ggstatsplot
: ggplot2
Based Plots with Statistical DetailsPackage  Status  Usage  GitHub  Miscellaneous 

“What is to be sought in designs for the display of information is the clear portrayal of complexity. Not the complication of the simple; rather … the revelation of the complex.”
 Edward R. Tufte
ggstatsplot
is an
extension of ggplot2
package
for creating graphics with details from statistical tests included in
the informationrich plots themselves. In a typical exploratory data
analysis workflow, data visualization and statistical modeling are two
different phases: visualization informs modeling, and modeling in its
turn can suggest a different visualization method, and so on and so
forth. The central idea of ggstatsplot
is simple: combine these two
phases into one in the form of graphics with statistical details, which
makes data exploration simpler and faster.
To get the latest, stable CRAN
release:
install.packages("ggstatsplot")
Note:
Linux users may encounter some installation problems, as several R
packages require external libraries on the system, especially for
PMCMRplus
package. The following README
file briefly describes the
installation procedure:
https://CRAN.Rproject.org/package=PMCMRplus/readme/README.html
You can get the development version of the package from GitHub
.
If you are in hurry and want to reduce the time of installation, prefer
# needed package to download from GitHub repo
install.packages("remotes")
# downloading the package from GitHub (needs `remotes` package to be installed)
remotes::install_github(
repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = FALSE, # assumes you have already installed needed packages
quick = TRUE # skips docs, demos, and vignettes
)
If time is not a constraint
remotes::install_github(
repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = TRUE, # installs packages which ggstatsplot depends on
upgrade_dependencies = TRUE # updates any out of date dependencies
)
To see what new changes (and bug fixes) have been made to the package
since the last release on CRAN
, you can check the detailed log of
changes here:
https://indrajeetpatil.github.io/ggstatsplot/news/index.html
If you want to cite this package in a scientific journal or in any other
context, run the following code in your R
console:
citation("ggstatsplot")
Patil, I. (2018). Visualizations with statistical details: The
'ggstatsplot' approach. PsyArxiv. doi:10.31234/osf.io/p7mku
A BibTeX entry for LaTeX users is
@Article{,
title = {Visualizations with statistical details: The 'ggstatsplot' approach},
author = {Indrajeet Patil},
year = {2021},
journal = {PsyArxiv},
url = {https://psyarxiv.com/p7mku/},
doi = {10.31234/osf.io/p7mku},
}
There is currently a publication in preparation corresponding to this package and the citation will be updated once it’s published.
To see the detailed documentation for each function in the stable CRAN version of the package, see:
Presentation: https://indrajeetpatil.github.io/ggstatsplot_slides/slides/ggstatsplot_presentation.html#1
Vignettes: https://indrajeetpatil.github.io/ggstatsplot/articles/
To see the documentation relevant for the development version of the
package, see the dedicated website for ggstatplot
, which is updated
after every new commit: https://indrajeetpatil.github.io/ggstatsplot/.
It, therefore, produces a limited kinds of plots for the supported analyses:
In addition to these basic plots, ggstatsplot
also provides
grouped_
versions (see below) that makes it easy to repeat the
same analysis for any grouping variable.
The table below summarizes all the different types of analyses currently supported in this package
Functions  Description  Parametric  Nonparametric  Robust  Bayesian 

ggbetweenstats 
Between group/condition comparisons  ✅  ✅  ✅  ✅ 
ggwithinstats 
Within group/condition comparisons  ✅  ✅  ✅  ✅ 
gghistostats , ggdotplotstats

Distribution of a numeric variable  ✅  ✅  ✅  ✅ 
ggcorrmat 
Correlation matrix  ✅  ✅  ✅  ✅ 
ggscatterstats 
Correlation between two variables  ✅  ✅  ✅  ✅ 
ggpiestats , ggbarstats

Association between categorical variables  ✅  ✅  ❌  ✅ 
ggpiestats , ggbarstats

Equal proportions for categorical variable levels  ✅  ✅  ❌  ✅ 
ggcoefstats 
Regression model coefficients  ✅  ✅  ✅  ✅ 
ggcoefstats 
Randomeffects metaanalysis  ✅  ❌  ✅  ✅ 
Summary of Bayesian analysis
Analysis  Hypothesis testing  Estimation 

(one/twosample) ttest  ✅  ✅ 
oneway ANOVA  ✅  ✅ 
correlation  ✅  ✅ 
(one/twoway) contingency table  ✅  ✅ 
randomeffects metaanalysis  ✅  ✅ 
For all statistical tests reported in the plots, the default template abides by the APA gold standard for statistical reporting. For example, here are results from Yuen’s test for trimmed means (robust ttest):
Here is a summary table of all the statistical tests currently supported across various functions: https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html
Here are examples of the main functions currently supported in
ggstatsplot
.
Note: If you are reading this on GitHub
repository, the
documentation below is for the development version of the package.
So you may see some features available here that are not currently
present in the stable version of this package on CRAN. For
documentation relevant for the CRAN
version, see:
https://CRAN.Rproject.org/package=ggstatsplot/readme/README.html
ggbetweenstats
This function creates either a violin plot, a box plot, or a mix of two for betweengroup or betweencondition comparisons with results from statistical tests in the subtitle. The simplest function call looks like this
# for reproducibility
set.seed(123)
library(ggstatsplot)
# plot
ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
title = "Distribution of sepal length across Iris species"
)
📝 Defaults return
✅ raw data + distributions
✅ descriptive statistics
✅
inferential statistics
✅ effect size + CIs
✅ pairwise
comparisons
✅ Bayesian hypothesistesting
✅ Bayesian
estimation
A number of other arguments can be specified to make this plot even more
informative or change some of the default options. Additionally, there
is also a grouped_
variant of this function that makes it easy to
repeat the same operation across a single grouping variable:
# for reproducibility
set.seed(123)
# plot
grouped_ggbetweenstats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = title, # variable to be used for tagging outliers
outlier.coef = 2,
ggsignif.args = list(textsize = 4, tip_length = 0.01),
p.adjust.method = "bonferroni", # method for adjusting pvalues for multiple comparisons
# adding new components to `ggstatsplot` default
ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
caption = substitute(paste(italic("Source"), ": IMDb (Internet Movie Database)")),
palette = "default_jama",
package = "ggsci",
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Differences in movie length by mpaa ratings for different genres")
)
Note here that the function can be used to tag outliers!
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  Fisher’s or Welch’s oneway ANOVA  stats::oneway.test 
Nonparametric  > 2  Kruskal–Wallis oneway ANOVA  stats::kruskal.test 
Robust  > 2  Heteroscedastic oneway ANOVA for trimmed means  WRS2::t1way 
Bayes Factor  > 2  Fisher’s ANOVA  BayesFactor::anovaBF 
Parametric  2  Student’s or Welch’s ttest  stats::t.test 
Nonparametric  2  Mann–Whitney U test  stats::wilcox.test 
Robust  2  Yuen’s test for trimmed means  WRS2::yuen 
Bayesian  2  Student’s ttest  BayesFactor::ttestBF 
Effect size estimation
Type  No. of groups  Effect size  CI?  Function used 

Parametric  > 2  η_{p}^{2}, ω_{p}^{2}  ✅ 
effectsize::omega_squared , effectsize::eta_squared

Nonparametric  > 2  ϵ_{ordinal}^{2}  ✅  effectsize::rank_epsilon_squared 
Robust  > 2  ξ (Explanatory measure of effect size)  ✅  WRS2::t1way 
Bayes Factor  > 2  R_{posterior}^{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  ξ (Explanatory measure of effect size)  ✅  WRS2::yuen.effect.ci 
Bayesian  2  δ_{posterior}  ✅  bayestestR::describe_posterior 
Pairwise comparison tests
Type  Equal variance?  Test  pvalue adjustment?  Function used 

Parametric  No  GamesHowell test  ✅  stats::pairwise.t.test 
Parametric  Yes  Student’s ttest  ✅  PMCMRplus::gamesHowellTest 
Nonparametric  No  Dunn test  ✅  PMCMRplus::kwAllPairsDunnTest 
Robust  No  Yuen’s trimmed means test  ✅  WRS2::lincon 
Bayes Factor  ❌  Student’s ttest  ❌  BayesFactor::ttestBF 
For more, see the ggbetweenstats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html
ggwithinstats
ggbetweenstats
function has an identical twin function ggwithinstats
for repeated measures designs that behaves in the same fashion with a
few minor tweaks introduced to properly visualize the repeated measures
design. As can be seen from an example below, the only difference
between the plot structure is that now the group means are connected by
paths to highlight the fact that these data are paired with each other.
# for reproducibility and data
set.seed(123)
library(WRS2) # for data
library(afex) # to run anova
# plot
ggwithinstats(
data = WineTasting,
x = Wine,
y = Taste,
title = "Wine tasting",
caption = "Data source: `WRS2` R package",
ggtheme = ggthemes::theme_fivethirtyeight(),
ggstatsplot.layer = FALSE
)
📝 Defaults return
✅ raw data + distributions
✅ descriptive statistics
✅
inferential statistics
✅ effect size + CIs
✅ pairwise
comparisons
✅ Bayesian hypothesistesting
✅ Bayesian
estimation
The central tendency measure displayed will depend on the statistics:
Type  Measure  Function used 

Parametric  mean  parameters::describe_distribution 
Nonparametric  median  parameters::describe_distribution 
Robust  trimmed mean  parameters::describe_distribution 
Bayesian  MAP estimate  parameters::describe_distribution 
As with the ggbetweenstats
, this function also has a grouped_
variant that makes repeating the same analysis across a single grouping
variable quicker. We will see an example with only repeated
measurements
# common setup
set.seed(123)
# plot
grouped_ggwithinstats(
data = dplyr::filter(
.data = bugs_long,
region %in% c("Europe", "North America"),
condition %in% c("LDLF", "LDHF")
),
x = condition,
y = desire,
type = "np", # nonparametric statistics
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region,
outlier.tagging = TRUE,
outlier.label = education
)
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  η_{p}^{2}, ω_{p}^{2}  ✅ 
effectsize::omega_squared , effectsize::eta_squared

Nonparametric  > 2  W_{Kendall} (Kendall’s coefficient of concordance)  ✅  effectsize::kendalls_w 
Robust  > 2  δ_{R − avg}^{AKP} (AlginaKeselmanPenfield robust standardized difference average)  ✅  WRS2::wmcpAKP 
Bayes Factor  > 2  R_{Bayesia**n}^{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  δ_{R}^{AKP} (AlginaKeselmanPenfield robust standardized difference)  ✅  WRS2::wmcpAKP 
Bayesian  2  δ_{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 
For more, see the ggwithinstats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html
gghistostats
To visualize the distribution of a single variable and check if its mean
is significantly different from a specified value with a onesample
test, gghistostats
can be used.
# for reproducibility
set.seed(123)
# plot
gghistostats(
data = ggplot2::msleep, # dataframe from which variable is to be taken
x = awake, # numeric variable whose distribution is of interest
title = "Amount of time spent awake", # title for the plot
caption = substitute(paste(italic("Source: "), "Mammalian sleep data set")),
test.value = 12, # default value is 0
binwidth = 1, # binwidth value (experiment)
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
📝 Defaults return
✅ counts + proportion for bins
✅ descriptive statistics
✅
inferential statistics
✅ effect size + CIs
✅ Bayesian
hypothesistesting
✅ Bayesian estimation
There is also a grouped_
variant of this function that makes it easy
to repeat the same operation across a single grouping variable:
# for reproducibility
set.seed(123)
# plot
grouped_gghistostats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = budget,
test.value = 50,
type = "nonparametric",
xlab = "Movies budget (in million US$)",
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.args = list(color = "red", size = 1),
ggtheme = ggthemes::theme_tufte(),
# modify the defaults from `ggstatsplot` for each plot
ggplot.component = ggplot2::labs(caption = "Source: IMDB.com"),
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Movies budgets for different genres")
)
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  Test  Function used 

Parametric  Onesample Student’s ttest  stats::t.test 
Nonparametric  Onesample Wilcoxon test  stats::wilcox.test 
Robust  Bootstrapt method for onesample test 
trimcibt (custom) 
Bayesian  Onesample Student’s ttest  BayesFactor::ttestBF 
Effect size estimation
Type  Effect size  CI?  Function used 

Parametric  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  trimmed mean  ✅ 
trimcibt (custom) 
Bayes Factor  δ_{posterior}  ✅  bayestestR::describe_posterior 
For more, including information about the variant of this function
grouped_gghistostats
, see the gghistostats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/gghistostats.html
ggdotplotstats
This function is similar to gghistostats
, but is intended to be used
when the numeric variable also has a label.
# for reproducibility
set.seed(123)
# plot
ggdotplotstats(
data = dplyr::filter(.data = gapminder::gapminder, continent == "Asia"),
y = country,
x = lifeExp,
test.value = 55,
type = "robust",
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy",
caption = substitute(
paste(
italic("Source"),
": Gapminder dataset from https://www.gapminder.org/"
)
)
)
📝 Defaults return
✅ descriptives (mean + sample size)
✅ inferential statistics
✅
effect size + CIs
✅ Bayesian hypothesistesting
✅ Bayesian
estimation
As with the rest of the functions in this package, there is also a
grouped_
variant of this function to facilitate looping the same
operation for all levels of a single grouping variable.
# for reproducibility
set.seed(123)
# plot
grouped_ggdotplotstats(
data = dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
x = cty,
y = manufacturer,
type = "bayes", # Bayesian test
xlab = "city miles per gallon",
ylab = "car manufacturer",
grouping.var = cyl, # grouping variable
test.value = 15.5,
point.args = list(color = "red", size = 5, shape = 13),
annotation.args = list(title = "Fuel economy data")
)
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  Test  Function used 

Parametric  Onesample Student’s ttest  stats::t.test 
Nonparametric  Onesample Wilcoxon test  stats::wilcox.test 
Robust  Bootstrapt method for onesample test 
trimcibt (custom) 
Bayesian  Onesample Student’s ttest  BayesFactor::ttestBF 
Effect size estimation
Type  Effect size  CI?  Function used 

Parametric  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  trimmed mean  ✅ 
trimcibt (custom) 
Bayes Factor  δ_{posterior}  ✅  bayestestR::describe_posterior 
ggscatterstats
This function creates a scatterplot with marginal distributions overlaid
on the axes (from ggExtra::ggMarginal
) and results from statistical
tests in the subtitle:
ggscatterstats(
data = ggplot2::msleep,
x = sleep_rem,
y = awake,
xlab = "REM sleep (in hours)",
ylab = "Amount of time spent awake (in hours)",
title = "Understanding mammalian sleep"
)
📝 Defaults return
✅ raw data + distributions
✅ marginal distributions
✅
inferential statistics
✅ effect size + CIs
✅ Bayesian
hypothesistesting
✅ Bayesian estimation
The available marginal distributions are
Number of other arguments can be specified to modify this basic plot
# for reproducibility
set.seed(123)
# plot
ggscatterstats(
data = dplyr::filter(movies_long, genre == "Action"),
x = budget,
y = rating,
type = "robust", # type of test that needs to be run
xlab = "Movie budget (in million/ US$)", # label for x axis
ylab = "IMDB rating", # label for y axis
label.var = title, # variable for labeling data points
label.expression = rating < 5 & budget > 100, # expression that decides which points to label
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression(paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")),
ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
marginal.type = "boxplot", # type of marginal distribution to be displayed
xfill = "pink", # color fill for xaxis marginal distribution
yfill = "#009E73" # color fill for yaxis marginal distribution
)
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable. Also, note that, as opposed to the other functions, this
function does not return a ggplot
object and any modification you want
to make can be made in advance using ggplot.component
argument
(available for all functions, but especially useful here):
# for reproducibility
set.seed(123)
# plot
grouped_ggscatterstats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = rating,
y = length,
grouping.var = genre, # grouping variable
label.var = title,
label.expression = length > 200,
xlab = "IMDB rating",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Relationship between movie length and IMDB ratings")
)
Hypothesis testing and Effect size estimation
Type  Test  CI?  Function used 

Parametric  Pearson’s correlation coefficient  ✅  correlation::correlation 
Nonparametric  Spearman’s rank correlation coefficient  ✅  correlation::correlation 
Robust  Winsorized Pearson correlation coefficient  ✅  correlation::correlation 
Bayesian  Pearson’s correlation coefficient  ✅  correlation::correlation 
For more, see the ggscatterstats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html
ggcorrmat
ggcorrmat
makes a correlalogram (a matrix of correlation coefficients)
with minimal amount of code. Just sticking to the defaults itself
produces publicationready correlation matrices. But, for the sake of
exploring the available options, let’s change some of the defaults. For
example, multiple aestheticsrelated arguments can be modified to change
the appearance of the correlation matrix.
# for reproducibility
set.seed(123)
# as a default this function outputs a correlation matrix plot
ggcorrmat(
data = ggplot2::msleep,
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms"
)
📝 Defaults return
✅ effect size + significance
✅ careful handling of NA
s
If there are NA
s present in the selected variables, the legend will
display minimum, median, and maximum number of pairs used for
correlation tests.
There is also a grouped_
variant of this function that makes it easy
to repeat the same operation across a single grouping variable:
# for reproducibility
set.seed(123)
# plot
grouped_ggcorrmat(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
type = "robust", # correlation method
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre, # grouping variable
matrix.type = "lower" # type of matrix
)
You can also get a dataframe containing all relevant details from the statistical tests:
# setup
set.seed(123)
# dataframe in long format
ggcorrmat(
data = ggplot2::msleep,
type = "bayes",
output = "dataframe"
)
#> # A tibble: 15 x 14
#> parameter1 parameter2 estimate conf.level conf.low conf.high pd
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total sleep_rem 0.731 0.95 0.617 0.810 1
#> 2 sleep_total sleep_cycle 0.432 0.95 0.678 0.223 0.995
#> 3 sleep_total awake 1.00 0.95 1.00 1.00 1
#> 4 sleep_total brainwt 0.339 0.95 0.523 0.156 0.996
#> 5 sleep_total bodywt 0.300 0.95 0.458 0.142 0.997
#> 6 sleep_rem sleep_cycle 0.306 0.95 0.535 0.0555 0.965
#> 7 sleep_rem awake 0.734 0.95 0.824 0.638 1
#> 8 sleep_rem brainwt 0.202 0.95 0.410 0.0130 0.927
#> 9 sleep_rem bodywt 0.315 0.95 0.481 0.120 0.994
#> 10 sleep_cycle awake 0.441 0.95 0.226 0.662 0.995
#> 11 sleep_cycle brainwt 0.823 0.95 0.720 0.911 1
#> 12 sleep_cycle bodywt 0.386 0.95 0.145 0.610 0.992
#> 13 awake brainwt 0.341 0.95 0.154 0.524 0.992
#> 14 awake bodywt 0.299 0.95 0.139 0.454 0.998
#> 15 brainwt bodywt 0.926 0.95 0.896 0.957 1
#> rope.percentage prior.distribution prior.location prior.scale bayes.factor
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 0 beta 1.41 1.41 3.00e+ 9
#> 2 0.0173 beta 1.41 1.41 8.85e+ 0
#> 3 0 beta 1.41 1.41 NA
#> 4 0.028 beta 1.41 1.41 7.29e+ 0
#> 5 0.0292 beta 1.41 1.41 9.28e+ 0
#> 6 0.091 beta 1.41 1.41 1.42e+ 0
#> 7 0 beta 1.41 1.41 3.01e+ 9
#> 8 0.212 beta 1.41 1.41 6.54e 1
#> 9 0.0362 beta 1.41 1.41 4.80e+ 0
#> 10 0.0158 beta 1.41 1.41 8.85e+ 0
#> 11 0 beta 1.41 1.41 3.80e+ 6
#> 12 0.0392 beta 1.41 1.41 3.76e+ 0
#> 13 0.0253 beta 1.41 1.41 7.29e+ 0
#> 14 0.0265 beta 1.41 1.41 9.27e+ 0
#> 15 0 beta 1.41 1.41 1.58e+22
#> method n.obs
#> <chr> <int>
#> 1 Bayesian Pearson correlation 61
#> 2 Bayesian Pearson correlation 32
#> 3 Bayesian Pearson correlation 83
#> 4 Bayesian Pearson correlation 56
#> 5 Bayesian Pearson correlation 83
#> 6 Bayesian Pearson correlation 32
#> 7 Bayesian Pearson correlation 61
#> 8 Bayesian Pearson correlation 48
#> 9 Bayesian Pearson correlation 61
#> 10 Bayesian Pearson correlation 32
#> 11 Bayesian Pearson correlation 30
#> 12 Bayesian Pearson correlation 32
#> 13 Bayesian Pearson correlation 56
#> 14 Bayesian Pearson correlation 83
#> 15 Bayesian Pearson correlation 56
Additionally, partial correlation are also supported:
# setup
set.seed(123)
# dataframe in long format
ggcorrmat(
data = ggplot2::msleep,
type = "bayes",
partial = TRUE,
output = "dataframe"
)
#> # A tibble: 15 x 14
#> parameter1 parameter2 estimate conf.level conf.low conf.high pd
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total sleep_rem 0.279 0.95 0.0202 0.550 0.940
#> 2 sleep_total sleep_cycle 0.0181 0.95 0.306 0.254 0.543
#> 3 sleep_total awake 1 0.95 1 1 1
#> 4 sleep_total brainwt 0.0818 0.95 0.352 0.192 0.678
#> 5 sleep_total bodywt 0.163 0.95 0.425 0.121 0.818
#> 6 sleep_rem sleep_cycle 0.0666 0.95 0.335 0.222 0.643
#> 7 sleep_rem awake 0.0505 0.95 0.212 0.328 0.611
#> 8 sleep_rem brainwt 0.0811 0.95 0.235 0.326 0.668
#> 9 sleep_rem bodywt 0.0190 0.95 0.296 0.265 0.544
#> 10 sleep_cycle awake 0.00603 0.95 0.278 0.279 0.516
#> 11 sleep_cycle brainwt 0.764 0.95 0.637 0.871 1
#> 12 sleep_cycle bodywt 0.0865 0.95 0.351 0.187 0.691
#> 13 awake brainwt 0.0854 0.95 0.349 0.205 0.690
#> 14 awake bodywt 0.407 0.95 0.630 0.146 0.991
#> 15 brainwt bodywt 0.229 0.95 0.0341 0.484 0.904
#> rope.percentage prior.distribution prior.location prior.scale bayes.factor
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 0.133 beta 1.41 1.41 1.04
#> 2 0.418 beta 1.41 1.41 0.277
#> 3 0 beta 1.41 1.41 NA
#> 4 0.390 beta 1.41 1.41 0.311
#> 5 0.294 beta 1.41 1.41 0.417
#> 6 0.404 beta 1.41 1.41 0.297
#> 7 0.411 beta 1.41 1.41 0.287
#> 8 0.380 beta 1.41 1.41 0.303
#> 9 0.424 beta 1.41 1.41 0.280
#> 10 0.422 beta 1.41 1.41 0.276
#> 11 0 beta 1.41 1.41 131029.
#> 12 0.393 beta 1.41 1.41 0.309
#> 13 0.390 beta 1.41 1.41 0.310
#> 14 0.033 beta 1.41 1.41 4.82
#> 15 0.206 beta 1.41 1.41 0.637
#> method n.obs
#> <chr> <int>
#> 1 Bayesian Pearson correlation 30
#> 2 Bayesian Pearson correlation 30
#> 3 Bayesian Pearson correlation 30
#> 4 Bayesian Pearson correlation 30
#> 5 Bayesian Pearson correlation 30
#> 6 Bayesian Pearson correlation 30
#> 7 Bayesian Pearson correlation 30
#> 8 Bayesian Pearson correlation 30
#> 9 Bayesian Pearson correlation 30
#> 10 Bayesian Pearson correlation 30
#> 11 Bayesian Pearson correlation 30
#> 12 Bayesian Pearson correlation 30
#> 13 Bayesian Pearson correlation 30
#> 14 Bayesian Pearson correlation 30
#> 15 Bayesian Pearson correlation 30
Hypothesis testing and Effect size estimation
Type  Test  CI?  Function used 

Parametric  Pearson’s correlation coefficient  ✅  correlation::correlation 
Nonparametric  Spearman’s rank correlation coefficient  ✅  correlation::correlation 
Robust  Winsorized Pearson correlation coefficient  ✅  correlation::correlation 
Bayesian  Pearson’s correlation coefficient  ✅  correlation::correlation 
For examples and more information, see the ggcorrmat
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html
ggpiestats
This function creates a pie chart for categorical or nominal variables with results from contingency table analysis (Pearson’s chisquared test for betweensubjects design and McNemar’s chisquared test for withinsubjects design) included in the subtitle of the plot. If only one categorical variable is entered, results from onesample proportion test (i.e., a chisquared goodness of fit test) will be displayed as a subtitle.
To study an interaction between two categorical variables:
# for reproducibility
set.seed(123)
# plot
ggpiestats(
data = mtcars,
x = am,
y = cyl,
package = "wesanderson",
palette = "Royal1",
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
legend.title = "Transmission", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)
📝 Defaults return
✅ descriptives (frequency + %s)
✅ inferential statistics
✅
effect size + CIs
✅ Goodnessoffit tests
✅ Bayesian
hypothesistesting
✅ Bayesian estimation
There is also a grouped_
variant of this function that makes it easy
to repeat the same operation across a single grouping variable.
Following example is a case where the theoretical question is about
proportions for different levels of a single nominal variable:
# for reproducibility
set.seed(123)
# plot
grouped_ggpiestats(
data = mtcars,
x = cyl,
grouping.var = am, # grouping variable
label.repel = TRUE, # repel labels (helpful for overlapping labels)
package = "ggsci", # package from which color palette is to be taken
palette = "default_ucscgb" # choosing a different color palette
)
twoway table
Hypothesis testing
Type  Design  Test  Function used 

Parametric/Nonparametric  Unpaired  Pearson’s χ^{2} test  stats::chisq.test 
Bayesian  Unpaired  Bayesian Pearson’s χ^{2} test  BayesFactor::contingencyTableBF 
Parametric/Nonparametric  Paired  McNemar’s χ^{2} test  stats::mcnemar.test 
Bayesian  Paired  ❌  ❌ 
Effect size estimation
Type  Design  Effect size  CI?  Function used 

Parametric/Nonparametric  Unpaired  Cramer’s V  ✅  effectsize::cramers_v 
Bayesian  Unpaired  Cramer’s V  ✅  effectsize::cramers_v 
Parametric/Nonparametric  Paired  Cohen’s g  ✅  effectsize::cohens_g 
Bayesian  Paired  ❌  ❌  ❌ 
oneway table
Hypothesis testing
Type  Test  Function used 

Parametric/Nonparametric  Goodness of fit χ^{2} test  stats::chisq.test 
Bayesian  Bayesian Goodness of fit χ^{2} test  (custom) 
Effect size estimation
Type  Effect size  CI?  Function used 

Parametric/Nonparametric  Cramer’s V  ✅  bayestestR::describe_posterior 
Bayesian  ❌  ❌  ❌ 
For more, see the ggpiestats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html
ggbarstats
In case you are not a fan of pie charts (for very good reasons), you can
alternatively use ggbarstats
function which has a similar syntax.
N.B. The pvalues from onesample proportion test are displayed on top of each bar.
# for reproducibility
set.seed(123)
library(ggplot2)
# plot
ggbarstats(
data = movies_long,
x = mpaa,
y = genre,
title = "MPAA Ratings by Genre",
xlab = "movie genre",
legend.title = "MPAA rating",
ggtheme = hrbrthemes::theme_ipsum_pub(),
ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
palette = "Set2"
)
📝 Defaults return
✅ descriptives (frequency + %s)
✅ inferential statistics
✅
effect size + CIs
✅ Goodnessoffit tests
✅ Bayesian
hypothesistesting
✅ Bayesian estimation
And, needless to say, there is also a grouped_
variant of this
function
# setup
set.seed(123)
# plot
grouped_ggbarstats(
data = mtcars,
x = am,
y = cyl,
grouping.var = vs,
package = "wesanderson",
palette = "Darjeeling2",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE
)
twoway table
Hypothesis testing
Type  Design  Test  Function used 

Parametric/Nonparametric  Unpaired  Pearson’s χ^{2} test  stats::chisq.test 
Bayesian  Unpaired  Bayesian Pearson’s χ^{2} test  BayesFactor::contingencyTableBF 
Parametric/Nonparametric  Paired  McNemar’s χ^{2} test  stats::mcnemar.test 
Bayesian  Paired  ❌  ❌ 
Effect size estimation
Type  Design  Effect size  CI?  Function used 

Parametric/Nonparametric  Unpaired  Cramer’s V  ✅  effectsize::cramers_v 
Bayesian  Unpaired  Cramer’s V  ✅  effectsize::cramers_v 
Parametric/Nonparametric  Paired  Cohen’s g  ✅  effectsize::cohens_g 
Bayesian  Paired  ❌  ❌  ❌ 
oneway table
Hypothesis testing
Type  Test  Function used 

Parametric/Nonparametric  Goodness of fit χ^{2} test  stats::chisq.test 
Bayesian  Bayesian Goodness of fit χ^{2} test  (custom) 
Effect size estimation
Type  Effect size  CI?  Function used 

Parametric/Nonparametric  Cramer’s V  ✅  bayestestR::describe_posterior 
Bayesian  ❌  ❌  ❌ 
ggcoefstats
The function ggcoefstats
generates dotandwhisker plots for
regression models saved in a tidy data frame. The tidy dataframes are
prepared using parameters::model_parameters
. Additionally, if
available, the model summary indices are also extracted from
performance::model_performance
.
Although the statistical models displayed in the plot may differ based on the class of models being investigated, there are few aspects of the plot that will be invariant across models:
The dotwhisker plot contains a dot representing the estimate
and their confidence intervals (95%
is the default). The
estimate can either be effect sizes (for tests that depend on the
F
statistic) or regression coefficients (for tests with t
,
χ^{2}, and z
statistic), etc. The function will, by
default, display a helpful x
axis label that should clear up what
estimates are being displayed. The confidence intervals can
sometimes be asymmetric if bootstrapping was used.
The label attached to dot will provide more details from the statistical test carried out and it will typically contain estimate, statistic, and pvalue.
The caption will contain diagnostic information, if available, about models that can be useful for model selection: The smaller the Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values, the “better” the model is.
The output of this function will be a ggplot2
object and, thus, it
can be further modified (e.g., change themes, etc.) with ggplot2
functions.
# for reproducibility
set.seed(123)
# model
mod < stats::lm(formula = mpg ~ am * cyl, data = mtcars)
# plot
ggcoefstats(mod)
📝 Defaults return
✅ inferential statistics
✅ estimate + CIs
✅ model summary (AIC
and BIC)
This default plot can be further modified to one’s liking with additional arguments (also, let’s use a different model now):
# for reproducibility
set.seed(123)
# model
mod < MASS::rlm(formula = mpg ~ am * cyl, data = mtcars)
# plot
ggcoefstats(
x = mod,
point.args = list(color = "red", size = 3, shape = 15),
vline.args = list(size = 1, color = "#CC79A7", linetype = "dotdash"),
title = "Car performance predicted by transmission & cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine",
exclude.intercept = TRUE,
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
) + # note the order in which the labels are entered
ggplot2::scale_y_discrete(labels = c("transmission", "cylinders", "interaction")) +
ggplot2::labs(x = "regression coefficient", y = NULL)
Most of the regression models that are supported in the underlying
packages are also supported by ggcoefstats
. For example
aareg
, afex_aov
, anova
, anova.mlm
, anova
, aov
, aovlist
,
Arima
, bam
, bayesx
, bayesGARCH
, bayesQR
, BBmm
, BBreg
,
bcplm
, betamfx
, betaor
, BFBayesFactor
, BGGM
, bglmerMod
,
bife
, bigglm
, biglm
, blavaan
, bmlm
, blmerMod
, blrm
,
bracl
, brglm
, brglm2
, brmsfit
, brmultinom
, btergm
, cch
,
censReg
, cgam
, cgamm
, cglm
, clm
, clm2
, clmm
, clmm2
,
coeftest
, complmrob
, confusionMatrix
, coxme
, coxph
, coxr
,
coxph.penal
, cpglm
, cpglmm
, crch
, crq
, crr
, DirichReg
,
drc
, eglm
, elm
, emmGrid
, epi.2by2
, ergm
, feis
, felm
,
fitdistr
, fixest
, flexsurvreg
, gam
, Gam
, gamlss
, garch
,
geeglm
, gjrm
, glmc
, glmerMod
, glmmTMB
, gls
, glht
, glm
,
glmm
, glmmadmb
, glmmPQL
, glmRob
, glmrob
, glmx
, gmm
,
HLfit
, hurdle
, ivFixed
, ivprobit
, ivreg
, iv_robust
,
lavaan
, lm
, lm.beta
, lmerMod
, lmerModLmerTest
, lmodel2
,
lmRob
, lmrob
, lm_robust
, logitmfx
, logitor
, logitsf
,
LORgee
, lqm
, lqmm
, lrm
, manova
, maov
, margins
, mcmc
,
mcmc.list
, MCMCglmm
, mclogit
, mice
, mmclogit
, mediate
,
metafor
, merMod
, merModList
, metaplus
, mhurdle
, mixor
,
mjoint
, mle2
, mlm
, multinom
, mvord
, negbin
, negbinmfx
,
negbinirr
, nlmerMod
, nlrq
, nlreg
, nls
, orcutt
, orm
, plm
,
poissonmfx
, poissonirr
, polr
, probitmfx
, ridgelm
,
riskRegression
, rjags
, rlm
, rlmerMod
, robmixglm
, rq
, rqs
,
rqss
, rrvglm
, scam
, selection
, semLm
, semLme
, slm
,
speedglm
, speedlm
, stanfit
, stanreg
, summary.lm
, survreg
,
svyglm
, svy_vglm
, svyolr
, tobit
, truncreg
, varest
, vgam
,
vglm
, wbgee
, wblm
, zeroinfl
, etc.
Although not shown here, this function can also be used to carry out parametric, robust, and Bayesian randomeffects metaanalysis.
Hypothesis testing and Effect size estimation
Type  Test  Effect size  CI?  Function used 

Parametric  Metaanalysis via randomeffects models  β  ✅  metafor::metafor 
Robust  Metaanalysis via robust randomeffects models  β  ✅  metaplus::metaplus 
Bayes  Metaanalysis via Bayesian randomeffects models  β  ✅  metaBMA::meta_random 
For a more exhaustive account of this function, see the associated vignette https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html
combine_plots
The full power of ggstatsplot
can be leveraged with a functional
programming package like purrr
that
replaces for
loops with code that is both more succinct and easier to
read and, therefore, purrr
should be preferrred 😻.
In such cases, ggstatsplot
contains a helper function combine_plots
to combine multiple plots, which can be useful for combining a list of
plots produced with purrr
. This is a wrapper around
patchwork::wrap_plots
and lets you combine multiple plots and add a
combination of title, caption, and annotation texts with suitable
defaults.
For examples (both with plyr
and purrr
), see the associated
vignette
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/combine_plots.html
ggstatsplot
statistical details with custom plotsSometimes you may not like the default plots produced by ggstatsplot
.
In such cases, you can use other custom plots (from ggplot2
or
other plotting packages) and still use ggstatsplot
functions to
display results from relevant statistical test.
For example, in the following chunk, we will create plot (ridgeplot)
using ggridges
package and use ggstatsplot
function for extracting
results.
# loading the needed libraries
set.seed(123)
library(ggridges)
library(ggplot2)
library(ggstatsplot)
# using `ggstatsplot` to get call with statistical results
stats_results <
ggbetweenstats(
data = morley,
x = Expt,
y = Speed,
output = "subtitle"
)
# using `ggridges` to create plot
ggplot(morley, aes(x = Speed, y = as.factor(Expt), fill = as.factor(Expt))) +
geom_density_ridges(
jittered_points = TRUE,
quantile_lines = TRUE,
scale = 0.9,
alpha = 0.7,
vline_size = 1,
vline_color = "red",
point_size = 0.4,
point_alpha = 1,
position = position_raincloud(adjust_vlines = TRUE)
) + # adding annotations
labs(
title = "MichelsonMorley experiments",
subtitle = stats_results,
x = "Speed of light",
y = "Experiment number"
) + # remove the legend
theme(legend.position = "none")
No need to use scores of packages for statistical analysis (e.g., one to get stats, one to get effect sizes, another to get Bayes Factors, and yet another to get pairwise comparisons, etc.).
Minimal amount of code needed for all functions (typically only
data
, x
, and y
), which minimizes chances of error and makes
for tidy scripts.
Conveniently toggle between statistical approaches.
Truly makes your figures worth a thousand words.
No need to copypaste results to the text editor (MSWord, e.g.).
Disembodied figures stand on their own and are easy to evaluate for the reader.
More breathing room for theoretical discussion and other text.
No need to worry about updating figures and statistical details separately.
All functions produce publicationready plots that require very few arguments if one finds the aesthetic and statistical defaults satisfying make the syntax much less cognitively demanding and easy to remember.
This package is…
❌ an alternative to learning ggplot2
✅ (The better you know
ggplot2
, the more you can modify the defaults to your liking.)
❌ meant to be used in talks/presentations
✅ (Default plots can be
too complicated for effectively communicating results in
timeconstrained presentation settings, e.g. conference talks.)
❌ the only game in town
✅ (GUI software alternatives:
JASP and jamovi).
ggstatsverse
: Components of ggstatsplot
To make the maintenance and development of ggstatsplot
more
manageable, it is being broken into smaller pieces. Currently, the
package internally relies on the following packages that manage
different aspects of statistical analyses:
statsExpressions
The statsExpressions
package forms the statistical backend that
processes data and creates expressions containing results from
statistical tests.
For more exhaustive documentation for this package, see: https://indrajeetpatil.github.io/statsExpressions/
pairwiseComparisons
The pairwiseComparisons
package forms the pairwise comparison backend
for creating results that are used to display post hoc multiple
comparisons displayed in ggbetweenstats
and ggwithinstats
functions.
For more exhaustive documentation for this package, see: https://indrajeetpatil.github.io/pairwiseComparisons/
ipmisc
The ipmisc
package contains the data wrangling/cleaning functions and
a few other miscellaneous functions.
For more exhaustive documentation for this package, see: https://indrajeetpatil.github.io/ipmisc/
In case you use the GUI software jamovi
,
you can install a module called
jjstatsplot
, which is a
wrapper around ggstatsplot
.
I would like to thank all the contributors to ggstatsplot
who pointed
out bugs or requested features I hadn’t considered. I would especially
like to thank other package developers (especially Daniel Lüdecke,
Dominique Makowski, Mattan S. BenShachar, Patrick Mair, Salvatore
Mangiafico, etc.) who have patiently and diligently answered my
relentless number of questions and added feature requests I wanted. I
also want to thank Chuck Powell for his initial contributions to the
package.
The hexsticker was generously designed by Sarah Otterstetter (Max Planck
Institute for Human Development, Berlin). This package has also
benefited from the larger rstats
community on Twitter and
StackOverflow
.
Thanks are also due to my postdoc advisers (Mina Cikara and Fiery Cushman at Harvard University; Iyad Rahwan at Max Planck Institute for Human Development) who patiently supported me spending hundreds of hours working on this package rather than what I was paid to do. 😄
As the code stands right now, here is the code coverage for all primary functions involved: https://codecov.io/gh/IndrajeetPatil/ggstatsplot/tree/master/R
I’m happy to receive bug reports, suggestions, questions, and (most of
all) contributions to fix problems and add features. I personally prefer
using the GitHub
issues system over trying to reach out to me in other
ways (personal email, Twitter, etc.). Pull Requests for contributions
are encouraged.
Here are some simple ways in which you can contribute (in the increasing order of commitment):
Read and correct any inconsistencies in the documentation
Raise issues about bugs or wanted features
Review code
Add new functionality (in the form of new plotting functions or helpers for preparing subtitles)
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
For reproducibility purposes, the details about the session information in which this document was rendered, see https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/session_info.html