vignettes/web_only/benchmarking.Rmd
benchmarking.Rmd
This is mostly to keep track of how the performance of different functions changes across time.
ggbetweenstats
library(ggstatsplot)
set.seed(123)
bench::mark(
ggbetweenstats(
data = dplyr::filter(
.data = movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length
)
) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 1.68s 1.68s 0.597 233MB 3.58
set.seed(123)
bench::mark(
grouped_ggbetweenstats(
data = dplyr::filter(
.data = movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length,
grouping.var = genre
)
) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 3.21s 3.21s 0.312 244MB 1.87
ggwithinstats
library(ggstatsplot)
set.seed(123)
bench::mark(
ggwithinstats(
data = bugs_long,
x = condition,
y = desire
)
) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 13.9s 13.9s 0.0719 659MB 0.863
set.seed(123)
bench::mark(
grouped_ggwithinstats(
data = bugs_long,
x = condition,
y = desire,
grouping.var = gender
)
) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 9.81s 9.81s 0.102 627MB 0.917
gghistostats
library(ggstatsplot)
set.seed(123)
bench::mark(gghistostats(mtcars, wt, test.value = 3)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 177ms 195ms 5.25 4.72MB 0
set.seed(123)
bench::mark(grouped_gghistostats(mtcars, wt, test.value = 3, grouping.var = am)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 393ms 403ms 2.48 5.95MB 2.48
ggdotplotstats
library(ggstatsplot)
set.seed(123)
bench::mark(ggdotplotstats(
dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
cty,
manufacturer,
test.value = 15
)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 193ms 199ms 5.02 3.18MB 2.51
set.seed(123)
bench::mark(
grouped_ggdotplotstats(
dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
cty,
manufacturer,
test.value = 15,
grouping.var = cyl
)
) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 404ms 404ms 2.48 5.82MB 2.48
ggscatterstats
library(ggstatsplot)
set.seed(123)
bench::mark(ggscatterstats(mtcars, wt, mpg)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 404ms 408ms 2.45 19.8MB 2.45
set.seed(123)
bench::mark(grouped_ggscatterstats(mtcars, wt, mpg, grouping.var = am)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 788ms 788ms 1.27 16.4MB 2.54
ggcorrmat
library(ggstatsplot)
set.seed(123)
bench::mark(ggcorrmat(iris)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 27ms 27.7ms 35.4 1.11MB 2.08
set.seed(123)
bench::mark(grouped_ggcorrmat(iris, grouping.var = Species)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 92.4ms 97.9ms 10.3 482KB 2.06
ggpiestats
library(ggstatsplot)
set.seed(123)
bench::mark(ggpiestats(mtcars, cyl)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 2.44s 2.44s 0.411 14MB 2.87
set.seed(123)
bench::mark(grouped_ggpiestats(mtcars, cyl, grouping.var = am)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 5.11s 5.11s 0.196 25.4MB 2.54
ggbarstats
library(ggstatsplot)
set.seed(123)
bench::mark(ggbarstats(ggplot2::mpg, fl, class)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 5.87s 5.87s 0.170 286MB 1.36
set.seed(123)
bench::mark(grouped_ggbarstats(ggplot2::mpg, fl, class, grouping.var = drv)) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 14.7s 14.7s 0.0681 538MB 1.91
ggcoefstats
library(ggstatsplot)
set.seed(123)
bench::mark(ggcoefstats(stats::lm(formula = wt ~ am * cyl, data = mtcars))) %>%
dplyr::select(-expression)
#> # A tibble: 1 x 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 348ms 348ms 2.88 4MB 2.88
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues
For details, see- https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/session_info.html