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

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

mlply(.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

list of results

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 list of length 0 (list()).

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

mlply(cbind(1:4, 4:1), rep)
#> $`1`
#> [1] 1 1 1 1
#> 
#> $`2`
#> [1] 2 2 2
#> 
#> $`3`
#> [1] 3 3
#> 
#> $`4`
#> [1] 4
#> 
#> attr(,"split_type")
#> [1] "array"
#> attr(,"split_labels")
#>      
#> 1 1 4
#> 2 2 3
#> 3 3 2
#> 4 4 1
mlply(cbind(1:4, times = 4:1), rep)
#> $`1`
#> [1] 1 1 1 1
#> 
#> $`2`
#> [1] 2 2 2
#> 
#> $`3`
#> [1] 3 3
#> 
#> $`4`
#> [1] 4
#> 
#> attr(,"split_type")
#> [1] "array"
#> attr(,"split_labels")
#>     times
#> 1 1     4
#> 2 2     3
#> 3 3     2
#> 4 4     1

mlply(cbind(1:4, 4:1), seq)
#> $`1`
#> [1] 1 2 3 4
#> 
#> $`2`
#> [1] 2 3
#> 
#> $`3`
#> [1] 3 2
#> 
#> $`4`
#> [1] 4 3 2 1
#> 
#> attr(,"split_type")
#> [1] "array"
#> attr(,"split_labels")
#>      
#> 1 1 4
#> 2 2 3
#> 3 3 2
#> 4 4 1
mlply(cbind(1:4, length = 4:1), seq)
#> $`1`
#> [1] 1 2 3 4
#> 
#> $`2`
#> [1] 2 3 4
#> 
#> $`3`
#> [1] 3 4
#> 
#> $`4`
#> [1] 4
#> 
#> attr(,"split_type")
#> [1] "array"
#> attr(,"split_labels")
#>     length
#> 1 1      4
#> 2 2      3
#> 3 3      2
#> 4 4      1
mlply(cbind(1:4, by = 4:1), seq, to = 20)
#> $`1`
#> [1]  1  5  9 13 17
#> 
#> $`2`
#> [1]  2  5  8 11 14 17 20
#> 
#> $`3`
#> [1]  3  5  7  9 11 13 15 17 19
#> 
#> $`4`
#>  [1]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
#> 
#> attr(,"split_type")
#> [1] "array"
#> attr(,"split_labels")
#>     by
#> 1 1  4
#> 2 2  3
#> 3 3  2
#> 4 4  1