matrixStats.benchmarks
anyMissing() benchmarks on subsetted computation
This report benchmark the performance of anyMissing() 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)
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5151279 275.2 8529671 455.6 8529671 455.6
Vcells 25677157 196.0 64769951 494.2 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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 | anyMissing_x_S | 0.001645 | 0.0016850 | 0.0017744 | 0.0017540 | 0.0017945 | 0.002745 |
| 3 | anyMissing(x[idxs]) | 0.004362 | 0.0044615 | 0.0058445 | 0.0045185 | 0.0046150 | 0.132779 |
| 2 | anyMissing(x, idxs) | 0.004668 | 0.0047940 | 0.0049665 | 0.0048725 | 0.0049760 | 0.009968 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 3 | anyMissing(x[idxs]) | 2.651672 | 2.647775 | 3.293864 | 2.576112 | 2.571747 | 48.37122 |
| 2 | anyMissing(x, idxs) | 2.837690 | 2.845104 | 2.799037 | 2.777936 | 2.772917 | 3.63133 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5140768 274.6 8529671 455.6 8529671 455.6
Vcells 14517815 110.8 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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 | anyMissing_x_S | 0.009050 | 0.0098585 | 0.0114122 | 0.0100365 | 0.0135600 | 0.016474 |
| 3 | anyMissing(x[idxs]) | 0.029842 | 0.0320590 | 0.0385811 | 0.0327690 | 0.0473635 | 0.075336 |
| 2 | anyMissing(x, idxs) | 0.035678 | 0.0373965 | 0.0439836 | 0.0401065 | 0.0508850 | 0.062054 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | anyMissing(x[idxs]) | 3.297459 | 3.251915 | 3.380692 | 3.264983 | 3.492884 | 4.573024 |
| 2 | anyMissing(x, idxs) | 3.942320 | 3.793326 | 3.854090 | 3.996064 | 3.752581 | 3.766784 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5140840 274.6 8529671 455.6 8529671 455.6
Vcells 14581375 111.3 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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 | anyMissing_x_S | 0.055034 | 0.0586245 | 0.0695209 | 0.069160 | 0.0768080 | 0.106304 |
| 3 | anyMissing(x[idxs]) | 0.205272 | 0.2285690 | 0.2612897 | 0.256588 | 0.2854245 | 0.421763 |
| 2 | anyMissing(x, idxs) | 0.237792 | 0.2566085 | 0.2900522 | 0.281582 | 0.3060665 | 0.424174 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | anyMissing(x[idxs]) | 3.729912 | 3.898865 | 3.758432 | 3.710064 | 3.716078 | 3.967518 |
| 2 | anyMissing(x, idxs) | 4.320820 | 4.377155 | 4.172156 | 4.071458 | 3.984826 | 3.990198 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5140912 274.6 8529671 455.6 8529671 455.6
Vcells 15211424 116.1 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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 | anyMissing_x_S | 0.656676 | 0.729587 | 0.7490933 | 0.741139 | 0.763536 | 0.982561 |
| 3 | anyMissing(x[idxs]) | 3.233937 | 4.354772 | 4.5173914 | 4.507199 | 4.636216 | 6.954612 |
| 2 | anyMissing(x, idxs) | 3.850509 | 5.864651 | 6.0772101 | 6.057391 | 6.251347 | 17.330158 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | anyMissing(x[idxs]) | 4.924707 | 5.968818 | 6.030479 | 6.081450 | 6.072034 | 7.078046 |
| 2 | anyMissing(x, idxs) | 5.863636 | 8.038317 | 8.112755 | 8.173083 | 8.187364 | 17.637743 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000000 vector
> x <- data[["n = 10000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5140984 274.6 8529671 455.6 8529671 455.6
Vcells 21511677 164.2 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 8.608837 | 10.56064 | 12.66146 | 11.79244 | 15.07779 | 17.01961 |
| 3 | anyMissing(x[idxs]) | 123.287634 | 135.30060 | 139.57775 | 139.81796 | 142.53043 | 158.02745 |
| 2 | anyMissing(x, idxs) | 135.227423 | 145.17896 | 151.47525 | 150.28209 | 156.62428 | 176.52571 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.000000 | 1.000000 |
| 3 | anyMissing(x[idxs]) | 14.32106 | 12.81178 | 11.02383 | 11.85658 | 9.453004 | 9.285023 |
| 2 | anyMissing(x, idxs) | 15.70798 | 13.74718 | 11.96349 | 12.74393 | 10.387747 | 10.371903 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.
Data type “double”
Data
> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+ mode <- match.arg(mode)
+ if (mode == "logical") {
+ x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+ } else {
+ x <- runif(n, min = range[1], max = range[2])
+ }
+ storage.mode(x) <- mode
+ if (na_prob > 0)
+ x[sample(n, size = na_prob * n)] <- NA
+ x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+ set.seed(seed)
+ data <- list()
+ data[[1]] <- rvector(n = scale * 100, ...)
+ data[[2]] <- rvector(n = scale * 1000, ...)
+ data[[3]] <- rvector(n = scale * 10000, ...)
+ data[[4]] <- rvector(n = scale * 1e+05, ...)
+ data[[5]] <- rvector(n = scale * 1e+06, ...)
+ names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+ data
+ }
> data <- rvectors(mode = mode)
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5141065 274.6 8529671 455.6 8529671 455.6
Vcells 20068542 153.2 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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 | anyMissing_x_S | 0.001654 | 0.0016870 | 0.0018159 | 0.0017885 | 0.0018505 | 0.002675 |
| 3 | anyMissing(x[idxs]) | 0.003922 | 0.0041425 | 0.0044248 | 0.0042240 | 0.0043280 | 0.019221 |
| 2 | anyMissing(x, idxs) | 0.004482 | 0.0046305 | 0.0048128 | 0.0047295 | 0.0048290 | 0.006648 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | anyMissing(x[idxs]) | 2.371221 | 2.455542 | 2.436641 | 2.361756 | 2.338827 | 7.185421 |
| 2 | anyMissing(x, idxs) | 2.709794 | 2.744813 | 2.650345 | 2.644395 | 2.609565 | 2.485234 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5141128 274.6 8529671 455.6 8529671 455.6
Vcells 20078332 153.2 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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 | anyMissing_x_S | 0.009484 | 0.0099275 | 0.0103565 | 0.010092 | 0.0102445 | 0.015067 |
| 3 | anyMissing(x[idxs]) | 0.028564 | 0.0298665 | 0.0309744 | 0.030411 | 0.0309435 | 0.056522 |
| 2 | anyMissing(x, idxs) | 0.035223 | 0.0369805 | 0.0374642 | 0.037193 | 0.0374100 | 0.051636 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | anyMissing(x[idxs]) | 3.011809 | 3.008461 | 2.990818 | 3.013377 | 3.020499 | 3.751377 |
| 2 | anyMissing(x, idxs) | 3.713939 | 3.725057 | 3.617465 | 3.685394 | 3.651716 | 3.427092 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5141200 274.6 8529671 455.6 8529671 455.6
Vcells 20173246 154.0 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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 | anyMissing_x_S | 0.055014 | 0.0620335 | 0.0726542 | 0.069601 | 0.0804640 | 0.113970 |
| 3 | anyMissing(x[idxs]) | 0.254718 | 0.2897140 | 0.3243564 | 0.315644 | 0.3652550 | 0.551803 |
| 2 | anyMissing(x, idxs) | 0.271002 | 0.3082915 | 0.3498256 | 0.333323 | 0.4115245 | 0.437846 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | anyMissing(x[idxs]) | 4.630058 | 4.670283 | 4.464386 | 4.535050 | 4.539359 | 4.841651 |
| 2 | anyMissing(x, idxs) | 4.926055 | 4.969758 | 4.814940 | 4.789055 | 5.114393 | 3.841765 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5141272 274.6 8529671 455.6 8529671 455.6
Vcells 21118295 161.2 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[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 | anyMissing_x_S | 0.884450 | 1.006420 | 1.070587 | 1.050933 | 1.127081 | 1.285333 |
| 3 | anyMissing(x[idxs]) | 6.866668 | 7.698575 | 9.099393 | 9.636115 | 10.166210 | 18.387087 |
| 2 | anyMissing(x, idxs) | 7.327605 | 8.010414 | 9.606912 | 9.881673 | 10.148229 | 19.164685 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 3 | anyMissing(x[idxs]) | 7.763772 | 7.649470 | 8.499442 | 9.169105 | 9.019946 | 14.30531 |
| 2 | anyMissing(x, idxs) | 8.284928 | 7.959319 | 8.973499 | 9.402763 | 9.003993 | 14.91029 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000000 vector
> x <- data[["n = 10000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5141344 274.6 8529671 455.6 8529671 455.6
Vcells 30568777 233.3 51815961 395.4 60562128 462.1
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs),
+ `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")
Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 9.109918 | 10.87158 | 13.01311 | 12.27992 | 15.45226 | 17.1029 |
| 2 | anyMissing(x, idxs) | 144.642119 | 158.67185 | 169.23774 | 165.86743 | 172.07069 | 491.1547 |
| 3 | anyMissing(x[idxs]) | 163.183915 | 171.95101 | 179.12347 | 177.47141 | 185.43935 | 200.9944 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | anyMissing_x_S | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 |
| 2 | anyMissing(x, idxs) | 15.87743 | 14.59510 | 13.00517 | 13.50721 | 11.13564 | 28.71763 |
| 3 | anyMissing(x[idxs]) | 17.91278 | 15.81655 | 13.76485 | 14.45216 | 12.00079 | 11.75207 |
Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.
Appendix
Session information
R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] microbenchmark_1.4-7 matrixStats_0.60.1 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 1.36 mins.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('anyMissing_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 18:48:23 (+0200 UTC). Powered by RSP.