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  5340860 285.3    7916910 422.9  7916910 422.9
Vcells 37506549 286.2   57645510 439.9 53339345 407.0
> 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	0.003120	0.0032720	0.0036362	0.0034155	0.0035445	0.023587
2	sum(x == value)	0.004364	0.0044715	0.0046578	0.0045425	0.0046575	0.013465

	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)	1.398718	1.366595	1.280956	1.329966	1.314008	0.5708653

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  5338627 285.2    7916910 422.9  7916910 422.9
Vcells 15871293 121.1   57645510 439.9 53339345 407.0
> 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	0.003210	0.0034375	0.0043672	0.003896	0.0042845	0.024847
2	sum(x == value)	0.038699	0.0389085	0.0401716	0.039055	0.0392785	0.057316

	expr	min	lq	mean	median	uq	max
1	count	1.00000	1.00000	1.000000	1.00000	1.000000	1.000000
2	sum(x == value)	12.05576	11.31884	9.198576	10.02438	9.167581	2.306757

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  5338690 285.2    7916910 422.9  7916910 422.9
Vcells 15871335 121.1   57645510 439.9 53339345 407.0
> 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	0.002458	0.0028375	0.0039312	0.0036480	0.0044205	0.02412
2	sum(x == value)	0.293736	0.3039170	0.3315467	0.3196135	0.3615910	0.40162

	expr	min	lq	mean	median	uq	max
1	count	1.000	1.0000	1.00000	1.00000	1.00000	1.00000
2	sum(x == value)	119.502	107.1073	84.33685	87.61335	81.79867	16.65091

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  5338753 285.2    7916910 422.9  7916910 422.9
Vcells 15871890 121.1   57645510 439.9 53339345 407.0
> 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	0.001994	0.0024515	0.0110779	0.0095445	0.0191595	0.03722
2	sum(x == value)	3.525143	3.5498585	4.0291448	3.6116965	3.9686680	21.94083

	expr	min	lq	mean	median	uq	max
1	count	1.000	1.000	1.0000	1.000	1.0000	1.0000
2	sum(x == value)	1767.875	1448.035	363.7111	378.406	207.1384	589.4904

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  5338816 285.2    7916910 422.9  7916910 422.9
Vcells 15871932 121.1   57645510 439.9 57374079 437.8
> 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	0.001994	0.002471	0.0145752	0.003806	0.0284725	0.033778
2	sum(x == value)	35.821991	36.044041	43.5296509	36.527983	37.7079685	448.575286

	expr	min	lq	mean	median	uq	max
1	count	1.00	1.00	1.000	1.000	1.000	1.0
2	sum(x == value)	17964.89	14586.82	2986.562	9597.473	1324.365	13280.1

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  5338879 285.2    7916910 422.9  7916910 422.9
Vcells 21427804 163.5   57645510 439.9 57374079 437.8
> 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	0.003213	0.003367	0.0038157	0.003506	0.0036730	0.028853
2	sum(x == value)	0.003962	0.004087	0.0044005	0.004177	0.0043135	0.014167

	expr	min	lq	mean	median	uq	max
1	count	1.000000	1.00000	1.000000	1.000000	1.000000	1.0000000
2	sum(x == value)	1.233116	1.21384	1.153242	1.191386	1.174381	0.4910061

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  5338942 285.2    7916910 422.9  7916910 422.9
Vcells 21427845 163.5   57645510 439.9 57374079 437.8
> 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	0.003353	0.0035805	0.0044607	0.0039775	0.004524	0.041792
2	sum(x == value)	0.034566	0.0348665	0.0362590	0.0362600	0.036475	0.049126

	expr	min	lq	mean	median	uq	max
1	count	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	sum(x == value)	10.30898	9.737886	8.128577	9.116279	8.062555	1.175488

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  5339005 285.2    7916910 422.9  7916910 422.9
Vcells 21428169 163.5   57645510 439.9 57374079 437.8
> 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	0.002589	0.003010	0.0041296	0.003672	0.0046585	0.024336
2	sum(x == value)	0.262221	0.271943	0.3024479	0.299344	0.3333360	0.369890

	expr	min	lq	mean	median	uq	max
1	count	1.0000	1.00000	1.00000	1.0000	1.00000	1.00000
2	sum(x == value)	101.2827	90.34651	73.23974	81.5207	71.55436	15.19929

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  5339068 285.2    7916910 422.9  7916910 422.9
Vcells 21428541 163.5   57645510 439.9 57374079 437.8
> 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	0.001974	0.002463	0.0101218	0.009207	0.016290	0.033455
2	sum(x == value)	3.327507	3.421146	3.9162957	3.604121	4.058308	19.164505

	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)	1685.667	1389.016	386.9173	391.4544	249.1288	572.8443

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  5339131 285.2    7916910 422.9  7916910 422.9
Vcells 21428583 163.5   57645510 439.9 57430649 438.2
> 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	0.001941	0.0024535	0.013511	0.0037645	0.025913	0.031865
2	sum(x == value)	32.971714	33.0920835	37.258351	33.4237675	35.454502	66.968885

	expr	min	lq	mean	median	uq	max
1	count	1.00	1.0	1.000	1.000	1.000	1.000
2	sum(x == value)	16986.97	13487.7	2757.623	8878.674	1368.213	2101.644

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_0.60.0     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_0.15.0-9000    
[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 23.11 secs.

Reproducibility

To reproduce this report, do:

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