For each slice of an array, apply function, keeping results as an array.
aaply(.data, .margins, .fun = NULL, ..., .expand = TRUE, .progress = "none", .inform = FALSE, .drop = TRUE, .parallel = FALSE, .paropts = NULL)
- matrix, array or data frame to be processed
- a vector giving the subscripts to split up
databy. 1 splits up by rows, 2 by columns and c(1,2) by rows and columns, and so on for higher dimensions
- function to apply to each piece
- other arguments passed on to
.datais a data frame, should output be 1d (expand = FALSE), with an element for each row; or nd (expand = TRUE), with a dimension for each variable.
- name of the progress bar to use, see
- produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging
- should extra dimensions of length 1 in the output be dropped, simplifying the output. Defaults to
TRUE, apply function in parallel, using parallel backend provided by foreach
- a list of additional options passed into the
foreachfunction when parallel computation is enabled. This is important if (for example) your code relies on external data or packages: use the
.packagesarguments to supply them so that all cluster nodes have the correct environment set up for computing.
This function is very similar to
apply, except that it will always return an array, and when the function returns >1 d data structures, those dimensions are added on to the highest dimensions, rather than the lowest dimensions. This makes
aaply idempotent, so that
aaply(input, X, identity) is equivalent to
if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)
Passing a data frame as first argument may lead to unexpected results, see https://github.com/hadley/plyr/issues/212.
This function splits matrices, arrays and data frames by dimensions
If there are no results, then this function will return a vector of length 0 (
Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. http://www.jstatsoft.org/v40/i01/.
Other array input:
Other array output:
dim(ozone) aaply(ozone, 1, mean) aaply(ozone, 1, mean, .drop = FALSE) aaply(ozone, 3, mean) aaply(ozone, c(1,2), mean) dim(aaply(ozone, c(1,2), mean)) dim(aaply(ozone, c(1,2), mean, .drop = FALSE)) aaply(ozone, 1, each(min, max)) aaply(ozone, 3, each(min, max)) standardise <- function(x) (x - min(x)) / (max(x) - min(x)) aaply(ozone, 3, standardise) aaply(ozone, 1:2, standardise) aaply(ozone, 1:2, diff)
Documentation reproduced from package plyr, version 1.8.4. License: MIT + file LICENSE