https://github.com/hadley/dplyr
Tip revision: bfc37e1b54aac7a094622a6fc874e6a869ba3957 authored by Romain Francois on 28 April 2020, 15:34:35 UTC
use funs::grouped_mean() from https://github.com/r-lib/funs/pull/50
use funs::grouped_mean() from https://github.com/r-lib/funs/pull/50
Tip revision: bfc37e1
group_map.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/group_map.R
\name{group_map}
\alias{group_map}
\alias{group_modify}
\alias{group_walk}
\title{Apply a function to each group}
\usage{
group_map(.data, .f, ..., keep = FALSE)
group_modify(.data, .f, ..., keep = FALSE)
group_walk(.data, .f, ...)
}
\arguments{
\item{.data}{A grouped tibble}
\item{.f}{A function or formula to apply to each group. It must return a data frame.
If a \strong{function}, it is used as is. It should have at least 2 formal arguments.
If a \strong{formula}, e.g. \code{~ head(.x)}, it is converted to a function.
In the formula, you can use
\itemize{
\item \code{.} or \code{.x} to refer to the subset of rows of \code{.tbl}
for the given group
\item \code{.y} to refer to the key, a one row tibble with one column per grouping variable
that identifies the group
}}
\item{...}{Additional arguments passed on to \code{.f}}
\item{keep}{are the grouping variables kept in \code{.x}}
}
\value{
\itemize{
\item \code{group_modify()} returns a grouped tibble. In that case \code{.f} must return a data frame.
\item \code{group_map()} returns a list of results from calling \code{.f} on each group
\item \code{group_walk()} calls \code{.f} for side effects and returns the input \code{.tbl}, invisibly
}
}
\description{
\Sexpr[results=rd, stage=render]{lifecycle::badge("experimental")}
\code{group_map()}, \code{group_modify()} and \code{group_walk()} are purrr-style functions that can
be used to iterate on grouped tibbles.
}
\details{
Use \code{group_modify()} when \code{summarize()} is too limited, in terms of what you need
to do and return for each group. \code{group_modify()} is good for "data frame in, data frame out".
If that is too limited, you need to use a \link[=group_nest]{nested} or \link[=group_split]{split} workflow.
\code{group_modify()} is an evolution of \code{\link[=do]{do()}}, if you have used that before.
Each conceptual group of the data frame is exposed to the function \code{.f} with two pieces of information:
\itemize{
\item The subset of the data for the group, exposed as \code{.x}.
\item The key, a tibble with exactly one row and columns for each grouping variable, exposed as \code{.y}.
}
For completeness, \code{group_modify()}, \code{group_map} and \code{group_walk()} also work on
ungrouped data frames, in that case the function is applied to the
entire data frame (exposed as \code{.x}), and \code{.y} is a one row tibble with no
column, consistently with \code{\link[=group_keys]{group_keys()}}.
}
\examples{
# return a list
mtcars \%>\%
group_by(cyl) \%>\%
group_map(~ head(.x, 2L))
# return a tibble grouped by `cyl` with 2 rows per group
# the grouping data is recalculated
mtcars \%>\%
group_by(cyl) \%>\%
group_modify(~ head(.x, 2L))
if (requireNamespace("broom", quietly = TRUE)) {
# a list of tibbles
iris \%>\%
group_by(Species) \%>\%
group_map(~ broom::tidy(lm(Petal.Length ~ Sepal.Length, data = .x)))
# a restructured grouped tibble
iris \%>\%
group_by(Species) \%>\%
group_modify(~ broom::tidy(lm(Petal.Length ~ Sepal.Length, data = .x)))
}
# a list of vectors
iris \%>\%
group_by(Species) \%>\%
group_map(~ quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)))
# to use group_modify() the lambda must return a data frame
iris \%>\%
group_by(Species) \%>\%
group_modify(~ {
quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)) \%>\%
tibble::enframe(name = "prob", value = "quantile")
})
iris \%>\%
group_by(Species) \%>\%
group_modify(~ {
.x \%>\%
purrr::map_dfc(fivenum) \%>\%
mutate(nms = c("min", "Q1", "median", "Q3", "max"))
})
# group_walk() is for side effects
dir.create(temp <- tempfile())
iris \%>\%
group_by(Species) \%>\%
group_walk(~ write.csv(.x, file = file.path(temp, paste0(.y$Species, ".csv"))))
list.files(temp, pattern = "csv$")
unlink(temp, recursive = TRUE)
# group_modify() and ungrouped data frames
mtcars \%>\%
group_modify(~ head(.x, 2L))
}
\seealso{
Other grouping functions:
\code{\link{group_by}()},
\code{\link{group_nest}()},
\code{\link{group_split}()},
\code{\link{group_trim}()}
}
\concept{grouping functions}