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.

adply(.data, .margins, .fun = NULL, ..., .expand = TRUE,
  .progress = "none", .inform = FALSE, .parallel = FALSE,
  .paropts = NULL, .id = NA)

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
.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.
.id	name(s) of the index column(s). Pass `NULL` to avoid creation of the index column(s). Omit or pass `NA` to use the default names `"X1"`, `"X2"`, .... Otherwise, this argument must have the same length as `.margins`.

Value

A data frame, as described in the output section.

Input

This function splits matrices, arrays and data frames by dimensions

Output

The most unambiguous behaviour is achieved when .fun returns a data frame - in that case pieces will be combined with rbind.fill. If .fun returns an atomic vector of fixed length, it will be rbinded together and converted to a data frame. Any other values will result in an error.

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/.