colRowAnyMissings - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

colAnyMissings() and rowAnyMissings() benchmarks

This report benchmark the performance of colAnyMissings() and rowAnyMissings() against alternative methods.

Alternative methods

  • colAnyMissings() and rowAnyMissings()
  • apply() + anyMissing()
  • colSums() + is.na() and rowSums() + is.na()

where

> colAnyMissings <- function(x, ...) colAnys(x, value = NA)

and

> rowAnyMissings <- function(x, ...) rowAnys(x, value = NA)

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 matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3070635 164.0    5709258 305.0  5709258 305.0
Vcells 5584481  42.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3069549 164.0    5709258 305.0  5709258 305.0
Vcells 5581643  42.6   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.001718 0.0020040 0.0025483 0.0024800 0.0026575 0.019309
3 colSums 0.003164 0.0035820 0.0045810 0.0039990 0.0046480 0.049358
2 apply+anyMissing 0.022496 0.0232115 0.0243481 0.0236175 0.0239760 0.077427
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums 1.841676 1.787425 1.797690 1.612500 1.749012 2.556217
2 apply+anyMissing 13.094296 11.582585 9.554741 9.523186 9.022013 4.009892

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.001647 0.0019670 0.0024760 0.0023825 0.0026000 0.018659
3 rowSums 0.003730 0.0040785 0.0049448 0.0044770 0.0049445 0.043257
2 apply+anyMissing 0.022250 0.0229470 0.0239494 0.0232480 0.0235625 0.078709
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 2.264724 2.073462 1.997145 1.879119 1.901731 2.318291
2 apply+anyMissing 13.509411 11.665989 9.672757 9.757817 9.062500 4.218286

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on integer+10x10 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 rowAnyMissings 1.647 1.967 2.47596 2.3825 2.6000 18.659
1 colAnyMissings 1.718 2.004 2.54827 2.4800 2.6575 19.309
expr min lq mean median uq max
2 rowAnyMissings 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
1 colAnyMissings 1.043109 1.01881 1.029205 1.040923 1.022115 1.034836

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on integer+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 3068101 163.9    5709258 305.0  5709258 305.0
Vcells 5198280  39.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3068095 163.9    5709258 305.0  5709258 305.0
Vcells 5203323  39.7   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.005658 0.0061945 0.0066545 0.0065850 0.0068605 0.016830
3 colSums 0.011000 0.0115230 0.0125151 0.0124345 0.0129090 0.028033
2 apply+anyMissing 0.165038 0.1664370 0.1721996 0.1675205 0.1702105 0.307964
expr min lq mean median uq max
1 colAnyMissings 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums 1.94415 1.860199 1.880706 1.888307 1.881641 1.665657
2 apply+anyMissing 29.16896 26.868512 25.877280 25.439712 24.810218 18.298515

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.007974 0.0084310 0.0090807 0.008863 0.009280 0.019760
3 rowSums 0.040060 0.0405430 0.0414272 0.041348 0.041754 0.050165
2 apply+anyMissing 0.165285 0.1666975 0.1733659 0.167829 0.172191 0.309644
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 5.023827 4.808801 4.562119 4.665237 4.499353 2.538715
2 apply+anyMissing 20.727991 19.771973 19.091691 18.935913 18.555065 15.670243

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on integer+100x100 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 5.658 6.1945 6.65447 6.585 6.8605 16.83
2 rowAnyMissings 7.974 8.4310 9.08070 8.863 9.2800 19.76
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnyMissings 1.409332 1.361046 1.364602 1.345938 1.352671 1.174094

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on integer+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 3068859 163.9    5709258 305.0  5709258 305.0
Vcells 5202064  39.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3068850 163.9    5709258 305.0  5709258 305.0
Vcells 5207102  39.8   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.004528 0.0049910 0.0054688 0.0054495 0.0056605 0.015334
3 colSums 0.011033 0.0115325 0.0123142 0.0123355 0.0127040 0.024995
2 apply+anyMissing 0.070639 0.0747955 0.0767843 0.0753150 0.0762035 0.175063
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums 2.436617 2.310659 2.251727 2.263602 2.244325 1.630038
2 apply+anyMissing 15.600486 14.986075 14.040462 13.820534 13.462327 11.416656

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.007779 0.0081110 0.0088606 0.008565 0.0088575 0.041227
2 apply+anyMissing 0.071069 0.0748305 0.0766471 0.075590 0.0765005 0.137070
3 rowSums 0.137214 0.1378560 0.1386933 0.138336 0.1387145 0.159324
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+anyMissing 9.136007 9.225805 8.650354 8.825452 8.636805 3.324763
3 rowSums 17.639028 16.996178 15.652861 16.151314 15.660683 3.864555

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on integer+1000x10 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 4.528 4.991 5.46879 5.4495 5.6605 15.334
2 rowAnyMissings 7.779 8.111 8.86057 8.5650 8.8575 41.227
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnyMissings 1.717977 1.625125 1.620207 1.571704 1.564791 2.688601

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on integer+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 3069061 164.0    5709258 305.0  5709258 305.0
Vcells 5202854  39.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3069055 164.0    5709258 305.0  5709258 305.0
Vcells 5207897  39.8   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.008065 0.0088440 0.0097604 0.0095400 0.0102780 0.017663
3 colSums 0.011586 0.0127905 0.0142201 0.0138025 0.0150635 0.029127
2 apply+anyMissing 1.022693 1.1110770 1.1551814 1.1500245 1.2128560 1.379744
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 colSums 1.436578 1.446235 1.456925 1.446803 1.465606 1.64904
2 apply+anyMissing 126.806324 125.630597 118.354258 120.547641 118.005059 78.11493

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.008756 0.0096875 0.0104656 0.0104145 0.011002 0.018797
3 rowSums 0.032151 0.0337365 0.0354815 0.0347135 0.035997 0.080521
2 apply+anyMissing 1.026136 1.0952340 1.1482911 1.1374905 1.206508 1.341381
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000
3 rowSums 3.671882 3.482477 3.390315 3.333189 3.27186 4.283715
2 apply+anyMissing 117.192325 113.056413 109.720938 109.221806 109.66256 71.361441

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on integer+10x1000 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 8.065 8.8440 9.76037 9.5400 10.278 17.663
2 rowAnyMissings 8.756 9.6875 10.46556 10.4145 11.002 18.797
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
2 rowAnyMissings 1.085679 1.095375 1.07225 1.091667 1.070442 1.064202

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on integer+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 3069274 164.0    5709258 305.0  5709258 305.0
Vcells 5203438  39.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3069268 164.0    5709258 305.0  5709258 305.0
Vcells 5253481  40.1   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.038997 0.0409690 0.0438670 0.041997 0.0439250 0.085250
3 colSums 0.077633 0.0821635 0.0845942 0.084590 0.0862585 0.100376
2 apply+anyMissing 1.505619 1.5885610 1.6492087 1.647717 1.7056425 1.957127
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums 1.990743 2.005504 1.928423 2.014191 1.963768 1.177431
2 apply+anyMissing 38.608585 38.774708 37.595641 39.234148 38.830791 22.957501

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.056641 0.0609035 0.0615156 0.0614335 0.0622650 0.073963
3 rowSums 0.274373 0.2902230 0.2940135 0.2974325 0.2991725 0.325074
2 apply+anyMissing 1.537276 1.6170405 1.6635327 1.6565425 1.7144425 1.926334
expr min lq mean median uq max
1 rowAnyMissings 1.00000 1.000000 1.00000 1.000000 1.000000 1.000000
3 rowSums 4.84407 4.765293 4.77950 4.841536 4.804826 4.395089
2 apply+anyMissing 27.14069 26.550863 27.04248 26.964807 27.534610 26.044563

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on integer+100x1000 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 38.997 40.9690 43.86702 41.9970 43.925 85.250
2 rowAnyMissings 56.641 60.9035 61.51555 61.4335 62.265 73.963
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.00000 1.0000000
2 rowAnyMissings 1.452445 1.486575 1.402319 1.462807 1.41753 0.8676012

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on integer+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 3069486 164.0    5709258 305.0  5709258 305.0
Vcells 5204107  39.8   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3069480 164.0    5709258 305.0  5709258 305.0
Vcells 5254150  40.1   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.030001 0.0319850 0.0327559 0.0328565 0.0333575 0.040529
3 colSums 0.081262 0.0834525 0.0868371 0.0865440 0.0888915 0.115576
2 apply+anyMissing 0.593065 0.6077225 0.6206371 0.6201165 0.6279245 0.743372
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums 2.708643 2.609114 2.651037 2.633999 2.664813 2.851686
2 apply+anyMissing 19.768174 19.000235 18.947344 18.873480 18.824088 18.341731

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.062917 0.0650115 0.0713341 0.0667215 0.068170 0.125828
3 rowSums 0.373836 0.3787040 0.3901189 0.3946015 0.396410 0.417912
2 apply+anyMissing 0.620900 0.6357370 0.6547821 0.6440605 0.654291 1.084129
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 5.941733 5.825185 5.468899 5.914158 5.815021 3.321296
2 apply+anyMissing 9.868557 9.778839 9.179092 9.652968 9.597932 8.615960

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on integer+1000x100 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 30.001 31.9850 32.75589 32.8565 33.3575 40.529
2 rowAnyMissings 62.917 65.0115 71.33408 66.7215 68.1700 125.828
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnyMissings 2.097163 2.032562 2.177748 2.030694 2.043618 3.104641

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() 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 matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3069700 164.0    5709258 305.0  5709258 305.0
Vcells 5319929  40.6   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3069682 164.0    5709258 305.0  5709258 305.0
Vcells 5320052  40.6   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.001819 0.0022745 0.0035873 0.002755 0.0033560 0.031580
3 colSums 0.003137 0.0037025 0.0056931 0.004514 0.0050970 0.064079
2 apply+anyMissing 0.022773 0.0236325 0.0307131 0.024044 0.0330695 0.084984
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
3 colSums 1.724574 1.62783 1.587011 1.638475 1.518772 2.029101
2 apply+anyMissing 12.519516 10.39020 8.561599 8.727405 9.853844 2.691070

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.001620 0.0019670 0.0025163 0.0023535 0.0025445 0.022849
3 rowSums 0.002514 0.0029355 0.0039530 0.0033415 0.0038405 0.052999
2 apply+anyMissing 0.021953 0.0228160 0.0246138 0.0231025 0.0234995 0.124548
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 1.551852 1.492374 1.570960 1.419800 1.509334 2.319533
2 apply+anyMissing 13.551235 11.599390 9.781872 9.816231 9.235410 5.450917

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on double+10x10 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 rowAnyMissings 1.620 1.9670 2.51627 2.3535 2.5445 22.849
1 colAnyMissings 1.819 2.2745 3.58731 2.7550 3.3560 31.580
expr min lq mean median uq max
2 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 colAnyMissings 1.122839 1.156329 1.425646 1.170597 1.318923 1.382117

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on double+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 3069893 164.0    5709258 305.0  5709258 305.0
Vcells 5320834  40.6   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3069887 164.0    5709258 305.0  5709258 305.0
Vcells 5330877  40.7   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.007107 0.0076770 0.0081280 0.0080405 0.0083785 0.018760
3 colSums 0.010312 0.0110705 0.0118222 0.0116055 0.0121975 0.026635
2 apply+anyMissing 0.208762 0.2103105 0.2168101 0.2117025 0.2160180 0.356309
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.00000 1.00000 1.000000 1.000000
3 colSums 1.450964 1.442035 1.45449 1.44338 1.455809 1.419776
2 apply+anyMissing 29.374138 27.394881 26.67434 26.32952 25.782419 18.993017

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.008187 0.0085160 0.0090631 0.0089225 0.0091945 0.019184
3 rowSums 0.021389 0.0219900 0.0225394 0.0225180 0.0228760 0.030473
2 apply+anyMissing 0.166758 0.1688355 0.1743099 0.1698705 0.1729725 0.273976
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 2.612557 2.582198 2.486937 2.523732 2.488009 1.588459
2 apply+anyMissing 20.368633 19.825681 19.232899 19.038442 18.812605 14.281485

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on double+100x100 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 7.107 7.677 8.12804 8.0405 8.3785 18.760
2 rowAnyMissings 8.187 8.516 9.06311 8.9225 9.1945 19.184
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnyMissings 1.151963 1.109287 1.115042 1.109695 1.097392 1.022601

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on double+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 3070109 164.0    5709258 305.0  5709258 305.0
Vcells 5321890  40.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3070100 164.0    5709258 305.0  5709258 305.0
Vcells 5331928  40.7   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.006251 0.006547 0.0073076 0.0069710 0.0073350 0.018011
3 colSums 0.011060 0.011543 0.0125758 0.0124015 0.0130405 0.027134
2 apply+anyMissing 0.116353 0.117901 0.1243409 0.1200470 0.1243990 0.223303
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums 1.769317 1.763098 1.720933 1.779013 1.777846 1.506524
2 apply+anyMissing 18.613502 18.008401 17.015376 17.220915 16.959646 12.398146

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.007567 0.0080310 0.0088064 0.0084455 0.008688 0.044808
3 rowSums 0.021999 0.0224215 0.0232173 0.0229490 0.023306 0.045368
2 apply+anyMissing 0.075030 0.0781560 0.0822301 0.0791490 0.081567 0.145654
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 2.907229 2.791869 2.636427 2.717305 2.682551 1.012498
2 apply+anyMissing 9.915422 9.731789 9.337580 9.371736 9.388467 3.250625

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on double+1000x10 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 6.251 6.547 7.30756 6.9710 7.335 18.011
2 rowAnyMissings 7.567 8.031 8.80636 8.4455 8.688 44.808
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnyMissings 1.210526 1.226669 1.205103 1.211519 1.184458 2.487813

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on double+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 3070311 164.0    5709258 305.0  5709258 305.0
Vcells 5322021  40.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3070305 164.0    5709258 305.0  5709258 305.0
Vcells 5332064  40.7   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.008865 0.010012 0.0109351 0.010653 0.0115545 0.021670
3 colSums 0.012431 0.013672 0.0148562 0.014700 0.0155670 0.028429
2 apply+anyMissing 1.023012 1.117141 1.1719342 1.175741 1.2425840 1.353891
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colSums 1.402256 1.365561 1.358581 1.379893 1.347267 1.311906
2 apply+anyMissing 115.398985 111.580154 107.171888 110.367080 107.541131 62.477665

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.008871 0.0100845 0.0114888 0.0110380 0.012122 0.026134
3 rowSums 0.020792 0.0226130 0.0248835 0.0234955 0.025047 0.072088
2 apply+anyMissing 1.021304 1.1250015 1.1898753 1.1814915 1.248977 1.769955
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 2.343817 2.242352 2.165891 2.128601 2.066243 2.758399
2 apply+anyMissing 115.128396 111.557489 103.568377 107.038549 103.033905 67.726142

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on double+10x1000 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 8.865 10.0120 10.93509 10.653 11.5545 21.670
2 rowAnyMissings 8.871 10.0845 11.48879 11.038 12.1220 26.134
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000
2 rowAnyMissings 1.000677 1.007241 1.050635 1.03614 1.049115 1.205999

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on double+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 3070524 164.0    5709258 305.0  5709258 305.0
Vcells 5323260  40.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3070518 164.0    5709258 305.0  5709258 305.0
Vcells 5423303  41.4   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.054174 0.059639 0.0664943 0.0614840 0.068437 0.146194
3 colSums 0.068635 0.076323 0.0839455 0.0793505 0.085531 0.298706
2 apply+anyMissing 1.976548 2.114569 2.3397263 2.1820875 2.237111 15.016490
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
3 colSums 1.266936 1.27975 1.262446 1.290588 1.249777 2.043217
2 apply+anyMissing 36.485177 35.45615 35.186849 35.490331 32.688619 102.716185

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.057760 0.0629830 0.0676968 0.065282 0.071874 0.099589
3 rowSums 0.182438 0.1947895 0.2059753 0.203771 0.212620 0.262827
2 apply+anyMissing 1.543800 1.6446905 1.8434543 1.695164 1.753697 13.961009
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 3.158553 3.092731 3.042616 3.121396 2.958232 2.639117
2 apply+anyMissing 26.727839 26.113245 27.231048 25.966783 24.399602 140.186255

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on double+100x1000 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 54.174 59.639 66.49434 61.484 68.437 146.194
2 rowAnyMissings 57.760 62.983 67.69678 65.282 71.874 99.589
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowAnyMissings 1.066194 1.056071 1.018083 1.061772 1.050221 0.6812113

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() on double+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 3070739 164.0    5709258 305.0  5709258 305.0
Vcells 5323408  40.7   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnyMissings = colAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 2L, 
+     FUN = anyMissing), colSums = is.na(colSums(X, na.rm = FALSE)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3070730 164.0    5709258 305.0  5709258 305.0
Vcells 5423446  41.4   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnyMissings = rowAnyMissings(X), `apply+anyMissing` = apply(X, MARGIN = 1L, 
+     FUN = anyMissing), rowSums = is.na(rowSums(X, na.rm = FALSE)), unit = "ms")

Table: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() 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 colAnyMissings 0.045287 0.0481780 0.0509085 0.0498250 0.0525545 0.086146
3 colSums 0.079204 0.0820255 0.0851201 0.0843330 0.0867525 0.108378
2 apply+anyMissing 0.601911 0.6307570 0.7094821 0.6432745 0.6718125 5.878546
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
3 colSums 1.748935 1.702551 1.67202 1.692584 1.650715 1.258074
2 apply+anyMissing 13.291033 13.092220 13.93641 12.910677 12.783158 68.239338

Table: Benchmarking of rowAnyMissings(), apply+anyMissing() and rowSums() 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 rowAnyMissings 0.062566 0.067849 0.0705182 0.0703415 0.072111 0.097521
3 rowSums 0.190387 0.202358 0.2105402 0.2118745 0.214736 0.238846
2 apply+anyMissing 0.652861 0.690647 0.7667233 0.7038675 0.732173 6.031251
expr min lq mean median uq max
1 rowAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums 3.042979 2.982476 2.985615 3.012084 2.977854 2.449175
2 apply+anyMissing 10.434757 10.179177 10.872702 10.006433 10.153416 61.845664

Figure: Benchmarking of colAnyMissings(), apply+anyMissing() and colSums() on double+1000x100 data as well as rowAnyMissings(), apply+anyMissing() and rowSums() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnyMissings() and rowAnyMissings() 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 colAnyMissings 45.287 48.178 50.90852 49.8250 52.5545 86.146
2 rowAnyMissings 62.566 67.849 70.51819 70.3415 72.1110 97.521
expr min lq mean median uq max
1 colAnyMissings 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAnyMissings 1.381544 1.408298 1.385194 1.411771 1.372119 1.132043

Figure: Benchmarking of colAnyMissings() and rowAnyMissings() 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 24.03 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:35:46 (-0700 UTC). Powered by RSP.

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