matrixStats.benchmarks

colCounts() and rowCounts() benchmarks on subsetted computation

This report benchmark the performance of colCounts() and rowCounts() on subsetted computation.

Data type “logical”

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"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.007606	0.007801	0.0109994	0.0079300	0.0080450	0.309395
2	colCounts(X, rows, cols)	0.008064	0.008298	0.0084776	0.0083755	0.0085375	0.012016
3	colCounts(X[rows, cols])	0.008854	0.009214	0.0094591	0.0093510	0.0095015	0.015203

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	colCounts(X, rows, cols)	1.060216	1.063710	0.7707346	1.056179	1.061218	0.0388371
3	colCounts(X[rows, cols])	1.164081	1.181131	0.8599695	1.179193	1.181044	0.0491378

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on logical+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	rowCounts_X_S	0.006708	0.0069125	0.0070966	0.0070305	0.0071560	0.010921
2	rowCounts(X, cols, rows)	0.007063	0.0072965	0.0106442	0.0074065	0.0075370	0.312444
3	rowCounts(X[cols, rows])	0.007837	0.0081360	0.0084116	0.0082715	0.0084395	0.013255

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.052922	1.055551	1.499901	1.053481	1.053242	28.609468
3	rowCounts(X[cols, rows])	1.168306	1.176998	1.185293	1.176517	1.179360	1.213717

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+10x10 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+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	rowCounts_X_S	6.708	6.9125	7.09660	7.0305	7.156	10.921
1	colCounts_X_S	7.606	7.8010	10.99939	7.9300	8.045	309.395

	expr	min	lq	mean	median	uq	max
2	rowCounts_X_S	1.00000	1.000000	1.000000	1.000000	1.000000	1.00000
1	colCounts_X_S	1.13387	1.128535	1.549952	1.127943	1.124231	28.33028

Figure: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.026727	0.0293985	0.0309949	0.0308655	0.0319700	0.045398
3	colCounts(X[rows, cols])	0.042368	0.0456020	0.0485646	0.0476885	0.0493335	0.100923
2	colCounts(X, rows, cols)	0.044052	0.0468630	0.0490066	0.0489265	0.0509660	0.062108

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colCounts(X[rows, cols])	1.585214	1.551168	1.566859	1.545042	1.543119	2.223072
2	colCounts(X, rows, cols)	1.648221	1.594061	1.581120	1.585152	1.594182	1.368078

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on logical+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	rowCounts_X_S	0.033296	0.0365775	0.0401433	0.0389525	0.0410605	0.074452
2	rowCounts(X, cols, rows)	0.044428	0.0502940	0.0553237	0.0533370	0.0563160	0.100177
3	rowCounts(X[cols, rows])	0.047997	0.0540510	0.0605435	0.0564860	0.0597290	0.092350

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.334334	1.374998	1.378155	1.369283	1.371537	1.345525
3	rowCounts(X[cols, rows])	1.441525	1.477712	1.508185	1.450125	1.454658	1.240396

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+100x100 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+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	colCounts_X_S	26.727	29.3985	30.99487	30.8655	31.9700	45.398
2	rowCounts_X_S	33.296	36.5775	40.14329	38.9525	41.0605	74.452

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts_X_S	1.245781	1.244196	1.295159	1.262008	1.284345	1.639984

Figure: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.026247	0.0274760	0.0288377	0.0284345	0.0295340	0.042165
2	colCounts(X, rows, cols)	0.039907	0.0427590	0.0449772	0.0440400	0.0459525	0.060219
3	colCounts(X[rows, cols])	0.040739	0.0427805	0.0457096	0.0442160	0.0461720	0.102585

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	1.520440	1.556231	1.559664	1.548823	1.555919	1.428175
3	colCounts(X[rows, cols])	1.552139	1.557013	1.585061	1.555012	1.563351	2.432942

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on logical+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	rowCounts_X_S	0.033610	0.035637	0.0383250	0.0372100	0.0397070	0.063654
2	rowCounts(X, cols, rows)	0.042230	0.049076	0.0523427	0.0515825	0.0546205	0.072425
3	rowCounts(X[cols, rows])	0.049348	0.052592	0.0575921	0.0562475	0.0601805	0.113429

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.256471	1.377108	1.365759	1.386254	1.375589	1.137792
3	rowCounts(X[cols, rows])	1.468254	1.475770	1.502729	1.511623	1.515614	1.781962

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+1000x10 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+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	colCounts_X_S	26.247	27.476	28.83772	28.4345	29.534	42.165
2	rowCounts_X_S	33.610	35.637	38.32498	37.2100	39.707	63.654

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowCounts_X_S	1.280527	1.297023	1.328988	1.308622	1.34445	1.509641

Figure: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.028019	0.0317380	0.0342072	0.0332630	0.0345640	0.085610
3	colCounts(X[rows, cols])	0.044574	0.0499660	0.0527359	0.0521100	0.0541495	0.073907
2	colCounts(X, rows, cols)	0.050994	0.0542415	0.0571011	0.0560025	0.0596855	0.074828

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	colCounts(X[rows, cols])	1.590849	1.574327	1.541661	1.566606	1.566644	0.8632987
2	colCounts(X, rows, cols)	1.819979	1.709040	1.669271	1.683628	1.726811	0.8740568

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on logical+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	rowCounts_X_S	0.033982	0.0375150	0.0392909	0.0392765	0.0410075	0.051869
2	rowCounts(X, cols, rows)	0.040886	0.0494235	0.0527877	0.0522650	0.0555445	0.098516
3	rowCounts(X[cols, rows])	0.049282	0.0539425	0.0565967	0.0570660	0.0587680	0.070242

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.203166	1.317433	1.343509	1.330694	1.354496	1.899323
3	rowCounts(X[cols, rows])	1.450238	1.437892	1.440452	1.452930	1.433104	1.354219

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+10x1000 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+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	colCounts_X_S	28.019	31.738	34.20720	33.2630	34.5640	85.610
2	rowCounts_X_S	33.982	37.515	39.29091	39.2765	41.0075	51.869

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.00000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowCounts_X_S	1.21282	1.182022	1.148615	1.180786	1.186422	0.6058755

Figure: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.182594	0.1842540	0.2118112	0.1940690	0.2313655	0.339551
2	colCounts(X, rows, cols)	0.257979	0.2612925	0.2991797	0.2740175	0.3341320	0.477315
3	colCounts(X[rows, cols])	0.274274	0.2768960	0.3246128	0.3055570	0.3505870	0.476297

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	1.412856	1.418110	1.412483	1.411959	1.444174	1.405724
3	colCounts(X[rows, cols])	1.502098	1.502795	1.532557	1.574476	1.515295	1.402726

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on logical+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	rowCounts_X_S	0.223760	0.226404	0.2639432	0.2447350	0.2860445	0.403284
2	rowCounts(X, cols, rows)	0.302837	0.304981	0.3388351	0.3078700	0.3700850	0.532897
3	rowCounts(X[cols, rows])	0.315416	0.317979	0.3623893	0.3284875	0.4020495	0.525957

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.353401	1.347065	1.283743	1.257973	1.293802	1.321394
3	rowCounts(X[cols, rows])	1.409617	1.404476	1.372982	1.342217	1.405549	1.304185

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+100x1000 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+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	colCounts_X_S	182.594	184.254	211.8112	194.069	231.3655	339.551
2	rowCounts_X_S	223.760	226.404	263.9432	244.735	286.0445	403.284

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	rowCounts_X_S	1.225451	1.22876	1.246125	1.261072	1.236332	1.187698

Figure: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.175062	0.1769075	0.2046092	0.1835485	0.2197940	0.352683
2	colCounts(X, rows, cols)	0.247622	0.2516000	0.2979273	0.2859790	0.3225025	0.413297
3	colCounts(X[rows, cols])	0.267559	0.2696250	0.3201011	0.3014480	0.3494225	0.494151

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	1.414482	1.422212	1.456080	1.558057	1.467294	1.171865
3	colCounts(X[rows, cols])	1.528367	1.524102	1.564451	1.642334	1.589773	1.401119

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on logical+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	rowCounts_X_S	0.228251	0.2304465	0.2636989	0.2361255	0.2890480	0.384564
2	rowCounts(X, cols, rows)	0.309545	0.3122550	0.3579008	0.3364640	0.3911205	0.583966
3	rowCounts(X[cols, rows])	0.320670	0.3223990	0.3699081	0.3365320	0.4050320	0.542895

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.356161	1.355000	1.357233	1.424937	1.353133	1.518514
3	rowCounts(X[cols, rows])	1.404901	1.399019	1.402767	1.425225	1.401262	1.411716

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on logical+1000x100 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+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	colCounts_X_S	175.062	176.9075	204.6092	183.5485	219.794	352.683
2	rowCounts_X_S	228.251	230.4465	263.6989	236.1255	289.048	384.564

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts_X_S	1.303829	1.302638	1.288793	1.286447	1.315086	1.090396

Figure: Benchmarking of colCounts_X_S() and rowCounts_X_S() on logical+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

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"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() 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	colCounts_X_S	0.007513	0.007701	0.0082643	0.007860	0.0080170	0.043130
2	colCounts(X, rows, cols)	0.007995	0.008244	0.0084457	0.008396	0.0085530	0.011258
3	colCounts(X[rows, cols])	0.008836	0.009187	0.0094558	0.009331	0.0095195	0.014535

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colCounts(X, rows, cols)	1.064156	1.070510	1.021946	1.068193	1.066858	0.2610248
3	colCounts(X[rows, cols])	1.176095	1.192962	1.144175	1.187150	1.187414	0.3370044

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.006611	0.0068420	0.0070167	0.0069415	0.0071010	0.010494
2	rowCounts(X, cols, rows)	0.007148	0.0073205	0.0078472	0.0074480	0.0075940	0.040368
3	rowCounts(X[cols, rows])	0.007829	0.0081120	0.0084132	0.0082750	0.0084995	0.012678

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.081228	1.069936	1.118365	1.072967	1.069427	3.846770
3	rowCounts(X[cols, rows])	1.184238	1.185618	1.199028	1.192106	1.196944	1.208119

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on integer+10x10 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	rowCounts_X_S	6.611	6.842	7.01671	6.9415	7.101	10.494
1	colCounts_X_S	7.513	7.701	8.26429	7.8600	8.017	43.130

	expr	min	lq	mean	median	uq	max
2	rowCounts_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
1	colCounts_X_S	1.136439	1.125548	1.177801	1.13232	1.128996	4.109968

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

100x100 matrix

> X <- data[["100x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() 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	colCounts_X_S	0.012714	0.0133915	0.0136908	0.0136985	0.0139190	0.016865
2	colCounts(X, rows, cols)	0.019719	0.0208075	0.0213002	0.0210855	0.0213155	0.035328
3	colCounts(X[rows, cols])	0.024512	0.0255960	0.0265992	0.0260005	0.0263190	0.083441

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	1.550967	1.553784	1.555797	1.539256	1.531396	2.094752
3	colCounts(X[rows, cols])	1.927953	1.911362	1.942846	1.898054	1.890869	4.947584

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.017063	0.0175975	0.0182973	0.0182610	0.0187895	0.026396
2	rowCounts(X, cols, rows)	0.024707	0.0253420	0.0272027	0.0263685	0.0273255	0.066962
3	rowCounts(X[cols, rows])	0.027998	0.0287235	0.0299832	0.0296720	0.0310535	0.040161

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.447987	1.440091	1.486703	1.443979	1.454296	2.536824
3	rowCounts(X[cols, rows])	1.640860	1.632249	1.638668	1.624884	1.652705	1.521481

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on integer+100x100 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	12.714	13.3915	13.69084	13.6985	13.9190	16.865
2	rowCounts_X_S	17.063	17.5975	18.29731	18.2610	18.7895	26.396

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	rowCounts_X_S	1.342064	1.31408	1.336464	1.333066	1.349917	1.565135

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

1000x10 matrix

> X <- data[["1000x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() 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	colCounts_X_S	0.012230	0.0127000	0.0130172	0.012842	0.0130465	0.024588
2	colCounts(X, rows, cols)	0.019635	0.0201025	0.0203527	0.020284	0.0204735	0.027571
3	colCounts(X[rows, cols])	0.024969	0.0255820	0.0263802	0.025783	0.0260185	0.075489

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	1.605478	1.582874	1.563520	1.579505	1.569271	1.121319
3	colCounts(X[rows, cols])	2.041619	2.014331	2.026556	2.007709	1.994290	3.070156

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.016813	0.0174410	0.0178744	0.0176530	0.0179620	0.024657
2	rowCounts(X, cols, rows)	0.027087	0.0284215	0.0286642	0.0286180	0.0287660	0.036448
3	rowCounts(X[cols, rows])	0.030488	0.0320110	0.0333142	0.0323755	0.0327865	0.100551

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.611075	1.629580	1.603645	1.621141	1.601492	1.478201
3	rowCounts(X[cols, rows])	1.813359	1.835388	1.863794	1.833994	1.825326	4.077990

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on integer+1000x10 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	12.230	12.700	13.01724	12.842	13.0465	24.588
2	rowCounts_X_S	16.813	17.441	17.87439	17.653	17.9620	24.657

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	rowCounts_X_S	1.374734	1.373307	1.373132	1.37463	1.376768	1.002806

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

10x1000 matrix

> X <- data[["10x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() 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	colCounts_X_S	0.016314	0.0167410	0.0174460	0.016918	0.0172865	0.056636
2	colCounts(X, rows, cols)	0.024995	0.0254325	0.0260099	0.025652	0.0259635	0.038758
3	colCounts(X[rows, cols])	0.030517	0.0314510	0.0319397	0.031827	0.0322755	0.036712

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colCounts(X, rows, cols)	1.532120	1.519175	1.490881	1.516255	1.501952	0.6843351
3	colCounts(X[rows, cols])	1.870602	1.878681	1.830778	1.881251	1.867093	0.6482096

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.017967	0.0184085	0.0188572	0.0186210	0.0188260	0.033670
2	rowCounts(X, cols, rows)	0.027994	0.0284885	0.0297140	0.0286725	0.0289640	0.066172
3	rowCounts(X[cols, rows])	0.030557	0.0310485	0.0322977	0.0312450	0.0317005	0.052640

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowCounts(X, cols, rows)	1.558079	1.547573	1.575735	1.539794	1.538511	1.96531
3	rowCounts(X[cols, rows])	1.700729	1.686639	1.712749	1.677944	1.683868	1.56341

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on integer+10x1000 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	16.314	16.7410	17.44599	16.918	17.2865	56.636
2	rowCounts_X_S	17.967	18.4085	18.85724	18.621	18.8260	33.670

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowCounts_X_S	1.101324	1.099606	1.080893	1.100662	1.089058	0.5944982

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

100x1000 matrix

> X <- data[["100x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() 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	colCounts_X_S	0.046676	0.0487955	0.0553676	0.0532065	0.0607630	0.107275
2	colCounts(X, rows, cols)	0.100991	0.1049005	0.1178285	0.1161850	0.1308215	0.201266
3	colCounts(X[rows, cols])	0.130573	0.1360795	0.1535592	0.1534255	0.1690285	0.190869

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	2.163660	2.149799	2.128113	2.183662	2.152980	1.876169
3	colCounts(X[rows, cols])	2.797433	2.788772	2.773449	2.883586	2.781767	1.779250

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.075797	0.0856670	0.0940439	0.0915810	0.0990450	0.207333
2	rowCounts(X, cols, rows)	0.129970	0.1413480	0.1551654	0.1551495	0.1603630	0.300093
3	rowCounts(X[cols, rows])	0.141723	0.1581225	0.1743555	0.1697115	0.1766965	0.336824

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.714712	1.649970	1.649924	1.694123	1.619092	1.447396
3	rowCounts(X[cols, rows])	1.869771	1.845781	1.853979	1.853130	1.784002	1.624556

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on integer+100x1000 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	46.676	48.7955	55.36758	53.2065	60.763	107.275
2	rowCounts_X_S	75.797	85.6670	94.04394	91.5810	99.045	207.333

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts_X_S	1.623897	1.755633	1.698538	1.721237	1.630022	1.932724

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

1000x100 matrix

> X <- data[["1000x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() 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	colCounts_X_S	0.044101	0.0459635	0.0501267	0.0465015	0.0498065	0.135071
2	colCounts(X, rows, cols)	0.089817	0.0930005	0.0986317	0.0962020	0.1041575	0.134039
3	colCounts(X[rows, cols])	0.124070	0.1289540	0.1395573	0.1342155	0.1492490	0.248636

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colCounts(X, rows, cols)	2.036621	2.023356	1.967648	2.068793	2.091243	0.9923596
3	colCounts(X[rows, cols])	2.813315	2.805574	2.784092	2.886262	2.996577	1.8407800

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.089520	0.0949835	0.1043018	0.0985495	0.1145930	0.131387
2	rowCounts(X, cols, rows)	0.147056	0.1572455	0.1743220	0.1624015	0.1882625	0.284750
3	rowCounts(X[cols, rows])	0.167580	0.1787585	0.1982546	0.1858020	0.2172495	0.380611

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.642717	1.655503	1.671323	1.647918	1.642880	2.167262
3	rowCounts(X[cols, rows])	1.871984	1.881995	1.900778	1.885367	1.895836	2.896870

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on integer+1000x100 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	44.101	45.9635	50.12668	46.5015	49.8065	135.071
2	rowCounts_X_S	89.520	94.9835	104.30181	98.5495	114.5930	131.387

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowCounts_X_S	2.029886	2.066498	2.080764	2.119276	2.300764	0.9727255

Figure: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() 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	colCounts_X_S	0.007594	0.007904	0.0085451	0.0080280	0.0081845	0.052906
2	colCounts(X, rows, cols)	0.008068	0.008312	0.0085567	0.0084045	0.0085425	0.015894
3	colCounts(X[rows, cols])	0.009234	0.009494	0.0098161	0.0096190	0.0097705	0.017977

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colCounts(X, rows, cols)	1.062418	1.051619	1.001363	1.046898	1.043741	0.3004196
3	colCounts(X[rows, cols])	1.215960	1.201164	1.148741	1.198181	1.193781	0.3397913

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.006584	0.0067485	0.0069682	0.006928	0.0070895	0.010480
2	rowCounts(X, cols, rows)	0.006942	0.0072185	0.0077354	0.007338	0.0074765	0.041329
3	rowCounts(X[cols, rows])	0.007857	0.0081405	0.0084955	0.008310	0.0084830	0.016240

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.054374	1.069645	1.110101	1.05918	1.054588	3.943607
3	rowCounts(X[cols, rows])	1.193348	1.206268	1.219172	1.19948	1.196558	1.549618

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+10x10 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	rowCounts_X_S	6.584	6.7485	6.96823	6.928	7.0895	10.480
1	colCounts_X_S	7.594	7.9040	8.54507	8.028	8.1845	52.906

	expr	min	lq	mean	median	uq	max
2	rowCounts_X_S	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
1	colCounts_X_S	1.153402	1.171223	1.22629	1.158776	1.154454	5.048282

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

100x100 matrix

> X <- data[["100x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.014196	0.0149005	0.0160255	0.015541	0.0165315	0.028720
2	colCounts(X, rows, cols)	0.019523	0.0202550	0.0217847	0.021346	0.0223615	0.043914
3	colCounts(X[rows, cols])	0.029427	0.0302390	0.0333228	0.031621	0.0343275	0.085521

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	colCounts(X, rows, cols)	1.375247	1.359350	1.359380	1.373528	1.35266	1.529039
3	colCounts(X[rows, cols])	2.072908	2.029395	2.079368	2.034683	2.07649	2.977751

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.014996	0.0156835	0.0160830	0.0159800	0.0163350	0.023253
2	rowCounts(X, cols, rows)	0.019778	0.0205145	0.0213926	0.0208505	0.0212270	0.064550
3	rowCounts(X[cols, rows])	0.031784	0.0327605	0.0333413	0.0332260	0.0334115	0.047861

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.318885	1.308031	1.330137	1.304787	1.299480	2.775986
3	rowCounts(X[cols, rows])	2.119499	2.088851	2.073074	2.079224	2.045393	2.058272

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+100x100 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	14.196	14.9005	16.02547	15.541	16.5315	28.720
2	rowCounts_X_S	14.996	15.6835	16.08300	15.980	16.3350	23.253

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.00000	1.000000	1.0000000	1.0000000
2	rowCounts_X_S	1.056354	1.052549	1.00359	1.028248	0.9881136	0.8096448

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

1000x10 matrix

> X <- data[["1000x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.014980	0.0156275	0.0160117	0.0158745	0.0161145	0.029877
2	colCounts(X, rows, cols)	0.021944	0.0226060	0.0230566	0.0231265	0.0233025	0.030112
3	colCounts(X[rows, cols])	0.031671	0.0324815	0.0340358	0.0335905	0.0338065	0.086349

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	1.464886	1.446553	1.439985	1.456833	1.446058	1.007866
3	colCounts(X[rows, cols])	2.114219	2.078483	2.125680	2.116004	2.097893	2.890150

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.015797	0.016212	0.0165174	0.016444	0.0167005	0.023335
2	rowCounts(X, cols, rows)	0.022139	0.022956	0.0234583	0.023331	0.0236270	0.039029
3	rowCounts(X[cols, rows])	0.036470	0.036897	0.0385065	0.037038	0.0373425	0.091142

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.401469	1.415988	1.420222	1.418815	1.414748	1.672552
3	rowCounts(X[cols, rows])	2.308666	2.275907	2.331278	2.252372	2.236011	3.905807

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+1000x10 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	14.980	15.6275	16.01171	15.8745	16.1145	29.877
2	rowCounts_X_S	15.797	16.2120	16.51735	16.4440	16.7005	23.335

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowCounts_X_S	1.054539	1.037402	1.031579	1.035875	1.036365	0.7810356

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

10x1000 matrix

> X <- data[["10x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() 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	colCounts_X_S	0.016762	0.0176135	0.0183643	0.0179445	0.0182860	0.055232
2	colCounts(X, rows, cols)	0.024951	0.0257595	0.0265658	0.0264230	0.0268410	0.046776
3	colCounts(X[rows, cols])	0.036602	0.0381720	0.0387702	0.0386365	0.0389275	0.055876

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colCounts(X, rows, cols)	1.488546	1.462486	1.446599	1.472485	1.467844	0.8469003
3	colCounts(X[rows, cols])	2.183630	2.167201	2.111168	2.153111	2.128814	1.0116599

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.015399	0.016227	0.0164746	0.0164215	0.0166505	0.023916
2	rowCounts(X, cols, rows)	0.021132	0.021912	0.0228900	0.0223440	0.0227310	0.065568
3	rowCounts(X[cols, rows])	0.032397	0.033395	0.0340964	0.0341285	0.0343205	0.046137

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.372297	1.350342	1.389414	1.360655	1.365184	2.741596
3	rowCounts(X[cols, rows])	2.103838	2.057990	2.069636	2.078282	2.061229	1.929127

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+10x1000 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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
2	rowCounts_X_S	15.399	16.2270	16.47458	16.4215	16.6505	23.916
1	colCounts_X_S	16.762	17.6135	18.36431	17.9445	18.2860	55.232

	expr	min	lq	mean	median	uq	max
2	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colCounts_X_S	1.088512	1.085444	1.114706	1.092744	1.098225	2.309416

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

100x1000 matrix

> X <- data[["100x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.060844	0.0667720	0.0733271	0.069616	0.0786415	0.146509
2	colCounts(X, rows, cols)	0.105129	0.1138780	0.1254892	0.119462	0.1348265	0.251047
3	colCounts(X[rows, cols])	0.169897	0.1879605	0.2036843	0.197494	0.2162130	0.255094

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	1.727845	1.705475	1.711362	1.716014	1.714445	1.713526
3	colCounts(X[rows, cols])	2.792338	2.814960	2.777749	2.836905	2.749350	1.741149

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.067017	0.0728570	0.0806024	0.0777475	0.0891165	0.134436
2	rowCounts(X, cols, rows)	0.099029	0.1070465	0.1161260	0.1100545	0.1228175	0.235963
3	rowCounts(X[cols, rows])	0.172720	0.1902700	0.2068109	0.1982245	0.2257600	0.280559

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.477670	1.469269	1.440726	1.415537	1.378168	1.755207
3	rowCounts(X[cols, rows])	2.577256	2.611554	2.565816	2.549593	2.533313	2.086934

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+100x1000 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	60.844	66.772	73.32710	69.6160	78.6415	146.509
2	rowCounts_X_S	67.017	72.857	80.60239	77.7475	89.1165	134.436

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowCounts_X_S	1.101456	1.091131	1.099217	1.116805	1.133199	0.9175955

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

1000x100 matrix

> X <- data[["1000x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> value <- 42

> colStats <- microbenchmark(colCounts_X_S = colCounts(X_S, value = value, na.rm = FALSE), `colCounts(X, rows, cols)` = colCounts(X, 
+     value = value, na.rm = FALSE, rows = rows, cols = cols), `colCounts(X[rows, cols])` = colCounts(X[rows, 
+     cols], value = value, na.rm = FALSE), unit = "ms")

> X <- t(X)
> X_S <- t(X_S)

> rowStats <- microbenchmark(rowCounts_X_S = rowCounts(X_S, value = value, na.rm = FALSE), `rowCounts(X, cols, rows)` = rowCounts(X, 
+     value = value, na.rm = FALSE, rows = cols, cols = rows), `rowCounts(X[cols, rows])` = rowCounts(X[cols, 
+     rows], value = value, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	0.062221	0.0665745	0.0717962	0.0685375	0.0750450	0.133687
2	colCounts(X, rows, cols)	0.104465	0.1117950	0.1221637	0.1156155	0.1290025	0.172953
3	colCounts(X[rows, cols])	0.171978	0.1854650	0.2035955	0.1959230	0.2195515	0.337777

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colCounts(X, rows, cols)	1.678935	1.679247	1.701535	1.686894	1.719002	1.293716
3	colCounts(X[rows, cols])	2.763986	2.785826	2.835742	2.858625	2.925598	2.526626

Table: Benchmarking of rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() 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	rowCounts_X_S	0.063717	0.070028	0.0765499	0.0742865	0.0804110	0.104947
2	rowCounts(X, cols, rows)	0.099519	0.112721	0.1224868	0.1166290	0.1295765	0.288866
3	rowCounts(X[cols, rows])	0.172380	0.190783	0.2058479	0.1978375	0.2225660	0.265248

	expr	min	lq	mean	median	uq	max
1	rowCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCounts(X, cols, rows)	1.561891	1.609656	1.600091	1.569989	1.611428	2.752494
3	rowCounts(X[cols, rows])	2.705400	2.724382	2.689069	2.663169	2.767855	2.527447

Figure: Benchmarking of colCounts_X_S(), colCounts(X, rows, cols)() and colCounts(X[rows, cols])() on double+1000x100 data as well as rowCounts_X_S(), rowCounts(X, cols, rows)() and rowCounts(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCounts_X_S() and rowCounts_X_S() 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	colCounts_X_S	62.221	66.5745	71.79619	68.5375	75.045	133.687
2	rowCounts_X_S	63.717	70.0280	76.54990	74.2865	80.411	104.947

	expr	min	lq	mean	median	uq	max
1	colCounts_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowCounts_X_S	1.024043	1.051874	1.066211	1.083881	1.071504	0.7850202

Figure: Benchmarking of colCounts_X_S() and rowCounts_X_S() on double+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

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

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

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

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

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

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

Total processing time was 34.17 secs.

Reproducibility

To reproduce this report, do:

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