Call function with arguments in array or data frame, returning an array.

Source: R/maply.r

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)

Arguments

.data

matrix or data frame to use as source of arguments
.fun

function to apply to each piece
...

other arguments passed on to .fun
.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 create_progress_bar
.inform

produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging
.drop

should extra dimensions of length 1 in the output be dropped, simplifying the output. Defaults to TRUE
.parallel

if TRUE, apply function in parallel, using parallel backend provided by foreach
.paropts

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.

Value

if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)

Details

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.

Input

Call a multi-argument function with values taken from columns of an data frame or array

Output

If there are no results, then this function will return a vector of length 0 (vector()).

References

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/.

See also

Other array output: aaply, daply, laply

Other multiple arguments input: m_ply, mdply, mlply

Examples

maply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)
#> , ,  = 1
#> 
#>     sd
#> mean        1        2        3        4         5
#>    1 1.979957       NA       NA       NA        NA
#>    2       NA 5.065483       NA       NA        NA
#>    3       NA       NA 6.157616       NA        NA
#>    4       NA       NA       NA 7.217646        NA
#>    5       NA       NA       NA       NA -3.952959
#> 
#> , ,  = 2
#> 
#>     sd
#> mean        1        2        3         4        5
#>    1 2.321781       NA       NA        NA       NA
#>    2       NA 2.858295       NA        NA       NA
#>    3       NA       NA 5.033051        NA       NA
#>    4       NA       NA       NA -3.600243       NA
#>    5       NA       NA       NA        NA 1.058706
#> 
#> , ,  = 3
#> 
#>     sd
#> mean          1        2        3        4          5
#>    1 -0.1197108       NA       NA       NA         NA
#>    2         NA 2.244207       NA       NA         NA
#>    3         NA       NA 3.115499       NA         NA
#>    4         NA       NA       NA 7.743137         NA
#>    5         NA       NA       NA       NA -0.6651083
#> 
#> , ,  = 4
#> 
#>     sd
#> mean      1          2        3        4        5
#>    1 1.5146         NA       NA       NA       NA
#>    2     NA -0.2760248       NA       NA       NA
#>    3     NA         NA 1.930856       NA       NA
#>    4     NA         NA       NA 2.763794       NA
#>    5     NA         NA       NA       NA 6.818263
#> 
#> , ,  = 5
#> 
#>     sd
#> mean          1         2        3        4       5
#>    1 -0.5090998        NA       NA       NA      NA
#>    2         NA 0.8839697       NA       NA      NA
#>    3         NA        NA 5.348532       NA      NA
#>    4         NA        NA       NA 5.052267      NA
#>    5         NA        NA       NA       NA 3.57056
#> 
maply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 5)
#> , ,  = 1
#> 
#>     sd
#> mean        1         2           3         4        5
#>    1 1.517669  4.527579  3.82361837 -4.725083 2.199798
#>    2 2.768721  3.224182 -0.99529765  2.515692 2.039431
#>    3 5.211769 -1.893360 -1.58687614  8.823366 1.458943
#>    4 4.554186  9.297864  4.21316008  9.840441 5.833372
#>    5 6.142695  6.084283 -0.05951423  1.374639 3.081335
#> 
#> , ,  = 2
#> 
#>     sd
#> mean         1        2           3          4         5
#>    1 0.8970913 1.265984 -0.01680762  6.5316434  1.304494
#>    2 3.0359308 1.141240  5.70017019 -1.3208570 11.388719
#>    3 1.9583316 3.130973  3.71227604 -0.3144141  8.060009
#>    4 5.9931103 1.693203  2.08139569  4.5987174  9.649176
#>    5 6.1238388 5.150212  2.29155515 -3.4086099  4.129568
#> 
#> , ,  = 3
#> 
#>     sd
#> mean          1         2          3           4          5
#>    1 0.02593041 1.7529987  0.7732773  1.01250376 -9.8878801
#>    2 1.52611293 4.7209227  3.0212735 -0.01437164 12.7937828
#>    3 1.85347615 0.8029822 -0.9384427  4.15909784 -1.5952580
#>    4 3.84587926 3.3187242  1.4644128 -1.73328440 -0.7074904
#>    5 4.60299851 6.1170288  8.9529011 12.57344185  3.8912774
#> 
#> , ,  = 4
#> 
#>     sd
#> mean         1        2         3          4           5
#>    1 2.2706723 3.277415 1.1206132  0.6884527  0.41069928
#>    2 0.7246651 1.858285 0.5818926 -2.7745647  5.54857261
#>    3 1.3246727 1.733644 5.2410858  1.0797861  5.81690039
#>    4 6.5644083 5.572725 6.0257341  3.9587867  5.08918821
#>    5 4.1767388 5.830813 8.3005692  1.1274967 -0.04764361
#> 
#> , ,  = 5
#> 
#>     sd
#> mean        1         2         3          4         5
#>    1 1.960865  3.482526  1.372903  2.7657129  1.561474
#>    2 1.694379  1.455693  4.126259 -1.0068933  5.834917
#>    3 4.525939 -1.127309 -1.687555  0.5806826  4.612414
#>    4 5.061999  1.458974  7.460127  3.1510559 11.077061
#>    5 4.421115  2.095400  8.611304  4.5895879  7.403626
#> 
maply(cbind(1:5, 1:5), rnorm, n = 5)
#> , ,  = 1
#> 
#>    
#>            1        2        3         4       5
#>   1 2.604407       NA       NA        NA      NA
#>   2       NA 6.224555       NA        NA      NA
#>   3       NA       NA 3.595176        NA      NA
#>   4       NA       NA       NA -2.354482      NA
#>   5       NA       NA       NA        NA 6.96672
#> 
#> , ,  = 2
#> 
#>    
#>              1        2        3        4        5
#>   1 -0.5150245       NA       NA       NA       NA
#>   2         NA 1.287751       NA       NA       NA
#>   3         NA       NA 1.798784       NA       NA
#>   4         NA       NA       NA 1.840307       NA
#>   5         NA       NA       NA       NA 5.210671
#> 
#> , ,  = 3
#> 
#>    
#>              1          2        3          4        5
#>   1 -0.4160239         NA       NA         NA       NA
#>   2         NA -0.1289284       NA         NA       NA
#>   3         NA         NA 4.848463         NA       NA
#>   4         NA         NA       NA -0.6778459       NA
#>   5         NA         NA       NA         NA 10.89832
#> 
#> , ,  = 4
#> 
#>    
#>            1        2       3        4        5
#>   1 1.876777       NA      NA       NA       NA
#>   2       NA 4.154233      NA       NA       NA
#>   3       NA       NA 8.92247       NA       NA
#>   4       NA       NA      NA 6.236424       NA
#>   5       NA       NA      NA       NA 3.715394
#> 
#> , ,  = 5
#> 
#>    
#>            1        2        3         4          5
#>   1 1.624132       NA       NA        NA         NA
#>   2       NA 4.363151       NA        NA         NA
#>   3       NA       NA 8.653987        NA         NA
#>   4       NA       NA       NA -3.277389         NA
#>   5       NA       NA       NA        NA -0.2816805
#>