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Separate a character column into multiple columns with a regular expression or numeric locations

Source: R/separate.R
separate.Rd

Given either a regular expression or a vector of character positions, separate() turns a single character column into multiple columns.

Usage

separate(
  data,
  col,
  into,
  sep = "[^[:alnum:]]+",
  remove = TRUE,
  convert = FALSE,
  extra = "warn",
  fill = "warn",
  ...
)

Arguments

data

A data frame.

col

Column name or position. This is passed to tidyselect::vars_pull().

This argument is passed by expression and supports quasiquotation (you can unquote column names or column positions).

into

Names of new variables to create as character vector. Use NA to omit the variable in the output.

sep

Separator between columns.

If character, sep is interpreted as a regular expression. The default value is a regular expression that matches any sequence of non-alphanumeric values.

If numeric, sep is interpreted as character positions to split at. Positive values start at 1 at the far-left of the string; negative value start at -1 at the far-right of the string. The length of sep should be one less than into.

remove

If TRUE, remove input column from output data frame.

convert

If TRUE, will run type.convert() with as.is = TRUE on new columns. This is useful if the component columns are integer, numeric or logical.

NB: this will cause string "NA"s to be converted to NAs.

extra

If sep is a character vector, this controls what happens when there are too many pieces. There are three valid options:

  • "warn" (the default): emit a warning and drop extra values.

  • "drop": drop any extra values without a warning.

  • "merge": only splits at most length(into) times

fill

If sep is a character vector, this controls what happens when there are not enough pieces. There are three valid options:

  • "warn" (the default): emit a warning and fill from the right

  • "right": fill with missing values on the right

  • "left": fill with missing values on the left

...

Additional arguments passed on to methods.

See also

unite(), the complement, extract() which uses regular expression capturing groups.

Examples

library(dplyr)
# If you want to split by any non-alphanumeric value (the default):
df <- data.frame(x = c(NA, "x.y", "x.z", "y.z"))
df %>% separate(x, c("A", "B"))
#>      A    B
#> 1 <NA> <NA>
#> 2    x    y
#> 3    x    z
#> 4    y    z

# If you just want the second variable:
df %>% separate(x, c(NA, "B"))
#>      B
#> 1 <NA>
#> 2    y
#> 3    z
#> 4    z

# If every row doesn't split into the same number of pieces, use
# the extra and fill arguments to control what happens:
df <- data.frame(x = c("x", "x y", "x y z", NA))
df %>% separate(x, c("a", "b"))
#> Warning: Expected 2 pieces. Additional pieces discarded in 1 rows [3].
#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1].
#>      a    b
#> 1    x <NA>
#> 2    x    y
#> 3    x    y
#> 4 <NA> <NA>
# The same behaviour as previous, but drops the c without warnings:
df %>% separate(x, c("a", "b"), extra = "drop", fill = "right")
#>      a    b
#> 1    x <NA>
#> 2    x    y
#> 3    x    y
#> 4 <NA> <NA>
# Opposite of previous, keeping the c and filling left:
df %>% separate(x, c("a", "b"), extra = "merge", fill = "left")
#>      a    b
#> 1 <NA>    x
#> 2    x    y
#> 3    x  y z
#> 4 <NA> <NA>
# Or you can keep all three:
df %>% separate(x, c("a", "b", "c"))
#> Warning: Expected 3 pieces. Missing pieces filled with `NA` in 2 rows [1, 2].
#>      a    b    c
#> 1    x <NA> <NA>
#> 2    x    y <NA>
#> 3    x    y    z
#> 4 <NA> <NA> <NA>

# To only split a specified number of times use extra = "merge":
df <- data.frame(x = c("x: 123", "y: error: 7"))
df %>% separate(x, c("key", "value"), ": ", extra = "merge")
#>   key    value
#> 1   x      123
#> 2   y error: 7

# Use regular expressions to separate on multiple characters:
df <- data.frame(x = c(NA, "a1b", "c4d", "e9g"))
df %>% separate(x, c("A","B"), sep = "[0-9]")
#>      A    B
#> 1 <NA> <NA>
#> 2    a    b
#> 3    c    d
#> 4    e    g

# convert = TRUE detects column classes:
df <- data.frame(x = c("x:1", "x:2", "y:4", "z", NA))
df %>% separate(x, c("key","value"), ":") %>% str
#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [4].
#> 'data.frame':	5 obs. of  2 variables:
#>  $ key  : chr  "x" "x" "y" "z" ...
#>  $ value: chr  "1" "2" "4" NA ...
df %>% separate(x, c("key","value"), ":", convert = TRUE) %>% str
#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [4].
#> 'data.frame':	5 obs. of  2 variables:
#>  $ key  : chr  "x" "x" "y" "z" ...
#>  $ value: int  1 2 4 NA NA