d_ply: Split data frame, apply function, and discard results.

Description

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.

Usage

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

Value

Nothing

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.

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