This is mostly to keep track of how the performance of different functions changes across time.
ggbetweenstats
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 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 700ms 700ms 1.43 327MB 5.71
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 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 1.31s 1.31s 0.765 226MB 6.12
ggwithinstats
bench::mark(
ggwithinstats(
data = bugs_long,
x = condition,
y = desire
)
) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 4.5s 4.5s 0.222 600MB 2.00
bench::mark(
grouped_ggwithinstats(
data = bugs_long,
x = condition,
y = desire,
grouping.var = gender
)
) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 3.22s 3.22s 0.310 577MB 2.48
gghistostats
bench::mark(gghistostats(mtcars, wt, test.value = 3)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 82.1ms 83.9ms 11.4 3.59MB 5.72
bench::mark(grouped_gghistostats(mtcars, wt, test.value = 3, grouping.var = am)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 180ms 180ms 5.56 5.13MB 11.1
ggdotplotstats
bench::mark(ggdotplotstats(
dplyr::filter(ggplot2::mpg, cyl %in% c("4", "6")),
cty,
manufacturer,
test.value = 15
)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 87.8ms 91.4ms 11.0 2.88MB 2.21
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 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 179ms 181ms 5.52 5.19MB 2.76
ggscatterstats
bench::mark(ggscatterstats(mtcars, wt, mpg)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 64.3ms 64.7ms 14.8 6.33MB 4.94
bench::mark(grouped_ggscatterstats(mtcars, wt, mpg, grouping.var = am)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 133ms 134ms 7.48 5.53MB 7.48
ggcorrmat
bench::mark(ggcorrmat(iris)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 12ms 12.4ms 80.2 1.52MB 6.68
bench::mark(grouped_ggcorrmat(iris, grouping.var = Species)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 39.2ms 40.1ms 24.8 609KB 6.20
ggpiestats
bench::mark(ggpiestats(mtcars, cyl)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 818ms 818ms 1.22 13.4MB 4.89
bench::mark(grouped_ggpiestats(mtcars, cyl, grouping.var = am)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 1.73s 1.73s 0.579 20.6MB 4.64
ggbarstats
bench::mark(ggbarstats(ggplot2::mpg, fl, class)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 2.33s 2.33s 0.428 138MB 4.71
bench::mark(grouped_ggbarstats(ggplot2::mpg, fl, class, grouping.var = drv)) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 5.89s 5.89s 0.170 183MB 4.42
ggcoefstats
bench::mark(ggcoefstats(stats::lm(formula = wt ~ am * cyl, data = mtcars))) %>%
dplyr::select(-expression)
#> # A tibble: 1 × 5
#> min median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 34.4ms 35.3ms 27.9 5.01MB 4.65
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues