data_separate: Separate single variable into multiple variables

In datawizard: Easy Data Wrangling and Statistical Transformations

Separate single variable into multiple variables

Description

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"), or a character vector of variable names (e.g., c("col1", "col2", "col3")), 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.

See Also

data_unite()

Examples

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

# 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
data_separate(d, guess_columns = "mode")

data_separate(d, guess_columns = "max")

# drop left-most column
data_separate(d, guess_columns = "mode", extra = "drop_left")

# merge right-most column
data_separate(d, guess_columns = "mode", extra = "merge_right")

# fill columns with fewer values with left-most values
data_separate(d, guess_columns = "mode", fill = "value_left")

# fill and merge
data_separate(
  d,
  guess_columns = "mode",
  fill = "value_left",
  extra = "merge_right"
)

# 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
# split two columns, default column names
data_separate(d, guess_columns = "mode")

# split into new named columns, repeating column names
data_separate(d, new_columns = c("a", "b", "c"))

# split selected variable new columns
data_separate(d, select = "y", new_columns = c("a", "b", "c"))

# merge multiple split columns
data_separate(
  d,
  new_columns = c("a", "b", "c"),
  merge_multiple = TRUE
)

# merge multiple split columns
data_separate(
  d,
  new_columns = c("a", "b", "c"),
  merge_multiple = TRUE,
  merge_separator = "-"
)

# 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
)

datawizard documentation built on Oct. 6, 2024, 1:08 a.m.