matrixStats.benchmarks

---

colVars() and rowVars() benchmarks

This report benchmark the performance of colVars() and rowVars() against alternative methods.

Alternative methods

• apply() + var()
• colVarColMeans() and rowVarColMeans()
• genefilter::rowVars(t(.)) and genefilter::rowVars()

where

> colVarColMeans <- function(x, na.rm = TRUE) {
+     if (na.rm) {
+         n <- colSums(!is.na(x))
+     }     else {
+         n <- nrow(x)
+     }
+     var <- colMeans(x * x, na.rm = na.rm) - (colMeans(x, na.rm = na.rm))^2
+     var * n/(n - 1)
+ }

and

> rowVarRowMeans <- function(x, na.rm = TRUE) {
+     if (na.rm) {
+         n <- rowSums(!is.na(x))
+     }     else {
+         n <- ncol(x)
+     }
+     mu <- rowMeans(x, na.rm = na.rm)
+     var <- rowMeans(x * x, na.rm = na.rm) - mu^2
+     var * (n/(n - 1))
+ }

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 integer matrix

> X <- data[["10x10"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5324574 284.4    8529671 455.6  8529671 455.6
Vcells 10675378  81.5   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311190 283.7    8529671 455.6  8529671 455.6
Vcells 10630747  81.2   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVars	0.002360	0.0031560	0.0046498	0.0044535	0.0055355	0.011795
3	colVarColMeans	0.013864	0.0168240	0.0212893	0.0184410	0.0213865	0.122530
5	genefilter::rowVars(t(.))	0.035432	0.0425200	0.0590438	0.0471030	0.0557940	1.006462
2	colVarsCenter	0.036826	0.0450000	0.0616142	0.0489495	0.0569050	0.961093
4	apply+var	0.122563	0.1328825	0.1513238	0.1453265	0.1617335	0.350631

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
3	colVarColMeans	5.874576	5.330799	4.578599	4.140788	3.863517	10.38830
5	genefilter::rowVars(t(.))	15.013559	13.472750	12.698283	10.576625	10.079306	85.32955
2	colVarsCenter	15.604237	14.258555	13.251067	10.991243	10.280011	81.48309
4	apply+var	51.933475	42.104721	32.544511	32.631975	29.217505	29.72709

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.002373	0.0035160	0.0042179	0.0040805	0.0046895	0.010942
3	rowVarRowMeans	0.015161	0.0169170	0.0189191	0.0184695	0.0199395	0.033628
2	rowVarsCenter	0.023000	0.0267620	0.0319457	0.0293280	0.0320235	0.173975
5	genefilter::rowVars	0.030653	0.0352425	0.0382160	0.0374810	0.0401580	0.078717
4	apply+var	0.119604	0.1247110	0.1362969	0.1323155	0.1442730	0.242408

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowVarRowMeans	6.388959	4.811433	4.485468	4.526283	4.251946	3.073296
2	rowVarsCenter	9.692372	7.611490	7.573901	7.187354	6.828766	15.899744
5	genefilter::rowVars	12.917404	10.023464	9.060493	9.185394	8.563386	7.194023
4	apply+var	50.402023	35.469568	32.314156	32.426296	30.765114	22.153902

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on integer+10x10 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	rowVars	2.373	3.516	4.21787	4.0805	4.6895	10.942
1	colVars	2.360	3.156	4.64975	4.4535	5.5355	11.795

	expr	min	lq	mean	median	uq	max
2	rowVars	1.0000000	1.0000000	1.000000	1.00000	1.000000	1.000000
1	colVars	0.9945217	0.8976109	1.102393	1.09141	1.180403	1.077956

Figure: Benchmarking of colVars() and rowVars() on integer+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 integer matrix

> X <- data[["100x100"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5309786 283.6    8529671 455.6  8529671 455.6
Vcells 10247410  78.2   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5309780 283.6    8529671 455.6  8529671 455.6
Vcells 10252517  78.3   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVars	0.038146	0.0413890	0.0451367	0.0432765	0.0464960	0.082799
3	colVarColMeans	0.060688	0.0647700	0.0703947	0.0689990	0.0730820	0.106353
5	genefilter::rowVars(t(.))	0.164999	0.1731930	0.1896125	0.1821255	0.1969555	0.304063
2	colVarsCenter	0.207949	0.2204515	0.2433081	0.2276340	0.2409900	0.631655
4	apply+var	0.892923	0.9234845	0.9989406	0.9687440	1.0103940	1.495626

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colVarColMeans	1.590940	1.564909	1.559590	1.594376	1.571791	1.284472
5	genefilter::rowVars(t(.))	4.325460	4.184518	4.200854	4.208416	4.235966	3.672303
2	colVarsCenter	5.451397	5.326331	5.390477	5.259991	5.183027	7.628776
4	apply+var	23.408037	22.312317	22.131470	22.384989	21.730773	18.063334

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.043003	0.0454375	0.0503081	0.0479315	0.0539205	0.097518
2	rowVarsCenter	0.099361	0.1064440	0.1200966	0.1105360	0.1182625	0.375241
3	rowVarRowMeans	0.106450	0.1109485	0.1218221	0.1154390	0.1221825	0.205070
5	genefilter::rowVars	0.147794	0.1548060	0.1670031	0.1617555	0.1700555	0.244416
4	apply+var	0.885880	0.9060705	1.0053500	0.9554105	1.0036455	1.888583

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowVarsCenter	2.310560	2.342646	2.387223	2.306124	2.193275	3.847915
3	rowVarRowMeans	2.475409	2.441783	2.421522	2.408416	2.265975	2.102894
5	genefilter::rowVars	3.436830	3.407010	3.319607	3.374722	3.153819	2.506368
4	apply+var	20.600423	19.941029	19.983863	19.932831	18.613431	19.366507

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on integer+100x100 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	38.146	41.3890	45.13666	43.2765	46.4960	82.799
2	rowVars	43.003	45.4375	50.30809	47.9315	53.9205	97.518

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowVars	1.127327	1.097816	1.114573	1.107564	1.15968	1.177768

Figure: Benchmarking of colVars() and rowVars() on integer+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 integer matrix

> X <- data[["1000x10"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5310596 283.7    8529671 455.6  8529671 455.6
Vcells 10251685  78.3   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5310584 283.7    8529671 455.6  8529671 455.6
Vcells 10256782  78.3   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVars	0.037941	0.0400425	0.0451982	0.0430165	0.0495390	0.067072
3	colVarColMeans	0.058470	0.0619070	0.0691308	0.0662595	0.0735880	0.114414
2	colVarsCenter	0.135759	0.1450995	0.1678398	0.1544730	0.1801345	0.478353
4	apply+var	0.189094	0.2013655	0.2262694	0.2115195	0.2421755	0.370089
5	genefilter::rowVars(t(.))	0.359036	0.3675440	0.4118899	0.3827155	0.4625595	0.604099

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colVarColMeans	1.541077	1.546032	1.529502	1.540328	1.485456	1.705839
2	colVarsCenter	3.578161	3.623637	3.713416	3.591017	3.636216	7.131933
4	apply+var	4.983896	5.028794	5.006155	4.917171	4.888583	5.517787
5	genefilter::rowVars(t(.))	9.463008	9.178847	9.112966	8.896947	9.337280	9.006724

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.043226	0.0445885	0.0500476	0.0455225	0.0528190	0.088238
2	rowVarsCenter	0.197012	0.2011390	0.2271245	0.2043530	0.2347250	0.689770
4	apply+var	0.191455	0.2004800	0.2195763	0.2053075	0.2302720	0.369756
3	rowVarRowMeans	0.302150	0.3041035	0.3362109	0.3064550	0.3589295	0.527757
5	genefilter::rowVars	0.344107	0.3475805	0.3767046	0.3510630	0.3872640	0.599410

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowVarsCenter	4.557720	4.511006	4.538171	4.489055	4.443950	7.817154
4	apply+var	4.429163	4.496227	4.387351	4.510022	4.359643	4.190440
3	rowVarRowMeans	6.990006	6.820223	6.717823	6.731946	6.795462	5.981063
5	genefilter::rowVars	7.960649	7.795295	7.526928	7.711857	7.331907	6.793105

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on integer+1000x10 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	37.941	40.0425	45.19823	43.0165	49.539	67.072
2	rowVars	43.226	44.5885	50.04759	45.5225	52.819	88.238

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowVars	1.139295	1.113529	1.107291	1.058257	1.06621	1.315571

Figure: Benchmarking of colVars() and rowVars() on integer+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 integer matrix

> X <- data[["10x1000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5310831 283.7    8529671 455.6  8529671 455.6
Vcells 10252708  78.3   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5310825 283.7    8529671 455.6  8529671 455.6
Vcells 10257815  78.3   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVars	0.040310	0.044805	0.0484361	0.0473075	0.0506900	0.084852
3	colVarColMeans	0.069532	0.077210	0.0831507	0.0821375	0.0868505	0.140173
5	genefilter::rowVars(t(.))	0.160276	0.176805	0.1919223	0.1864665	0.2021215	0.278555
2	colVarsCenter	0.842742	0.926340	1.0948278	0.9689705	1.0150505	7.110711
4	apply+var	7.727900	8.404078	8.8476474	8.6144495	8.8371020	15.560164

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colVarColMeans	1.724932	1.723245	1.716710	1.736247	1.713366	1.651971
5	genefilter::rowVars(t(.))	3.976085	3.946100	3.962384	3.941584	3.987404	3.282834
2	colVarsCenter	20.906524	20.674925	22.603563	20.482387	20.024670	83.801336
4	apply+var	191.711734	187.570093	182.666500	182.094795	174.336200	183.380050

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.040909	0.0458035	0.0484437	0.0475970	0.0501660	0.066115
3	rowVarRowMeans	0.095888	0.1040220	0.1109857	0.1077580	0.1139725	0.239718
2	rowVarsCenter	0.097298	0.1112460	0.1246629	0.1185145	0.1297625	0.277787
5	genefilter::rowVars	0.139836	0.1540495	0.1672120	0.1605695	0.1722090	0.283510
4	apply+var	7.679890	8.2745725	9.0351688	8.6155415	8.9748085	25.272514

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowVarRowMeans	2.343934	2.271049	2.291026	2.263966	2.271907	3.625773
2	rowVarsCenter	2.378401	2.428766	2.573358	2.489957	2.586662	4.201573
5	genefilter::rowVars	3.418221	3.363269	3.451678	3.373521	3.432783	4.288134
4	apply+var	187.731062	180.653716	186.508759	181.010179	178.902215	382.250836

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on integer+10x1000 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	40.310	44.8050	48.43607	47.3075	50.690	84.852
2	rowVars	40.909	45.8035	48.44367	47.5970	50.166	66.115

	expr	min	lq	mean	median	uq	max
1	colVars	1.00000	1.000000	1.000000	1.00000	1.0000000	1.0000000
2	rowVars	1.01486	1.022285	1.000157	1.00612	0.9896627	0.7791802

Figure: Benchmarking of colVars() and rowVars() on integer+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 integer matrix

> X <- data[["100x1000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311079 283.7    8529671 455.6  8529671 455.6
Vcells 10253445  78.3   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311067 283.7    8529671 455.6  8529671 455.6
Vcells 10303542  78.7   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVars	0.370829	0.4085235	0.4195262	0.4157715	0.426228	0.519336
3	colVarColMeans	0.503366	0.5655590	0.5905095	0.5838090	0.601591	0.780460
5	genefilter::rowVars(t(.))	1.270763	1.3460095	1.7212105	1.3984925	1.446581	16.919878
2	colVarsCenter	1.914315	2.0718710	2.1389776	2.1026925	2.151651	3.248097
4	apply+var	8.841118	9.6817635	10.4105899	9.8669370	10.065778	25.771648

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colVarColMeans	1.357407	1.384398	1.407563	1.404158	1.411430	1.502804
5	genefilter::rowVars(t(.))	3.426817	3.294815	4.102748	3.363608	3.393914	32.579829
2	colVarsCenter	5.162258	5.071608	5.098555	5.057327	5.048121	6.254327
4	apply+var	23.841496	23.699404	24.815110	23.731634	23.615947	49.624228

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.418112	0.4630555	0.4845348	0.4746995	0.4897880	0.916874
2	rowVarsCenter	0.740568	0.8279065	0.8805668	0.8497355	0.8723015	1.988345
3	rowVarRowMeans	0.773923	0.8634255	0.8888374	0.8765830	0.8993800	1.268610
5	genefilter::rowVars	1.173905	1.2380160	1.3416203	1.2660140	1.3133850	2.995484
4	apply+var	8.836777	9.7173340	10.4228695	9.8858500	10.0527290	23.224188

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowVarsCenter	1.771219	1.787921	1.817345	1.790049	1.780978	2.168613
3	rowVarRowMeans	1.850995	1.864626	1.834414	1.846606	1.836264	1.383625
5	genefilter::rowVars	2.807633	2.673580	2.768883	2.666980	2.681538	3.267062
4	apply+var	21.134952	20.985247	21.511084	20.825491	20.524653	25.329749

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on integer+100x1000 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	370.829	408.5235	419.5262	415.7715	426.228	519.336
2	rowVars	418.112	463.0555	484.5348	474.6995	489.788	916.874

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowVars	1.127506	1.133486	1.154957	1.141732	1.149122	1.765474

Figure: Benchmarking of colVars() and rowVars() on integer+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 integer matrix

> X <- data[["1000x100"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311322 283.7    8529671 455.6  8529671 455.6
Vcells 10254291  78.3   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311310 283.7    8529671 455.6  8529671 455.6
Vcells 10304388  78.7   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVars	0.363207	0.4007100	0.4253631	0.4098655	0.4388995	0.861742
3	colVarColMeans	0.482134	0.5276105	0.5627123	0.5550705	0.5885535	0.783706
2	colVarsCenter	1.132922	1.2575280	1.3980389	1.3109680	1.3451710	8.618698
5	genefilter::rowVars(t(.))	1.370261	1.4955900	1.6599965	1.5673965	1.6268775	9.540546
4	apply+var	1.737615	1.9083930	2.1507354	1.9615955	2.0303695	10.291710

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	colVarColMeans	1.327436	1.316689	1.322899	1.354275	1.340976	0.9094439
2	colVarsCenter	3.119219	3.138250	3.286695	3.198532	3.064872	10.0014830
5	genefilter::rowVars(t(.))	3.772672	3.732350	3.902540	3.824173	3.706720	11.0712325
4	apply+var	4.784090	4.762529	5.056234	4.785949	4.626047	11.9429133

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.417132	0.4528270	0.4700069	0.4648665	0.472382	0.978441
2	rowVarsCenter	0.848116	0.9121935	0.9626401	0.9328475	0.961714	1.944915
3	rowVarRowMeans	0.963181	1.0461160	1.0901473	1.0766520	1.107744	1.699706
5	genefilter::rowVars	1.266613	1.4009005	1.5936395	1.4462570	1.475210	8.766786
4	apply+var	1.775390	1.9011820	2.0848153	1.9612635	2.045497	9.556229

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowVarsCenter	2.033208	2.014442	2.048140	2.006700	2.035882	1.987769
3	rowVarRowMeans	2.309056	2.310189	2.319428	2.316046	2.345017	1.737157
5	genefilter::rowVars	3.036480	3.093677	3.390672	3.111123	3.122916	8.959954
4	apply+var	4.256183	4.198473	4.435712	4.218982	4.330176	9.766791

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on integer+1000x100 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	363.207	400.710	425.3631	409.8655	438.8995	861.742
2	rowVars	417.132	452.827	470.0069	464.8665	472.3820	978.441

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowVars	1.148469	1.130062	1.104955	1.134193	1.076287	1.135422

Figure: Benchmarking of colVars() and rowVars() 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 double matrix

> X <- data[["10x10"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311574 283.7    8529671 455.6  8529671 455.6
Vcells 10370330  79.2   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311559 283.7    8529671 455.6  8529671 455.6
Vcells 10370522  79.2   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVars	0.002324	0.0030675	0.0042399	0.0042840	0.0049775	0.011146
3	colVarColMeans	0.013662	0.0156890	0.0181517	0.0171560	0.0196865	0.064575
5	genefilter::rowVars(t(.))	0.034680	0.0399530	0.0445390	0.0429920	0.0477220	0.106259
2	colVarsCenter	0.035354	0.0429560	0.0488164	0.0454875	0.0519625	0.175900
4	apply+var	0.118836	0.1294370	0.1399149	0.1370680	0.1471245	0.212091

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colVarColMeans	5.878658	5.114588	4.281139	4.004669	3.955098	5.793558
5	genefilter::rowVars(t(.))	14.922547	13.024613	10.504657	10.035481	9.587544	9.533375
2	colVarsCenter	15.212564	14.003586	11.513483	10.617997	10.439478	15.781446
4	apply+var	51.134251	42.196251	32.999347	31.995331	29.557911	19.028441

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.002332	0.0035340	0.0041633	0.0040995	0.0046600	0.011632
3	rowVarRowMeans	0.012076	0.0138485	0.0153650	0.0151250	0.0164810	0.025843
2	rowVarsCenter	0.021286	0.0258045	0.0306393	0.0278605	0.0306815	0.165175
5	genefilter::rowVars	0.029458	0.0334900	0.0366212	0.0359425	0.0386085	0.075654
4	apply+var	0.120389	0.1268535	0.1391906	0.1366650	0.1447080	0.245290

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowVarRowMeans	5.178388	3.918647	3.690571	3.689474	3.536695	2.221716
2	rowVarsCenter	9.127787	7.301783	7.359341	6.796073	6.584013	14.200052
5	genefilter::rowVars	12.632075	9.476514	8.796158	8.767533	8.285086	6.503955
4	apply+var	51.624786	35.895161	33.432585	33.336992	31.053219	21.087517

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on double+10x10 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() on double+10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rowVars	2.332	3.5340	4.16332	4.0995	4.6600	11.632
1	colVars	2.324	3.0675	4.23993	4.2840	4.9775	11.146

	expr	min	lq	mean	median	uq	max
2	rowVars	1.0000000	1.0000000	1.000000	1.000000	1.000000	1.0000000
1	colVars	0.9965695	0.8679966	1.018401	1.045006	1.068133	0.9582187

Figure: Benchmarking of colVars() and rowVars() on double+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 double matrix

> X <- data[["100x100"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311790 283.7    8529671 455.6  8529671 455.6
Vcells 10371438  79.2   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5311793 283.7    8529671 455.6  8529671 455.6
Vcells 10381560  79.3   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVarColMeans	0.034550	0.0376165	0.0403220	0.0394090	0.0417280	0.054983
1	colVars	0.038112	0.0407025	0.0448693	0.0430195	0.0467095	0.080185
5	genefilter::rowVars(t(.))	0.154983	0.1632025	0.1801385	0.1692275	0.1896000	0.302693
2	colVarsCenter	0.180966	0.1930835	0.2117339	0.1990085	0.2079165	0.535319
4	apply+var	0.878946	0.9064145	0.9826759	0.9481085	0.9967995	1.483164

	expr	min	lq	mean	median	uq	max
3	colVarColMeans	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colVars	1.103097	1.082038	1.112776	1.091616	1.119380	1.458360
5	genefilter::rowVars(t(.))	4.485760	4.338588	4.467501	4.294133	4.543712	5.505211
2	colVarsCenter	5.237800	5.132947	5.251080	5.049824	4.982661	9.736082
4	apply+var	25.439826	24.096194	24.370726	24.058172	23.888025	26.974956

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.038347	0.0411570	0.0455176	0.0431400	0.0486570	0.066723
3	rowVarRowMeans	0.065138	0.0675215	0.0733791	0.0696150	0.0756715	0.121582
2	rowVarsCenter	0.088767	0.0943445	0.1055336	0.0981400	0.1079580	0.314286
5	genefilter::rowVars	0.138098	0.1439415	0.1558099	0.1496205	0.1607345	0.234906
4	apply+var	0.880335	0.9126775	0.9974756	0.9485235	1.0044210	1.532095

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowVarRowMeans	1.698647	1.640584	1.612103	1.613700	1.555203	1.822190
2	rowVarsCenter	2.314836	2.292308	2.318522	2.274919	2.218756	4.710310
5	genefilter::rowVars	3.601273	3.497376	3.423068	3.468255	3.303420	3.520615
4	apply+var	22.957076	22.175511	21.914055	21.987100	20.642888	22.962022

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on double+100x100 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	38.112	40.7025	44.86932	43.0195	46.7095	80.185
2	rowVars	38.347	41.1570	45.51762	43.1400	48.6570	66.723

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowVars	1.006166	1.011166	1.014449	1.002801	1.041694	0.8321132

Figure: Benchmarking of colVars() and rowVars() on double+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 double matrix

> X <- data[["1000x10"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5312056 283.7    8529671 455.6  8529671 455.6
Vcells 10372817  79.2   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5312044 283.7    8529671 455.6  8529671 455.6
Vcells 10382914  79.3   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVarColMeans	0.036424	0.0413250	0.0453536	0.0445445	0.0481710	0.068859
1	colVars	0.038044	0.0407880	0.0470514	0.0463525	0.0511450	0.069190
2	colVarsCenter	0.115508	0.1243390	0.1440905	0.1360350	0.1520300	0.395856
4	apply+var	0.184691	0.1982325	0.2233855	0.2129610	0.2389775	0.355468
5	genefilter::rowVars(t(.))	0.252518	0.2626890	0.3005084	0.2869555	0.3331290	0.444355

	expr	min	lq	mean	median	uq	max
3	colVarColMeans	1.000000	1.0000000	1.000000	1.000000	1.000000	1.000000
1	colVars	1.044476	0.9870054	1.037433	1.040589	1.061738	1.004807
2	colVarsCenter	3.171206	3.0088082	3.177045	3.053912	3.156048	5.748791
4	apply+var	5.070585	4.7969147	4.925416	4.780860	4.961024	5.162259
5	genefilter::rowVars(t(.))	6.932737	6.3566606	6.625895	6.441996	6.915551	6.453114

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.038129	0.0404745	0.0474356	0.0454810	0.0521865	0.088187
3	rowVarRowMeans	0.063686	0.0666385	0.0770592	0.0737510	0.0842280	0.139195
2	rowVarsCenter	0.087531	0.0942935	0.1137596	0.1048190	0.1266610	0.320216
4	apply+var	0.184153	0.1954625	0.2231553	0.2173145	0.2444120	0.347457
5	genefilter::rowVars	0.235445	0.2414380	0.2754153	0.2625005	0.3001445	0.416986

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowVarRowMeans	1.670277	1.646432	1.624502	1.621578	1.613981	1.578407
2	rowVarsCenter	2.295654	2.329701	2.398191	2.304677	2.427084	3.631102
4	apply+var	4.829736	4.829275	4.704387	4.778138	4.683433	3.940002
5	genefilter::rowVars	6.174959	5.965188	5.806093	5.771652	5.751382	4.728429

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on double+1000x10 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() on double+1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rowVars	38.129	40.4745	47.43557	45.4810	52.1865	88.187
1	colVars	38.044	40.7880	47.05136	46.3525	51.1450	69.190

	expr	min	lq	mean	median	uq	max
2	rowVars	1.0000000	1.000000	1.0000000	1.000000	1.0000000	1.0000000
1	colVars	0.9977707	1.007746	0.9919004	1.019162	0.9800427	0.7845828

Figure: Benchmarking of colVars() and rowVars() on double+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 double matrix

> X <- data[["10x1000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5312290 283.8    8529671 455.6  8529671 455.6
Vcells 10372966  79.2   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5312284 283.8    8529671 455.6  8529671 455.6
Vcells 10383073  79.3   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVars	0.039794	0.0429090	0.0469636	0.0452155	0.0484165	0.091483
3	colVarColMeans	0.042958	0.0466380	0.0521861	0.0501135	0.0569770	0.078319
5	genefilter::rowVars(t(.))	0.158553	0.1796210	0.2005337	0.1895110	0.2033935	0.461371
2	colVarsCenter	0.833458	0.9014035	0.9466516	0.9471060	0.9731435	1.530369
4	apply+var	7.765121	8.4133765	8.9748003	8.7040295	8.9040455	16.613906

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	colVarColMeans	1.079509	1.086905	1.111204	1.108326	1.176810	0.8561044
5	genefilter::rowVars(t(.))	3.984344	4.186091	4.269986	4.191284	4.200913	5.0432430
2	colVarsCenter	20.944313	21.007330	20.157157	20.946490	20.099419	16.7284523
4	apply+var	195.132960	196.074868	191.101403	192.501012	183.905187	181.6064843

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.038568	0.0441540	0.0463789	0.0457460	0.0476350	0.066105
3	rowVarRowMeans	0.072553	0.0781125	0.0833582	0.0810600	0.0867100	0.113056
2	rowVarsCenter	0.097160	0.1089710	0.1223411	0.1167165	0.1282330	0.252790
5	genefilter::rowVars	0.135930	0.1559825	0.1667408	0.1643475	0.1729475	0.244898
4	apply+var	7.734458	8.5229970	9.0260429	8.7360185	8.8759365	16.427359

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowVarRowMeans	1.881171	1.769092	1.797331	1.771958	1.820300	1.710249
2	rowVarsCenter	2.519187	2.467976	2.637863	2.551403	2.691991	3.824068
5	genefilter::rowVars	3.524424	3.532692	3.595189	3.592609	3.630681	3.704682
4	apply+var	200.540811	193.028876	194.615412	190.967921	186.332245	248.504031

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on double+10x1000 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	39.794	42.909	46.96355	45.2155	48.4165	91.483
2	rowVars	38.568	44.154	46.37887	45.7460	47.6350	66.105

	expr	min	lq	mean	median	uq	max
1	colVars	1.0000000	1.000000	1.0000000	1.000000	1.0000000	1.0000000
2	rowVars	0.9691913	1.029015	0.9875503	1.011733	0.9838588	0.7225933

Figure: Benchmarking of colVars() and rowVars() on double+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 double matrix

> X <- data[["100x1000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5312538 283.8    8529671 455.6  8529671 455.6
Vcells 10374556  79.2   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5312526 283.8    8529671 455.6  8529671 455.6
Vcells 10474653  80.0   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVarColMeans	0.227658	0.2568100	0.4061417	0.280031	0.297967	12.848085
1	colVars	0.366654	0.3971055	0.4143027	0.413039	0.422013	0.533167
5	genefilter::rowVars(t(.))	1.241417	1.3575785	1.5669587	1.427919	1.503113	13.640083
2	colVarsCenter	1.587058	1.7256375	2.0593262	1.781643	1.853342	14.534955
4	apply+var	8.771338	9.6010645	9.9796321	9.758297	9.934980	23.449087

	expr	min	lq	mean	median	uq	max
3	colVarColMeans	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	colVars	1.610547	1.546301	1.020094	1.474976	1.416308	0.0414978
5	genefilter::rowVars(t(.))	5.452991	5.286315	3.858158	5.099144	5.044564	1.0616433
2	colVarsCenter	6.971238	6.719511	5.070463	6.362308	6.219957	1.1312935
4	apply+var	38.528574	37.385867	24.571801	34.847203	33.342550	1.8251037

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.383108	0.4212025	0.4443792	0.4335005	0.4644170	0.577450
3	rowVarRowMeans	0.557170	0.5967800	0.6316420	0.6073605	0.6280675	1.328177
2	rowVarsCenter	0.748677	0.8087930	0.8737887	0.8343895	0.8868905	1.608309
5	genefilter::rowVars	1.144970	1.2229115	1.4326800	1.2636820	1.3533885	12.169686
4	apply+var	8.836759	9.6938980	10.2881584	9.8871080	10.1604960	21.798754

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowVarRowMeans	1.454342	1.416848	1.421403	1.401061	1.352378	2.300073
2	rowVarsCenter	1.954219	1.920200	1.966313	1.924772	1.909686	2.785192
5	genefilter::rowVars	2.988635	2.903381	3.224003	2.915065	2.914167	21.074874
4	apply+var	23.065974	23.014816	23.151754	22.807605	21.877959	37.750029

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on double+100x1000 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	366.654	397.1055	414.3027	413.0390	422.013	533.167
2	rowVars	383.108	421.2025	444.3792	433.5005	464.417	577.450

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowVars	1.044876	1.060682	1.072596	1.049539	1.10048	1.083057

Figure: Benchmarking of colVars() and rowVars() on double+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 double matrix

> X <- data[["1000x100"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5312781 283.8    8529671 455.6  8529671 455.6
Vcells 10374718  79.2   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colVars = colVars(X, na.rm = FALSE), colVarsCenter = colVars(X, center = colMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), colVarColMeans = colVarColMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 2L, FUN = var, na.rm = FALSE), `genefilter::rowVars(t(.))` = genefilter_colVars(X, na.rm = FALSE), 
+     unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5312769 283.8    8529671 455.6  8529671 455.6
Vcells 10474815  80.0   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowVars = rowVars(X, na.rm = FALSE), rowVarsCenter = rowVars(X, center = rowMeans(X, 
+     na.rm = FALSE), na.rm = FALSE), rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE), `apply+var` = apply(X, 
+     MARGIN = 1L, FUN = var, na.rm = FALSE), `genefilter::rowVars` = genefilter_rowVars(X, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() 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	colVarColMeans	0.236591	0.2548605	0.3502374	0.2667690	0.2795570	7.219906
1	colVars	0.362819	0.3896030	0.4205326	0.4064870	0.4257745	0.848092
2	colVarsCenter	0.846065	0.9378680	1.0836001	0.9712455	1.0618485	8.273782
5	genefilter::rowVars(t(.))	1.285118	1.3944565	1.5633854	1.4472140	1.5514135	9.008731
4	apply+var	1.666260	1.7751905	1.9326401	1.8247425	1.9000110	9.021567

	expr	min	lq	mean	median	uq	max
3	colVarColMeans	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	colVars	1.533528	1.528691	1.200708	1.523742	1.523033	0.1174658
2	colVarsCenter	3.576066	3.679927	3.093902	3.640773	3.798326	1.1459681
5	genefilter::rowVars(t(.))	5.431813	5.471450	4.463788	5.424971	5.549543	1.2477629
4	apply+var	7.042787	6.965342	5.518086	6.840160	6.796507	1.2495408

Table: Benchmarking of rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() 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	rowVars	0.369447	0.3981980	0.4138544	0.4101275	0.420431	0.565742
3	rowVarRowMeans	0.522479	0.5821485	0.6792477	0.6035575	0.630286	7.376799
2	rowVarsCenter	0.729655	0.7770475	0.9085696	0.8090230	0.849332	7.632795
5	genefilter::rowVars	1.128522	1.2424880	1.4133518	1.3286465	1.373558	8.355827
4	apply+var	1.678533	1.8045680	1.9414004	1.9013635	2.018127	2.691588

	expr	min	lq	mean	median	uq	max
1	rowVars	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowVarRowMeans	1.414219	1.461957	1.641272	1.471634	1.499143	13.039157
2	rowVarsCenter	1.974992	1.951410	2.195385	1.972613	2.020146	13.491653
5	genefilter::rowVars	3.054625	3.120277	3.415094	3.239594	3.267024	14.769678
4	apply+var	4.543366	4.531836	4.691023	4.636030	4.800140	4.757625

Figure: Benchmarking of colVars(), colVarsCenter(), colVarColMeans(), apply+var() and genefilter::rowVars(t(.))() on double+1000x100 data as well as rowVars(), rowVarsCenter(), rowVarRowMeans(), apply+var() and genefilter::rowVars() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colVars() and rowVars() 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	colVars	362.819	389.603	420.5326	406.4870	425.7745	848.092
2	rowVars	369.447	398.198	413.8544	410.1275	420.4310	565.742

	expr	min	lq	mean	median	uq	max
1	colVars	1.000000	1.000000	1.0000000	1.000000	1.0000000	1.0000000
2	rowVars	1.018268	1.022061	0.9841197	1.008956	0.9874499	0.6670762

Figure: Benchmarking of colVars() and rowVars() 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 36.81 secs.

Reproducibility

To reproduce this report, do:

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

---

Copyright Henrik Bengtsson. Last updated on 2021-08-25 19:11:09 (+0200 UTC). Powered by RSP.