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.
separate(
data,
col,
into,
sep = "[^[:alnum:]]+",
remove = TRUE,
convert = FALSE,
extra = "warn",
fill = "warn",
...
)
A data frame.
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).
Names of new variables to create as character vector.
Use NA
to omit the variable in the output.
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
.
If TRUE
, remove input column from output data frame.
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.
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
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.
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