melt.data.table.Rd
melt
is data.table
's wide-to-long 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 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 non-measure 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
non-numeric/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 function
patterns
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.
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="2013-09-01"), d_2 = as.Date(6:1, origin="2012-01-01") ) # add a couple of list cols DT[, l_1 := DT[, list(c=list(rep(i_1, sample(5, 1L)))), by = i_1]$c]#> i_1 n_1 f_1 f_2 c_1 c_2 d_1 d_2 l_1 #> <int> <num> <fctr> <ord> <char> <char> <Date> <Date> <list> #> 1: 1 NA a z c A 2013-09-02 2012-01-07 1,1,1,1,1 #> 2: 2 6 c a c B 2013-09-03 2012-01-06 2 #> 3: 3 7 b x <NA> A 2013-09-04 2012-01-05 3,3 #> 4: 4 8 <NA> c c A <NA> 2012-01-04 4,4,4,4,4 #> 5: 5 9 c x <NA> <NA> 2013-09-05 2012-01-03 5,5,5 #> 6: NA 10 b x c <NA> 2013-09-06 2012-01-02 NA,NA#> i_1 n_1 f_1 f_2 c_1 c_2 d_1 d_2 l_1 #> <int> <num> <fctr> <ord> <char> <char> <Date> <Date> <list> #> 1: 1 NA a z c A 2013-09-02 2012-01-07 1,1,1,1,1 #> 2: 2 6 c a c B 2013-09-03 2012-01-06 2 #> 3: 3 7 b x <NA> A 2013-09-04 2012-01-05 3,3 #> 4: 4 8 <NA> c c A <NA> 2012-01-04 4,4,4,4,4 #> 5: 5 9 c x <NA> <NA> 2013-09-05 2012-01-03 5,5,5 #> 6: NA 10 b x c <NA> 2013-09-06 2012-01-02 NA,NA #> l_2 #> <list> #> 1: c,c #> 2: c,c,c #> 3: NA,NA,NA,NA,NA #> 4: c,c #> 5: NA,NA,NA,NA,NA #> 6: c# id.vars, measure.vars as character/integer/numeric vectors melt(DT, id.vars=1:2, measure.vars="f_1")#> i_1 n_1 variable value #> <int> <num> <fctr> <char> #> 1: 1 NA f_1 a #> 2: 2 6 f_1 c #> 3: 3 7 f_1 b #> 4: 4 8 f_1 <NA> #> 5: 5 9 f_1 c #> 6: NA 10 f_1 b#> i_1 n_1 variable value #> <int> <num> <fctr> <char> #> 1: 1 NA f_1 a #> 2: 2 6 f_1 c #> 3: 3 7 f_1 b #> 4: 4 8 f_1 <NA> #> 5: 5 9 f_1 c #> 6: NA 10 f_1 bmelt(DT, id.vars=1:2, measure.vars=3L, value.factor=TRUE) # same, but 'value' is factor#> i_1 n_1 variable value #> <int> <num> <fctr> <fctr> #> 1: 1 NA f_1 a #> 2: 2 6 f_1 c #> 3: 3 7 f_1 b #> 4: 4 8 f_1 <NA> #> 5: 5 9 f_1 c #> 6: NA 10 f_1 bmelt(DT, id.vars=1:2, measure.vars=3:4, value.factor=TRUE) # 'value' is *ordered* factor#> i_1 n_1 variable value #> <int> <num> <fctr> <ord> #> 1: 1 NA f_1 a #> 2: 2 6 f_1 c #> 3: 3 7 f_1 b #> 4: 4 8 f_1 <NA> #> 5: 5 9 f_1 c #> 6: NA 10 f_1 b #> 7: 1 NA f_2 z #> 8: 2 6 f_2 a #> 9: 3 7 f_2 x #> 10: 4 8 f_2 c #> 11: 5 9 f_2 x #> 12: NA 10 f_2 x# preserves attribute when types are identical, ex: Date melt(DT, id.vars=3:4, measure.vars=c("d_1", "d_2"))#> f_1 f_2 variable value #> <fctr> <ord> <fctr> <Date> #> 1: a z d_1 2013-09-02 #> 2: c a d_1 2013-09-03 #> 3: b x d_1 2013-09-04 #> 4: <NA> c d_1 <NA> #> 5: c x d_1 2013-09-05 #> 6: b x d_1 2013-09-06 #> 7: a z d_2 2012-01-07 #> 8: c a d_2 2012-01-06 #> 9: b x d_2 2012-01-05 #> 10: <NA> c d_2 2012-01-04 #> 11: c x d_2 2012-01-03 #> 12: b x d_2 2012-01-02#> f_1 f_2 variable value #> <fctr> <ord> <fctr> <num> #> 1: a z n_1 NA #> 2: c a n_1 6 #> 3: b x n_1 7 #> 4: <NA> c n_1 8 #> 5: c x n_1 9 #> 6: b x n_1 10 #> 7: a z d_1 15950 #> 8: c a d_1 15951 #> 9: b x d_1 15952 #> 10: <NA> c d_1 NA #> 11: c x d_1 15953 #> 12: b x d_1 15954#> i_1 variable value #> <int> <fctr> <list> #> 1: 1 l_1 1,1,1,1,1 #> 2: 2 l_1 2 #> 3: 3 l_1 3,3 #> 4: 4 l_1 4,4,4,4,4 #> 5: 5 l_1 5,5,5 #> 6: NA l_1 NA,NA #> 7: 1 l_2 c,c #> 8: 2 l_2 c,c,c #> 9: 3 l_2 NA,NA,NA,NA,NA #> 10: 4 l_2 c,c #> 11: 5 l_2 NA,NA,NA,NA,NA #> 12: NA l_2 csuppressWarnings( melt(DT, id.vars=1, measure.vars=c("c_1", "l_1")) # c1 coerced to list, with warning )#> i_1 variable value #> <int> <fctr> <list> #> 1: 1 c_1 c #> 2: 2 c_1 c #> 3: 3 c_1 NA #> 4: 4 c_1 c #> 5: 5 c_1 NA #> 6: NA c_1 c #> 7: 1 l_1 1,1,1,1,1 #> 8: 2 l_1 2 #> 9: 3 l_1 3,3 #> 10: 4 l_1 4,4,4,4,4 #> 11: 5 l_1 5,5,5 #> 12: NA l_1 NA,NA#> i_1 variable value #> <int> <fctr> <char> #> 1: 1 c_1 c #> 2: 2 c_1 c #> 3: 3 c_1 <NA> #> 4: 4 c_1 c #> 5: 5 c_1 <NA> #> 6: NA c_1 c #> 7: 1 f_1 a #> 8: 2 f_1 c #> 9: 3 f_1 b #> 10: 4 f_1 <NA> #> 11: 5 f_1 c #> 12: NA f_1 bsuppressWarnings( melt(DT, id.vars=1, measure.vars=c("c_1", "n_1")) # n_1 coerced to char, with warning )#> i_1 variable value #> <int> <fctr> <char> #> 1: 1 c_1 c #> 2: 2 c_1 c #> 3: 3 c_1 <NA> #> 4: 4 c_1 c #> 5: 5 c_1 <NA> #> 6: NA c_1 c #> 7: 1 n_1 <NA> #> 8: 2 n_1 6 #> 9: 3 n_1 7 #> 10: 4 n_1 8 #> 11: 5 n_1 9 #> 12: NA n_1 10# 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#> i_1 variable value #> <int> <fctr> <char> #> 1: 1 c_1 c #> 2: 2 c_1 c #> 3: 4 c_1 c #> 4: NA c_1 c #> 5: 1 c_2 A #> 6: 2 c_2 B #> 7: 3 c_2 A #> 8: 4 c_2 A# 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)#> i_1 n_1 variable value1 value2 #> <int> <num> <fctr> <ord> <Date> #> 1: 1 NA 1 a 2013-09-02 #> 2: 2 6 1 c 2013-09-03 #> 3: 3 7 1 b 2013-09-04 #> 4: 4 8 1 <NA> <NA> #> 5: 5 9 1 c 2013-09-05 #> 6: NA 10 1 b 2013-09-06 #> 7: 1 NA 2 z 2012-01-07 #> 8: 2 6 2 a 2012-01-06 #> 9: 3 7 2 x 2012-01-05 #> 10: 4 8 2 c 2012-01-04 #> 11: 5 9 2 x 2012-01-03 #> 12: NA 10 2 x 2012-01-02# 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)#> i_1 n_1 variable value1 value2 #> <int> <num> <fctr> <ord> <Date> #> 1: 1 NA 1 a 2013-09-02 #> 2: 2 6 1 c 2013-09-03 #> 3: 3 7 1 b 2013-09-04 #> 4: 4 8 1 <NA> <NA> #> 5: 5 9 1 c 2013-09-05 #> 6: NA 10 1 b 2013-09-06 #> 7: 1 NA 2 z 2012-01-07 #> 8: 2 6 2 a 2012-01-06 #> 9: 3 7 2 x 2012-01-05 #> 10: 4 8 2 c 2012-01-04 #> 11: 5 9 2 x 2012-01-03 #> 12: NA 10 2 x 2012-01-02# same as above, but provide names directly: melt(DT, id.vars=1:2, measure.vars=patterns(f="^f_", d="^d_"), value.factor=TRUE)#> i_1 n_1 variable f d #> <int> <num> <fctr> <ord> <Date> #> 1: 1 NA 1 a 2013-09-02 #> 2: 2 6 1 c 2013-09-03 #> 3: 3 7 1 b 2013-09-04 #> 4: 4 8 1 <NA> <NA> #> 5: 5 9 1 c 2013-09-05 #> 6: NA 10 1 b 2013-09-06 #> 7: 1 NA 2 z 2012-01-07 #> 8: 2 6 2 a 2012-01-06 #> 9: 3 7 2 x 2012-01-05 #> 10: 4 8 2 c 2012-01-04 #> 11: 5 9 2 x 2012-01-03 #> 12: NA 10 2 x 2012-01-02# 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)#> i_1 n_1 variable value1 value2 #> <int> <num> <fctr> <ord> <Date> #> 1: 1 NA 1 a 2013-09-02 #> 2: 2 6 1 c 2013-09-03 #> 3: 3 7 1 b 2013-09-04 #> 4: 5 9 1 c 2013-09-05 #> 5: NA 10 1 b 2013-09-06 #> 6: 1 NA 2 z 2012-01-07 #> 7: 2 6 2 a 2012-01-06 #> 8: 3 7 2 x 2012-01-05 #> 9: 4 8 2 c 2012-01-04 #> 10: 5 9 2 x 2012-01-03 #> 11: NA 10 2 x 2012-01-02# '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)))#> one.NA two.NA #> TRUE FALSE#> i_1 n_1 variable value1 value2 #> <int> <num> <fctr> <list> <Date> #> 1: 1 NA 1 1,1,1,1,1 2013-09-02 #> 2: 2 6 1 2 2013-09-03 #> 3: 3 7 1 3,3 2013-09-04 #> 4: 4 8 1 4,4,4,4,4 <NA> #> 5: 5 9 1 5,5,5 2013-09-05 #> 6: NA 10 1 NA,NA 2013-09-06 #> 7: 1 NA 2 c,c 2012-01-07 #> 8: 2 6 2 c,c,c 2012-01-06 #> 9: 3 7 2 NA,NA,NA,NA,NA 2012-01-05 #> 10: 4 8 2 c,c 2012-01-04 #> 11: 5 9 2 NA,NA,NA,NA,NA 2012-01-03 #> 12: NA 10 2 c 2012-01-02#> i_1 n_1 variable value1 value2 #> <int> <num> <fctr> <list> <Date> #> 1: 1 NA 1 1,1,1,1,1 2013-09-02 #> 2: 2 6 1 2 2013-09-03 #> 3: 3 7 1 3,3 2013-09-04 #> 4: 5 9 1 5,5,5 2013-09-05 #> 5: NA 10 1 NA,NA 2013-09-06 #> 6: 1 NA 2 c,c 2012-01-07 #> 7: 2 6 2 c,c,c 2012-01-06 #> 8: 3 7 2 NA,NA,NA,NA,NA 2012-01-05 #> 9: 4 8 2 c,c 2012-01-04 #> 10: 5 9 2 NA,NA,NA,NA,NA 2012-01-03 #> 11: NA 10 2 c 2012-01-02# 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")))#> variable d c f #> <fctr> <Date> <char> <char> #> 1: 1 2013-09-02 c <NA> #> 2: 1 2013-09-03 c <NA> #> 3: 1 2013-09-04 <NA> <NA> #> 4: 1 <NA> c <NA> #> 5: 1 2013-09-05 <NA> <NA> #> 6: 1 2013-09-06 c <NA> #> 7: 2 2012-01-07 <NA> z #> 8: 2 2012-01-06 <NA> a #> 9: 2 2012-01-05 <NA> x #> 10: 2 2012-01-04 <NA> c #> 11: 2 2012-01-03 <NA> x #> 12: 2 2012-01-02 <NA> x# specifying columns to melt via separator. melt(DT.missing.cols, measure.vars=measure(value.name, number=as.integer, sep="_"))#> number d c f #> <int> <Date> <char> <char> #> 1: 1 2013-09-02 c <NA> #> 2: 1 2013-09-03 c <NA> #> 3: 1 2013-09-04 <NA> <NA> #> 4: 1 <NA> c <NA> #> 5: 1 2013-09-05 <NA> <NA> #> 6: 1 2013-09-06 c <NA> #> 7: 2 2012-01-07 <NA> z #> 8: 2 2012-01-06 <NA> a #> 9: 2 2012-01-05 <NA> x #> 10: 2 2012-01-04 <NA> c #> 11: 2 2012-01-03 <NA> x #> 12: 2 2012-01-02 <NA> x# specifying columns to melt via regex. melt(DT.missing.cols, measure.vars=measure(value.name, number=as.integer, pattern="(.)_(.)"))#> number d c f #> <int> <Date> <char> <char> #> 1: 1 2013-09-02 c <NA> #> 2: 1 2013-09-03 c <NA> #> 3: 1 2013-09-04 <NA> <NA> #> 4: 1 <NA> c <NA> #> 5: 1 2013-09-05 <NA> <NA> #> 6: 1 2013-09-06 c <NA> #> 7: 2 2012-01-07 <NA> z #> 8: 2 2012-01-06 <NA> a #> 9: 2 2012-01-05 <NA> x #> 10: 2 2012-01-04 <NA> c #> 11: 2 2012-01-03 <NA> x #> 12: 2 2012-01-02 <NA> x