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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 = NULL, ..., .expand = TRUE,
.progress = "none", .parallel = FALSE)
}
\arguments{
\item{.fun}{function to apply to each piece}
\item{...}{other arguments passed on to \code{.fun}}
\item{.progress}{name of the progress bar to use, see
\code{\link{create_progress_bar}}}
\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{.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}
\item{.parallel}{if \code{TRUE}, apply function in
parallel, using parallel backend provided by foreach
dimension for each variable.}
}
\value{
A data frame, as described in the output section.
}
\description{
For each slice of an array, apply function then combine
results into a data frame.
}
\section{Input}{
This function splits matrices, arrays and data frames by
dimensions
}
\section{Output}{
The most unambiguous behaviour is achieved when
\code{.fun} returns a data frame - in that case pieces
will be combined with \code{\link{rbind.fill}}. If
\code{.fun} returns an atomic vector of fixed length, it
will be \code{rbind}ed together and converted to a data
frame. Any other values will result in an error.
If there are no results, then this function will return a
data frame with zero rows and columns
(\code{data.frame()}).
}
\references{
Hadley Wickham (2011). The Split-Apply-Combine Strategy
for Data Analysis. Journal of Statistical Software,
40(1), 1-29. \url{http://www.jstatsoft.org/v40/i01/}.
}
\seealso{
Other array input: \code{\link{aaply}},
\code{\link{alply}}
Other data frame output: \code{\link{ddply}},
\code{\link{ldply}}
}
\keyword{manip}
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