mdply: Call function with arguments in array or data frame,...
In plyr: Tools for Splitting, Applying and Combining Data

Description Usage Arguments Details Value Input Output References See Also Examples

Call a multi-argument function with values taken from columns of an data frame or array, and combine results into a data frame

1 2	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 `.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.

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 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()).

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/.

Other data frame output: adply, ddply, ldply

Other multiple arguments input: m_ply, maply, mlply

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)

  mean sd         V1         V2
1    1  1  0.0281318  0.9603721
2    2  2  3.6647118  5.7467023
3    3  3  6.4070505 -2.7127433
4    4  4  1.2098160  9.5733648
5    5  5 -5.9447450 -1.0400300
   mean sd         V1         V2
1     1  1  1.2506739  0.7594330
2     2  1  3.3733821  2.0537217
3     3  1  5.4556120  2.3165622
4     4  1  3.5802295  5.3253231
5     5  1  5.4384257  5.7115446
6     1  2  1.2419571 -2.0223169
7     2  2 -1.6976060  2.3125690
8     3  2  2.1137534  2.2454386
9     4  2  4.2050767  4.2625454
10    5  2  3.2627306  6.3577257
11    1  3 -3.6870592  7.8840932
12    2  3  5.6079898  1.5860156
13    3  3 -4.0223132 -0.1752903
14    4  3 -2.6084191  2.0463315
15    5  3  1.5435458  3.5188400
16    1  4 -2.4372223  0.5647384
17    2  4  4.8898557  2.1102315
18    3  4 -0.3103671  3.6353763
19    4  4  1.8073910  3.0327481
20    5  4  1.6112743  3.4861216
21    1  5  2.3765304 -5.1859103
22    2  5  9.6538316 -0.5331513
23    3  5 -4.2507880 -3.7359422
24    4  5  8.2971204  0.9058580
25    5  5  7.5059693 11.8869157
  mean sd        V1       V2       V3        V4        V5
1    1  1 2.3735868 0.458740 0.433311 0.4914755 0.1916439
2    2  2 0.5876147 6.308201 4.792018 0.9086048 2.3942631
3    3  3 0.8936273 9.725387 3.006424 3.3535177 8.3397970
4    4  4 2.7855436 3.689438 3.858531 6.2899890 3.6810345
5    5  5 4.4560882 1.327426 7.759834 8.1707367 5.6068403
   mean sd      value
1     1  1  2.5241037
2     1  1  1.1133229
3     1  1  1.2999567
4     1  1  1.6144720
5     1  1  0.1030883
6     2  2  1.3409487
7     2  2  1.6047732
8     2  2  1.9683570
9     2  2  5.2817933
10    2  2  2.4033436
11    3  3  9.1567933
12    3  3  1.9877205
13    3  3  7.3993067
14    3  3  3.1268524
15    3  3  5.2726129
16    4  4  5.7998091
17    4  4  0.1764000
18    4  4  2.0118731
19    4  4  4.7002937
20    4  4  5.6200545
21    5  5 -4.0449753
22    5  5  0.5471754
23    5  5  2.0633721
24    5  5  4.6482450
25    5  5  7.4577557