| ldply | R Documentation | 
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
)
| .data | list to be processed | 
| .fun | function to apply to each piece | 
| ... | other arguments passed on to  | 
| .progress | name of the progress bar to use, see
 | 
| .inform | produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging | 
| .parallel | if  | 
| .paropts | a list of additional options passed into
the  | 
| .id | name of the index column (used if  | 
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. https://www.jstatsoft.org/v40/i01/.
Other list input: 
l_ply(),
laply(),
llply()
Other data frame output: 
adply(),
ddply(),
mdply()
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