colRowAlls - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

colAlls() and rowAlls() benchmarks

This report benchmark the performance of colAlls() and rowAlls() against alternative methods.

Alternative methods

  • apply() + all()
  • colSums() == n or rowSums() == n

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 3062839 163.6    5709258 305.0  5709258 305.0
Vcells 5337890  40.8   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3061972 163.6    5709258 305.0  5709258 305.0
Vcells 5335730  40.8   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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 colAlls 0.001456 0.0016925 0.0021430 0.0020870 0.0022825 0.014512
3 colSums==n 0.003712 0.0040890 0.0052483 0.0046245 0.0052255 0.058173
2 apply+all 0.019053 0.0197280 0.0207568 0.0200725 0.0204470 0.064866
expr min lq mean median uq max
1 colAlls 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums==n 2.54945 2.415953 2.449002 2.215860 2.289376 4.008614
2 apply+all 13.08585 11.656130 9.685704 9.617873 8.958160 4.469818

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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 rowAlls 0.001317 0.0015415 0.0020103 0.0019760 0.0021695 0.014576
3 rowSums==n 0.003993 0.0044455 0.0053485 0.0048405 0.0053980 0.047883
2 apply+all 0.018248 0.0190860 0.0201142 0.0194350 0.0198600 0.067070
expr min lq mean median uq max
1 rowAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums==n 3.031891 2.883879 2.660601 2.449646 2.488131 3.285058
2 apply+all 13.855733 12.381447 10.005750 9.835526 9.154183 4.601400

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x10 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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 rowAlls 1.317 1.5415 2.01026 1.976 2.1695 14.576
1 colAlls 1.456 1.6925 2.14304 2.087 2.2825 14.512
expr min lq mean median uq max
2 rowAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 colAlls 1.105543 1.097957 1.066051 1.056174 1.052086 0.9956092

Figure: Benchmarking of colAlls() and rowAlls() 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 3060526 163.5    5709258 305.0  5709258 305.0
Vcells 5141926  39.3   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3060520 163.5    5709258 305.0  5709258 305.0
Vcells 5146969  39.3   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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 colAlls 0.001991 0.0024595 0.0030387 0.0030415 0.0033560 0.013028
3 colSums==n 0.011997 0.0128015 0.0138879 0.0137880 0.0142750 0.028365
2 apply+all 0.170807 0.1723080 0.1772172 0.1734345 0.1762225 0.284037
expr min lq mean median uq max
1 colAlls 1.000000 1.00000 1.000000 1.00000 1.000000 1.000000
3 colSums==n 6.025615 5.20492 4.570314 4.53329 4.253576 2.177234
2 apply+all 85.789553 70.05814 58.319869 57.02269 52.509684 21.802042

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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 rowAlls 0.005430 0.005711 0.0063150 0.0061340 0.0064950 0.018429
3 rowSums==n 0.035304 0.035952 0.0366582 0.0365790 0.0370105 0.048019
2 apply+all 0.128607 0.130724 0.1357184 0.1313515 0.1341430 0.266220
expr min lq mean median uq max
1 rowAlls 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
3 rowSums==n 6.501658 6.29522 5.804955 5.963319 5.698306 2.605622
2 apply+all 23.684530 22.88986 21.491475 21.413678 20.653272 14.445711

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x100 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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 colAlls 1.991 2.4595 3.03871 3.0415 3.356 13.028
2 rowAlls 5.430 5.7110 6.31499 6.1340 6.495 18.429
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000
2 rowAlls 2.727273 2.322017 2.078181 2.016768 1.93534 1.414569

Figure: Benchmarking of colAlls() and rowAlls() 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 3061281 163.5    5709258 305.0  5709258 305.0
Vcells 5145460  39.3   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3061272 163.5    5709258 305.0  5709258 305.0
Vcells 5150498  39.3   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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 colAlls 0.001435 0.0017835 0.0022827 0.0022645 0.0025915 0.011481
3 colSums==n 0.011634 0.0121230 0.0130242 0.0128585 0.0135305 0.026541
2 apply+all 0.104368 0.1063765 0.1080376 0.1072015 0.1078570 0.163115
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums==n 8.107317 6.797309 5.705521 5.678295 5.221107 2.311732
2 apply+all 72.730314 59.644800 47.328023 47.340031 41.619525 14.207386

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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 rowAlls 0.005958 0.006369 0.0072919 0.006920 0.0076210 0.035577
2 apply+all 0.062092 0.064786 0.0664165 0.065730 0.0665915 0.119653
3 rowSums==n 0.133095 0.133928 0.1350352 0.134499 0.1349295 0.159741
expr min lq mean median uq max
1 rowAlls 1.00000 1.00000 1.000000 1.000000 1.000000 1.000000
2 apply+all 10.42162 10.17208 9.108271 9.498555 8.737895 3.363212
3 rowSums==n 22.33887 21.02810 18.518541 19.436272 17.704960 4.490008

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x10 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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 colAlls 1.435 1.7835 2.28274 2.2645 2.5915 11.481
2 rowAlls 5.958 6.3690 7.29189 6.9200 7.6210 35.577
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAlls 4.151916 3.571068 3.194358 3.055862 2.940768 3.098772

Figure: Benchmarking of colAlls() and rowAlls() 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 3061483 163.6    5709258 305.0  5709258 305.0
Vcells 5146180  39.3   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3061477 163.6    5709258 305.0  5709258 305.0
Vcells 5151223  39.4   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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 colAlls 0.008884 0.010476 0.0122303 0.0118190 0.013187 0.025558
3 colSums==n 0.013942 0.015299 0.0173466 0.0166725 0.018284 0.033008
2 apply+all 0.749683 0.789936 0.8352648 0.8179545 0.861084 1.281145
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums==n 1.569338 1.460386 1.418328 1.410652 1.386517 1.291494
2 apply+all 84.385750 75.404353 68.294713 69.206743 65.297945 50.126966

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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 rowAlls 0.013450 0.0158825 0.0182027 0.0171120 0.0191295 0.040438
3 rowSums==n 0.028157 0.0299995 0.0325026 0.0312035 0.0327855 0.081287
2 apply+all 0.750827 0.7848980 0.8408515 0.8123460 0.8575355 1.320873
expr min lq mean median uq max
1 rowAlls 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
3 rowSums==n 2.093457 1.88884 1.785592 1.823486 1.713871 2.010164
2 apply+all 55.823569 49.41905 46.193802 47.472300 44.827910 32.664153

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x1000 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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 colAlls 8.884 10.4760 12.23030 11.819 13.1870 25.558
2 rowAlls 13.450 15.8825 18.20269 17.112 19.1295 40.438
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAlls 1.513958 1.516084 1.488327 1.447838 1.450633 1.582205

Figure: Benchmarking of colAlls() and rowAlls() 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 3061703 163.6    5709258 305.0  5709258 305.0
Vcells 5146710  39.3   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3061691 163.6    5709258 305.0  5709258 305.0
Vcells 5196743  39.7   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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 colAlls 0.010682 0.0123640 0.0148551 0.0131580 0.0153845 0.064374
3 colSums==n 0.083959 0.0886235 0.0931459 0.0914115 0.0942260 0.149290
2 apply+all 1.574836 1.6613425 1.7092781 1.7042370 1.7260300 2.316910
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums==n 7.859858 7.167866 6.270311 6.947218 6.124736 2.319104
2 apply+all 147.428946 134.369338 115.063617 129.520976 112.192791 35.991394

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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 rowAlls 0.038797 0.0412470 0.0431752 0.042569 0.0446765 0.060725
3 rowSums==n 0.224029 0.2295035 0.2348777 0.236617 0.2387210 0.265381
2 apply+all 1.158340 1.2152695 1.2458592 1.234323 1.2673915 1.555215
expr min lq mean median uq max
1 rowAlls 1.00000 1.000000 1.000000 1.000000 1.000000 1.00000
3 rowSums==n 5.77439 5.564126 5.440107 5.558434 5.343324 4.37021
2 apply+all 29.85643 29.463222 28.855899 28.995819 28.368191 25.61079

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x1000 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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 colAlls 10.682 12.364 14.85507 13.158 15.3845 64.374
2 rowAlls 38.797 41.247 43.17520 42.569 44.6765 60.725
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowAlls 3.631998 3.336056 2.906429 3.235218 2.903994 0.9433156

Figure: Benchmarking of colAlls() and rowAlls() 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 3061915 163.6    5709258 305.0  5709258 305.0
Vcells 5147379  39.3   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3061903 163.6    5709258 305.0  5709258 305.0
Vcells 5197412  39.7   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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 colAlls 0.002035 0.0028485 0.003774 0.0032960 0.0041875 0.013992
3 colSums==n 0.082210 0.0830130 0.085607 0.0848105 0.0872915 0.101987
2 apply+all 0.486788 0.4983560 0.510808 0.5038850 0.5200695 0.642772
expr min lq mean median uq max
1 colAlls 1.00000 1.00000 1.0000 1.00000 1.00000 1.000000
3 colSums==n 40.39803 29.14271 22.6833 25.73134 20.84573 7.288951
2 apply+all 239.20786 174.95384 135.3489 152.87773 124.19570 45.938536

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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 rowAlls 0.040659 0.0415455 0.0441671 0.0432225 0.0449320 0.067793
3 rowSums==n 0.322712 0.3248410 0.3387711 0.3402920 0.3443395 0.410126
2 apply+all 0.516653 0.5375725 0.5625644 0.5458500 0.5586570 1.268665
expr min lq mean median uq max
1 rowAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums==n 7.937037 7.818921 7.670212 7.873029 7.663569 6.049681
2 apply+all 12.706978 12.939368 12.737180 12.628839 12.433388 18.713805

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x100 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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 colAlls 2.035 2.8485 3.77401 3.2960 4.1875 13.992
2 rowAlls 40.659 41.5455 44.16711 43.2225 44.9320 67.793
expr min lq mean median uq max
1 colAlls 1.00000 1.00000 1.00000 1.00000 1.00000 1.000000
2 rowAlls 19.97985 14.58504 11.70297 13.11362 10.73003 4.845126

Figure: Benchmarking of colAlls() and rowAlls() 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 11.85 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:34:58 (-0700 UTC). Powered by RSP.

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