Split list, apply function, and return results in a list.

Source: R/llply.r

For each element of a list, apply function, keeping results as a list.

llply(
  .data,
  .fun = NULL,
  ...,
  .progress = "none",
  .inform = FALSE,
  .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

.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

llply is equivalent to lapply except that it will preserve labels and can display a progress bar.

Input

This function splits lists by elements.

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

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

Examples

llply(llply(mtcars, round), table)
#> $mpg
#> 
#> 10 13 14 15 16 17 18 19 20 21 22 23 24 26 27 30 32 34 
#>  2  1  1  4  3  1  2  3  1  4  1  2  1  1  1  2  1  1 
#> 
#> $cyl
#> 
#>  4  6  8 
#> 11  7 14 
#> 
#> $disp
#> 
#>  71  76  79  95 108 120 121 141 145 147 160 168 225 258 276 301 304 318 350 351 
#>   1   1   2   1   1   2   1   1   1   1   2   2   1   1   3   1   1   1   1   1 
#> 360 400 440 460 472 
#>   2   1   1   1   1 
#> 
#> $hp
#> 
#>  52  62  65  66  91  93  95  97 105 109 110 113 123 150 175 180 205 215 230 245 
#>   1   1   1   2   1   1   1   1   1   1   3   1   2   2   3   3   1   1   1   2 
#> 264 335 
#>   1   1 
#> 
#> $drat
#> 
#>  3  4  5 
#> 13 18  1 
#> 
#> $wt
#> 
#>  2  3  4  5 
#>  8 13  8  3 
#> 
#> $qsec
#> 
#> 14 15 16 17 18 19 20 23 
#>  1  2  3  9  5  7  4  1 
#> 
#> $vs
#> 
#>  0  1 
#> 18 14 
#> 
#> $am
#> 
#>  0  1 
#> 19 13 
#> 
#> $gear
#> 
#>  3  4  5 
#> 15 12  5 
#> 
#> $carb
#> 
#>  1  2  3  4  6  8 
#>  7 10  3 10  1  1 
#> 
llply(baseball, summary)
#> $id
#>    Length     Class      Mode 
#>     21699 character character 
#> 
#> $year
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    1871    1937    1970    1961    1988    2007 
#> 
#> $stint
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   1.000   1.000   1.000   1.093   1.000   4.000 
#> 
#> $team
#>    Length     Class      Mode 
#>     21699 character character 
#> 
#> $lg
#>    Length     Class      Mode 
#>     21699 character character 
#> 
#> $g
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00   29.00   59.00   72.82  125.00  165.00 
#> 
#> $ab
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>     0.0    25.0   131.0   225.4   435.0   705.0 
#> 
#> $r
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00    2.00   15.00   31.78   58.00  177.00 
#> 
#> $h
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00    4.00   32.00   61.76  119.00  257.00 
#> 
#> $X2b
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00    0.00    5.00   10.45   19.00   64.00 
#> 
#> $X3b
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   0.000   0.000   1.000   2.194   3.000  28.000 
#> 
#> $hr
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   0.000   0.000   1.000   5.234   7.000  73.000 
#> 
#> $rbi
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>    0.00    1.00   14.00   29.59   51.00  184.00      12 
#> 
#> $sb
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   0.000   0.000   1.000   5.168   5.000 130.000     250 
#> 
#> $cs
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>     0.0     0.0     0.0     2.1     3.0    42.0    4525 
#> 
#> $bb
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00    1.00   11.00   22.49   38.00  232.00 
#> 
#> $so
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>    0.00    4.00   19.00   29.26   45.00  189.00    1305 
#> 
#> $ibb
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   0.000   0.000   0.000   2.292   3.000 120.000    7528 
#> 
#> $hbp
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   0.000   0.000   0.000   1.543   2.000  51.000     377 
#> 
#> $sh
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   0.000   0.000   1.000   3.388   5.000  52.000     960 
#> 
#> $sf
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   0.000   0.000   1.000   1.843   3.000  19.000    7390 
#> 
#> $gidp
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   0.000   0.000   2.000   4.774   8.000  36.000    5272 
#> 
# Examples from ?lapply
x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE))

llply(x, mean)
#> $a
#> [1] 5.5
#> 
#> $beta
#> [1] 4.535125
#> 
#> $logic
#> [1] 0.5
#> 
llply(x, quantile, probs = 1:3/4)
#> $a
#>  25%  50%  75% 
#> 3.25 5.50 7.75 
#> 
#> $beta
#>       25%       50%       75% 
#> 0.2516074 1.0000000 5.0536690 
#> 
#> $logic
#> 25% 50% 75% 
#> 0.0 0.5 1.0 
#>