Split list, apply function, and return results in an array.

For each element of a list, apply function then combine results into an array.

Usage

laply(
  .data,
  .fun = NULL,
  ...,
  .progress = "none",
  .inform = FALSE,
  .drop = TRUE,
  .parallel = FALSE,
  .paropts = NULL
)

Arguments

.data

list to be processed

.fun

function to apply to each piece

...

other arguments passed on to .fun

.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

.drop

should extra dimensions of length 1 in the output be dropped, simplifying the output. Defaults to TRUE

.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

if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)

Details

laply is similar in spirit to sapply except that it will always return an array, and the output is transposed with respect sapply - each element of the list corresponds to a row, not a column.

Input

This function splits lists by elements.

Output

If there are no results, then this function will return a vector of length 0 (vector()).

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 list input: l_ply(), ldply(), llply()

Other array output: aaply(), daply(), maply()

Examples

laply(baseball, is.factor)
#>  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# cf
ldply(baseball, is.factor)
#>      .id    V1
#> 1     id FALSE
#> 2   year FALSE
#> 3  stint FALSE
#> 4   team FALSE
#> 5     lg FALSE
#> 6      g FALSE
#> 7     ab FALSE
#> 8      r FALSE
#> 9      h FALSE
#> 10   X2b FALSE
#> 11   X3b FALSE
#> 12    hr FALSE
#> 13   rbi FALSE
#> 14    sb FALSE
#> 15    cs FALSE
#> 16    bb FALSE
#> 17    so FALSE
#> 18   ibb FALSE
#> 19   hbp FALSE
#> 20    sh FALSE
#> 21    sf FALSE
#> 22  gidp FALSE
colwise(is.factor)(baseball)
#>      id  year stint  team    lg     g    ab     r     h   X2b   X3b    hr   rbi
#> 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>      sb    cs    bb    so   ibb   hbp    sh    sf  gidp
#> 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

laply(seq_len(10), identity)
#>  [1]  1  2  3  4  5  6  7  8  9 10
laply(seq_len(10), rep, times = 4)
#>        1  2  3  4
#>  [1,]  1  1  1  1
#>  [2,]  2  2  2  2
#>  [3,]  3  3  3  3
#>  [4,]  4  4  4  4
#>  [5,]  5  5  5  5
#>  [6,]  6  6  6  6
#>  [7,]  7  7  7  7
#>  [8,]  8  8  8  8
#>  [9,]  9  9  9  9
#> [10,] 10 10 10 10
laply(seq_len(10), matrix, nrow = 2, ncol = 2)
#> , , 1
#> 
#>        1  2
#>  [1,]  1  1
#>  [2,]  2  2
#>  [3,]  3  3
#>  [4,]  4  4
#>  [5,]  5  5
#>  [6,]  6  6
#>  [7,]  7  7
#>  [8,]  8  8
#>  [9,]  9  9
#> [10,] 10 10
#> 
#> , , 2
#> 
#>        1  2
#>  [1,]  1  1
#>  [2,]  2  2
#>  [3,]  3  3
#>  [4,]  4  4
#>  [5,]  5  5
#>  [6,]  6  6
#>  [7,]  7  7
#>  [8,]  8  8
#>  [9,]  9  9
#> [10,] 10 10
#>