Split data frame, apply function, and discard results.

For each subset of a data frame, apply function and discard results. To apply a function for each row, use a_ply with .margins set to 1.

d_ply(.data, .variables, .fun = NULL, ..., .progress = "none",
  .inform = FALSE, .drop = TRUE, .print = FALSE, .parallel = FALSE,
  .paropts = NULL)

Arguments

.data	data frame to be processed
.variables	variables to split data frame by, as `as.quoted` variables, a formula or character vector
.fun	function to apply to each piece
...	other arguments passed on to `.fun`
.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
.drop	should combinations of variables that do not appear in the input data be preserved (FALSE) or dropped (TRUE, default)
.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 data frames by variables.

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. http://www.jstatsoft.org/v40/i01/.