mdply | R Documentation |
Call a multi-argument function with values taken from columns of an data frame or array, and combine results into a data frame
mdply(
.data,
.fun = NULL,
...,
.expand = TRUE,
.progress = "none",
.inform = FALSE,
.parallel = FALSE,
.paropts = NULL
)
.data |
matrix or data frame to use as source of arguments |
.fun |
function to apply to each piece |
... |
other arguments passed on to |
.expand |
should output be 1d (expand = FALSE), with an element for each row; or nd (expand = TRUE), with a dimension for each variable. |
.progress |
name of the progress bar to use, see
|
.inform |
produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging |
.parallel |
if |
.paropts |
a list of additional options passed into
the |
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
.
A data frame, as described in the output section.
Call a multi-argument function with values taken from columns of an data frame or array
The most unambiguous behaviour is achieved when .fun
returns a
data frame - in that case pieces will be combined with
rbind.fill
. If .fun
returns an atomic vector of
fixed length, it will be rbind
ed together and converted to a data
frame. Any other values will result in an error.
If there are no results, then this function will return a data
frame with zero rows and columns (data.frame()
).
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()
,
maply()
,
mlply()
Other data frame output:
adply()
,
ddply()
,
ldply()
mdply(data.frame(mean = 1:5, sd = 1:5), rnorm, n = 2)
mdply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 2)
mdply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)
mdply(cbind(mean = 1:5, sd = 1:5), as.data.frame(rnorm), n = 5)
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