Split array, apply function, and discard results.

For each slice of an array, apply function and discard results

Usage

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

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
.inform: produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging
.print: automatically print each result? (default: FALSE)
.parallel: if TRUE, apply function in parallel, using parallel backend provided by foreach
.paropts: 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.

Value

Nothing

Input

This function splits matrices, arrays and data frames by dimensions

Output

All output is discarded. This is useful for functions that you are calling purely for their side effects like displaying plots or saving output.

References

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