daply: Split data frame, apply function, and return results in an...
In plyr: Tools for Splitting, Applying and Combining Data

daply

R Documentation

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

Description

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.

Usage

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

Arguments

`.data`	data frame to be processed
`.variables`	variables to split data frame by, 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_i`	should combinations of variables that do not appear in the input data be preserved (FALSE) or dropped (TRUE, default)
`.drop_o`	should extra dimensions of length 1 in the output be dropped, simplifying the output. Defaults to `TRUE`
`.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

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

Input

This function splits data frames by variables.

Output

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

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/.

Examples

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]))

plyr documentation built on Oct. 2, 2023, 9:07 a.m.