split {data.table} | R Documentation

## Split data.table into chunks in a list

### Description

Split method for data.table. Faster and more flexible. Be aware that processing list of data.tables will be generally much slower than manipulation in single data.table by group using `by` argument, read more on `data.table`.

### Usage

    ## S3 method for class 'data.table'
    split(x, f, drop = FALSE,
          by, sorted = FALSE, keep.by = TRUE, flatten = TRUE,
          ..., verbose = getOption("datatable.verbose"))

### Arguments

`x` | data.table
`f` | factor or list of factors. Same as `split.data.frame`. Use `by` argument instead, this is just for consistency with data.frame method.
`drop` | logical. Default `FALSE` will not drop empty list elements caused by factor levels not refered by that factors. Works also with new arguments of split data.table method.
`by` | character vector. Column names on which split should be made. For `length(by) > 1L` and `flatten` FALSE it will result nested lists with data.tables on leafs.
`sorted` | When default `FALSE` it will retain the order of groups we are splitting on. When `TRUE` then sorted list(s) are returned. Does not have effect for `f` argument.
`keep.by` | logical default `TRUE`. Keep column provided to `by` argument.
`flatten` | logical default `TRUE` will unlist nested lists of data.tables. When using `f` results are always flattened to list of data.tables.
`...` | passed to data.frame way of processing when using `f` argument.
`verbose` | logical default `FALSE`. When `TRUE` it will print to console data.table split query used to split data.

### Details

Argument `f` is just for consistency in usage to data.frame method. Recommended is to use `by` argument instead, it will be faster, more flexible, and by default will preserve order according to order in data.

### Value

List of `data.table`s. If using `flatten` FALSE and `length(by) > 1L` then recursively nested lists having `data.table`s as leafs of grouping according to `by` argument.

### See Also

`data.table`, `rbindlist`

### Examples

    set.seed(123)
    dt = data.table(x1 = rep(letters[1:2], 6),
                    x2 = rep(letters[3:5], 4),
                    x3 = rep(letters[5:8], 3),
                    y = rnorm(12))
    dt = dt[sample(.N)]
    df = as.data.frame(dt)

    # split consistency with data.frame: `x, f, drop`
    all.equal(
        split(dt, list(dt$x1, dt$x2)),
        lapply(split(df, list(df$x1, df$x2)), setDT)
    )

    # nested list using `flatten` arguments
    split(dt, by=c("x1", "x2"))
    split(dt, by=c("x1", "x2"), flatten=FALSE)

    # dealing with factors
    fdt = dt[, c(lapply(.SD, as.factor), list(y=y)), .SDcols=x1:x3]
    fdf = as.data.frame(fdt)
    sdf = split(fdf, list(fdf$x1, fdf$x2))
    all.equal(
        split(fdt, by=c("x1", "x2"), sorted=TRUE),
        lapply(sdf[sort(names(sdf))], setDT)
    )

    # factors having unused levels, drop FALSE, TRUE
    fdt = dt[, .(x1 = as.factor(c(as.character(x1), "c"))[-13L],
                 x2 = as.factor(c("a", as.character(x2)))[-1L],
                 x3 = as.factor(c("a", as.character(x3), "z"))[c(-1L,-14L)],
                 y = y)]
    fdf = as.data.frame(fdt)
    sdf = split(fdf, list(fdf$x1, fdf$x2))
    all.equal(
        split(fdt, by=c("x1", "x2"), sorted=TRUE),
        lapply(sdf[sort(names(sdf))], setDT)
    )
    sdf = split(fdf, list(fdf$x1, fdf$x2), drop=TRUE)
    all.equal(
        split(fdt, by=c("x1", "x2"), sorted=TRUE, drop=TRUE),
        lapply(sdf[sort(names(sdf))], setDT)
    )

[Package data.table version 1.10.0 Index]