matrixStats.benchmarks
mean2() benchmarks
This report benchmark the performance of mean2() against alternative methods.
Alternative methods
- mean() + [()
- mean.default() + [() - avoids method dispatching
as below
> mean2_R_v1 <- function(x, na.rm = FALSE, idxs) {
+ mean(x[idxs], na.rm = na.rm)
+ }
and
> mean2_R_v2 <- function(x, na.rm = FALSE, idxs) {
+ mean.default(x[idxs], na.rm = na.rm)
+ }
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
All elements
> x <- data[["n = 1000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347263 285.6 7916910 422.9 7916910 422.9
Vcells 37517075 286.3 68615206 523.5 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 1000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 0.002799 | 0.0029020 | 0.0030745 | 0.0029335 | 0.0030210 | 0.012954 |
| 2 | mean2_no_refine | 0.003068 | 0.0031415 | 0.0033200 | 0.0032090 | 0.0033545 | 0.007081 |
| 1 | mean2 | 0.003065 | 0.0031340 | 0.0034238 | 0.0032610 | 0.0033640 | 0.016945 |
| 3 | mean | 0.004613 | 0.0047810 | 0.0050974 | 0.0048745 | 0.0050025 | 0.020661 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | mean2_no_refine | 1.096106 | 1.082529 | 1.079844 | 1.093915 | 1.110394 | 0.5466265 |
| 1 | mean2 | 1.095034 | 1.079945 | 1.113599 | 1.111641 | 1.113539 | 1.3080902 |
| 3 | mean | 1.648089 | 1.647485 | 1.657973 | 1.661667 | 1.655909 | 1.5949514 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 1000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 1000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345120 285.5 7916910 422.9 7916910 422.9
Vcells 15881619 121.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.002064 | 0.0021475 | 0.0024638 | 0.002225 | 0.0023215 | 0.022281 |
| 1 | mean2 | 0.002059 | 0.0021525 | 0.0022641 | 0.002227 | 0.0023335 | 0.002860 |
| 4 | mean.default+[() | 0.003339 | 0.0036365 | 0.0039478 | 0.003760 | 0.0039260 | 0.018079 |
| 3 | mean+[() | 0.005438 | 0.0057050 | 0.0061576 | 0.005895 | 0.0061545 | 0.025670 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.0000000 | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | mean2 | 0.9975775 | 1.002328 | 0.9189186 | 1.000899 | 1.005169 | 0.1283605 |
| 4 | mean.default+[() | 1.6177326 | 1.693364 | 1.6022924 | 1.689888 | 1.691148 | 0.8114088 |
| 3 | mean+[() | 2.6346899 | 2.656577 | 2.4992248 | 2.649438 | 2.651088 | 1.1521027 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 1000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345204 285.5 7916910 422.9 7916910 422.9
Vcells 15882295 121.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.002315 | 0.0024935 | 0.0034461 | 0.0026655 | 0.0044160 | 0.016416 |
| 1 | mean2 | 0.002319 | 0.0024495 | 0.0035524 | 0.0026815 | 0.0045770 | 0.017283 |
| 4 | mean.default+[() | 0.003571 | 0.0040185 | 0.0057829 | 0.0058445 | 0.0070055 | 0.029692 |
| 3 | mean+[() | 0.005303 | 0.0056080 | 0.0077779 | 0.0060660 | 0.0102080 | 0.031665 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.001728 | 0.9823541 | 1.030855 | 1.006003 | 1.036458 | 1.052814 |
| 4 | mean.default+[() | 1.542549 | 1.6115901 | 1.678105 | 2.192647 | 1.586390 | 1.808723 |
| 3 | mean+[() | 2.290713 | 2.2490475 | 2.257027 | 2.275746 | 2.311594 | 1.928911 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 1000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345285 285.5 7916910 422.9 7916910 422.9
Vcells 15882549 121.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 0.003677 | 0.0038805 | 0.0046441 | 0.0039715 | 0.0041005 | 0.036367 |
| 2 | mean2_no_refine | 0.003715 | 0.0038585 | 0.0041854 | 0.0039775 | 0.0041675 | 0.009074 |
| 4 | mean.default+[() | 0.005256 | 0.0055545 | 0.0060895 | 0.0057035 | 0.0059990 | 0.019459 |
| 3 | mean+[() | 0.007326 | 0.0075905 | 0.0086258 | 0.0078090 | 0.0081560 | 0.049528 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 1.000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | mean2_no_refine | 1.010334 | 0.9943306 | 0.9012131 | 1.001511 | 1.016339 | 0.2495119 |
| 4 | mean.default+[() | 1.429426 | 1.4313877 | 1.3112266 | 1.436107 | 1.462992 | 0.5350730 |
| 3 | mean+[() | 1.992385 | 1.9560624 | 1.8573406 | 1.966260 | 1.989026 | 1.3618940 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000+0.8 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
All elements
> x <- data[["n = 10000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345295 285.5 7916910 422.9 7916910 422.9
Vcells 15882329 121.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 10000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 0.012779 | 0.0133225 | 0.0138461 | 0.0135730 | 0.0141105 | 0.028004 |
| 3 | mean | 0.014545 | 0.0152885 | 0.0161326 | 0.0157375 | 0.0162270 | 0.040861 |
| 1 | mean2 | 0.016360 | 0.0170340 | 0.0178260 | 0.0172390 | 0.0178105 | 0.044082 |
| 2 | mean2_no_refine | 0.016388 | 0.0170300 | 0.0177870 | 0.0172665 | 0.0179135 | 0.032551 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean | 1.138195 | 1.147570 | 1.165137 | 1.159471 | 1.149995 | 1.459113 |
| 1 | mean2 | 1.280225 | 1.278589 | 1.287436 | 1.270095 | 1.262216 | 1.574132 |
| 2 | mean2_no_refine | 1.282417 | 1.278289 | 1.284619 | 1.272121 | 1.269516 | 1.162370 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 10000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 10000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345447 285.5 7916910 422.9 7916910 422.9
Vcells 15883722 121.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 0.007452 | 0.0076215 | 0.0079020 | 0.0077615 | 0.0079765 | 0.012544 |
| 2 | mean2_no_refine | 0.007461 | 0.0076190 | 0.0082991 | 0.0077965 | 0.0080595 | 0.022231 |
| 4 | mean.default+[() | 0.009629 | 0.0098760 | 0.0104579 | 0.0101015 | 0.0105330 | 0.015865 |
| 3 | mean+[() | 0.011588 | 0.0121195 | 0.0137225 | 0.0123370 | 0.0130215 | 0.072490 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | mean2_no_refine | 1.001208 | 0.999672 | 1.050253 | 1.004509 | 1.010406 | 1.772242 |
| 4 | mean.default+[() | 1.292136 | 1.295808 | 1.323447 | 1.301488 | 1.320504 | 1.264748 |
| 3 | mean+[() | 1.555019 | 1.590172 | 1.736573 | 1.589512 | 1.632483 | 5.778858 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 10000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345528 285.5 7916910 422.9 7916910 422.9
Vcells 15885078 121.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 0.015590 | 0.0159015 | 0.0165469 | 0.0164185 | 0.0170750 | 0.018352 |
| 2 | mean2_no_refine | 0.015607 | 0.0160440 | 0.0168530 | 0.0165460 | 0.0171715 | 0.027121 |
| 4 | mean.default+[() | 0.018600 | 0.0202175 | 0.0213759 | 0.0208940 | 0.0217700 | 0.039835 |
| 3 | mean+[() | 0.021621 | 0.0226610 | 0.0245148 | 0.0236765 | 0.0247240 | 0.070578 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | mean2_no_refine | 1.001090 | 1.008961 | 1.018500 | 1.007766 | 1.005651 | 1.477823 |
| 4 | mean.default+[() | 1.193072 | 1.271421 | 1.291837 | 1.272589 | 1.274963 | 2.170608 |
| 3 | mean+[() | 1.386850 | 1.425086 | 1.481530 | 1.442062 | 1.447965 | 3.845793 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 10000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345609 285.5 7916910 422.9 7916910 422.9
Vcells 15887496 121.3 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.026708 | 0.0278605 | 0.0301429 | 0.0289540 | 0.0312570 | 0.056221 |
| 1 | mean2 | 0.026650 | 0.0285610 | 0.0305719 | 0.0297945 | 0.0318585 | 0.052113 |
| 4 | mean.default+[() | 0.032061 | 0.0337195 | 0.0363694 | 0.0358005 | 0.0379250 | 0.064048 |
| 3 | mean+[() | 0.034290 | 0.0362305 | 0.0397354 | 0.0377100 | 0.0406250 | 0.075920 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | mean2 | 0.9978284 | 1.025143 | 1.014232 | 1.029029 | 1.019244 | 0.9269312 |
| 4 | mean.default+[() | 1.2004268 | 1.210298 | 1.206564 | 1.236461 | 1.213328 | 1.1392184 |
| 3 | mean+[() | 1.2838850 | 1.300425 | 1.318234 | 1.302411 | 1.299709 | 1.3503851 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000+0.8 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
All elements
> x <- data[["n = 100000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345619 285.5 7916910 422.9 7916910 422.9
Vcells 15887501 121.3 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 100000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 0.093988 | 0.1040215 | 0.1243721 | 0.1183500 | 0.1426235 | 0.186819 |
| 3 | mean | 0.095835 | 0.1036230 | 0.1306482 | 0.1200370 | 0.1551335 | 0.199532 |
| 2 | mean2_no_refine | 0.123333 | 0.1349320 | 0.1474210 | 0.1413460 | 0.1628710 | 0.185379 |
| 1 | mean2 | 0.123388 | 0.1362255 | 0.1490978 | 0.1456175 | 0.1605965 | 0.206744 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean | 1.019651 | 0.9961691 | 1.050462 | 1.014254 | 1.087713 | 1.068050 |
| 2 | mean2_no_refine | 1.312221 | 1.2971549 | 1.185322 | 1.194305 | 1.141965 | 0.992292 |
| 1 | mean2 | 1.312806 | 1.3095898 | 1.198804 | 1.230397 | 1.126017 | 1.106654 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 100000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 100000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345771 285.5 7916910 422.9 7916910 422.9
Vcells 15894040 121.3 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 100000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.062371 | 0.0669800 | 0.0760080 | 0.0720105 | 0.0797415 | 0.135274 |
| 1 | mean2 | 0.062362 | 0.0670355 | 0.0786025 | 0.0749805 | 0.0827105 | 0.145381 |
| 4 | mean.default+[() | 0.073925 | 0.0800490 | 0.0908226 | 0.0867730 | 0.0961165 | 0.141635 |
| 3 | mean+[() | 0.076271 | 0.0821560 | 0.0944314 | 0.0905120 | 0.1003580 | 0.184158 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 0.9998557 | 1.000829 | 1.034136 | 1.041244 | 1.037233 | 1.074715 |
| 4 | mean.default+[() | 1.1852463 | 1.195118 | 1.194909 | 1.205005 | 1.205351 | 1.047023 |
| 3 | mean+[() | 1.2228600 | 1.226575 | 1.242388 | 1.256928 | 1.258542 | 1.361370 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 100000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 100000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345852 285.5 7916910 422.9 7916910 422.9
Vcells 15904619 121.4 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 100000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.105058 | 0.1114755 | 0.1712444 | 0.1299190 | 0.2306875 | 0.295112 |
| 4 | mean.default+[() | 0.122963 | 0.1348490 | 0.1886266 | 0.1453550 | 0.2508830 | 0.339519 |
| 3 | mean+[() | 0.124876 | 0.1355110 | 0.1864570 | 0.1475750 | 0.2502520 | 0.335273 |
| 1 | mean2 | 0.105087 | 0.1115920 | 0.1750653 | 0.1669525 | 0.2318520 | 0.281113 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 4 | mean.default+[() | 1.170430 | 1.209674 | 1.101505 | 1.118812 | 1.087545 | 1.1504751 |
| 3 | mean+[() | 1.188639 | 1.215612 | 1.088836 | 1.135900 | 1.084809 | 1.1360873 |
| 1 | mean2 | 1.000276 | 1.001045 | 1.022312 | 1.285051 | 1.005048 | 0.9525638 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 100000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 100000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345933 285.6 7916910 422.9 7916910 422.9
Vcells 15924673 121.5 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 100000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.186719 | 0.1871585 | 0.2345470 | 0.1984955 | 0.2757345 | 0.406197 |
| 1 | mean2 | 0.186731 | 0.1896190 | 0.2519452 | 0.2321785 | 0.2970505 | 0.458958 |
| 3 | mean+[() | 0.221482 | 0.2258150 | 0.2820589 | 0.2483130 | 0.3293260 | 0.593510 |
| 4 | mean.default+[() | 0.218936 | 0.2217100 | 0.2793557 | 0.2508805 | 0.3262965 | 0.533550 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.000064 | 1.013147 | 1.074178 | 1.169692 | 1.077306 | 1.129890 |
| 3 | mean+[() | 1.186178 | 1.206544 | 1.202569 | 1.250976 | 1.194359 | 1.461138 |
| 4 | mean.default+[() | 1.172543 | 1.184611 | 1.191044 | 1.263910 | 1.183372 | 1.313525 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 100000+0.8 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
All elements
> x <- data[["n = 1000000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5345943 285.6 7916910 422.9 7916910 422.9
Vcells 15924869 121.5 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 1000000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 0.907361 | 0.995682 | 1.150988 | 1.074253 | 1.325117 | 1.567060 |
| 3 | mean | 0.943019 | 1.025310 | 1.198732 | 1.117517 | 1.373515 | 1.566173 |
| 2 | mean2_no_refine | 1.178194 | 1.253573 | 1.325687 | 1.315514 | 1.384903 | 1.706064 |
| 1 | mean2 | 1.171185 | 1.263374 | 1.338025 | 1.321410 | 1.402878 | 1.619958 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean | 1.039299 | 1.029756 | 1.041481 | 1.040274 | 1.036524 | 0.999434 |
| 2 | mean2_no_refine | 1.298484 | 1.259010 | 1.151782 | 1.224585 | 1.045117 | 1.088704 |
| 1 | mean2 | 1.290760 | 1.268853 | 1.162502 | 1.230074 | 1.058683 | 1.033756 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 1000000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 1000000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346095 285.6 7916910 422.9 7916910 422.9
Vcells 15985407 122.0 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 0.533091 | 0.5941275 | 0.6773973 | 0.6582845 | 0.7374295 | 1.032028 |
| 2 | mean2_no_refine | 0.544434 | 0.5959685 | 0.7081223 | 0.6618695 | 0.8063780 | 1.126720 |
| 4 | mean.default+[() | 0.664087 | 0.8367260 | 0.9719835 | 0.9699980 | 1.0899330 | 1.356483 |
| 3 | mean+[() | 0.659317 | 0.9135430 | 1.0246188 | 1.0231725 | 1.1581980 | 1.425969 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | mean2_no_refine | 1.021278 | 1.003099 | 1.045357 | 1.005446 | 1.093498 | 1.091753 |
| 4 | mean.default+[() | 1.245729 | 1.408327 | 1.434880 | 1.473524 | 1.478017 | 1.314386 |
| 3 | mean+[() | 1.236781 | 1.537621 | 1.512582 | 1.554301 | 1.570588 | 1.381715 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 1000000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346176 285.6 7916910 422.9 7916910 422.9
Vcells 16085463 122.8 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.072838 | 1.193715 | 1.377496 | 1.285649 | 1.462301 | 2.575293 |
| 1 | mean2 | 1.023994 | 1.187117 | 1.352711 | 1.301637 | 1.433191 | 2.157889 |
| 3 | mean+[() | 1.291677 | 1.613168 | 2.064294 | 1.884502 | 2.140823 | 16.232211 |
| 4 | mean.default+[() | 1.255464 | 1.675893 | 1.925433 | 1.909858 | 2.174402 | 2.917143 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.0000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | mean2 | 0.9544722 | 0.9944727 | 0.9820072 | 1.012435 | 0.980093 | 0.8379198 |
| 3 | mean+[() | 1.2039814 | 1.3513845 | 1.4985847 | 1.465799 | 1.464009 | 6.3030541 |
| 4 | mean.default+[() | 1.1702270 | 1.4039306 | 1.3977775 | 1.485521 | 1.486973 | 1.1327422 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 1000000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346257 285.6 7916910 422.9 7916910 422.9
Vcells 16286272 124.3 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.901145 | 1.982795 | 2.191301 | 2.085029 | 2.325310 | 3.499716 |
| 1 | mean2 | 1.921770 | 2.022821 | 2.215527 | 2.117431 | 2.338416 | 3.516379 |
| 4 | mean.default+[() | 2.843862 | 3.268609 | 3.629056 | 3.378196 | 3.546221 | 18.510310 |
| 3 | mean+[() | 2.787402 | 3.267502 | 3.634881 | 3.415418 | 3.694373 | 18.642724 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.010849 | 1.020187 | 1.011055 | 1.015540 | 1.005636 | 1.004761 |
| 4 | mean.default+[() | 1.495868 | 1.648485 | 1.656119 | 1.620215 | 1.525053 | 5.289089 |
| 3 | mean+[() | 1.466170 | 1.647927 | 1.658778 | 1.638067 | 1.588766 | 5.326925 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 1000000+0.8 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000000 vector
All elements
> x <- data[["n = 10000000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346267 285.6 7916910 422.9 7916910 422.9
Vcells 16285840 124.3 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 10000000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 3 | mean | 8.910474 | 9.316434 | 10.18797 | 9.749757 | 10.53794 | 14.44465 |
| 4 | mean.default | 8.853049 | 9.314250 | 10.17287 | 9.864505 | 10.78925 | 15.27491 |
| 2 | mean2_no_refine | 11.009040 | 11.300674 | 12.30224 | 11.951052 | 13.04010 | 15.03995 |
| 1 | mean2 | 11.026848 | 11.407820 | 12.44913 | 12.280653 | 13.33792 | 15.53697 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 3 | mean | 1.0000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default | 0.9935553 | 0.9997655 | 0.9985174 | 1.011769 | 1.023848 | 1.057478 |
| 2 | mean2_no_refine | 1.2355168 | 1.2129828 | 1.2075257 | 1.225780 | 1.237443 | 1.041212 |
| 1 | mean2 | 1.2375153 | 1.2244835 | 1.2219439 | 1.259586 | 1.265704 | 1.075621 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 10000000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 10000000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346419 285.6 7916910 422.9 7916910 422.9
Vcells 16886378 128.9 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 6.629129 | 6.857459 | 7.449166 | 7.100306 | 7.651725 | 12.641119 |
| 2 | mean2_no_refine | 6.644606 | 6.830097 | 7.348730 | 7.105475 | 7.594907 | 9.892778 |
| 4 | mean.default+[() | 8.861437 | 10.966095 | 12.014368 | 11.263540 | 11.826247 | 38.922241 |
| 3 | mean+[() | 8.804789 | 10.971579 | 12.019712 | 11.351643 | 11.954465 | 26.449184 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 1.000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.0000000 | 1.0000000 |
| 2 | mean2_no_refine | 1.002335 | 0.9960099 | 0.9865171 | 1.000728 | 0.9925745 | 0.7825872 |
| 4 | mean.default+[() | 1.336742 | 1.5991485 | 1.6128474 | 1.586346 | 1.5455662 | 3.0790186 |
| 3 | mean+[() | 1.328197 | 1.5999482 | 1.6135648 | 1.598754 | 1.5623228 | 2.0923135 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 10000000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346500 285.6 7916910 422.9 7916910 422.9
Vcells 17887347 136.5 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 10.95015 | 11.65684 | 12.38789 | 11.96634 | 12.88033 | 19.96879 |
| 2 | mean2_no_refine | 11.08275 | 11.69462 | 12.39908 | 12.06658 | 12.58402 | 20.05557 |
| 3 | mean+[() | 13.71837 | 15.81739 | 18.95038 | 18.84776 | 20.14947 | 31.91055 |
| 4 | mean.default+[() | 13.72225 | 16.84050 | 19.07589 | 19.07357 | 20.53107 | 29.24286 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 | 1.000000 |
| 2 | mean2_no_refine | 1.012109 | 1.003242 | 1.000903 | 1.008376 | 0.9769958 | 1.004346 |
| 3 | mean+[() | 1.252802 | 1.356919 | 1.529750 | 1.575064 | 1.5643604 | 1.598021 |
| 4 | mean.default+[() | 1.253156 | 1.444689 | 1.539881 | 1.593935 | 1.5939868 | 1.464428 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 10000000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346581 285.6 7916910 422.9 7916910 422.9
Vcells 19887401 151.8 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 20.66110 | 21.83599 | 22.60181 | 22.26913 | 23.00120 | 33.34457 |
| 2 | mean2_no_refine | 20.68084 | 21.81241 | 22.77915 | 22.50213 | 23.14509 | 28.56826 |
| 4 | mean.default+[() | 34.11083 | 35.55587 | 39.38754 | 36.83349 | 38.44051 | 64.57649 |
| 3 | mean+[() | 33.82848 | 35.73351 | 39.98734 | 37.33651 | 38.96290 | 63.50868 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | mean2 | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | mean2_no_refine | 1.000955 | 0.9989203 | 1.007847 | 1.010463 | 1.006256 | 0.8567588 |
| 4 | mean.default+[() | 1.650968 | 1.6283150 | 1.742672 | 1.654016 | 1.671239 | 1.9366417 |
| 3 | mean+[() | 1.637303 | 1.6364503 | 1.769210 | 1.676604 | 1.693951 | 1.9046181 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on integer+n = 10000000+0.8 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
All elements
> x <- data[["n = 1000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346591 285.6 7916910 422.9 7916910 422.9
Vcells 25442469 194.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 1000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.002933 | 0.0031125 | 0.0032647 | 0.0032385 | 0.0033290 | 0.006978 |
| 4 | mean.default | 0.003917 | 0.0041370 | 0.0044087 | 0.0042170 | 0.0043200 | 0.018609 |
| 1 | mean2 | 0.004567 | 0.0047790 | 0.0051201 | 0.0048695 | 0.0050135 | 0.023907 |
| 3 | mean | 0.005680 | 0.0060120 | 0.0063855 | 0.0061355 | 0.0063200 | 0.025811 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default | 1.335493 | 1.329157 | 1.350441 | 1.302146 | 1.297687 | 2.666810 |
| 1 | mean2 | 1.557109 | 1.535422 | 1.568353 | 1.503628 | 1.506008 | 3.426053 |
| 3 | mean | 1.936584 | 1.931566 | 1.955950 | 1.894550 | 1.898468 | 3.698911 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 1000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 1000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346740 285.6 7916910 422.9 7916910 422.9
Vcells 21444195 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.002334 | 0.0024905 | 0.0031571 | 0.0026165 | 0.0027885 | 0.024405 |
| 1 | mean2 | 0.002784 | 0.0029205 | 0.0033334 | 0.0030745 | 0.0032765 | 0.006376 |
| 4 | mean.default+[() | 0.003939 | 0.0042665 | 0.0050883 | 0.0044880 | 0.0047995 | 0.019943 |
| 3 | mean+[() | 0.006492 | 0.0067825 | 0.0084807 | 0.0069955 | 0.0076530 | 0.038641 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | mean2 | 1.192802 | 1.172656 | 1.055823 | 1.175043 | 1.175005 | 0.2612579 |
| 4 | mean.default+[() | 1.687661 | 1.713110 | 1.611671 | 1.715269 | 1.721176 | 0.8171686 |
| 3 | mean+[() | 2.781491 | 2.723349 | 2.686184 | 2.673610 | 2.744486 | 1.5833231 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 1000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346824 285.6 7916910 422.9 7916910 422.9
Vcells 21444356 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.003106 | 0.0031965 | 0.0035391 | 0.0033355 | 0.0034855 | 0.014889 |
| 1 | mean2 | 0.003909 | 0.0040290 | 0.0045060 | 0.0041685 | 0.0043615 | 0.010720 |
| 4 | mean.default+[() | 0.004986 | 0.0053090 | 0.0061101 | 0.0055230 | 0.0059350 | 0.033285 |
| 3 | mean+[() | 0.007438 | 0.0078490 | 0.0086463 | 0.0080190 | 0.0083450 | 0.031917 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | mean2 | 1.258532 | 1.260441 | 1.273204 | 1.249738 | 1.251327 | 0.7199946 |
| 4 | mean.default+[() | 1.605280 | 1.660879 | 1.726457 | 1.655824 | 1.702769 | 2.2355430 |
| 3 | mean+[() | 2.394720 | 2.455498 | 2.443101 | 2.404137 | 2.394205 | 2.1436631 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 1000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346905 285.6 7916910 422.9 7916910 422.9
Vcells 21444610 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.004601 | 0.0047285 | 0.0048831 | 0.0048230 | 0.0049595 | 0.006286 |
| 1 | mean2 | 0.006301 | 0.0064380 | 0.0067323 | 0.0065425 | 0.0066785 | 0.021662 |
| 4 | mean.default+[() | 0.006837 | 0.0073140 | 0.0076880 | 0.0074800 | 0.0076455 | 0.015266 |
| 3 | mean+[() | 0.009484 | 0.0098535 | 0.0106035 | 0.0100335 | 0.0102700 | 0.038918 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.369485 | 1.361531 | 1.378692 | 1.356521 | 1.346607 | 3.446071 |
| 4 | mean.default+[() | 1.485981 | 1.546791 | 1.574410 | 1.550902 | 1.541587 | 2.428571 |
| 3 | mean+[() | 2.061291 | 2.083853 | 2.171469 | 2.080344 | 2.070773 | 6.191219 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000+0.8 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
All elements
> x <- data[["n = 10000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5346915 285.6 7916910 422.9 7916910 422.9
Vcells 21444178 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 10000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.016343 | 0.0168855 | 0.0176837 | 0.0178010 | 0.0181985 | 0.025474 |
| 4 | mean.default | 0.024008 | 0.0246560 | 0.0259579 | 0.0259465 | 0.0265315 | 0.041457 |
| 3 | mean | 0.025674 | 0.0264595 | 0.0282590 | 0.0282540 | 0.0288150 | 0.056288 |
| 1 | mean2 | 0.031372 | 0.0326195 | 0.0340836 | 0.0340820 | 0.0348160 | 0.059319 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default | 1.469008 | 1.460188 | 1.467901 | 1.457587 | 1.457895 | 1.627424 |
| 3 | mean | 1.570948 | 1.566995 | 1.598030 | 1.587214 | 1.583372 | 2.209626 |
| 1 | mean2 | 1.919599 | 1.931805 | 1.927404 | 1.914611 | 1.913125 | 2.328610 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 10000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 10000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347064 285.6 7916910 422.9 7916910 422.9
Vcells 21446619 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.008969 | 0.0093850 | 0.0100392 | 0.0096345 | 0.0097975 | 0.023925 |
| 1 | mean2 | 0.013063 | 0.0135375 | 0.0137817 | 0.0137670 | 0.0140580 | 0.014790 |
| 4 | mean.default+[() | 0.013210 | 0.0140580 | 0.0146024 | 0.0144055 | 0.0147235 | 0.019816 |
| 3 | mean+[() | 0.016091 | 0.0170300 | 0.0181583 | 0.0173760 | 0.0176285 | 0.062644 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | mean2 | 1.456461 | 1.442461 | 1.372791 | 1.428927 | 1.434856 | 0.6181818 |
| 4 | mean.default+[() | 1.472851 | 1.497922 | 1.454544 | 1.495200 | 1.502781 | 0.8282550 |
| 3 | mean+[() | 1.794069 | 1.814598 | 1.808751 | 1.803519 | 1.799286 | 2.6183490 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 10000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347148 285.6 7916910 422.9 7916910 422.9
Vcells 21447680 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.015054 | 0.0157015 | 0.0165805 | 0.0164360 | 0.0170095 | 0.028136 |
| 4 | mean.default+[() | 0.021503 | 0.0226135 | 0.0239531 | 0.0235040 | 0.0244930 | 0.038894 |
| 1 | mean2 | 0.022561 | 0.0232685 | 0.0243081 | 0.0244435 | 0.0250640 | 0.028230 |
| 3 | mean+[() | 0.024111 | 0.0254995 | 0.0271320 | 0.0264410 | 0.0274415 | 0.067673 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default+[() | 1.428391 | 1.440213 | 1.444656 | 1.430032 | 1.439960 | 1.382357 |
| 1 | mean2 | 1.498671 | 1.481928 | 1.466069 | 1.487193 | 1.473529 | 1.003341 |
| 3 | mean+[() | 1.601634 | 1.624017 | 1.636382 | 1.608725 | 1.613304 | 2.405210 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 10000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347229 285.6 7916910 422.9 7916910 422.9
Vcells 21449734 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.027086 | 0.0279495 | 0.0301745 | 0.0298760 | 0.0319795 | 0.048777 |
| 4 | mean.default+[() | 0.037885 | 0.0402285 | 0.0425009 | 0.0421915 | 0.0451705 | 0.052830 |
| 3 | mean+[() | 0.040519 | 0.0423820 | 0.0455732 | 0.0449230 | 0.0477910 | 0.082372 |
| 1 | mean2 | 0.040718 | 0.0436405 | 0.0465917 | 0.0465470 | 0.0491415 | 0.064083 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default+[() | 1.398693 | 1.439328 | 1.408501 | 1.412221 | 1.412483 | 1.083092 |
| 3 | mean+[() | 1.495939 | 1.516378 | 1.510320 | 1.503648 | 1.494426 | 1.688747 |
| 1 | mean2 | 1.503286 | 1.561405 | 1.544073 | 1.558006 | 1.536656 | 1.313795 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000+0.8 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
All elements
> x <- data[["n = 100000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347239 285.6 7916910 422.9 7916910 422.9
Vcells 21449302 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 100000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.104291 | 0.1071575 | 0.1249470 | 0.1194430 | 0.1360095 | 0.175672 |
| 3 | mean | 0.157285 | 0.1614785 | 0.1849504 | 0.1726970 | 0.2045740 | 0.287513 |
| 4 | mean.default | 0.155755 | 0.1607070 | 0.1874724 | 0.1840785 | 0.2026845 | 0.265754 |
| 1 | mean2 | 0.207217 | 0.2150335 | 0.2530091 | 0.2455205 | 0.2737330 | 0.377974 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean | 1.508136 | 1.506927 | 1.480231 | 1.445853 | 1.504115 | 1.636647 |
| 4 | mean.default | 1.493465 | 1.499727 | 1.500416 | 1.541141 | 1.490223 | 1.512785 |
| 1 | mean2 | 1.986912 | 2.006705 | 2.024932 | 2.055545 | 2.012602 | 2.151589 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 100000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 100000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347388 285.6 7916910 422.9 7916910 422.9
Vcells 21455836 163.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 100000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.059471 | 0.0631450 | 0.0687657 | 0.0655690 | 0.0753455 | 0.088766 |
| 4 | mean.default+[() | 0.090426 | 0.0922270 | 0.1025668 | 0.0971795 | 0.1126480 | 0.133103 |
| 1 | mean2 | 0.090747 | 0.0966125 | 0.1064545 | 0.1007720 | 0.1162475 | 0.186528 |
| 3 | mean+[() | 0.090600 | 0.0966045 | 0.1069094 | 0.1043795 | 0.1136130 | 0.163617 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default+[() | 1.520506 | 1.460559 | 1.491541 | 1.482095 | 1.495086 | 1.499482 |
| 1 | mean2 | 1.525903 | 1.530010 | 1.548077 | 1.536885 | 1.542859 | 2.101345 |
| 3 | mean+[() | 1.523432 | 1.529884 | 1.554692 | 1.591903 | 1.507894 | 1.843239 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 100000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 100000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347472 285.6 7916910 422.9 7916910 422.9
Vcells 21465897 163.8 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 100000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.099580 | 0.1041280 | 0.1186855 | 0.1152010 | 0.1273065 | 0.168712 |
| 4 | mean.default+[() | 0.145084 | 0.1554260 | 0.1761674 | 0.1684695 | 0.1887120 | 0.292940 |
| 3 | mean+[() | 0.146749 | 0.1533735 | 0.1742661 | 0.1712600 | 0.1863825 | 0.243424 |
| 1 | mean2 | 0.153840 | 0.1629160 | 0.1847121 | 0.1836745 | 0.1970805 | 0.248477 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default+[() | 1.456959 | 1.492644 | 1.484322 | 1.462396 | 1.482344 | 1.736332 |
| 3 | mean+[() | 1.473680 | 1.472932 | 1.468302 | 1.486619 | 1.464045 | 1.442837 |
| 1 | mean2 | 1.544889 | 1.564574 | 1.556316 | 1.594383 | 1.548079 | 1.472788 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 100000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 100000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347553 285.6 7916910 422.9 7916910 422.9
Vcells 21487559 164.0 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 100000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.186943 | 0.1875200 | 0.2121366 | 0.2025785 | 0.228860 | 0.317327 |
| 4 | mean.default+[() | 0.261512 | 0.2683015 | 0.3235838 | 0.3020175 | 0.370536 | 0.520009 |
| 3 | mean+[() | 0.264410 | 0.2786960 | 0.3385849 | 0.3165955 | 0.384946 | 0.625382 |
| 1 | mean2 | 0.289844 | 0.2908345 | 0.3382736 | 0.3217065 | 0.376748 | 0.515944 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default+[() | 1.398886 | 1.430789 | 1.525355 | 1.490867 | 1.619051 | 1.638716 |
| 3 | mean+[() | 1.414388 | 1.486220 | 1.596070 | 1.562829 | 1.682015 | 1.970781 |
| 1 | mean2 | 1.550441 | 1.550952 | 1.594602 | 1.588059 | 1.646194 | 1.625906 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 100000+0.8 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
All elements
> x <- data[["n = 1000000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347563 285.6 7916910 422.9 7916910 422.9
Vcells 21487127 164.0 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 1000000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.084968 | 1.119546 | 1.200236 | 1.165453 | 1.203855 | 1.870008 |
| 4 | mean.default | 1.647657 | 1.705947 | 1.806631 | 1.756751 | 1.837757 | 2.478214 |
| 3 | mean | 1.644539 | 1.731426 | 1.818524 | 1.800603 | 1.869951 | 2.495900 |
| 1 | mean2 | 2.172183 | 2.227528 | 2.346593 | 2.291418 | 2.377251 | 3.759157 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default | 1.518623 | 1.523784 | 1.505229 | 1.507355 | 1.526560 | 1.325243 |
| 3 | mean | 1.515749 | 1.546542 | 1.515138 | 1.544981 | 1.553302 | 1.334700 |
| 1 | mean2 | 2.002071 | 1.989670 | 1.955109 | 1.966118 | 1.974698 | 2.010236 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 1000000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 1000000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347712 285.6 7916910 422.9 7916910 422.9
Vcells 21547660 164.4 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.745584 | 0.830596 | 0.9484822 | 0.8859205 | 0.9779515 | 1.575263 |
| 1 | mean2 | 1.194177 | 1.343805 | 1.5391119 | 1.4253620 | 1.6089785 | 2.834571 |
| 3 | mean+[() | 1.130618 | 1.383927 | 1.7654041 | 1.6769295 | 1.7737485 | 17.628140 |
| 4 | mean.default+[() | 1.166562 | 1.474412 | 1.7155767 | 1.7129150 | 1.7970410 | 3.548100 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.601667 | 1.617880 | 1.622710 | 1.608905 | 1.645254 | 1.799427 |
| 3 | mean+[() | 1.516419 | 1.666186 | 1.861294 | 1.892867 | 1.813739 | 11.190601 |
| 4 | mean.default+[() | 1.564628 | 1.775126 | 1.808760 | 1.933486 | 1.837556 | 2.252386 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 1000000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347796 285.7 7916910 422.9 7916910 422.9
Vcells 21647721 165.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.154667 | 1.240007 | 1.312643 | 1.307033 | 1.363352 | 1.644477 |
| 1 | mean2 | 1.836903 | 1.970395 | 2.083372 | 2.049998 | 2.167930 | 2.531606 |
| 3 | mean+[() | 1.992156 | 2.686151 | 2.723728 | 2.758100 | 2.895196 | 3.356348 |
| 4 | mean.default+[() | 1.926728 | 2.680360 | 3.070589 | 2.786370 | 2.896028 | 23.352091 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.590851 | 1.589020 | 1.587158 | 1.568436 | 1.590147 | 1.539460 |
| 3 | mean+[() | 1.725308 | 2.166240 | 2.074994 | 2.110199 | 2.123587 | 2.040982 |
| 4 | mean.default+[() | 1.668644 | 2.161569 | 2.339240 | 2.131828 | 2.124197 | 14.200315 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 1000000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347877 285.7 7916910 422.9 7916910 422.9
Vcells 21847775 166.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 2.076573 | 2.159642 | 2.322205 | 2.228099 | 2.392206 | 3.587241 |
| 1 | mean2 | 3.217868 | 3.366200 | 3.584199 | 3.503211 | 3.727764 | 5.057597 |
| 4 | mean.default+[() | 3.248398 | 3.635093 | 4.665304 | 4.763008 | 4.861496 | 14.865250 |
| 3 | mean+[() | 3.260758 | 3.620653 | 4.530265 | 4.822929 | 4.913578 | 14.827025 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.549605 | 1.558684 | 1.543446 | 1.572288 | 1.558296 | 1.409885 |
| 4 | mean.default+[() | 1.564307 | 1.683193 | 2.008998 | 2.137701 | 2.032223 | 4.143923 |
| 3 | mean+[() | 1.570259 | 1.676506 | 1.950847 | 2.164594 | 2.053995 | 4.133267 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 1000000+0.8 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000000 vector
All elements
> x <- data[["n = 10000000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5347887 285.7 7916910 422.9 7916910 422.9
Vcells 21847343 166.7 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, refine = TRUE), mean2_no_refine = mean2(x, refine = FALSE),
+ mean = mean(x), mean.default = mean.default(x), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 10000000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 12.05742 | 12.34299 | 13.30771 | 12.61662 | 13.12648 | 20.39061 |
| 3 | mean | 18.26289 | 19.06504 | 19.89328 | 19.20690 | 19.90218 | 28.48310 |
| 4 | mean.default | 18.55781 | 19.05214 | 19.81446 | 19.20971 | 19.67350 | 29.96884 |
| 1 | mean2 | 24.13884 | 24.60549 | 25.54137 | 25.15560 | 25.70356 | 35.64816 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean | 1.514659 | 1.544605 | 1.494869 | 1.522349 | 1.516186 | 1.396874 |
| 4 | mean.default | 1.539119 | 1.543560 | 1.488946 | 1.522572 | 1.498765 | 1.469737 |
| 1 | mean2 | 2.001990 | 1.993480 | 1.919291 | 1.993846 | 1.958146 | 1.748264 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean() and mean.default() on n = 10000000+all data. Outliers are displayed as crosses. Times are in milliseconds.
A 20% subset
> x <- data[["n = 10000000"]]
> subset
[1] 0.2
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5348036 285.7 7916910 422.9 7916910 422.9
Vcells 22449766 171.3 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000000+0.2 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 8.842612 | 9.108199 | 9.70163 | 9.385096 | 9.876549 | 14.74497 |
| 1 | mean2 | 14.343695 | 14.947731 | 15.79439 | 15.359806 | 16.088828 | 22.05634 |
| 3 | mean+[() | 14.026245 | 18.405489 | 19.39216 | 18.930086 | 20.034273 | 31.86399 |
| 4 | mean.default+[() | 13.649966 | 18.462709 | 19.51494 | 19.329358 | 20.303463 | 30.02308 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.622111 | 1.641129 | 1.628014 | 1.636617 | 1.628993 | 1.495855 |
| 3 | mean+[() | 1.586211 | 2.020761 | 1.998856 | 2.017037 | 2.028469 | 2.161008 |
| 4 | mean.default+[() | 1.543658 | 2.027043 | 2.011511 | 2.059580 | 2.055724 | 2.036158 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000000+0.2 data. Outliers are displayed as crosses. Times are in milliseconds.
A 40% subset
> x <- data[["n = 10000000"]]
> subset
[1] 0.4
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5348120 285.7 7916910 422.9 7916910 422.9
Vcells 23449827 179.0 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000000+0.4 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 13.38557 | 14.22482 | 14.60752 | 14.46934 | 14.89307 | 17.41966 |
| 1 | mean2 | 21.57257 | 22.66196 | 23.39498 | 23.16886 | 23.73333 | 28.22251 |
| 4 | mean.default+[() | 21.93596 | 22.43135 | 26.86379 | 23.67327 | 31.90814 | 38.55702 |
| 3 | mean+[() | 22.01277 | 22.68463 | 27.24690 | 23.78662 | 32.35292 | 41.31454 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.611628 | 1.593128 | 1.601571 | 1.601238 | 1.593582 | 1.620153 |
| 4 | mean.default+[() | 1.638776 | 1.576916 | 1.839038 | 1.636099 | 2.142482 | 2.213419 |
| 3 | mean+[() | 1.644515 | 1.594722 | 1.865265 | 1.643933 | 2.172347 | 2.371719 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000000+0.4 data. Outliers are displayed as crosses. Times are in milliseconds.
A 80% subset
> x <- data[["n = 10000000"]]
> subset
[1] 0.8
> idxs <- sort(sample(length(x), size = subset * length(x), replace = FALSE))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5348195 285.7 7916910 422.9 7916910 422.9
Vcells 25449871 194.2 54892165 418.8 94934136 724.3
> stats <- microbenchmark(mean2 = mean2(x, idxs = idxs, refine = TRUE), mean2_no_refine = mean2(x,
+ idxs = idxs, refine = FALSE), `mean+[()` = mean2_R_v1(x, idxs = idxs), `mean.default+[()` = mean2_R_v2(x,
+ idxs = idxs), unit = "ms")
Table: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000000+0.8 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 22.57832 | 23.60605 | 24.70810 | 24.29803 | 25.13072 | 33.07540 |
| 1 | mean2 | 35.72502 | 37.29216 | 38.70859 | 38.23601 | 39.48672 | 45.97980 |
| 4 | mean.default+[() | 51.40088 | 54.59220 | 62.16602 | 56.73165 | 73.34072 | 86.73393 |
| 3 | mean+[() | 50.37513 | 54.05168 | 65.55149 | 57.02706 | 73.28914 | 421.26474 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.582271 | 1.579771 | 1.566636 | 1.573626 | 1.571253 | 1.390151 |
| 4 | mean.default+[() | 2.276559 | 2.312636 | 2.516018 | 2.334825 | 2.918369 | 2.622309 |
| 3 | mean+[() | 2.231129 | 2.289738 | 2.653037 | 2.346983 | 2.916316 | 12.736498 |
Figure: Benchmarking of mean2(), mean2_no_refine(), mean+[()() and mean.default+[()() on double+n = 10000000+0.8 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 2.12 mins.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('mean2')
Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:45:58 (+0200 UTC). Powered by RSP.