melt.data.table  R Documentation 
melt
is data.table
's widetolong reshaping tool.
We provide an S3 method for melting data.table
s. It is written in C for speed and memory
efficiency. Since v1.9.6
, melt.data.table
allows melting into
multiple columns simultaneously.
## fast melt a data.table
## S3 method for class 'data.table'
melt(data, id.vars, measure.vars,
variable.name = "variable", value.name = "value",
..., na.rm = FALSE, variable.factor = TRUE,
value.factor = FALSE,
verbose = getOption("datatable.verbose"))
data 
A 
id.vars 
vector of id variables. Can be integer (corresponding id column numbers) or character (id column names) vector. If missing, all nonmeasure columns will be assigned to it. If integer, must be positive; see Details. 
measure.vars 
Measure variables for
For convenience/clarity in the case of multiple 
variable.name 
name (default 
value.name 
name for the molten data values column(s). The default name is 
na.rm 
If 
variable.factor 
If 
value.factor 
If 
verbose 

... 
any other arguments to be passed to/from other methods. 
If id.vars
and measure.vars
are both missing, all
nonnumeric/integer/logical
columns are assigned as id variables and
the rest as measure variables. If only one of id.vars
or
measure.vars
is supplied, the rest of the columns will be assigned to
the other. Both id.vars
and measure.vars
can have the same column
more than once and the same column can be both as id and measure variables.
melt.data.table
also accepts list
columns for both id and measure
variables.
When all measure.vars
are not of the same type, they'll be coerced
according to the hierarchy list
> character
> numeric >
integer > logical
. For example, if any of the measure variables is a
list
, then entire value column will be coerced to a list.
From version 1.9.6
, melt
gains a feature with measure.vars
accepting a list of character
or integer
vectors as well to melt
into multiple columns in a single function call efficiently.
If a vector in the list contains missing values, or is shorter than the
max length of the list elements, then the output will include runs of
missing values at the specified position, or at the end.
The functions
patterns
and measure
can be used to provide regular expression patterns. When
used along with melt
, if cols
argument is not provided, the
patterns will be matched against names(data)
, for convenience.
Attributes are preserved if all value
columns are of the same type. By
default, if any of the columns to be melted are of type factor
, it'll
be coerced to character
type. To get a factor
column, set
value.factor = TRUE
. melt.data.table
also preserves
ordered
factors.
Historical note: melt.data.table
was originally designed as an enhancement to reshape2::melt
in terms of computing and memory efficiency. reshape2
has since been superseded in favour of tidyr
, and melt
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.
An unkeyed data.table
containing the molten data.
dcast
, https://cran.rproject.org/package=reshape
set.seed(45)
require(data.table)
DT < data.table(
i_1 = c(1:5, NA),
n_1 = c(NA, 6, 7, 8, 9, 10),
f_1 = factor(sample(c(letters[1:3], NA), 6L, TRUE)),
f_2 = factor(c("z", "a", "x", "c", "x", "x"), ordered=TRUE),
c_1 = sample(c(letters[1:3], NA), 6L, TRUE),
c_2 = sample(c(LETTERS[1:2], NA), 6L, TRUE),
d_1 = as.Date(c(1:3,NA,4:5), origin="20130901"),
d_2 = as.Date(6:1, origin="20120101")
)
# add a couple of list cols
DT[, l_1 := DT[, list(c=list(rep(i_1, sample(5, 1L)))), by = i_1]$c]
DT[, l_2 := DT[, list(c=list(rep(c_1, sample(5, 1L)))), by = i_1]$c]
# id.vars, measure.vars as character/integer/numeric vectors
melt(DT, id.vars=1:2, measure.vars="f_1")
melt(DT, id.vars=c("i_1", "n_1"), measure.vars=3) # same as above
melt(DT, id.vars=1:2, measure.vars=3L, value.factor=TRUE) # same, but 'value' is factor
melt(DT, id.vars=1:2, measure.vars=3:4, value.factor=TRUE) # 'value' is *ordered* factor
# preserves attribute when types are identical, ex: Date
melt(DT, id.vars=3:4, measure.vars=c("d_1", "d_2"))
melt(DT, id.vars=3:4, measure.vars=c("n_1", "d_1")) # attribute not preserved
# on list
melt(DT, id.vars=1, measure.vars=c("l_1", "l_2")) # value is a list
suppressWarnings(
melt(DT, id.vars=1, measure.vars=c("c_1", "l_1")) # c1 coerced to list, with warning
)
# on character
melt(DT, id.vars=1, measure.vars=c("c_1", "f_1")) # value is char
suppressWarnings(
melt(DT, id.vars=1, measure.vars=c("c_1", "n_1")) # n_1 coerced to char, with warning
)
# on na.rm=TRUE. NAs are removed efficiently, from within C
melt(DT, id.vars=1, measure.vars=c("c_1", "c_2"), na.rm=TRUE) # remove NA
# measure.vars can be also 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.vars=1:2, measure.vars=patterns("^f_", "^d_"), value.factor=TRUE)
# same as above, but provide list of columns directly by column names or indices
melt(DT, id.vars=1:2, measure.vars=list(3:4, c("d_1", "d_2")), value.factor=TRUE)
# same as above, but provide names directly:
melt(DT, id.vars=1:2, measure.vars=patterns(f="^f_", d="^d_"), value.factor=TRUE)
# na.rm=TRUE removes rows with NAs in any 'value' columns
melt(DT, id.vars=1:2, measure.vars=patterns("f_", "d_"), value.factor=TRUE, na.rm=TRUE)
# 'na.rm=TRUE' also works with list column, but note that is.na only
# returns TRUE if the list element is a length=1 vector with an NA.
is.na(list(one.NA=NA, two.NA=c(NA,NA)))
melt(DT, id.vars=1:2, measure.vars=patterns("l_", "d_"), na.rm=FALSE)
melt(DT, id.vars=1:2, measure.vars=patterns("l_", "d_"), na.rm=TRUE)
# measure list with missing/short entries results in output with runs of NA
DT.missing.cols < DT[, .(d_1, d_2, c_1, f_2)]
melt(DT.missing.cols, measure.vars=list(d=1:2, c="c_1", f=c(NA, "f_2")))
# specifying columns to melt via separator.
melt(DT.missing.cols, measure.vars=measure(value.name, number=as.integer, sep="_"))
# specifying columns to melt via regex.
melt(DT.missing.cols, measure.vars=measure(value.name, number=as.integer, pattern="(.)_(.)"))
melt(DT.missing.cols, measure.vars=measure(value.name, number=as.integer, pattern="([dc])_(.)"))
# cols arg of measure can be used if you do not want to use regex
melt(DT.missing.cols, measure.vars=measure(
value.name, number=as.integer, sep="_", cols=c("d_1","d_2","c_1")))
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