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.

Usage

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

.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.

Value

list of results

Input

This function splits data frames by variables.

Output

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

References

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/.

See also

Other data frame input: d_ply(), daply(), ddply()

Other list output: alply(), llply(), mlply()

Examples

linmod <- function(df) {
  lm(rbi ~ year, data = mutate(df, year = year - min(year)))
}
models <- dlply(baseball, .(id), linmod)
models[[1]]
#> 
#> Call:
#> lm(formula = rbi ~ year, data = mutate(df, year = year - min(year)))
#> 
#> Coefficients:
#> (Intercept)         year  
#>     118.924       -1.732  
#> 

coef <- ldply(models, coef)
with(coef, plot(`(Intercept)`, year))

qual <- laply(models, function(mod) summary(mod)$r.squared)
hist(qual)