Package	Status	Usage	GitHub	References

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 plots themselves and targeted primarily at behavioral sciences community to provide a one-line code to produce information-rich 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`	dot-and-whisker 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 t-test/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	Non-parametric	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 t-test):

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
`ggbetweenstats` (2 groups)	Parametric	Student’s and Welch’s t-test	Cohen’s d, Hedge’s g
`ggbetweenstats` (> 2 groups)	Parametric	Fisher’s and Welch’s one-way ANOVA
`ggbetweenstats` (2 groups)	Non-parametric	Mann-Whitney U-test	r
`ggbetweenstats` (> 2 groups)	Non-parametric	Kruskal-Wallis Rank Sum Test
`ggbetweenstats` (2 groups)	Robust	Yuen’s test for trimmed means
`ggbetweenstats` (> 2 groups)	Robust	Heteroscedastic one-way ANOVA for trimmed means
`ggwithinstats` (2 groups)	Parametric	Student’s t-test	Cohen’s d, Hedge’s g
`ggwithinstats` (> 2 groups)	Parametric	Fisher’s one-way repeated measures ANOVA
`ggwithinstats` (2 groups)	Non-parametric	Wilcoxon signed-rank test	r
`ggwithinstats` (> 2 groups)	Non-parametric	Friedman rank sum test
`ggwithinstats` (2 groups)	Robust	Yuen’s test on trimmed means for dependent samples
`ggwithinstats` (> 2 groups)	Robust	Heteroscedastic one-way 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	One-sample proportion test	Cramér’s V
`ggscatterstats` and `ggcorrmat`	Parametric	Pearson’s r	r
`ggscatterstats` and `ggcorrmat`	Non-parametric
`ggscatterstats`and `ggcorrmat`	Robust	Percentage bend correlation	r
`gghistostats` and `ggdotplotstats`	Parametric	One-sample t-test	Cohen’s d, Hedge’s g
`gghistostats`	Non-parametric	One-sample Wilcoxon signed rank test	r
`gghistostats` and `ggdotplotstats`	Robust	One-sample percentile bootstrap	robust estimator
`ggcoefstats`	Parametric	Regression models

Installation

To get the latest, stable CRAN release (0.1.2):

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 (0.1.2.9000). 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:

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: 0x0000000030e4fc98>
#> <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.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-

# 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 t-test.

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 t-test 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 x-axis variable
  ylab = "Attribute: Sepal Length", # label for the y-axis 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 t-test). If the null hypothesis can’t be rejected with the null hypothesis significance testing (NHST) approach, the Bayesian approach can help index evidence in favor of the null hypothesis (i.e., ).

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 p-values 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 (between-subjects) tests are carried out for each type of analyses-

Type	No. of groups	Test
Parametric	> 2	Fisher’s or Welch’s one-way ANOVA
Non-parametric	> 2	Kruskal–Wallis one-way ANOVA
Robust	> 2	Heteroscedastic one-way ANOVA for trimmed means
Bayes Factor	> 2	Fisher’s ANOVA
Parametric	2	Student’s or Welch’s t-test
Non-parametric	2	Mann–Whitney U test
Robust	2	Yuen’s test for trimmed means
Bayes Factor	2	Student’s t-test

The omnibus effect in one-way 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	p-value adjustment?
Parametric	No	Games-Howell test	Yes
Parametric	Yes	Student’s t-test	Yes
Non-parametric	No	Dwass-Steel-Crichtlow-Fligner 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 x-axis 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 (within-subjects) tests are carried out for each type of analyses-

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

The omnibus effect in one-way 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	p-value adjustment?
Parametric	Student’s t-test	Yes
Non-parametric	Durbin-Conover test	Yes
Robust	Yuen’s trimmed means test	Yes
Bayes Factor	No	No

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 x-axis marginal distribution
  yfill = "#009E73", # color fill for y-axis marginal distribution
  xalpha = 0.6, # transparency for x-axis marginal distribution
  yalpha = 0.6, # transparency for y-axis 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"
)

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
Non-parametric	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 between-subjects design and McNemar’s 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 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, # within-subjects 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 statistic   p.value
#>   <fct>         <int> <dbl> <chr>     <chr>   <chr>          <dbl>     <dbl>
#> 1 Disapprove      720   45  (n = 720) 20.83%  79.17%          245  3.20e- 55
#> 2 Approve         880   55. (n = 880) 90.23%  9.77%           570. 6.80e-126
#>   parameter method                                   significance
#>       <dbl> <chr>                                    <chr>       
#> 1         1 Chi-squared test for given probabilities ***         
#> 2         1 Chi-squared 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 chi-squared 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 p-values for results from one-sample 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 one-sample 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 one-sample test
  test.value.line = TRUE, # display a vertical line at test value
  type = "bayes", # bayes factor for one sample t-test
  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 t-test
  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	One-sample Student’s t-test
Non-parametric	One-sample Wilcoxon test
Robust	One-sample percentile bootstrap
Bayes Factor	One-sample Student’s t-test

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 (central-and noncentral-t distribution based)	Yes
Non-parametric	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", # non-parametric 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 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 correlalogram plot
ggstatsplot::ggcorrmat(
  data = ggplot2::msleep,
  corr.method = "robust", # correlation method
  sig.level = 0.001, # threshold of significance
  p.adjust.method = "holm", # p-value 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 NAs 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 p-values (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 p-value matrix
ggstatsplot::ggcorrmat(
  data = ggplot2::msleep,
  cor.vars = sleep_total:bodywt,
  corr.method = "robust",
  output = "p.values", # only "p" or "p-values" will also work
  p.adjust.method = "holm"
)
#> # 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   0.        5.291e-12   9.138e- 3 0.        3.170e- 5 2.568e- 6
#> 2 sleep_rem     4.070e-13 0.          1.978e- 2 5.291e-12 9.698e- 3 3.762e- 3
#> 3 sleep_cycle   2.285e- 3 1.978e- 2   0.        9.138e- 3 1.637e- 9 1.696e- 5
#> 4 awake         0.        4.070e-13   2.285e- 3 0.        3.170e- 5 2.568e- 6
#> 5 brainwt       4.528e- 6 4.849e- 3   1.488e-10 4.528e- 6 0.        4.509e-17
#> 6 bodywt        2.568e- 7 7.524e- 4   2.120e- 6 2.568e- 7 3.221e-18 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: p-values adjusted for multiple comparisons
#> the lower triangle: unadjusted p-values.
#> # A tibble: 15 x 7
#>    pair                          r   lower    upper         p lower.adj
#>    <chr>                     <dbl>   <dbl>    <dbl>     <dbl>     <dbl>
#>  1 sleep_total-sleep_rem    0.7641  0.6344  0.8520  7.806e-13   0.5632 
#>  2 sleep_total-sleep_cycle -0.4888 -0.7155 -0.1689  4.530e- 3  -0.7609 
#>  3 sleep_total-awake       -1      -1      -1       0.         -1      
#>  4 sleep_total-brainwt     -0.5935 -0.7408 -0.3918  1.426e- 6  -0.7852 
#>  5 sleep_total-bodywt      -0.5346 -0.6727 -0.3605  1.931e- 7  -0.7121 
#>  6 sleep_rem-sleep_cycle   -0.3344 -0.6118  0.01617 6.139e- 2  -0.6118 
#>  7 sleep_rem-awake         -0.7641 -0.8520 -0.6344  7.806e-13  -0.8807 
#>  8 sleep_rem-brainwt       -0.4139 -0.6246 -0.1471  3.451e- 3  -0.6495 
#>  9 sleep_rem-bodywt        -0.4517 -0.6317 -0.2255  2.580e- 4  -0.6647 
#> 10 sleep_cycle-awake        0.4888  0.1689  0.7155  4.530e- 3   0.05610
#> 11 sleep_cycle-brainwt      0.8727  0.7474  0.9380  3.250e-10   0.6573 
#> 12 sleep_cycle-bodywt       0.8464  0.7061  0.9228  1.040e- 9   0.6115 
#> 13 awake-brainwt            0.5935  0.3918  0.7408  1.426e- 6   0.2934 
#> 14 awake-bodywt             0.5346  0.3605  0.6727  1.931e- 7   0.2875 
#> 15 brainwt-bodywt           0.9572  0.9277  0.9748  9.694e-31   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
Non-parametric	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 dot-and-whisker 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 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 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 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 log-likelihood 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.) with ggplot2 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 different 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, bglmerMod, bigglm, biglm, blmerMod, bracl, brglm2, brmsfit, btergm, cch, clm, clmm, confusionMatrix, coxph, drc, emmGrid, epi.2by2, ergm, felm, fitdistr, glmc, 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, slm, speedglm, speedlm, stanreg, survreg, svyglm, svyolr, svyglm, tobit, wbgee, wblm, etc.

Although not shown here, this function can also be used to carry out both frequentist and Bayesian random-effects meta-analysis.

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 = "Michelson-Morley 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 non-standard 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 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/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 non-parametric)
"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

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.

Session Information

For reproducibility purposes, the details about the session information in which this document was rendered, see- https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/session_info.html

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

Raison d’être

Summary of available plots

Summary of types of statistical analyses

Statistical reporting

Summary of statistical tests and effect sizes

Installation

Citation

Documentation and Examples

Help

Primary functions

`ggbetweenstats`

Summary of tests

`ggwithinstats`

Summary of tests

`ggscatterstats`

Summary of tests

`ggpiestats`

Summary of tests

`ggbarstats`

Summary of tests

`gghistostats`

Summary of tests

`ggdotplotstats`

Summary of tests

`ggcorrmat`

Summary of tests

`ggcoefstats`

`combine_plots`

`theme_ggstatsplot`

Using `ggstatsplot` statistical details with custom plots

Usage and syntax simplicity

Code coverage

Contributing

Session Information

Links

License

Community

Citation

Developers

ggstatsplot: ggplot2 Based Plots with Statistical Details

Raison d’être

Summary of available plots

Summary of types of statistical analyses

Statistical reporting

Summary of statistical tests and effect sizes

Installation

Citation

Documentation and Examples

Help

Primary functions

ggbetweenstats

Summary of tests

ggwithinstats

Summary of tests

ggscatterstats

Summary of tests

ggpiestats

Summary of tests

ggbarstats

Summary of tests

gghistostats

Summary of tests

ggdotplotstats

Summary of tests

ggcorrmat

Summary of tests

ggcoefstats

combine_plots

theme_ggstatsplot

Using ggstatsplot statistical details with custom plots

Usage and syntax simplicity

Code coverage

Contributing

Session Information

Links

License

Community

Citation

Developers

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

`ggbetweenstats`

`ggwithinstats`

`ggscatterstats`

`ggpiestats`

`ggbarstats`

`gghistostats`

`ggdotplotstats`

`ggcorrmat`

`ggcoefstats`

`combine_plots`

`theme_ggstatsplot`

Using `ggstatsplot` statistical details with custom plots