Package	Status	Usage	GitHub	Miscellaneous

Raison d’être

“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 information-rich 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.

Installation

Type	Source	Command
Release	CRAN	`install.packages("ggstatsplot")`
Development	GitHub	`remotes::install_github("IndrajeetPatil/ggstatsplot")`

Linux users may encounter some installation problems. In particular, the ggstatsplot package depends on the PMCMRplus package.

ERROR: dependencies ‘gmp’, ‘Rmpfr’ are not available for package ‘PMCMRplus’
ERROR: dependency ‘pairwiseComparisons’ is not available for package ‘ggstatsplot’

This means that your operating system lacks gmp and Rmpfr libraries.

If you use Ubuntu, you can install these dependencies:

sudo apt-get install libgmp3-dev
sudo apt-get install libmpfr-dev

The following README file briefly describes the installation procedure: https://CRAN.R-project.org/package=PMCMRplus/readme/README.html

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")

  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}},
  }

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:

Website: https://indrajeetpatil.github.io/ggstatsplot/
Presentation: https://indrajeetpatil.github.io/ggstatsplot_slides/slides/ggstatsplot_presentation.html#1
Vignettes: https://indrajeetpatil.github.io/ggstatsplot/articles/

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
`ggscatterstats`	scatterplots	for correlation between two variables
`ggcorrmat`	correlation matrices	for correlations between multiple variables
`ggpiestats`	pie charts	for categorical data
`ggbarstats`	bar charts	for categorical data
`ggcoefstats`	dot-and-whisker plots	for regression models and meta-analysis

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

The table below summarizes all the different types of analyses currently supported in this package-

Functions	Description	Parametric	Non-parametric	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`	Random-effects meta-analysis	✅	❌	✅	✅

Summary of Bayesian analysis

Analysis	Hypothesis testing	Estimation
(one/two-sample) t-test	✅	✅
one-way ANOVA	✅	✅
correlation	✅	✅
(one/two-way) contingency table	✅	✅
random-effects meta-analysis	✅	✅

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 t-test):

Summary of statistical tests and effect sizes

Here is a summary table of all the statistical tests currently supported across various functions: https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html

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.R-project.org/package=ggstatsplot/readme/README.html

`ggbetweenstats`

This function creates either a violin plot, a box plot, or a mix of two for between-group or between-condition 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 hypothesis-testing
✅ 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 p-values 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!

Summary of graphics

graphical element	`geom_` used	argument for further modification
raw data	`ggplot2::geom_point`	`point.args`
box plot	`ggplot2::geom_boxplot`	❌
density plot	`ggplot2::geom_violin`	`violin.args`
centrality measure point	`ggplot2::geom_point`	`centrality.point.args`
centrality measure label	`ggrepel::geom_label_repel`	`centrality.label.args`
outlier point	`ggplot2::stat_boxplot`	❌
outlier label	`ggrepel::geom_label_repel`	`outlier.label.args`
pairwise comparisons	`ggsignif::geom_ggsignif`	`ggsignif.args`

Summary of tests

Central tendency measure

Type	Measure	Function used
Parametric	mean	`parameters::describe_distribution`
Non-parametric	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 one-way ANOVA	`stats::oneway.test`
Non-parametric	> 2	Kruskal–Wallis one-way ANOVA	`stats::kruskal.test`
Robust	> 2	Heteroscedastic one-way ANOVA for trimmed means	`WRS2::t1way`
Bayes Factor	> 2	Fisher’s ANOVA	`BayesFactor::anovaBF`
Parametric	2	Student’s or Welch’s t-test	`stats::t.test`
Non-parametric	2	Mann–Whitney U test	`stats::wilcox.test`
Robust	2	Yuen’s test for trimmed means	`WRS2::yuen`
Bayesian	2	Student’s t-test	`BayesFactor::ttestBF`

Effect size estimation

Type	No. of groups	Effect size	CI?	Function used
Parametric	> 2	$\eta_{p}^2$ , $\omega_{p}^2$	✅	`effectsize::omega_squared`, `effectsize::eta_squared`
Non-parametric	> 2	$\epsilon_{ordinal}^2$	✅	`effectsize::rank_epsilon_squared`
Robust	> 2	$\xi$ (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`
Non-parametric	2	r (rank-biserial correlation)	✅	`effectsize::rank_biserial`
Robust	2	$\xi$ (Explanatory measure of effect size)	✅	`WRS2::yuen.effect.ci`
Bayesian	2	$\delta_{posterior}$	✅	`bayestestR::describe_posterior`

Pairwise comparison tests

Type	Equal variance?	Test	p-value adjustment?	Function used
Parametric	No	Games-Howell test	✅	`stats::pairwise.t.test`
Parametric	Yes	Student’s t-test	✅	`PMCMRplus::gamesHowellTest`
Non-parametric	No	Dunn test	✅	`PMCMRplus::kwAllPairsDunnTest`
Robust	No	Yuen’s trimmed means test	✅	`WRS2::lincon`
Bayes Factor	❌	Student’s t-test	❌	`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()
)

Defaults return

✅ raw data + distributions
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ pairwise comparisons
✅ Bayesian hypothesis-testing
✅ Bayesian estimation

The central tendency measure displayed will depend on the statistics:

Type	Measure	Function used
Parametric	mean	`parameters::describe_distribution`
Non-parametric	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(
    bugs_long,
    region %in% c("Europe", "North America"),
    condition %in% c("LDLF", "LDHF")
  ),
  x = condition,
  y = desire,
  type = "np", # non-parametric statistics
  xlab = "Condition",
  ylab = "Desire to kill an artrhopod",
  grouping.var = region,
  outlier.tagging = TRUE,
  outlier.label = education
)

Summary of graphics

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`

Summary of tests

Central tendency measure

Type	Measure	Function used
Parametric	mean	`parameters::describe_distribution`
Non-parametric	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	One-way repeated measures ANOVA	`afex::aov_ez`
Non-parametric	> 2	Friedman rank sum test	`stats::friedman.test`
Robust	> 2	Heteroscedastic one-way repeated measures ANOVA for trimmed means	`WRS2::rmanova`
Bayes Factor	> 2	One-way repeated measures ANOVA	`BayesFactor::anovaBF`
Parametric	2	Student’s t-test	`stats::t.test`
Non-parametric	2	Wilcoxon signed-rank test	`stats::wilcox.test`
Robust	2	Yuen’s test on trimmed means for dependent samples	`WRS2::yuend`
Bayesian	2	Student’s t-test	`BayesFactor::ttestBF`

Effect size estimation

Type	No. of groups	Effect size	CI?	Function used
Parametric	> 2	$\eta_{p}^2$ , $\omega_{p}^2$	✅	`effectsize::omega_squared`, `effectsize::eta_squared`
Non-parametric	> 2	$W_{Kendall}$ (Kendall’s coefficient of concordance)	✅	`effectsize::kendalls_w`
Robust	> 2	$\delta_{R-avg}^{AKP}$ (Algina-Keselman-Penfield 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`
Non-parametric	2	r (rank-biserial correlation)	✅	`effectsize::rank_biserial`
Robust	2	$\delta_{R}^{AKP}$ (Algina-Keselman-Penfield robust standardized difference)	✅	`WRS2::wmcpAKP`
Bayesian	2	$\delta_{posterior}$	✅	`bayestestR::describe_posterior`

Pairwise comparison tests

Type	Test	p-value adjustment?	Function used
Parametric	Student’s t-test	✅	`stats::pairwise.t.test`
Non-parametric	Durbin-Conover test	✅	`PMCMRplus::durbinAllPairsTest`
Robust	Yuen’s trimmed means test	✅	`WRS2::rmmcp`
Bayesian	Student’s t-test	❌	`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 one-sample 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()
)

Defaults return

✅ counts + proportion for bins
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesis-testing
✅ 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")
)

Summary of graphics

graphical element	`geom_` used	argument for further modification
histogram bin	`ggplot2::stat_bin`	`bin.args`
centrality measure line	`ggplot2::geom_vline`	`centrality.line.args`
normality curve	`ggplot2::stat_function`	`normal.curve.args`

Summary of tests

Central tendency measure

Type	Measure	Function used
Parametric	mean	`parameters::describe_distribution`
Non-parametric	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	One-sample Student’s t-test	`stats::t.test`
Non-parametric	One-sample Wilcoxon test	`stats::wilcox.test`
Robust	Bootstrap-t method for one-sample test	`WRS2::trimcibt`
Bayesian	One-sample Student’s t-test	`BayesFactor::ttestBF`

Effect size estimation

Type	Effect size	CI?	Function used
Parametric	Cohen’s d, Hedge’s g	✅	`effectsize::cohens_d`, `effectsize::hedges_g`
Non-parametric	r (rank-biserial correlation)	✅	`effectsize::rank_biserial`
Robust	trimmed mean	✅	`WRS2::trimcibt`
Bayes Factor	$\delta_{posterior}$	✅	`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(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 hypothesis-testing
✅ 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(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")
)

Summary of graphics

graphical element	`geom_` used	argument for further modification
raw data	`ggplot2::geom_point`	`point.args`
centrality measure line	`ggplot2::geom_vline`	`centrality.line.args`

Summary of tests

Central tendency measure

Type	Measure	Function used
Parametric	mean	`parameters::describe_distribution`
Non-parametric	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	One-sample Student’s t-test	`stats::t.test`
Non-parametric	One-sample Wilcoxon test	`stats::wilcox.test`
Robust	Bootstrap-t method for one-sample test	`WRS2::trimcibt`
Bayesian	One-sample Student’s t-test	`BayesFactor::ttestBF`

Effect size estimation

Type	Effect size	CI?	Function used
Parametric	Cohen’s d, Hedge’s g	✅	`effectsize::cohens_d`, `effectsize::hedges_g`
Non-parametric	r (rank-biserial correlation)	✅	`effectsize::rank_biserial`
Robust	trimmed mean	✅	`WRS2::trimcibt`
Bayes Factor	$\delta_{posterior}$	✅	`bayestestR::describe_posterior`

`ggscatterstats`

This function creates a scatterplot with marginal distributions overlaid on the axes 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 hypothesis-testing
✅ 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_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")
)

Summary of graphics

graphical element	`geom_` used	argument for further modification
raw data	`ggplot2::geom_point`	`point.args`
labels for raw data	`ggrepel::geom_label_repel`	`point.label.args`
smooth line	`ggplot2::geom_smooth`	`smooth.line.args`
marginal histograms	`ggside::geom_xsidehistogram`, `ggside::geom_ysidehistogram`	`xsidehistogram.args`, `ysidehistogram.args`
marginal density plots	`ggside::geom_xsidedensity`, `ggside::geom_ysidedensity`	`xsidedensity.args`, `ysidedensity.args`

Summary of tests

Hypothesis testing and Effect size estimation

Type	Test	CI?	Function used
Parametric	Pearson’s correlation coefficient	✅	`correlation::correlation`
Non-parametric	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 publication-ready correlation matrices. But, for the sake of exploring the available options, let’s change some of the defaults. For example, multiple aesthetics-related 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 NAs

If there are NAs 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)

# tidy data as output
ggcorrmat(
  data = dplyr::select(ggplot2::msleep, dplyr::matches("sleep|awake")),
  type = "bayes",
  output = "dataframe"
)
#> # A tibble: 6 × 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_rem   sleep_cycle   -0.304       0.95   -0.534   -0.0465 0.963
#> 5 sleep_rem   awake         -0.733       0.95   -0.826   -0.639  1    
#> 6 sleep_cycle awake          0.439       0.95    0.220    0.653  0.998
#>   rope.percentage prior.distribution prior.location prior.scale  bayes.factor
#>             <dbl> <chr>                       <dbl>       <dbl>         <dbl>
#> 1          0      beta                         1.41        1.41 3000790806.  
#> 2          0.0173 beta                         1.41        1.41          8.85
#> 3          0      beta                         1.41        1.41         NA   
#> 4          0.100  beta                         1.41        1.41          1.42
#> 5          0      beta                         1.41        1.41 3005546544.  
#> 6          0.015  beta                         1.41        1.41          8.85
#>   method                       n.obs
#>   <chr>                        <int>
#> 1 Bayesian Pearson correlation    61
#> 2 Bayesian Pearson correlation    32
#> 3 Bayesian Pearson correlation    83
#> 4 Bayesian Pearson correlation    32
#> 5 Bayesian Pearson correlation    61
#> 6 Bayesian Pearson correlation    32

Additionally, partial correlation are also supported:

# setup
set.seed(123)

# tidy data as output
ggcorrmat(
  data = dplyr::select(ggplot2::msleep, dplyr::matches("sleep|awake")),
  type = "bayes",
  partial = TRUE,
  output = "dataframe"
)
#> # A tibble: 6 × 14
#>   parameter1  parameter2  estimate conf.level conf.low conf.high    pd
#>   <chr>       <chr>          <dbl>      <dbl>    <dbl>     <dbl> <dbl>
#> 1 sleep_total sleep_rem    0.0938        0.95   -0.165     0.385 0.704
#> 2 sleep_total sleep_cycle  0.00194       0.95   -0.263     0.279 0.505
#> 3 sleep_total awake       -1             0.95   -1        -1     1    
#> 4 sleep_rem   sleep_cycle -0.0274        0.95   -0.283     0.248 0.558
#> 5 sleep_rem   awake        0.0889        0.95   -0.191     0.356 0.698
#> 6 sleep_cycle awake        0.00381       0.95   -0.259     0.283 0.508
#>   rope.percentage prior.distribution prior.location prior.scale bayes.factor
#>             <dbl> <chr>                       <dbl>       <dbl>        <dbl>
#> 1           0.378 beta                         1.41        1.41        0.311
#> 2           0.434 beta                         1.41        1.41        0.267
#> 3           0     beta                         1.41        1.41       NA    
#> 4           0.429 beta                         1.41        1.41        0.272
#> 5           0.386 beta                         1.41        1.41        0.309
#> 6           0.444 beta                         1.41        1.41        0.267
#>   method                       n.obs
#>   <chr>                        <int>
#> 1 Bayesian Pearson correlation    32
#> 2 Bayesian Pearson correlation    32
#> 3 Bayesian Pearson correlation    32
#> 4 Bayesian Pearson correlation    32
#> 5 Bayesian Pearson correlation    32
#> 6 Bayesian Pearson correlation    32

Summary of graphics

graphical element	`geom_` used	argument for further modification
correlation matrix	`ggcorrplot::ggcorrplot`	`ggcorrplot.args`

Summary of tests

Hypothesis testing and Effect size estimation

Type	Test	CI?	Function used
Parametric	Pearson’s correlation coefficient	✅	`correlation::correlation`
Non-parametric	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 chi-squared test for between-subjects design and McNemar’s chi-squared test for within-subjects design) included in the subtitle of the plot. If only one categorical variable is entered, results from one-sample proportion test (i.e., a chi-squared 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
✅ Goodness-of-fit tests
✅ Bayesian hypothesis-testing
✅ 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
)

Summary of graphics

graphical element	`geom_` used	argument for further modification
pie slices	`ggplot2::geom_col`	❌
descriptive labels	`ggplot2::geom_label`/`ggrepel::geom_label_repel`	`label.args`

Summary of tests

two-way table

Hypothesis testing

Type	Design	Test	Function used
Parametric/Non-parametric	Unpaired	Pearson’s $\chi^2$ test	`stats::chisq.test`
Bayesian	Unpaired	Bayesian Pearson’s $\chi^2$ test	`BayesFactor::contingencyTableBF`
Parametric/Non-parametric	Paired	McNemar’s $\chi^2$ test	`stats::mcnemar.test`
Bayesian	Paired	❌	❌

Effect size estimation

Type	Design	Effect size	CI?	Function used
Parametric/Non-parametric	Unpaired	Cramer’s $V$	✅	`effectsize::cramers_v`
Bayesian	Unpaired	Cramer’s $V$	✅	`effectsize::cramers_v`
Parametric/Non-parametric	Paired	Cohen’s $g$	✅	`effectsize::cohens_g`
Bayesian	Paired	❌	❌	❌

one-way table

Hypothesis testing

Type	Test	Function used
Parametric/Non-parametric	Goodness of fit $\chi^2$ test	`stats::chisq.test`
Bayesian	Bayesian Goodness of fit $\chi^2$ test	(custom)

Effect size estimation

Type	Effect size	CI?	Function used
Parametric/Non-parametric	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 p-values from one-sample 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
✅ Goodness-of-fit tests
✅ Bayesian hypothesis-testing
✅ 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)
)

Summary of graphics

graphical element	`geom_` used	argument for further modification
bars	`ggplot2::geom_bar`	❌
descriptive labels	`ggplot2::geom_label`	`label.args`

Summary of tests

two-way table

Hypothesis testing

Type	Design	Test	Function used
Parametric/Non-parametric	Unpaired	Pearson’s $\chi^2$ test	`stats::chisq.test`
Bayesian	Unpaired	Bayesian Pearson’s $\chi^2$ test	`BayesFactor::contingencyTableBF`
Parametric/Non-parametric	Paired	McNemar’s $\chi^2$ test	`stats::mcnemar.test`
Bayesian	Paired	❌	❌

Effect size estimation

Type	Design	Effect size	CI?	Function used
Parametric/Non-parametric	Unpaired	Cramer’s $V$	✅	`effectsize::cramers_v`
Bayesian	Unpaired	Cramer’s $V$	✅	`effectsize::cramers_v`
Parametric/Non-parametric	Paired	Cohen’s $g$	✅	`effectsize::cohens_g`
Bayesian	Paired	❌	❌	❌

one-way table

Hypothesis testing

Type	Test	Function used
Parametric/Non-parametric	Goodness of fit $\chi^2$ test	`stats::chisq.test`
Bayesian	Bayesian Goodness of fit $\chi^2$ test	(custom)

Effect size estimation

Type	Effect size	CI?	Function used
Parametric/Non-parametric	Cramer’s $V$	✅	`bayestestR::describe_posterior`
Bayesian	❌	❌	❌

`ggcoefstats`

The function ggcoefstats generates dot-and-whisker 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 dot-whisker 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-, $\chi^{2}$ -, 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 p-value.
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),
  title = "Car performance predicted by transmission & cylinder count",
  subtitle = "Source: 1974 Motor Trend US magazine",
  exclude.intercept = TRUE,
  ggtheme = hrbrthemes::theme_ipsum_ps()
) + # 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)

Supported models

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 random-effects meta-analysis.

Summary of graphics

graphical element	`geom_` used	argument for further modification
regression estimate	`ggplot2::geom_point`	`point.args`
error bars	`ggplot2::geom_errorbarh`	`errorbar.args`
vertical line	`ggplot2::geom_vline`	`vline.args`
label with statistical details	`ggrepel::geom_label_repel`	`stats.label.args`

Summary of meta-analysis tests

Hypothesis testing and Effect size estimation

Type	Test	Effect size	CI?	Function used
Parametric	Meta-analysis via random-effects models	$\beta$	✅	`metafor::metafor`
Robust	Meta-analysis via robust random-effects models	$\beta$	✅	`metaplus::metaplus`
Bayes	Meta-analysis via Bayesian random-effects models	$\beta$	✅	`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

Extracting dataframes with statistical details

ggstatsplot also offers a convenience function to extract dataframes with statistical details that are used to create expressions displayed in ggstatsplot plots.

set.seed(123)

# a list of tibbles containing statistical analysis summaries
ggbetweenstats(mtcars, cyl, mpg) %>%
  extract_stats()
#> $subtitle_data
#> # A tibble: 1 × 13
#>   statistic    df df.error    p.value
#>       <dbl> <dbl>    <dbl>      <dbl>
#> 1      31.6     2     18.0 0.00000127
#>   method                                                   estimate conf.level
#>   <chr>                                                       <dbl>      <dbl>
#> 1 One-way analysis of means (not assuming equal variances)    0.744       0.95
#>   conf.low conf.high effectsize conf.method conf.distribution expression
#>      <dbl>     <dbl> <chr>      <chr>       <chr>             <list>    
#> 1    0.475     0.853 Omega2     ncp         F                 <language>
#> 
#> $caption_data
#> # A tibble: 6 × 20
#>   term  estimate conf.level conf.low conf.high    pd rope.percentage
#>   <chr>    <dbl>      <dbl>    <dbl>     <dbl> <dbl>           <dbl>
#> 1 mu      20.5         0.95  19.3        21.9  1              0     
#> 2 cyl-4    5.90        0.95   4.11        7.52 1              0     
#> 3 cyl-6   -0.704       0.95  -2.64        1.06 0.780          0.416 
#> 4 cyl-8   -5.18        0.95  -6.76       -3.55 1              0     
#> 5 sig2    11.0         0.95   6.24       18.3  1              0     
#> 6 g_cyl    2.69        0.95   0.0911     18.7  1              0.0438
#>   prior.distribution prior.location prior.scale component       bf10
#>   <chr>                       <dbl>       <dbl> <chr>          <dbl>
#> 1 cauchy                          0       0.707 extra       3008850.
#> 2 cauchy                          0       0.707 conditional 3008850.
#> 3 cauchy                          0       0.707 conditional 3008850.
#> 4 cauchy                          0       0.707 conditional 3008850.
#> 5 cauchy                          0       0.707 extra       3008850.
#> 6 cauchy                          0       0.707 extra       3008850.
#>   method                          log_e_bf10    r2 std.dev r2.conf.level
#>   <chr>                                <dbl> <dbl>   <dbl>         <dbl>
#> 1 Bayes factors for linear models       14.9 0.714  0.0503          0.95
#> 2 Bayes factors for linear models       14.9 0.714  0.0503          0.95
#> 3 Bayes factors for linear models       14.9 0.714  0.0503          0.95
#> 4 Bayes factors for linear models       14.9 0.714  0.0503          0.95
#> 5 Bayes factors for linear models       14.9 0.714  0.0503          0.95
#> 6 Bayes factors for linear models       14.9 0.714  0.0503          0.95
#>   r2.conf.low r2.conf.high expression
#>         <dbl>        <dbl> <list>    
#> 1       0.574        0.788 <language>
#> 2       0.574        0.788 <language>
#> 3       0.574        0.788 <language>
#> 4       0.574        0.788 <language>
#> 5       0.574        0.788 <language>
#> 6       0.574        0.788 <language>
#> 
#> $pairwise_comparisons_data
#> # A tibble: 3 × 11
#>   group1 group2 statistic   p.value alternative method            distribution
#>   <chr>  <chr>      <dbl>     <dbl> <chr>       <chr>             <chr>       
#> 1 4      6          -6.67 0.00110   two.sided   Games-Howell test q           
#> 2 4      8         -10.7  0.0000140 two.sided   Games-Howell test q           
#> 3 6      8          -7.48 0.000257  two.sided   Games-Howell test q           
#>   p.adjustment test.details      p.value.adjustment
#>   <chr>        <chr>             <chr>             
#> 1 none         Games-Howell test Holm              
#> 2 none         Games-Howell test Holm              
#> 3 none         Games-Howell test Holm              
#>   label                                     
#>   <chr>                                     
#> 1 list(~italic(p)[Holm-corrected]==0.001)   
#> 2 list(~italic(p)[Holm-corrected]==1.4e-05) 
#> 3 list(~italic(p)[Holm-corrected]==2.57e-04)
#> 
#> $descriptive_data
#> NULL
#> 
#> $one_sample_data
#> NULL

Note that all of this analysis is carried out by statsExpressions package: https://indrajeetpatil.github.io/statsExpressions/

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.

# 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 = "Michelson-Morley experiments",
    subtitle = stats_results,
    x = "Speed of light",
    y = "Experiment number"
  ) + # remove the legend
  theme(legend.position = "none")

Summary of benefits

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 copy-paste results to the text editor (MS-Word, 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.

Syntax simplicity

All functions produce publication-ready 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.

Misconceptions about ggstatsplot

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 time-constrained 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, 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/

Extensions

In case you use the GUI software jamovi, you can install a module called jjstatsplot, which is a wrapper around ggstatsplot.

Acknowledgments

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. Ben-Shachar, 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. 😁

Contributing

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 e-mail, 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.

ggstatsplot: ggplot2 Based Plots with Statistical Details

Raison d’être

Installation

Citation

Documentation and Examples

Summary of available plots

Summary of types of statistical analyses

Statistical reporting

Summary of statistical tests and effect sizes

Primary functions

ggbetweenstats

Summary of graphics

Summary of tests

ggwithinstats

Summary of graphics

Summary of tests

gghistostats

Summary of graphics

Summary of tests

ggdotplotstats

Summary of graphics

Summary of tests

ggscatterstats

Summary of graphics

Summary of tests

ggcorrmat

Summary of graphics

Summary of tests

ggpiestats

Summary of graphics

Summary of tests

ggbarstats

Summary of graphics

Summary of tests

ggcoefstats

Supported models

Summary of graphics

Summary of meta-analysis tests

Extracting dataframes with statistical details

Using ggstatsplot statistical details with custom plots

Summary of benefits

Syntax simplicity

Misconceptions about ggstatsplot

ggstatsverse: Components of ggstatsplot

statsExpressions

pairwiseComparisons

Extensions

Acknowledgments

Contributing

Links

License

Community

Citation

Developers

`ggstatsplot`: `ggplot2` Based Plots with Statistical Details

`ggbetweenstats`

`ggwithinstats`

`gghistostats`

`ggdotplotstats`

`ggscatterstats`

`ggcorrmat`

`ggpiestats`

`ggbarstats`

`ggcoefstats`

Using `ggstatsplot` statistical details with custom plots

`ggstatsverse`: Components of `ggstatsplot`

`statsExpressions`

`pairwiseComparisons`