dlply
From plyr v1.8.4
by Hadley Wickham
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
.
 Keywords
 manip
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 SplitApplyCombine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 129. http://www.jstatsoft.org/v40/i01/.
See Also
Examples
library(plyr)
linmod < function(df) {
lm(rbi ~ year, data = mutate(df, year = year  min(year)))
}
models < dlply(baseball, .(id), linmod)
models[[1]]
coef < ldply(models, coef)
with(coef, plot(`(Intercept)`, year))
qual < laply(models, function(mod) summary(mod)$r.squared)
hist(qual)
Community examples
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