matrixStats.benchmarks

---

colAlls() and rowAlls() benchmarks

This report benchmark the performance of colAlls() and rowAlls() against alternative methods.

Alternative methods

• apply() + all()
• colSums() == n or rowSums() == n

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 = "logical")

Results

10x10 matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5178000 276.6    7916910 422.9  7916910 422.9
Vcells 9439896  72.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5164311 275.9    7916910 422.9  7916910 422.9
Vcells 9394591  71.7   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls	0.002918	0.0033165	0.0040829	0.0040155	0.004426	0.015997
3	colSums==n	0.006390	0.0069745	0.0081633	0.0076470	0.008282	0.041658
2	apply+all	0.032027	0.0346345	0.0360910	0.0352475	0.036222	0.086653

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
3	colSums==n	2.189856	2.10297	1.999383	1.904371	1.871215	2.604113
2	apply+all	10.975668	10.44309	8.839505	8.777861	8.183913	5.416828

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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	rowAlls	0.002918	0.0032065	0.0039516	0.0039845	0.0042960	0.017575
3	rowSums==n	0.006662	0.0073095	0.0084375	0.0079800	0.0085190	0.053558
2	apply+all	0.030570	0.0324275	0.0341682	0.0330865	0.0341165	0.081091

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums==n	2.283071	2.279588	2.135227	2.002761	1.983007	3.047397
2	apply+all	10.476354	10.113052	8.646735	8.303802	7.941457	4.613997

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x10 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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	rowAlls	2.918	3.2065	3.95157	3.9845	4.296	17.575
1	colAlls	2.918	3.3165	4.08292	4.0155	4.426	15.997

	expr	min	lq	mean	median	uq	max
2	rowAlls	1	1.000000	1.00000	1.00000	1.000000	1.0000000
1	colAlls	1	1.034305	1.03324	1.00778	1.030261	0.9102134

Figure: Benchmarking of colAlls() and rowAlls() on 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 5162874 275.8    7916910 422.9  7916910 422.9
Vcells 9200790  70.2   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5162868 275.8    7916910 422.9  7916910 422.9
Vcells 9205833  70.3   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls	0.002933	0.0038555	0.0049758	0.0047205	0.005187	0.025572
3	colSums==n	0.015744	0.0178695	0.0200639	0.0191665	0.021673	0.048562
2	apply+all	0.194001	0.2085905	0.2306413	0.2294020	0.250898	0.341773

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.00000	1.00000
3	colSums==n	5.367883	4.634807	4.032323	4.060269	4.17833	1.89903
2	apply+all	66.144221	54.102062	46.352981	48.596971	48.37054	13.36513

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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	rowAlls	0.007883	0.0091750	0.0103561	0.0098445	0.0107805	0.025822
3	rowSums==n	0.045572	0.0481485	0.0536306	0.0524420	0.0584220	0.085888
2	apply+all	0.185799	0.1944955	0.2200996	0.2165005	0.2421380	0.335716

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums==n	5.781048	5.247793	5.178666	5.327035	5.419229	3.326156
2	apply+all	23.569580	21.198420	21.253200	21.992026	22.460739	13.001162

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x100 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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	colAlls	2.933	3.8555	4.97576	4.7205	5.1870	25.572
2	rowAlls	7.883	9.1750	10.35607	9.8445	10.7805	25.822

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls	2.687692	2.379717	2.081304	2.085478	2.078369	1.009776

Figure: Benchmarking of colAlls() and rowAlls() on 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 5163624 275.8    7916910 422.9  7916910 422.9
Vcells 9204309  70.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5163618 275.8    7916910 422.9  7916910 422.9
Vcells 9209352  70.3   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls	0.002853	0.0035520	0.0043257	0.0041145	0.0047010	0.017084
3	colSums==n	0.017351	0.0194050	0.0207849	0.0201080	0.0213510	0.038464
2	apply+all	0.106709	0.1122245	0.1243842	0.1259220	0.1296695	0.202048

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
3	colSums==n	6.081668	5.463119	4.805017	4.887107	4.54180	2.251463
2	apply+all	37.402383	31.594735	28.754896	30.604448	27.58339	11.826738

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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	rowAlls	0.008121	0.0095930	0.0124842	0.0107530	0.0134965	0.025416
2	apply+all	0.096428	0.1016105	0.1246757	0.1089125	0.1305395	0.250102
3	rowSums==n	0.167941	0.1753380	0.1949295	0.1870195	0.2103385	0.262439

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.00000	1.00000	1.000000	1.00000	1.00000	1.000000
2	apply+all	11.87391	10.59215	9.986648	10.12857	9.67210	9.840337
3	rowSums==n	20.67984	18.27770	15.614044	17.39231	15.58467	10.325740

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x10 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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	colAlls	2.853	3.552	4.32567	4.1145	4.7010	17.084
2	rowAlls	8.121	9.593	12.48424	10.7530	13.4965	25.416

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	rowAlls	2.846477	2.700732	2.886082	2.61344	2.870985	1.487708

Figure: Benchmarking of colAlls() and rowAlls() on 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 5163838 275.8    7916910 422.9  7916910 422.9
Vcells 9205044  70.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5163832 275.8    7916910 422.9  7916910 422.9
Vcells 9210087  70.3   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls	0.009408	0.0118870	0.0140773	0.0128055	0.0148165	0.035856
3	colSums==n	0.013396	0.0143105	0.0170158	0.0159030	0.0184325	0.039828
2	apply+all	0.858664	0.8714435	1.0008542	0.9282170	1.0878390	1.492602

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums==n	1.423895	1.203878	1.208737	1.241888	1.244052	1.110776
2	apply+all	91.269558	73.310633	71.096930	72.485807	73.420781	41.627677

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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	rowAlls	0.014285	0.0186485	0.0226967	0.0210525	0.0241460	0.067273
3	rowSums==n	0.028580	0.0311925	0.0361926	0.0347380	0.0387395	0.062046
2	apply+all	0.859912	0.8921850	1.0275150	0.9945310	1.1099595	1.457040

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.00000	1.000000	1.00000	1.000000	1.000000	1.0000000
3	rowSums==n	2.00070	1.672655	1.59462	1.650065	1.604386	0.9223017
2	apply+all	60.19685	47.842186	45.27160	47.240518	45.968670	21.6586149

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x1000 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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	colAlls	9.408	11.8870	14.07732	12.8055	14.8165	35.856
2	rowAlls	14.285	18.6485	22.69668	21.0525	24.1460	67.273

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.00000	1.00000	1.000000
2	rowAlls	1.518389	1.568815	1.612287	1.64402	1.62967	1.876199

Figure: Benchmarking of colAlls() and rowAlls() on 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 5164047 275.8    7916910 422.9  7916910 422.9
Vcells 9205557  70.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5164035 275.8    7916910 422.9  7916910 422.9
Vcells 9255590  70.7   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls	0.011273	0.0132780	0.0157696	0.0147690	0.016544	0.040375
3	colSums==n	0.081055	0.0825335	0.0932127	0.0871075	0.095800	0.193283
2	apply+all	1.347308	1.3854070	1.5263032	1.3998815	1.611465	2.504890

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
3	colSums==n	7.190189	6.215808	5.91093	5.897996	5.790619	4.787195
2	apply+all	119.516367	104.338530	96.78800	94.785124	97.404769	62.040619

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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	rowAlls	0.036765	0.0383855	0.0443383	0.0407925	0.0451965	0.108958
3	rowSums==n	0.224237	0.2258145	0.2543480	0.2275955	0.2768195	0.412877
2	apply+all	1.350284	1.3681070	1.4806585	1.3936725	1.5038460	2.501126

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums==n	6.099198	5.882807	5.736533	5.579347	6.124799	3.789322
2	apply+all	36.727431	35.641245	33.394586	34.164920	33.273506	22.954955

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x1000 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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	colAlls	11.273	13.2780	15.76955	14.7690	16.5440	40.375
2	rowAlls	36.765	38.3855	44.33828	40.7925	45.1965	108.958

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.00000	1.000000	1.000000	1.000000	1.00000
2	rowAlls	3.261332	2.89091	2.811639	2.762035	2.731897	2.69865

Figure: Benchmarking of colAlls() and rowAlls() on 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 5164253 275.9    7916910 422.9  7916910 422.9
Vcells 9206220  70.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5164241 275.9    7916910 422.9  7916910 422.9
Vcells 9256253  70.7   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAlls	0.002754	0.0036585	0.0047849	0.0042275	0.0053800	0.022840
3	colSums==n	0.082708	0.0839100	0.0986052	0.0910275	0.1086585	0.168674
2	apply+all	0.624761	0.6350055	0.7332335	0.6904375	0.7986980	1.114454

	expr	min	lq	mean	median	uq	max
1	colAlls	1.00000	1.00000	1.00000	1.00000	1.00000	1.000000
3	colSums==n	30.03195	22.93563	20.60766	21.53223	20.19675	7.385026
2	apply+all	226.85585	173.56991	153.23969	163.32052	148.45688	48.793958

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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	rowAlls	0.040793	0.0419205	0.0475654	0.0435670	0.0531445	0.082903
3	rowSums==n	0.324184	0.3255680	0.3669326	0.3330525	0.4075610	0.550911
2	apply+all	0.628847	0.6355465	0.7025238	0.6401720	0.7288735	1.145160

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.00000	1.00000	1.000000	1.000000	1.000000	1.000000
3	rowSums==n	7.94705	7.76632	7.714273	7.644605	7.668921	6.645248
2	apply+all	15.41556	15.16076	14.769633	14.693966	13.714938	13.813252

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x100 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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	colAlls	2.754	3.6585	4.78488	4.2275	5.3800	22.840
2	rowAlls	40.793	41.9205	47.56542	43.5670	53.1445	82.903

	expr	min	lq	mean	median	uq	max
1	colAlls	1.00000	1.00000	1.000000	1.00000	1.00000	1.000000
2	rowAlls	14.81227	11.45838	9.940776	10.30562	9.87816	3.629729

Figure: Benchmarking of colAlls() and rowAlls() on 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 11.79 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:10:11 (+0200 UTC). Powered by RSP.