adply: Split array, apply function, and return results in a data frame.

Description

Split array, apply function, and return results in a data frame. For each slice of an array, apply function then combine results into a data frame

Usage

adply(.data, .margins, .fun, ..., .expand=TRUE,
    .progress="none", .parallel=FALSE)

Arguments

.data

matrix, array or data frame to be processed

.margins

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

.fun

function to apply to each piece

...

other arguments passed on to .fun

.expand

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.

.progress

name of the progress bar to use, see create_progress_bar

.parallel

if TRUE, apply function in parallel, using parallel backend provided by foreach

Value

a data frame

Details

All plyr functions use the same split-apply-combine strategy: they split the input into simpler pieces, apply .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 combines the result into a data frame. If there are no results, then this function will return a data frame with zero rows and columns (data.frame()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. http://www.jstatsoft.org/v40/i01/.