pivot_wider.dtplyr_step.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/step-call-pivot_wider.R
\name{pivot_wider.dtplyr_step}
\alias{pivot_wider.dtplyr_step}
\title{Pivot data from long to wide}
\usage{
\method{pivot_wider}{dtplyr_step}(
  data,
  id_cols = NULL,
  names_from = name,
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE,
  names_repair = "check_unique",
  values_from = value,
  values_fill = NULL,
  values_fn = NULL,
  ...
)
}
\arguments{
\item{data}{A \code{\link[=lazy_dt]{lazy_dt()}}.}

\item{id_cols}{<\code{\link[tidyr:tidyr_tidy_select]{tidy-select}}> A set of columns that
uniquely identify each observation. Typically used when you have
redundant variables, i.e. variables whose values are perfectly correlated
with existing variables.

Defaults to all columns in \code{data} except for the columns specified through
\code{names_from} and \code{values_from}. If a tidyselect expression is supplied, it
will be evaluated on \code{data} after removing the columns specified through
\code{names_from} and \code{values_from}.}

\item{names_from, values_from}{<\code{\link[tidyr:tidyr_tidy_select]{tidy-select}}> A pair of
arguments describing which column (or columns) to get the name of the
output column (\code{names_from}), and which column (or columns) to get the
cell values from (\code{values_from}).

If \code{values_from} contains multiple values, the value will be added to the
front of the output column.}

\item{names_prefix}{String added to the start of every variable name. This is
particularly useful if \code{names_from} is a numeric vector and you want to
create syntactic variable names.}

\item{names_sep}{If \code{names_from} or \code{values_from} contains multiple
variables, this will be used to join their values together into a single
string to use as a column name.}

\item{names_glue}{Instead of \code{names_sep} and \code{names_prefix}, you can supply
a glue specification that uses the \code{names_from} columns (and special
\code{.value}) to create custom column names.}

\item{names_sort}{Should the column names be sorted? If \code{FALSE}, the default,
column names are ordered by first appearance.}

\item{names_repair}{What happens if the output has invalid column names?
The default, \code{"check_unique"} is to error if the columns are duplicated.
Use \code{"minimal"} to allow duplicates in the output, or \code{"unique"} to
de-duplicated by adding numeric suffixes. See \code{\link[vctrs:vec_as_names]{vctrs::vec_as_names()}}
for more options.}

\item{values_fill}{Optionally, a (scalar) value that specifies what each
\code{value} should be filled in with when missing.

This can be a named list if you want to apply different fill values to
different value columns.}

\item{values_fn}{A function, the default is \code{length()}. Note this is different
behavior than \code{tidyr::pivot_wider()}, which returns a list column by default.}

\item{...}{Additional arguments passed on to methods.}
}
\description{
This is a method for the tidyr \code{pivot_wider()} generic. It is translated to
\code{\link[data.table:dcast.data.table]{data.table::dcast()}}
}
\examples{
library(tidyr)

fish_encounters_dt <- lazy_dt(fish_encounters)
fish_encounters_dt
fish_encounters_dt \%>\%
  pivot_wider(names_from = station, values_from = seen)
# Fill in missing values
fish_encounters_dt \%>\%
  pivot_wider(names_from = station, values_from = seen, values_fill = 0)

# Generate column names from multiple variables
us_rent_income_dt <- lazy_dt(us_rent_income)
us_rent_income_dt
us_rent_income_dt \%>\%
  pivot_wider(names_from = variable, values_from = c(estimate, moe))

# When there are multiple `names_from` or `values_from`, you can use
# use `names_sep` or `names_glue` to control the output variable names
us_rent_income_dt \%>\%
  pivot_wider(
    names_from = variable,
    names_sep = ".",
    values_from = c(estimate, moe)
  )

# Can perform aggregation with values_fn
warpbreaks_dt <- lazy_dt(as_tibble(warpbreaks[c("wool", "tension", "breaks")]))
warpbreaks_dt
warpbreaks_dt \%>\%
  pivot_wider(
    names_from = wool,
    values_from = breaks,
    values_fn = mean
  )
}