Evaluate expression n times then combine results into a data frame
Arguments
- .n
number of times to evaluate the expression
- .expr
expression to evaluate
- .progress
name of the progress bar to use, see
create_progress_bar- .id
name of the index column. Pass
NULLto avoid creation of the index column. For compatibility, omit this argument or passNAto use".n"as column name.
Details
This function runs an expression multiple times, and combines the result into
a data frame. If there are no results, then this function returns a data
frame with zero rows and columns (data.frame()). This function is
equivalent to replicate, but will always return results as a
data frame.
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/.
Examples
rdply(20, mean(runif(100)))
#> .n V1
#> 1 1 0.4795744
#> 2 2 0.4619230
#> 3 3 0.4937163
#> 4 4 0.5056349
#> 5 5 0.4755529
#> 6 6 0.4724123
#> 7 7 0.5372040
#> 8 8 0.5301065
#> 9 9 0.5131290
#> 10 10 0.5444707
#> 11 11 0.5657078
#> 12 12 0.4684942
#> 13 13 0.5239426
#> 14 14 0.5541316
#> 15 15 0.5258364
#> 16 16 0.5119269
#> 17 17 0.4832072
#> 18 18 0.5097692
#> 19 19 0.4745947
#> 20 20 0.5303042
rdply(20, each(mean, var)(runif(100)))
#> .n mean var
#> 1 1 0.4873160 0.08598864
#> 2 2 0.5221509 0.09519466
#> 3 3 0.4947220 0.08025103
#> 4 4 0.5368994 0.08335525
#> 5 5 0.4739039 0.08891457
#> 6 6 0.5091936 0.07533008
#> 7 7 0.4926832 0.08071563
#> 8 8 0.4969253 0.08208359
#> 9 9 0.5180824 0.09649493
#> 10 10 0.5293319 0.09211816
#> 11 11 0.5258403 0.09443793
#> 12 12 0.4434285 0.07515195
#> 13 13 0.5472506 0.09163580
#> 14 14 0.4875347 0.07910611
#> 15 15 0.4781630 0.08854618
#> 16 16 0.4916634 0.08125938
#> 17 17 0.5343236 0.07783850
#> 18 18 0.5042167 0.08035243
#> 19 19 0.5101281 0.07934224
#> 20 20 0.5199815 0.07614262
rdply(20, data.frame(x = runif(2)))
#> .n x
#> 1 1 0.140353269
#> 2 1 0.545322403
#> 3 2 0.904697092
#> 4 2 0.253702644
#> 5 3 0.602473737
#> 6 3 0.441347882
#> 7 4 0.647197510
#> 8 4 0.394503333
#> 9 5 0.957982466
#> 10 5 0.203466160
#> 11 6 0.292937938
#> 12 6 0.936537911
#> 13 7 0.982041245
#> 14 7 0.479236994
#> 15 8 0.330646341
#> 16 8 0.924491715
#> 17 9 0.715084739
#> 18 9 0.149491671
#> 19 10 0.297409046
#> 20 10 0.389014139
#> 21 11 0.181730398
#> 22 11 0.503558082
#> 23 12 0.972787563
#> 24 12 0.775012026
#> 25 13 0.350684260
#> 26 13 0.474066010
#> 27 14 0.029976892
#> 28 14 0.668548816
#> 29 15 0.818863351
#> 30 15 0.521741578
#> 31 16 0.338766052
#> 32 16 0.625411691
#> 33 17 0.320346392
#> 34 17 0.701813391
#> 35 18 0.004481501
#> 36 18 0.110020100
#> 37 19 0.339631328
#> 38 19 0.177673390
#> 39 20 0.470992930
#> 40 20 0.784546703