Here are few examples that demonstrate how the basic plots generated by
ggstatsplot can be further modified with either with
ggplot2 functions or other additional
themes, etc. from
ggplot2 extensions. This is because the class of the object from all functions is still
# for reproducibility set.seed(123) # plot ggstatsplot::ggscatterstats( data = ggplot2::msleep, x = brainwt, y = sleep_total, xlab = "Brain weight (in kilograms)", ylab = "Total amount of sleep (in hours)", label.var = "name", title = "Mammalian sleep", marginal = FALSE, type = "robust" ) + # further modifications with `ggplot2` ggplot2::geom_rug(sides = "b") + ggplot2::scale_x_log10() #> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.
# for reproducibility set.seed(123) # needed library (download from GitHub) # devtools::install_github("gadenbuie/ggpomological") library(ggpomological) # plot ggpomological::paint_pomological( pomo_gg = ggstatsplot::ggbetweenstats( data = dplyr::filter(.data = ggstatsplot::movies_long, genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")), x = genre, y = rating, messages = FALSE, xlab = "movie genre", ylab = "IMDB rating", title = "Differences in IMDB ratings by genre" ) + # further modifications with `ggplot2` ggpomological::theme_pomological_fancy() + ggplot2::theme(legend.position = "none"), res = 110, width = 1000, height = 700 )
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues