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hb edited this page Mar 3, 2015 · 2 revisions

matrixStats: Benchmark report

colDiffs() and rowDiffs() benchmarks

This report benchmark the performance of colDiffs() and rowDiffs() against alternative methods.

Alternative methods

  • apply() + diff()
  • apply() + diff2()
  • diff()

Data type "integer"

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), naProb = 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 (naProb > 0) 
+         X[sample(n, size = naProb * 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   652121 34.9    1168576  62.5  1168576  62.5
Vcells 12127277 92.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651609 34.8    1168576  62.5  1168576  62.5
Vcells 12126201 92.6   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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 colDiffs 0.0023 0.0050 0.0073 0.0060 0.0077 0.1120
4 diff 0.0158 0.0196 0.0247 0.0235 0.0262 0.0801
3 apply+diff2 0.0932 0.0974 0.1210 0.1005 0.1497 0.6698
2 apply+diff 0.1871 0.1952 0.2284 0.1984 0.2931 0.3110
expr min lq mean median uq max
1 colDiffs 1.00 1.000 1.000 1.000 1.00 1.0000
4 diff 6.83 3.922 3.391 3.935 3.40 0.7148
3 apply+diff2 40.31 19.457 16.608 16.835 19.45 5.9794
2 apply+diff 80.96 38.992 31.336 33.252 38.07 2.7766
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.0019 0.0035 0.0079 0.0058 0.0083 0.1440
4 diff + t 0.0239 0.0327 0.0418 0.0356 0.0493 0.1190
3 apply+diff2 0.0932 0.0982 0.1226 0.1007 0.1644 0.2140
2 apply+diff 0.1863 0.1952 0.2456 0.1977 0.3239 0.5466
expr min lq mean median uq max
1 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.0000
4 diff + t 12.39 9.444 5.291 6.166 5.953 0.8262
3 apply+diff2 48.37 28.330 15.540 17.431 19.858 1.4866
2 apply+diff 96.74 56.327 31.115 34.229 39.135 3.7968
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on integer+10x10 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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 rowDiffs 1.926 3.465 7.893 5.775 8.277 144
1 colDiffs 2.311 5.005 7.288 5.968 7.700 112
expr min lq mean median uq max
2 rowDiffs 1.0 1.000 1.0000 1.000 1.0000 1.0000
1 colDiffs 1.2 1.445 0.9234 1.033 0.9302 0.7781
Figure: Benchmarking of colDiffs() and rowDiffs() 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   651707 34.9    1168576  62.5  1168576  62.5
Vcells 12127600 92.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651701 34.9    1168576  62.5  1168576  62.5
Vcells 12132643 92.6   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.0212 0.0389 0.0459 0.0456 0.0545 0.0674
4 diff 0.1963 0.2352 0.2914 0.2910 0.3336 0.4492
3 apply+diff2 0.7360 0.8313 1.2553 1.2848 1.3776 12.5830
2 apply+diff 1.8878 2.0122 2.7597 3.0379 3.2109 10.8445
expr min lq mean median uq max
1 colDiffs 1.000 1.000 1.000 1.00 1.000 1.000
4 diff 9.273 6.049 6.354 6.38 6.124 6.668
3 apply+diff2 34.763 21.381 27.365 28.16 25.290 186.780
2 apply+diff 89.161 51.752 60.161 66.59 58.946 160.975
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.0208 0.0416 0.0508 0.0527 0.0602 0.0716
4 diff + t 0.2275 0.3199 0.3680 0.3759 0.4275 0.5436
3 apply+diff2 0.8165 1.3289 1.4804 1.3960 1.4732 13.0218
2 apply+diff 2.0745 3.1376 3.3738 3.3146 3.4390 14.8169
expr min lq mean median uq max
1 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.000
4 diff + t 10.94 7.694 7.245 7.128 7.096 7.591
3 apply+diff2 39.28 31.962 29.148 26.470 24.453 181.864
2 apply+diff 99.79 75.466 66.430 62.849 57.082 206.934
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on integer+100x100 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
colDiffs 21.17 38.88 45.87 45.62 54.47 67.37
rowDiffs 20.79 41.58 50.79 52.74 60.25 71.60
expr min lq mean median uq max
colDiffs 1.0000 1.000 1.000 1.000 1.000 1.000
rowDiffs 0.9818 1.069 1.107 1.156 1.106 1.063
Figure: Benchmarking of colDiffs() and rowDiffs() 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   651746 34.9    1168576  62.5  1168576  62.5
Vcells 12127838 92.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651740 34.9    1168576  62.5  1168576  62.5
Vcells 12132881 92.6   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.0196 0.0277 0.0393 0.0391 0.0477 0.0631
4 diff 0.2048 0.2173 0.3008 0.3218 0.3630 0.5085
3 apply+diff2 0.3896 0.4733 0.6279 0.6802 0.7322 1.2319
2 apply+diff 0.6775 0.7674 1.0440 1.1435 1.2093 1.3585
expr min lq mean median uq max
1 colDiffs 1.00 1.00 1.000 1.000 1.000 1.000
4 diff 10.43 7.84 7.664 8.236 7.605 8.055
3 apply+diff2 19.84 17.08 15.996 17.409 15.338 19.512
2 apply+diff 34.51 27.69 26.597 29.265 25.334 21.518
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.0239 0.0308 0.0408 0.0431 0.0485 0.0685
4 diff + t 0.2360 0.2572 0.3070 0.3124 0.3430 0.4912
3 apply+diff2 0.3884 0.4702 0.5599 0.5951 0.6406 0.7484
2 apply+diff 0.6833 0.7191 0.9294 0.9941 1.0867 1.2696
expr min lq mean median uq max
1 rowDiffs 1.000 1.00 1.000 1.000 1.000 1.000
4 diff + t 9.887 8.35 7.527 7.245 7.071 7.168
3 apply+diff2 16.274 15.27 13.728 13.803 13.206 10.921
2 apply+diff 28.628 23.35 22.786 23.057 22.404 18.528
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on integer+1000x10 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
colDiffs 19.63 27.72 39.25 39.07 47.73 63.13
rowDiffs 23.87 30.80 40.79 43.12 48.51 68.52
expr min lq mean median uq max
colDiffs 1.000 1.000 1.000 1.000 1.000 1.000
rowDiffs 1.216 1.111 1.039 1.103 1.016 1.085
Figure: Benchmarking of colDiffs() and rowDiffs() 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   651779 34.9    1168576  62.5  1168576  62.5
Vcells 12128497 92.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651773 34.9    1168576  62.5  1168576  62.5
Vcells 12133540 92.6   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.0223 0.0398 0.0466 0.0447 0.0583 0.0805
4 diff 0.1809 0.2171 0.2562 0.2508 0.2985 0.4789
3 apply+diff2 4.0228 4.4108 7.3171 6.2407 7.4412 119.2660
2 apply+diff 13.8972 14.9422 19.4433 18.7034 22.1824 37.5157
expr min lq mean median uq max
1 colDiffs 1.000 1.000 1.000 1.000 1.000 1.000
4 diff 8.103 5.449 5.497 5.616 5.119 5.952
3 apply+diff2 180.167 110.703 157.009 139.753 127.589 1482.376
2 apply+diff 622.412 375.022 417.207 418.841 380.347 466.288
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.0177 0.0325 0.0392 0.0395 0.0489 0.0631
4 diff + t 0.2121 0.2758 0.3019 0.2956 0.3357 0.4508
3 apply+diff2 4.1929 4.5197 6.1341 5.5814 7.3547 12.0691
2 apply+diff 13.8668 14.5501 17.7744 15.4222 20.7244 32.2541
expr min lq mean median uq max
1 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.00
4 diff + t 11.98 8.479 7.707 7.493 6.866 7.14
3 apply+diff2 236.78 138.943 156.571 141.451 150.434 191.17
2 apply+diff 783.08 447.289 453.687 390.846 423.898 510.88
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on integer+10x1000 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
2 rowDiffs 17.71 32.53 39.18 39.46 48.89 63.13
1 colDiffs 22.33 39.84 46.60 44.66 58.32 80.46
expr min lq mean median uq max
2 rowDiffs 1.000 1.000 1.00 1.000 1.000 1.000
1 colDiffs 1.261 1.225 1.19 1.132 1.193 1.274
Figure: Benchmarking of colDiffs() and rowDiffs() 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   651818 34.9    1168576  62.5  1168576  62.5
Vcells 12128910 92.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651812 34.9    1168576  62.5  1168576  62.5
Vcells 12178953 93.0   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.1875 0.225 0.283 0.2416 0.3622 0.4269
4 diff 2.1750 2.340 2.862 2.5484 3.3814 4.5629
3 apply+diff2 7.1082 7.572 10.720 9.7053 12.5422 29.5795
2 apply+diff 18.6753 21.836 27.337 26.7275 31.0019 43.9205
expr min lq mean median uq max
1 colDiffs 1.00 1.00 1.00 1.00 1.000 1.00
4 diff 11.60 10.40 10.11 10.55 9.335 10.69
3 apply+diff2 37.92 33.65 37.87 40.18 34.624 69.29
2 apply+diff 99.62 97.04 96.58 110.65 85.583 102.88
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.1759 0.231 0.2781 0.2421 0.3572 0.5039
4 diff + t 2.4645 2.641 2.9138 2.6841 3.2914 3.9265
3 apply+diff2 7.3572 8.879 10.7272 9.4393 11.9757 27.1173
2 apply+diff 19.5868 21.590 26.3636 25.4653 29.2603 46.4524
expr min lq mean median uq max
1 rowDiffs 1.00 1.00 1.00 1.00 1.000 1.000
4 diff + t 14.01 11.43 10.48 11.09 9.213 7.792
3 apply+diff2 41.82 38.44 38.57 38.98 33.523 53.814
2 apply+diff 111.34 93.48 94.79 105.17 81.907 92.185
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on integer+100x1000 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
colDiffs 187.5 225 283.0 241.6 362.2 426.9
rowDiffs 175.9 231 278.1 242.1 357.2 503.9
expr min lq mean median uq max
colDiffs 1.0000 1.000 1.0000 1.000 1.0000 1.00
rowDiffs 0.9384 1.026 0.9826 1.002 0.9862 1.18
Figure: Benchmarking of colDiffs() and rowDiffs() 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   651854 34.9    1168576  62.5  1168576  62.5
Vcells 12129470 92.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651848 34.9    1168576  62.5  1168576  62.5
Vcells 12179513 93.0   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.1782 0.2227 0.2739 0.241 0.3459 0.4242
4 diff 1.8974 2.1779 2.4874 2.282 2.7595 4.1113
3 apply+diff2 3.4973 3.5718 4.5654 4.277 5.1853 14.4731
2 apply+diff 6.4684 6.6347 8.1138 7.354 9.5434 15.5883
expr min lq mean median uq max
1 colDiffs 1.00 1.00 1.000 1.000 1.000 1.000
4 diff 10.65 9.78 9.081 9.469 7.978 9.691
3 apply+diff2 19.62 16.04 16.666 17.748 14.992 34.117
2 apply+diff 36.29 29.79 29.620 30.515 27.591 36.746
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.1882 0.2381 0.271 0.2443 0.2853 0.4196
4 diff + t 2.1904 2.2991 2.551 2.3282 2.4814 4.3404
3 apply+diff2 3.5131 3.7225 4.753 4.4778 5.6942 11.3488
2 apply+diff 6.4980 6.7209 8.358 7.5251 9.7076 20.5862
expr min lq mean median uq max
1 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.00
4 diff + t 11.64 9.656 9.413 9.532 8.699 10.34
3 apply+diff2 18.66 15.635 17.541 18.332 19.962 27.05
2 apply+diff 34.52 28.228 30.846 30.808 34.032 49.06
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on integer+1000x100 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
colDiffs 178.2 222.7 273.9 241.0 345.9 424.2
rowDiffs 188.2 238.1 271.0 244.3 285.3 419.6
expr min lq mean median uq max
colDiffs 1.000 1.000 1.0000 1.000 1.0000 1.0000
rowDiffs 1.056 1.069 0.9892 1.014 0.8247 0.9891
Figure: Benchmarking of colDiffs() and rowDiffs() 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), naProb = 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 (naProb > 0) 
+         X[sample(n, size = naProb * 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   651900 34.9    1168576  62.5  1168576  62.5
Vcells 12245170 93.5   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651885 34.9    1168576  62.5  1168576  62.5
Vcells 12245298 93.5   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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 colDiffs 0.0027 0.0058 0.0081 0.0075 0.0094 0.0227
4 diff 0.0166 0.0244 0.0318 0.0320 0.0354 0.0885
3 apply+diff2 0.0928 0.1440 0.1443 0.1492 0.1519 0.3434
2 apply+diff 0.1848 0.2874 0.2917 0.2933 0.2999 0.6637
expr min lq mean median uq max
1 colDiffs 1.000 1.000 1.000 1.000 1.000 1.000
4 diff 6.143 4.233 3.928 4.256 3.755 3.898
3 apply+diff2 34.425 24.930 17.843 19.868 16.101 15.118
2 apply+diff 68.564 49.761 36.075 39.070 31.794 29.219
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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
1 rowDiffs 0.0023 0.0035 0.0057 0.0054 0.0062 0.0535
4 diff + t 0.0243 0.0285 0.0357 0.0341 0.0366 0.1259
3 apply+diff2 0.0928 0.0974 0.1073 0.0993 0.1028 0.1794
2 apply+diff 0.1871 0.1950 0.2042 0.1973 0.2017 0.3314
expr min lq mean median uq max
1 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.000
4 diff + t 10.50 8.221 6.264 6.321 5.936 2.353
3 apply+diff2 40.16 28.108 18.805 18.427 16.683 3.353
2 apply+diff 80.99 56.271 35.794 36.603 32.741 6.194
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on double+10x10 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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 rowDiffs 2.310 3.465 5.706 5.390 6.161 53.51
1 colDiffs 2.695 5.775 8.085 7.508 9.432 22.71
expr min lq mean median uq max
2 rowDiffs 1.000 1.000 1.000 1.000 1.000 1.0000
1 colDiffs 1.167 1.667 1.417 1.393 1.531 0.4245
Figure: Benchmarking of colDiffs() and rowDiffs() 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   651923 34.9    1168576  62.5  1168576  62.5
Vcells 12245964 93.5   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651917 34.9    1168576  62.5  1168576  62.5
Vcells 12256007 93.6   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.0200 0.0335 0.0460 0.0464 0.0579 0.0758
4 diff 0.1424 0.2271 0.2707 0.2602 0.3422 0.3780
3 apply+diff2 0.7507 1.2303 1.2811 1.3169 1.3774 3.8992
2 apply+diff 1.8920 2.1965 3.1496 3.1135 3.2292 16.7012
expr min lq mean median uq max
1 colDiffs 1.000 1.000 1.000 1.00 1.000 1.000
4 diff 7.115 6.781 5.886 5.61 5.907 4.985
3 apply+diff2 37.499 36.734 27.860 28.39 23.774 51.416
2 apply+diff 94.517 65.584 68.493 67.12 55.737 220.225
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.0192 0.0212 0.0361 0.0333 0.0516 0.0666
4 diff + t 0.1721 0.1807 0.2240 0.2281 0.2392 0.3630
3 apply+diff2 0.7510 0.8300 1.0368 0.8819 0.9722 10.9681
2 apply+diff 1.8651 2.0175 2.2546 2.0730 2.1783 12.2207
expr min lq mean median uq max
1 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.000
4 diff + t 8.94 8.536 6.197 6.849 4.638 5.451
3 apply+diff2 39.02 39.197 28.681 26.485 18.847 164.691
2 apply+diff 96.90 95.284 62.372 62.252 42.227 183.500
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on double+100x100 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
2 rowDiffs 19.25 21.17 36.15 33.30 51.58 66.60
1 colDiffs 20.02 33.49 45.98 46.39 57.94 75.84
expr min lq mean median uq max
2 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.000
1 colDiffs 1.04 1.582 1.272 1.393 1.123 1.139
Figure: Benchmarking of colDiffs() and rowDiffs() 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   651949 34.9    1168576  62.5  1168576  62.5
Vcells 12246811 93.5   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651952 34.9    1168576  62.5  1168576  62.5
Vcells 12256869 93.6   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.0208 0.0327 0.0384 0.0371 0.0431 0.0566
4 diff 0.1528 0.2433 0.2561 0.2587 0.2851 0.3288
3 apply+diff2 0.3980 0.6361 0.6614 0.6812 0.7243 0.8931
2 apply+diff 0.6479 1.0375 1.0505 1.0800 1.1327 1.3077
expr min lq mean median uq max
1 colDiffs 1.000 1.000 1.000 1.000 1.000 1.000
4 diff 7.351 7.435 6.662 6.964 6.612 5.809
3 apply+diff2 19.147 19.441 17.203 18.336 16.799 15.782
2 apply+diff 31.165 31.705 27.323 29.072 26.272 23.108
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.0192 0.0246 0.0310 0.0295 0.0366 0.0693
4 diff + t 0.1836 0.1952 0.2230 0.2140 0.2306 0.3646
3 apply+diff2 0.3923 0.4244 0.4934 0.4806 0.5230 0.7499
2 apply+diff 0.6444 0.7064 0.7840 0.7470 0.8063 1.1091
expr min lq mean median uq max
1 rowDiffs 1.000 1.000 1.00 1.000 1.000 1.000
4 diff + t 9.539 7.922 7.19 7.268 6.305 5.261
3 apply+diff2 20.379 17.226 15.91 16.319 14.300 10.822
2 apply+diff 33.478 28.671 25.28 25.365 22.047 16.005
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on double+1000x10 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
2 rowDiffs 19.25 24.64 31.01 29.45 36.57 69.29
1 colDiffs 20.79 32.72 38.45 37.15 43.12 56.59
expr min lq mean median uq max
2 rowDiffs 1.00 1.000 1.00 1.000 1.000 1.0000
1 colDiffs 1.08 1.328 1.24 1.261 1.179 0.8167
Figure: Benchmarking of colDiffs() and rowDiffs() 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   652000 34.9    1168576  62.5  1168576  62.5
Vcells 12246856 93.5   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   651994 34.9    1168576  62.5  1168576  62.5
Vcells 12256899 93.6   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.0185 0.0339 0.0464 0.0508 0.0587 0.0720
4 diff 0.1582 0.2008 0.2332 0.2212 0.2724 0.4489
3 apply+diff2 4.2965 4.6662 6.5428 6.9925 7.5439 22.4528
2 apply+diff 13.9034 15.0863 19.0904 17.8191 22.2090 31.2763
expr min lq mean median uq max
1 colDiffs 1.000 1.000 1.000 1.000 1.000 1.000
4 diff 8.562 5.926 5.023 4.352 4.639 6.235
3 apply+diff2 232.506 137.740 140.917 137.608 128.503 311.901
2 apply+diff 752.388 445.327 411.164 350.670 378.305 434.472
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.0173 0.0296 0.0429 0.0448 0.0554 0.1105
4 diff + t 0.1913 0.2558 0.2801 0.2662 0.3160 0.4612
3 apply+diff2 4.0817 4.5700 6.1980 5.9098 7.5210 14.2406
2 apply+diff 13.8279 14.8917 18.9992 18.0859 22.1860 34.2963
expr min lq mean median uq max
1 rowDiffs 1.00 1.00 1.000 1.000 1.000 1.000
4 diff + t 11.04 8.63 6.529 5.936 5.701 4.174
3 apply+diff2 235.61 154.17 144.461 131.773 135.674 128.894
2 apply+diff 798.19 502.38 442.829 403.267 400.221 310.422
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on double+10x1000 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
2 rowDiffs 17.32 29.64 42.90 44.85 55.43 110.48
1 colDiffs 18.48 33.88 46.43 50.81 58.71 71.99
expr min lq mean median uq max
2 rowDiffs 1.000 1.000 1.000 1.000 1.000 1.0000
1 colDiffs 1.067 1.143 1.082 1.133 1.059 0.6516
Figure: Benchmarking of colDiffs() and rowDiffs() 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   652033 34.9    1168576  62.5  1168576  62.5
Vcells 12247891 93.5   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   652027 34.9    1168576  62.5  1168576  62.5
Vcells 12347934 94.3   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.1786 0.2847 0.3489 0.3638 0.3734 1.687
4 diff 2.1011 2.6652 3.1360 2.8827 3.6598 4.719
3 apply+diff2 7.3934 8.6341 11.2506 10.9654 12.8413 25.931
2 apply+diff 19.0575 21.4077 27.4937 25.6127 31.6163 46.958
expr min lq mean median uq max
1 colDiffs 1.00 1.000 1.000 1.000 1.000 1.000
4 diff 11.76 9.362 8.989 7.924 9.801 2.798
3 apply+diff2 41.39 30.330 32.249 30.143 34.390 15.376
2 apply+diff 106.69 75.201 78.810 70.407 84.670 27.844
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.1775 0.308 0.3305 0.357 0.3667 0.4785
4 diff + t 2.4933 2.723 3.0462 2.823 3.3662 4.6826
3 apply+diff2 7.7537 10.111 12.1467 10.654 13.3417 32.7014
2 apply+diff 19.4848 22.295 26.9076 25.571 30.6418 46.9913
expr min lq mean median uq max
1 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.000
4 diff + t 14.05 8.841 9.216 7.907 9.181 9.786
3 apply+diff2 43.69 32.831 36.750 29.839 36.386 68.342
2 apply+diff 109.80 72.395 81.409 71.619 83.568 98.206
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on double+100x1000 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
2 rowDiffs 177.5 308.0 330.5 357.0 366.7 478.5
1 colDiffs 178.6 284.7 348.9 363.8 373.4 1686.5
expr min lq mean median uq max
2 rowDiffs 1.000 1.0000 1.000 1.000 1.000 1.000
1 colDiffs 1.006 0.9244 1.056 1.019 1.018 3.525
Figure: Benchmarking of colDiffs() and rowDiffs() 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   652068 34.9    1168576  62.5  1168576  62.5
Vcells 12247914 93.5   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colDiffs = colDiffs(X), `apply+diff` = apply(X, MARGIN = 2L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 2L, FUN = diff2), diff = diff(X), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   652062 34.9    1168576  62.5  1168576  62.5
Vcells 12347957 94.3   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowDiffs = rowDiffs(X), `apply+diff` = apply(X, MARGIN = 1L, FUN = diff), 
+     `apply+diff2` = apply(X, MARGIN = 1L, FUN = diff2), `diff + t` = diff(t(X)), unit = "ms")

Table: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() 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
1 colDiffs 0.1786 0.2758 0.3995 0.3692 0.4458 0.8954
4 diff 1.5518 1.9740 2.8880 2.2114 3.5845 13.8699
3 apply+diff2 3.6959 4.1153 5.4097 5.5884 6.2940 10.0434
2 apply+diff 6.3182 7.3878 9.4272 9.6958 10.8253 15.2954
expr min lq mean median uq max
1 colDiffs 1.000 1.000 1.00 1.00 1.000 1.00
4 diff 8.687 7.157 7.23 5.99 8.041 15.49
3 apply+diff2 20.692 14.920 13.54 15.14 14.119 11.22
2 apply+diff 35.373 26.785 23.60 26.26 24.284 17.08
Table: Benchmarking of rowDiffs(), apply+diff(), apply+diff2() and diff + t() 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 rowDiffs 0.1836 0.3374 0.4164 0.3674 0.3826 7.341
4 diff + t 1.9579 2.2749 2.9718 3.0140 3.4453 4.567
3 apply+diff2 3.8268 5.8088 6.4758 6.3787 7.7231 11.845
2 apply+diff 6.5792 8.2859 9.8003 9.4483 10.7860 18.800
expr min lq mean median uq max
1 rowDiffs 1.00 1.000 1.000 1.000 1.000 1.0000
4 diff + t 10.66 6.742 7.136 8.203 9.004 0.6221
3 apply+diff2 20.84 17.216 15.551 17.360 20.184 1.6135
2 apply+diff 35.83 24.557 23.535 25.714 28.188 2.5608
Figure: Benchmarking of colDiffs(), apply+diff(), apply+diff2() and diff() on double+1000x100 data as well as rowDiffs(), apply+diff(), apply+diff2() and diff + t() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colDiffs() and rowDiffs() 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
2 rowDiffs 183.6 337.4 416.4 367.4 382.6 7341.5
1 colDiffs 178.6 275.8 399.5 369.2 445.8 895.4
expr min lq mean median uq max
2 rowDiffs 1.0000 1.0000 1.0000 1.000 1.000 1.000
1 colDiffs 0.9727 0.8175 0.9593 1.005 1.165 0.122
Figure: Benchmarking of colDiffs() and rowDiffs() on double+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R Under development (unstable) (2015-02-27 r67909)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
[1] markdown_0.7.7          microbenchmark_1.4-2    matrixStats_0.14.0-9000
[4] ggplot2_1.0.0           knitr_1.9.3             R.devices_2.13.0       
[7] R.utils_2.0.0           R.oo_1.19.0             R.methodsS3_1.7.0      

loaded via a namespace (and not attached):
 [1] Rcpp_0.11.4         splines_3.2.0       MASS_7.3-39        
 [4] munsell_0.4.2       lattice_0.20-30     colorspace_1.2-4   
 [7] R.cache_0.11.1-9000 multcomp_1.3-9      stringr_0.6.2      
[10] plyr_1.8.1          tools_3.2.0         grid_3.2.0         
[13] gtable_0.1.2        TH.data_1.0-6       survival_2.38-1    
[16] digest_0.6.8        R.rsp_0.20.0        reshape2_1.4.1     
[19] formatR_1.0.3       base64enc_0.1-3     mime_0.2.1         
[22] evaluate_0.5.7      labeling_0.3        sandwich_2.3-2     
[25] scales_0.2.4        mvtnorm_1.0-2       zoo_1.7-12         
[28] Cairo_1.5-6         proto_0.3-10

Total processing time was 1.25 mins.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2015-03-02 17:02:54 (-0800 UTC). Powered by RSP.

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