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
)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.
if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)
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.
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()).
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/.
maply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)
#> , , = 1
#>
#> sd
#> mean 1 2 3 4 5
#> 1 1.363653 NA NA NA NA
#> 2 NA 4.541345 NA NA NA
#> 3 NA NA -0.8260046 NA NA
#> 4 NA NA NA -2.701309 NA
#> 5 NA NA NA NA 17.82204
#>
#> , , = 2
#>
#> sd
#> mean 1 2 3 4 5
#> 1 0.7141121 NA NA NA NA
#> 2 NA 3.92173 NA NA NA
#> 3 NA NA 2.083138 NA NA
#> 4 NA NA NA 10.10375 NA
#> 5 NA NA NA NA 10.31
#>
#> , , = 3
#>
#> sd
#> mean 1 2 3 4 5
#> 1 1.517669 NA NA NA NA
#> 2 NA 3.537443 NA NA NA
#> 3 NA NA 9.635308 NA NA
#> 4 NA NA NA 6.216742 NA
#> 5 NA NA NA NA 10.71347
#>
#> , , = 4
#>
#> sd
#> mean 1 2 3 4 5
#> 1 0.8970913 NA NA NA NA
#> 2 NA 4.071862 NA NA NA
#> 3 NA NA -0.1250051 NA NA
#> 4 NA NA NA 11.97244 NA
#> 5 NA NA NA NA 10.61919
#>
#> , , = 5
#>
#> sd
#> mean 1 2 3 4 5
#> 1 0.02593041 NA NA NA NA
#> 2 NA 1.052226 NA NA NA
#> 3 NA NA -0.4395716 NA NA
#> 4 NA NA NA 3.383517 NA
#> 5 NA NA NA NA 3.014993
#>
maply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 5)
#> , , = 1
#>
#> sd
#> mean 1 2 3 4 5
#> 1 0.1767388 1.830813 4.3005692 -2.872503 -4.047644
#> 2 3.1387077 2.080409 1.7663395 1.528559 6.383887
#> 3 2.9291426 2.054595 -0.5809235 5.838858 8.385583
#> 4 3.3668218 5.494057 2.5598395 6.253520 13.870784
#> 5 5.7863626 6.350489 4.9690900 5.871351 7.795530
#>
#> , , = 2
#>
#> sd
#> mean 1 2 3 4 5
#> 1 0.4211154 -1.9045995 4.611304 0.5895879 3.403626
#> 2 3.2412631 2.2486021 3.324285 2.4491791 5.120662
#> 3 2.7278463 4.4175061 0.744830 6.0679335 8.907878
#> 4 1.9363455 0.8749631 2.185512 5.2899310 13.423312
#> 5 3.7294869 7.3067516 4.363292 10.6616492 -4.096736
#>
#> , , = 3
#>
#> sd
#> mean 1 2 3 4 5
#> 1 2.763789 2.882412243 -3.293812 1.959838 9.022037
#> 2 2.612091 0.003134899 2.386769 2.031545 12.561386
#> 3 0.553320 -0.057917429 7.367524 1.767154 3.991960
#> 4 6.648932 4.142106719 8.380331 5.466697 -3.943103
#> 5 5.542142 1.626990515 2.280979 3.465068 6.966720
#>
#> , , = 4
#>
#> sd
#> mean 1 2 3 4 5
#> 1 1.132992 0.3221283 5.1487326 1.243596 -6.5751226
#> 2 1.570620 4.4667801 -0.4906428 9.510975 0.2193779
#> 3 3.065487 3.4748507 0.5141894 7.048007 0.9979738
#> 4 2.846602 2.7209305 4.4490381 8.519341 1.3003842
#> 5 5.075106 3.1943701 -1.3064574 4.303655 5.2106705
#>
#> , , = 5
#>
#> sd
#> mean 1 2 3 4 5
#> 1 1.376499 0.8488515 1.0093778 -7.7103041 -6.080120
#> 2 3.360461 2.6808490 0.4892213 10.6350262 -3.322321
#> 3 1.901491 0.3743715 3.8693234 -0.6762064 6.080771
#> 4 3.659362 2.3096085 -0.2999633 0.2340077 -1.847307
#> 5 5.558514 7.6352674 10.6800814 4.1130219 10.898321
#>
maply(cbind(1:5, 1:5), rnorm, n = 5)
#> , , = 1
#>
#>
#> 1 2 3 4 5
#> 1 0.7430788 NA NA NA NA
#> 2 NA 4.955065 NA NA NA
#> 3 NA NA 7.181933 NA NA
#> 4 NA NA NA 8.833542 NA
#> 5 NA NA NA NA -0.655269
#>
#> , , = 2
#>
#>
#> 1 2 3 4 5
#> 1 -0.0563361 NA NA NA NA
#> 2 NA 2.144051 NA NA NA
#> 3 NA NA 4.080835 NA NA
#> 4 NA NA NA 3.32288 NA
#> 5 NA NA NA NA 5.549967
#>
#> , , = 3
#>
#>
#> 1 2 3 4 5
#> 1 1.198777 NA NA NA NA
#> 2 NA 6.252889 NA NA NA
#> 3 NA NA 4.963651 NA NA
#> 4 NA NA NA 5.180119 NA
#> 5 NA NA NA NA 9.264527
#>
#> , , = 4
#>
#>
#> 1 2 3 4 5
#> 1 1.650534 NA NA NA NA
#> 2 NA -0.9523938 NA NA NA
#> 3 NA NA 6.156466 NA NA
#> 4 NA NA NA 9.065362 NA
#> 5 NA NA NA NA 3.828311
#>
#> , , = 5
#>
#>
#> 1 2 3 4 5
#> 1 1.343913 NA NA NA NA
#> 2 NA 2.815777 NA NA NA
#> 3 NA NA -2.938665 NA NA
#> 4 NA NA NA -0.541373 NA
#> 5 NA NA NA NA 15.43344
#>