vignettes/web_only/ggwithinstats.Rmd
ggwithinstats.Rmd
You can cite this package/vignette as:
Patil, I. (2021). Visualizations with statistical details: The
'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167,
doi:10.21105/joss.03167
A BibTeX entry for LaTeX users is
@Article{,
doi = {10.21105/joss.03167},
url = {https://doi.org/10.21105/joss.03167},
year = {2021},
publisher = {{The Open Journal}},
volume = {6},
number = {61},
pages = {3167},
author = {Indrajeet Patil},
title = {{Visualizations with statistical details: The {'ggstatsplot'} approach}},
journal = {{Journal of Open Source Software}},
}
The function ggstatsplot::ggwithinstats
is designed to facilitate data exploration, and for making highly customizable publicationready 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: http://r4ds.had.co.nz/pipes.html
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).
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
set.seed(123)
library(ggstatsplot)
# function call
ggstatsplot::ggwithinstats(
data = bugs_long,
x = condition,
y = desire
)
Note:
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.
As can be seen from the plot, the function by default returns Bayes Factor for the test. If the null hypothesis can’t be rejected with the null hypothesis significance testing (NHST) approach, the Bayesian approach can help index evidence in favor of the null hypothesis (i.e., \(BF_{01}\)).
By default, natural logarithms are shown because Bayes Factor values can sometimes be pretty large. Having values on logarithmic scale also makes it easy to compare evidence in favor alternative (\(BF_{10}\)) versus null (\(BF_{01}\)) hypotheses (since \(log_{e}(BF_{01}) =  log_{e}(BF_{10})\)).
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
set.seed(123)
library(ggstatsplot)
# plot
ggstatsplot::ggwithinstats(
data = bugs_long,
x = condition,
y = desire,
type = "nonparametric", # type of statistical test
xlab = "Condition", # label for the xaxis
ylab = "Desire to kill an artrhopod", # label for the yaxis
effsize.type = "biased", # type of effect size
sphericity.correction = FALSE, # don't display sphericity corrected dfs and pvalues
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
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
ggplot2::scale_y_continuous(
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 disgustinglooking 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
set.seed(123)
library(ggstatsplot)
# selecting subset of the data
df_disgust < dplyr::filter(bugs_long, condition %in% c("LDHF", "HDHF"))
# parametric ttest
p1 < ggstatsplot::ggwithinstats(
data = df_disgust,
x = condition,
y = desire,
type = "p",
effsize.type = "d",
conf.level = 0.99,
title = "Parametric test",
package = "ggsci",
palette = "nrc_npg",
ggtheme = hrbrthemes::theme_ipsum_pub()
)
# MannWhitney U test (nonparametric test)
p2 < ggstatsplot::ggwithinstats(
data = df_disgust,
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill bugs",
type = "np",
conf.level = 0.99,
title = "Nonparametric Test",
package = "ggsci",
palette = "uniform_startrek",
ggtheme = ggthemes::theme_map()
)
# robust ttest
p3 < ggstatsplot::ggwithinstats(
data = df_disgust,
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill bugs",
type = "r",
conf.level = 0.99,
title = "Robust Test",
package = "wesanderson",
palette = "Royal2",
ggtheme = hrbrthemes::theme_ipsum_tw()
)
# Bayes Factor for parametric ttest
p4 < ggstatsplot::ggwithinstats(
data = df_disgust,
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill bugs",
type = "bayes",
title = "Bayesian Test",
package = "ggsci",
palette = "nrc_npg",
ggtheme = ggthemes::theme_fivethirtyeight()
)
# combining the individual plots into a single plot
ggstatsplot::combine_plots(
plotlist = list(p1, p2, p3, p4),
plotgrid.args = list(nrow = 2),
annotation.args = list(
title = "Effect of disgust on desire to kill bugs ",
caption = "Source: Bugs dataset from `jmv` R package"
)
)
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
set.seed(123)
library(ggstatsplot)
ggstatsplot::grouped_ggwithinstats(
# arguments relevant for ggstatsplot::ggwithinstats
data = bugs_long,
x = condition,
y = desire,
grouping.var = gender,
xlab = "Continent",
ylab = "Desire to kill bugs",
type = "nonparametric", # type of test
pairwise.display = "significant", # display only significant pairwise comparisons
p.adjust.method = "BH", # adjust pvalues for multiple tests using this method
ggtheme = ggthemes::theme_tufte(),
package = "ggsci",
palette = "default_jco",
outlier.tagging = TRUE,
outlier.label = education,
k = 3,
# arguments relevant for ggstatsplot::combine_plots
annotation.args = list(title = "Desire to kill bugs across genders"),
plotgrid.args = list(ncol = 1)
)
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: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/purrr_examples.html
For independent measures designs, ggbetweenstats
function can be used: 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 
point path  ggplot2::geom_path 
point.path.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 point path  ggplot2::geom_path 
centrality.path.args 
centrality measure label  ggrepel::geom_label_repel 
centrality.label.args 
outlier point  ggplot2::stat_boxplot 
❌ 
outlier label  ggrepel::geom_label_repel 
outlier.label.args 
pairwise comparisons  ggsignif::geom_ggsignif 
ggsignif.args 
Central tendency measure
Type  Measure  Function used 

Parametric  mean  parameters::describe_distribution 
Nonparametric  median  parameters::describe_distribution 
Robust  trimmed mean  parameters::describe_distribution 
Bayesian  MAP (maximum a posteriori probability) estimate  parameters::describe_distribution 
Hypothesis testing
Type  No. of groups  Test  Function used 

Parametric  > 2  Oneway repeated measures ANOVA  afex::aov_ez 
Nonparametric  > 2  Friedman rank sum test  stats::friedman.test 
Robust  > 2  Heteroscedastic oneway repeated measures ANOVA for trimmed means  WRS2::rmanova 
Bayes Factor  > 2  Oneway repeated measures ANOVA  BayesFactor::anovaBF 
Parametric  2  Student’s ttest  stats::t.test 
Nonparametric  2  Wilcoxon signedrank test  stats::wilcox.test 
Robust  2  Yuen’s test on trimmed means for dependent samples  WRS2::yuend 
Bayesian  2  Student’s ttest  BayesFactor::ttestBF 
Effect size estimation
Type  No. of groups  Effect size  CI?  Function used 

Parametric  > 2  \(\eta_{p}^2\), \(\omega_{p}^2\)  ✅ 
effectsize::omega_squared , effectsize::eta_squared

Nonparametric  > 2  \(W_{Kendall}\) (Kendall’s coefficient of concordance)  ✅  effectsize::kendalls_w 
Robust  > 2  \(\delta_{Ravg}^{AKP}\) (AlginaKeselmanPenfield robust standardized difference average)  ✅  WRS2::wmcpAKP 
Bayes Factor  > 2  \(R_{Bayesian}^2\)  ✅  performance::r2_bayes 
Parametric  2  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  2  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  2  \(\delta_{R}^{AKP}\) (AlginaKeselmanPenfield robust standardized difference)  ✅  WRS2::wmcpAKP 
Bayesian  2  \(\delta_{posterior}\)  ✅  bayestestR::describe_posterior 
Pairwise comparison tests
Type  Test  pvalue adjustment?  Function used 

Parametric  Student’s ttest  ✅  stats::pairwise.t.test 
Nonparametric  DurbinConover test  ✅  PMCMRplus::durbinAllPairsTest 
Robust  Yuen’s trimmed means test  ✅  WRS2::rmmcp 
Bayesian  Student’s ttest  ❌  BayesFactor::ttestBF 
If you wish to include statistical analysis results in a publication/report, the ideal reporting practice will be a hybrid of two approaches:
the ggstatsplot
approach, where the plot contains both the visual and numerical summaries about a statistical model, and
the standard narrative approach, which provides interpretive context for the reported statistics.
For example, let’s see the following example:
library(WRS2) # for data
ggwithinstats(WineTasting, Wine, Taste)
The narrative context (assuming type = "parametric"
) can complement this plot either as a figure caption or in the main text
Fisher’s repeated measures oneway ANOVA revealed that, across 22 friends to taste each of the three wines, there was a statistically significant difference across persons preference for each wine. The effect size \((\omega_{p} = 0.02)\) was medium, as per Field’s (2013) conventions. The Bayes Factor for the same analysis revealed that the data were 8.25 times more probable under the alternative hypothesis as compared to the null hypothesis. This can be considered moderate evidence (Jeffreys, 1961) in favor of the alternative hypothesis. This global effect was carried out by post hoc pairwise ttests, which revealed that Wine C was preferred across participants to be the least desirable compared to Wines A and B.
Similar reporting style can be followed when the function performs ttest instead of a oneway ANOVA.
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub
: https://github.com/IndrajeetPatil/ggstatsplot/issues