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

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)

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
.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

A data frame, as described in the output section.

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

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 rbinded 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()).

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/.

Examples

mdply(data.frame(mean = 1:5, sd = 1:5), rnorm, n = 2)
#>   mean sd        V1       V2
#> 1    1  1  1.198777 1.650534
#> 2    2  2  2.687827 4.955065
#> 3    3  3  3.216077 9.379334
#> 4    4  4 -1.904788 5.631554
#> 5    5  5 11.969889 6.801391
mdply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 2)
#>    mean sd          V1         V2
#> 1     1  1  1.65455025  2.0521554
#> 2     2  1  0.02044487  3.2083856
#> 3     3  1  2.83071992  3.2950298
#> 4     4  1  5.26634059  2.8646567
#> 5     5  1  3.86894620  5.1099934
#> 6     1  2  2.70581082  0.5313243
#> 7     2  2  6.17337713  1.7781613
#> 8     3  2  0.21430589  0.7154185
#> 9     4  2  7.40921747  3.8398527
#> 10    5  2  4.12543752  4.7615698
#> 11    1  3  3.35938859 -0.7368357
#> 12    2  3  1.56371934  3.5793740
#> 13    3  3  8.20073433  7.3459717
#> 14    4  3  8.55457945  2.8479782
#> 15    5  3 10.48137553  3.3455248
#> 16    1  4 -2.46301416 -0.3753259
#> 17    2  4  6.25150583  5.2522328
#> 18    3  4 10.21393344  2.5797252
#> 19    4  4  7.92981345 -2.8532105
#> 20    5  4  1.67192189  9.4019675
#> 21    1  5  0.13089947  1.8940601
#> 22    2  5 -1.49214725 -2.8022458
#> 23    3  5 -1.87711521  1.3071175
#> 24    4  5  9.76173537  6.0255059
#> 25    5  5  2.64538751  4.3337449
mdply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)
#>   mean sd       V1        V2         V3        V4         V5
#> 1    1  1 2.226682  1.332944  0.6529115 0.9014493  1.0347661
#> 2    2  2 2.772254  2.041662  2.0151736 3.8616881  0.6305001
#> 3    3  3 4.012205  1.763587  5.8027834 8.5209502  0.8855410
#> 4    4  4 4.034041 12.136760 -1.3667443 8.6359167  3.1871642
#> 5    5  5 3.109857 13.680555  0.7737609 0.1921425 10.0874553
mdply(cbind(mean = 1:5, sd = 1:5), as.data.frame(rnorm), n = 5)
#>    mean sd       value
#> 1     1  1 -0.49605374
#> 2     1  1 -0.18481873
#> 3     1  1  1.63023437
#> 4     1  1  3.10125251
#> 5     1  1  0.38626319
#> 6     2  2 -1.26927654
#> 7     2  2  1.97911777
#> 8     2  2  0.68698772
#> 9     2  2  0.66093312
#> 10    2  2  1.04282194
#> 11    3  3  6.95836895
#> 12    3  3  4.90968828
#> 13    3  3  4.54298335
#> 14    3  3 -2.25412534
#> 15    3  3  5.68079256
#> 16    4  4  4.89215349
#> 17    4  4  6.32326637
#> 18    4  4  3.28871432
#> 19    4  4  6.96386683
#> 20    4  4  0.01022768
#> 21    5  5 -9.69488781
#> 22    5  5  8.59507831
#> 23    5  5  1.50997479
#> 24    5  5 -4.47062922
#> 25    5  5  5.38149624