Call a multi-argument function with values taken from columns of an data frame or array, and combine results into an array
maply(
.data,
.fun = NULL,
...,
.expand = TRUE,
.progress = "none",
.inform = FALSE,
.drop = TRUE,
.parallel = FALSE,
.paropts = NULL
)
if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)
matrix or data frame to use as source of arguments
function to apply to each piece
other arguments passed on to .fun
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
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.
Call a multi-argument function with values taken from columns of an data frame or array
If there are no results, then this function will return a vector of
length 0 (vector()
).
The m*ply
functions are the plyr
version of mapply
,
specialised according to the type of output they produce. These functions
are just a convenient wrapper around a*ply
with margins = 1
and .fun
wrapped in splat
.
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 multiple arguments input:
m_ply()
,
mdply()
,
mlply()
Other array output:
aaply()
,
daply()
,
laply()
maply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)
maply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 5)
maply(cbind(1:5, 1:5), rnorm, n = 5)
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