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 5173263 276.3    8529671 455.6  8529671 455.6
Vcells 9649620  73.7   31876688 243.2 60562128 462.1
> 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 5164851 275.9    8529671 455.6  8529671 455.6
Vcells 9622208  73.5   31876688 243.2 60562128 462.1
> 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	0.004152	0.0045730	0.0055188	0.0055150	0.0058835	0.025830
3	colSums	0.005915	0.0063460	0.0074479	0.0069075	0.0078875	0.035323
2	apply+anyMissing	0.041145	0.0421355	0.0437230	0.0428330	0.0436395	0.098740

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	1.424615	1.387711	1.349549	1.252493	1.340614	1.367518
2	apply+anyMissing	9.909682	9.213973	7.922559	7.766636	7.417269	3.822687

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	0.004328	0.0046645	0.0056704	0.0057380	0.0059540	0.023656
3	rowSums	0.006724	0.0070750	0.0080048	0.0075205	0.0083910	0.031508
2	apply+anyMissing	0.041078	0.0419675	0.0433506	0.0424490	0.0432625	0.095971

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.553604	1.516776	1.411692	1.310648	1.409305	1.331924
2	apply+anyMissing	9.491220	8.997213	7.645142	7.397874	7.266124	4.056941

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
1	colAnyMissings	4.152	4.5730	5.51880	5.515	5.8835	25.830
2	rowAnyMissings	4.328	4.6645	5.67035	5.738	5.9540	23.656

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnyMissings	1.042389	1.020009	1.027461	1.040435	1.011983	0.9158343

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 5163428 275.8    8529671 455.6  8529671 455.6
Vcells 9238876  70.5   31876688 243.2 60562128 462.1
> 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 5163404 275.8    8529671 455.6  8529671 455.6
Vcells 9243889  70.6   31876688 243.2 60562128 462.1
> 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
3	colSums	0.016000	0.017601	0.0197373	0.0184605	0.0213410	0.043355
1	colAnyMissings	0.017705	0.018906	0.0215564	0.0201235	0.0233870	0.047997
2	apply+anyMissing	0.246295	0.253925	0.2814964	0.2724580	0.2972355	0.407821

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.106562	1.074143	1.092167	1.090084	1.095872	1.107069
2	apply+anyMissing	15.393437	14.426737	14.262176	14.758972	13.927909	9.406551

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	0.018271	0.0210740	0.0246279	0.0233630	0.0257595	0.064017
3	rowSums	0.044517	0.0475015	0.0555271	0.0537035	0.0587230	0.097315
2	apply+anyMissing	0.245325	0.2610140	0.3120101	0.2852185	0.3253155	0.709762

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	2.436484	2.254033	2.254641	2.298656	2.279664	1.520143
2	apply+anyMissing	13.427015	12.385594	12.668957	12.208128	12.628952	11.087086

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	17.705	18.906	21.55639	20.1235	23.3870	47.997
2	rowAnyMissings	18.271	21.074	24.62792	23.3630	25.7595	64.017

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.031968	1.114673	1.142488	1.160981	1.101445	1.333771

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 5164163 275.8    8529671 455.6  8529671 455.6
Vcells 9242622  70.6   31876688 243.2 60562128 462.1
> 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 5164157 275.8    8529671 455.6  8529671 455.6
Vcells 9247665  70.6   31876688 243.2 60562128 462.1
> 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
3	colSums	0.015642	0.017862	0.0192017	0.0190945	0.0199650	0.034222
1	colAnyMissings	0.018089	0.019810	0.0213207	0.0212070	0.0222605	0.037839
2	apply+anyMissing	0.112795	0.118537	0.1295417	0.1284490	0.1384660	0.214005

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.156438	1.109058	1.110352	1.110634	1.114976	1.105692
2	apply+anyMissing	7.211034	6.636267	6.746361	6.727016	6.935437	6.253433

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	0.019821	0.0220475	0.0248644	0.0238195	0.0265080	0.042560
2	apply+anyMissing	0.105758	0.1103845	0.1258374	0.1200485	0.1371815	0.253293
3	rowSums	0.168489	0.1739700	0.1900289	0.1866410	0.2024465	0.230999

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	apply+anyMissing	5.335654	5.006667	5.060956	5.039925	5.175098	5.951433
3	rowSums	8.500530	7.890691	7.642626	7.835639	7.637185	5.427608

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	18.089	19.8100	21.32067	21.2070	22.2605	37.839
2	rowAnyMissings	19.821	22.0475	24.86435	23.8195	26.5080	42.560

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	rowAnyMissings	1.095749	1.112948	1.166209	1.12319	1.190809	1.124766

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 5164395 275.9    8529671 455.6  8529671 455.6
Vcells 9243463  70.6   31876688 243.2 60562128 462.1
> 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 5164371 275.9    8529671 455.6  8529671 455.6
Vcells 9248476  70.6   31876688 243.2 60562128 462.1
> 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
3	colSums	0.015172	0.0167680	0.0185027	0.0178915	0.0190845	0.039948
1	colAnyMissings	0.016558	0.0180085	0.0201090	0.0193335	0.0207530	0.044990
2	apply+anyMissing	1.231870	1.3360010	1.4178813	1.3889800	1.4404045	2.019945

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.091352	1.07398	1.086816	1.080597	1.087427	1.126214
2	apply+anyMissing	81.193646	79.67563	76.631050	77.633513	75.475098	50.564359

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	0.015909	0.018860	0.0221714	0.020855	0.0233990	0.057988
3	rowSums	0.027829	0.030573	0.0340773	0.031732	0.0353285	0.070356
2	apply+anyMissing	1.229049	1.339528	1.4923336	1.402618	1.5593800	3.116060

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.749261	1.62105	1.536994	1.521554	1.509829	1.213286
2	apply+anyMissing	77.254950	71.02484	67.308980	67.255694	66.643019	53.736290

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	16.558	18.0085	20.10903	19.3335	20.753	44.990
2	rowAnyMissings	15.909	18.8600	22.17139	20.8550	23.399	57.988

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.0000000	1.000000	1.000000	1.000000	1.0000	1.000000
2	rowAnyMissings	0.9608044	1.047283	1.102559	1.078698	1.1275	1.288909

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 5164585 275.9    8529671 455.6  8529671 455.6
Vcells 9244012  70.6   31876688 243.2 60562128 462.1
> 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 5164573 275.9    8529671 455.6  8529671 455.6
Vcells 9294045  71.0   31876688 243.2 60562128 462.1
> 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
3	colSums	0.090408	0.0987670	0.1038478	0.101548	0.1067305	0.152955
1	colAnyMissings	0.115819	0.1254885	0.1299807	0.129344	0.1322780	0.187385
2	apply+anyMissing	1.826588	1.9727960	2.0741151	2.022147	2.0801860	3.259332

	expr	min	lq	mean	median	uq	max
3	colSums	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.28107	1.270551	1.251646	1.273723	1.239365	1.225099
2	apply+anyMissing	20.20383	19.974242	19.972653	19.913214	19.490080	21.309091

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	0.110603	0.1224845	0.1278250	0.1239075	0.1271055	0.224174
3	rowSums	0.223137	0.2488885	0.2623729	0.2505605	0.2644200	0.370899
2	apply+anyMissing	1.827371	2.0025165	2.0684907	2.0354200	2.0903510	3.049776

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
3	rowSums	2.017459	2.03200	2.052594	2.022158	2.080319	1.654514
2	apply+anyMissing	16.521894	16.34914	16.182201	16.426931	16.445795	13.604504

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
2	rowAnyMissings	110.603	122.4845	127.8251	123.9075	127.1055	224.174
1	colAnyMissings	115.819	125.4885	129.9807	129.3440	132.2780	187.385

	expr	min	lq	mean	median	uq	max
2	rowAnyMissings	1.00000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	colAnyMissings	1.04716	1.024526	1.016864	1.043875	1.040695	0.8358909

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 5164791 275.9    8529671 455.6  8529671 455.6
Vcells 9244673  70.6   31876688 243.2 60562128 462.1
> 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 5164779 275.9    8529671 455.6  8529671 455.6
Vcells 9294706  71.0   31876688 243.2 60562128 462.1
> 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
3	colSums	0.083760	0.0902095	0.1012488	0.0933785	0.1096735	0.170515
1	colAnyMissings	0.107897	0.1145115	0.1322218	0.1201660	0.1503085	0.209809
2	apply+anyMissing	0.691620	0.7374295	0.8200929	0.7719145	0.8877480	1.238088

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
1	colAnyMissings	1.288169	1.269395	1.30591	1.286870	1.370509	1.230443
2	apply+anyMissing	8.257163	8.174632	8.09978	8.266512	8.094462	7.260874

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	0.117486	0.1240050	0.1365120	0.128367	0.146813	0.206941
3	rowSums	0.323155	0.3405910	0.3718618	0.352058	0.393595	0.537099
2	apply+anyMissing	0.705295	0.7330845	0.8019749	0.761286	0.794242	1.287498

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	2.750583	2.746591	2.724023	2.742590	2.680927	2.595421
2	apply+anyMissing	6.003226	5.911733	5.874758	5.930543	5.409889	6.221570

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	107.897	114.5115	132.2218	120.166	150.3085	209.809
2	rowAnyMissings	117.486	124.0050	136.5120	128.367	146.8130	206.941

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.0000000	1.0000000
2	rowAnyMissings	1.088872	1.082904	1.032447	1.068247	0.9767445	0.9863304

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 5165019 275.9    8529671 455.6  8529671 455.6
Vcells 9360522  71.5   31876688 243.2 60562128 462.1
> 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 5164986 275.9    8529671 455.6  8529671 455.6
Vcells 9360620  71.5   31876688 243.2 60562128 462.1
> 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	0.004183	0.0045845	0.0055005	0.0055000	0.005831	0.023252
3	colSums	0.005828	0.0063930	0.0074972	0.0071465	0.008012	0.031594
2	apply+anyMissing	0.041914	0.0428770	0.0442545	0.0434440	0.044194	0.094950

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums	1.393258	1.394481	1.363010	1.299364	1.374035	1.358765
2	apply+anyMissing	10.020081	9.352601	8.045609	7.898909	7.579146	4.083520

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	0.004364	0.0048200	0.0056987	0.0056735	0.0060610	0.022892
3	rowSums	0.004848	0.0053185	0.0062513	0.0060010	0.0066915	0.027379
2	apply+anyMissing	0.041745	0.0423780	0.0438045	0.0428670	0.0434475	0.095406

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.110907	1.103423	1.096983	1.057724	1.104026	1.196007
2	apply+anyMissing	9.565765	8.792116	7.686824	7.555654	7.168372	4.167657

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	4.183	4.5845	5.50046	5.5000	5.831	23.252
2	rowAnyMissings	4.364	4.8200	5.69865	5.6735	6.061	22.892

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.00000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnyMissings	1.04327	1.051369	1.036031	1.031546	1.039444	0.9845175

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 5165212 275.9    8529671 455.6  8529671 455.6
Vcells 9361413  71.5   31876688 243.2 60562128 462.1
> 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 5165188 275.9    8529671 455.6  8529671 455.6
Vcells 9371426  71.5   31876688 243.2 60562128 462.1
> 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
3	colSums	0.013272	0.0147110	0.0168459	0.0156600	0.018058	0.038820
1	colAnyMissings	0.017963	0.0194415	0.0220184	0.0207050	0.024018	0.052582
2	apply+anyMissing	0.250737	0.2616875	0.2901419	0.2775855	0.315537	0.410093

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.353451	1.321562	1.307046	1.322158	1.330048	1.354508
2	apply+anyMissing	18.892179	17.788560	17.223292	17.725766	17.473530	10.563962

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	0.023835	0.0261650	0.0308886	0.0278850	0.0305080	0.095873
3	rowSums	0.031138	0.0329495	0.0375498	0.0355995	0.0392675	0.111720
2	apply+anyMissing	0.242400	0.2611820	0.3028223	0.2836630	0.3106240	0.720852

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.306398	1.259297	1.215650	1.276654	1.287121	1.165292
2	apply+anyMissing	10.169918	9.982114	9.803689	10.172602	10.181723	7.518822

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	17.963	19.4415	22.01837	20.705	24.018	52.582
2	rowAnyMissings	23.835	26.1650	30.88861	27.885	30.508	95.873

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.326894	1.345832	1.402856	1.346776	1.270214	1.823305

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 5165423 275.9    8529671 455.6  8529671 455.6
Vcells 9362461  71.5   31876688 243.2 60562128 462.1
> 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 5165399 275.9    8529671 455.6  8529671 455.6
Vcells 9372474  71.6   31876688 243.2 60562128 462.1
> 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
3	colSums	0.016003	0.0177055	0.0191410	0.0189050	0.0198145	0.035648
1	colAnyMissings	0.018330	0.0202330	0.0217923	0.0215055	0.0225825	0.038811
2	apply+anyMissing	0.118946	0.1284225	0.1377918	0.1365365	0.1459090	0.227962

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.145410	1.142752	1.138512	1.137556	1.139696	1.088729
2	apply+anyMissing	7.432731	7.253255	7.198764	7.222243	7.363749	6.394805

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	0.025614	0.030294	0.0327506	0.0324670	0.0338925	0.053688
3	rowSums	0.033268	0.036110	0.0388449	0.0385370	0.0413465	0.053762
2	apply+anyMissing	0.114821	0.123882	0.1368627	0.1382005	0.1464095	0.233176

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.298821	1.191985	1.186082	1.186959	1.219931	1.001378
2	apply+anyMissing	4.482744	4.089325	4.178940	4.256645	4.319820	4.343168

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	18.330	20.233	21.79230	21.5055	22.5825	38.811
2	rowAnyMissings	25.614	30.294	32.75058	32.4670	33.8925	53.688

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowAnyMissings	1.397381	1.497257	1.502851	1.509707	1.50083	1.383319

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 5165637 275.9    8529671 455.6  8529671 455.6
Vcells 9362606  71.5   31876688 243.2 60562128 462.1
> 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 5165613 275.9    8529671 455.6  8529671 455.6
Vcells 9372619  71.6   31876688 243.2 60562128 462.1
> 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
3	colSums	0.012113	0.0133090	0.0157057	0.0144020	0.0158420	0.049407
1	colAnyMissings	0.017947	0.0199695	0.0226568	0.0213455	0.0237925	0.046400
2	apply+anyMissing	1.249203	1.3442290	1.4602180	1.4036045	1.5026120	2.120474

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.000000	1.000000	1.00000	1.000000	1.0000000
1	colAnyMissings	1.481631	1.500451	1.442589	1.48212	1.501862	0.9391382
2	apply+anyMissing	103.129118	101.001503	92.973997	97.45900	94.849893	42.9184933

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	0.020984	0.023676	0.0269468	0.0250505	0.0278775	0.065245
3	rowSums	0.026256	0.028679	0.0313051	0.0299870	0.0318135	0.054268
2	apply+anyMissing	1.222901	1.338548	1.4503159	1.3913760	1.4882110	2.238517

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	rowSums	1.251239	1.211311	1.161737	1.197062	1.141189	0.8317572
2	apply+anyMissing	58.277783	56.536049	53.821412	55.542843	53.383948	34.3094030

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	17.947	19.9695	22.65681	21.3455	23.7925	46.400
2	rowAnyMissings	20.984	23.6760	26.94682	25.0505	27.8775	65.245

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.16922	1.185608	1.189348	1.173573	1.171693	1.406142

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 5165827 275.9    8529671 455.6  8529671 455.6
Vcells 9363813  71.5   31876688 243.2 60562128 462.1
> 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 5165815 275.9    8529671 455.6  8529671 455.6
Vcells 9463846  72.3   31876688 243.2 60562128 462.1
> 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
3	colSums	0.066902	0.0737645	0.0800034	0.0769425	0.0845035	0.124964
1	colAnyMissings	0.117715	0.1283685	0.1376021	0.1324485	0.1425200	0.206157
2	apply+anyMissing	1.858225	2.0137385	2.3072266	2.0690435	2.2104915	17.597055

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.759514	1.740248	1.719953	1.721396	1.686557	1.649731
2	apply+anyMissing	27.775328	27.299561	28.839096	26.890776	26.158579	140.816995

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	0.161246	0.1792265	0.1899426	0.1828285	0.196273	0.312658
3	rowSums	0.213240	0.2367400	0.2534892	0.2393485	0.260552	0.372628
2	apply+anyMissing	1.877465	2.0376830	2.3019401	2.0778825	2.179920	17.639980

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.322451	1.320898	1.334557	1.309142	1.327498	1.191807
2	apply+anyMissing	11.643483	11.369318	12.119136	11.365200	11.106571	56.419410

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	117.715	128.3685	137.6021	132.4485	142.520	206.157
2	rowAnyMissings	161.246	179.2265	189.9426	182.8285	196.273	312.658

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.0000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.3698	1.396187	1.380375	1.380374	1.377161	1.516601

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 5166045 275.9    8529671 455.6  8529671 455.6
Vcells 9363970  71.5   31876688 243.2 60562128 462.1
> 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 5166021 275.9    8529671 455.6  8529671 455.6
Vcells 9463983  72.3   31876688 243.2 60562128 462.1
> 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
3	colSums	0.080691	0.0853730	0.0991938	0.0950485	0.1066455	0.161523
1	colAnyMissings	0.108974	0.1145825	0.1381865	0.1288255	0.1514760	0.272347
2	apply+anyMissing	0.727379	0.7471060	0.9229441	0.8041740	0.9356230	6.545080

	expr	min	lq	mean	median	uq	max
3	colSums	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAnyMissings	1.350510	1.342140	1.393097	1.355366	1.420369	1.686119
2	apply+anyMissing	9.014376	8.751081	9.304458	8.460670	8.773207	40.521040

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	0.166913	0.1763320	0.1953518	0.1893330	0.2064165	0.290477
3	rowSums	0.217193	0.2299690	0.2547578	0.2494075	0.2707735	0.365161
2	apply+anyMissing	0.759895	0.7800215	0.9386052	0.8212835	0.9339740	6.762341

	expr	min	lq	mean	median	uq	max
1	rowAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums	1.301235	1.304182	1.304098	1.317295	1.311782	1.257108
2	apply+anyMissing	4.552641	4.423596	4.804693	4.337773	4.524706	23.280125

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	108.974	114.5825	138.1865	128.8255	151.4760	272.347
2	rowAnyMissings	166.913	176.3320	195.3518	189.3330	206.4165	290.477

	expr	min	lq	mean	median	uq	max
1	colAnyMissings	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnyMissings	1.531677	1.538909	1.413682	1.469686	1.362701	1.066569

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_0.60.1     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] rappdirs_0.3.3          startup_0.15.0          labeling_0.4.2         
[67] bitops_1.0-7            base64enc_0.1-3         boot_1.3-28            
[70] gtable_0.3.0            DBI_1.1.1               markdown_1.1           
[73] R6_2.5.1                lpSolveAPI_5.5.2.0-17.7 rle_0.9.2              
[76] dplyr_1.0.7             fastmap_1.1.0           bit_4.0.4              
[79] utf8_1.2.2              parallel_4.1.1          Rcpp_1.0.7             
[82] vctrs_0.3.8             png_0.1-7               DEoptimR_1.0-9         
[85] tidyselect_1.1.1        xfun_0.25               coda_0.19-4            

Total processing time was 24.17 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 18:50:38 (+0200 UTC). Powered by RSP.