For each element of a list, apply function and discard results
l_ply(
.data,
.fun = NULL,
...,
.progress = "none",
.inform = FALSE,
.print = FALSE,
.parallel = FALSE,
.paropts = NULL
)list to be processed
function to apply to each piece
other arguments passed on to .fun
name of the progress bar to use, see
create_progress_bar
produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging
automatically print each result? (default: FALSE)
if TRUE, apply function in parallel, using parallel
backend provided by foreach
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.
Nothing
This function splits lists by elements.
All output is discarded. This is useful for functions that you are calling purely for their side effects like displaying plots or saving output.
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/.
l_ply(llply(mtcars, round), table, .print = TRUE)
#> piece
#> 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
#> piece
#> 4 6 8
#> 11 7 14
#> piece
#> 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
#> piece
#> 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
#> piece
#> 3 4 5
#> 13 18 1
#> piece
#> 2 3 4 5
#> 8 13 8 3
#> piece
#> 14 15 16 17 18 19 20 23
#> 1 2 3 9 5 7 4 1
#> piece
#> 0 1
#> 18 14
#> piece
#> 0 1
#> 19 13
#> piece
#> 3 4 5
#> 15 12 5
#> piece
#> 1 2 3 4 6 8
#> 7 10 3 10 1 1
l_ply(baseball, function(x) print(summary(x)))
#> Length Class Mode
#> 21699 character character
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1871 1937 1970 1961 1988 2007
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.000 1.000 1.000 1.093 1.000 4.000
#> Length Class Mode
#> 21699 character character
#> Length Class Mode
#> 21699 character character
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 29.00 59.00 72.82 125.00 165.00
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0 25.0 131.0 225.4 435.0 705.0
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 2.00 15.00 31.78 58.00 177.00
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 4.00 32.00 61.76 119.00 257.00
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 0.00 5.00 10.45 19.00 64.00
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.000 0.000 1.000 2.194 3.000 28.000
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.000 0.000 1.000 5.234 7.000 73.000
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.00 1.00 14.00 29.59 51.00 184.00 12
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.000 0.000 1.000 5.168 5.000 130.000 250
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.0 0.0 0.0 2.1 3.0 42.0 4525
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 1.00 11.00 22.49 38.00 232.00
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.00 4.00 19.00 29.26 45.00 189.00 1305
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.000 0.000 0.000 2.292 3.000 120.000 7528
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.000 0.000 0.000 1.543 2.000 51.000 377
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.000 0.000 1.000 3.388 5.000 52.000 960
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.000 0.000 1.000 1.843 3.000 19.000 7390
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.000 0.000 2.000 4.774 8.000 36.000 5272