# public hadley /plyr

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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 \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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