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(
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 3.48s 3.48s 0.288 266MB 2.59
set.seed(123)
bench::mark(
grouped_ggbetweenstats(
data = dplyr::filter(
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 7.11s 7.11s 0.141 252MB 1.27
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 13.5s 13.5s 0.0740 656MB 0.740
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 10.4s 10.4s 0.0957 631MB 0.861
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 321ms 321ms 3.11 4.06MB 3.11
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 645ms 645ms 1.55 4.42MB 1.55
ggdotplotstats
library(ggstatsplot)
set.seed(123)
bench::mark(ggdotplotstats(
dplyr::filter(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 416ms 416ms 2.40 2.35MB 2.40
set.seed(123)
bench::mark(
grouped_ggdotplotstats(
dplyr::filter(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 722ms 722ms 1.38 4.13MB 1.38
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 80.7ms 91ms 11.0 5.48MB 0
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 196ms 213ms 4.69 3.92MB 2.35
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 30.1ms 33.3ms 30.3 1.52MB 2.16
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 79.6ms 86.7ms 11.0 478KB 2.19
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 2.63s 2.63s 0.381 13.9MB 2.28
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 5.31s 5.31s 0.188 25.5MB 2.26
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 8.96s 8.96s 0.112 286MB 2.01
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 41.9s 41.9s 0.0239 537MB 0.668
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 461ms 461ms 2.17 4.32MB 2.17
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues