matrixStats.benchmarks

---

allocVector() benchmarks

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

Alternative methods

• vector() + assignment
• rep()
• matrix() + as.vector()

where

> allocVector_R1 <- function(length, value = NA) {
+     x <- vector(mode = typeof(value), length = length)
+     if (!is.finite(value) || value != 0) 
+         x[] <- value
+     x
+ }
> allocVector_R2 <- function(length, value = NA) {
+     x <- matrix(data = value, nrow = length, ncol = 1L)
+     as.vector(x)
+ }

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)
> values <- list(zero = 0, one = 1, `NA` = NA_real_)
> if (mode != "double") 
+     values <- lapply(values, FUN = function(x) {
+         storage.mode(x) <- mode
+         x
+     })

Results

n = 1000 matrix

> n <- length(data[["n = 1000"]])
> str(value)
 int 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.001207	0.0015755	0.0024554	0.0017380	0.0019005	0.070186
2	rep	0.002024	0.0022360	0.0024954	0.0023425	0.0024865	0.009744
3	allocVector_R1	0.002680	0.0030830	0.0035664	0.0033290	0.0035985	0.017822
4	allocVector_R2	0.004625	0.0050665	0.0055169	0.0052575	0.0055825	0.020414

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rep	1.676885	1.419232	1.016286	1.347814	1.308340	0.1388311
3	allocVector_R1	2.220381	1.956839	1.452458	1.915420	1.893449	0.2539253
4	allocVector_R2	3.831814	3.215804	2.246833	3.025029	2.937385	0.2908557

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 1000"]])
> str(value)
 int 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.001494	0.0018995	0.0021898	0.0020030	0.0022105	0.009982
2	rep	0.002167	0.0023285	0.0026053	0.0025065	0.0026485	0.008874
4	allocVector_R2	0.004711	0.0052180	0.0059140	0.0055500	0.0059060	0.033372
3	allocVector_R1	0.006308	0.0068715	0.0073591	0.0071210	0.0075000	0.021515

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rep	1.450468	1.225849	1.189758	1.251373	1.198145	0.8890002
4	allocVector_R2	3.153280	2.747039	2.700697	2.770844	2.671794	3.3432178
3	allocVector_R1	4.222222	3.617531	3.360637	3.555167	3.392898	2.1553797

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 1000"]])
> str(value)
 int NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.001601	0.0018860	0.0022406	0.002098	0.0023700	0.009687
2	rep	0.002125	0.0022880	0.0026007	0.002435	0.0025570	0.015160
4	allocVector_R2	0.004699	0.0051900	0.0057627	0.005567	0.0059280	0.016872
3	allocVector_R1	0.006213	0.0067775	0.0073564	0.006959	0.0072925	0.035589

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rep	1.327295	1.213150	1.160740	1.160629	1.078903	1.564984
4	allocVector_R2	2.935041	2.751856	2.571993	2.653479	2.501266	1.741716
3	allocVector_R1	3.880700	3.593584	3.283294	3.316969	3.077004	3.673893

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 matrix

> n <- length(data[["n = 10000"]])
> str(value)
 int 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.003097	0.0036030	0.0039692	0.0037665	0.0040225	0.011831
3	allocVector_R1	0.004596	0.0051095	0.0055898	0.0054600	0.0058380	0.014070
4	allocVector_R2	0.012145	0.0134350	0.0145650	0.0139685	0.0146185	0.046007
2	rep	0.016329	0.0164995	0.0167631	0.0166105	0.0167380	0.023181

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	allocVector_R1	1.484017	1.418124	1.408302	1.449622	1.451336	1.189249
4	allocVector_R2	3.921537	3.728837	3.669525	3.708615	3.634183	3.888682
2	rep	5.272522	4.579378	4.223321	4.410062	4.161094	1.959344

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 10000"]])
> str(value)
 int 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.006589	0.0071495	0.0076573	0.0073930	0.0077470	0.018073
4	allocVector_R2	0.012832	0.0139145	0.0146466	0.0144695	0.0150000	0.027984
2	rep	0.015819	0.0165415	0.0167551	0.0166530	0.0167985	0.022610
3	allocVector_R1	0.030526	0.0319380	0.0325804	0.0323585	0.0327435	0.045655

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
4	allocVector_R2	1.947488	1.946220	1.912753	1.957189	1.936233	1.548387
2	rep	2.400819	2.313658	2.188110	2.252536	2.168388	1.251038
3	allocVector_R1	4.632873	4.467166	4.254805	4.376911	4.226604	2.526144

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 10000"]])
> str(value)
 int NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.006206	0.0069575	0.0073615	0.0072990	0.0075975	0.014405
4	allocVector_R2	0.011756	0.0138505	0.0150380	0.0143845	0.0149755	0.044435
2	rep	0.015812	0.0165340	0.0167107	0.0166510	0.0167925	0.020548
3	allocVector_R1	0.030063	0.0319225	0.0323812	0.0322610	0.0326025	0.044110

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
4	allocVector_R2	1.894296	1.990729	2.042782	1.970749	1.971109	3.084693
2	rep	2.547857	2.376428	2.269999	2.281271	2.210266	1.426449
3	allocVector_R1	4.844183	4.588214	4.398700	4.419920	4.291214	3.062131

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 matrix

> n <- length(data[["n = 100000"]])
> str(value)
 int 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 100000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.017219	0.0200070	0.0759101	0.0227995	0.1609475	0.205651
3	allocVector_R1	0.018154	0.0212425	0.0719590	0.0241340	0.1597315	0.233512
4	allocVector_R2	0.073308	0.0864205	0.2441712	0.0978160	0.3415975	5.458121
2	rep	0.126543	0.1362765	0.2448996	0.1584245	0.2594165	5.544453

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.0000000	1.000000	1.0000000	1.000000
3	allocVector_R1	1.054301	1.061753	0.9479505	1.058532	0.9924447	1.135477
4	allocVector_R2	4.257390	4.319513	3.2165851	4.290269	2.1224157	26.540698
2	rep	7.349033	6.811441	3.2261800	6.948595	1.6118082	26.960496

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 100000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 100000"]])
> str(value)
 int 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 100000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.043335	0.0456530	0.1437595	0.0517275	0.1675860	5.353385
4	allocVector_R2	0.069954	0.0774700	0.2085445	0.0855720	0.3296740	5.208113
2	rep	0.122660	0.1270560	0.1738057	0.1463460	0.2482715	0.306157
3	allocVector_R1	0.214804	0.2252585	0.3433585	0.2660250	0.4863515	0.592387

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
4	allocVector_R2	1.614261	1.696931	1.450649	1.654284	1.967193	0.9728635
2	rep	2.830506	2.783081	1.209003	2.829172	1.481457	0.0571894
3	allocVector_R1	4.956825	4.934145	2.388423	5.142816	2.902101	0.1106565

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 100000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 100000"]])
> str(value)
 int NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 100000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.042508	0.0449285	0.0811267	0.0481785	0.1560825	0.199457
4	allocVector_R2	0.071047	0.0784890	0.1667413	0.0899735	0.3270300	0.387363
2	rep	0.122501	0.1268385	0.2188964	0.1407625	0.1653190	5.556213
3	allocVector_R1	0.214654	0.2226760	0.3780174	0.2542545	0.4858145	5.622163

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
4	allocVector_R2	1.671380	1.746976	2.055319	1.867503	2.095238	1.942088
2	rep	2.881834	2.823119	2.698203	2.921687	1.059177	27.856696
3	allocVector_R1	5.049732	4.956231	4.659590	5.277344	3.112550	28.187344

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 100000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 matrix

> n <- length(data[["n = 1000000"]])
> str(value)
 int 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.161781	0.1808590	0.6202326	0.1942385	0.208862	6.246709
3	allocVector_R1	0.170806	0.1900815	0.5925710	0.2023540	0.232190	4.052854
4	allocVector_R2	0.839776	0.9285695	1.5797688	0.9736050	1.024361	6.547366
2	rep	1.257761	1.3129645	1.8984884	1.3857345	1.463321	7.256379

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
3	allocVector_R1	1.055785	1.050993	0.9554013	1.041781	1.111691	0.6487983
4	allocVector_R2	5.190820	5.134218	2.5470585	5.012420	4.904490	1.0481305
2	rep	7.774467	7.259603	3.0609296	7.134191	7.006164	1.1616323

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 1000000"]])
> str(value)
 int 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.408741	0.428375	4.308654	0.4384040	0.528319	333.321278
4	allocVector_R2	0.820420	0.872034	1.700392	0.9274495	3.033707	6.748338
2	rep	1.144560	1.225901	1.559570	1.2619730	1.305289	6.854616
3	allocVector_R1	2.082066	2.208751	3.002041	2.2696910	2.731126	7.881858

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
4	allocVector_R2	2.007188	2.035679	0.3946458	2.115513	5.742187	0.0202457
2	rep	2.800208	2.861746	0.3619622	2.878562	2.470646	0.0205646
3	allocVector_R1	5.093852	5.156117	0.6967469	5.177168	5.169463	0.0236464

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 1000000"]])
> str(value)
 int NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.413900	0.432972	0.9158277	0.442799	0.5179325	6.271777
4	allocVector_R2	0.820146	0.877243	1.6536557	0.933258	1.3049465	6.882525
2	rep	1.179737	1.223618	1.5982229	1.262436	1.3497245	7.009429
3	allocVector_R1	2.131479	2.217995	3.1299068	2.279055	3.2363245	8.883135

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
4	allocVector_R2	1.981508	2.026096	1.805640	2.107633	2.519530	1.097380
2	rep	2.850295	2.826091	1.745113	2.851036	2.605985	1.117614
3	allocVector_R1	5.149744	5.122720	3.417572	5.146929	6.248545	1.416367

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 1000000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000000 matrix

> n <- length(data[["n = 10000000"]])
> str(value)
 int 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
3	allocVector_R1	12.48484	12.87243	16.07658	13.38199	20.99177	24.23849
1	allocVector	12.46579	12.89216	20.49525	15.28668	21.44455	344.06724
2	rep	20.30834	20.78174	35.41318	24.65478	29.38401	383.14429
4	allocVector_R2	29.53574	34.97074	36.49151	37.35883	38.83788	45.86025

	expr	min	lq	mean	median	uq	max
3	allocVector_R1	1.0000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	allocVector	0.9984744	1.001533	1.274851	1.142332	1.021570	14.195078
2	rep	1.6266410	1.614438	2.202780	1.842385	1.399788	15.807269
4	allocVector_R2	2.3657296	2.716717	2.269855	2.791724	1.850148	1.892043

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 10000000"]])
> str(value)
 int 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	13.19570	14.10907	32.13831	16.68228	22.69588	390.66040
2	rep	20.22937	21.27525	26.23988	26.99304	30.48453	35.37265
4	allocVector_R2	29.60304	38.12044	43.35905	39.91478	42.12181	377.14408
3	allocVector_R1	40.16599	49.48983	62.32039	51.47486	55.76479	397.89815

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rep	1.533027	1.507913	0.8164672	1.618067	1.343174	0.0905458
4	allocVector_R2	2.243386	2.701840	1.3491389	2.392646	1.855923	0.9654013
3	allocVector_R1	3.043870	3.507662	1.9391307	3.085602	2.457044	1.0185270

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 10000000"]])
> str(value)
 int NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	13.14410	16.99748	25.64399	21.39350	26.83883	379.44009
2	rep	20.42353	25.48343	44.73435	28.64958	34.08759	415.55887
4	allocVector_R2	31.17431	40.19437	45.05834	44.76693	50.02818	63.26161
3	allocVector_R1	45.79004	54.37136	73.01122	60.67593	67.26059	423.91220

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rep	1.553817	1.499247	1.744438	1.339172	1.270085	1.0951897
4	allocVector_R2	2.371733	2.364725	1.757072	2.092548	1.864023	0.1667236
3	allocVector_R1	3.483695	3.198790	2.847108	2.836186	2.506093	1.1172045

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on integer+n = 10000000+NA 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)
> values <- list(zero = 0, one = 1, `NA` = NA_real_)
> if (mode != "double") 
+     values <- lapply(values, FUN = function(x) {
+         storage.mode(x) <- mode
+         x
+     })

Results

n = 1000 matrix

> n <- length(data[["n = 1000"]])
> str(value)
 num 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.001259	0.0014825	0.0023722	0.0017395	0.0029765	0.021729
2	rep	0.001810	0.0019070	0.0026352	0.0021055	0.0034560	0.006048
3	allocVector_R1	0.002546	0.0029835	0.0045033	0.0033035	0.0063625	0.016274
4	allocVector_R2	0.004289	0.0048775	0.0072078	0.0051745	0.0101200	0.026711

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rep	1.437649	1.286341	1.110840	1.210405	1.161095	0.2783377
3	allocVector_R1	2.022240	2.012479	1.898337	1.899109	2.137578	0.7489530
4	allocVector_R2	3.406672	3.290051	3.038403	2.974705	3.399966	1.2292788

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 1000"]])
> str(value)
 num 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.001422	0.001743	0.0021571	0.0018840	0.0021275	0.014491
2	rep	0.001687	0.001808	0.0020665	0.0019280	0.0020655	0.004987
4	allocVector_R2	0.004030	0.004550	0.0056235	0.0047575	0.0050425	0.045010
3	allocVector_R1	0.005145	0.005460	0.0064151	0.0057040	0.0060510	0.024704

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.0000000	1.000000	1.0000000	1.0000000
2	rep	1.186357	1.037292	0.9579905	1.023355	0.9708578	0.3441446
4	allocVector_R2	2.834037	2.610442	2.6069129	2.525212	2.3701528	3.1060658
3	allocVector_R1	3.618143	3.132530	2.9738820	3.027601	2.8441833	1.7047823

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 1000"]])
> str(value)
 num NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rep	0.002137	0.002257	0.0025928	0.0023560	0.0025325	0.015994
1	allocVector	0.001857	0.002189	0.0025469	0.0023780	0.0025890	0.016976
4	allocVector_R2	0.005103	0.005568	0.0061317	0.0058410	0.0062185	0.025025
3	allocVector_R1	0.006296	0.006648	0.0072987	0.0068505	0.0071515	0.041025

	expr	min	lq	mean	median	uq	max
2	rep	1.0000000	1.0000000	1.0000000	1.000000	1.000000	1.000000
1	allocVector	0.8689752	0.9698715	0.9822627	1.009338	1.022310	1.061398
4	allocVector_R2	2.3879270	2.4669916	2.3648663	2.479202	2.455479	1.564649
3	allocVector_R1	2.9461862	2.9455029	2.8149327	2.907682	2.823889	2.565024

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 matrix

> n <- length(data[["n = 10000"]])
> str(value)
 num 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.005278	0.0059925	0.0071286	0.0063065	0.0076170	0.022418
3	allocVector_R1	0.007145	0.0082195	0.0096153	0.0092150	0.0104980	0.016950
2	rep	0.016506	0.0168900	0.0194588	0.0171165	0.0207635	0.048388
4	allocVector_R2	0.018069	0.0223440	0.0245975	0.0235805	0.0263385	0.042436

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	allocVector_R1	1.353733	1.371631	1.348826	1.461191	1.378233	0.7560889
2	rep	3.127321	2.818523	2.729672	2.714104	2.725942	2.1584441
4	allocVector_R2	3.423456	3.728661	3.450517	3.739079	3.457857	1.8929432

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 10000"]])
> str(value)
 num 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.008135	0.0089675	0.0099416	0.0094730	0.0105050	0.016818
2	rep	0.016543	0.0167100	0.0171143	0.0168565	0.0171865	0.024020
4	allocVector_R2	0.017665	0.0190135	0.0207219	0.0195470	0.0226105	0.037304
3	allocVector_R1	0.031283	0.0319630	0.0336748	0.0323975	0.0333210	0.064274

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rep	2.033559	1.863396	1.721486	1.779426	1.636030	1.428232
4	allocVector_R2	2.171481	2.120268	2.084357	2.063443	2.152356	2.218100
3	allocVector_R1	3.845483	3.564316	3.387263	3.419983	3.171918	3.821739

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 10000"]])
> str(value)
 num NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.007814	0.0088285	0.0100072	0.0092185	0.0099435	0.041077
2	rep	0.015961	0.0166460	0.0170889	0.0168560	0.0171340	0.025815
4	allocVector_R2	0.017514	0.0186930	0.0205903	0.0194995	0.0214825	0.040296
3	allocVector_R1	0.029559	0.0314550	0.0322915	0.0320290	0.0325030	0.046611

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rep	2.042616	1.885485	1.707663	1.828497	1.723136	0.6284539
4	allocVector_R2	2.241362	2.117347	2.057551	2.115257	2.160457	0.9809869
3	allocVector_R1	3.782826	3.562893	3.226831	3.474426	3.268769	1.1347226

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 matrix

> n <- length(data[["n = 100000"]])
> str(value)
 num 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 100000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.032975	0.0445010	0.2552944	0.3371345	0.3607805	0.515925
3	allocVector_R1	0.033665	0.0496920	0.2560893	0.3381810	0.3662975	0.474873
2	rep	0.126966	0.1615215	0.5220916	0.4098125	0.4410205	9.735218
4	allocVector_R2	0.138870	0.1877330	0.5510740	0.6731745	0.7102845	0.884302

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	allocVector_R1	1.020925	1.116649	1.003113	1.003104	1.015292	0.9204303
2	rep	3.850372	3.629615	2.045057	1.215576	1.222407	18.8694442
4	allocVector_R2	4.211372	4.218624	2.158582	1.996754	1.968744	1.7140127

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 100000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 100000"]])
> str(value)
 num 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 100000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.062230	0.0700165	0.1309885	0.0767860	0.1001945	0.445361
2	rep	0.119678	0.1283640	0.2241331	0.1420500	0.3334435	0.566131
4	allocVector_R2	0.132306	0.1610060	0.3878265	0.1777770	0.3559725	4.469379
3	allocVector_R1	0.211214	0.2219980	0.3977451	0.2336005	0.5315830	4.103197

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rep	1.923156	1.833339	1.711091	1.849947	3.327962	1.271173
4	allocVector_R2	2.126081	2.299544	2.960769	2.315227	3.552815	10.035407
3	allocVector_R1	3.394086	3.170653	3.036490	3.042228	5.305511	9.213193

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 100000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 100000"]])
> str(value)
 num NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 100000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.060898	0.0705405	0.3723784	0.2894075	0.3151010	8.264352
2	rep	0.127419	0.1590620	0.3073524	0.3701820	0.4051885	0.544962
3	allocVector_R1	0.216292	0.2265175	0.5392802	0.5762815	0.6383735	7.983009
4	allocVector_R2	0.129893	0.1579815	0.4931727	0.6243855	0.6631820	0.876573

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rep	2.092335	2.254903	0.8253765	1.279103	1.285900	0.0659413
3	allocVector_R1	3.551709	3.211170	1.4482049	1.991246	2.025933	0.9659570
4	allocVector_R2	2.132960	2.239586	1.3243860	2.157461	2.104665	0.1060668

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 100000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 matrix

> n <- length(data[["n = 1000000"]])
> str(value)
 num 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
3	allocVector_R1	0.321378	0.4081640	1.545236	0.4848415	3.088989	7.951424
1	allocVector	0.314506	0.3781115	1.726715	0.4936290	3.144486	9.375319
2	rep	1.193094	1.3386675	2.806585	1.6701390	3.652778	20.428340
4	allocVector_R2	1.352299	1.5610275	4.029003	2.3514385	7.009819	10.039261

	expr	min	lq	mean	median	uq	max
3	allocVector_R1	1.0000000	1.0000000	1.000000	1.000000	1.000000	1.000000
1	allocVector	0.9786171	0.9263715	1.117444	1.018125	1.017966	1.179074
2	rep	3.7124321	3.2797295	1.816282	3.444711	1.182516	2.569142
4	allocVector_R2	4.2078145	3.8245105	2.607371	4.849912	2.269293	1.262574

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 1000000"]])
> str(value)
 num 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.640098	0.664248	1.514574	0.6993095	2.704990	9.212924
2	rep	1.155785	1.237724	2.213403	1.2760855	3.440713	12.252703
4	allocVector_R2	1.335175	1.398490	6.586466	1.4785205	6.331370	329.164961
3	allocVector_R1	2.105089	2.247377	3.778901	2.2781760	5.742554	10.931701

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rep	1.805638	1.863346	1.461403	1.824779	1.271987	1.329947
4	allocVector_R2	2.085891	2.105373	4.348725	2.114258	2.340626	35.728609
3	allocVector_R1	3.288698	3.383340	2.495025	3.257751	2.122948	1.186562

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 1000000"]])
> str(value)
 num NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	0.643766	0.6671845	1.318058	0.683923	0.788271	8.101824
2	rep	1.157254	1.2346510	1.660541	1.275901	1.361646	5.398941
4	allocVector_R2	1.302117	1.3870670	2.476507	1.440087	2.239600	10.112007
3	allocVector_R1	2.059051	2.2200330	2.681154	2.262782	2.441831	7.953408

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rep	1.797631	1.850539	1.259840	1.865562	1.727383	0.6663859
4	allocVector_R2	2.022656	2.078986	1.878907	2.105627	2.841154	1.2481149
3	allocVector_R1	3.198446	3.327465	2.034171	3.308533	3.097704	0.9816812

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 1000000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000000 matrix

> n <- length(data[["n = 10000000"]])
> str(value)
 num 0

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	25.53924	26.31628	36.88158	29.63464	41.07488	409.94440
3	allocVector_R1	25.22212	26.29911	40.92258	29.96420	42.05531	388.95371
2	rep	31.20185	32.24085	47.36722	42.58038	47.38334	384.88675
4	allocVector_R2	56.96028	67.86871	71.98907	73.29372	76.68337	94.41727

	expr	min	lq	mean	median	uq	max
1	allocVector	1.0000000	1.0000000	1.000000	1.000000	1.000000	1.0000000
3	allocVector_R1	0.9875831	0.9993475	1.109567	1.011121	1.023869	0.9487962
2	rep	1.2217221	1.2251294	1.284306	1.436845	1.153584	0.9388755
4	allocVector_R2	2.2303049	2.5789630	1.951898	2.473245	1.866916	0.2303172

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 10000000"]])
> str(value)
 num 1

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	23.86580	24.80692	39.34795	30.88632	39.53323	383.2569
2	rep	31.15383	32.37567	44.60568	40.18790	47.33339	385.3664
3	allocVector_R1	51.98065	63.37773	74.32429	67.24214	72.40544	411.2283
4	allocVector_R2	56.99035	69.27100	80.79715	73.64805	79.10371	429.5774

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rep	1.305375	1.305106	1.133622	1.301155	1.197307	1.005504
3	allocVector_R1	2.178040	2.554841	1.888899	2.177085	1.831508	1.072983
4	allocVector_R2	2.387951	2.792406	2.053402	2.384488	2.000942	1.120860

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> n <- length(data[["n = 10000000"]])
> str(value)
 num NA

> stats <- microbenchmark(allocVector = allocVector(length = n, value = value), rep = rep(value, times = n), 
+     allocVector_R1 = allocVector_R1(length = n, value = value), allocVector_R2 = allocVector_R2(length = n, 
+         value = value), unit = "ms")

Table: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocVector	23.95989	25.90227	43.90854	31.04191	40.65225	387.8995
2	rep	31.63185	33.35264	45.92046	40.45574	49.01193	411.6979
3	allocVector_R1	52.54272	64.85379	74.94758	68.04997	72.06090	416.6914
4	allocVector_R2	57.28902	71.93656	79.57958	75.60285	80.23777	410.3189

	expr	min	lq	mean	median	uq	max
1	allocVector	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rep	1.320200	1.287634	1.045821	1.303262	1.205639	1.061352
3	allocVector_R1	2.192945	2.503788	1.706902	2.192197	1.772618	1.074225
4	allocVector_R2	2.391038	2.777230	1.812394	2.435509	1.973760	1.057797

Figure: Benchmarking of allocVector(), rep(), allocVector_R1() and allocVector_R2() on double+n = 10000000+NA 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         
[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 2.51 mins.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:07:29 (+0200 UTC). Powered by RSP.