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

Source: R/mlply.r

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

See also

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

Other list output: alply(), dlply(), llply()

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