matrixStats.benchmarks
weightedMean() benchmarks on subsetted computation
This report benchmark the performance of weightedMean() on subsetted computation.
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)
> data <- data[1:4]
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5361703 286.4 8529671 455.6 8529671 455.6
Vcells 12013100 91.7 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.003030 | 0.0031235 | 0.0032381 | 0.003199 | 0.0032590 | 0.006265 |
| 2 | weightedMean(x, w, idxs) | 0.006109 | 0.0062925 | 0.0075168 | 0.006445 | 0.0065945 | 0.112131 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.008183 | 0.0083415 | 0.0085273 | 0.008460 | 0.0085940 | 0.011408 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 2 | weightedMean(x, w, idxs) | 2.016172 | 2.014567 | 2.321338 | 2.014692 | 2.023474 | 17.89800 |
| 3 | weightedMean(x[idxs], w[idxs]) | 2.700660 | 2.670562 | 2.633411 | 2.644576 | 2.637005 | 1.82091 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5349309 285.7 8529671 455.6 8529671 455.6
Vcells 10865967 83.0 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.014392 | 0.0148975 | 0.0158303 | 0.0156915 | 0.016372 | 0.023731 |
| 2 | weightedMean(x, w, idxs) | 0.038463 | 0.0394330 | 0.0424078 | 0.0425210 | 0.044238 | 0.056879 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.050966 | 0.0539020 | 0.0589082 | 0.0575475 | 0.060693 | 0.107980 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 2.672526 | 2.646954 | 2.678908 | 2.709811 | 2.702052 | 2.396823 |
| 3 | weightedMean(x[idxs], w[idxs]) | 3.541273 | 3.618191 | 3.721243 | 3.667431 | 3.707122 | 4.550166 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5349381 285.7 8529671 455.6 8529671 455.6
Vcells 11082527 84.6 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.103117 | 0.120849 | 0.1409248 | 0.1335115 | 0.1461600 | 0.360549 |
| 2 | weightedMean(x, w, idxs) | 0.450204 | 0.488171 | 0.5840349 | 0.5497170 | 0.6286915 | 1.223040 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.522812 | 0.551163 | 0.6967387 | 0.6436165 | 0.7789510 | 1.360351 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 4.365953 | 4.039512 | 4.144302 | 4.117376 | 4.301392 | 3.392160 |
| 3 | weightedMean(x[idxs], w[idxs]) | 5.070085 | 4.560758 | 4.944045 | 4.820682 | 5.329440 | 3.772999 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5349453 285.7 8529671 455.6 8529671 455.6
Vcells 13242576 101.1 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.543149 | 1.672434 | 1.767486 | 1.738379 | 1.876264 | 2.088636 |
| 2 | weightedMean(x, w, idxs) | 11.815077 | 12.703033 | 14.230083 | 14.345170 | 14.757426 | 29.054454 |
| 3 | weightedMean(x[idxs], w[idxs]) | 11.839894 | 14.813293 | 15.541099 | 15.826799 | 16.222686 | 28.754855 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 2 | weightedMean(x, w, idxs) | 7.656472 | 7.595536 | 8.051030 | 8.252041 | 7.865325 | 13.91073 |
| 3 | weightedMean(x[idxs], w[idxs]) | 7.672554 | 8.857326 | 8.792771 | 9.104346 | 8.646271 | 13.76729 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000000 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)
> data <- data[1:4]
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5349528 285.7 8529671 455.6 8529671 455.6
Vcells 11401451 87.0 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.003421 | 0.0035815 | 0.0037345 | 0.0036360 | 0.0037865 | 0.006879 |
| 2 | weightedMean(x, w, idxs) | 0.006397 | 0.0066325 | 0.0070208 | 0.0067615 | 0.0069680 | 0.024667 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.008167 | 0.0084590 | 0.0087905 | 0.0086590 | 0.0089525 | 0.013348 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 1.869921 | 1.851878 | 1.879982 | 1.859598 | 1.840222 | 3.585841 |
| 3 | weightedMean(x[idxs], w[idxs]) | 2.387314 | 2.361860 | 2.353845 | 2.381463 | 2.364321 | 1.940398 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5349597 285.7 8529671 455.6 8529671 455.6
Vcells 11426563 87.2 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.018424 | 0.0189400 | 0.0208679 | 0.0205745 | 0.0215095 | 0.028179 |
| 2 | weightedMean(x, w, idxs) | 0.042496 | 0.0430270 | 0.0466539 | 0.0459825 | 0.0491950 | 0.065586 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.055149 | 0.0574275 | 0.0623475 | 0.0613545 | 0.0647675 | 0.117008 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 2.306557 | 2.271753 | 2.235682 | 2.234927 | 2.287129 | 2.327478 |
| 3 | weightedMean(x[idxs], w[idxs]) | 2.993324 | 3.032075 | 2.987730 | 2.982065 | 3.011111 | 4.152312 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5349669 285.8 8529671 455.6 8529671 455.6
Vcells 11674111 89.1 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.115510 | 0.1206845 | 0.1385982 | 0.132096 | 0.1470275 | 0.227056 |
| 2 | weightedMean(x, w, idxs) | 0.330913 | 0.3529380 | 0.3964054 | 0.371142 | 0.4198300 | 0.579781 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.471444 | 0.4774910 | 0.5490967 | 0.496952 | 0.6147105 | 1.133490 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 2.864800 | 2.924468 | 2.860104 | 2.809638 | 2.855452 | 2.553471 |
| 3 | weightedMean(x[idxs], w[idxs]) | 4.081413 | 3.956523 | 3.961787 | 3.762052 | 4.180922 | 4.992116 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5349741 285.8 8529671 455.6 8529671 455.6
Vcells 14149573 108.0 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 2.071744 | 2.387315 | 2.542025 | 2.550411 | 2.697675 | 3.819459 |
| 2 | weightedMean(x, w, idxs) | 14.878945 | 16.011812 | 17.164947 | 16.538214 | 17.235565 | 30.703660 |
| 3 | weightedMean(x[idxs], w[idxs]) | 14.769285 | 16.204172 | 17.735854 | 16.547055 | 17.570960 | 29.165115 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 7.181845 | 6.707039 | 6.752469 | 6.484529 | 6.389044 | 8.038746 |
| 3 | weightedMean(x[idxs], w[idxs]) | 7.128914 | 6.787615 | 6.977057 | 6.487996 | 6.513372 | 7.635928 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000000 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 15.58 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('weightedMean_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 19:31:55 (+0200 UTC). Powered by RSP.