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

Description

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