For each slice of an array, apply function then combine results into a list.
alply(
.data,
.margins,
.fun = NULL,
...,
.expand = TRUE,
.progress = "none",
.inform = FALSE,
.parallel = FALSE,
.paropts = NULL,
.dims = FALSE
)list of results
matrix, array or data frame to be processed
a vector giving the subscripts to split up data by.
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 .fun
if .data is 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
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
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 TRUE, copy over dimensions and names from input.
This function splits matrices, arrays and data frames by dimensions
If there are no results, then this function will return
a list of length 0 (list()).
The list will have "dims" and "dimnames" corresponding to the
margins given. For instance alply(x, c(3,2), ...) where
x has dims c(4,3,2) will give a result with dims
c(2,3).
alply is somewhat similar to apply for cases
where the results are not atomic.
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/.
Other array input:
a_ply(),
aaply(),
adply()
Other list output:
dlply(),
llply(),
mlply()
alply(ozone, 3, quantile)
alply(ozone, 3, function(x) table(round(x)))
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