plyr (version 1.8.6)

llply: Split list, apply function, and return results in a list.

Description

For each element of a list, apply function, keeping results as a list.

Usage

llply(
  .data,
  .fun = NULL,
  ...,
  .progress = "none",
  .inform = FALSE,
  .parallel = FALSE,
  .paropts = NULL
)

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.

Value

list of results

Input

This function splits lists by elements.

Output

If there are no results, then this function will return a list of length 0 (list()).

Details

llply is equivalent to lapply except that it will preserve labels and can display a progress bar.

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/.

See Also

Other list input: l_ply(), laply(), ldply()

Other list output: alply(), dlply(), mlply()

Examples

# NOT RUN {
llply(llply(mtcars, round), table)
llply(baseball, summary)
# Examples from ?lapply
x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE))

llply(x, mean)
llply(x, quantile, probs = 1:3/4)
# }