colRowAnys - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

colAnys() and rowAnys() benchmarks

This report benchmark the performance of colAnys() and rowAnys() against alternative methods.

Alternative methods

  • apply() + any()
  • colSums() > 0 or rowSums() > 0

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
> data <- rmatrices(mode = "logical")

Results

10x10 matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3076924 164.4    5709258 305.0  5709258 305.0
Vcells 5446537  41.6   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075852 164.3    5709258 305.0  5709258 305.0
Vcells 5443727  41.6   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 0.001409 0.0016820 0.0021317 0.0020960 0.0022740 0.014822
3 colSums > 0 0.003203 0.0035375 0.0045709 0.0040560 0.0046105 0.050292
2 apply+any 0.018943 0.0198790 0.0211223 0.0202665 0.0206385 0.096390
expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 2.273243 2.103151 2.144281 1.935115 2.027485 3.393064
2 apply+any 13.444287 11.818668 9.908818 9.669132 9.075857 6.503171

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowAnys 0.001352 0.001613 0.0020306 0.0019925 0.0021370 0.014867
3 rowSums > 0 0.003700 0.004049 0.0048250 0.0044065 0.0047855 0.042995
2 apply+any 0.018271 0.019153 0.0201576 0.0196070 0.0200150 0.066538
expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums > 0 2.736686 2.510229 2.376183 2.211543 2.239354 2.891976
2 apply+any 13.514053 11.874148 9.927041 9.840402 9.365934 4.475550

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 10x10 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 rowAnys 1.352 1.613 2.03058 1.9925 2.137 14.867
1 colAnys 1.409 1.682 2.13167 2.0960 2.274 14.822
expr min lq mean median uq max
2 rowAnys 1.00000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 colAnys 1.04216 1.042777 1.049784 1.051945 1.064109 0.9969732

Figure: Benchmarking of colAnys() and rowAnys() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3074405 164.2    5709258 305.0  5709258 305.0
Vcells 5249923  40.1   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3074399 164.2    5709258 305.0  5709258 305.0
Vcells 5254966  40.1   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 0.001983 0.0026210 0.0032875 0.0030400 0.003373 0.017689
3 colSums > 0 0.012002 0.0128435 0.0136077 0.0135200 0.013936 0.027762
2 apply+any 0.170574 0.1759735 0.1829443 0.1804605 0.186364 0.307078
expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 colSums > 0 6.052446 4.900229 4.139171 4.447368 4.131634 1.56945
2 apply+any 86.018154 67.139832 55.647767 59.362007 55.251705 17.35983

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowAnys 0.007374 0.007807 0.0090621 0.0084775 0.0089945 0.031446
3 rowSums > 0 0.034992 0.035657 0.0379811 0.0365035 0.0373175 0.072237
2 apply+any 0.128059 0.130017 0.1448516 0.1337435 0.1387050 0.421439
expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums > 0 4.745321 4.567311 4.191221 4.305928 4.148924 2.297176
2 apply+any 17.366287 16.653900 15.984387 15.776290 15.421091 13.401991

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 100x100 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 1.983 2.621 3.28754 3.0400 3.3730 17.689
2 rowAnys 7.374 7.807 9.06207 8.4775 8.9945 31.446
expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
2 rowAnys 3.718608 2.978634 2.75649 2.788651 2.666617 1.777715

Figure: Benchmarking of colAnys() and rowAnys() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075160 164.3    5709258 305.0  5709258 305.0
Vcells 5253453  40.1   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075151 164.3    5709258 305.0  5709258 305.0
Vcells 5258491  40.2   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 0.001420 0.0018105 0.0022909 0.0022640 0.0025450 0.013141
3 colSums > 0 0.011362 0.0117590 0.0125820 0.0125435 0.0131395 0.025477
2 apply+any 0.106342 0.1074235 0.1102312 0.1085720 0.1107750 0.196663
expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 8.001409 6.494891 5.492084 5.540415 5.162868 1.938741
2 apply+any 74.888732 59.333610 48.116372 47.955830 43.526523 14.965604

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowAnys 0.007637 0.008313 0.0089298 0.008670 0.0090945 0.033725
2 apply+any 0.062229 0.064914 0.0665394 0.065440 0.0662155 0.121197
3 rowSums > 0 0.133206 0.133676 0.1345420 0.134236 0.1345585 0.157303
expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+any 8.148357 7.808733 7.451395 7.547866 7.280829 3.593684
3 rowSums > 0 17.442189 16.080356 15.066651 15.482814 14.795591 4.664285

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 1000x10 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 1.420 1.8105 2.29093 2.264 2.5450 13.141
2 rowAnys 7.637 8.3130 8.92979 8.670 9.0945 33.725
expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnys 5.378169 4.591549 3.897889 3.829505 3.573477 2.566395

Figure: Benchmarking of colAnys() and rowAnys() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075362 164.3    5709258 305.0  5709258 305.0
Vcells 5254179  40.1   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075356 164.3    5709258 305.0  5709258 305.0
Vcells 5259222  40.2   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 0.007631 0.0084180 0.0103535 0.0094965 0.0109950 0.024751
3 colSums > 0 0.013443 0.0160035 0.0178000 0.0169520 0.0179195 0.052343
2 apply+any 0.748316 0.8504210 0.8765576 0.8680990 0.9004565 1.336996
expr min lq mean median uq max
1 colAnys 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 1.76163 1.901105 1.719221 1.785079 1.629786 2.114783
2 apply+any 98.06264 101.024115 84.662926 91.412520 81.896908 54.017858

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowAnys 0.019140 0.0215825 0.0228858 0.0227170 0.0238075 0.035336
3 rowSums > 0 0.028248 0.0306170 0.0329904 0.0321730 0.0332045 0.076977
2 apply+any 0.749158 0.8338955 0.8688107 0.8689875 0.8818345 1.748149
expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 rowSums > 0 1.475862 1.418603 1.441523 1.416252 1.394708 2.17843
2 apply+any 39.140961 38.637577 37.962857 38.252740 37.040197 49.47218

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 10x1000 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 7.631 8.4180 10.35350 9.4965 10.9950 24.751
2 rowAnys 19.140 21.5825 22.88581 22.7170 23.8075 35.336
expr min lq mean median uq max
1 colAnys 1.00000 1.000000 1.000000 1.000000 1.000000 1.00000
2 rowAnys 2.50819 2.563851 2.210442 2.392145 2.165302 1.42766

Figure: Benchmarking of colAnys() and rowAnys() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075581 164.3    5709258 305.0  5709258 305.0
Vcells 5254705  40.1   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075569 164.3    5709258 305.0  5709258 305.0
Vcells 5304738  40.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 0.008099 0.0095220 0.0119732 0.0109315 0.012789 0.053653
3 colSums > 0 0.083496 0.0957385 0.0971840 0.0972690 0.099108 0.114800
2 apply+any 1.600610 1.7995685 1.8141498 1.8099180 1.835619 2.166671
expr min lq mean median uq max
1 colAnys 1.00000 1.00000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 10.30942 10.05445 8.116831 8.898047 7.749472 2.139675
2 apply+any 197.63057 188.99060 151.518174 165.569044 143.531081 40.383035

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowAnys 0.067779 0.0746215 0.076382 0.0762285 0.0778680 0.092209
3 rowSums > 0 0.223956 0.2489675 0.252571 0.2565880 0.2580455 0.288709
2 apply+any 1.191257 1.2977970 1.325457 1.3312255 1.3451045 1.534203
expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums > 0 3.304209 3.336404 3.306683 3.366038 3.313884 3.131028
2 apply+any 17.575606 17.391730 17.353005 17.463619 17.274163 16.638322

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 100x1000 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 8.099 9.5220 11.97315 10.9315 12.789 53.653
2 rowAnys 67.779 74.6215 76.38199 76.2285 77.868 92.209
expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.00000 1.000000 1.00000 1.000000
2 rowAnys 8.368811 7.836747 6.37944 6.973288 6.08867 1.718618

Figure: Benchmarking of colAnys() and rowAnys() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075793 164.3    5709258 305.0  5709258 305.0
Vcells 5255375  40.1   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3075781 164.3    5709258 305.0  5709258 305.0
Vcells 5305408  40.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 0.002520 0.0032355 0.0041383 0.003961 0.0044305 0.016411
3 colSums > 0 0.081511 0.0889975 0.0919926 0.091922 0.0958630 0.108950
2 apply+any 0.512329 0.5432960 0.5562859 0.555951 0.5703010 0.645138
expr min lq mean median uq max
1 colAnys 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
3 colSums > 0 32.34563 27.50657 22.22972 23.20677 21.63706 6.63884
2 apply+any 203.30516 167.91717 134.42474 140.35622 128.72159 39.31132

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowAnys 0.057554 0.0643550 0.0661932 0.0663185 0.0676865 0.114304
3 rowSums > 0 0.314876 0.3501235 0.3556642 0.3596680 0.3608840 0.409574
2 apply+any 0.546965 0.5674115 0.5772502 0.5720150 0.5803790 0.685904
expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums > 0 5.470966 5.440502 5.373123 5.423343 5.331698 3.583199
2 apply+any 9.503510 8.816899 8.720688 8.625270 8.574516 6.000700

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 1000x100 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAnys 2.520 3.2355 4.13827 3.9610 4.4305 16.411
2 rowAnys 57.554 64.3550 66.19320 66.3185 67.6865 114.304
expr min lq mean median uq max
1 colAnys 1.00000 1.00000 1.00000 1.00000 1.0000 1.000000
2 rowAnys 22.83889 19.89028 15.99538 16.74287 15.2774 6.965084

Figure: Benchmarking of colAnys() and rowAnys() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 3.6.1 Patched (2019-08-27 r77078)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-6    matrixStats_0.55.0-9000 ggplot2_3.2.1          
[4] knitr_1.24              R.devices_2.16.0        R.utils_2.9.0          
[7] R.oo_1.22.0             R.methodsS3_1.7.1       history_0.0.0-9002     

loaded via a namespace (and not attached):
 [1] Biobase_2.45.0       bit64_0.9-7          splines_3.6.1       
 [4] network_1.15         assertthat_0.2.1     highr_0.8           
 [7] stats4_3.6.1         blob_1.2.0           robustbase_0.93-5   
[10] pillar_1.4.2         RSQLite_2.1.2        backports_1.1.4     
[13] lattice_0.20-38      glue_1.3.1           digest_0.6.20       
[16] colorspace_1.4-1     sandwich_2.5-1       Matrix_1.2-17       
[19] XML_3.98-1.20        lpSolve_5.6.13.3     pkgconfig_2.0.2     
[22] genefilter_1.66.0    purrr_0.3.2          ergm_3.10.4         
[25] xtable_1.8-4         mvtnorm_1.0-11       scales_1.0.0        
[28] tibble_2.1.3         annotate_1.62.0      IRanges_2.18.2      
[31] TH.data_1.0-10       withr_2.1.2          BiocGenerics_0.30.0 
[34] lazyeval_0.2.2       mime_0.7             survival_2.44-1.1   
[37] magrittr_1.5         crayon_1.3.4         statnet.common_4.3.0
[40] memoise_1.1.0        laeken_0.5.0         R.cache_0.13.0      
[43] MASS_7.3-51.4        R.rsp_0.43.1         tools_3.6.1         
[46] multcomp_1.4-10      S4Vectors_0.22.1     trust_0.1-7         
[49] munsell_0.5.0        AnnotationDbi_1.46.1 compiler_3.6.1      
[52] rlang_0.4.0          grid_3.6.1           RCurl_1.95-4.12     
[55] cwhmisc_6.6          rappdirs_0.3.1       labeling_0.3        
[58] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         
[61] gtable_0.3.0         codetools_0.2-16     DBI_1.0.0           
[64] markdown_1.1         R6_2.4.0             zoo_1.8-6           
[67] dplyr_0.8.3          bit_1.1-14           zeallot_0.1.0       
[70] parallel_3.6.1       Rcpp_1.0.2           vctrs_0.2.0         
[73] DEoptimR_1.0-8       tidyselect_0.2.5     xfun_0.9            
[76] coda_0.19-3

Total processing time was 11.72 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('colAnys')

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:36:12 (-0700 UTC). Powered by RSP.

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