Replicate expression and return results in a data frame. — rdply • plyr

Evaluate expression n times then combine results into a data frame

rdply(.n, .expr, .progress = "none", .id = NA)

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 `NULL` to avoid creation of the index column. For compatibility, omit this argument or pass `NA` to use `".n"` as column name.

Value

a data frame

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

Examples

rdply(20, mean(runif(100)))
#>    .n        V1
#> 1   1 0.4836436
#> 2   2 0.4799288
#> 3   3 0.4783763
#> 4   4 0.4661561
#> 5   5 0.5193975
#> 6   6 0.4826382
#> 7   7 0.4853779
#> 8   8 0.5033093
#> 9   9 0.5436864
#> 10 10 0.5370971
#> 11 11 0.5527163
#> 12 12 0.5311481
#> 13 13 0.4714964
#> 14 14 0.5445611
#> 15 15 0.5348261
#> 16 16 0.5191539
#> 17 17 0.5051741
#> 18 18 0.5046961
#> 19 19 0.4912406
#> 20 20 0.4951647
rdply(20, each(mean, var)(runif(100)))
#>    .n      mean        var
#> 1   1 0.4959134 0.09544314
#> 2   2 0.5327163 0.08448444
#> 3   3 0.5004168 0.09229451
#> 4   4 0.5050083 0.08060376
#> 5   5 0.4887997 0.08813860
#> 6   6 0.5287587 0.08785057
#> 7   7 0.4687391 0.06609100
#> 8   8 0.5024661 0.09130735
#> 9   9 0.5375696 0.07506986
#> 10 10 0.4911553 0.09991435
#> 11 11 0.5305912 0.09428006
#> 12 12 0.5042991 0.09284471
#> 13 13 0.4733392 0.07500645
#> 14 14 0.5335691 0.09423053
#> 15 15 0.4964951 0.07716722
#> 16 16 0.4655572 0.09399317
#> 17 17 0.5125365 0.07466841
#> 18 18 0.5391303 0.08478118
#> 19 19 0.4733673 0.06991162
#> 20 20 0.5367983 0.07694283
rdply(20, data.frame(x = runif(2)))
#>    .n          x
#> 1   1 0.11080562
#> 2   1 0.45940211
#> 3   2 0.66353209
#> 4   2 0.57198812
#> 5   3 0.59525637
#> 6   3 0.20911521
#> 7   4 0.66956041
#> 8   4 0.49112037
#> 9   5 0.69588151
#> 10  5 0.64594813
#> 11  6 0.75810868
#> 12  6 0.43120516
#> 13  7 0.69651659
#> 14  7 0.21022653
#> 15  8 0.14072162
#> 16  8 0.15714079
#> 17  9 0.68212874
#> 18  9 0.48151583
#> 19 10 0.66666017
#> 20 10 0.87981722
#> 21 11 0.89144040
#> 22 11 0.25595099
#> 23 12 0.86164285
#> 24 12 0.27330167
#> 25 13 0.34253370
#> 26 13 0.10788219
#> 27 14 0.88664160
#> 28 14 0.74232748
#> 29 15 0.76179493
#> 30 15 0.07399478
#> 31 16 0.34894525
#> 32 16 0.16098949
#> 33 17 0.72017991
#> 34 17 0.88904095
#> 35 18 0.03013248
#> 36 18 0.45054664
#> 37 19 0.24646247
#> 38 19 0.72652932
#> 39 20 0.33401874
#> 40 20 0.42803925