% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_separate.R
\name{data_separate}
\alias{data_separate}
\title{Separate single variable into multiple 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{
\item{data}{A data frame.}

\item{select}{Variables that will be included when performing the required
tasks. Can be either
\itemize{
\item a variable specified as a literal variable name (e.g., \code{column_name}),
\item a string with the variable name (e.g., \code{"column_name"}), a character
vector of variable names (e.g., \code{c("col1", "col2", "col3")}), or a
character vector of variable names including ranges specified via \code{:}
(e.g., \code{c("col1:col3", "col5")}),
\item for some functions, like \code{data_select()} or \code{data_rename()}, \code{select} can
be a named character vector. In this case, the names are used to rename
the columns in the output data frame. See 'Details' in the related
functions to see where this option applies.
\item a formula with variable names (e.g., \code{~column_1 + column_2}),
\item a vector of positive integers, giving the positions counting from the left
(e.g. \code{1} or \code{c(1, 3, 5)}),
\item a vector of negative integers, giving the positions counting from the
right (e.g., \code{-1} or \code{-1:-3}),
\item one of the following select-helpers: \code{starts_with()}, \code{ends_with()},
\code{contains()}, a range using \code{:}, or \code{regex()}. \code{starts_with()},
\code{ends_with()}, and  \code{contains()} accept several patterns, e.g
\code{starts_with("Sep", "Petal")}. \code{regex()} can be used to define regular
expression patterns.
\item a function testing for logical conditions, e.g. \code{is.numeric()} (or
\code{is.numeric}), or any user-defined function that selects the variables
for which the function returns \code{TRUE} (like: \code{foo <- function(x) mean(x) > 3}),
\item ranges specified via literal variable names, select-helpers (except
\code{regex()}) and (user-defined) functions can be negated, i.e. return
non-matching elements, when prefixed with a \code{-}, e.g. \code{-ends_with()},
\code{-is.numeric} or \code{-(Sepal.Width:Petal.Length)}. \strong{Note:} Negation means
that matches are \emph{excluded}, and thus, the \code{exclude} argument can be
used alternatively. For instance, \code{select=-ends_with("Length")} (with
\code{-}) is equivalent to \code{exclude=ends_with("Length")} (no \code{-}). In case
negation should not work as expected, use the \code{exclude} argument instead.
}

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

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

\item{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.}

\item{guess_columns}{If \code{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 \code{"split_1"},
\code{"split_2"} and \code{"split_3"}. When values from a variable are split into
different amount of new columns, the \code{guess_column} can be either \code{"mode"}
(number of new columns is based on the most common number of splits), \code{"min"}
or \code{"max"} to use the minimum resp. maximum number of possible splits as
required number of columns.}

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

\item{merge_separator}{Separator string when \code{merge_multiple = TRUE}. Defines
the string that is used to merge values together.}

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

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

\item{convert_na}{Logical, if \code{TRUE}, character \code{"NA"} values are converted
into real \code{NA} values.}

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

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

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

\item{verbose}{Toggle warnings.}

\item{regex}{Logical, if \code{TRUE}, the search pattern from \code{select} will be
treated as regular expression. When \code{regex = TRUE}, select \emph{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. \code{regex = TRUE} is comparable to using one of the two
select-helpers, \code{select = contains()} or \code{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.}

\item{...}{Currently not used.}
}
\value{
A data frame with the newly created variable(s), or - when \code{append = TRUE} -
\code{data} including new variables.
}
\description{
Separates a single variable into multiple new variables.
}
\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
)
}
\seealso{
\code{\link[=data_unite]{data_unite()}}
}