# d_ply

0th

Percentile

##### 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.

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

Nothing

##### Input

This function splits data frames by variables.

##### Output

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

##### 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/.

Other data frame input: daply, ddply, dlply
Other no output: a_ply, l_ply, m_ply