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 5335209 285.0 8529671 455.6 8529671 455.6
Vcells 37464398 285.9 58379130 445.4 101881463 777.3
> 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 | 0.002644 | 0.0027400 | 0.0030311 | 0.0027865 | 0.0028750 | 0.019038 |
| 1 | mean2 | 0.002992 | 0.0030355 | 0.0034202 | 0.0031365 | 0.0032185 | 0.023013 |
| 2 | mean2_no_refine | 0.002983 | 0.0030465 | 0.0033331 | 0.0031375 | 0.0032170 | 0.011124 |
| 3 | mean | 0.004267 | 0.0044290 | 0.0050100 | 0.0045415 | 0.0046795 | 0.024184 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | mean2 | 1.131619 | 1.107847 | 1.128375 | 1.125606 | 1.119478 | 1.2087929 |
| 2 | mean2_no_refine | 1.128215 | 1.111861 | 1.099637 | 1.125965 | 1.118956 | 0.5843051 |
| 3 | mean | 1.613843 | 1.616423 | 1.652860 | 1.629822 | 1.627652 | 1.2703015 |
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 5333066 284.9 8529671 455.6 8529671 455.6
Vcells 15828942 120.8 46703304 356.4 101881463 777.3
> 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 | 0.002748 | 0.0030090 | 0.0034480 | 0.0031075 | 0.003252 | 0.019823 |
| 1 | mean2 | 0.002736 | 0.0030055 | 0.0031542 | 0.0031385 | 0.003242 | 0.003930 |
| 4 | mean.default+[() | 0.003742 | 0.0039725 | 0.0042215 | 0.0040670 | 0.004234 | 0.015538 |
| 3 | mean+[() | 0.005980 | 0.0062890 | 0.0066827 | 0.0064585 | 0.006637 | 0.024305 |
| 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 | 0.9956332 | 0.9988368 | 0.9148018 | 1.009976 | 0.996925 | 0.1982546 |
| 4 | mean.default+[() | 1.3617176 | 1.3202060 | 1.2243472 | 1.308769 | 1.301968 | 0.7838370 |
| 3 | mean+[() | 2.1761281 | 2.0900631 | 1.9381489 | 2.078359 | 2.040898 | 1.2261010 |
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 5333150 284.9 8529671 455.6 8529671 455.6
Vcells 15829618 120.8 46703304 356.4 101881463 777.3
> 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 | 0.003707 | 0.0039070 | 0.0041698 | 0.0040185 | 0.0041995 | 0.015867 |
| 1 | mean2 | 0.003713 | 0.0038895 | 0.0041802 | 0.0040240 | 0.0042490 | 0.009105 |
| 4 | mean.default+[() | 0.004356 | 0.0046555 | 0.0049403 | 0.0048135 | 0.0049160 | 0.018109 |
| 3 | mean+[() | 0.006681 | 0.0070390 | 0.0074300 | 0.0071950 | 0.0074245 | 0.027253 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | mean2 | 1.001619 | 0.9955209 | 1.002499 | 1.001369 | 1.011787 | 0.5738325 |
| 4 | mean.default+[() | 1.175074 | 1.1915792 | 1.184804 | 1.197835 | 1.170616 | 1.1412996 |
| 3 | mean+[() | 1.802266 | 1.8016381 | 1.781887 | 1.790469 | 1.767949 | 1.7175900 |
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 5333231 284.9 8529671 455.6 8529671 455.6
Vcells 15829872 120.8 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 0.005617 | 0.0058795 | 0.0060997 | 0.0059950 | 0.0062935 | 0.008362 |
| 1 | mean2 | 0.005550 | 0.0058745 | 0.0065841 | 0.0060490 | 0.0063270 | 0.025825 |
| 4 | mean.default+[() | 0.006109 | 0.0063475 | 0.0066216 | 0.0065025 | 0.0067620 | 0.011049 |
| 3 | mean+[() | 0.008464 | 0.0087735 | 0.0093324 | 0.0090215 | 0.0092860 | 0.034636 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 0.9880719 | 0.9991496 | 1.079412 | 1.009008 | 1.005323 | 3.088376 |
| 4 | mean.default+[() | 1.0875912 | 1.0795986 | 1.085570 | 1.084654 | 1.074442 | 1.321335 |
| 3 | mean+[() | 1.5068542 | 1.4922187 | 1.529975 | 1.504837 | 1.475491 | 4.142071 |
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 5333241 284.9 8529671 455.6 8529671 455.6
Vcells 15829656 120.8 46703304 356.4 101881463 777.3
> 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 | 0.013168 | 0.0138065 | 0.0142189 | 0.0139930 | 0.0144305 | 0.027982 |
| 3 | mean | 0.014819 | 0.0155985 | 0.0163233 | 0.0159980 | 0.0163880 | 0.034170 |
| 2 | mean2_no_refine | 0.017081 | 0.0177570 | 0.0183532 | 0.0179305 | 0.0185955 | 0.032276 |
| 1 | mean2 | 0.016948 | 0.0178315 | 0.0184025 | 0.0180020 | 0.0186490 | 0.032773 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean | 1.12538 | 1.129794 | 1.147993 | 1.143286 | 1.135650 | 1.221142 |
| 2 | mean2_no_refine | 1.29716 | 1.286133 | 1.290756 | 1.281391 | 1.288625 | 1.153456 |
| 1 | mean2 | 1.28706 | 1.291529 | 1.294226 | 1.286500 | 1.292332 | 1.171217 |
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 5333393 284.9 8529671 455.6 8529671 455.6
Vcells 15831048 120.8 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 0.011244 | 0.0118025 | 0.0122085 | 0.0120220 | 0.0123640 | 0.018002 |
| 1 | mean2 | 0.011402 | 0.0119860 | 0.0124435 | 0.0122490 | 0.0126350 | 0.018282 |
| 2 | mean2_no_refine | 0.011353 | 0.0120910 | 0.0127748 | 0.0124010 | 0.0128515 | 0.034642 |
| 3 | mean+[() | 0.013511 | 0.0143220 | 0.0156329 | 0.0146925 | 0.0152425 | 0.051375 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | mean2 | 1.014052 | 1.015548 | 1.019248 | 1.018882 | 1.021918 | 1.015554 |
| 2 | mean2_no_refine | 1.009694 | 1.024444 | 1.046381 | 1.031526 | 1.039429 | 1.924342 |
| 3 | mean+[() | 1.201619 | 1.213472 | 1.280493 | 1.222134 | 1.232813 | 2.853850 |
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 5333474 284.9 8529671 455.6 8529671 455.6
Vcells 15832404 120.8 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 0.018318 | 0.0195020 | 0.0204484 | 0.0203160 | 0.0208745 | 0.033990 |
| 2 | mean2_no_refine | 0.020049 | 0.0209755 | 0.0223444 | 0.0218995 | 0.0230970 | 0.036959 |
| 1 | mean2 | 0.020087 | 0.0209700 | 0.0221402 | 0.0219190 | 0.0225585 | 0.039007 |
| 3 | mean+[() | 0.021083 | 0.0222545 | 0.0239472 | 0.0230885 | 0.0238945 | 0.069703 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | mean2_no_refine | 1.094497 | 1.075556 | 1.092718 | 1.077943 | 1.106470 | 1.087349 |
| 1 | mean2 | 1.096572 | 1.075274 | 1.082733 | 1.078903 | 1.080673 | 1.147602 |
| 3 | mean+[() | 1.150944 | 1.141139 | 1.171100 | 1.136469 | 1.144674 | 2.050691 |
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 5333555 284.9 8529671 455.6 8529671 455.6
Vcells 15834822 120.9 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 0.033108 | 0.0351345 | 0.0377840 | 0.0366405 | 0.0384795 | 0.052429 |
| 3 | mean+[() | 0.035239 | 0.0379350 | 0.0417208 | 0.0397670 | 0.0424440 | 0.074999 |
| 2 | mean2_no_refine | 0.037113 | 0.0386835 | 0.0425239 | 0.0403430 | 0.0432415 | 0.075843 |
| 1 | mean2 | 0.036976 | 0.0397130 | 0.0451191 | 0.0410610 | 0.0432870 | 0.304684 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean+[() | 1.064365 | 1.079708 | 1.104193 | 1.085329 | 1.103029 | 1.430487 |
| 2 | mean2_no_refine | 1.120968 | 1.101012 | 1.125449 | 1.101049 | 1.123754 | 1.446585 |
| 1 | mean2 | 1.116830 | 1.130313 | 1.194134 | 1.120645 | 1.124937 | 5.811364 |
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 5333565 284.9 8529671 455.6 8529671 455.6
Vcells 15834826 120.9 46703304 356.4 101881463 777.3
> 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 | 0.086089 | 0.0939410 | 0.1017803 | 0.1000040 | 0.1069845 | 0.133384 |
| 3 | mean | 0.087773 | 0.0958705 | 0.1042470 | 0.1020055 | 0.1118700 | 0.153118 |
| 2 | mean2_no_refine | 0.113125 | 0.1235325 | 0.1354121 | 0.1313805 | 0.1454950 | 0.190802 |
| 1 | mean2 | 0.113166 | 0.1238950 | 0.1370114 | 0.1315580 | 0.1479895 | 0.191542 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean | 1.019561 | 1.020539 | 1.024235 | 1.020014 | 1.045665 | 1.147949 |
| 2 | mean2_no_refine | 1.314047 | 1.315001 | 1.330435 | 1.313752 | 1.359963 | 1.430471 |
| 1 | mean2 | 1.314523 | 1.318860 | 1.346148 | 1.315527 | 1.383280 | 1.436019 |
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 5333717 284.9 8529671 455.6 8529671 455.6
Vcells 15841365 120.9 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 0.067272 | 0.070955 | 0.0779639 | 0.0743770 | 0.0830760 | 0.119719 |
| 3 | mean+[() | 0.069493 | 0.073628 | 0.0799708 | 0.0773515 | 0.0854515 | 0.114088 |
| 2 | mean2_no_refine | 0.076796 | 0.081816 | 0.0883674 | 0.0847455 | 0.0942295 | 0.114397 |
| 1 | mean2 | 0.077077 | 0.081859 | 0.0892513 | 0.0867050 | 0.0936555 | 0.142096 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | mean+[() | 1.033015 | 1.037672 | 1.025741 | 1.039992 | 1.028594 | 0.9529649 |
| 2 | mean2_no_refine | 1.141574 | 1.153069 | 1.133439 | 1.139405 | 1.134257 | 0.9555459 |
| 1 | mean2 | 1.145752 | 1.153675 | 1.144776 | 1.165750 | 1.127347 | 1.1869127 |
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 5333798 284.9 8529671 455.6 8529671 455.6
Vcells 15851943 121.0 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 0.110645 | 0.1216670 | 0.1385319 | 0.135250 | 0.1464605 | 0.217912 |
| 3 | mean+[() | 0.112323 | 0.1188815 | 0.1365603 | 0.136013 | 0.1467645 | 0.200972 |
| 2 | mean2_no_refine | 0.127548 | 0.1362975 | 0.1540432 | 0.150394 | 0.1659490 | 0.211932 |
| 1 | mean2 | 0.127682 | 0.1372890 | 0.1554826 | 0.154730 | 0.1654435 | 0.222608 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | mean+[() | 1.015166 | 0.9771055 | 0.9857681 | 1.005641 | 1.002076 | 0.9222622 |
| 2 | mean2_no_refine | 1.152768 | 1.1202504 | 1.1119690 | 1.111970 | 1.133063 | 0.9725577 |
| 1 | mean2 | 1.153979 | 1.1283996 | 1.1223596 | 1.144030 | 1.129612 | 1.0215500 |
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 5333879 284.9 8529671 455.6 8529671 455.6
Vcells 15871997 121.1 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 3 | mean+[() | 0.220048 | 0.2270255 | 0.2741137 | 0.2705260 | 0.3147935 | 0.407134 |
| 4 | mean.default+[() | 0.218661 | 0.2199195 | 0.2706278 | 0.2729675 | 0.3123145 | 0.345561 |
| 2 | mean2_no_refine | 0.254661 | 0.2622925 | 0.3371748 | 0.3114755 | 0.4364735 | 0.454337 |
| 1 | mean2 | 0.255069 | 0.2713740 | 0.3351383 | 0.3210450 | 0.4143855 | 0.457223 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 3 | mean+[() | 1.0000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 4 | mean.default+[() | 0.9936968 | 0.9686996 | 0.9872832 | 1.009025 | 0.992125 | 0.8487648 |
| 2 | mean2_no_refine | 1.1572975 | 1.1553438 | 1.2300548 | 1.151370 | 1.386539 | 1.1159397 |
| 1 | mean2 | 1.1591516 | 1.1953459 | 1.2226252 | 1.186744 | 1.316372 | 1.1230283 |
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 5333889 284.9 8529671 455.6 8529671 455.6
Vcells 15872197 121.1 46703304 356.4 101881463 777.3
> 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 | 0.781505 | 0.8023825 | 0.8331030 | 0.8240795 | 0.832862 | 1.192908 |
| 3 | mean | 0.784452 | 0.8052410 | 0.8382436 | 0.8268195 | 0.832684 | 1.341355 |
| 1 | mean2 | 1.030187 | 1.0576885 | 1.0999319 | 1.0822270 | 1.102979 | 1.815785 |
| 2 | mean2_no_refine | 1.030142 | 1.0335435 | 1.1019452 | 1.0862900 | 1.102585 | 1.785978 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 | 1.000000 |
| 3 | mean | 1.003771 | 1.003562 | 1.006170 | 1.003325 | 0.9997863 | 1.124441 |
| 1 | mean2 | 1.318209 | 1.318185 | 1.320283 | 1.313256 | 1.3243232 | 1.522150 |
| 2 | mean2_no_refine | 1.318151 | 1.288093 | 1.322700 | 1.318186 | 1.3238514 | 1.497163 |
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 5334041 284.9 8529671 455.6 8529671 455.6
Vcells 15932735 121.6 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 3 | mean+[() | 0.567962 | 0.6327910 | 0.8461862 | 0.7494230 | 0.8619645 | 11.109744 |
| 4 | mean.default+[() | 0.564959 | 0.5874640 | 0.7434595 | 0.7777125 | 0.8554040 | 1.123919 |
| 2 | mean2_no_refine | 0.675981 | 0.7142115 | 0.9288030 | 0.8646745 | 1.1634940 | 1.410423 |
| 1 | mean2 | 0.680770 | 0.7195735 | 0.9591212 | 0.8990550 | 1.2039735 | 1.287770 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 3 | mean+[() | 1.0000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.0000000 | 1.0000000 |
| 4 | mean.default+[() | 0.9947127 | 0.9283697 | 0.8786003 | 1.037748 | 0.9923889 | 0.1011652 |
| 2 | mean2_no_refine | 1.1901870 | 1.1286689 | 1.0976343 | 1.153787 | 1.3498166 | 0.1269537 |
| 1 | mean2 | 1.1986189 | 1.1371424 | 1.1334634 | 1.199663 | 1.3967785 | 0.1159136 |
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 5334116 284.9 8529671 455.6 8529671 455.6
Vcells 16032781 122.4 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.167260 | 1.542802 | 1.709931 | 1.647669 | 1.684692 | 13.413051 |
| 3 | mean+[() | 1.176652 | 1.513987 | 1.587801 | 1.650256 | 1.687054 | 1.769663 |
| 2 | mean2_no_refine | 1.350894 | 1.635193 | 2.256859 | 2.267421 | 2.313971 | 13.259026 |
| 1 | mean2 | 1.377870 | 1.921315 | 2.103943 | 2.277221 | 2.301809 | 2.805382 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | mean+[() | 1.008046 | 0.9813229 | 0.9285761 | 1.001570 | 1.001402 | 0.1319359 |
| 2 | mean2_no_refine | 1.157321 | 1.0598855 | 1.3198542 | 1.376138 | 1.373527 | 0.9885168 |
| 1 | mean2 | 1.180431 | 1.2453409 | 1.2304257 | 1.382086 | 1.366308 | 0.2091532 |
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 5334203 284.9 8529671 455.6 8529671 455.6
Vcells 16233598 123.9 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 2.318165 | 2.394410 | 2.556104 | 2.452503 | 2.491433 | 8.348316 |
| 3 | mean+[() | 2.326090 | 2.412050 | 2.566803 | 2.457890 | 2.527220 | 8.420793 |
| 1 | mean2 | 2.713277 | 2.779487 | 3.046277 | 2.866725 | 2.982606 | 8.860754 |
| 2 | mean2_no_refine | 2.706951 | 2.841883 | 3.168071 | 2.898226 | 2.998219 | 8.864914 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean+[() | 1.003419 | 1.007367 | 1.004185 | 1.002196 | 1.014364 | 1.008682 |
| 1 | mean2 | 1.170442 | 1.160823 | 1.191766 | 1.168898 | 1.197145 | 1.061382 |
| 2 | mean2_no_refine | 1.167713 | 1.186882 | 1.239414 | 1.181742 | 1.203412 | 1.061881 |
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 5334213 284.9 8529671 455.6 8529671 455.6
Vcells 16233166 123.9 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 8.820035 | 9.338323 | 9.481309 | 9.401161 | 9.533841 | 12.94580 |
| 3 | mean | 8.850172 | 9.348933 | 9.431105 | 9.403455 | 9.509620 | 11.47250 |
| 1 | mean2 | 11.220452 | 11.505320 | 11.779207 | 11.718151 | 11.943236 | 14.36670 |
| 2 | mean2_no_refine | 11.219526 | 11.512978 | 11.751696 | 11.741934 | 11.920455 | 13.29831 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default | 1.000000 | 1.000000 | 1.0000000 | 1.000000 | 1.0000000 | 1.0000000 |
| 3 | mean | 1.003417 | 1.001136 | 0.9947049 | 1.000244 | 0.9974594 | 0.8861952 |
| 1 | mean2 | 1.272155 | 1.232054 | 1.2423608 | 1.246458 | 1.2527203 | 1.1097575 |
| 2 | mean2_no_refine | 1.272050 | 1.232874 | 1.2394592 | 1.248988 | 1.2503308 | 1.0272300 |
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 5334365 284.9 8529671 455.6 8529671 455.6
Vcells 16833704 128.5 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 8.105865 | 8.821421 | 10.00521 | 9.983128 | 10.24088 | 20.72092 |
| 3 | mean+[() | 8.337124 | 9.947527 | 10.66760 | 10.132872 | 10.60189 | 20.82084 |
| 1 | mean2 | 9.381135 | 12.821609 | 13.13456 | 13.100914 | 13.64462 | 22.03093 |
| 2 | mean2_no_refine | 9.472205 | 12.778382 | 13.50176 | 13.210631 | 14.22950 | 22.34785 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean+[() | 1.028530 | 1.127656 | 1.066205 | 1.015000 | 1.035252 | 1.004822 |
| 1 | mean2 | 1.157327 | 1.453463 | 1.312773 | 1.312305 | 1.332368 | 1.063222 |
| 2 | mean2_no_refine | 1.168562 | 1.448563 | 1.349473 | 1.323296 | 1.389481 | 1.078517 |
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 5334440 284.9 8529671 455.6 8529671 455.6
Vcells 17834651 136.1 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 13.45022 | 15.00510 | 17.29576 | 17.71355 | 18.03555 | 28.16862 |
| 3 | mean+[() | 13.35736 | 14.98070 | 17.65791 | 17.84439 | 18.44718 | 35.18150 |
| 1 | mean2 | 17.36632 | 23.75696 | 24.61952 | 24.18407 | 25.44178 | 28.58307 |
| 2 | mean2_no_refine | 16.49944 | 23.80050 | 24.68057 | 24.48683 | 26.88420 | 28.82598 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | mean+[() | 0.9930961 | 0.9983741 | 1.020939 | 1.007386 | 1.022823 | 1.248961 |
| 1 | mean2 | 1.2911552 | 1.5832595 | 1.423442 | 1.365286 | 1.410646 | 1.014713 |
| 2 | mean2_no_refine | 1.2267037 | 1.5861609 | 1.426972 | 1.382378 | 1.490623 | 1.023337 |
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 5334527 284.9 8529671 455.6 8529671 455.6
Vcells 19834715 151.4 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 25.81895 | 26.51238 | 30.21581 | 28.05782 | 35.32258 | 48.05117 |
| 3 | mean+[() | 25.81136 | 26.52639 | 33.27037 | 28.08455 | 30.11104 | 399.47393 |
| 1 | mean2 | 29.67431 | 41.22107 | 43.72055 | 42.64755 | 48.19389 | 50.79746 |
| 2 | mean2_no_refine | 29.80945 | 41.46204 | 44.23641 | 47.05902 | 48.32213 | 52.20627 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 | 1.000000 |
| 3 | mean+[() | 0.9997062 | 1.000528 | 1.101091 | 1.000952 | 0.8524585 | 8.313511 |
| 1 | mean2 | 1.1493228 | 1.554786 | 1.446943 | 1.519988 | 1.3643931 | 1.057153 |
| 2 | mean2_no_refine | 1.1545569 | 1.563875 | 1.464015 | 1.677215 | 1.3680238 | 1.086472 |
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 5334537 284.9 8529671 455.6 8529671 455.6
Vcells 25389783 193.8 46703304 356.4 101881463 777.3
> 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 | 0.003397 | 0.0035125 | 0.0036706 | 0.0036035 | 0.0037195 | 0.008628 |
| 4 | mean.default | 0.003853 | 0.0040150 | 0.0043051 | 0.0041085 | 0.0041940 | 0.018430 |
| 1 | mean2 | 0.005006 | 0.0052320 | 0.0054801 | 0.0053370 | 0.0054530 | 0.018092 |
| 3 | mean | 0.005515 | 0.0058415 | 0.0061428 | 0.0059525 | 0.0060955 | 0.021552 |
| 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 | 1.134236 | 1.143061 | 1.172858 | 1.140141 | 1.127571 | 2.136069 |
| 1 | mean2 | 1.473653 | 1.489537 | 1.492974 | 1.481060 | 1.466057 | 2.096894 |
| 3 | mean | 1.623491 | 1.663061 | 1.673515 | 1.651866 | 1.638796 | 2.497914 |
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 5334686 285.0 8529671 455.6 8529671 455.6
Vcells 21391506 163.3 46703304 356.4 101881463 777.3
> 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 | 0.002829 | 0.0030780 | 0.0033358 | 0.0031720 | 0.003297 | 0.016660 |
| 1 | mean2 | 0.003176 | 0.0034155 | 0.0036594 | 0.0035295 | 0.003669 | 0.010323 |
| 4 | mean.default+[() | 0.003777 | 0.0040305 | 0.0042983 | 0.0041570 | 0.004339 | 0.014093 |
| 3 | mean+[() | 0.006017 | 0.0064345 | 0.0068636 | 0.0066250 | 0.006828 | 0.024158 |
| 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 | 1.122658 | 1.109649 | 1.097018 | 1.112705 | 1.112830 | 0.6196279 |
| 4 | mean.default+[() | 1.335101 | 1.309454 | 1.288555 | 1.310530 | 1.316045 | 0.8459184 |
| 3 | mean+[() | 2.126900 | 2.090481 | 2.057588 | 2.088588 | 2.070974 | 1.4500600 |
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 5334770 285.0 8529671 455.6 8529671 455.6
Vcells 21391667 163.3 46703304 356.4 101881463 777.3
> 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 | 0.003955 | 0.0041900 | 0.0044788 | 0.0043285 | 0.0045640 | 0.013814 |
| 1 | mean2 | 0.004634 | 0.0048675 | 0.0051350 | 0.0050510 | 0.0053005 | 0.006276 |
| 4 | mean.default+[() | 0.004870 | 0.0051460 | 0.0054945 | 0.0053365 | 0.0055185 | 0.019900 |
| 3 | mean+[() | 0.007242 | 0.0076560 | 0.0082527 | 0.0079435 | 0.0081550 | 0.026346 |
| 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 | 1.171681 | 1.161695 | 1.146505 | 1.166917 | 1.161372 | 0.4543217 |
| 4 | mean.default+[() | 1.231353 | 1.228162 | 1.226784 | 1.232875 | 1.209137 | 1.4405675 |
| 3 | mean+[() | 1.831100 | 1.827208 | 1.842612 | 1.835162 | 1.786810 | 1.9071956 |
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 5334851 285.0 8529671 455.6 8529671 455.6
Vcells 21391921 163.3 46703304 356.4 101881463 777.3
> 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 | 0.006096 | 0.0063290 | 0.0067313 | 0.0065355 | 0.0067290 | 0.021217 |
| 4 | mean.default+[() | 0.006673 | 0.0070425 | 0.0072381 | 0.0072185 | 0.0073795 | 0.009505 |
| 1 | mean2 | 0.007397 | 0.0076115 | 0.0080315 | 0.0077860 | 0.0080540 | 0.025651 |
| 3 | mean+[() | 0.009168 | 0.0095680 | 0.0100987 | 0.0097825 | 0.0100630 | 0.034877 |
| 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+[() | 1.094652 | 1.112735 | 1.075295 | 1.104506 | 1.096671 | 0.4479898 |
| 1 | mean2 | 1.213419 | 1.202639 | 1.193172 | 1.191340 | 1.196909 | 1.2089834 |
| 3 | mean+[() | 1.503937 | 1.511771 | 1.500270 | 1.496825 | 1.495467 | 1.6438233 |
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 5334861 285.0 8529671 455.6 8529671 455.6
Vcells 21391489 163.3 46703304 356.4 101881463 777.3
> 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 | 0.019592 | 0.0204710 | 0.0216919 | 0.0212445 | 0.0224825 | 0.037104 |
| 4 | mean.default | 0.023796 | 0.0242965 | 0.0257009 | 0.0258625 | 0.0267565 | 0.040319 |
| 3 | mean | 0.025122 | 0.0262995 | 0.0282379 | 0.0280265 | 0.0292220 | 0.049169 |
| 1 | mean2 | 0.033995 | 0.0354585 | 0.0377419 | 0.0380060 | 0.0395335 | 0.055808 |
| 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 | 1.214577 | 1.186874 | 1.184816 | 1.217374 | 1.190103 | 1.086648 |
| 3 | mean | 1.282258 | 1.284720 | 1.301773 | 1.319236 | 1.299767 | 1.325167 |
| 1 | mean2 | 1.735147 | 1.732133 | 1.739913 | 1.788981 | 1.758412 | 1.504097 |
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 5335010 285.0 8529671 455.6 8529671 455.6
Vcells 21393922 163.3 46703304 356.4 101881463 777.3
> 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 | 0.012235 | 0.0128715 | 0.0137211 | 0.013318 | 0.0137245 | 0.024455 |
| 4 | mean.default+[() | 0.012882 | 0.0136635 | 0.0142658 | 0.013986 | 0.0144090 | 0.028018 |
| 1 | mean2 | 0.015782 | 0.0162710 | 0.0171119 | 0.016755 | 0.0173325 | 0.031873 |
| 3 | mean+[() | 0.015611 | 0.0164135 | 0.0174252 | 0.016794 | 0.0173245 | 0.064889 |
| 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+[() | 1.052881 | 1.061531 | 1.039705 | 1.050158 | 1.049874 | 1.145696 |
| 1 | mean2 | 1.289906 | 1.264111 | 1.247130 | 1.258072 | 1.262887 | 1.303333 |
| 3 | mean+[() | 1.275930 | 1.275182 | 1.269963 | 1.261000 | 1.262305 | 2.653404 |
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 5335094 285.0 8529671 455.6 8529671 455.6
Vcells 21394983 163.3 46703304 356.4 101881463 777.3
> 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 | 0.020904 | 0.0219150 | 0.0236334 | 0.0228335 | 0.0238410 | 0.039096 |
| 4 | mean.default+[() | 0.020723 | 0.0221710 | 0.0231311 | 0.0228425 | 0.0239555 | 0.031711 |
| 3 | mean+[() | 0.023336 | 0.0250565 | 0.0270711 | 0.0258095 | 0.0274085 | 0.081607 |
| 1 | mean2 | 0.026991 | 0.0283175 | 0.0297291 | 0.0293300 | 0.0306085 | 0.046726 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 4 | mean.default+[() | 0.9913414 | 1.011681 | 0.978747 | 1.000394 | 1.004803 | 0.811106 |
| 3 | mean+[() | 1.1163414 | 1.143349 | 1.145461 | 1.130335 | 1.149637 | 2.087349 |
| 1 | mean2 | 1.2911883 | 1.292151 | 1.257931 | 1.284516 | 1.283860 | 1.195161 |
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 5335175 285.0 8529671 455.6 8529671 455.6
Vcells 21397037 163.3 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 0.036775 | 0.0382140 | 0.0407724 | 0.0405630 | 0.0423160 | 0.053501 |
| 2 | mean2_no_refine | 0.036998 | 0.0389655 | 0.0426207 | 0.0416175 | 0.0447005 | 0.077864 |
| 3 | mean+[() | 0.039122 | 0.0409575 | 0.0442101 | 0.0435920 | 0.0456245 | 0.073438 |
| 1 | mean2 | 0.047836 | 0.0505150 | 0.0542753 | 0.0538305 | 0.0575540 | 0.065101 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | mean2_no_refine | 1.006064 | 1.019666 | 1.045334 | 1.025997 | 1.056350 | 1.455375 |
| 3 | mean+[() | 1.063821 | 1.071793 | 1.084315 | 1.074674 | 1.078186 | 1.372647 |
| 1 | mean2 | 1.300775 | 1.321898 | 1.331178 | 1.327084 | 1.360100 | 1.216818 |
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 5335185 285.0 8529671 455.6 8529671 455.6
Vcells 21396605 163.3 46703304 356.4 101881463 777.3
> 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 | 0.123641 | 0.1301225 | 0.1480198 | 0.1367445 | 0.1636720 | 0.212153 |
| 3 | mean | 0.157203 | 0.1574605 | 0.1760000 | 0.1586010 | 0.1949485 | 0.265719 |
| 4 | mean.default | 0.155662 | 0.1558715 | 0.1776203 | 0.1642010 | 0.1959210 | 0.264739 |
| 1 | mean2 | 0.232905 | 0.2330720 | 0.2702550 | 0.2524035 | 0.2967165 | 0.416678 |
| 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 | 1.271447 | 1.210094 | 1.189031 | 1.159835 | 1.191093 | 1.252488 |
| 4 | mean.default | 1.258984 | 1.197883 | 1.199977 | 1.200787 | 1.197034 | 1.247868 |
| 1 | mean2 | 1.883720 | 1.791174 | 1.825803 | 1.845804 | 1.812873 | 1.964045 |
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 5335334 285.0 8529671 455.6 8529671 455.6
Vcells 21403139 163.3 46703304 356.4 101881463 777.3
> 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 | 0.077349 | 0.0856255 | 0.0927483 | 0.0903835 | 0.0989490 | 0.119778 |
| 4 | mean.default+[() | 0.081374 | 0.0861535 | 0.0951665 | 0.0919175 | 0.1009670 | 0.131160 |
| 3 | mean+[() | 0.083170 | 0.0885990 | 0.0973453 | 0.0966945 | 0.1043025 | 0.133494 |
| 1 | mean2 | 0.105852 | 0.1158745 | 0.1261990 | 0.1235155 | 0.1353835 | 0.232145 |
| 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+[() | 1.052037 | 1.006166 | 1.026072 | 1.016972 | 1.020394 | 1.095026 |
| 3 | mean+[() | 1.075256 | 1.034727 | 1.049564 | 1.069825 | 1.054104 | 1.114512 |
| 1 | mean2 | 1.368499 | 1.353271 | 1.360661 | 1.366571 | 1.368215 | 1.938127 |
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 5335418 285.0 8529671 455.6 8529671 455.6
Vcells 21414762 163.4 46703304 356.4 101881463 777.3
> 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 | 0.136383 | 0.1369515 | 0.1596459 | 0.1564385 | 0.177603 | 0.224354 |
| 4 | mean.default+[() | 0.134424 | 0.1386260 | 0.1634526 | 0.1571330 | 0.180167 | 0.300216 |
| 3 | mean+[() | 0.136195 | 0.1387200 | 0.1625829 | 0.1589555 | 0.177135 | 0.230900 |
| 1 | mean2 | 0.181882 | 0.1827200 | 0.2163775 | 0.2123575 | 0.236955 | 0.305140 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 | 1.000000 |
| 4 | mean.default+[() | 0.9856360 | 1.012227 | 1.023845 | 1.004439 | 1.0144367 | 1.338135 |
| 3 | mean+[() | 0.9986215 | 1.012913 | 1.018397 | 1.016089 | 0.9973649 | 1.029177 |
| 1 | mean2 | 1.3336120 | 1.334195 | 1.355359 | 1.357450 | 1.3341835 | 1.360083 |
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 5335499 285.0 8529671 455.6 8529671 455.6
Vcells 21434816 163.6 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 0.261269 | 0.2649040 | 0.3562759 | 0.2869375 | 0.3184455 | 6.092981 |
| 2 | mean2_no_refine | 0.272242 | 0.2749425 | 0.3049602 | 0.2906005 | 0.3257420 | 0.412461 |
| 3 | mean+[() | 0.263600 | 0.2718540 | 0.3079954 | 0.2970435 | 0.3242510 | 0.527499 |
| 1 | mean2 | 0.357708 | 0.3648395 | 0.4119429 | 0.3919220 | 0.4516170 | 0.598076 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | mean2_no_refine | 1.041999 | 1.037895 | 0.8559665 | 1.012766 | 1.022913 | 0.0676945 |
| 3 | mean+[() | 1.008922 | 1.026236 | 0.8644855 | 1.035220 | 1.018231 | 0.0865749 |
| 1 | mean2 | 1.369118 | 1.377252 | 1.1562469 | 1.365879 | 1.418192 | 0.0981582 |
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 5335509 285.0 8529671 455.6 8529671 455.6
Vcells 21434384 163.6 46703304 356.4 101881463 777.3
> 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 | 1.333625 | 1.352267 | 1.423952 | 1.381003 | 1.414618 | 2.574833 |
| 4 | mean.default | 1.636013 | 1.670561 | 1.742952 | 1.700897 | 1.776353 | 2.402908 |
| 3 | mean | 1.651583 | 1.688845 | 1.752163 | 1.723132 | 1.776223 | 2.350706 |
| 1 | mean2 | 2.424576 | 2.475733 | 2.578822 | 2.534032 | 2.581672 | 4.091119 |
| 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 | 1.226741 | 1.235378 | 1.224024 | 1.231639 | 1.255712 | 0.9332287 |
| 3 | mean | 1.238416 | 1.248900 | 1.230493 | 1.247739 | 1.255621 | 0.9129547 |
| 1 | mean2 | 1.818034 | 1.830803 | 1.811031 | 1.834921 | 1.824996 | 1.5888871 |
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 5335658 285 8529671 455.6 8529671 455.6
Vcells 21494917 164 46703304 356.4 101881463 777.3
> 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 | 1.060349 | 1.089580 | 1.255321 | 1.118643 | 1.145223 | 7.532557 |
| 4 | mean.default+[() | 1.134807 | 1.164546 | 1.272702 | 1.190851 | 1.228322 | 7.659987 |
| 3 | mean+[() | 1.143280 | 1.180238 | 1.219480 | 1.199141 | 1.238282 | 1.641473 |
| 1 | mean2 | 1.594557 | 1.632402 | 1.698210 | 1.665925 | 1.729671 | 2.319990 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 4 | mean.default+[() | 1.070220 | 1.068803 | 1.0138459 | 1.064549 | 1.072561 | 1.0169172 |
| 3 | mean+[() | 1.078211 | 1.083204 | 0.9714482 | 1.071960 | 1.081258 | 0.2179171 |
| 1 | mean2 | 1.503804 | 1.498193 | 1.3528091 | 1.489236 | 1.510335 | 0.3079950 |
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 5335742 285.0 8529671 455.6 8529671 455.6
Vcells 21594978 164.8 46703304 356.4 101881463 777.3
> 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 | 1.745365 | 1.819536 | 2.011218 | 1.864065 | 1.956688 | 8.094959 |
| 4 | mean.default+[() | 1.848216 | 1.932346 | 2.128681 | 1.971524 | 2.031969 | 8.231800 |
| 3 | mean+[() | 1.888923 | 1.947911 | 2.003362 | 1.994406 | 2.046650 | 2.196784 |
| 1 | mean2 | 2.496994 | 2.586266 | 2.824170 | 2.650161 | 2.760435 | 8.884958 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | mean2_no_refine | 1.000000 | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 4 | mean.default+[() | 1.058928 | 1.061999 | 1.0584043 | 1.057648 | 1.038473 | 1.0169045 |
| 3 | mean+[() | 1.082251 | 1.070553 | 0.9960942 | 1.069923 | 1.045976 | 0.2713768 |
| 1 | mean2 | 1.430643 | 1.421387 | 1.4042092 | 1.421711 | 1.410769 | 1.0975915 |
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 5335823 285.0 8529671 455.6 8529671 455.6
Vcells 21795032 166.3 46703304 356.4 101881463 777.3
> 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 | 3.062606 | 3.205199 | 3.579960 | 3.296143 | 3.539412 | 9.760095 |
| 4 | mean.default+[() | 3.185006 | 3.296816 | 3.738387 | 3.419195 | 3.705976 | 9.599141 |
| 3 | mean+[() | 3.240967 | 3.365845 | 3.784418 | 3.511828 | 3.694897 | 9.764755 |
| 1 | mean2 | 4.242442 | 4.399191 | 4.692116 | 4.514841 | 4.854763 | 10.971203 |
| 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+[() | 1.039966 | 1.028584 | 1.044254 | 1.037332 | 1.047060 | 0.983509 |
| 3 | mean+[() | 1.058238 | 1.050121 | 1.057112 | 1.065436 | 1.043929 | 1.000477 |
| 1 | mean2 | 1.385239 | 1.372517 | 1.310662 | 1.369734 | 1.371630 | 1.124088 |
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 5335833 285.0 8529671 455.6 8529671 455.6
Vcells 21794600 166.3 46703304 356.4 101881463 777.3
> 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 | 14.49868 | 14.62616 | 14.80666 | 14.73758 | 14.85474 | 17.19605 |
| 3 | mean | 18.12252 | 18.34603 | 18.68131 | 18.48286 | 18.88111 | 21.76762 |
| 4 | mean.default | 18.14405 | 18.28320 | 18.64565 | 18.50771 | 18.82398 | 24.29008 |
| 1 | mean2 | 26.54792 | 26.91221 | 27.22419 | 27.10253 | 27.43443 | 28.98659 |
| 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 | 1.249943 | 1.254330 | 1.261683 | 1.254131 | 1.271049 | 1.265850 |
| 4 | mean.default | 1.251428 | 1.250034 | 1.259275 | 1.255817 | 1.267203 | 1.412538 |
| 1 | mean2 | 1.831058 | 1.840005 | 1.838645 | 1.839008 | 1.846846 | 1.685654 |
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 5335982 285.0 8529671 455.6 8529671 455.6
Vcells 22397007 170.9 46703304 356.4 101881463 777.3
> 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 | 11.81605 | 11.93566 | 12.69844 | 12.05150 | 12.41685 | 20.45035 |
| 4 | mean.default+[() | 13.33752 | 13.62064 | 14.50310 | 13.75737 | 14.36470 | 21.41216 |
| 3 | mean+[() | 13.38795 | 13.67508 | 14.34312 | 13.84662 | 14.20438 | 21.41130 |
| 1 | mean2 | 18.04404 | 18.23449 | 19.43780 | 18.39911 | 18.87307 | 26.55891 |
| 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+[() | 1.128764 | 1.141172 | 1.142117 | 1.141548 | 1.156871 | 1.047031 |
| 3 | mean+[() | 1.133032 | 1.145733 | 1.129518 | 1.148954 | 1.143960 | 1.046989 |
| 1 | mean2 | 1.527080 | 1.527732 | 1.530723 | 1.526707 | 1.519956 | 1.298702 |
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 5336066 285.0 8529671 455.6 8529671 455.6
Vcells 23397068 178.6 46703304 356.4 101881463 777.3
> 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 | 18.72819 | 19.47158 | 21.36158 | 19.88508 | 20.33191 | 27.27118 |
| 3 | mean+[() | 21.62594 | 21.92370 | 23.54343 | 22.16877 | 22.54664 | 29.87771 |
| 4 | mean.default+[() | 21.50124 | 21.95887 | 23.79628 | 22.28273 | 23.29906 | 38.36955 |
| 1 | mean2 | 26.87450 | 27.83740 | 33.39097 | 28.42617 | 29.09137 | 408.78697 |
| 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+[() | 1.154726 | 1.125933 | 1.102139 | 1.114845 | 1.108929 | 1.095578 |
| 4 | mean.default+[() | 1.148068 | 1.127740 | 1.113976 | 1.120576 | 1.145935 | 1.406963 |
| 1 | mean2 | 1.434976 | 1.429643 | 1.563132 | 1.429523 | 1.430823 | 14.989705 |
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 5336147 285.0 8529671 455.6 8529671 455.6
Vcells 25397122 193.8 46703304 356.4 101881463 777.3
> 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 | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 45.08694 | 48.03676 | 50.27481 | 49.96531 | 52.91864 | 54.96800 |
| 3 | mean+[() | 46.00506 | 47.96573 | 50.41644 | 50.01278 | 52.99518 | 57.83929 |
| 2 | mean2_no_refine | 40.67148 | 45.62297 | 49.37627 | 51.05324 | 51.85897 | 56.11946 |
| 1 | mean2 | 57.04986 | 58.86777 | 65.67113 | 63.45499 | 64.26397 | 428.41058 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 4 | mean.default+[() | 1.000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.0000000 | 1.000000 |
| 3 | mean+[() | 1.020363 | 0.9985213 | 1.0028172 | 1.000950 | 1.0014463 | 1.052236 |
| 2 | mean2_no_refine | 0.902068 | 0.9497511 | 0.9821276 | 1.021774 | 0.9799754 | 1.020948 |
| 1 | mean2 | 1.265330 | 1.2254735 | 1.3062433 | 1.269981 | 1.2143918 | 7.793818 |
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_0.60.1 ggplot2_3.3.5
[4] knitr_1.33 R.devices_2.17.0 R.utils_2.10.1
[7] R.oo_1.24.0 R.methodsS3_1.8.1-9001 history_0.0.1-9000
loaded via a namespace (and not attached):
[1] Biobase_2.52.0 httr_1.4.2 splines_4.1.1
[4] bit64_4.0.5 network_1.17.1 assertthat_0.2.1
[7] highr_0.9 stats4_4.1.1 blob_1.2.2
[10] GenomeInfoDbData_1.2.6 robustbase_0.93-8 pillar_1.6.2
[13] RSQLite_2.2.8 lattice_0.20-44 glue_1.4.2
[16] digest_0.6.27 XVector_0.32.0 colorspace_2.0-2
[19] Matrix_1.3-4 XML_3.99-0.7 pkgconfig_2.0.3
[22] zlibbioc_1.38.0 genefilter_1.74.0 purrr_0.3.4
[25] ergm_4.1.2 xtable_1.8-4 scales_1.1.1
[28] tibble_3.1.4 annotate_1.70.0 KEGGREST_1.32.0
[31] farver_2.1.0 generics_0.1.0 IRanges_2.26.0
[34] ellipsis_0.3.2 cachem_1.0.6 withr_2.4.2
[37] BiocGenerics_0.38.0 mime_0.11 survival_3.2-13
[40] magrittr_2.0.1 crayon_1.4.1 statnet.common_4.5.0
[43] memoise_2.0.0 laeken_0.5.1 fansi_0.5.0
[46] R.cache_0.15.0 MASS_7.3-54 R.rsp_0.44.0
[49] progressr_0.8.0 tools_4.1.1 lifecycle_1.0.0
[52] S4Vectors_0.30.0 trust_0.1-8 munsell_0.5.0
[55] tabby_0.0.1-9001 AnnotationDbi_1.54.1 Biostrings_2.60.2
[58] compiler_4.1.1 GenomeInfoDb_1.28.1 rlang_0.4.11
[61] grid_4.1.1 RCurl_1.98-1.4 cwhmisc_6.6
[64] rappdirs_0.3.3 startup_0.15.0 labeling_0.4.2
[67] bitops_1.0-7 base64enc_0.1-3 boot_1.3-28
[70] gtable_0.3.0 DBI_1.1.1 markdown_1.1
[73] R6_2.5.1 lpSolveAPI_5.5.2.0-17.7 rle_0.9.2
[76] dplyr_1.0.7 fastmap_1.1.0 bit_4.0.4
[79] utf8_1.2.2 parallel_4.1.1 Rcpp_1.0.7
[82] vctrs_0.3.8 png_0.1-7 DEoptimR_1.0-9
[85] tidyselect_1.1.1 xfun_0.25 coda_0.19-4
Total processing time was 2.22 mins.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('mean2')
Copyright Henrik Bengtsson. Last updated on 2021-08-25 19:25:30 (+0200 UTC). Powered by RSP.