melt.data.table {data.table} | R Documentation

## Fast melt for data.table

### Description

An S3 method for melting `data.table`s written in C for speed and memory efficiency. Since `v1.9.6`, `melt.data.table` allows melting into multiple columns simultaneously.

It is not necessary to load `reshape2` anymore. But if you have to, then load `reshape2` package before loading `data.table`.

### Usage

    ## 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"))

### Arguments

`data` | A `data.table` object to melt.
`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.

`measure.vars`

vector of measure variables. Can be integer (corresponding measure column numbers) or character (measure column names) vector. If missing, all non-id columns will be assigned to it.

`measure.vars` also now accepts a list of character/integer vectors to melt into multiple columns - i.e., melt into more than one `value` columns simultaneously. Use `patterns` to provide multiple patterns conveniently. See also `Examples`.

`variable.name` | name for the measured variable names column. The default name is 'variable'.
`value.name` | name for the molten data values column. The default name is 'value'.
`na.rm` | If `TRUE`, `NA` values will be removed from the molten data.
`variable.factor` | If `TRUE`, the `variable` column will be converted to `factor`, else it will be a `character` column.
`value.factor` | If `TRUE`, the `value` column will be converted to `factor`, else the molten value type is left unchanged.
`verbose` | `TRUE` turns on status and information messages to the console. Turn this on by default using `options(datatable.verbose=TRUE)`. The quantity and types of verbosity may be expanded in future.
`...` | any other arguments to be passed to/from other methods.

### Details

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. Note that, if the type of `value` column is a list, `na.rm = TRUE` will have no effect.

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. 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. This is to be compatible with `reshape2`'s `melt.data.frame`. To get a `factor` column, set `value.factor = TRUE`. `melt.data.table` also preserves `ordered` factors.

### Value

An unkeyed `data.table` containing the molten data.

### See Also

`dcast`, http://had.co.nz/reshape/

### Examples

    set.seed(45)
    require(data.table)
    DT <- data.table(
          i_1 = c(1:5, NA),
          i_2 = c(NA,6,7,8,9,10),
          f_1 = factor(sample(c(letters[1:3], NA), 6, TRUE)),
          f_2 = factor(c("z", "a", "x", "c", "x", "x"), ordered=TRUE),
          c_1 = sample(c(letters[1:3], NA), 6, 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(5,1)))), by = i_1]$c]
    DT[, l_2 := DT[, list(c=list(rep(c_1, sample(5,1)))), by = i_1]$c]

    # id, measure as character/integer/numeric vectors
    melt(DT, id=1:2, measure="f_1")
    melt(DT, id=c("i_1", "i_2"), measure=3) # same as above
    melt(DT, id=1:2, measure=3L, value.factor=TRUE) # same, but 'value' is factor
    melt(DT, id=1:2, measure=3:4, value.factor=TRUE) # 'value' is *ordered* factor

    # preserves attribute when types are identical, ex: Date
    melt(DT, id=3:4, measure=c("d_1", "d_2"))
    melt(DT, id=3:4, measure=c("i_1", "d_1")) # attribute not preserved

    # on list
    melt(DT, id=1, measure=c("l_1", "l_2")) # value is a list
    melt(DT, id=1, measure=c("c_1", "l_1")) # c1 coerced to list

    # on character
    melt(DT, id=1, measure=c("c_1", "f_1")) # value is char
    melt(DT, id=1, measure=c("c_1", "i_2")) # i2 coerced to char

    # on na.rm=TRUE. NAs are removed efficiently, from within C
    melt(DT, id=1, measure=c("c_1", "i_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=1:2, measure=patterns("^f_", "^d_"), value.factor=TRUE)
    # same as above, but provide list of columns directly by column names or indices
    melt(DT, id=1:2, measure=list(3:4, c("d_1", "d_2")), value.factor=TRUE)

    # na.rm=TRUE removes rows with NAs in any 'value' columns
    melt(DT, id=1:2, measure=patterns("f_", "d_"), value.factor=TRUE, na.rm=TRUE)

    # return 'NA' for missing columns, 'na.rm=TRUE' ignored due to list column
    melt(DT, id=1:2, measure=patterns("l_", "c_"), na.rm=TRUE)

[Package data.table version 1.10.4-1 Index]