

Arguments

Details

as.matrix is a generic function in base R. It dispatches to as.matrix.data.table if its x argument is a data.table.

The method for data.tables will return a character matrix if there are only atomic columns and any non-(numeric/logical/complex) column, applying as.vector to factors and format to other non-character columns. Otherwise, the usual coercion hierarchy (logical < integer < double < complex) will be used, e.g., all-logical data frames will be coerced to a logical matrix, mixed logical-integer will give an integer matrix, etc.

Value

A new matrix containing the contents of x.

See Also

data.table, as.matrix, data.matrix array

Examples

DT <- data.table(A = letters[1:10], X = 1:10, Y = 11:20)
as.matrix(DT) # character matrix
as.matrix(DT, rownames = "A")
as.matrix(DT, rownames = 1)
as.matrix(DT, rownames = TRUE)

setkey(DT, A)
as.matrix(DT, rownames = TRUE)

Description

Efficient conversion of data.table to xts, data.table must have a time based type in first column. See ?xts::timeBased for supported types

Usage

as.xts.data.table(x, numeric.only = TRUE, ...)

Arguments

See Also

as.data.table.xts

Examples

if (requireNamespace("xts", quietly = TRUE)) {
  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 data.table
  print(sample.dt)
  # print head of xts
  print(head(as.xts.data.table(sample.dt))) # xts might not be attached on search path
}

Description

Intended for use in i in [.data.table.

between is equivalent to lower<=x & x<=upper when incbounds=TRUE, or lower<x & y<upper when FALSE. With a caveat that NA in lower or upper are taken as unlimited bounds not NA. This can be changed by setting NAbounds to NA.

inrange checks whether each value in x is in between any of the intervals provided in lower,upper.

Usage

between(x, lower, upper, incbounds=TRUE, NAbounds=TRUE, check=FALSE)
x %between% y

inrange(x, lower, upper, incbounds=TRUE)
x %inrange% y

Arguments

Details

non-equi joins were implemented in v1.9.8. They extend binary search based joins in data.table to other binary operators including >=, <=, >, <. inrange makes use of this new functionality and performs a range join.

Value

Logical vector the same length as x with value TRUE for those that lie within the specified range.

Note

Current implementation does not make use of ordered keys for %between%.

See Also

data.table, like, %chin%

Examples

X = data.table(a=1:5, b=6:10, c=c(5:1))
X[b %between% c(7,9)]
X[between(b, 7, 9)] # same as above
# NEW feature in v1.9.8, vectorised between
X[c %between% list(a,b)]
X[between(c, a, b)] # same as above
X[between(c, a, b, incbounds=FALSE)] # open interval

# inrange()
Y = data.table(a=c(8,3,10,7,-10), val=runif(5))
range = data.table(start = 1:5, end = 6:10)
Y[a %inrange% range]
Y[inrange(a, range$start, range$end)] # same as above
Y[inrange(a, range$start, range$end, incbounds=FALSE)] # open interval

Description

Some of internally used C routines are now exported. This interface should be considered experimental. List of exported C routines and their signatures are provided below in the usage section.

Usage

# SEXP DT_subsetDT(SEXP x, SEXP rows, SEXP cols);
# p_DT_subsetDT = R_GetCCallable("data.table", "DT_subsetDT");

Details

Details how to use those can be found in Writing R Extensions manual Linking to native routines in other packages section. An example use with Rcpp:

  dt = data.table::as.data.table(iris)
  Rcpp::cppFunction("SEXP mysub2(SEXP x, SEXP rows, SEXP cols) { return DT_subsetDT(x,rows,cols); }",
    include="#include <datatableAPI.h>",
    depends="data.table")
  mysub2(dt, 1:4, 1:4)

Note

Be aware C routines are likely to have less input validation than their corresponding R interface. For example one should not expect DT[-5L] will be equal to .Call(DT_subsetDT, DT, -5L, seq_along(DT)) because translation of i=-5L to seq_len(nrow(DT))[-5L] might be happening on R level. Moreover checks that i argument is in range of 1:nrow(DT), missingness, etc. might be happening on R level too.

References

https://cran.r-project.org/doc/manuals/r-release/R-exts.html

Description

chmatch returns a vector of the positions of (first) matches of its first argument in its second. Both arguments must be character vectors.

%chin% is like %in%, but for character vectors.

Usage

chmatch(x, table, nomatch=NA_integer_)
x %chin% table
chorder(x)
chgroup(x)

Arguments

Details

Fast versions of match, %in% and order, optimised for character vectors. 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 (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 base::sort.list(x,method="radix"), see setkey) is implemented using the (almost) unused truelength of CHARSXP as the counter. Where R has used truelength of CHARSXP (where a character value is shared by a variable name), the non zero truelengths are stored first and reinstated afterwards. Each of the ch* functions implements a variation on this theme. Remember that internally in R, length of a CHARSXP is the nchar of the string and DATAPTR is the string itself.

Methods that do build and cache a hash table (such as the fastmatch package) are much faster on subsequent calls (almost instant) but a little slower on the first. Therefore 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 x and table, how many unique strings each contains, and whether the position of the first match is all that is required.

It may be possible to speed up fastmatch's hash table build time by using the technique in data.table, and we have suggested this to its author. If successful, fastmatch would then be fastest in all cases.

Value

As match and %in%. chorder and chgroup return an integer index vector.

Note

The name charmatch was taken by charmatch, hence chmatch.

See Also

match, %in%

Examples

# Please type 'example(chmatch)' to run this and see timings on your machine

N = 1e5
# N is set small here (1e5) to reduce runtime because every day CRAN runs and checks
# all documentation examples in addition to the package's test suite.
# The comments here apply when N has been changed to 1e8 and were run on 2018-05-13
# with R 3.5.0 and data.table 1.11.2.

u = as.character(as.hexmode(1:10000))
y = sample(u,N,replace=TRUE)
x = sample(u)
                                           #  With N=1e8 ...
system.time(a <- match(x,y))               #  4.6s
system.time(b <- chmatch(x,y))             #  1.8s
identical(a,b)

system.time(a <- x %in% y)               #  4.5s
system.time(b <- x %chin% y)             #  1.7s
identical(a,b)

# 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))               # 46s
system.time(b <- chmatch(x,y))             # 16s
identical(a,b)

Description

In data.table parlance, all set* functions change their input 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 data.table operator that modifies input by reference is :=. Check out the See Also section below for other set* function data.table provides.

copy() copies an entire object.

Usage

copy(x)

Arguments

Details

data.table provides functions that operate on objects 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 DT.copy <- copy(DT). It may also be sometimes useful before := (or set) is used to subassign to a column by reference.

A copy() may be required when doing dt_names = names(DT). Due to R's copy-on-modify, dt_names still points to the same location in memory as names(DT). Therefore modifying DT by reference now, say by adding a new column, dt_names will also get updated. To avoid this, one has to explicitly copy: dt_names <- copy(names(DT)).

Value

Returns a copy of the object.

Note

To confirm precisely whether an object is a copy of another, compare their exact memory address with address.

See Also

data.table, address, setkey, setDT, setDF, set :=, setorder, setattr, 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)

Description

A data.table can be used in S4 class definitions as either a parent class (inside a contains argument of setClass), or as an element of an S4 slot.

Author(s)

Steve Lianoglou

See Also

data.table

Examples

## Used in inheritance.
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))

Description

data.table internally optimises certain expressions in order to improve performance. This section briefly summarises those optimisations.

Note that there's no additional input needed from the user to take advantage of these optimisations. They happen automatically.

Run the code under the example section to get a feel for the performance benefits from these optimisations.

Note that for all optimizations involving efficient sorts, the caveat mentioned in setorder applies – whenever data.table does the sorting, it does so in "C-locale". This has some subtle implications; see Examples.

Details

data.table reads the global option datatable.optimize to figure out what level of optimisation is required. The default value Inf activates all available optimisations.

At optimisation level >= 1, i.e., getOption("datatable.optimize") >= 1, these are the optimisations:

• The base function order is internally replaced with data.table's fast ordering. That is, DT[order(...)] gets internally optimised to DT[forder(...)].

• The expression DT[, lapply(.SD, fun), by=.] gets optimised to DT[, list(fun(a), fun(b), ...), by=.] where a,b, ... are columns in .SD. This improves performance tremendously.

• Similarly, the expression DT[, c(.N, lapply(.SD, fun)), by=.] gets optimised to DT[, list(.N, fun(a), fun(b), ...)]. .N is just for example here.

• base::mean function is internally optimised to use data.table's fastmean function. mean() from base is an S3 generic and gets slow with many groups.

At optimisation level >= 2, i.e., getOption("datatable.optimize") >= 2, additional optimisations are implemented on top of the optimisations already shown above.

• Expressions in j which contain only the functions min, max, mean, median, var, sd, sum, prod, first, last, head, tail (for example, DT[, list(mean(x), median(x), min(y), max(y)), by=z]), they are very effectively optimised using what we call GForce. These functions are automatically replaced with a corresponding GForce version with pattern g*, e.g., prod becomes gprod.

  Normally, once the rows belonging to each group are identified, the values corresponding to the group are gathered and the j-expression is evaluated. This can be improved by computing the result directly without having to gather the values or evaluating the expression for each group (which can get costly with large number of groups) by implementing it specifically for a particular function. As a result, it is extremely fast.

• In addition to all the functions above, '.N' is also optimised to use GForce, when used separately or when combined with the functions mentioned above. Note further that GForce-optimized functions must be used separately, i.e., code like DT[ , max(x) - min(x), by=z] will not currently be optimized to use gmax, gmin.

• Expressions of the form DT[i, j, by] are also optimised when i is a subset operation and j is any/all of the functions discussed above.

At optimisation level >= 3, i.e., getOption("datatable.optimize") >= 3, additional optimisations for subsets in i are implemented on top of the optimisations already shown above. Subsetting operations are - if possible - translated into joins to make use of blazing fast binary search using indices and keys. The following queries are optimized:

• Supported operators: ==, %in%. Non-equi operators(>, <, etc.) are not supported yet because non-equi joins are slower than vector based subsets.

• Queries on multiple columns are supported, if the connector is '&', e.g. DT[x == 2 & y == 3] is supported, but DT[x == 2 | y == 3] is not.

• Optimization will currently be turned off when doing subset when cross product of elements provided to filter on exceeds > 1e4. This most likely happens if multiple %in%, or %chin% queries are combined, e.g. DT[x %in% 1:100 & y %in% 1:200] will not be optimized since 100 * 200 = 2e4 > 1e4.

• Queries with multiple criteria on one column are not supported, e.g. DT[x == 2 & x %in% c(2,5)] is not supported.

• Queries with non-missing j are supported, e.g. DT[x == 3 & y == 5, .(new = x-y)] or DT[x == 3 & y == 5, new := x-y] are supported. Also extends to queries using with = FALSE.

• "notjoin" queries, i.e. queries that start with !, are only supported if there are no & connections, e.g. DT[!x==3] is supported, but DT[!x==3 & y == 4] is not.

If in doubt, whether your query benefits from optimization, call it with the verbose = TRUE argument. You should see "Optimized subsetting...".

Auto indexing: In case a query is optimized, but no appropriate key or index is found, data.table automatically creates an index on the first run. Any successive subsets on the same column then reuse this index to binary search (instead of vector scan) and is therefore fast. Auto indexing can be switched off with the global option options(datatable.auto.index = FALSE). To switch off using existing indices set global option options(datatable.use.index = FALSE).

See Also

setNumericRounding, getNumericRounding

Examples

## Not run: 
old = options(datatable.optimize = Inf)

# Generate a big data.table with a relatively many columns
set.seed(1L)
DT = lapply(1:20, function(x) sample(c(-100:100), 5e6L, TRUE))
setDT(DT)[, id := sample(1e5, 5e6, TRUE)]
print(object.size(DT), units="Mb") # 400MB, not huge, but will do

# 'order' optimisation
options(datatable.optimize = 1L) # optimisation 'on'
system.time(ans1 <- DT[order(id)])
options(datatable.optimize = 0L) # optimisation 'off'
system.time(ans2 <- DT[order(id)])
identical(ans1, ans2)

# optimisation of 'lapply(.SD, fun)'
options(datatable.optimize = 1L) # optimisation 'on'
system.time(ans1 <- DT[, lapply(.SD, min), by=id])
options(datatable.optimize = 0L) # optimisation 'off'
system.time(ans2 <- DT[, lapply(.SD, min), by=id])
identical(ans1, ans2)

# optimisation of 'mean'
options(datatable.optimize = 1L) # optimisation 'on'
system.time(ans1 <- DT[, lapply(.SD, mean), by=id])
system.time(ans2 <- DT[, lapply(.SD, base::mean), by=id])
identical(ans1, ans2)

# optimisation of 'c(.N, lapply(.SD, ))'
options(datatable.optimize = 1L) # optimisation 'on'
system.time(ans1 <- DT[, c(.N, lapply(.SD, min)), by=id])
options(datatable.optimize = 0L) # optimisation 'off'
system.time(ans2 <- DT[, c(N=.N, lapply(.SD, min)), by=id])
identical(ans1, ans2)

# GForce
options(datatable.optimize = 2L) # optimisation 'on'
system.time(ans1 <- DT[, lapply(.SD, median), by=id])
system.time(ans2 <- DT[, lapply(.SD, function(x) as.numeric(stats::median(x))), by=id])
identical(ans1, ans2)

# optimized subsets
options(datatable.optimize = 2L)
system.time(ans1 <- DT[id == 100L]) # vector scan
system.time(ans2 <- DT[id == 100L]) # vector scan
system.time(DT[id %in% 100:500])    # vector scan

options(datatable.optimize = 3L)
system.time(ans1 <- DT[id == 100L]) # index + binary search subset
system.time(ans2 <- DT[id == 100L]) # only binary search subset
system.time(DT[id %in% 100:500])    # only binary search subset again

# sensitivity to collate order
old_lc_collate = Sys.getlocale("LC_COLLATE")

if (old_lc_collate == "C") {
  Sys.setlocale("LC_COLLATE", "")
}
DT = data.table(
  grp = rep(1:2, each = 4L),
  var = c("A", "a", "0", "1", "B", "b", "0", "1")
)
options(datatable.optimize = Inf)
DT[, .(max(var), min(var)), by=grp]
# GForce is deactivated because of the ad-hoc column 'tolower(var)',
#   through which the result for 'max(var)' may also change
DT[, .(max(var), min(tolower(var))), by=grp]

Sys.setlocale("LC_COLLATE", old_lc_collate)
options(old)

## End(Not run)

Description

dcast.data.table is data.table's long-to-wide reshaping tool. In the spirit of data.table, it is very fast and memory efficient, making it well-suited to handling large data sets in RAM. More importantly, it is capable of handling very large data quite efficiently in terms of memory usage. dcast.data.table can also cast multiple value.var columns and accepts multiple functions to fun.aggregate. See Examples for more.

Usage

## S3 method for class 'data.table'
dcast(data, formula, fun.aggregate = NULL, sep = "_",
    ..., margins = NULL, subset = NULL, fill = NULL,
    drop = TRUE, value.var = guess(data),
    verbose = getOption("datatable.verbose"),
    value.var.in.dots = FALSE, value.var.in.LHSdots = value.var.in.dots, 
    value.var.in.RHSdots = value.var.in.dots)

Arguments

Details

The cast formula takes the form LHS ~ RHS, ex: var1 + var2 ~ var3. The order of entries in the formula is essential. There are two special variables: . represents no variable, while ... represents all variables not otherwise mentioned in formula, and value.var depending on value.var.in.LHSdots and value.var.in.RHSdots arguments; see Examples.

When not all combinations of LHS & RHS values are present in the data, some or all (in accordance with drop) missing combinations will replaced with the value specified by fill. Note that fill will be converted to the class of value.var; see Examples.

dcast also allows value.var columns of type list.

When variable combinations in formula don't identify a unique value, fun.aggregate will have to be specified, which defaults to length. For the formula var1 ~ var2, this means there are some (var1, var2) combinations in the data corresponding to multiple rows (i.e. x is not unique by (var1, var2).

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 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: dcast(DT, x+x~ y), then the answer will have duplicate names. In those cases, the duplicate names are renamed using 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, _) from the (unique) values in each column mentioned in the formula RHS.

From v1.9.4, dcast tries to preserve attributes wherever possible.

From v1.9.6, it is possible to cast multiple value.var columns and also cast by providing multiple fun.aggregate functions. Multiple fun.aggregate functions should be provided as a list, for e.g., list(mean, sum, function(x) paste(x, collapse=""). value.var can be either a character vector or list of length one, or a list of length equal to length(fun.aggregate). When value.var is a character vector or a list of length one, each function mentioned under fun.aggregate is applied to every column specified under value.var column. When value.var is a list of length equal to length(fun.aggregate) each element of fun.aggregate is applied to each element of value.var column.

Historical note: dcast.data.table was originally designed as an enhancement to reshape2::dcast in terms of computing and memory efficiency. reshape2 has since been superseded in favour of tidyr, and dcast has had a generic defined within data.table since v1.9.6 in 2015, at which point the dependency between the packages became more etymological than programmatic. We thank the reshape2 authors for the inspiration.

Value

A keyed data.table that has been cast. The key columns are equal to the variables in the formula LHS in the same order.

See Also

melt.data.table, rowid, https://cran.r-project.org/package=reshape

Examples

ChickWeight = as.data.table(ChickWeight)
setnames(ChickWeight, tolower(names(ChickWeight)))
DT <- melt(as.data.table(ChickWeight), id.vars=2:4) # calls melt.data.table

# dcast is an S3 method in data.table from v1.9.6
dcast(DT, time ~ variable, fun.aggregate=mean)
dcast(DT, diet ~ variable, fun.aggregate=mean)
dcast(DT, diet+chick ~ time, drop=FALSE)
dcast(DT, diet+chick ~ time, drop=FALSE, fill=0)

# using subset
dcast(DT, chick ~ time, fun.aggregate=mean, subset=.(time < 10 & chick < 20))

# drop argument, #1512
DT <- data.table(v1 = c(1.1, 1.1, 1.1, 2.2, 2.2, 2.2),
                 v2 = factor(c(1L, 1L, 1L, 3L, 3L, 3L), levels=1:3),
                 v3 = factor(c(2L, 3L, 5L, 1L, 2L, 6L), levels=1:6),
                 v4 = c(3L, 2L, 2L, 5L, 4L, 3L))
# drop=TRUE
dcast(DT, v1+v2~v3, value.var='v4')                      # default is drop=TRUE
dcast(DT, v1+v2~v3, value.var='v4', drop=FALSE)          # all missing combinations of LHS and RHS
dcast(DT, v1+v2~v3, value.var='v4', drop=c(FALSE, TRUE)) # all missing combinations of LHS only
dcast(DT, v1+v2~v3, value.var='v4', drop=c(TRUE, FALSE)) # all missing combinations of RHS only

# using . and ...
DT <- data.table(v1 = rep(1:2, each = 6),
                 v2 = rep(rep(1:3, 2), each = 2),
                 v3 = rep(1:2, 6),
                 v4 = rnorm(6))
dcast(DT, ... ~ v3, value.var="v4") # same as v1+v2 ~ v3, value.var="v4"
dcast(DT, ... ~ v3, value.var="v4", value.var.in.dots=TRUE) # same as v1+v2+v4~v3, value.var="v4"
dcast(DT, v1+v2+v3 ~ ., value.var="v4")

## for each combination of (v1, v2), add up all values of v4
dcast(DT, v1+v2 ~ ., value.var="v4", fun.aggregate=sum)

# fill and types
dcast(DT, v2~v3, value.var='v1', fun.aggregate=length, fill=0L)  #  0L --> 0
dcast(DT, v2~v3, value.var='v4', fun.aggregate=length, fill=1.1) # 1.1 --> 1L

# 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.aggregate=sum, value.var=c("d1","d2"))
# multiple fun.aggregate
dcast(DT, x+y ~ z, fun.aggregate=list(sum, mean), value.var="d1")
# multiple fun.agg and value.var (all combinations)
dcast(DT, x+y ~ z, fun.aggregate=list(sum, mean), value.var=c("d1", "d2"))
# multiple fun.agg and value.var (one-to-one)
dcast(DT, x+y ~ z, fun.aggregate=list(sum, mean), value.var=list("d1", "d2"))

Description

duplicated returns a logical vector indicating which rows of a data.table are duplicates of a row with smaller subscripts.

unique returns a data.table with duplicated rows removed, by columns specified in by argument. When no by then duplicated rows by all columns are removed.

anyDuplicated returns the index i of the first duplicated entry if there is one, and 0 otherwise.

uniqueN is equivalent to length(unique(x)) when x is an atomic vector, and nrow(unique(x)) when x is a data.frame or data.table. The number of unique rows are computed directly without materialising the intermediate unique data.table and is therefore faster and memory efficient.

Usage

## S3 method for class 'data.table'
duplicated(x, incomparables=FALSE, fromLast=FALSE, by=seq_along(x), ...)

## S3 method for class 'data.table'
unique(x, incomparables=FALSE, fromLast=FALSE,
by=seq_along(x), cols=NULL, ...)

## S3 method for class 'data.table'
anyDuplicated(x, incomparables=FALSE, fromLast=FALSE, by=seq_along(x), ...)

uniqueN(x, by=if (is.list(x)) seq_along(x) else NULL, na.rm=FALSE)

Arguments

Details

Because data.tables are usually sorted by key, tests for duplication are especially quick when only the keyed columns are considered. Unlike unique.data.frame, paste is not used to ensure equality of floating point data. It is instead accomplished directly and is therefore quite fast. data.table provides setNumericRounding to handle cases where limitations in floating point representation is undesirable.

v1.9.4 introduces anyDuplicated method for data.tables and is similar to base in functionality. It also implements the logical argument fromLast for all three functions, with default value FALSE.

Note: When cols is specified, the resulting table will have columns c(by, cols), in that order.

Value

duplicated returns a logical vector of length nrow(x) indicating which rows are duplicates.

unique returns a data table with duplicated rows removed.

anyDuplicated returns a integer value with the index of first duplicate. If none exists, 0L is returned.

uniqueN returns the number of unique elements in the vector, data.frame or data.table.

See Also

setNumericRounding, data.table, duplicated, unique, all.equal, fsetdiff, funion, fintersect, fsetequal

Examples

DT <- data.table(A = rep(1:3, each=4), B = rep(1:4, each=3),
                  C = rep(1:2, 6), key = c("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 vs. FALSE
DT <- data.table(A = c(1, 1, 2, 2, 3), B = c(1, 2, 1, 1, 2), C = c("a", "b", "a", "b", "a"))

duplicated(DT, by="B", fromLast=FALSE) # rows 3,4,5 are duplicates
unique(DT, by="B", fromLast=FALSE) # equivalent: DT[!duplicated(DT, by="B", fromLast=FALSE)]

duplicated(DT, by="B", fromLast=TRUE) # rows 1,2,3 are duplicates
unique(DT, by="B", fromLast=TRUE) # equivalent: DT[!duplicated(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, by = key(DT))
# uniqueN, unique rows on all columns
uniqueN(DT)
# uniqueN while grouped by "A"
DT[, .(uN=uniqueN(.SD)), by=A]

# uniqueN's na.rm=TRUE
x = sample(c(NA, NaN, runif(3)), 10, TRUE)
uniqueN(x, na.rm = FALSE) # 5, default
uniqueN(x, na.rm=TRUE) # 3

Description

fcase is a fast implementation of SQL CASE WHEN statement for R. Conceptually, fcase is a nested version of fifelse (with smarter implementation than manual nesting). It is comparable to dplyr::case_when and supports bit64's integer64 and nanotime classes.

Usage

fcase(..., default=NA)

Arguments

Details

fcase evaluates each when-value pair in order, until it finds a when that is TRUE. It then returns the corresponding value. During evaluation, value will be evaluated regardless of whether the corresponding when is TRUE or not, which means recursive calls should be placed in the last when-value pair, see Examples.

default is always evaluated, regardless of whether it is returned or not.

Value

Vector with the same length as the logical conditions (when) in ..., filled with the corresponding values (value) from ..., or eventually default. Attributes of output values value1, value2, ...valueN in ... are preserved.

See Also

fifelse

Examples

x = 1:10
fcase(
	x < 5L, 1L,
	x > 5L, 3L
)

fcase(
	x < 5L, 1L:10L,
	x > 5L, 3L:12L
)

# Lazy evaluation example
fcase(
	x < 5L, 1L,
	x >= 5L, 3L,
	x == 5L, stop("provided value is an unexpected one!")
)

# fcase preserves attributes, example with dates
fcase(
	x < 5L, as.Date("2019-10-11"),
	x > 5L, as.Date("2019-10-14")
)

# fcase example with factor; note the matching levels
fcase(
	x < 5L, factor("a", levels=letters[1:3]),
	x > 5L, factor("b", levels=letters[1:3])
)

# Example of using the 'default' argument
fcase(
	x < 5L, 1L,
	x > 5L, 3L,
	default = 5L
)

# fcase can be used for recursion, unlike fifelse
# Recursive function to calculate the Greatest Common Divisor
gcd_dt = function(x,y) {
  r = x%%y
  fcase(!r, y, r, gcd_dt(x, y)) # Recursive call must be in the last when-value pair
}
gcd_dt(10L, 1L)

Description

Fill in missing values in a vector by successively pulling from candidate vectors in order. As per the ANSI SQL function COALESCE, dplyr::coalesce and hutils::coalesce. Unlike BBmisc::coalesce which just returns the first non-NULL vector. Written in C, and multithreaded for numeric and factor types.

Usage

fcoalesce(...)

Arguments

Details

Factor type is supported only when the factor levels of each item are equal.

NaN is considered missing (note is.na(NaN) and all.equal(NA_real_, NaN) are both TRUE).

Value

Atomic vector of the same type and length as the first vector, having NA values replaced by corresponding non-NA values from the other vectors. If the first item is NULL, the result is NULL.

See Also

fifelse

Examples

x = c(11L, NA, 13L, NA, 15L, NA)
y = c(NA, 12L, 5L, NA, NA, NA)
z = c(11L, NA, 1L, 14L, NA, NA)
fcoalesce(x, y, z)
fcoalesce(list(x,y,z))   # same
fcoalesce(x, list(y,z))  # same

Description

Similar to base::droplevels but much faster.

Usage

fdroplevels(x, exclude = if (anyNA(levels(x))) NULL else NA, ...)
setdroplevels(x, except = NULL, exclude = NULL)

## S3 method for class 'data.table'
droplevels(x, except = NULL, exclude, in.place = FALSE, ...)

Arguments

Value

fdroplevels returns a factor.

droplevels returns a data.table where levels are dropped at factor columns.

See Also

data.table, duplicated, unique

Examples

# on vectors
x = factor(letters[1:10])
fdroplevels(x[1:5])
# exclude levels from drop
fdroplevels(x[1:5], exclude = c("a", "c"))

# on data.table
DT = data.table(a = factor(1:10), b = factor(letters[1:10]))
droplevels(head(DT))[["b"]]
# exclude levels
droplevels(head(DT), exclude = c("b", "c"))[["b"]]
# except columns from drop
droplevels(head(DT), except = 2)[["b"]]
droplevels(head(DT), except = 1)[["b"]]

Description

fifelse is a faster and more robust replacement of ifelse. It is comparable to dplyr::if_else and hutils::if_else. It returns a value with the same length as test filled with corresponding values from yes, no or eventually na, depending on test. Supports bit64's integer64 and nanotime classes.

Usage

fifelse(test, yes, no, na=NA)

Arguments

Details

In contrast to ifelse attributes are copied from the first non-NA argument to the output. This is useful when returning Date, factor or other classes.

Unlike ifelse, fifelse evaluates both yes and no arguments for type checking regardless of the result of test. This means that neither yes nor no should be recursive function calls. For recursion, use fcase instead.

Value

A vector of the same length as test and attributes as yes. Data values are taken from the values of yes and no, eventually na.

See Also

fcoalesce

fcase

Examples

x = c(1:4, 3:2, 1:4)
fifelse(x > 2L, x, x - 1L)

# unlike ifelse, fifelse preserves attributes, taken from the 'yes' argument
dates = as.Date(c("2011-01-01","2011-01-02","2011-01-03","2011-01-04","2011-01-05"))
ifelse(dates == "2011-01-01", dates - 1, dates)
fifelse(dates == "2011-01-01", dates - 1, dates)
yes = factor(c("a","b","c"))
no = yes[1L]
ifelse(c(TRUE,FALSE,TRUE), yes, no)
fifelse(c(TRUE,FALSE,TRUE), yes, no)

# Example of using the 'na' argument
fifelse(test = c(-5L:5L < 0L, NA), yes = 1L, no = 0L, na = 2L)

# Example showing both 'yes' and 'no' arguments are evaluated, unlike ifelse
fifelse(1 == 1, print("yes"), print("no"))
ifelse(1 == 1, print("yes"), print("no"))

Description

A fast binary-search based overlap join of two data.tables. This is very much inspired by findOverlaps function from the Bioconductor package IRanges (see link below under See Also).

Usually, x is a very large data.table with small interval ranges, and y is much smaller keyed data.table with relatively larger interval spans. For a usage in genomics, see the examples section.

NOTE: This is still under development, meaning it is 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 = NA,
    which = FALSE, verbose = getOption("datatable.verbose"))

Arguments

Details

Very briefly, foverlaps() collapses the two-column interval in y to one-column of unique values to generate a lookup table, and then performs the join depending on the type of overlap, using the already available binary search feature of data.table. The time (and space) required to generate the lookup is therefore proportional to the number of unique values present in the interval columns of y when combined together.

Overlap joins takes advantage of the fact that y is sorted to speed-up finding overlaps. Therefore y has to be keyed (see ?setkey) prior to running foverlaps(). A key on x is not necessary, although it might speed things further. The columns in by.x argument should correspond to the columns specified in by.y. The last two columns should be the interval columns in both by.x and by.y. The first interval column in by.x should always be <= the second interval column in by.x, and likewise for by.y. The storage.mode of the interval columns must be either double or integer. It therefore works with bit64::integer64 type as well.

The lookup generation step could be quite time consuming if the number of unique values in 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 y.

Value

A new data.table by joining over the interval columns (along with other additional identifier columns) specified in by.x and by.y.

NB: When which=TRUE: a) mult="first" or "last" returns a vector of matching row numbers in y, and b) when mult="all" returns a data.table with two columns with the first containing row numbers of x and the second column with corresponding row numbers of y.

nomatch=NA|NULL also influences whether non-matching rows are returned or not, as explained above.

See Also

data.table, https://www.bioconductor.org/packages/release/bioc/html/IRanges.html, setNumericRounding

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=NULL)
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)

Description

Similar to base::rank but much faster. And it accepts vectors, lists, data.frames or data.tables as input. In addition to the ties.method possibilities provided by base::rank, it also provides ties.method="dense".

Like forder, sorting is done in "C-locale"; in particular, this may affect how capital/lowercase letters are ranked. See Details on forder for more.

bit64::integer64 type is also supported.

Usage

frank(x, ..., na.last=TRUE, ties.method=c("average",
  "first", "last", "random", "max", "min", "dense"))

frankv(x, cols=seq_along(x), order=1L, na.last=TRUE,
      ties.method=c("average", "first", "last", "random",
        "max", "min", "dense"))

Arguments

Details

To be consistent with other data.table operations, NAs are considered identical to other NAs (and NaNs to other NaNs), unlike base::rank. Therefore, for na.last=TRUE and na.last=FALSE, NAs (and NaNs) are given identical ranks, unlike rank.