colRowRanks

matrixStats: Benchmark report

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colRanks() and rowRanks() benchmarks

This report benchmark the performance of colRanks() and rowRanks() against alternative methods.

Alternative methods

• apply() + rank()

Data type "integer"

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), naProb = 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 (naProb > 0) 
+         X[sample(n, size = naProb * 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   805858 43.1    1442291  77.1  1442291  77.1
Vcells 12281216 93.7   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   804682 43.0    1442291  77.1  1442291  77.1
Vcells 12277671 93.7   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	0.0219	0.0370	0.0634	0.0458	0.0835	0.4666
apply+rank	0.6086	0.6679	0.9051	0.9907	1.0781	1.6838

expr	min	lq	mean	median	uq	max
colRanks	1.00	1.00	1.00	1.00	1.00	1.000
apply+rank	27.73	18.07	14.27	21.63	12.91	3.609
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	0.0112	0.0192	0.0332	0.0258	0.0423	0.216
apply+rank	0.6194	0.9162	1.0319	0.9658	1.0848	2.591

expr	min	lq	mean	median	uq	max
rowRanks	1.00	1.0	1.00	1.00	1.00	1
apply+rank	55.48	47.6	31.09	37.45	25.62	12
Figure: Benchmarking of colRanks() and apply+rank() on integer+10x10 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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	rowRanks	11.16	19.25	33.19	25.79	42.35	216.0
1	colRanks	21.94	36.96	63.43	45.81	83.54	466.6

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.000	1.00	1.000	1.000	1.000	1.00
1	colRanks	1.965	1.92	1.911	1.776	1.973	2.16
Figure: Benchmarking of colRanks() and rowRanks() 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   804858 43.0    1442291  77.1  1442291  77.1
Vcells 12278904 93.7   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   804852 43.0    1442291  77.1  1442291  77.1
Vcells 12283947 93.8   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	0.4677	0.5372	0.6366	0.6071	0.6731	5.088
apply+rank	6.6997	8.6540	10.1316	10.2144	10.8734	19.869

expr	min	lq	mean	median	uq	max
colRanks	1.00	1.00	1.00	1.00	1.00	1.000
apply+rank	14.32	16.11	15.92	16.83	16.15	3.905
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	0.430	0.5565	0.5758	0.5713	0.6182	1.011
apply+rank	6.774	10.0188	10.6447	10.2421	10.6195	22.862

expr	min	lq	mean	median	uq	max
rowRanks	1.00	1	1.00	1.00	1.00	1.00
apply+rank	15.75	18	18.49	17.93	17.18	22.61
Figure: Benchmarking of colRanks() and apply+rank() on integer+100x100 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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
2	rowRanks	430.0	556.5	575.8	571.3	618.2	1011
1	colRanks	467.7	537.2	636.6	607.1	673.1	5088

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.000	1.0000	1.000	1.000	1.000	1.000
1	colRanks	1.088	0.9654	1.106	1.063	1.089	5.031
Figure: Benchmarking of colRanks() and rowRanks() 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   804893 43.0    1442291  77.1  1442291  77.1
Vcells 12278927 93.7   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   804887 43.0    1442291  77.1  1442291  77.1
Vcells 12283970 93.8   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	0.5867	0.6458	0.6699	0.6581	0.7141	0.8808
apply+rank	2.1369	2.2239	2.5919	2.3563	2.9588	3.3664

expr	min	lq	mean	median	uq	max
colRanks	1.000	1.000	1.000	1.000	1.000	1.000
apply+rank	3.642	3.444	3.869	3.581	4.143	3.822
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	0.5613	0.6327	0.6648	0.6569	0.6679	1.409
apply+rank	2.2978	2.9965	3.2506	3.0893	3.1749	7.486

expr	min	lq	mean	median	uq	max
rowRanks	1.000	1.000	1.00	1.000	1.000	1.000
apply+rank	4.094	4.736	4.89	4.703	4.754	5.312
Figure: Benchmarking of colRanks() and apply+rank() on integer+1000x10 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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
2	rowRanks	561.3	632.7	664.8	656.9	667.9	1409.3
1	colRanks	586.7	645.8	669.9	658.1	714.1	880.8

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.000	1.000	1.000	1.000	1.000	1.000
1	colRanks	1.045	1.021	1.008	1.002	1.069	0.625
Figure: Benchmarking of colRanks() and rowRanks() 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   804931 43.0    1442291  77.1  1442291  77.1
Vcells 12279515 93.7   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   804925 43.0    1442291  77.1  1442291  77.1
Vcells 12284558 93.8   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	0.3218	0.3823	0.4225	0.3988	0.4566	0.788
apply+rank	56.2760	70.3403	79.6412	80.8709	87.5793	142.845

expr	min	lq	mean	median	uq	max
colRanks	1.0	1	1.0	1.0	1.0	1.0
apply+rank	174.9	184	188.5	202.8	191.8	181.3
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	0.291	0.2951	0.3405	0.3465	0.3503	0.5401
apply+rank	55.647	63.4144	69.8360	67.9174	72.6635	204.0764

expr	min	lq	mean	median	uq	max
rowRanks	1.0	1.0	1.0	1	1.0	1.0
apply+rank	191.2	214.9	205.1	196	207.4	377.9
Figure: Benchmarking of colRanks() and apply+rank() on integer+10x1000 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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
2	rowRanks	291.0	295.1	340.5	346.5	350.3	540.1
1	colRanks	321.8	382.3	422.5	398.8	456.6	788.0

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.000	1.000	1.000	1.000	1.000	1.000
1	colRanks	1.106	1.296	1.241	1.151	1.303	1.459
Figure: Benchmarking of colRanks() and rowRanks() 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   804966 43.0    1442291  77.1  1442291  77.1
Vcells 12279858 93.7   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   804960 43.0    1442291  77.1  1442291  77.1
Vcells 12329901 94.1   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	4.616	4.664	5.255	5.019	5.529	10.61
apply+rank	66.844	80.143	90.069	91.047	95.747	124.23

expr	min	lq	mean	median	uq	max
colRanks	1.00	1.00	1.00	1.00	1.00	1.0
apply+rank	14.48	17.18	17.14	18.14	17.32	11.7
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	4.049	4.40	4.782	4.612	5.195	5.655
apply+rank	67.583	76.33	86.739	82.782	93.011	212.121

expr	min	lq	mean	median	uq	max
rowRanks	1.00	1.00	1.00	1.00	1.0	1.00
apply+rank	16.69	17.35	18.14	17.95	17.9	37.51
Figure: Benchmarking of colRanks() and apply+rank() on integer+100x1000 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() on integer+100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rowRanks	4.049	4.400	4.782	4.612	5.195	5.655
1	colRanks	4.616	4.664	5.255	5.019	5.529	10.614

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.00	1.00	1.000	1.000	1.000	1.000
1	colRanks	1.14	1.06	1.099	1.088	1.064	1.877
Figure: Benchmarking of colRanks() and rowRanks() 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   805003 43.0    1442291  77.1  1442291  77.1
Vcells 12280280 93.7   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   804997 43.0    1442291  77.1  1442291  77.1
Vcells 12330323 94.1   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	5.555	6.245	7.023	7.29	7.689	9.302
apply+rank	21.082	26.730	30.861	29.52	32.120	143.992

expr	min	lq	mean	median	uq	max
colRanks	1.000	1.000	1.000	1.00	1.000	1.00
apply+rank	3.795	4.281	4.394	4.05	4.177	15.48
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	5.331	5.563	6.555	6.465	7.429	10.13
apply+rank	20.240	22.058	27.375	26.859	31.184	66.52

expr	min	lq	mean	median	uq	max
rowRanks	1.000	1.000	1.000	1.000	1.000	1.000
apply+rank	3.796	3.965	4.176	4.154	4.198	6.566
Figure: Benchmarking of colRanks() and apply+rank() on integer+1000x100 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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
2	rowRanks	5.331	5.563	6.555	6.465	7.429	10.131
1	colRanks	5.555	6.245	7.023	7.290	7.689	9.302

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.000	1.000	1.000	1.000	1.000	1.0000
1	colRanks	1.042	1.123	1.071	1.128	1.035	0.9182
Figure: Benchmarking of colRanks() and rowRanks() 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), naProb = 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 (naProb > 0) 
+         X[sample(n, size = naProb * 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   805046 43.0    1442291  77.1  1442291  77.1
Vcells 12396419 94.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   805031 43.0    1442291  77.1  1442291  77.1
Vcells 12396547 94.6   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	0.0227	0.0277	0.0468	0.0385	0.0601	0.0978
apply+rank	0.5963	0.6175	0.7297	0.6298	0.9014	1.3262

expr	min	lq	mean	median	uq	max
colRanks	1.00	1.00	1.0	1.00	1.00	1.00
apply+rank	26.25	22.28	15.6	16.36	15.01	13.56
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	0.0127	0.0204	0.0351	0.0333	0.047	0.0789
apply+rank	0.6175	0.9206	0.9927	0.9464	1.014	3.9700

expr	min	lq	mean	median	uq	max
rowRanks	1.0	1.00	1.0	1.00	1.0	1.00
apply+rank	48.6	45.12	28.3	28.42	21.6	50.31
Figure: Benchmarking of colRanks() and apply+rank() on double+10x10 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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	rowRanks	12.70	20.40	35.08	33.3	46.97	78.92
1	colRanks	22.71	27.72	46.79	38.5	60.05	97.78

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.000	1.000	1.000	1.000	1.000	1.000
1	colRanks	1.788	1.358	1.334	1.156	1.279	1.239
Figure: Benchmarking of colRanks() and rowRanks() 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   805074 43.0    1442291  77.1  1442291  77.1
Vcells 12396430 94.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   805068 43.0    1442291  77.1  1442291  77.1
Vcells 12406473 94.7   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	0.4993	0.5718	0.6483	0.6825	0.7181	0.8019
apply+rank	6.5873	7.1349	9.7630	10.2284	10.9937	19.8936

expr	min	lq	mean	median	uq	max
colRanks	1.00	1.00	1.00	1.00	1.00	1.00
apply+rank	13.19	12.48	15.06	14.99	15.31	24.81
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	0.4727	0.4958	0.5727	0.5399	0.6533	0.7749
apply+rank	6.7336	7.1317	9.0582	8.3650	10.4796	19.3332

expr	min	lq	mean	median	uq	max
rowRanks	1.00	1.00	1.00	1.00	1.00	1.00
apply+rank	14.24	14.38	15.82	15.49	16.04	24.95
Figure: Benchmarking of colRanks() and apply+rank() on double+100x100 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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
2	rowRanks	472.7	495.8	572.7	539.9	653.3	774.9
1	colRanks	499.3	571.8	648.3	682.5	718.1	801.9

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.000	1.000	1.000	1.000	1.000	1.000
1	colRanks	1.056	1.153	1.132	1.264	1.099	1.035
Figure: Benchmarking of colRanks() and rowRanks() 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   805109 43.0    1442291  77.1  1442291  77.1
Vcells 12397126 94.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   805103 43.0    1442291  77.1  1442291  77.1
Vcells 12407169 94.7   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	0.6656	0.6742	0.7494	0.7316	0.7418	1.035
apply+rank	2.1288	2.2208	2.4954	2.2566	2.7636	3.455

expr	min	lq	mean	median	uq	max
colRanks	1.000	1.000	1.00	1.000	1.000	1.000
apply+rank	3.198	3.294	3.33	3.084	3.725	3.338
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	0.6394	0.7622	0.813	0.8173	0.8625	0.952
apply+rank	2.1700	3.1711	3.131	3.2519	3.3481	3.532

expr	min	lq	mean	median	uq	max
rowRanks	1.000	1.00	1.000	1.000	1.000	1.00
apply+rank	3.394	4.16	3.851	3.979	3.882	3.71
Figure: Benchmarking of colRanks() and apply+rank() on double+1000x10 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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
colRanks	665.6	674.2	749.4	731.6	741.8	1035
rowRanks	639.4	762.2	813.0	817.3	862.5	952

expr	min	lq	mean	median	uq	max
colRanks	1.0000	1.000	1.000	1.000	1.000	1.00
rowRanks	0.9607	1.131	1.085	1.117	1.163	0.92
Figure: Benchmarking of colRanks() and rowRanks() 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   805147 43.0    1442291  77.1  1442291  77.1
Vcells 12397152 94.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   805141 43.0    1442291  77.1  1442291  77.1
Vcells 12407195 94.7   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	0.3511	0.4217	0.4466	0.4302	0.4693	1.164
apply+rank	56.5898	62.7835	71.7817	69.7821	79.0790	96.098

expr	min	lq	mean	median	uq	max
colRanks	1.0	1.0	1.0	1.0	1.0	1.00
apply+rank	161.2	148.9	160.7	162.2	168.5	82.58
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	0.3064	0.313	0.3898	0.3653	0.4238	0.8196
apply+rank	56.2872	63.234	72.5931	70.1948	77.5813	213.3468

expr	min	lq	mean	median	uq	max
rowRanks	1.0	1	1.0	1.0	1	1.0
apply+rank	183.7	202	186.2	192.1	183	260.3
Figure: Benchmarking of colRanks() and apply+rank() on double+10x1000 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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	rowRanks	306.4	313.0	389.8	365.3	423.8	819.6
1	colRanks	351.1	421.7	446.6	430.2	469.3	1163.7

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.000	1.000	1.000	1.000	1.000	1.00
1	colRanks	1.146	1.347	1.146	1.178	1.107	1.42
Figure: Benchmarking of colRanks() and rowRanks() 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   805182 43.1    1442291  77.1  1442291  77.1
Vcells 12398076 94.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   805176 43.1    1442291  77.1  1442291  77.1
Vcells 12498119 95.4   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	4.919	4.956	5.697	5.231	6.466	9.23
apply+rank	69.764	81.853	93.062	90.987	104.885	135.71

expr	min	lq	mean	median	uq	max
colRanks	1.00	1.00	1.00	1.0	1.00	1.0
apply+rank	14.18	16.52	16.34	17.4	16.22	14.7
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	5.028	5.054	5.356	5.069	5.436	6.513
apply+rank	69.930	80.867	90.392	88.045	95.610	239.730

expr	min	lq	mean	median	uq	max
rowRanks	1.00	1	1.00	1.00	1.00	1.00
apply+rank	13.91	16	16.88	17.37	17.59	36.81
Figure: Benchmarking of colRanks() and apply+rank() on double+100x1000 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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
2	rowRanks	5.028	5.054	5.356	5.069	5.436	6.513
1	colRanks	4.919	4.956	5.697	5.231	6.466	9.230

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.0000	1.0000	1.000	1.000	1.000	1.000
1	colRanks	0.9783	0.9806	1.064	1.032	1.189	1.417
Figure: Benchmarking of colRanks() and rowRanks() 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   805219 43.1    1442291  77.1  1442291  77.1
Vcells 12398101 94.6   35610798 271.7 68120027 519.8
> colStats <- microbenchmark(colRanks = colRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 2L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")
> X <- t(X)
> gc()
           used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells   805213 43.1    1442291  77.1  1442291  77.1
Vcells 12498144 95.4   35610798 271.7 68120027 519.8
> rowStats <- microbenchmark(rowRanks = rowRanks(X, na.rm = FALSE), `apply+rank` = apply(X, MARGIN = 1L, 
+     FUN = rank, na.last = "keep", ties.method = "max"), unit = "ms")

Table: Benchmarking of colRanks() and apply+rank() 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
colRanks	6.608	7.098	7.898	7.838	8.574	11.36
apply+rank	20.612	26.252	28.988	30.054	30.827	39.66

expr	min	lq	mean	median	uq	max
colRanks	1.000	1.000	1.00	1.000	1.000	1.000
apply+rank	3.119	3.699	3.67	3.835	3.595	3.492
Table: Benchmarking of rowRanks() and apply+rank() 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
rowRanks	6.623	6.664	7.701	7.201	8.491	19.56
apply+rank	20.811	22.743	26.504	25.474	30.661	40.62

expr	min	lq	mean	median	uq	max
rowRanks	1.000	1.000	1.000	1.000	1.000	1.000
apply+rank	3.142	3.413	3.442	3.538	3.611	2.077
Figure: Benchmarking of colRanks() and apply+rank() on double+1000x100 data as well as rowRanks() and apply+rank() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanks() and rowRanks() 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
2	rowRanks	6.623	6.664	7.701	7.201	8.491	19.56
1	colRanks	6.608	7.098	7.898	7.838	8.574	11.36

	expr	min	lq	mean	median	uq	max
2	rowRanks	1.0000	1.000	1.000	1.000	1.00	1.0000
1	colRanks	0.9978	1.065	1.026	1.088	1.01	0.5808
Figure: Benchmarking of colRanks() and rowRanks() on double+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R Under development (unstable) (2015-02-27 r67909)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
[1] markdown_0.7.7          microbenchmark_1.4-2    matrixStats_0.14.0-9000
[4] ggplot2_1.0.0           knitr_1.9.3             R.devices_2.13.0       
[7] R.utils_2.0.0           R.oo_1.19.0             R.methodsS3_1.7.0      

loaded via a namespace (and not attached):
 [1] Rcpp_0.11.4         BiocGenerics_0.13.6 splines_3.2.0      
 [4] MASS_7.3-39         munsell_0.4.2       lattice_0.20-30    
 [7] colorspace_1.2-4    R.cache_0.11.1-9000 multcomp_1.3-9     
[10] stringr_0.6.2       plyr_1.8.1          tools_3.2.0        
[13] parallel_3.2.0      grid_3.2.0          Biobase_2.27.2     
[16] gtable_0.1.2        TH.data_1.0-6       survival_2.38-1    
[19] digest_0.6.8        R.rsp_0.20.0        reshape2_1.4.1     
[22] formatR_1.0.3       base64enc_0.1-3     mime_0.2.1         
[25] evaluate_0.5.7      labeling_0.3        sandwich_2.3-2     
[28] scales_0.2.4        mvtnorm_1.0-2       zoo_1.7-12         
[31] Cairo_1.5-6         proto_0.3-10       

Total processing time was 2.02 mins.

Reproducibility

To reproduce this report, do:

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

---

Copyright Henrik Bengtsson. Last updated on 2015-03-02 17:21:28 (-0800 UTC). Powered by RSP.

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