Replicate expression and return results in a array.

Source: R/raply.r

Evalulate expression n times then combine results into an array

raply(.n, .expr, .progress = "none", .drop = TRUE)

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
.drop

should extra dimensions of length 1 be dropped, simplifying the output. Defaults to TRUE

Value

if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)

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 vector of length 0 (vector(0)). This function is equivalent to replicate, but will always return results as a vector, matrix or array.

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

raply(100, mean(runif(100)))
#>   [1] 0.5584955 0.4697370 0.4822532 0.5305808 0.5377929 0.4881434 0.5381100
#>   [8] 0.4703026 0.5472282 0.4935907 0.4952039 0.4839420 0.5138925 0.4327108
#>  [15] 0.4688585 0.5252160 0.5253965 0.4718530 0.4846889 0.4451547 0.5268910
#>  [22] 0.5137286 0.4906905 0.5082787 0.4739030 0.5015941 0.5305796 0.4748503
#>  [29] 0.5220966 0.5346173 0.4899333 0.4792032 0.4698322 0.4609836 0.4723003
#>  [36] 0.5433753 0.4762456 0.4902146 0.4785824 0.4964821 0.4854702 0.4597122
#>  [43] 0.4461904 0.5131031 0.5121412 0.4940802 0.4946290 0.4743156 0.5248818
#>  [50] 0.5638900 0.4820735 0.5043396 0.5463671 0.5407433 0.4644628 0.5068428
#>  [57] 0.4820315 0.5153432 0.5178457 0.5197146 0.4941293 0.4959801 0.5069841
#>  [64] 0.4783945 0.4552871 0.4552612 0.5027126 0.4955604 0.5412673 0.5189458
#>  [71] 0.5015168 0.4429296 0.5418324 0.4546798 0.4767133 0.5676088 0.4873053
#>  [78] 0.5176180 0.4695523 0.4387623 0.5689078 0.5131628 0.4832881 0.4821332
#>  [85] 0.5258128 0.5188157 0.5388142 0.5231157 0.5218042 0.5554773 0.4771688
#>  [92] 0.5270716 0.5156222 0.4588663 0.5291597 0.5041095 0.4705824 0.4556904
#>  [99] 0.5876905 0.5139431
raply(100, each(mean, var)(runif(100)))
#>             mean        var
#>   [1,] 0.5064787 0.09021675
#>   [2,] 0.5222287 0.07982721
#>   [3,] 0.5002795 0.07679700
#>   [4,] 0.4403795 0.08527059
#>   [5,] 0.5768071 0.08834411
#>   [6,] 0.4904158 0.07663819
#>   [7,] 0.5582532 0.10447992
#>   [8,] 0.5062552 0.07693396
#>   [9,] 0.5400883 0.07151249
#>  [10,] 0.5155815 0.08577656
#>  [11,] 0.4426911 0.07847692
#>  [12,] 0.5148081 0.08126050
#>  [13,] 0.5296468 0.08506841
#>  [14,] 0.4931644 0.07433117
#>  [15,] 0.5433389 0.08772457
#>  [16,] 0.4776649 0.08191829
#>  [17,] 0.4769341 0.09080778
#>  [18,] 0.5146941 0.08305615
#>  [19,] 0.5110821 0.07343913
#>  [20,] 0.5075705 0.07275980
#>  [21,] 0.4900907 0.09737132
#>  [22,] 0.4612607 0.08595145
#>  [23,] 0.4491314 0.07709163
#>  [24,] 0.5587151 0.06810705
#>  [25,] 0.4817064 0.09346954
#>  [26,] 0.4774691 0.07466746
#>  [27,] 0.4682821 0.08319741
#>  [28,] 0.4843510 0.09205859
#>  [29,] 0.5105021 0.08332798
#>  [30,] 0.5364541 0.07954959
#>  [31,] 0.5125006 0.09704554
#>  [32,] 0.4674663 0.08826359
#>  [33,] 0.5013050 0.09193559
#>  [34,] 0.5072940 0.07669015
#>  [35,] 0.4873498 0.07698108
#>  [36,] 0.5123859 0.07942558
#>  [37,] 0.5349697 0.10539271
#>  [38,] 0.4798644 0.08764293
#>  [39,] 0.4830029 0.08383848
#>  [40,] 0.4878720 0.08896036
#>  [41,] 0.4600546 0.08585740
#>  [42,] 0.4895523 0.06848075
#>  [43,] 0.4841702 0.08800731
#>  [44,] 0.5326503 0.09800090
#>  [45,] 0.5179845 0.07593739
#>  [46,] 0.4829349 0.07971595
#>  [47,] 0.5006757 0.07488661
#>  [48,] 0.4918405 0.08106829
#>  [49,] 0.4964587 0.08937489
#>  [50,] 0.4464064 0.07566823
#>  [51,] 0.4923931 0.07025832
#>  [52,] 0.4726734 0.08606211
#>  [53,] 0.4851818 0.08386350
#>  [54,] 0.4978580 0.08815816
#>  [55,] 0.4668338 0.07695100
#>  [56,] 0.4399210 0.09181129
#>  [57,] 0.5112859 0.07759263
#>  [58,] 0.4882721 0.08871280
#>  [59,] 0.4919637 0.08360487
#>  [60,] 0.4641138 0.08458284
#>  [61,] 0.5419229 0.08094339
#>  [62,] 0.4978082 0.09316553
#>  [63,] 0.5503442 0.07506029
#>  [64,] 0.4920230 0.08141070
#>  [65,] 0.4977952 0.08393787
#>  [66,] 0.4920332 0.08967688
#>  [67,] 0.5046889 0.07816585
#>  [68,] 0.5100983 0.09149569
#>  [69,] 0.5486922 0.08244488
#>  [70,] 0.5133056 0.07832304
#>  [71,] 0.5794162 0.07911560
#>  [72,] 0.5015504 0.09281853
#>  [73,] 0.4949633 0.08409770
#>  [74,] 0.5643648 0.07531413
#>  [75,] 0.5024826 0.08841003
#>  [76,] 0.5081734 0.07739172
#>  [77,] 0.5370159 0.08291375
#>  [78,] 0.4890444 0.07785362
#>  [79,] 0.5126101 0.08873069
#>  [80,] 0.5126939 0.07547086
#>  [81,] 0.5378120 0.07980691
#>  [82,] 0.5138487 0.07923766
#>  [83,] 0.5132048 0.08222338
#>  [84,] 0.4601113 0.07988469
#>  [85,] 0.5339971 0.08385979
#>  [86,] 0.4573957 0.08061159
#>  [87,] 0.4596440 0.08407839
#>  [88,] 0.4950845 0.08552175
#>  [89,] 0.4911690 0.07275004
#>  [90,] 0.5307175 0.08123803
#>  [91,] 0.4885752 0.09477539
#>  [92,] 0.4841624 0.08715988
#>  [93,] 0.4489604 0.09313719
#>  [94,] 0.4919678 0.07736353
#>  [95,] 0.5207776 0.08348864
#>  [96,] 0.5086462 0.07340560
#>  [97,] 0.5231152 0.07866158
#>  [98,] 0.4887706 0.07695537
#>  [99,] 0.4379047 0.08020732
#> [100,] 0.4811070 0.08384652

raply(10, runif(4))
#>                1         2          3         4
#>  [1,] 0.54145249 0.8907091 0.32151545 0.1250310
#>  [2,] 0.66251793 0.9076187 0.06790063 0.3817729
#>  [3,] 0.09742514 0.2887907 0.42288886 0.4029146
#>  [4,] 0.26808516 0.1966407 0.40262705 0.7738335
#>  [5,] 0.51317455 0.2063446 0.34925567 0.9825930
#>  [6,] 0.41201474 0.9733992 0.20484583 0.5971397
#>  [7,] 0.84129918 0.2892940 0.62101429 0.7080306
#>  [8,] 0.55622021 0.9469919 0.31842539 0.5562842
#>  [9,] 0.40948053 0.5751130 0.51273323 0.8527832
#> [10,] 0.90699040 0.4192694 0.82711846 0.9345553
raply(10, matrix(runif(4), nrow=2))
#> , , 1
#> 
#>                1          2
#>  [1,] 0.30128874 0.44078373
#>  [2,] 0.45309542 0.13070042
#>  [3,] 0.97522098 0.85046352
#>  [4,] 0.74889236 0.69268308
#>  [5,] 0.93545440 0.94320610
#>  [6,] 0.79375497 0.60836588
#>  [7,] 0.07492938 0.01811218
#>  [8,] 0.92322266 0.75240169
#>  [9,] 0.72741335 0.72724533
#> [10,] 0.38768796 0.77172367
#> 
#> , , 2
#> 
#>                1          2
#>  [1,] 0.06584878 0.15140966
#>  [2,] 0.91119418 0.95694045
#>  [3,] 0.43453848 0.98714552
#>  [4,] 0.69394622 0.99208567
#>  [5,] 0.62401968 0.46227475
#>  [6,] 0.45477137 0.60268634
#>  [7,] 0.09800884 0.94325154
#>  [8,] 0.22652712 0.39899366
#>  [9,] 0.94387626 0.09961407
#> [10,] 0.46811674 0.69185158
#> 

# See the central limit theorem in action
hist(raply(1000, mean(rexp(10))))
hist(raply(1000, mean(rexp(100))))
hist(raply(1000, mean(rexp(1000))))