extract: Extract a character column into multiple columns using...

In tidyr: Tidy Messy Data

Extract a character column into multiple columns using regular expression groups

Description

extract() has been superseded in favour of separate_wider_regex() because it has a more polished API and better handling of problems. Superseded functions will not go away, but will only receive critical bug fixes.

Given a regular expression with capturing groups, extract() turns each group into a new column. If the groups don't match, or the input is NA, the output will be NA.

Usage

extract(
  data,
  col,
  into,
  regex = "([[:alnum:]]+)",
  remove = TRUE,
  convert = FALSE,
  ...
)

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.
regex	A string representing a regular expression used to extract the desired values. There should be one group (defined by ⁠()⁠) for each element of 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.
...	Additional arguments passed on to methods.

See Also

separate() to split up by a separator.

Examples

df <- tibble(x = c(NA, "a-b", "a-d", "b-c", "d-e"))
df %>% extract(x, "A")
df %>% extract(x, c("A", "B"), "([[:alnum:]]+)-([[:alnum:]]+)")

# Now recommended
df %>%
  separate_wider_regex(
    x,
    patterns = c(A = "[[:alnum:]]+", "-", B = "[[:alnum:]]+")
  )

# If no match, NA:
df %>% extract(x, c("A", "B"), "([a-d]+)-([a-d]+)")

tidyr documentation built on June 24, 2024, 5:14 p.m.