separate: Separate a character column into multiple columns with a...

In tidyverse/tidyr: Tidy Messy Data

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

Description

separate() has been superseded in favour of separate_wider_position() and separate_wider_delim() because the two functions make the two uses more obvious, the API is more polished, and the handling of problems is better. Superseded functions will not go away, but will only receive critical bug fixes.

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	<tidy-select> Column to expand.
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

# If you want to split by any non-alphanumeric value (the default):
df <- tibble(x = c(NA, "x.y", "x.z", "y.z"))
df %>% separate(x, c("A", "B"))

# If you just want the second variable:
df %>% separate(x, c(NA, "B"))

# We now recommend separate_wider_delim() instead:
df %>% separate_wider_delim(x, ".", names = c("A", "B"))
df %>% separate_wider_delim(x, ".", names = c(NA, "B"))

# Controlling uneven splits -------------------------------------------------
# If every row doesn't split into the same number of pieces, use
# the extra and fill arguments to control what happens:
df <- tibble(x = c("x", "x y", "x y z", NA))
df %>% separate(x, c("a", "b"))
# The same behaviour as previous, but drops the c without warnings:
df %>% separate(x, c("a", "b"), extra = "drop", fill = "right")
# Opposite of previous, keeping the c and filling left:
df %>% separate(x, c("a", "b"), extra = "merge", fill = "left")
# Or you can keep all three:
df %>% separate(x, c("a", "b", "c"))

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

# Controlling column types --------------------------------------------------
# convert = TRUE detects column classes:
df <- tibble(x = c("x:1", "x:2", "y:4", "z", NA))
df %>% separate(x, c("key", "value"), ":") %>% str()
df %>% separate(x, c("key", "value"), ":", convert = TRUE) %>% str()

tidyverse/tidyr documentation built on Oct. 30, 2024, 1:53 a.m.