The function ggstatsplot::ggwithinstats is designed to facilitate data exploration, and for making highly customizable publication-ready plots, with relevant statistical details included in the plot itself if desired. We will see examples of how to use this function in this vignette.

To begin with, here are some instances where you would want to use ggwithinstats-

  • to check if a continuous variable differs across multiple groups/conditions

  • to compare distributions visually and check for outliers

Note: This vignette uses the pipe operator (%>%), if you are not familiar with this operator, here is a good explanation:

Comparisons between groups with ggwithistats

To illustrate how this function can be used, we will use the bugs dataset throughout this vignette. This data set, “Bugs”, provides the extent to which men and women want to kill arthropods that vary in freighteningness (low, high) and disgustingness (low, high). Each participant rates their attitudes towards all anthropods. Subset of the data reported by Ryan et al. (2013). Note that this is a repeated measures design because the same participant gave four different ratings across four different conditions (LDLF, LDHF, HDLF, HDHF).

Let’s have a look at the data-

data("bugs", package = "jmv")

# getting data in tidy format
data_bugs <- bugs %>%
  tibble::as_tibble(x = .) %>%
  tidyr::gather(data = ., key, value, LDLF:HDHF) %>%
  dplyr::filter(.data = ., Region %in% c("Europe", "North America"))

dplyr::glimpse(x = data_bugs)
#> Observations: 344
#> Variables: 6
#> $ Subject   <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18...
#> $ Gender    <fct> Female, Female, Female, Female, Female, Female, Female, F...
#> $ Region    <fct> North America, North America, Europe, North America, Nort...
#> $ Education <fct> some, advance, college, college, some, some, some, high, ...
#> $ key       <chr> "LDLF", "LDLF", "LDLF", "LDLF", "LDLF", "LDLF", "LDLF", "...
#> $ value     <dbl> 6.0, 10.0, 5.0, 6.0, 3.0, 2.0, 10.0, 10.0, 9.5, 0.0, 9.5,...

Suppose the first thing we want to inspect is the distribution of desire to kill across all conditions (disregarding the factorial structure of the experiment). We also want to know if the mean differences in this desire across conditions is statistically significant.

The simplest form of the function call is-

# since the confidence intervals for the effect sizes are computed using
# bootstrapping, important to set a seed for reproducibility

# function call
  data = data_bugs,
  x = key,
  y = value,
  messages = FALSE


  • The function automatically decides whether a dependent samples test is preferred (for 2 groups) or an ANOVA (3 or more groups). based on the number of levels in the grouping variable.
  • The output of the function is a ggplot object which means that it can be further modified with ggplot2 functions.

We can make the output much more aesthetically pleasing as well as informative by making use of the many optional parameters in ggwithinstats. We’ll add a title and caption, better x and y axis labels, and tag and label the outliers in the data. We can and will change the overall theme as well as the color palette in use.


# for reproducibility

# plot
  data = data_bugs,
  x = key, # grouping/independent variable
  y = value, # dependent variables
  xlab = "Condition", # label for the x-axis
  ylab = "Desire to kill an artrhopod", # label for the y-axis
  type = "parametric", # type of statistical test
  effsize.type = "biased", # type of effect size
  nboot = 10, # number of bootstrap samples used
  bf.message = TRUE, # display bayes factor in favor of null hypothesis
  sphericity.correction = FALSE, # don't display sphericity corrected dfs and p-values
  pairwise.comparisons = TRUE, # display pairwise comparisons
  outlier.tagging = TRUE, # whether outliers should be flagged
  outlier.coef = 1.5, # coefficient for Tukey's rule
  outlier.label = Region, # label to attach to outlier values
  outlier.label.color = "red", # outlier point label color
  mean.plotting = TRUE, # whether the mean is to be displayed
  mean.color = "darkblue", # color for mean
  messages = FALSE, # turn off messages
  ggtheme = firatheme::theme_fira(), # a different theme
  ggstatsplot.layer = FALSE, # turn off default modification of the used theme
  package = "yarrr", # package from which color palette is to be taken
  palette = "info2", # choosing a different color palette
  title = "Comparison of desire to kill bugs",
  caption = "Source: Ryan et al., 2013"
) + # modifying the plot further
    limits = c(0, 10),
    breaks = seq(from = 0, to = 10, by = 1)

As can be appreciated from the effect size (partial eta squared) of 0.18, there are small differences in the mean desire to kill across conditions. Importantly, this plot also helps us appreciate the distributions within any given condition.

So far we have only used a classic parametric test, but we can also use other available options: The type (of test) argument also accepts the following abbreviations: "p" (for parametric), "np" (for nonparametric), "r" (for robust), "bf" (for Bayes Factor).

Let’s use the combine_plots function to make one plot from four separate plots that demonstrates all of these options. Let’s compare desire to kill bugs only for low versus high disgust conditions to see how much of a difference whether a bug is disgusting-looking or not makes to the desire to kill that bug. We will generate the plots one by one and then use combine_plots to merge them into one plot with some common labeling. It is possible, but not necessarily recommended, to make each plot have different colors or themes.

For example,

# for reproducibility

# selecting subset of the data
df_disgust <- dplyr::filter(.data = data_bugs, key %in% c("LDHF", "HDHF"))

# parametric t-test
p1 <-
    data = df_disgust,
    x = key,
    y = value,
    type = "p",
    effsize.type = "d",
    conf.level = 0.99,
    title = "Parametric test",
    package = "ggsci",
    palette = "nrc_npg",
    ggtheme = ggthemr::ggthemr(palette = "light"),
    messages = FALSE

# Mann-Whitney U test (nonparametric test)
p2 <-
    data = df_disgust,
    x = key,
    y = value,
    xlab = "Condition",
    ylab = "Desire to kill bugs",
    type = "np",
    conf.level = 0.99,
    title = "Non-parametric Test",
    package = "ggsci",
    palette = "uniform_startrek",
    ggtheme = ggthemes::theme_map(),
    ggstatsplot.layer = FALSE,
    messages = FALSE

# robust t-test
p3 <-
    data = df_disgust,
    x = key,
    y = value,
    xlab = "Condition",
    ylab = "Desire to kill bugs",
    type = "r",
    conf.level = 0.99,
    title = "Robust Test",
    tr = 0.2,
    package = "wesanderson",
    palette = "Royal2",
    ggtheme = hrbrthemes::theme_ipsum_tw(),
    ggstatsplot.layer = FALSE,
    messages = FALSE

# Bayes Factor for parametric t-test
p4 <-
    data = df_disgust,
    x = key,
    y = value,
    xlab = "Condition",
    ylab = "Desire to kill bugs",
    type = "bf",
    title = "Bayesian Test",
    package = "ggsci",
    palette = "nrc_npg",
    ggtheme = ggthemes::theme_fivethirtyeight(),
    messages = FALSE

# combining the individual plots into a single plot
  p1, p2, p3, p4,
  nrow = 2,
  title.text = "Effect of disgust on desire to kill bugs ",
  caption.text = "Source: Bugs dataset from `jmv` R package",
  title.size = 14,
  caption.size = 12

Grouped analysis with grouped_ggwithinstats

What if we want to carry out this same analysis but for each region (or gender)?

ggstatsplot provides a special helper function for such instances: grouped_ggwithinstats. This is merely a wrapper function around ggstatsplot::combine_plots. It applies ggwithinstats across all levels of a specified grouping variable and then combines list of individual plots into a single plot. Note that the grouping variable can be anything: conditions in a given study, groups in a study sample, different studies, etc.

Let’s focus on the two regions and for years: 1967, 1987, 2007. Also, let’s carry out pairwise comparisons to see if there differences between every pair of continents.

# for reproducibility

  # arguments relevant for ggstatsplot::ggwithinstats
  data = data_bugs,
  x = key,
  y = value,
  grouping.var = Gender,
  xlab = "Continent",
  ylab = "Desire to kill bugs",
  sort = "ascending", = median,
  nboot = 10,
  type = "np", # type of test
  pairwise.comparisons = TRUE, # display results from pairwise comparisons
  pairwise.display = "significant", # display only significant pairwise comparisons
  pairwise.annotation = "p.value", # annotate the pairwise comparisons using p-values
  p.adjust.method = "fdr", # adjust p-values for multiple tests using this method
  ggtheme = ggthemes::theme_tufte(),
  package = "ggsci",
  palette = "default_jco",
  notch = TRUE,
  outlier.tagging = TRUE,
  ggstatsplot.layer = FALSE,
  outlier.label = Education,
  k = 3, = TRUE,
  title.prefix = "Year",
  messages = FALSE,
  # arguments relevant for ggstatsplot::combine_plots
  title.text = "Desire to kill bugs across genders",
  plotgrid.args = list(ncol = 1, labels = c("(a)", "(b)"))

Grouped analysis with ggwithinstats + purrr

Although this grouping function provides a quick way to explore the data, it leaves much to be desired. For example, the same type of test and theme is applied for all genders, but maybe we want to change this for different genders, or maybe we want to gave different effect sizes for different years. This type of customization for different levels of a grouping variable is not possible with grouped_ggwithinstats, but this can be easily achieved using the purrr package.

See the associated vignette here:

Between-subjects designs

For independent measures designs, ggbetweenstats function can be used:

Summary of tests

Following (within-subjects) tests are carried out for each type of analyses-

Type No. of groups Test
Parametric > 2 One-way repeated measures ANOVA
Non-parametric > 2 Friedman test
Robust > 2 Heteroscedastic one-way repeated measures ANOVA for trimmed means
Bayes Factor > 2 One-way repeated measures ANOVA
Parametric 2 Student’s t-test
Non-parametric 2 Wilcoxon signed-rank test
Robust 2 Yuen’s test on trimmed means for dependent samples
Bayes Factor 2 Student’s t-test

Following effect sizes (and confidence intervals/CI) are available for each type of test-

Type No. of groups Effect size CI?
Parametric > 2 \(\eta_{p}^2\), \(\omega^2\) Yes
Non-parametric > 2 \(W_{Kendall}\) (Kendall’s coefficient of concordance) Yes
Robust > 2 No No
Bayes Factor > 2 No No
Parametric 2 Cohen’s d, Hedge’s g (central-and noncentral-t distribution based) Yes
Non-parametric 2 r (computed as \(Z/\sqrt{N}\)) Yes
Robust 2 \(\xi\) (Explanatory measure of effect size) Yes
Bayes Factor 2 No No

Here is a summary of multiple pairwise comparison tests supported in ggwithinstats-

Type Test p-value adjustment?
Parametric Student’s t-test Yes
Non-parametric Durbin-Conover test Yes
Robust Yuen’s trimmed means test Yes
Bayes Factor No No

Effect size interpretation

To see how the effect sizes displayed in these tests can be interpreted, see:


If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: