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

Usage

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. https://www.jstatsoft.org/v40/i01/.

See also

Other multiple arguments input: m_ply(), maply(), mlply()

Other data frame output: adply(), ddply(), ldply()

Examples

mdply(data.frame(mean = 1:5, sd = 1:5), rnorm, n = 2)
#>   mean sd        V1         V2
#> 1    1  1  2.704609  0.9199264
#> 2    2  2  1.125438  1.7615698
#> 3    3  3  5.359389  1.2631643
#> 4    4  4  3.418292  6.1058320
#> 5    5  5 13.667891 12.2432861
mdply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 2)
#>    mean sd         V1         V2
#> 1     1  1  2.5181931  0.6159927
#> 2     2  1  3.8271252  1.4485083
#> 3     3  1  2.1342465  2.6561685
#> 4     4  1  5.0628765  4.8130582
#> 5     5  1  6.8034834  4.8949313
#> 6     1  2  2.9649067 -2.4266052
#> 7     2  2  0.3359609  4.2009838
#> 8     3  2  2.6523598  3.3576240
#> 9     4  2  2.6031411  2.0791017
#> 10    5  2  3.0491539  4.3228470
#> 11    1  3  4.4570412  2.2153036
#> 12    2  3  0.5872325  1.6002469
#> 13    3  3  6.6800471  3.9988320
#> 14    4  3  2.9587346  3.7043479
#> 15    5  3  5.1042982  6.1583811
#> 16    1  4  1.0833249  1.0303471
#> 17    2  4  5.7233761 -0.7389998
#> 18    3  4  4.3496061  1.3514492
#> 19    4  4  7.7370445 11.3612670
#> 20    5  4  2.1807213  5.0340412
#> 21    1  5 11.1709494 -5.7084303
#> 22    2  5  7.7948959  0.9839552
#> 23    3  5  1.1098572 11.6805552
#> 24    4  5 -0.2262391 -0.8078575
#> 25    5  5 10.0874553 -2.4802687
mdply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)
#>   mean sd         V1        V2         V3         V4         V5
#> 1    1  1 -0.1848187 1.6302344  3.1012525 0.38626319 -0.6346383
#> 2    2  2  1.9791178 0.6869877  0.6609331 1.04282194  4.6389126
#> 3    3  3  4.9096883 4.5429833 -2.2541253 5.68079256  3.6691151
#> 4    4  4  6.3232664 3.2887143  6.9638668 0.01022768 -7.7559102
#> 5    5  5  8.5950783 1.5099748 -4.4706292 5.38149624  9.3765425
mdply(cbind(mean = 1:5, sd = 1:5), as.data.frame(rnorm), n = 5)
#>    mean sd      value
#> 1     1  1  1.4538274
#> 2     1  1  0.1492831
#> 3     1  1  1.5662016
#> 4     1  1  2.1522120
#> 5     1  1  0.2438026
#> 6     2  2  1.0214833
#> 7     2  2 -0.3321047
#> 8     2  2  1.0406621
#> 9     2  2  2.2306964
#> 10    2  2 -1.5360968
#> 11    3  3 -1.2229168
#> 12    3  3  5.1275354
#> 13    3  3 -0.7225288
#> 14    3  3  1.8950180
#> 15    3  3  4.3862403
#> 16    4  4  2.7086676
#> 17    4  4 -1.1488592
#> 18    4  4 -0.1201610
#> 19    4  4 10.0563573
#> 20    4  4  5.3876143
#> 21    5  5 13.8972077
#> 22    5  5  6.9331546
#> 23    5  5  0.4065238
#> 24    5  5 -2.9216824
#> 25    5  5  4.5797055