Split list, apply function, and return results in a data frame.

For each element of a list, apply function then combine results into a data frame.

Usage

ldply(
  .data,
  .fun = NULL,
  ...,
  .progress = "none",
  .inform = FALSE,
  .parallel = FALSE,
  .paropts = NULL,
  .id = NA
)

Arguments

.data: list to be processed
.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
.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 of the index column (used if .data is a named list). Pass NULL to avoid creation of the index column. For compatibility, omit this argument or pass NA to avoid converting the index column to a factor; in this case, ".id" is used as colum name.

Value

A data frame, as described in the output section.

Input

This function splits lists by elements.

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