matrixStats.benchmarks

---

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 5186058  277    7916910 422.9  7916910 422.9
Vcells 9686828   74   33191153 253.3 53339345 407.0
> 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 5172353 276.3    7916910 422.9  7916910 422.9
Vcells 9641495  73.6   33191153 253.3 53339345 407.0
> 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	0.003809	0.0072100	0.0086075	0.0083990	0.010001	0.023819
3	colSums	0.006060	0.0110605	0.0131979	0.0123710	0.014875	0.049497
2	apply+anyMissing	0.042024	0.0748170	0.0769931	0.0806825	0.086153	0.105722

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
3	colSums	1.590969	1.53405	1.533312	1.472913	1.487351	2.078047
2	apply+anyMissing	11.032817	10.37684	8.944930	9.606203	8.614439	4.438557

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	0.003602	0.0041560	0.0049236	0.0048900	0.0052365	0.021287
3	rowSums	0.006268	0.0069095	0.0079021	0.0074495	0.0082510	0.029873
2	apply+anyMissing	0.038887	0.0411740	0.0431419	0.0420480	0.0432080	0.096701

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.740144	1.662536	1.604952	1.523415	1.575671	1.403345
2	apply+anyMissing	10.795947	9.907122	8.762293	8.598773	8.251313	4.542726

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	3.602	4.156	4.92358	4.890	5.2365	21.287
1	colAnyMissings	3.809	7.210	8.60746	8.399	10.0010	23.819

	expr	min	lq	mean	median	uq	max
2	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.057468	1.734841	1.748212	1.717587	1.909863	1.118946

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 5170912 276.2    7916910 422.9  7916910 422.9
Vcells 9258133  70.7   33191153 253.3 53339345 407.0
> 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 5170906 276.2    7916910 422.9  7916910 422.9
Vcells 9263176  70.7   33191153 253.3 53339345 407.0
> 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	0.007478	0.008555	0.0097983	0.0094525	0.0103845	0.030946
3	colSums	0.016013	0.017199	0.0190291	0.0182335	0.0201185	0.043141
2	apply+anyMissing	0.233762	0.253799	0.2714024	0.2593775	0.2846385	0.408694

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	2.141348	2.010403	1.942086	1.928961	1.937359	1.394073
2	apply+anyMissing	31.259963	29.666745	27.698985	27.440095	27.409938	13.206683

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	0.010866	0.0124065	0.0136336	0.0132000	0.0142105	0.029364
3	rowSums	0.042151	0.0453000	0.0493053	0.0474800	0.0518395	0.078197
2	apply+anyMissing	0.229116	0.2461195	0.2647529	0.2541975	0.2768255	0.383094

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
3	rowSums	3.879164	3.651312	3.616455	3.59697	3.647972	2.663023
2	apply+anyMissing	21.085588	19.837948	19.419133	19.25739	19.480349	13.046383

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	7.478	8.5550	9.79828	9.4525	10.3845	30.946
2	rowAnyMissings	10.866	12.4065	13.63361	13.2000	14.2105	29.364

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnyMissings	1.453062	1.450205	1.391429	1.396456	1.368434	0.9488787

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 5171665 276.2    7916910 422.9  7916910 422.9
Vcells 9261909  70.7   33191153 253.3 53339345 407.0
> 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 5171659 276.2    7916910 422.9  7916910 422.9
Vcells 9266952  70.8   33191153 253.3 53339345 407.0
> 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	0.007049	0.0080435	0.0088473	0.008437	0.0090805	0.021892
3	colSums	0.015188	0.0170840	0.0183648	0.017961	0.0188000	0.034795
2	apply+anyMissing	0.105127	0.1106220	0.1197459	0.118856	0.1252275	0.198053

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	2.154632	2.123951	2.075755	2.128837	2.070371	1.589393
2	apply+anyMissing	14.913747	13.752968	13.534738	14.087472	13.790816	9.046821

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	0.009904	0.011500	0.0124859	0.0121255	0.0130485	0.025463
2	apply+anyMissing	0.095213	0.103436	0.1126797	0.1071910	0.1202915	0.208917
3	rowSums	0.158646	0.168908	0.1791885	0.1743620	0.1866530	0.220276

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	apply+anyMissing	9.613591	8.994435	9.024575	8.84013	9.218799	8.204728
3	rowSums	16.018376	14.687652	14.351304	14.37978	14.304556	8.650827

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	7.049	8.0435	8.84730	8.4370	9.0805	21.892
2	rowAnyMissings	9.904	11.5000	12.48587	12.1255	13.0485	25.463

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowAnyMissings	1.405022	1.429726	1.411263	1.437181	1.43698	1.163119

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 5171879 276.3    7916910 422.9  7916910 422.9
Vcells 9262714  70.7   33191153 253.3 53339345 407.0
> 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 5171873 276.3    7916910 422.9  7916910 422.9
Vcells 9267757  70.8   33191153 253.3 53339345 407.0
> 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	0.008279	0.0089980	0.0104391	0.009740	0.0108185	0.024007
3	colSums	0.014411	0.0150095	0.0172416	0.016228	0.0174725	0.049457
2	apply+anyMissing	1.223089	1.2735145	1.3670440	1.289249	1.4162525	2.011674

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	1.740669	1.668093	1.651645	1.666119	1.615058	2.060107
2	apply+anyMissing	147.733905	141.533063	130.954580	132.366427	130.910246	83.795310

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	0.009444	0.0106160	0.0121598	0.011230	0.0125085	0.040141
3	rowSums	0.026942	0.0277665	0.0302508	0.028774	0.0302270	0.052397
2	apply+anyMissing	1.229076	1.2678990	1.3675467	1.286207	1.4201360	2.021531

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	2.852817	2.615533	2.487785	2.562244	2.416517	1.305324
2	apply+anyMissing	130.143583	119.432837	112.465031	114.533170	113.533677	50.360753

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	8.279	8.998	10.43907	9.74	10.8185	24.007
2	rowAnyMissings	9.444	10.616	12.15975	11.23	12.5085	40.141

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.140718	1.179818	1.164831	1.152977	1.156214	1.672054

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 5172093 276.3    7916910 422.9  7916910 422.9
Vcells 9263307  70.7   33191153 253.3 53339345 407.0
> 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 5172075 276.3    7916910 422.9  7916910 422.9
Vcells 9313330  71.1   33191153 253.3 53339345 407.0
> 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	0.039274	0.039847	0.0427368	0.040893	0.0426735	0.073255
3	colSums	0.090102	0.090739	0.0973042	0.092236	0.0944665	0.156048
2	apply+anyMissing	1.768765	1.810264	1.9352011	1.823029	1.8750790	3.187346

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	2.294189	2.277185	2.276822	2.255545	2.213704	2.130203
2	apply+anyMissing	45.036538	45.430384	45.281812	44.580466	43.940127	43.510286

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	0.057653	0.0581690	0.0635540	0.058692	0.0604780	0.148521
3	rowSums	0.222719	0.2236545	0.2482212	0.224808	0.2523015	0.383250
2	apply+anyMissing	1.771039	1.8009920	1.9164027	1.825411	1.8692320	3.141921

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
3	rowSums	3.863095	3.844909	3.905674	3.830301	4.17179	2.580443
2	apply+anyMissing	30.718939	30.961371	30.153922	31.101530	30.90764	21.154726

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	39.274	39.847	42.73683	40.893	42.6735	73.255
2	rowAnyMissings	57.653	58.169	63.55401	58.692	60.4780	148.521

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.467969	1.459809	1.487102	1.435258	1.417226	2.027452

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 5172299 276.3    7916910 422.9  7916910 422.9
Vcells 9263970  70.7   33191153 253.3 53339345 407.0
> 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 5172281 276.3    7916910 422.9  7916910 422.9
Vcells 9313993  71.1   33191153 253.3 53339345 407.0
> 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	0.031041	0.0322385	0.0394072	0.0358605	0.0446895	0.087170
3	colSums	0.083186	0.0844970	0.0982269	0.0909645	0.1089015	0.158834
2	apply+anyMissing	0.663584	0.6760930	0.7758048	0.7308360	0.8508990	1.197154

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	2.679875	2.620997	2.492617	2.536621	2.436848	1.822118
2	apply+anyMissing	21.377662	20.971602	19.686904	20.379972	19.040244	13.733555

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	0.068845	0.0697250	0.0782112	0.0708675	0.0862475	0.146025
3	rowSums	0.322388	0.3238260	0.3631519	0.3298095	0.4000005	0.539408
2	apply+anyMissing	0.671254	0.6773885	0.7446619	0.6828805	0.7592760	1.256254

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	4.682809	4.644331	4.643219	4.653889	4.637821	3.693943
2	apply+anyMissing	9.750222	9.715145	9.521162	9.636018	8.803455	8.603006

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	31.041	32.2385	39.40715	35.8605	44.6895	87.170
2	rowAnyMissings	68.845	69.7250	78.21124	70.8675	86.2475	146.025

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	2.217873	2.162787	1.984697	1.976199	1.929928	1.675175

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 5172503 276.3    7916910 422.9  7916910 422.9
Vcells 9379776  71.6   33191153 253.3 53339345 407.0
> 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 5172488 276.3    7916910 422.9  7916910 422.9
Vcells 9379904  71.6   33191153 253.3 53339345 407.0
> 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	0.003581	0.0041315	0.0049224	0.0046810	0.0050555	0.019806
3	colSums	0.005513	0.0060460	0.0071663	0.0068855	0.0075965	0.031905
2	apply+anyMissing	0.037558	0.0410005	0.0423185	0.0416070	0.0423830	0.093578

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	1.539514	1.463391	1.455853	1.470946	1.502621	1.610875
2	apply+anyMissing	10.488132	9.923877	8.597086	8.888485	8.383543	4.724730

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	0.003654	0.0040280	0.0048360	0.0047425	0.0051905	0.020096
3	rowSums	0.004608	0.0051185	0.0062236	0.0058080	0.0064145	0.031750
2	apply+anyMissing	0.037789	0.0396555	0.0418397	0.0410250	0.0422060	0.096883

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.261084	1.27073	1.286945	1.224670	1.235815	1.579916
2	apply+anyMissing	10.341817	9.84496	8.651758	8.650501	8.131394	4.821009

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
1	colAnyMissings	3.581	4.1315	4.92242	4.6810	5.0555	19.806
2	rowAnyMissings	3.654	4.0280	4.83598	4.7425	5.1905	20.096

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.0000000	1.0000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.020385	0.9749486	0.9824395	1.013138	1.026704	1.014642

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 5172696 276.3    7916910 422.9  7916910 422.9
Vcells 9380684  71.6   33191153 253.3 53339345 407.0
> 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 5172690 276.3    7916910 422.9  7916910 422.9
Vcells 9390727  71.7   33191153 253.3 53339345 407.0
> 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	0.010568	0.0116260	0.0132199	0.0126215	0.0134595	0.046017
3	colSums	0.014621	0.0158655	0.0179084	0.0170930	0.0183270	0.048197
2	apply+anyMissing	0.268224	0.2804840	0.2967699	0.2848725	0.3034100	0.430302

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	1.383516	1.364657	1.354652	1.354276	1.361641	1.047374
2	apply+anyMissing	25.380772	24.125581	22.448639	22.570416	22.542442	9.350936

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	0.010763	0.0123595	0.0136147	0.0130415	0.0143355	0.030891
3	rowSums	0.031449	0.0327610	0.0357766	0.0340155	0.0377875	0.067907
2	apply+anyMissing	0.233782	0.2465895	0.2714195	0.2567235	0.2849800	0.407697

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	2.921955	2.650674	2.627789	2.608251	2.635939	2.198278
2	apply+anyMissing	21.720896	19.951414	19.935750	19.685121	19.879321	13.197922

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	10.568	11.6260	13.21995	12.6215	13.4595	46.017
2	rowAnyMissings	10.763	12.3595	13.61471	13.0415	14.3355	30.891

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnyMissings	1.018452	1.063091	1.029861	1.033277	1.065084	0.6712954

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 5172907 276.3    7916910 422.9  7916910 422.9
Vcells 9381734  71.6   33191153 253.3 53339345 407.0
> 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 5172901 276.3    7916910 422.9  7916910 422.9
Vcells 9391777  71.7   33191153 253.3 53339345 407.0
> 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	0.009523	0.0111850	0.0119533	0.0117325	0.012410	0.026042
3	colSums	0.016357	0.0184395	0.0194881	0.0190915	0.020452	0.035045
2	apply+anyMissing	0.117981	0.1244735	0.1356701	0.1348180	0.141845	0.223501

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	1.717631	1.648592	1.630347	1.627232	1.648026	1.345711
2	apply+anyMissing	12.389058	11.128610	11.349992	11.490987	11.429895	8.582328

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	0.010802	0.0128360	0.0138888	0.0135210	0.0148770	0.026792
3	rowSums	0.033736	0.0368030	0.0393192	0.0393355	0.0413700	0.054038
2	apply+anyMissing	0.110934	0.1212575	0.1318602	0.1309920	0.1411225	0.217518

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	3.123125	2.867170	2.831011	2.909215	2.780803	2.016945
2	apply+anyMissing	10.269765	9.446673	9.494031	9.688041	9.485952	8.118767

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	9.523	11.185	11.95332	11.7325	12.410	26.042
2	rowAnyMissings	10.802	12.836	13.88875	13.5210	14.877	26.792

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.00000	1.000000	1.0000
2	rowAnyMissings	1.134306	1.147608	1.161916	1.15244	1.198791	1.0288

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 5173121 276.3    7916910 422.9  7916910 422.9
Vcells 9381879  71.6   33191153 253.3 53339345 407.0
> 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 5173115 276.3    7916910 422.9  7916910 422.9
Vcells 9391922  71.7   33191153 253.3 53339345 407.0
> 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	0.008927	0.0099830	0.0125789	0.0111975	0.0125595	0.068952
3	colSums	0.011348	0.0121525	0.0144350	0.0132575	0.0146240	0.036569
2	apply+anyMissing	1.200840	1.2472740	1.3499939	1.2682735	1.4201865	1.980458

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.0000	1.000000	1.000000	1.00000	1.000000	1.0000000
3	colSums	1.2712	1.217319	1.147559	1.18397	1.164378	0.5303544
2	apply+anyMissing	134.5178	124.939798	107.321923	113.26399	113.076675	28.7222706

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	0.009502	0.0107415	0.0131113	0.011807	0.0138640	0.046459
3	rowSums	0.025936	0.0270980	0.0305283	0.028370	0.0308415	0.073159
2	apply+anyMissing	1.187766	1.2390640	1.3551333	1.280582	1.4261220	2.035022

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
3	rowSums	2.729531	2.522739	2.328389	2.402812	2.224574	1.57470
2	apply+anyMissing	125.001684	115.352977	103.355744	108.459515	102.865118	43.80254

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	8.927	9.9830	12.57892	11.1975	12.5595	68.952
2	rowAnyMissings	9.502	10.7415	13.11135	11.8070	13.8640	46.459

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnyMissings	1.064411	1.075979	1.042327	1.054432	1.103866	0.6737876

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 5173336 276.3    7916910 422.9  7916910 422.9
Vcells 9383128  71.6   33191153 253.3 53339345 407.0
> 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 5173318 276.3    7916910 422.9  7916910 422.9
Vcells 9483151  72.4   33191153 253.3 53339345 407.0
> 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	0.054339	0.055505	0.0661051	0.0625085	0.0739490	0.120282
3	colSums	0.066439	0.067155	0.0799080	0.0749915	0.0865195	0.143436
2	apply+anyMissing	1.760925	1.805524	2.2101087	1.8913905	2.2497235	18.045320

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	1.222676	1.209891	1.208801	1.199701	1.169989	1.192498
2	apply+anyMissing	32.406283	32.529033	33.433240	30.258133	30.422636	150.025108

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	0.058057	0.0599710	0.0732221	0.0678525	0.0846755	0.134703
3	rowSums	0.212298	0.2148315	0.2461261	0.2273010	0.2784615	0.333167
2	apply+anyMissing	1.769613	1.8313000	2.1574124	1.8940445	2.0682460	18.275419

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	3.656717	3.582256	3.361364	3.349928	3.288572	2.473345
2	apply+anyMissing	30.480614	30.536426	29.463956	27.914145	24.425554	135.671952

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	54.339	55.505	66.10513	62.5085	73.9490	120.282
2	rowAnyMissings	58.057	59.971	73.22209	67.8525	84.6755	134.703

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.068422	1.080461	1.107661	1.085492	1.145053	1.119893

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 5173530 276.3    7916910 422.9  7916910 422.9
Vcells 9383245  71.6   33191153 253.3 53339345 407.0
> 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 5173524 276.3    7916910 422.9  7916910 422.9
Vcells 9483288  72.4   33191153 253.3 53339345 407.0
> 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	0.047434	0.0507065	0.0623801	0.0612045	0.0710025	0.133226
3	colSums	0.080218	0.0832945	0.0992983	0.0973190	0.1113380	0.157986
2	apply+anyMissing	0.694111	0.7165185	0.8960841	0.8045915	0.9224660	6.548673

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.00000	1.000000	1.000000	1.000000	1.000000	1.00000
3	colSums	1.69115	1.642679	1.591827	1.590063	1.568086	1.18585
2	apply+anyMissing	14.63320	14.130703	14.364906	13.145953	12.992021	49.15462

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	0.065476	0.0664530	0.0787625	0.0750730	0.0858680	0.128460
3	rowSums	0.217432	0.2191375	0.2580663	0.2529185	0.2794245	0.366757
2	apply+anyMissing	0.717454	0.7312975	0.9005127	0.7565085	0.9377745	6.505789

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	3.320789	3.297631	3.276513	3.368968	3.254117	2.855029
2	apply+anyMissing	10.957511	11.004733	11.433269	10.076972	10.921117	50.644473

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	47.434	50.7065	62.38009	61.2045	71.0025	133.226
2	rowAnyMissings	65.476	66.4530	78.76248	75.0730	85.8680	128.460

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.00000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnyMissings	1.38036	1.310542	1.262622	1.226593	1.209366	0.9642262

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

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] microbenchmark_1.4-7   matrixStats_0.60.0     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

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

Total processing time was 23.61 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:10:59 (+0200 UTC). Powered by RSP.