Split data frame, apply function, and discard results.

For each subset of a data frame, apply function and discard results. To apply a function for each row, use a_ply with .margins set to 1.

d_ply(.data, .variables, .fun = NULL, ..., .progress = "none", .inform = FALSE, .drop = TRUE, .print = FALSE, .parallel = FALSE, .paropts = NULL)
data frame to be processed
variables to split data frame by, as as.quoted variables, a formula or character vector
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 combinations of variables that do not appear in the input data be preserved (FALSE) or dropped (TRUE, default)
automatically print each result? (default: FALSE)
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.



This function splits data frames by variables.


All output is discarded. This is useful for functions that you are calling purely for their side effects like displaying plots or saving output.


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 input: daply, ddply, dlply

Other no output: a_ply, l_ply, m_ply

  • d_ply
Documentation reproduced from package plyr, version 1.8.4, License: MIT + file LICENSE

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