These functions compute an integer vector or list for use as the measure.vars argument to melt. Each measured variable name is converted into several groups that occupy different columns in the output melted data. measure allows specifying group names/conversions in R code (each group and conversion specified as an argument) whereas measurev allows specifying group names/conversions using data values (each group and conversion specified as a list element). See vignette("datatable-reshape") for more info.

measure(..., sep, pattern, cols, multiple.keyword="value.name")
measurev(fun.list, sep, pattern, cols, multiple.keyword="value.name",
  group.desc="elements of fun.list")

Arguments

...

One or more (1) symbols (without argument name; symbol is used for group name) or (2) functions to convert the groups (with argument name that is used for group name). Must have same number of arguments as groups that are specified by either sep or pattern arguments.

fun.list

Named list which must have the same number of elements as groups that are specified by either sep or pattern arguments. Each name used for a group name, and each value must be either a function (to convert the group from a character vector to an atomic vector of the same size) or NULL (no conversion).

sep

Separator to split each element of cols into groups. Columns that result in the maximum number of groups are considered measure variables.

pattern

Perl-compatible regex with capture groups to match to cols. Columns that match the regex are considered measure variables.

cols

A character vector of column names.

multiple.keyword

A string, if used as a group name, then measure returns a list and melt returns multiple value columns (with names defined by the unique values in that group). Otherwise if the string not used as a group name, then measure returns a vector and melt returns a single value column.

group.desc

Internal, used in error messages.

See also

Examples

(two.iris = data.table(datasets::iris)[c(1,150)])
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <num> <num> <num> <num> <fctr> #> 1: 5.1 3.5 1.4 0.2 setosa #> 2: 5.9 3.0 5.1 1.8 virginica
# melt into a single value column. melt(two.iris, measure.vars = measure(part, dim, sep="."))
#> Species part dim value #> <fctr> <char> <char> <num> #> 1: setosa Sepal Length 5.1 #> 2: virginica Sepal Length 5.9 #> 3: setosa Sepal Width 3.5 #> 4: virginica Sepal Width 3.0 #> 5: setosa Petal Length 1.4 #> 6: virginica Petal Length 5.1 #> 7: setosa Petal Width 0.2 #> 8: virginica Petal Width 1.8
# do the same, programmatically with measurev my.list = list(part=NULL, dim=NULL) melt(two.iris, measure.vars=measurev(my.list, sep="."))
#> Species part dim value #> <fctr> <char> <char> <num> #> 1: setosa Sepal Length 5.1 #> 2: virginica Sepal Length 5.9 #> 3: setosa Sepal Width 3.5 #> 4: virginica Sepal Width 3.0 #> 5: setosa Petal Length 1.4 #> 6: virginica Petal Length 5.1 #> 7: setosa Petal Width 0.2 #> 8: virginica Petal Width 1.8
# melt into two value columns, one for each part. melt(two.iris, measure.vars = measure(value.name, dim, sep="."))
#> Species dim Sepal Petal #> <fctr> <char> <num> <num> #> 1: setosa Length 5.1 1.4 #> 2: virginica Length 5.9 5.1 #> 3: setosa Width 3.5 0.2 #> 4: virginica Width 3.0 1.8
# melt into two value columns, one for each dim. melt(two.iris, measure.vars = measure(part, value.name, sep="."))
#> Species part Length Width #> <fctr> <char> <num> <num> #> 1: setosa Sepal 5.1 3.5 #> 2: virginica Sepal 5.9 3.0 #> 3: setosa Petal 1.4 0.2 #> 4: virginica Petal 5.1 1.8
# melt using sep, converting child number to integer. (two.families = data.table(sex_child1="M", sex_child2="F", age_child1=10, age_child2=20))
#> sex_child1 sex_child2 age_child1 age_child2 #> <char> <char> <num> <num> #> 1: M F 10 20
print(melt(two.families, measure.vars = measure( value.name, child=as.integer, sep="_child" )), class=TRUE)
#> child sex age #> <int> <char> <num> #> 1: 1 M 10 #> 2: 2 F 20
# same melt using pattern. print(melt(two.families, measure.vars = measure( value.name, child=as.integer, pattern="(.*)_child(.)" )), class=TRUE)
#> child sex age #> <int> <char> <num> #> 1: 1 M 10 #> 2: 2 F 20
# same melt with pattern and measurev function list. print(melt(two.families, measure.vars = measurev( list(value.name=NULL, child=as.integer), pattern="(.*)_child(.)" )), class=TRUE)
#> child sex age #> <int> <char> <num> #> 1: 1 M 10 #> 2: 2 F 20
# inspired by data(who, package="tidyr") (who <- data.table(id=1, new_sp_m5564=2, newrel_f65=3))
#> id new_sp_m5564 newrel_f65 #> <num> <num> <num> #> 1: 1 2 3
# melt to three variable columns, all character. melt(who, measure.vars = measure(diagnosis, gender, ages, pattern="new_?(.*)_(.)(.*)"))
#> id diagnosis gender ages value #> <num> <char> <char> <char> <num> #> 1: 1 sp m 5564 2 #> 2: 1 rel f 65 3
# melt to five variable columns, two numeric (with custom conversion). print(melt(who, measure.vars = measure( diagnosis, gender, ages, ymin=as.numeric, ymax=function(y)ifelse(y=="", Inf, as.numeric(y)), pattern="new_?(.*)_(.)(([0-9]{2})([0-9]{0,2}))" )), class=TRUE)
#> id diagnosis gender ages ymin ymax value #> <num> <char> <char> <char> <num> <num> <num> #> 1: 1 sp m 5564 55 64 2 #> 2: 1 rel f 65 65 Inf 3