matrixStats.benchmarks

colAnys() and rowAnys() benchmarks

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

Alternative methods

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 5192340 277.4    7916910 422.9  7916910 422.9
Vcells 9548887  72.9   33191153 253.3 53339345 407.0
> 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 5178649 276.6    7916910 422.9  7916910 422.9
Vcells 9503582  72.6   33191153 253.3 53339345 407.0
> 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 0.003015 0.0033380 0.0043223 0.0042475 0.0045720 0.019112
3 colSums > 0 0.005908 0.0063745 0.0080616 0.0071145 0.0079250 0.054657
2 apply+any 0.033156 0.0347580 0.0377213 0.0355990 0.0368595 0.084983
  expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 1.959536 1.909676 1.865101 1.674985 1.733377 2.859826
2 apply+any 10.997015 10.412822 8.727058 8.381165 8.062008 4.446578

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 0.003149 0.0035075 0.0044978 0.0044310 0.0047810 0.019036
3 rowSums > 0 0.006862 0.0072455 0.0086486 0.0078765 0.0084835 0.050237
2 apply+any 0.034071 0.0349290 0.0370099 0.0355310 0.0361085 0.083324
  expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums > 0 2.179105 2.065716 1.922851 1.777590 1.774420 2.639052
2 apply+any 10.819625 9.958375 8.228470 8.018732 7.552499 4.377180

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
1 colAnys 3.015 3.3380 4.32234 4.2475 4.572 19.112
2 rowAnys 3.149 3.5075 4.49779 4.4310 4.781 19.036
  expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowAnys 1.044444 1.050779 1.040591 1.043202 1.045713 0.9960234

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 5177209 276.5    7916910 422.9  7916910 422.9
Vcells 9309779  71.1   33191153 253.3 53339345 407.0
> 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 5177203 276.5    7916910 422.9  7916910 422.9
Vcells 9314822  71.1   33191153 253.3 53339345 407.0
> 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 0.003116 0.0039975 0.0053324 0.0048930 0.0055855 0.023137
3 colSums > 0 0.015894 0.0174145 0.0200416 0.0187445 0.0212160 0.054532
2 apply+any 0.193797 0.2074970 0.2319392 0.2248080 0.2493780 0.420242
  expr min lq mean median uq max
1 colAnys 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 5.10077 4.356348 3.758443 3.830881 3.798407 2.356918
2 apply+any 62.19416 51.906692 43.496134 45.944819 44.647391 18.163202

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 0.009437 0.0112045 0.0127247 0.012203 0.0134225 0.035027
3 rowSums > 0 0.046317 0.0480945 0.0542597 0.053143 0.0585380 0.124492
2 apply+any 0.193635 0.2000855 0.2295396 0.218877 0.2494360 0.410195
  expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums > 0 4.908022 4.292427 4.264129 4.354913 4.361185 3.554172
2 apply+any 20.518703 17.857602 18.038933 17.936327 18.583423 11.710823

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 3.116 3.9975 5.33241 4.893 5.5855 23.137
2 rowAnys 9.437 11.2045 12.72468 12.203 13.4225 35.027
  expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnys 3.028562 2.802877 2.386291 2.493971 2.403097 1.513896

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 5177959 276.6    7916910 422.9  7916910 422.9
Vcells 9313300  71.1   33191153 253.3 53339345 407.0
> 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 5177953 276.6    7916910 422.9  7916910 422.9
Vcells 9318343  71.1   33191153 253.3 53339345 407.0
> 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 0.003017 0.003642 0.0047623 0.0043505 0.0049420 0.022187
3 colSums > 0 0.017179 0.019061 0.0204056 0.0196370 0.0209315 0.037863
2 apply+any 0.111874 0.117432 0.1270182 0.1254865 0.1305995 0.207890
  expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 5.694067 5.233663 4.284792 4.513734 4.235431 1.706540
2 apply+any 37.081207 32.243822 26.671505 28.844156 26.426447 9.369901

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 0.008582 0.0100280 0.0113144 0.0109640 0.0119975 0.027385
2 apply+any 0.096734 0.1010950 0.1140153 0.1101305 0.1276375 0.198634
3 rowSums > 0 0.167370 0.1747045 0.1910449 0.1875660 0.2097510 0.229876
  expr min lq mean median uq max
1 rowAnys 1.00000 1.00000 1.00000 1.00000 1.00000 1.000000
2 apply+any 11.27173 10.08127 10.07703 10.04474 10.63867 7.253387
3 rowSums > 0 19.50245 17.42167 16.88514 17.10744 17.48289 8.394230

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 3.017 3.642 4.76232 4.3505 4.9420 22.187
2 rowAnys 8.582 10.028 11.31438 10.9640 11.9975 27.385
  expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000
2 rowAnys 2.844548 2.753432 2.375813 2.52017 2.427661 1.234281

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 5178173 276.6    7916910 422.9  7916910 422.9
Vcells 9314049  71.1   33191153 253.3 53339345 407.0
> 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 5178167 276.6    7916910 422.9  7916910 422.9
Vcells 9319092  71.1   33191153 253.3 53339345 407.0
> 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 0.007453 0.0092850 0.0118857 0.0106215 0.0129310 0.034985
3 colSums > 0 0.013042 0.0147795 0.0171880 0.0161970 0.0188685 0.039224
2 apply+any 0.851302 0.8912870 1.0151367 0.9475960 1.1103635 1.456249
  expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 1.749899 1.591761 1.446111 1.524926 1.459168 1.121166
2 apply+any 114.222729 95.992138 85.408528 89.214894 85.868340 41.624954

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 0.019350 0.0236435 0.0290122 0.0263760 0.0318340 0.068443
3 rowSums > 0 0.028014 0.0312395 0.0377033 0.0352885 0.0407545 0.124822
2 apply+any 0.847844 0.9052355 1.0829320 1.0152130 1.1993540 1.783223
  expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums > 0 1.447752 1.321272 1.299569 1.337902 1.280219 1.823737
2 apply+any 43.816227 38.286865 37.326820 38.490029 37.675253 26.054133

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 7.453 9.2850 11.88566 10.6215 12.931 34.985
2 rowAnys 19.350 23.6435 29.01217 26.3760 31.834 68.443
  expr min lq mean median uq max
1 colAnys 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnys 2.59627 2.546419 2.440939 2.483265 2.461836 1.956353

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 5178381 276.6    7916910 422.9  7916910 422.9
Vcells 9314568  71.1   33191153 253.3 53339345 407.0
> 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 5178369 276.6    7916910 422.9  7916910 422.9
Vcells 9364601  71.5   33191153 253.3 53339345 407.0
> 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 0.008363 0.0114285 0.0166425 0.0138285 0.0176545 0.054207
3 colSums > 0 0.080974 0.0836290 0.0967549 0.0930810 0.1028945 0.148354
2 apply+any 1.348433 1.3932855 1.5654713 1.4423025 1.6878120 2.607238
  expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums > 0 9.682411 7.317583 5.813722 6.731099 5.828231 2.736805
2 apply+any 161.237953 121.913243 94.064670 104.299273 95.602368 48.097810

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 0.054470 0.056363 0.0662752 0.059969 0.0705985 0.131714
3 rowSums > 0 0.224264 0.226941 0.2577230 0.235087 0.2797685 0.415196
2 apply+any 1.354361 1.384301 1.5227031 1.418225 1.5867925 2.406700
  expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
3 rowSums > 0 4.117202 4.026418 3.88868 3.920142 3.962811 3.152254
2 apply+any 24.864347 24.560456 22.97546 23.649310 22.476292 18.272165

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 8.363 11.4285 16.6425 13.8285 17.6545 54.207
2 rowAnys 54.470 56.3630 66.2752 59.9690 70.5985 131.714
  expr min lq mean median uq max
1 colAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnys 6.513213 4.931793 3.982286 4.336624 3.998896 2.429834

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 5178587 276.6    7916910 422.9  7916910 422.9
Vcells 9315221  71.1   33191153 253.3 53339345 407.0
> 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 5178575 276.6    7916910 422.9  7916910 422.9
Vcells 9365254  71.5   33191153 253.3 53339345 407.0
> 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 0.002769 0.0039285 0.0056187 0.0050880 0.0066740 0.026367
3 colSums > 0 0.082616 0.0849765 0.1018653 0.0940155 0.1148010 0.167160
2 apply+any 0.624251 0.6449835 0.7633539 0.7123450 0.8540075 1.186777
  expr min lq mean median uq max
1 colAnys 1.00000 1.00000 1.00000 1.00000 1.00000 1.000000
3 colSums > 0 29.83604 21.63078 18.12979 18.47789 17.20123 6.339743
2 apply+any 225.44276 164.18060 135.86026 140.00491 127.96037 45.009937

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 0.052694 0.0560710 0.0656955 0.0606060 0.0714685 0.114980
3 rowSums > 0 0.324020 0.3257205 0.3804144 0.3626460 0.4216195 0.541018
2 apply+any 0.630176 0.6376395 0.7300979 0.6797115 0.7868810 1.176739
  expr min lq mean median uq max
1 rowAnys 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums > 0 6.149087 5.809072 5.790572 5.983665 5.899375 4.705323
2 apply+any 11.959160 11.372002 11.113366 11.215251 11.010179 10.234293

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 2.769 3.9285 5.61867 5.088 6.6740 26.367
2 rowAnys 52.694 56.0710 65.69548 60.606 71.4685 114.980
  expr min lq mean median uq max
1 colAnys 1.00000 1.00000 1.00000 1.00000 1.0000 1.000000
2 rowAnys 19.02997 14.27288 11.69235 11.91156 10.7085 4.360754

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 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-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_1.4-7   matrixStats_0.60.0     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

loaded via a namespace (and not attached):
 [1] Biobase_2.52.0          httr_1.4.2              splines_4.1.1          
 [4] bit64_4.0.5             network_1.17.1          assertthat_0.2.1       
 [7] highr_0.9               stats4_4.1.1            blob_1.2.2             
[10] GenomeInfoDbData_1.2.6  robustbase_0.93-8       pillar_1.6.2           
[13] RSQLite_2.2.8           lattice_0.20-44         glue_1.4.2             
[16] digest_0.6.27           XVector_0.32.0          colorspace_2.0-2       
[19] Matrix_1.3-4            XML_3.99-0.7            pkgconfig_2.0.3        
[22] zlibbioc_1.38.0         genefilter_1.74.0       purrr_0.3.4            
[25] ergm_4.1.2              xtable_1.8-4            scales_1.1.1           
[28] tibble_3.1.4            annotate_1.70.0         KEGGREST_1.32.0        
[31] farver_2.1.0            generics_0.1.0          IRanges_2.26.0         
[34] ellipsis_0.3.2          cachem_1.0.6            withr_2.4.2            
[37] BiocGenerics_0.38.0     mime_0.11               survival_3.2-13        
[40] magrittr_2.0.1          crayon_1.4.1            statnet.common_4.5.0   
[43] memoise_2.0.0           laeken_0.5.1            fansi_0.5.0            
[46] R.cache_0.15.0          MASS_7.3-54             R.rsp_0.44.0           
[49] progressr_0.8.0         tools_4.1.1             lifecycle_1.0.0        
[52] S4Vectors_0.30.0        trust_0.1-8             munsell_0.5.0          
[55] tabby_0.0.1-9001        AnnotationDbi_1.54.1    Biostrings_2.60.2      
[58] compiler_4.1.1          GenomeInfoDb_1.28.1     rlang_0.4.11           
[61] grid_4.1.1              RCurl_1.98-1.4          cwhmisc_6.6            
[64] rstudioapi_0.13         rappdirs_0.3.3          startup_0.15.0         
[67] labeling_0.4.2          bitops_1.0-7            base64enc_0.1-3        
[70] boot_1.3-28             gtable_0.3.0            DBI_1.1.1              
[73] markdown_1.1            R6_2.5.1                lpSolveAPI_5.5.2.0-17.7
[76] rle_0.9.2               dplyr_1.0.7             fastmap_1.1.0          
[79] bit_4.0.4               utf8_1.2.2              parallel_4.1.1         
[82] Rcpp_1.0.7              vctrs_0.3.8             png_0.1-7              
[85] DEoptimR_1.0-9          tidyselect_1.1.1        xfun_0.25              
[88] coda_0.19-4

Total processing time was 12.11 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:11:25 (+0200 UTC). Powered by RSP.