Separate single variable into multiple variables

Separates a single variable into multiple new variables.

Usage

data_separate(
  data,
  select = NULL,
  new_columns = NULL,
  separator = "[^[:alnum:]]+",
  guess_columns = NULL,
  merge_multiple = FALSE,
  merge_separator = "",
  fill = "right",
  extra = "drop_right",
  convert_na = TRUE,
  exclude = NULL,
  append = FALSE,
  ignore_case = FALSE,
  verbose = TRUE,
  regex = FALSE,
  ...
)

Arguments

data

A data frame.

select

Variables that will be included when performing the required tasks. Can be either

a variable specified as a literal variable name (e.g., column_name),
a string with the variable name (e.g., "column_name"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")),
a formula with variable names (e.g., ~column_1 + column_2),
a vector of positive integers, giving the positions counting from the left (e.g. 1 or c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the right (e.g., -1 or -1:-3),
one of the following select-helpers: starts_with(), ends_with(), contains(), a range using : or regex(""). starts_with(), ends_with(), and contains() accept several patterns, e.g starts_with("Sep", "Petal").
or a function testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (like: foo <- function(x) mean(x) > 3),
ranges specified via literal variable names, select-helpers (except regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a -, e.g. -ends_with(""), -is.numeric or -(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, the exclude argument can be used alternatively. For instance, select=-ends_with("Length") (with -) is equivalent to exclude=ends_with("Length") (no -). In case negation should not work as expected, use the exclude argument instead.

If NULL, selects all columns. Patterns that found no matches are silently ignored, e.g. extract_column_names(iris, select = c("Species", "Test")) will just return "Species".

new_columns

The names of the new columns, as character vector. If more than one variable was selected (in select), the new names are prefixed with the name of the original column. new_columns can also be a list of (named) character vectors when multiple variables should be separated. See 'Examples'.

separator

Separator between columns. Can be a character vector, which is then treated as regular expression, or a numeric vector that indicates at which positions the string values will be split.

guess_columns

If new_columns is not given, the required number of new columns is guessed based on the results of value splitting. For example, if a variable is split into three new columns, this will be considered as the required number of new columns, and columns are named "split_1", "split_2" and "split_3". When values from a variable are split into different amount of new columns, the guess_column can be either "mode" (number of new columns is based on the most common number of splits), "min" or "max" to use the minimum resp. maximum number of possible splits as required number of columns.

merge_multiple

Logical, if TRUE and more than one variable is selected for separating, new columns can be merged. Value pairs of all split variables are merged.

merge_separator

Separator string when merge_multiple = TRUE. Defines the string that is used to merge values together.

fill

How to deal with values that return fewer new columns after splitting? Can be "left" (fill missing columns from the left with NA), "right" (fill missing columns from the right with NA) or "value_left" or "value_right" to fill missing columns from left or right with the left-most or right-most values.

extra

How to deal with values that return too many new columns after splitting? Can be "drop_left" or "drop_right" to drop the left-most or right-most values, or "merge_left" or "merge_right" to merge the left- or right-most value together, and keeping all remaining values as is.

convert_na

Logical, if TRUE, character "NA" values are converted into real NA values.

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

append

Logical, if FALSE (default), removes original columns that were separated. If TRUE, all columns are preserved and the new columns are appended to the data frame.

ignore_case

Logical, if TRUE and when one of the select-helpers or a regular expression is used in select, ignores lower/upper case in the search pattern when matching against variable names.

verbose

Toggle warnings.

regex

Logical, if TRUE, the search pattern from select will be treated as regular expression. When regex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1. regex = TRUE is comparable to using one of the two select-helpers, select = contains("") or select = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.

...

Currently not used.

Value

A data frame with the newly created variable(s), or - when append = TRUE - data including new variables.

Examples

# simple case
d <- data.frame(
  x = c("1.a.6", "2.b.7", "3.c.8"),
  stringsAsFactors = FALSE
)
d
#>       x
#> 1 1.a.6
#> 2 2.b.7
#> 3 3.c.8
data_separate(d, new_columns = c("a", "b", "c"))
#>   a b c
#> 1 1 a 6
#> 2 2 b 7
#> 3 3 c 8

# guess number of columns
d <- data.frame(
  x = c("1.a.6", NA, "2.b.6.7", "3.c", "x.y.z"),
  stringsAsFactors = FALSE
)
d
#>         x
#> 1   1.a.6
#> 2    <NA>
#> 3 2.b.6.7
#> 4     3.c
#> 5   x.y.z
data_separate(d, guess_columns = "mode")
#> Column `x` had different number of values after splitting. Variable was
#>   split into 3 columns.
#> `x` returned more columns than expected after splitting. Right-most
#>   columns have been dropped.
#> `x`returned fewer columns than expected after splitting. Right-most
#>   columns were filled with `NA`.
#>    x_1  x_2  x_3
#> 1    1    a    6
#> 2 <NA> <NA> <NA>
#> 3    2    b    6
#> 4    3    c <NA>
#> 5    x    y    z

data_separate(d, guess_columns = "max")
#> Column `x` had different number of values after splitting. Variable was
#>   split into 4 columns.
#> `x`returned fewer columns than expected after splitting. Right-most
#>   columns were filled with `NA`.
#>    x_1  x_2  x_3  x_4
#> 1    1    a    6 <NA>
#> 2 <NA> <NA> <NA> <NA>
#> 3    2    b    6    7
#> 4    3    c <NA> <NA>
#> 5    x    y    z <NA>

# drop left-most column
data_separate(d, guess_columns = "mode", extra = "drop_left")
#> Column `x` had different number of values after splitting. Variable was
#>   split into 3 columns.
#> `x` returned more columns than expected after splitting. Left-most
#>   columns have been dropped.
#> `x`returned fewer columns than expected after splitting. Right-most
#>   columns were filled with `NA`.
#>    x_1  x_2  x_3
#> 1    1    a    6
#> 2 <NA> <NA> <NA>
#> 3    b    6    7
#> 4    3    c <NA>
#> 5    x    y    z

# merge right-most column
data_separate(d, guess_columns = "mode", extra = "merge_right")
#> Column `x` had different number of values after splitting. Variable was
#>   split into 3 columns.
#> `x` returned more columns than expected after splitting. Right-most
#>   columns have been merged together.
#> `x`returned fewer columns than expected after splitting. Right-most
#>   columns were filled with `NA`.
#>    x_1  x_2  x_3
#> 1    1    a    6
#> 2 <NA> <NA> <NA>
#> 3    2    b  6 7
#> 4    3    c <NA>
#> 5    x    y    z

# fill columns with fewer values with left-most values
data_separate(d, guess_columns = "mode", fill = "value_left")
#> Column `x` had different number of values after splitting. Variable was
#>   split into 3 columns.
#> `x` returned more columns than expected after splitting. Right-most
#>   columns have been dropped.
#> `x`returned fewer columns than expected after splitting. Left-most
#>   columns were filled with first value.
#>    x_1  x_2  x_3
#> 1    1    a    6
#> 2 <NA> <NA> <NA>
#> 3    2    b    6
#> 4    3    3    c
#> 5    x    y    z

# fill and merge
data_separate(
  d,
  guess_columns = "mode",
  fill = "value_left",
  extra = "merge_right"
)
#> Column `x` had different number of values after splitting. Variable was
#>   split into 3 columns.
#> `x` returned more columns than expected after splitting. Right-most
#>   columns have been merged together.
#> `x`returned fewer columns than expected after splitting. Left-most
#>   columns were filled with first value.
#>    x_1  x_2  x_3
#> 1    1    a    6
#> 2 <NA> <NA> <NA>
#> 3    2    b  6 7
#> 4    3    3    c
#> 5    x    y    z

# multiple columns to split
d <- data.frame(
  x = c("1.a.6", "2.b.7", "3.c.8"),
  y = c("x.y.z", "10.11.12", "m.n.o"),
  stringsAsFactors = FALSE
)
d
#>       x        y
#> 1 1.a.6    x.y.z
#> 2 2.b.7 10.11.12
#> 3 3.c.8    m.n.o
# split two columns, default column names
data_separate(d, guess_columns = "mode")
#>   x_1 x_2 x_3 y_1 y_2 y_3
#> 1   1   a   6   x   y   z
#> 2   2   b   7  10  11  12
#> 3   3   c   8   m   n   o

# split into new named columns, repeating column names
data_separate(d, new_columns = c("a", "b", "c"))
#>   x_a x_b x_c y_a y_b y_c
#> 1   1   a   6   x   y   z
#> 2   2   b   7  10  11  12
#> 3   3   c   8   m   n   o

# split selected variable new columns
data_separate(d, select = "y", new_columns = c("a", "b", "c"))
#>       x  a  b  c
#> 1 1.a.6  x  y  z
#> 2 2.b.7 10 11 12
#> 3 3.c.8  m  n  o

# merge multiple split columns
data_separate(
  d,
  new_columns = c("a", "b", "c"),
  merge_multiple = TRUE
)
#>     a   b   c
#> 1  1x  ay  6z
#> 2 210 b11 712
#> 3  3m  cn  8o

# merge multiple split columns
data_separate(
  d,
  new_columns = c("a", "b", "c"),
  merge_multiple = TRUE,
  merge_separator = "-"
)
#>      a    b    c
#> 1  1-x  a-y  6-z
#> 2 2-10 b-11 7-12
#> 3  3-m  c-n  8-o

# separate multiple columns, give proper column names
d_sep <- data.frame(
  x = c("1.a.6", "2.b.7.d", "3.c.8", "5.j"),
  y = c("m.n.99.22", "77.f.g.34", "44.9", NA),
  stringsAsFactors = FALSE
)

data_separate(
  d_sep,
  select = c("x", "y"),
  new_columns = list(
    x = c("A", "B", "C"), # separate "x" into three columns
    y = c("EE", "FF", "GG", "HH") # separate "y" into four columns
  ),
  verbose = FALSE
)
#>   A B    C   EE   FF   GG   HH
#> 1 1 a    6    m    n   99   22
#> 2 2 b    7   77    f    g   34
#> 3 3 c    8   44    9 <NA> <NA>
#> 4 5 j <NA> <NA> <NA> <NA> <NA>

Usage

Arguments

Value

See also

Examples