Split list, apply function, and return results in a list.
For each element of a list, apply function, keeping results as a list.
llply(.data, .fun = NULL, ..., .progress = "none", .inform = FALSE, .parallel = FALSE, .paropts = NULL)
- list to be processed
- function to apply to each piece
- other arguments passed on to
- name of the progress bar to use, see
- produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging
TRUE, apply function in parallel, using parallel backend provided by foreach
- a list of additional options passed into
foreachfunction when parallel computation is enabled. This is important if (for example) your code relies on external data or packages: use the
.packagesarguments to supply them so that all cluster nodes have the correct environment set up for computing.
llply is equivalent to
lapply except that it will
preserve labels and can display a progress bar.
list of results
This function splits lists by elements.
If there are no results, then this function will return
a list of length 0 (
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/.
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)