Enhancing ggplot2 plots with statistical analysis
ggstatsplot
ggstatsplot is an extension of ggplot2 package for creating graphics with details from statistical tests included in the plots themselves and targeted primarily at behavioral sciences community to provide a oneline code to produce informationrich plots. 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.
Summary of available plots
It, therefore, produces a limited kinds of plots for the supported
analyses:
Function  Plot  Description 

ggbetweenstats 
violin plots  for comparisons between groups/conditions 
ggwithinstats 
violin plots  for comparisons within groups/conditions 
gghistostats 
histograms  for distribution about numeric variable 
ggdotplotstats 
dot plots/charts  for distribution about labeled numeric variable 
ggpiestats 
pie charts  for categorical data 
ggbarstats 
bar charts  for categorical data 
ggscatterstats 
scatterplots  for correlations between two variables 
ggcorrmat 
correlation matrices  for correlations between multiple variables 
ggcoefstats 
dotandwhisker plots  for regression models 
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.
Summary of types of statistical analyses
Currently, it supports only the most common types of statistical tests:
parametric, nonparametric, robust, and bayesian versions
of ttest/anova, correlation analyses, contingency table
analysis, and regression analyses.
The table below summarizes all the different types of analyses currently
supported in this package
Functions  Description  Parametric  Nonparametric  Robust  Bayes Factor 

ggbetweenstats 
Between group/condition comparisons  Yes  Yes  Yes  Yes 
ggwithinstats 
Within group/condition comparisons  Yes  Yes  Yes  Yes 
gghistostats , ggdotplotstats 
Distribution of a numeric variable  Yes  Yes  Yes  Yes 
ggcorrmat 
Correlation matrix  Yes  Yes  Yes  No 
ggscatterstats 
Correlation between two variables  Yes  Yes  Yes  Yes 
ggpiestats , ggbarstats 
Association between categorical variables  Yes  NA 
NA 
Yes 
ggpiestats , ggbarstats 
Equal proportions for categorical variable levels  Yes  NA 
NA 
Yes 
ggcoefstats 
Regression model coefficients  Yes  No  Yes  No 
Statistical reporting
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):
Summary of statistical tests and effect sizes
Here is a summary table of all the statistical tests currently supported
across various functions:
Functions  Type  Test  Effect size  95% CI available? 

ggbetweenstats (2 groups) 
Parametric  Student’s and Welch’s ttest  Cohen’s d, Hedge’s g  
ggbetweenstats (> 2 groups) 
Parametric  Fisher’s and Welch’s oneway ANOVA  
ggbetweenstats (2 groups) 
Nonparametric  MannWhitney Utest  r  
ggbetweenstats (> 2 groups) 
Nonparametric  KruskalWallis Rank Sum Test  
ggbetweenstats (2 groups) 
Robust  Yuen’s test for trimmed means  
ggbetweenstats (> 2 groups) 
Robust  Heteroscedastic oneway ANOVA for trimmed means  
ggwithinstats (2 groups) 
Parametric  Student’s ttest  Cohen’s d, Hedge’s g  
ggwithinstats (> 2 groups) 
Parametric  Fisher’s oneway repeated measures ANOVA  
ggwithinstats (2 groups) 
Nonparametric  Wilcoxon signedrank test  r  
ggwithinstats (> 2 groups) 
Nonparametric  Friedman rank sum test  
ggwithinstats (2 groups) 
Robust  Yuen’s test on trimmed means for dependent samples  
ggwithinstats (> 2 groups) 
Robust  Heteroscedastic oneway repeated measures ANOVA for trimmed means  
ggpiestats and ggbarstats (unpaired) 
Parametric  Cramér’s V  
ggpiestats and ggbarstats (paired) 
Parametric  McNemar’s test  Cohen’s g  
ggpiestats 
Parametric  Onesample proportion test  Cramér’s V  
ggscatterstats and ggcorrmat 
Parametric  Pearson’s r  r  
ggscatterstats and ggcorrmat 
Nonparametric  
ggscatterstats and ggcorrmat 
Robust  Percentage bend correlation  r  
gghistostats and ggdotplotstats 
Parametric  Onesample ttest  Cohen’s d, Hedge’s g  
gghistostats 
Nonparametric  Onesample Wilcoxon signed rank test  r  
gghistostats and ggdotplotstats 
Robust  Onesample percentile bootstrap  robust estimator  
ggcoefstats 
Parametric  Regression models 
Installation
To get the latest, stable CRAN
release (0.1.0
):
utils::install.packages(pkgs = "ggstatsplot")
Note: If you are on a linux machine, you will need to have OpenGL
libraries installed (specifically, libx11
, mesa
and Mesa OpenGL
Utility library  glu
) for the dependency package rgl
to work.
You can get the development version of the package from GitHub
(0.1.0.9000
). 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 are in hurry and want to reduce the time of installation, prefer
# needed package to download from GitHub repo
utils::install.packages(pkgs = "remotes")
# downloading the package from GitHub
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
)
If you are not using the RStudio IDE and you
get an error related to “pandoc” you will either need to remove the
argument build_vignettes = TRUE
(to avoid building the vignettes) or
install pandoc. If you have the rmarkdown
R
package installed then you can check if you have pandoc by running the
following in R:
rmarkdown::pandoc_available()
#> [1] TRUE
Citation
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")
There is currently a publication in preparation corresponding to this
package and the citation will be updated once it’s published.
Documentation and Examples
To see the detailed documentation for each function in the stable
CRAN version of the package, see:
 README:
https://CRAN.Rproject.org/package=ggstatsplot/readme/README.html  Presentation:
https://indrajeetpatil.github.io/ggstatsplot_slides/slides/ggstatsplot_presentation.html#1  Vignettes:
https://CRAN.Rproject.org/package=ggstatsplot/vignettes/additional.html
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/.
Help
In R
, documentation for any function can be accessed with the standard
help
command (e.g., ?ggbetweenstats
).
Another handy tool to see arguments to any of the functions is args
.
For example
args(name = ggstatsplot::specify_decimal_p)
#> function (x, k = 3, p.value = FALSE)
#> NULL
In case you want to look at the function body for any of the functions,
just type the name of the function without the parentheses:
# function to convert class of any object to `ggplot` class
ggstatsplot::ggplot_converter
#> function(plot) {
#> cowplot::ggdraw() + cowplot::draw_grob(grid::grobTree(plot))
#> }
#> <bytecode: 0x00000000255f6230>
#> <environment: namespace:ggstatsplot>
If you are not familiar either with what the namespace ::
does or how
to use pipe operator %>%
, something this package and its documentation
relies a lot on, you can check out these links
Primary functions
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
# loading needed libraries
library(ggstatsplot)
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
messages = FALSE
) + # further modification outside of ggstatsplot
ggplot2::coord_cartesian(ylim = c(3, 8)) +
ggplot2::scale_y_continuous(breaks = seq(3, 8, by = 1))
Note that this function returns object of class ggplot
and thus can be
further modified using ggplot2
functions.
A number of other arguments can be specified to make this plot even more
informative or change some of the default options. Additionally, this
time we will use a grouping variable that has only two levels. The
function will automatically switch from carrying out an ANOVA analysis
to a ttest.
The type
(of test) argument also accepts the following abbreviations:
"p"
(for parametric) or "np"
(for nonparametric) or "r"
(for
robust) or "bf"
(for Bayes Factor). Additionally, the type of plot
to be displayed can also be modified ("box"
, "violin"
, or
"boxviolin"
).
A number of other arguments can be specified to make this plot even more
informative or change some of the default options.
library(ggplot2)
# for reproducibility
set.seed(123)
# let's leave out one of the factor levels and see if instead of anova, a ttest will be run
iris2 < dplyr::filter(.data = iris, Species != "setosa")
# let's change the levels of our factors, a common routine in data analysis
# pipeline, to see if this function respects the new factor levels
iris2$Species < factor(x = iris2$Species, levels = c("virginica", "versicolor"))
# plot
ggstatsplot::ggbetweenstats(
data = iris2,
x = Species,
y = Sepal.Length,
notch = TRUE, # show notched box plot
mean.plotting = TRUE, # whether mean for each group is to be displayed
mean.ci = TRUE, # whether to display confidence interval for means
mean.label.size = 2.5, # size of the label for mean
type = "parametric", # which type of test is to be run
k = 3, # number of decimal places for statistical results
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = Sepal.Width, # variable to be used for the outlier tag
outlier.label.color = "darkgreen", # changing the color for the text label
xlab = "Type of Species", # label for the xaxis variable
ylab = "Attribute: Sepal Length", # label for the yaxis variable
title = "Dataset: Iris flower data set", # title text for the plot
ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off ggstatsplot theme layer
package = "wesanderson", # package from which color palette is to be taken
palette = "Darjeeling1", # choosing a different color palette
messages = FALSE
)
As can be seen from the plot, the function by default returns Bayes
Factor for the test (here, Student’s ttest). 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.,
).
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
() versus null
() hypotheses (since ).
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
ggstatsplot::grouped_ggbetweenstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
pairwise.comparisons = TRUE, # display significant pairwise comparisons
pairwise.annotation = "p.value", # how do you want to annotate the pairwise comparisons
p.adjust.method = "bonferroni", # method for adjusting pvalues for multiple comparisons
conf.level = 0.99, # changing confidence level to 99%
ggplot.component = list( # adding new components to `ggstatsplot` default
ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())
),
k = 3,
title.prefix = "Movie genre",
caption = substitute(paste(
italic("Source"),
":IMDb (Internet Movie Database)"
)),
palette = "default_jama",
package = "ggsci",
messages = FALSE,
nrow = 2,
title.text = "Differences in movie length by mpaa ratings for different genres"
)
Summary of tests
Following (betweensubjects) tests are carried out for each type of
analyses
Type  No. of groups  Test 

Parametric  > 2  Fisher’s or Welch’s oneway ANOVA 
Nonparametric  > 2  Kruskal–Wallis oneway ANOVA 
Robust  > 2  Heteroscedastic oneway ANOVA for trimmed means 
Bayes Factor  > 2  Fisher’s ANOVA 
Parametric  2  Student’s or Welch’s ttest 
Nonparametric  2  Mann–Whitney U test 
Robust  2  Yuen’s test for trimmed means 
Bayes Factor  2  Student’s ttest 
The omnibus effect in oneway ANOVA design can also be followed up with
more focal pairwise comparison tests. Here is a summary of multiple
pairwise comparison tests supported in ggbetweenstats
Type  Equal variance?  Test  pvalue adjustment? 

Parametric  No  GamesHowell test  Yes 
Parametric  Yes  Student’s ttest  Yes 
Nonparametric  No  DwassSteelCrichtlowFligner test  Yes 
Robust  No  Yuen’s trimmed means test  Yes 
Bayes Factor  No  No  No 
Bayes Factor  Yes  No  No 
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)
# plot
ggstatsplot::ggwithinstats(
data = WRS2::WineTasting,
x = Wine,
y = Taste,
sort = "descending", # ordering groups along the xaxis based on
sort.fun = median, # values of `y` variable
pairwise.comparisons = TRUE,
pairwise.display = "s",
pairwise.annotation = "p",
title = "Wine tasting",
caption = "Data from: `WRS2` R package",
ggtheme = ggthemes::theme_fivethirtyeight(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
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)
# getting data in tidy format
data_bugs < ggstatsplot::bugs_long %>%
dplyr::filter(.data = ., region %in% c("Europe", "North America"))
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(data_bugs, condition %in% c("LDLF", "LDHF")),
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region,
outlier.tagging = TRUE,
outlier.label = education,
ggtheme = hrbrthemes::theme_ipsum_tw(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
Summary of tests
Following (withinsubjects) tests are carried out for each type of
analyses
Type  No. of groups  Test 

Parametric  > 2  Oneway repeated measures ANOVA 
Nonparametric  > 2  Friedman test 
Robust  > 2  Heteroscedastic oneway repeated measures ANOVA for trimmed means 
Bayes Factor  > 2  Oneway repeated measures ANOVA 
Parametric  2  Student’s ttest 
Nonparametric  2  Wilcoxon signedrank test 
Robust  2  Yuen’s test on trimmed means for dependent samples 
Bayes Factor  2  Student’s ttest 
The omnibus effect in oneway ANOVA design can also be followed up with
more focal pairwise comparison tests. Here is a summary of multiple
pairwise comparison tests supported in ggwithinstats
Type  Test  pvalue adjustment? 

Parametric  Student’s ttest  Yes 
Nonparametric  DurbinConover test  Yes 
Robust  Yuen’s trimmed means test  Yes 
Bayes Factor  No  No 
For more, see the ggwithinstats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html
ggscatterstats
This function creates a scatterplot with marginal distributions overlaid
on the axes (from ggExtra::ggMarginal
) and results from statistical
tests in the subtitle:
ggstatsplot::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",
messages = FALSE
)
The available marginal distributions are
 histograms
 boxplots
 density
 violin
 densigram (density + histogram)
Number of other arguments can be specified to modify this basic plot
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggscatterstats(
data = dplyr::filter(.data = ggstatsplot::movies_long, genre == "Action"),
x = budget,
y = rating,
type = "robust", # type of test that needs to be run
conf.level = 0.99, # confidence level
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
line.color = "yellow", # changing regression line color line
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression( # caption text for the plot
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 = "density", # type of marginal distribution to be displayed
xfill = "#0072B2", # color fill for xaxis marginal distribution
yfill = "#009E73", # color fill for yaxis marginal distribution
xalpha = 0.6, # transparency for xaxis marginal distribution
yalpha = 0.6, # transparency for yaxis marginal distribution
centrality.para = "median", # central tendency lines to be displayed
messages = FALSE # turn off messages and notes
)
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 for this particular
function):
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggscatterstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = rating,
y = length,
label.var = title,
label.expression = length > 200,
conf.level = 0.99,
k = 3, # no. of decimal places in the results
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
grouping.var = genre, # grouping variable
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
messages = FALSE,
nrow = 2,
title.text = "Relationship between movie length by IMDB ratings for different genres"
)
Using ggscatterstats()
in R Notebooks or R Markdown
If you try including a ggscatterstats()
plot inside an R Notebook
or
R Markdown
code chunk, you will notice that the plot is not returned
in the output. In order to get a ggscatterstats()
to show up in these
contexts, you need to save the ggscatterstats
plot as a variable in
one code chunk, and explicitly print it using the grid
package in
another chunk, like this:
# include the following code in your code chunk inside R Notebook or Markdown
grid::grid.newpage()
grid::grid.draw(
ggstatsplot::ggscatterstats(
data = ggstatsplot::movies_wide,
x = budget,
y = rating,
marginal = TRUE,
messages = FALSE
)
)
Another option  or rather a compromise  is not to include marginal
distribution at all by setting marginal = FALSE
.
Summary of tests
Following tests are carried out for each type of analyses. Additionally,
the correlation coefficients (and their confidence intervals) are used
as effect sizes
Type  Test  CI? 

Parametric  Pearson’s correlation coefficient  Yes 
Nonparametric  Spearman’s rank correlation coefficient  Yes 
Robust  Percentage bend correlation coefficient  Yes 
Bayes Factor  Pearson’s correlation coefficient  No 
For more, see the ggscatterstats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html
ggpiestats
This function creates a pie chart for categorical or nominal variables
with results from contingency table analysis (Pearson’s
test for betweensubjects design and McNemar’s
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
goodness of fit test) will be displayed as a subtitle.
Here is an example of a case where the theoretical question is about
proportions for different levels of a single nominal variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = ggplot2::msleep,
x = vore,
title = "Composition of vore types among mammals",
messages = FALSE
)
This function can also be used to study an interaction between two
categorical variables:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = mtcars,
x = am,
y = cyl,
conf.level = 0.99, # confidence interval for effect size measure
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
stat.title = "interaction: ", # title for the results
legend.title = "Transmission", # title for the legend
factor.levels = c("1 = manual", "0 = automatic"), # renaming the factor level names (`x`)
facet.wrap.name = "No. of cylinders", # name for the facetting variable
slice.label = "counts", # show counts data instead of percentages
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
caption = substitute( # text for the caption
paste(italic("Source"), ": 1974 Motor Trend US magazine")
),
messages = FALSE # turn off messages and notes
)
In case of repeated measures designs, setting paired = TRUE
will
produce results from McNemar’s
test
# for reproducibility
set.seed(123)
# data
survey.data < data.frame(
`1st survey` = c("Approve", "Approve", "Disapprove", "Disapprove"),
`2nd survey` = c("Approve", "Disapprove", "Approve", "Disapprove"),
`Counts` = c(794, 150, 86, 570),
check.names = FALSE
)
# plot
ggstatsplot::ggpiestats(
data = survey.data,
x = `1st survey`,
y = `2nd survey`,
counts = Counts,
paired = TRUE, # withinsubjects design
conf.level = 0.99, # confidence interval for effect size measure
package = "wesanderson",
palette = "Royal1"
)
#> Note: 99% CI for effect size estimate was computed with 100 bootstrap samples.
#> # A tibble: 2 x 11
#> `2nd survey` counts perc N Approve Disapprove `Chisquared`
#> <fct> <int> <dbl> <chr> <chr> <chr> <dbl>
#> 1 Disapprove 720 45 (n = 720) 20.83% 79.17% 245
#> 2 Approve 880 55. (n = 880) 90.23% 9.77% 570.
#> p.value df method significance
#> <dbl> <dbl> <chr> <chr>
#> 1 3.20e 55 1 Chisquared test for given probabilities ***
#> 2 6.80e126 1 Chisquared test for given probabilities ***
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
ggstatsplot::grouped_ggpiestats(
dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
grouping.var = genre, # grouping variable
title.prefix = "Movie genre", # prefix for the facetted title
label.text.size = 3, # text size for slice labels
slice.label = "both", # show both counts and percentage data
perc.k = 1, # no. of decimal places for percentages
palette = "brightPastel",
package = "quickpalette",
messages = FALSE,
nrow = 2,
title.text = "Composition of MPAA ratings for different genres"
)
Summary of tests
Following tests are carried out for each type of analyses
Type of data  Design  Test 

Unpaired  contingency table  Pearson’s test 
Paired  contingency table  McNemar’s test 
Frequency  contingency table  Goodness of fit () 
Following effect sizes (and confidence intervals/CI) are available for
each type of test
Type  Effect size  CI? 

Pearson’s chisquared test  Cramér’s V  Yes 
McNemar’s test  Cohen’s g  Yes 
Goodness of fit  Cramér’s V  Yes 
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
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbarstats(
data = ggstatsplot::movies_long,
x = mpaa,
y = genre,
sampling.plan = "jointMulti",
title = "MPAA Ratings by Genre",
xlab = "movie genre",
perc.k = 1,
x.axis.orientation = "slant",
ggtheme = hrbrthemes::theme_modern_rc(),
ggstatsplot.layer = FALSE,
ggplot.component = ggplot2::theme(axis.text.x = ggplot2::element_text(face = "italic")),
palette = "Set2",
messages = FALSE
)
Note that pvalues for results from onesample proportion tests are
displayed for each bar in the form of asterisks with the following
convention:
 :
 :
 :
 :
And, needless to say, there is also a grouped_
variant of this
function
# setup
set.seed(123)
# smaller dataset
df < dplyr::filter(
.data = forcats::gss_cat,
race %in% c("Black", "White"),
relig %in% c("Protestant", "Catholic", "None"),
!partyid %in% c("No answer", "Don't know", "Other party")
)
# plot
ggstatsplot::grouped_ggbarstats(
data = df,
x = relig,
y = partyid,
grouping.var = race,
title.prefix = "Race",
xlab = "Party affiliation",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
messages = FALSE,
title.text = "Race, religion, and political affiliation",
nrow = 2
)
Summary of tests
This is identical to the ggpiestats
function summary of tests.
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.
ggstatsplot::gghistostats(
data = ToothGrowth, # dataframe from which variable is to be taken
x = len, # numeric variable whose distribution is of interest
xlab = "Tooth length", # `x`axis label
title = "Distribution of Tooth Length", # title for the plot
fill.gradient = TRUE, # use color gradient
test.value = 10, # the comparison value for onesample test
test.value.line = TRUE, # display a vertical line at test value
type = "bayes", # bayes factor for one sample ttest
bf.prior = 0.8, # prior width for calculating the bayes factor
messages = FALSE # turn off the messages
)
The aesthetic defaults can be easily modified
# for reproducibility
set.seed(123)
# plot
ggstatsplot::gghistostats(
data = iris, # dataframe from which variable is to be taken
x = Sepal.Length, # numeric variable whose distribution is of interest
title = "Distribution of Iris sepal length", # title for the plot
caption = substitute(paste(italic("Source:"), "Ronald Fisher's Iris data set")),
type = "parametric", # one sample ttest
conf.level = 0.99, # changing confidence level for effect size
bar.measure = "mix", # what does the bar length denote
test.value = 5, # default value is 0
test.value.line = TRUE, # display a vertical line at test value
test.value.color = "#0072B2", # color for the line for test value
centrality.para = "mean", # which measure of central tendency is to be plotted
centrality.color = "darkred", # decides color for central tendency line
binwidth = 0.10, # binwidth value (experiment)
bf.prior = 0.8, # prior width for computing bayes factor
messages = FALSE, # turn off the messages
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
As can be seen from the plot, bayes factor can be attached (bf.message = TRUE
) to assess evidence in favor of the null hypothesis.
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
ggstatsplot::grouped_gghistostats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = budget,
xlab = "Movies budget (in million US$)",
type = "robust", # use robust location measure
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.color = "red",
title.prefix = "Movie genre",
ggtheme = ggthemes::theme_tufte(),
ggplot.component = list( # modify the defaults from `ggstatsplot` for each plot
ggplot2::scale_x_continuous(breaks = seq(0, 200, 50), limits = (c(0, 200)))
),
messages = FALSE,
nrow = 2,
title.text = "Movies budgets for different genres"
)
Summary of tests
Following tests are carried out for each type of analyses
Type  Test 

Parametric  Onesample Student’s ttest 
Nonparametric  Onesample Wilcoxon test 
Robust  Onesample percentile bootstrap 
Bayes Factor  Onesample Student’s ttest 
Following effect sizes (and confidence intervals/CI) are available for
each type of test
Type  Effect size  CI? 

Parametric  Cohen’s d, Hedge’s g (centraland noncentralt distribution based)  Yes 
Nonparametric  r (computed as )  Yes 
Robust  (Robust location measure)  Yes 
Bayes Factor  No  No 
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,
test.value.line = TRUE,
test.line.labeller = TRUE,
test.value.color = "red",
centrality.para = "median",
centrality.k = 0,
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy",
messages = FALSE,
caption = substitute(
paste(
italic("Source"),
": Gapminder dataset from https://www.gapminder.org/"
)
)
)
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)
# removing factor level with very few no. of observations
df < dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6"))
# plot
ggstatsplot::grouped_ggdotplotstats(
data = df,
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "nonparametric", # nonparametric test
grouping.var = cyl, # grouping variable
test.value = 15.5,
title.prefix = "cylinder count",
point.color = "red",
point.size = 5,
point.shape = 13,
test.value.line = TRUE,
ggtheme = ggthemes::theme_par(),
messages = FALSE,
title.text = "Fuel economy data"
)
Summary of tests
This is identical to summary of tests for gghistostats
.
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 correlalogram plot
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
corr.method = "robust", # correlation method
sig.level = 0.001, # threshold of significance
p.adjust.method = "holm", # pvalue adjustment method for multiple comparisons
cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
cor.vars.names = c(
"REM sleep", # variable names
"time awake",
"brain weight",
"body weight"
),
matrix.type = "upper", # type of visualization matrix
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms"
)
Note that 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.
Alternatively, you can use it just to get the correlation matrices and
their corresponding pvalues (in a tibble
format).
# for reproducibility
set.seed(123)
# show four digits in a tibble
options(pillar.sigfig = 4)
# getting the correlation coefficient matrix
ggstatsplot::ggcorrmat(
data = iris, # all numeric variables from data will be used
corr.method = "robust",
output = "correlations", # specifying the needed output ("r" or "corr" will also work)
digits = 3 # number of digits to be dispayed for correlation coefficient
)
#> # A tibble: 4 x 5
#> variable Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Sepal.Length 1 0.143 0.878 0.837
#> 2 Sepal.Width 0.143 1 0.426 0.373
#> 3 Petal.Length 0.878 0.426 1 0.966
#> 4 Petal.Width 0.837 0.373 0.966 1
# getting the pvalue matrix
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "robust",
output = "p.values", # only "p" or "pvalues" will also work
p.adjust.method = "holm"
)
#> # A tibble: 6 x 7
#> variable sleep_total sleep_rem sleep_cycle awake brainwt
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total 0. 5.291e12 9.138e 3 0. 3.170e 5
#> 2 sleep_rem 4.070e13 0. 1.978e 2 5.291e12 9.698e 3
#> 3 sleep_cycle 2.285e 3 1.978e 2 0. 9.138e 3 1.637e 9
#> 4 awake 0. 4.070e13 2.285e 3 0. 3.170e 5
#> 5 brainwt 4.528e 6 4.849e 3 1.488e10 4.528e 6 0.
#> 6 bodywt 2.568e 7 7.524e 4 2.120e 6 2.568e 7 3.221e18
#> bodywt
#> <dbl>
#> 1 2.568e 6
#> 2 3.762e 3
#> 3 1.696e 5
#> 4 2.568e 6
#> 5 4.509e17
#> 6 0.
# getting the confidence intervals for correlations
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "spearman",
output = "ci",
p.adjust.method = "holm"
)
#> Note: In the correlation matrix,
#> the upper triangle: pvalues adjusted for multiple comparisons
#> the lower triangle: unadjusted pvalues.
#> # A tibble: 15 x 7
#> pair r lower upper p lower.adj
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_totalsleep_rem 0.7641 0.6344 0.8520 7.806e13 0.5632
#> 2 sleep_totalsleep_cycle 0.4888 0.7155 0.1689 4.530e 3 0.7609
#> 3 sleep_totalawake 1 1 1 0. 1
#> 4 sleep_totalbrainwt 0.5935 0.7408 0.3918 1.426e 6 0.7852
#> 5 sleep_totalbodywt 0.5346 0.6727 0.3605 1.931e 7 0.7121
#> 6 sleep_remsleep_cycle 0.3344 0.6118 0.01617 6.139e 2 0.6118
#> 7 sleep_remawake 0.7641 0.8520 0.6344 7.806e13 0.8807
#> 8 sleep_rembrainwt 0.4139 0.6246 0.1471 3.451e 3 0.6495
#> 9 sleep_rembodywt 0.4517 0.6317 0.2255 2.580e 4 0.6647
#> 10 sleep_cycleawake 0.4888 0.1689 0.7155 4.530e 3 0.05610
#> 11 sleep_cyclebrainwt 0.8727 0.7474 0.9380 3.250e10 0.6573
#> 12 sleep_cyclebodywt 0.8464 0.7061 0.9228 1.040e 9 0.6115
#> 13 awakebrainwt 0.5935 0.3918 0.7408 1.426e 6 0.2934
#> 14 awakebodywt 0.5346 0.3605 0.6727 1.931e 7 0.2875
#> 15 brainwtbodywt 0.9572 0.9277 0.9748 9.694e31 0.9071
#> upper.adj
#> <dbl>
#> 1 0.8798
#> 2 0.07055
#> 3 1
#> 4 0.2982
#> 5 0.2928
#> 6 0.01617
#> 7 0.5605
#> 8 0.1058
#> 9 0.1708
#> 10 0.7669
#> 11 0.9563
#> 12 0.9442
#> 13 0.7872
#> 14 0.7150
#> 15 0.9805
# getting the sample sizes for all pairs
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "robust",
output = "n" # note that n is different due to NAs
)
#> # A tibble: 6 x 7
#> variable sleep_total sleep_rem sleep_cycle awake brainwt bodywt
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total 83 61 32 83 56 83
#> 2 sleep_rem 61 61 32 61 48 61
#> 3 sleep_cycle 32 32 32 32 30 32
#> 4 awake 83 61 32 83 56 83
#> 5 brainwt 56 48 30 56 56 56
#> 6 bodywt 83 61 32 83 56 83
Note that if cor.vars
are not specified, all numeric variables will be
used.
There is 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
# let's use only 50% of the data to speed up the process
ggstatsplot::grouped_ggcorrmat(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
cor.vars = length:votes,
corr.method = "np",
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre, # grouping variable
digits = 3, # number of digits after decimal point
title.prefix = "Movie genre",
messages = FALSE,
nrow = 2
)
Summary of tests
Following tests are carried out for each type of analyses. Additionally,
the correlation coefficients (and their confidence intervals) are used
as effect sizes
Type  Test  CI? 

Parametric  Pearson’s correlation coefficient  Yes 
Nonparametric  Spearman’s rank correlation coefficient  Yes 
Robust  Percentage bend correlation coefficient  No 
Bayes Factor  Pearson’s correlation coefficient  No 
For examples and more information, see the ggcorrmat
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html
ggcoefstats
ggcoefstats
creates a dotandwhisker plot for regression models.
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 witht
and
z
statistic), etc. The function will, by default, display a
helpfulx
axis label that should clear up what estimates are being
displayed. The confidence intervals can sometimes be asymmetric if
bootstrapping was used. 
The caption will always 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. Additionally, the higher the loglikelihood value the
“better” is the model fit. 
The output of this function will be a
ggplot2
object and, thus, it
can be further modified (e.g., change themes, etc.) withggplot2
functions.
# for reproducibility
set.seed(123)
# model
mod < stats::lm(
formula = mpg ~ am * cyl,
data = mtcars
)
# plot
ggstatsplot::ggcoefstats(x = mod)
This default plot can be further modified to one’s liking with
additional arguments (also, let’s use a robust linear model instead of a
simple linear model now):
# for reproducibility
set.seed(123)
# model
mod < MASS::rlm(
formula = mpg ~ am * cyl,
data = mtcars
)
# plot
ggstatsplot::ggcoefstats(
x = mod,
point.color = "red",
point.shape = 15,
vline.color = "#CC79A7",
vline.linetype = "dotdash",
stats.label.size = 3.5,
stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
title = "Car performance predicted by transmission & cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine",
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
) + # further modification with the ggplot2 commands
# 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 broom
and
broom.mixed
packages with tidy
and glance
methods are also
supported by ggcoefstats
. For example
aareg
, anova
, aov
, aovlist
, Arima
, bigglm
, biglm
,
brmsfit
, btergm
, cch
, clm
, clmm
, confusionMatrix
, coxph
,
drc
, emmGrid
, epi.2by2
, ergm
, felm
, fitdistr
, glmerMod
,
glmmTMB
, gls
, gam
, Gam
, gamlss
, garch
, glm
, glmmadmb
,
glmmPQL
, glmmTMB
, glmRob
, glmrob
, gmm
, ivreg
, lm
,
lm.beta
, lmerMod
, lmodel2
, lmRob
, lmrob
, mcmc
, MCMCglmm
,
mclogit
, mmclogit
, mediate
, mjoint
, mle2
, mlm
, multinom
,
negbin
, nlmerMod
, nlrq
, nls
, orcutt
, plm
, polr
, ridgelm
,
rjags
, rlm
, rlmerMod
, rq
, speedglm
, speedlm
, stanreg
,
survreg
, svyglm
, svyolr
, svyglm
, etc.
Although not shown here, this function can also be used to carry out
both frequentist and Bayesian randomeffects metaanalysis.
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 😻. (Another old school
option to do this effectively is using the plyr
package.)
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
cowplot::plot_grid
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
theme_ggstatsplot
All plots from ggstatsplot
have a default theme: theme_ggstatsplot
.
You can change this theme by using the ggtheme
argument. It is
important to note that irrespective of which ggplot
theme you choose,
ggstatsplot
in the backdrop adds a new layer with its idiosyncratic
theme settings, chosen to make the graphs more readable or aesthetically
pleasing.
Let’s see an example with gghistostats
and see how a certain theme
from hrbrthemes
package looks like with and without the ggstatsplot
layer.
# to use hrbrthemes themes, first make sure you have all the necessary fonts
library(hrbrthemes)
# extrafont::ttf_import()
# extrafont::font_import()
# try this yourself
ggstatsplot::combine_plots(
# with the ggstatsplot layer
ggstatsplot::gghistostats(
data = iris,
x = Sepal.Width,
messages = FALSE,
title = "Distribution of Sepal Width",
test.value = 5,
ggtheme = hrbrthemes::theme_ipsum(),
ggstatsplot.layer = TRUE
),
# without the ggstatsplot layer
ggstatsplot::gghistostats(
data = iris,
x = Sepal.Width,
messages = FALSE,
title = "Distribution of Sepal Width",
test.value = 5,
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
),
nrow = 1,
labels = c("(a)", "(b)"),
title.text = "Behavior of ggstatsplot theme layer with and without chosen ggtheme"
)
For more on how to modify it, see the associated vignette
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/theme_ggstatsplot.html
Using ggstatsplot
statistical details with custom plots
Sometimes 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.
set.seed(123)
# loading the needed libraries
library(ggridges)
library(ggplot2)
library(ggstatsplot)
# using `ggstatsplot` to get call with statistical results
stats_results <
ggstatsplot::ggbetweenstats(
data = morley,
x = Expt,
y = Speed,
return = "subtitle",
messages = FALSE
)
# 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")
Usage and syntax simplicity
As seen from these examples, ggstatsplot
relies on nonstandard
evaluation (NSE)  implemented via rlang
 i.e., rather than looking
at the values of arguments (x
, y
), it instead looks at their
expressions. Therefore, the syntax is simpler and follows the following
principles
 When a given function depends on variables in a dataframe,
data
argument must always be specified.  The
$
operator cannot be used to specify variables in a dataframe.  All functions accept both quoted (
x = "var1"
) and unquoted (x = var1
) arguments.
These set principles combined with the fact that almost 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/reconstruct.
ggstatsplot
is a very chatty package and will by default print
helpful notes on assumptions about statistical tests, warnings, etc. If
you don’t want your console to be cluttered with such messages, they can
be turned off by setting argument messages = FALSE
in the function
call.
Most functions share a type
(of test) argument that is helpful to
specify the type of statistical analysis:
"p"
(for parametric)"np"
(for nonparametric)"r"
(for robust)"bf"
(for Bayes Factor)
All relevant functions in ggstatsplot
have a return
argument which
can be used to not only return plots (which is the default), but also to
return a subtitle
or caption
, which are objects of type call
and
can be used to display statistical details in conjunction with a custom
plot and at a custom location in the plot.
Additionally, all functions share the ggtheme
and palette
arguments
that can be used to specify your favorite ggplot
theme and color
palette.
Code coverage
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
GitHub
Comments
Subscribe to Python Awesome
Get the latest posts delivered right to your inbox