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

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

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

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
#>