tidyr: extract – R documentation

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extract

Extract a character column into multiple columns using regular expression groups

Description

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`	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.
`regex`	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 `NA`s.
`...`	Additional arguments passed on to methods.

Examples

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

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

tidyr

Tidy Messy Data

v1.1.3

MIT + file LICENSE

Authors

Hadley Wickham [aut, cre], RStudio [cph]

Initial release