Box/Violin plots for between-subjects comparisons
A combination of box and violin plots along with jittered data points for between-subjects designs with statistical details included in the plot as a subtitle.
ggbetweenstats( data, x, y, type = "parametric", pairwise.display = "significant", p.adjust.method = "holm", effsize.type = "unbiased", bf.prior = 0.707, bf.message = TRUE, results.subtitle = TRUE, xlab = NULL, ylab = NULL, caption = NULL, title = NULL, subtitle = NULL, digits = 2L, var.equal = FALSE, conf.level = 0.95, nboot = 100L, tr = 0.2, centrality.plotting = TRUE, centrality.type = type, centrality.point.args = list(size = 5, color = "darkred"), centrality.label.args = list(size = 3, nudge_x = 0.4, segment.linetype = 4, min.segment.length = 0), point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha = 0.4, size = 3, stroke = 0, na.rm = TRUE), boxplot.args = list(width = 0.3, alpha = 0.2, na.rm = TRUE), violin.args = list(width = 0.5, alpha = 0.2, na.rm = TRUE), ggsignif.args = list(textsize = 3, tip_length = 0.01, na.rm = TRUE), ggtheme = ggstatsplot::theme_ggstatsplot(), package = "RColorBrewer", palette = "Dark2", ggplot.component = NULL, ... )
data: A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not
be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.
x: The grouping (or independent) variable from data. In case of a repeated measures or within-subjects design, if subject.id argument is not available or not explicitly specified, the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted, the results can
be inaccurate when there are more than two levels in x and there are NAs present. The data is expected to be sorted by user in subject-1, subject-2, ..., pattern.
y: The response (or outcome or dependent) variable from data.
type: A character specifying the type of statistical approach:
"parametric""nonparametric""robust""bayes"You can specify just the initial letter.
pairwise.display: Decides which pairwise comparisons to display. Available options are:
"significant" (abbreviation accepted: "s")"non-significant" (abbreviation accepted: "ns")"all"You can use this argument to make sure that your plot is not uber-cluttered when you have multiple groups being compared and scores of pairwise comparisons being displayed. If set to "none", no pairwise comparisons will be displayed.
p.adjust.method: Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".
effsize.type: Type of effect size needed for parametric tests. The argument can be "eta" (partial eta-squared) or "omega" (partial omega-squared).
bf.prior: A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates. In addition to numeric arguments, several named values are also recognized: "medium", "wide", and "ultrawide", corresponding to r scale values of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value corresponds to scale for fixed effects.
bf.message: Logical that decides whether to display Bayes Factor in favor of the null hypothesis. This argument is relevant only for parametric test (Default: TRUE).
results.subtitle: Decides whether the results of statistical tests are to be displayed as a subtitle (Default: TRUE). If set to FALSE, only the plot will be returned.
xlab: Label for x axis variable. If NULL (default), variable name for x will be used.
ylab: Labels for y axis variable. If NULL (default), variable name for y will be used.
caption: The text for the plot caption. This argument is relevant only if bf.message = FALSE.
title: The text for the plot title.
subtitle: The text for the plot subtitle. Will work only if results.subtitle = FALSE.
digits: Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific"
to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).
var.equal: a logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.
conf.level: Scalar between 0 and 1 (default: 95%
confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.
nboot: Number of bootstrap samples for computing confidence interval for the effect size (Default: 100L).
tr: Trim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.
centrality.plotting: Logical that decides whether centrality tendency measure is to be displayed as a point with a label (Default: TRUE). Function decides which central tendency measure to show depending on the type argument.
If you want default centrality parameter, you can specify this using centrality.type argument.
centrality.type: Decides which centrality parameter is to be displayed. The default is to choose the same as type argument. You can specify this to be:
"parameteric" (for mean )"nonparametric" (for median )robust (for trimmed mean )bayes (for MAP estimator )Just as type argument, abbreviations are also accepted.
centrality.point.args, centrality.label.args: A list of additional aesthetic arguments to be passed to ggplot2::geom_point() and ggrepel::geom_label_repel() geoms, which are involved in mean plotting.
point.args: A list of additional aesthetic arguments to be passed to the ggplot2::geom_point().
boxplot.args: A list of additional aesthetic arguments passed on to ggplot2::geom_boxplot().
violin.args: A list of additional aesthetic arguments to be passed to the ggplot2::geom_violin().
ggsignif.args: A list of additional aesthetic arguments to be passed to ggsignif::geom_signif().
ggtheme: A {ggplot2} theme. Default value is theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., ggplot2::theme_bw()), or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). But note that sometimes these themes will remove some of the details that {ggstatsplot} plots typically contains. For example, if relevant, ggbetweenstats() shows details about multiple comparison test as a label on the secondary Y-axis. Some themes (e.g. ggthemes::theme_fivethirtyeight()) will remove the secondary Y-axis and thus the details as well.
package, palette: Name of the package from which the given palette is to be extracted. The available palettes and packages can be checked by running View(paletteer::palettes_d_names).
ggplot.component: A ggplot component to be added to the plot prepared by {ggstatsplot}. This argument is primarily helpful for grouped_
variants of all primary functions. Default is NULL. The argument should be entered as a {ggplot2} function or a list of {ggplot2} functions.
...: Currently ignored.
For details, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html
| graphical element | geom used | argument for further modification |
| raw data | ggplot2::geom_point() | point.args |
| box plot | ggplot2::geom_boxplot() | boxplot.args |
| density plot | ggplot2::geom_violin() | violin.args |
| centrality measure point | ggplot2::geom_point() | centrality.point.args |
| centrality measure label | ggrepel::geom_label_repel() | centrality.label.args |
| pairwise comparisons | ggsignif::geom_signif() | ggsignif.args |
The table below provides summary about:
| Type | Measure | Function used |
| Parametric | mean | datawizard::describe_distribution() |
| Non-parametric | median | datawizard::describe_distribution() |
| Robust | trimmed mean | datawizard::describe_distribution() |
| Bayesian | MAP | datawizard::describe_distribution() |
The table below provides summary about:
Hypothesis testing
| Type | No. of groups | Test | Function used |
| Parametric | 2 | Student's or Welch's t -test | stats::t.test() |
| Non-parametric | 2 | Mann-Whitney U test | stats::wilcox.test() |
| Robust | 2 | Yuen's test for trimmed means | WRS2::yuen() |
| Bayesian | 2 | Student's t -test | BayesFactor::ttestBF() |
Effect size estimation
| Type | No. of groups | Effect size | CI available? | Function used |
| Parametric | 2 | Cohen's d , Hedge's g | Yes | effectsize::cohens_d() , effectsize::hedges_g() |
| Non-parametric | 2 | r (rank-biserial correlation) | Yes | effectsize::rank_biserial() |
| Robust | 2 | Algina-Keselman-Penfield robust standardized difference | Yes | WRS2::akp.effect() |
| Bayesian | 2 | difference | Yes | bayestestR::describe_posterior() |
Hypothesis testing
| Type | No. of groups | Test | Function used |
| Parametric | 2 | Student's t -test | stats::t.test() |
| Non-parametric | 2 | Wilcoxon signed-rank test | stats::wilcox.test() |
| Robust | 2 | Yuen's test on trimmed means for dependent samples | WRS2::yuend() |
| Bayesian | 2 | Student's t -test | BayesFactor::ttestBF() |
Effect size estimation
| Type | No. of groups | Effect size | CI available? | Function used |
| Parametric | 2 | Cohen's d , Hedge's g | Yes | effectsize::cohens_d() , effectsize::hedges_g() |
| Non-parametric | 2 | r (rank-biserial correlation) | Yes | effectsize::rank_biserial() |
| Robust | 2 | Algina-Keselman-Penfield robust standardized difference | Yes | WRS2::wmcpAKP() |
| Bayesian | 2 | difference | Yes | bayestestR::describe_posterior() |
The table below provides summary about:
Hypothesis testing
| Type | No. of groups | Test | Function used |
| Parametric | > 2 | Fisher's or Welch's one-way ANOVA | stats::oneway.test() |
| Non-parametric | > 2 | Kruskal-Wallis one-way ANOVA | stats::kruskal.test() |
| Robust | > 2 | Heteroscedastic one-way ANOVA for trimmed means | WRS2::t1way() |
| Bayes Factor | > 2 | Fisher's ANOVA | BayesFactor::anovaBF() |
Effect size estimation
| Type | No. of groups | Effect size | CI available? | Function used |
| Parametric | > 2 | partial eta-squared, partial omega-squared | Yes | effectsize::omega_squared() , effectsize::eta_squared() |
| Non-parametric | > 2 | rank epsilon squared | Yes | effectsize::rank_epsilon_squared() |
| Robust | > 2 | Explanatory measure of effect size | Yes | WRS2::t1way() |
| Bayes Factor | > 2 | Bayesian R-squared | Yes | performance::r2_bayes() |
Hypothesis testing
| Type | No. of groups | Test | Function used |
| Parametric | > 2 | One-way repeated measures ANOVA | afex::aov_ez() |
| Non-parametric | > 2 | Friedman rank sum test | stats::friedman.test() |
| Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means | WRS2::rmanova() |
| Bayes Factor | > 2 | One-way repeated measures ANOVA | BayesFactor::anovaBF() |
Effect size estimation
| Type | No. of groups | Effect size | CI available? | Function used |
| Parametric | > 2 | partial eta-squared, partial omega-squared | Yes | effectsize::omega_squared() , effectsize::eta_squared() |
| Non-parametric | > 2 | Kendall's coefficient of concordance | Yes | effectsize::kendalls_w() |
| Robust | > 2 | Algina-Keselman-Penfield robust standardized difference average | Yes | WRS2::wmcpAKP() |
| Bayes Factor | > 2 | Bayesian R-squared | Yes | performance::r2_bayes() |
The table below provides summary about:
Hypothesis testing
| Type | Equal variance? | Test | p -value adjustment? | Function used |
| Parametric | No | Games-Howell test | Yes | PMCMRplus::gamesHowellTest() |
| Parametric | Yes | Student's t -test | Yes | stats::pairwise.t.test() |
| Non-parametric | No | Dunn test | Yes | PMCMRplus::kwAllPairsDunnTest() |
| Robust | No | Yuen's trimmed means test | Yes | WRS2::lincon() |
| Bayesian | NA | Student's t -test | NA | BayesFactor::ttestBF() |
Effect size estimation
Not supported.
Hypothesis testing
| Type | Test | p -value adjustment? | Function used |
| Parametric | Student's t -test | Yes | stats::pairwise.t.test() |
| Non-parametric | Durbin-Conover test | Yes | PMCMRplus::durbinAllPairsTest() |
| Robust | Yuen's trimmed means test | Yes | WRS2::rmmcp() |
| Bayesian | Student's t -test | NA | BayesFactor::ttestBF() |
Effect size estimation
Not supported.
# for reproducibility set.seed(123) p <- ggbetweenstats(mtcars, am, mpg) p # extracting details from statistical tests extract_stats(p) # modifying defaults ggbetweenstats( morley, x = Expt, y = Speed, type = "robust", xlab = "The experiment number", ylab = "Speed-of-light measurement" ) # you can remove a specific geom to reduce complexity of the plot ggbetweenstats( mtcars, am, wt, # to remove violin plot violin.args = list(width = 0, linewidth = 0), # to remove boxplot boxplot.args = list(width = 0), # to remove points point.args = list(alpha = 0) )
grouped_ggbetweenstats, ggwithinstats, grouped_ggwithinstats
Useful links