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.

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/.