matrixStats: Benchmark report

binCounts() benchmarks

This report benchmark the performance of binCounts() against alternative methods.

Alternative methods

hist()

as below

> hist <- graphics::hist
> binCounts_hist <- function(x, bx, right = FALSE, ...) {
+     hist(x, breaks = bx, right = right, include.lowest = TRUE, plot = FALSE)$counts
+ }

Data type “integer”

Non-sorted simulated data

> set.seed(48879)
> nx <- 1e+05
> xmax <- 0.01 * nx
> x <- runif(nx, min = 0, max = xmax)
> storage.mode(x) <- mode
> str(x)
 int [1:100000] 722 285 591 3 349 509 216 91 150 383 ...
> nb <- 10000
> bx <- seq(from = 0, to = xmax, length.out = nb + 1L)
> bx <- c(-1, bx, xmax + 1)

Results

> gc()
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells  832976 44.5    1602994 85.7  1238948 66.2
Vcells 1550951 11.9    8388608 64.0  3747923 28.6
> stats <- microbenchmark(binCounts = binCounts(x, bx = bx), hist = binCounts_hist(x, bx = bx), unit = "ms")

Table: Benchmarking of binCounts() and hist() on integer+unsorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	binCounts	6.0648	6.5985	7.15556	6.67395	6.88525	14.4020
2	hist	10.8401	12.5433	13.15207	12.76730	13.05785	20.3017

	expr	min	lq	mean	median	uq	max
1	binCounts	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	hist	1.78738	1.900932	1.838021	1.913005	1.896496	1.409644

Figure: Benchmarking of binCounts() and hist() on integer+unsorted data. Outliers are displayed as crosses. Times are in milliseconds.

Sorted simulated data

> x <- sort(x)

> gc()
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells 1014082 54.2    1602994 85.7  1602994 85.7
Vcells 1897117 14.5    8388608 64.0  8387727 64.0
> stats <- microbenchmark(binCounts = binCounts(x, bx = bx), hist = binCounts_hist(x, bx = bx), unit = "ms")

Table: Benchmarking of binCounts() and hist() on integer+sorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	binCounts	1.1791	1.43800	1.981658	1.6019	1.67240	6.9819
2	hist	3.6734	4.15625	4.655433	4.3932	4.60495	10.7287

	expr	min	lq	mean	median	uq	max
1	binCounts	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	hist	3.115427	2.890299	2.349262	2.742493	2.753498	1.536645

Figure: Benchmarking of binCounts() and hist() on integer+sorted data. Outliers are displayed as crosses. Times are in milliseconds.

Data type “double”

Non-sorted simulated data

> set.seed(48879)
> nx <- 1e+05
> xmax <- 0.01 * nx
> x <- runif(nx, min = 0, max = xmax)
> storage.mode(x) <- mode
> str(x)
 num [1:100000] 722.11 285.54 591.33 3.42 349.14 ...
> nb <- 10000
> bx <- seq(from = 0, to = xmax, length.out = nb + 1L)
> bx <- c(-1, bx, xmax + 1)

Results

> gc()
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells 1015597 54.3    1602994 85.7  1602994 85.7
Vcells 1955040 15.0    8388608 64.0  8387727 64.0
> stats <- microbenchmark(binCounts = binCounts(x, bx = bx), hist = binCounts_hist(x, bx = bx), unit = "ms")

Table: Benchmarking of binCounts() and hist() on double+unsorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	binCounts	9.9433	11.51275	12.05104	11.91325	12.07075	17.6203
2	hist	11.2240	13.21570	13.97113	13.55680	13.93655	20.2583

	expr	min	lq	mean	median	uq	max
1	binCounts	1.0000	1.000000	1.00000	1.00000	1.000000	1.000000
2	hist	1.1288	1.147919	1.15933	1.13796	1.154572	1.149714

Figure: Benchmarking of binCounts() and hist() on double+unsorted data. Outliers are displayed as crosses. Times are in milliseconds.

Sorted simulated data

> x <- sort(x)

> gc()
          used (Mb) gc trigger (Mb) max used (Mb)
Ncells 1015609 54.3    1602994 85.7  1602994 85.7
Vcells 1955560 15.0    8388608 64.0  8387727 64.0
> stats <- microbenchmark(binCounts = binCounts(x, bx = bx), hist = binCounts_hist(x, bx = bx), unit = "ms")

Table: Benchmarking of binCounts() and hist() on double+sorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	binCounts	0.7632	2.69325	3.262256	2.79545	3.11185	9.5363
2	hist	4.4010	5.07225	5.598413	5.37100	5.68055	11.0009

	expr	min	lq	mean	median	uq	max
1	binCounts	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	hist	5.766509	1.883319	1.716117	1.921337	1.825457	1.153582

Figure: Benchmarking of binCounts() and hist() on double+sorted data. Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R Under development (unstable) (2025-01-06 r87534 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows Server 2022 x64 (build 20348)

Matrix products: default
  LAPACK version 3.12.0

locale:
[1] LC_COLLATE=C                 LC_CTYPE=German_Germany.utf8
[3] LC_MONETARY=C                LC_NUMERIC=C                
[5] LC_TIME=C                   

time zone: Europe/Berlin
tzcode source: internal

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

other attached packages:
[1] microbenchmark_1.5.0 matrixStats_1.5.0    ggplot2_3.5.1       
[4] knitr_1.49           R.devices_2.17.2     R.utils_2.12.3      
[7] R.oo_1.27.0          R.methodsS3_1.8.2   

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5      cli_3.6.3        rlang_1.1.4      xfun_0.50       
 [5] labeling_0.4.3   glue_1.8.0       colorspace_2.1-1 markdown_1.13   
 [9] scales_1.3.0     R.cache_0.16.0   grid_4.5.0       munsell_0.5.1   
[13] evaluate_1.0.1   tibble_3.2.1     R.rsp_0.46.0     base64enc_0.1-3 
[17] lifecycle_1.0.4  compiler_4.5.0   pkgconfig_2.0.3  farver_2.1.2    
[21] digest_0.6.37    R6_2.5.1         pillar_1.10.1    magrittr_2.0.3  
[25] withr_3.0.2      tools_4.5.0      gtable_0.3.6

Total processing time was 8.72 secs.

Reproducibility

To reproduce this report, do:

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