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)
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
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 .e
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 ca

  • A data frame, as described in the output section.


This function splits lists by elements.


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()).


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 data frame output: adply; ddply; mdply

Other list input: l_ply; laply; llply

  • ldply
Documentation reproduced from package plyr, version 1.8.3, License: MIT + file LICENSE

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