plyr (version 1.8.4)

laply: Split list, apply function, and return results in an array.


For each element of a list, apply function then combine results into an array.


laply(.data, .fun = NULL, ..., .progress = "none", .inform = FALSE,
  .drop = TRUE, .parallel = FALSE, .paropts = NULL)



list to be processed


function to apply to each piece


other arguments passed on to .fun


name of the progress bar to use, see create_progress_bar


produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging


should extra dimensions of length 1 in the output be dropped, simplifying the output. Defaults to TRUE


if TRUE, apply function in parallel, using parallel backend provided by foreach


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.


if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)


This function splits lists by elements.


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


laply is similar in spirit to sapply except that it will always return an array, and the output is transposed with respect sapply - each element of the list corresponds to a row, not a column.


Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29.

See Also

Other array output: aaply, daply, maply

Other list input: l_ply, ldply, llply


Run this code
laply(baseball, is.factor)
# cf
ldply(baseball, is.factor)

laply(seq_len(10), identity)
laply(seq_len(10), rep, times = 4)
laply(seq_len(10), matrix, nrow = 2, ncol = 2)
# }

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