plyr (version 1.8.4)

daply: Split data frame, apply function, and return results in an array.


For each subset of data frame, apply function then combine results into an array. daply with a function that operates column-wise is similar to aggregate. To apply a function for each row, use aaply with .margins set to 1.


daply(.data, .variables, .fun = NULL, ..., .progress = "none",
  .inform = FALSE, .drop_i = TRUE, .drop_o = TRUE, .parallel = FALSE,
  .paropts = NULL)



data frame to be processed


variables to split data frame by, 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)


should extra dimensions of length 1 in the output be dropped, simplifying the output. Defaults to TRUE


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.


if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)


This function splits data frames by variables.


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


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

See Also

Other array output: aaply, laply, maply

Other data frame input: d_ply, ddply, dlply


Run this code
daply(baseball, .(year), nrow)

# Several different ways of summarising by variables that should not be
# included in the summary

daply(baseball[, c(2, 6:9)], .(year), colwise(mean))
daply(baseball[, 6:9], .(baseball$year), colwise(mean))
daply(baseball, .(year), function(df) colwise(mean)(df[, 6:9]))
# }

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