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
.margins set to
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.quotedvariables, a formula or character vector
- 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
- should combinations of variables that do not appear in the input data be preserved (FALSE) or dropped (TRUE, default)
- automatically print each result? (default:
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.
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/.