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
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
TRUE, apply function in parallel, using parallel
backend provided by foreach
a list of additional options passed into
foreach function when parallel computation
is enabled. This is important if (for example) your code relies on
external data or packages: use the
arguments to supply them so that all cluster nodes have the correct
environment set up for computing.
name of the index column (used if
.data is a named list).
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 case,
".id" is used as colum name.
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
.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 (
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/.