plyr (version 1.8.4)

dlply: Split data frame, apply function, and return results in a list.


For each subset of a data frame, apply function then combine results into a list. dlply is similar to by except that the results are returned in a different format. To apply a function for each row, use alply with .margins set to 1.


dlply(.data, .variables, .fun = NULL, ..., .progress = "none",
  .inform = FALSE, .drop = TRUE, .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)


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.


list of results


This function splits data frames by variables.


If there are no results, then this function will return a list of length 0 (list()).


Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29.

See Also

Other data frame input: d_ply, daply, ddply

Other list output: alply, llply, mlply


Run this code
linmod <- function(df) {
  lm(rbi ~ year, data = mutate(df, year = year - min(year)))
models <- dlply(baseball, .(id), linmod)

coef <- ldply(models, coef)
with(coef, plot(`(Intercept)`, year))
qual <- laply(models, function(mod) summary(mod)$r.squared)
# }

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