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adply.Rd
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adply.Rd
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\name{adply}
\alias{adply}
\title{Split array, apply function, and return results in a data frame.}
\usage{adply(.data, .margins, .fun, ..., .expand=TRUE,
.progress="none", .parallel=FALSE)}
\description{
Split array, apply function, and return results in a data
frame. For each slice of an array, apply function then
combine results into a data frame
}
\details{
All plyr functions use the same split-apply-combine
strategy: they split the input into simpler pieces, apply
\code{.fun} to each piece, and then combine the pieces
into a single data structure. This function splits
matrices, arrays and data frames by dimensions and
combines the result into a data frame. If there are no
results, then this function will return a data frame with
zero rows and columns (\code{data.frame()}).
}
\keyword{manip}
\value{a data frame}
\arguments{
\item{.data}{matrix, array or data frame to be processed}
\item{.margins}{a vector giving the subscripts to split up \code{data} by. 1 splits up by rows, 2 by columns and c(1,2) by rows and columns, and so on for higher dimensions}
\item{.fun}{function to apply to each piece}
\item{...}{other arguments passed on to \code{.fun}}
\item{.expand}{if \code{.data} is a data frame, should output be 1d
(expand = FALSE), with an element for each row; or nd (expand = TRUE),
with a dimension for each variable.}
\item{.progress}{name of the progress bar to use, see \code{\link{create_progress_bar}}}
\item{.parallel}{if \code{TRUE}, apply function in parallel, using parallel
backend provided by foreach}
}