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\name{a_ply}
\alias{a_ply}
\title{Split array, apply function, and discard results.}
\usage{
a_ply(.data, .margins, .fun = NULL, ..., .expand = TRUE,
.progress = "none", .print = FALSE)
}
\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{.print}{automatically print each result? (default:
\code{FALSE})}
}
\description{
For each slice of an array, apply function and discard
results
}
\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
discards the output. This is useful for functions that
you are calling purely for their side effects like
display plots and saving output.
}
\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/}.
}
\keyword{manip}
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