colRowMeans2 - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

colMeans2() and rowMeans2() benchmarks

This report benchmark the performance of colMeans2() and rowMeans2() against alternative methods.

Alternative methods

  • apply() + mean()
  • .colMeans() and .rowMeans()
  • colMeans() and rowMeans()

Data type "integer"

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 = mode)

Results

10x10 integer matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139851 167.7    5709258 305.0  5709258 305.0
Vcells 6077827  46.4   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3138984 167.7    5709258 305.0  5709258 305.0
Vcells 6075817  46.4   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colMeans2 0.001118 0.0013945 0.0018182 0.0018415 0.0019805 0.011639
2 .colMeans 0.001602 0.0018650 0.0023100 0.0021000 0.0025185 0.014027
3 colMeans 0.003010 0.0032895 0.0040018 0.0039165 0.0043600 0.013600
4 apply+mean 0.044338 0.0458210 0.0477659 0.0463565 0.0470120 0.144426
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 .colMeans 1.432916 1.337397 1.270511 1.140375 1.271649 1.205172
3 colMeans 2.692308 2.358910 2.200975 2.126799 2.201464 1.168485
4 apply+mean 39.658318 32.858372 26.271116 25.173228 23.737440 12.408798

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on integer+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 rowMeans2 0.001100 0.0013460 0.0017564 0.0017395 0.0019180 0.010729
2 .rowMeans 0.002473 0.0026470 0.0029007 0.0028050 0.0029790 0.009608
3 rowMeans 0.003673 0.0039865 0.0046466 0.0045145 0.0047140 0.029878
4 apply+mean 0.044563 0.0455930 0.0469140 0.0459645 0.0464085 0.103257
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 .rowMeans 2.248182 1.966568 1.651481 1.612532 1.553180 0.8955168
3 rowMeans 3.339091 2.961739 2.645451 2.595286 2.457768 2.7847889
4 apply+mean 40.511818 33.872957 26.709855 26.423972 24.196298 9.6241029

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+10x10 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on integer+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 rowMeans2 1.100 1.3460 1.75643 1.7395 1.9180 10.729
1 colMeans2 1.118 1.3945 1.81819 1.8415 1.9805 11.639
expr min lq mean median uq max
2 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 colMeans2 1.016364 1.036033 1.035162 1.058637 1.032586 1.084817

Figure: Benchmarking of colMeans2() and rowMeans2() on integer+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 integer matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3137564 167.6    5709258 305.0  5709258 305.0
Vcells 5692629  43.5   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3137555 167.6    5709258 305.0  5709258 305.0
Vcells 5697667  43.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.009152 0.0095320 0.0100594 0.0099955 0.0103290 0.015906
3 colMeans 0.010689 0.0113320 0.0119437 0.0118010 0.0123520 0.018471
1 colMeans2 0.011058 0.0114345 0.0122556 0.0118535 0.0121290 0.044265
4 apply+mean 0.389271 0.3951415 0.4015145 0.3986820 0.4022945 0.509034
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colMeans 1.167941 1.188838 1.187323 1.180631 1.195856 1.161260
1 colMeans2 1.208260 1.199591 1.218332 1.185884 1.174267 2.782912
4 apply+mean 42.533982 41.454207 39.914514 39.886149 38.948059 32.002640

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on integer+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 rowMeans2 0.011204 0.0115515 0.0121952 0.0120125 0.0124430 0.021796
2 .rowMeans 0.038564 0.0389080 0.0393451 0.0391395 0.0394070 0.049944
3 rowMeans 0.040023 0.0404875 0.0411525 0.0411120 0.0414240 0.048529
4 apply+mean 0.387624 0.3917800 0.3984268 0.3944985 0.3990515 0.500580
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 .rowMeans 3.441985 3.368221 3.226285 3.258231 3.167002 2.29143
3 rowMeans 3.572206 3.504956 3.374493 3.422435 3.329101 2.22651
4 apply+mean 34.596930 33.915942 32.670873 32.840666 32.070361 22.96660

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+100x100 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on integer+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 colMeans2 11.058 11.4345 12.25564 11.8535 12.129 44.265
2 rowMeans2 11.204 11.5515 12.19517 12.0125 12.443 21.796
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000
2 rowMeans2 1.013203 1.010232 0.9950659 1.013414 1.025888 0.4923981

Figure: Benchmarking of colMeans2() and rowMeans2() on integer+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 integer matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3138330 167.7    5709258 305.0  5709258 305.0
Vcells 5696415  43.5   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3138321 167.7    5709258 305.0  5709258 305.0
Vcells 5701453  43.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.009389 0.0098325 0.0105085 0.0102025 0.0105670 0.037245
3 colMeans 0.010948 0.0113905 0.0123146 0.0120045 0.0125850 0.031726
1 colMeans2 0.012227 0.0128430 0.0133656 0.0132430 0.0134815 0.028296
4 apply+mean 0.100870 0.1024890 0.1056013 0.1033105 0.1048780 0.173595
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 colMeans 1.166045 1.158454 1.171861 1.176623 1.190972 0.8518190
1 colMeans2 1.302269 1.306178 1.271878 1.298015 1.275812 0.7597261
4 apply+mean 10.743423 10.423493 10.049095 10.125998 9.925050 4.6608941

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on integer+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 rowMeans2 0.012525 0.0130485 0.0135167 0.0134115 0.0136675 0.027150
4 apply+mean 0.100688 0.1023380 0.1049152 0.1029525 0.1045485 0.209029
2 .rowMeans 0.135357 0.1361160 0.1364440 0.1363250 0.1365440 0.140769
3 rowMeans 0.136966 0.1377290 0.1384011 0.1383265 0.1386560 0.146218
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
4 apply+mean 8.038962 7.842894 7.761877 7.676434 7.649424 7.699042
2 .rowMeans 10.806946 10.431544 10.094450 10.164784 9.990415 5.184862
3 rowMeans 10.935409 10.555160 10.239241 10.314022 10.144942 5.385562

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+1000x10 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on integer+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 colMeans2 12.227 12.8430 13.36558 13.2430 13.4815 28.296
2 rowMeans2 12.525 13.0485 13.51673 13.4115 13.6675 27.150
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowMeans2 1.024372 1.016001 1.011309 1.012724 1.013797 0.9594996

Figure: Benchmarking of colMeans2() and rowMeans2() on integer+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 integer matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3138552 167.7    5709258 305.0  5709258 305.0
Vcells 5697223  43.5   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3138543 167.7    5709258 305.0  5709258 305.0
Vcells 5702261  43.6   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.009074 0.009620 0.0109615 0.0103155 0.011103 0.023892
1 colMeans2 0.010614 0.011105 0.0127047 0.0119750 0.013109 0.026440
3 colMeans 0.010567 0.011371 0.0130535 0.0125615 0.013851 0.023245
4 apply+mean 3.208715 3.267930 3.4315273 3.3277545 3.455468 8.267523
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 colMeans2 1.169716 1.154366 1.159031 1.160874 1.180672 1.1066466
3 colMeans 1.164536 1.182017 1.190846 1.217731 1.247501 0.9729198
4 apply+mean 353.616377 339.701715 313.052712 322.597499 311.219355 346.0372928

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on integer+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 rowMeans2 0.010936 0.0114295 0.0126284 0.012351 0.0133470 0.020203
2 .rowMeans 0.030953 0.0314140 0.0330664 0.031832 0.0331605 0.058767
3 rowMeans 0.032560 0.0330545 0.0351301 0.034382 0.0353905 0.052018
4 apply+mean 3.197030 3.2592550 3.4373507 3.341137 3.4499105 8.425955
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 .rowMeans 2.830377 2.748502 2.618415 2.577281 2.484491 2.908825
3 rowMeans 2.977323 2.892034 2.781836 2.783742 2.651570 2.574766
4 apply+mean 292.339978 285.161643 272.192098 270.515505 258.478347 417.064545

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+10x1000 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on integer+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 colMeans2 10.614 11.1050 12.70472 11.975 13.109 26.440
2 rowMeans2 10.936 11.4295 12.62840 12.351 13.347 20.203
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000
2 rowMeans2 1.030337 1.029221 0.9939928 1.031399 1.018155 0.7641074

Figure: Benchmarking of colMeans2() and rowMeans2() on integer+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 integer matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3138772 167.7    5709258 305.0  5709258 305.0
Vcells 5697806  43.5   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3138766 167.7    5709258 305.0  5709258 305.0
Vcells 5747849  43.9   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.074811 0.0755745 0.0785353 0.0770220 0.0788250 0.108906
3 colMeans 0.076820 0.0776875 0.0822842 0.0796755 0.0830435 0.135364
1 colMeans2 0.099775 0.1002240 0.1053356 0.1023415 0.1066470 0.131193
4 apply+mean 3.770532 3.8167745 4.1021140 3.8867665 4.0373260 15.632112
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colMeans 1.026854 1.027959 1.047736 1.034451 1.053517 1.242943
1 colMeans2 1.333694 1.326162 1.341251 1.328731 1.352959 1.204644
4 apply+mean 50.400770 50.503470 52.232741 50.463069 51.218852 143.537656

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on integer+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 rowMeans2 0.105809 0.1063705 0.1106376 0.1072835 0.1092985 0.204664
2 .rowMeans 0.272818 0.2733865 0.2794856 0.2739900 0.2760480 0.367536
3 rowMeans 0.274481 0.2752600 0.2806756 0.2763950 0.2789175 0.347585
4 apply+mean 3.788474 3.8303300 4.0666559 3.8958810 4.0174210 15.718836
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 .rowMeans 2.578401 2.570135 2.526135 2.553888 2.525634 1.795802
3 rowMeans 2.594118 2.587747 2.536891 2.576305 2.551888 1.698320
4 apply+mean 35.804837 36.009326 36.756538 36.313888 36.756415 76.803131

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+100x1000 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on integer+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 colMeans2 99.775 100.2240 105.3356 102.3415 106.6470 131.193
2 rowMeans2 105.809 106.3705 110.6376 107.2835 109.2985 204.664
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowMeans2 1.060476 1.061328 1.050335 1.048289 1.024862 1.560022

Figure: Benchmarking of colMeans2() and rowMeans2() on integer+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 integer matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139005 167.7    5709258 305.0  5709258 305.0
Vcells 5698593  43.5   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3138999 167.7    5709258 305.0  5709258 305.0
Vcells 5748636  43.9   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.079366 0.0796445 0.0803061 0.0800025 0.0803930 0.085562
3 colMeans 0.081143 0.0817155 0.0829258 0.0823115 0.0829675 0.129585
1 colMeans2 0.105009 0.1053790 0.1062905 0.1058115 0.1062135 0.124981
4 apply+mean 0.838260 0.8452160 0.8647765 0.8540085 0.8741790 1.017310
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colMeans 1.022390 1.026003 1.032622 1.028862 1.032024 1.514516
1 colMeans2 1.323098 1.323117 1.323566 1.322602 1.321179 1.460707
4 apply+mean 10.561953 10.612359 10.768500 10.674773 10.873820 11.889741

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on integer+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 rowMeans2 0.114807 0.1166280 0.1211237 0.1175665 0.1195020 0.219929
2 .rowMeans 0.371756 0.3728350 0.3784171 0.3732835 0.3743715 0.456133
3 rowMeans 0.373874 0.3746180 0.3790754 0.3753410 0.3780370 0.460845
4 apply+mean 0.852882 0.8707085 0.9019386 0.8792360 0.8988245 1.298366
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 .rowMeans 3.238095 3.196788 3.124220 3.175084 3.132763 2.074001
3 rowMeans 3.256544 3.212076 3.129655 3.192585 3.163437 2.095426
4 apply+mean 7.428833 7.465690 7.446426 7.478627 7.521418 5.903569

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on integer+1000x100 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on integer+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 colMeans2 105.009 105.379 106.2905 105.8115 106.2135 124.981
2 rowMeans2 114.807 116.628 121.1237 117.5665 119.5020 219.929
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowMeans2 1.093306 1.106748 1.139554 1.111094 1.125111 1.759699

Figure: Benchmarking of colMeans2() and rowMeans2() on integer+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Data type "double"

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 = mode)

Results

10x10 double matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139245 167.7    5709258 305.0  5709258 305.0
Vcells 5814528  44.4   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139227 167.7    5709258 305.0  5709258 305.0
Vcells 5814651  44.4   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colMeans2 0.001137 0.0014075 0.0018672 0.0018675 0.002051 0.012019
2 .colMeans 0.001644 0.0018460 0.0022731 0.0020850 0.002520 0.011391
3 colMeans 0.002991 0.0034025 0.0039936 0.0038965 0.004390 0.011282
4 apply+mean 0.045506 0.0463995 0.0481880 0.0470175 0.047545 0.145097
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 .colMeans 1.445910 1.311545 1.217387 1.116466 1.228669 0.9477494
3 colMeans 2.630607 2.417407 2.138835 2.086479 2.140419 0.9386804
4 apply+mean 40.022867 32.965897 25.807903 25.176707 23.181375 12.0723022

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on double+10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .rowMeans 0.001270 0.0014695 0.0017708 0.0016930 0.0019405 0.005853
1 rowMeans2 0.001105 0.0013700 0.0017901 0.0017550 0.0019500 0.010126
3 rowMeans 0.002554 0.0028565 0.0035454 0.0033785 0.0037425 0.022294
4 apply+mean 0.044823 0.0460120 0.0478368 0.0464970 0.0470385 0.100305
expr min lq mean median uq max
2 .rowMeans 1.0000000 1.0000000 1.000000 1.000000 1.000000 1.000000
1 rowMeans2 0.8700787 0.9322899 1.010899 1.036621 1.004896 1.730053
3 rowMeans 2.0110236 1.9438585 2.002163 1.995570 1.928627 3.808987
4 apply+mean 35.2937008 31.3113304 27.014683 27.464265 24.240402 17.137366

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+10x10 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on double+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 rowMeans2 1.105 1.3700 1.79007 1.7550 1.950 10.126
1 colMeans2 1.137 1.4075 1.86718 1.8675 2.051 12.019
expr min lq mean median uq max
2 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 colMeans2 1.028959 1.027372 1.043076 1.064103 1.051795 1.186945

Figure: Benchmarking of colMeans2() and rowMeans2() on double+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 double matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139462 167.7    5709258 305.0  5709258 305.0
Vcells 5815557  44.4   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139453 167.7    5709258 305.0  5709258 305.0
Vcells 5825595  44.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.008221 0.0087925 0.0096838 0.0092225 0.0096805 0.019484
3 colMeans 0.009916 0.0105470 0.0118815 0.0112720 0.0117985 0.036548
1 colMeans2 0.011310 0.0116740 0.0129054 0.0122155 0.0125745 0.046964
4 apply+mean 0.443032 0.4478055 0.4671414 0.4521345 0.4629810 0.650094
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colMeans 1.206179 1.199545 1.226943 1.222228 1.218790 1.875795
1 colMeans2 1.375745 1.327722 1.332675 1.324532 1.298952 2.410388
4 apply+mean 53.890281 50.930395 48.239418 49.025156 47.826145 33.365531

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on double+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 rowMeans2 0.011374 0.0117905 0.0123781 0.0122205 0.0126775 0.024969
2 .rowMeans 0.018457 0.0203275 0.0207877 0.0204935 0.0206905 0.043692
3 rowMeans 0.021277 0.0217830 0.0223839 0.0223900 0.0227155 0.030016
4 apply+mean 0.404403 0.4088945 0.4151887 0.4106470 0.4145620 0.532848
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 .rowMeans 1.622736 1.724058 1.679394 1.676977 1.632065 1.749850
3 rowMeans 1.870670 1.847504 1.808346 1.832167 1.791797 1.202131
4 apply+mean 35.555038 34.679997 33.542230 33.603126 32.700611 21.340382

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+100x100 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on double+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 colMeans2 11.310 11.6740 12.90537 12.2155 12.5745 46.964
2 rowMeans2 11.374 11.7905 12.37809 12.2205 12.6775 24.969
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000
2 rowMeans2 1.005659 1.009979 0.9591426 1.000409 1.008191 0.5316626

Figure: Benchmarking of colMeans2() and rowMeans2() on double+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 double matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139688 167.7    5709258 305.0  5709258 305.0
Vcells 5816683  44.4   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139679 167.7    5709258 305.0  5709258 305.0
Vcells 5826721  44.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.009459 0.0097845 0.0102891 0.010161 0.0104700 0.024951
3 colMeans 0.010823 0.0114160 0.0121760 0.011978 0.0125580 0.025641
1 colMeans2 0.012415 0.0129345 0.0134168 0.013192 0.0135985 0.025389
4 apply+mean 0.152337 0.1540075 0.1570167 0.155667 0.1576625 0.216901
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colMeans 1.144201 1.166743 1.183383 1.178821 1.199427 1.027654
1 colMeans2 1.312507 1.321938 1.303984 1.298297 1.298806 1.017554
4 apply+mean 16.104979 15.739946 15.260493 15.320047 15.058500 8.693078

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on double+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 rowMeans2 0.012531 0.0130100 0.0135245 0.0134345 0.0137750 0.021325
2 .rowMeans 0.020336 0.0205390 0.0208271 0.0207940 0.0209900 0.025154
3 rowMeans 0.021891 0.0222575 0.0228277 0.0227350 0.0230475 0.029208
4 apply+mean 0.110166 0.1129025 0.1155217 0.1139625 0.1153240 0.196645
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 .rowMeans 1.622855 1.578709 1.539953 1.547806 1.523775 1.179555
3 rowMeans 1.746948 1.710799 1.687874 1.692285 1.673140 1.369660
4 apply+mean 8.791477 8.678132 8.541647 8.482824 8.371978 9.221336

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+1000x10 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on double+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 colMeans2 12.415 12.9345 13.41682 13.1920 13.5985 25.389
2 rowMeans2 12.531 13.0100 13.52452 13.4345 13.7750 21.325
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowMeans2 1.009343 1.005837 1.008027 1.018382 1.012979 0.8399307

Figure: Benchmarking of colMeans2() and rowMeans2() on double+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 double matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139910 167.7    5709258 305.0  5709258 305.0
Vcells 5816830  44.4   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3139901 167.7    5709258 305.0  5709258 305.0
Vcells 5826868  44.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.009119 0.0096180 0.0108982 0.0103445 0.0111785 0.020629
3 colMeans 0.010592 0.0115365 0.0132383 0.0128655 0.0143210 0.023064
1 colMeans2 0.012080 0.0125470 0.0143993 0.0133060 0.0143800 0.065356
4 apply+mean 3.233188 3.3239640 3.5124924 3.4266600 3.5333660 8.546768
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colMeans 1.161531 1.199470 1.214727 1.243704 1.281120 1.118038
1 colMeans2 1.324707 1.304533 1.321260 1.286287 1.286398 3.168161
4 apply+mean 354.555105 345.598253 322.300532 331.254290 316.085879 414.308401

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on double+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 rowMeans2 0.012227 0.0127810 0.0146545 0.0137915 0.0147080 0.062302
2 .rowMeans 0.019566 0.0202355 0.0215739 0.0209840 0.0225900 0.026842
3 rowMeans 0.021130 0.0218835 0.0246065 0.0231680 0.0245015 0.077906
4 apply+mean 3.246822 3.3370775 3.5229248 3.4122410 3.5453170 8.447450
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 .rowMeans 1.600229 1.583249 1.472164 1.521517 1.535899 0.4308369
3 rowMeans 1.728143 1.712190 1.679107 1.679875 1.665862 1.2504574
4 apply+mean 265.545269 261.096745 240.398345 247.416235 241.046845 135.5887451

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+10x1000 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on double+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 colMeans2 12.080 12.547 14.39934 13.3060 14.380 65.356
2 rowMeans2 12.227 12.781 14.65453 13.7915 14.708 62.302
expr min lq mean median uq max
1 colMeans2 1.000000 1.00000 1.000000 1.000000 1.000000 1.0000000
2 rowMeans2 1.012169 1.01865 1.017722 1.036487 1.022809 0.9532713

Figure: Benchmarking of colMeans2() and rowMeans2() on double+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 double matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3140130 167.8    5709258 305.0  5709258 305.0
Vcells 5818143  44.4   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3140124 167.8    5709258 305.0  5709258 305.0
Vcells 5918186  45.2   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.064567 0.065199 0.0720234 0.0684560 0.0778060 0.114816
3 colMeans 0.066360 0.067253 0.0743774 0.0703435 0.0786795 0.161419
1 colMeans2 0.100799 0.101367 0.1175601 0.1050505 0.1103315 0.726208
4 apply+mean 4.343545 4.405486 4.6693956 4.4575465 4.5427805 18.057693
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colMeans 1.027770 1.031504 1.032684 1.027572 1.011227 1.405893
1 colMeans2 1.561154 1.554732 1.632250 1.534570 1.418033 6.324972
4 apply+mean 67.271904 67.569840 64.831691 65.115498 58.385992 157.275058

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on double+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 rowMeans2 0.107488 0.1080635 0.1176995 0.1089730 0.117304 0.241564
2 .rowMeans 0.180992 0.1825000 0.1893512 0.1835685 0.185773 0.251952
3 rowMeans 0.180206 0.1834630 0.1892023 0.1851485 0.188669 0.246761
4 apply+mean 3.915669 4.0004425 4.3035005 4.0729265 4.219132 17.113837
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 .rowMeans 1.683834 1.688822 1.608767 1.684532 1.583689 1.043003
3 rowMeans 1.676522 1.697733 1.607502 1.699031 1.608377 1.021514
4 apply+mean 36.428894 37.019368 36.563443 37.375556 35.967508 70.845975

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+100x1000 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on double+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 colMeans2 100.799 101.3670 117.5601 105.0505 110.3315 726.208
2 rowMeans2 107.488 108.0635 117.6995 108.9730 117.3040 241.564
expr min lq mean median uq max
1 colMeans2 1.00000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowMeans2 1.06636 1.066062 1.001186 1.037339 1.063196 0.3326375

Figure: Benchmarking of colMeans2() and rowMeans2() on double+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 double matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3140363 167.8    5709258 305.0  5709258 305.0
Vcells 5818301  44.4   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colMeans2 = colMeans2(X, na.rm = FALSE), .colMeans = .colMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), colMeans = colMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 2L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3140357 167.8    5709258 305.0  5709258 305.0
Vcells 5918344  45.2   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowMeans2 = rowMeans2(X, na.rm = FALSE), .rowMeans = .rowMeans(X, m = nrow(X), 
+     n = ncol(X), na.rm = FALSE), rowMeans = rowMeans(X, na.rm = FALSE), `apply+mean` = apply(X, MARGIN = 1L, 
+     FUN = mean, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 .colMeans 0.077399 0.0777785 0.0811696 0.0784920 0.0821365 0.117364
3 colMeans 0.079115 0.0797960 0.0836713 0.0808430 0.0838830 0.115651
1 colMeans2 0.104898 0.1053610 0.1102334 0.1063325 0.1139030 0.133721
4 apply+mean 0.938313 0.9515165 1.0568774 0.9737810 1.0373125 6.995496
expr min lq mean median uq max
2 .colMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 colMeans 1.022171 1.025939 1.030821 1.029952 1.021263 0.9854044
1 colMeans2 1.355289 1.354629 1.358062 1.354692 1.386753 1.1393698
4 apply+mean 12.123064 12.233670 13.020611 12.406118 12.629130 59.6051259

Table: Benchmarking of rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on double+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 rowMeans2 0.107895 0.1088235 0.1138603 0.1095450 0.1119620 0.206995
2 .rowMeans 0.187725 0.1883625 0.1950287 0.1887395 0.1905115 0.328856
3 rowMeans 0.189587 0.1902130 0.1967597 0.1910290 0.1935570 0.320987
4 apply+mean 0.965276 0.9926910 1.1065505 1.0498105 1.0774335 6.995676
expr min lq mean median uq max
1 rowMeans2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 .rowMeans 1.739886 1.730899 1.712878 1.722940 1.701573 1.588715
3 rowMeans 1.757143 1.747904 1.728080 1.743840 1.728774 1.550699
4 apply+mean 8.946439 9.122028 9.718496 9.583372 9.623207 33.796353

Figure: Benchmarking of colMeans2(), .colMeans(), colMeans() and apply+mean() on double+1000x100 data as well as rowMeans2(), .rowMeans(), rowMeans() and apply+mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMeans2() and rowMeans2() on double+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 colMeans2 104.898 105.3610 110.2334 106.3325 113.903 133.721
2 rowMeans2 107.895 108.8235 113.8603 109.5450 111.962 206.995
expr min lq mean median uq max
1 colMeans2 1.000000 1.000000 1.000000 1.000000 1.0000000 1.000000
2 rowMeans2 1.028571 1.032863 1.032902 1.030212 0.9829592 1.547962

Figure: Benchmarking of colMeans2() and rowMeans2() on double+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 28.82 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('colMeans2')

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:44:05 (-0700 UTC). Powered by RSP.

<script> var link = document.createElement('link'); link.rel = 'icon'; link.href = "data:image/png;base64,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" document.getElementsByTagName('head')[0].appendChild(link); </script>
Add a custom footer
⚠️ **GitHub.com Fallback** ⚠️