matrixStats.benchmarks

count() benchmarks

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

Alternative methods

sum(x == value)

Data type “integer”

Data

> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     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
+     x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rvector(n = scale * 100, ...)
+     data[[2]] <- rvector(n = scale * 1000, ...)
+     data[[3]] <- rvector(n = scale * 10000, ...)
+     data[[4]] <- rvector(n = scale * 1e+05, ...)
+     data[[5]] <- rvector(n = scale * 1e+06, ...)
+     names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+     data
+ }
> data <- rvectors(mode = mode)

Results

n = 1000 vector

> x <- data[["n = 1000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5328806 284.6    8529671 455.6  8529671 455.6
Vcells 37453872 285.8   66528939 507.6 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on integer+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.002963	0.0031560	0.0036359	0.003269	0.003465	0.023826
2	sum(x == value)	0.004211	0.0043245	0.0045332	0.004403	0.004481	0.013697

	expr	min	lq	mean	median	uq	max
1	count	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	sum(x == value)	1.421195	1.370247	1.246779	1.346895	1.293218	0.5748762

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 vector

> x <- data[["n = 10000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5326573 284.5    8529671 455.6  8529671 455.6
Vcells 15818616 120.7   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on integer+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.002987	0.0033400	0.0040824	0.003892	0.0042985	0.025761
2	sum(x == value)	0.035581	0.0374245	0.0387245	0.037691	0.0391610	0.052762

	expr	min	lq	mean	median	uq	max
1	count	1.00000	1.00000	1.000000	1.000000	1.000000	1.000000
2	sum(x == value)	11.91195	11.20494	9.485654	9.684224	9.110387	2.048135

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 vector

> x <- data[["n = 100000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5326636 284.5    8529671 455.6  8529671 455.6
Vcells 15818658 120.7   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on integer+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.002412	0.0027625	0.0037828	0.0033975	0.0043455	0.023250
2	sum(x == value)	0.276560	0.2957400	0.3162287	0.3059775	0.3342625	0.401091

	expr	min	lq	mean	median	uq	max
1	count	1.00	1.0000	1.00000	1.0000	1.00000	1.00000
2	sum(x == value)	114.66	107.0552	83.59668	90.0596	76.92153	17.25123

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5326699 284.5    8529671 455.6  8529671 455.6
Vcells 15819213 120.7   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on integer+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.001981	0.0025015	0.0102454	0.0063565	0.016595	0.030828
2	sum(x == value)	2.797049	3.5377980	3.6884905	3.5646060	3.593936	17.222212

	expr	min	lq	mean	median	uq	max
1	count	1.000	1.000	1.0000	1.0000	1.0000	1.0000
2	sum(x == value)	1411.938	1414.271	360.0129	560.7812	216.5674	558.6549

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000000 vector

> x <- data[["n = 10000000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5326762 284.5    8529671 455.6  8529671 455.6
Vcells 15819255 120.7   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on integer+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.002007	0.002561	0.0131692	0.0036965	0.0241115	0.035557
2	sum(x == value)	35.804868	36.474240	39.2176533	36.9697970	37.4299840	55.510647

	expr	min	lq	mean	median	uq	max
1	count	1.00	1.00	1.000	1.0	1.000	1.000
2	sum(x == value)	17839.99	14242.19	2977.987	10001.3	1552.371	1561.174

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.

Data type “double”

Data

> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     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
+     x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rvector(n = scale * 100, ...)
+     data[[2]] <- rvector(n = scale * 1000, ...)
+     data[[3]] <- rvector(n = scale * 10000, ...)
+     data[[4]] <- rvector(n = scale * 1e+05, ...)
+     data[[5]] <- rvector(n = scale * 1e+06, ...)
+     names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+     data
+ }
> data <- rvectors(mode = mode)

Results

n = 1000 vector

> x <- data[["n = 1000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5326825 284.5    8529671 455.6  8529671 455.6
Vcells 21375126 163.1   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on double+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.003170	0.0033640	0.0038077	0.0035055	0.003624	0.025294
2	sum(x == value)	0.003983	0.0040985	0.0042885	0.0041635	0.004260	0.014047

	expr	min	lq	mean	median	uq	max
1	count	1.000000	1.000000	1.00000	1.000000	1.000000	1.0000000
2	sum(x == value)	1.256467	1.218341	1.12628	1.187705	1.175497	0.5553491

Figure: Benchmarking of count() and sum(x == value)() on double+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 vector

> x <- data[["n = 10000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5326888 284.5    8529671 455.6  8529671 455.6
Vcells 21375167 163.1   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on double+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.003084	0.0033045	0.0040723	0.0037945	0.0043805	0.025023
2	sum(x == value)	0.032108	0.0335125	0.0342588	0.0336250	0.0337760	0.052027

	expr	min	lq	mean	median	uq	max
1	count	1.00000	1.00000	1.00000	1.00000	1.000000	1.000000
2	sum(x == value)	10.41115	10.14147	8.41259	8.86151	7.710535	2.079167

Figure: Benchmarking of count() and sum(x == value)() on double+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 vector

> x <- data[["n = 100000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5326951 284.5    8529671 455.6  8529671 455.6
Vcells 21375491 163.1   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on double+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.002565	0.002884	0.0041810	0.0036640	0.004688	0.028827
2	sum(x == value)	0.261388	0.271350	0.2949966	0.2892505	0.313009	0.374071

	expr	min	lq	mean	median	uq	max
1	count	1.0000	1.00000	1.00000	1.00000	1.00000	1.00000
2	sum(x == value)	101.9057	94.08807	70.55698	78.94391	66.76813	12.97641

Figure: Benchmarking of count() and sum(x == value)() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5327014 284.5    8529671 455.6  8529671 455.6
Vcells 21375863 163.1   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on double+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.001985	0.002481	0.0111833	0.009625	0.0183635	0.032442
2	sum(x == value)	3.321211	3.411456	3.6900379	3.441822	3.5675620	17.761495

	expr	min	lq	mean	median	uq	max
1	count	1.000	1.000	1.00	1.0000	1.0000	1.0000
2	sum(x == value)	1673.154	1375.032	329.96	357.5919	194.2746	547.4846

Figure: Benchmarking of count() and sum(x == value)() on double+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000000 vector

> x <- data[["n = 10000000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5327077 284.5    8529671 455.6  8529671 455.6
Vcells 21375905 163.1   53223152 406.1 60562128 462.1
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() on double+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	count	0.001998	0.0025375	0.0141113	0.003532	0.0268855	0.030452
2	sum(x == value)	32.952294	33.4601140	40.3936593	34.074120	35.2056820	413.800210

	expr	min	lq	mean	median	uq	max
1	count	1.00	1.00	1.000	1.000	1.000	1.00
2	sum(x == value)	16492.64	13186.25	2862.504	9647.259	1309.467	13588.61

Figure: Benchmarking of count() and sum(x == value)() on double+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

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

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

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

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

other attached packages:
[1] microbenchmark_1.4-7   matrixStats_0.60.1     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

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

Total processing time was 21.96 secs.

Reproducibility

To reproduce this report, do:

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