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aaply {plyr}

Split array, apply function, and return results in an array.


For each slice of an array, apply function, keeping results as an array.


aaply(.data, .margins, .fun = NULL, ..., .expand = TRUE,
    .progress = "none", .inform = FALSE, .drop = TRUE,
    .parallel = FALSE, .paropts = NULL)


function to apply to each piece
other arguments passed on to .fun
name of the progress bar to use, see create_progress_bar
if TRUE, apply function in parallel, using parallel backend provided by foreach
a list of additional options passed into the foreach function when parallel computation is enabled. This is important if (for example) your code relies on external data or packages: use the .export and .packages arguments to supply them so that all cluster nodes have the correct environment set up for computing.
produce informative error messages? This is turned off by by default because it substantially slows processing speed, but is very useful for debugging
matrix, array or data frame to be processed
a vector giving the subscripts to split up data by. 1 splits up by rows, 2 by columns and c(1,2) by rows and columns, and so on for higher dimensions
if .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.
should extra dimensions of length 1 in the output be dropped, simplifying the output. Defaults to TRUE


This function is very similar to 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 aaply idempotent, so that aaply(input, X, identity) is equivalent to aperm(input, X).


if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)


This function splits matrices, arrays and data frames by dimensions


If there are no results, then this function will return a vector of length 0 (vector()).


Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29.

See Also

Other array input: a_ply, adply, alply

Other array output: daply, laply, maply


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)

Documentation reproduced from package plyr, version 1.8. License: MIT