% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/aaply.r
\name{aaply}
\alias{aaply}
\title{Split array, apply function, and return results in an array.}
\usage{
aaply(.data, .margins, .fun = NULL, ..., .expand = TRUE,
  .progress = "none", .inform = FALSE, .drop = TRUE, .parallel = FALSE,
  .paropts = NULL)
}
\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{.inform}{produce informative error messages?  This is turned off
by default because it substantially slows processing speed, but is very
useful for debugging}

\item{.drop}{should extra dimensions of length 1 in the output be
dropped, simplifying the output.  Defaults to \code{TRUE}}

\item{.parallel}{if \code{TRUE}, apply function in parallel, using parallel
backend provided by foreach}

\item{.paropts}{a list of additional options passed into
the \code{\link[foreach]{foreach}} function when parallel computation
is enabled.  This is important if (for example) your code relies on
external data or packages: use the \code{.export} and \code{.packages}
arguments to supply them so that all cluster nodes have the correct
environment set up for computing.}
}
\value{
if results are atomic with same type and dimensionality, a
  vector, matrix or array; otherwise, a list-array (a list with
  dimensions)
}
\description{
For each slice of an array, apply function, keeping results as an array.
}
\details{
This function is very similar to \code{\link{apply}}, except that it will
always return an array, and when the function returns >1 d data structures,
those dimensions are added on to the highest dimensions, rather than the
lowest dimensions.  This makes \code{aaply} idempotent, so that
\code{aaply(input, X, identity)} is equivalent to \code{aperm(input, X)}.
}
\section{Warning}{
Passing a data frame as first argument may lead to
unexpected results, see \url{https://github.com/hadley/plyr/issues/212}.
}

\section{Input}{
 This function splits matrices, arrays and data frames by
  dimensions
}

\section{Output}{

  If there are no results, then this function will return a vector of
  length 0 (\code{vector()}).
}
\examples{
dim(ozone)
aaply(ozone, 1, mean)
aaply(ozone, 1, mean, .drop = FALSE)
aaply(ozone, 3, mean)
aaply(ozone, c(1,2), mean)

dim(aaply(ozone, c(1,2), mean))
dim(aaply(ozone, c(1,2), mean, .drop = FALSE))

aaply(ozone, 1, each(min, max))
aaply(ozone, 3, each(min, max))

standardise <- function(x) (x - min(x)) / (max(x) - min(x))
aaply(ozone, 3, standardise)
aaply(ozone, 1:2, standardise)

aaply(ozone, 1:2, diff)
}
\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{a_ply}};
  \code{\link{adply}}; \code{\link{alply}}

Other array output: \code{\link{daply}};
  \code{\link{laply}}; \code{\link{maply}}
}
\keyword{manip}

