# ggstatsplot v0.0.12

0

0th

Percentile

## 'ggplot2' Based Plots with Statistical Details

Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It is targeted primarily at behavioral sciences community to provide a one-line code to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. 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.

# ggstatsplot: ggplot2 Based Plots with Statistical Details

Package Status Usage GitHub References

# Overview

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 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.

It, therefore, produces a limited kinds of plots for the supported analyses:

• violin plots (for comparisons between groups or conditions),
• pie charts and bar charts (for categorical data),
• scatterplots (for correlations between two variables),
• correlation matrices (for correlations between multiple variables),
• histograms and dot plots/charts (for hypothesis about distributions),
• dot-and-whisker plots (for regression models).

In addition to these basic plots, ggstatsplot also provides grouped_ versions for most functions that makes it easy to repeat the same analysis for any grouping variable.

Future versions will include other types of statistical analyses and plots as well.

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 95% CI available?
ggbetweenstats Parametric Student’s and Welch’s t-test Cohen’s d, Hedge’s g $\checkmark$
ggbetweenstats Parametric Fisher’s and Welch’s one-way ANOVA $\eta^2, \eta^2_p, \omega^2, \omega^2_p$ $\checkmark$
ggbetweenstats Non-parametric Mann-Whitney U-test r $\checkmark$
ggbetweenstats Non-parametric Kruskal-Wallis Rank Sum Test $\epsilon^2$ $\checkmark$
ggbetweenstats Robust Yuen’s test for trimmed means $\xi$ $\checkmark$
ggbetweenstats Robust Heteroscedastic one-way ANOVA for trimmed means $\xi$ $\checkmark$
ggwithinstats Parametric Student’s t-test Cohen’s d, Hedge’s g $\checkmark$
ggwithinstats Parametric Fisher’s one-way repeated measures ANOVA $\eta^2_p, \omega^2$ $\checkmark$
ggwithinstats Non-parametric Wilcoxon signed-rank test r $\checkmark$
ggwithinstats Non-parametric Friedman test $W_{Kendall}$ $\checkmark$
ggwithinstats Robust Yuen’s test on trimmed means for dependent samples $\xi$ $\checkmark$
ggwithinstats Robust Heteroscedastic one-way repeated measures ANOVA for trimmed means $\times$ $\times$
ggpiestats Parametric $\text{Pearson's}~ \chi^2 ~\text{test}$ Cramér’s V $\checkmark$
ggpiestats Parametric McNemar’s test Cohen’s g $\checkmark$
ggpiestats Parametric One-sample proportion test Cramér’s V $\checkmark$
ggscatterstats/ggcorrmat Parametric Pearson’s r r $\checkmark$
ggscatterstats/ggcorrmat Non-parametric $\text{Spearman's}~ \rho$ $\rho$ $\checkmark$
ggscatterstats/ggcorrmat Robust Percentage bend correlation r $\checkmark$
gghistostats/ggdotplotstats Parametric One-sample t-test Cohen’s d, Hedge’s g $\checkmark$
gghistostats Non-parametric One-sample Wilcoxon signed rank test r $\checkmark$
gghistostats/ggdotplotstats Robust One-sample percentile bootstrap robust estimator $\checkmark$
gghistostats/ggdotplotstats Parametric Regression models $\beta$ $\checkmark$

Work is in progress to add some of the currently missing functionality.

# Installation

To get the latest, stable CRAN release (0.0.12):

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 (0.0.12.9000). 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")

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
)


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) {
#>   # convert the saved plot
#>   p <- cowplot::ggdraw() +
#>     cowplot::draw_grob(grid::grobTree(plot))
#>
#>   # returning the converted plot
#>   return(p)
#> }
#> <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-

# Usage and syntax

ggstatsplot relies on non-standard evaluation (NSE), i.e., rather than looking at the values of arguments (x, y), it instead looks at their expressions. This means that you shouldn’t enter arguments with the $ operator and set data = NULL (e.g., data = NULL, x = data$x, y = data$y). You must always specify the data argument for all functions. On the plus side, you can enter arguments either as a string (x = "x", y = "y") or as a bare expression (x = x, y = y) and it wouldn’t matter. To read more about NSE, see- http://adv-r.had.co.nz/Computing-on-the-language.html 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. # 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 a ggplot2 object and thus any of the graphics layers can be further modified. 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$Species <-
base::factor(
x = iris2$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 = "p", # 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., $BF_{01}$). By default, natural logarithms are shown because Bayes Factor values can sometimes be pretty large. Having values on logarithmic scale also makes it easy to compare evidence in favor alternative ($BF_{10}$) versus null ($BF_{01}$) hypotheses (since $log_{e}(BF_{01}) = - log_{e}(BF_{01})$). 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 Student’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 Student’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) library(jmv) data("bugs", package = "jmv") # getting data in tidy format data_bugs <- bugs %>% tibble::as_tibble(x = .) %>% tidyr::gather(data = ., key, value, LDLF:HDHF) %>% dplyr::filter(.data = ., Region %in% c("Europe", "North America")) # plot ggstatsplot::grouped_ggwithinstats( data = dplyr::filter(data_bugs, key %in% c("LDLF", "LDHF")), x = key, y = value, 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,
conf.level = 0.99,
k = 3, # no. of decimal places in the results
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
grouping.var = genre, # grouping variable
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
messages = FALSE,
nrow = 2,
title.text = "Relationship between movie length by IMDB ratings for different genres"
)


Using ggscatterstats() in R Notebooks or R Markdown

If you include a ggscatterstats() plot inside an R Notebook or R Markdown code chunk, you will notice that running the chunk doesn’t return any output nor does it give any error. In order to get a ggscatterstats() to show up in these contexts, you need to save the ggscatterstats plot as a variable in one code chunk, and explicitly print it using the grid package in another chunk, like this:

# include the following code in your code chunk inside R Notebook or Markdown
grid::grid.newpage()
grid::grid.draw(
ggstatsplot::ggscatterstats(
data = ggstatsplot::movies_wide,
x = budget,
y = rating,
marginal = TRUE,
messages = FALSE
)
)


Another option - or rather a compromise - is not to include marginal distribution at all by setting marginal = FALSE.

### Summary of tests

Following tests are carried out for each type of analyses. Additionally, the correlation coefficients (and their confidence intervals) are used as effect sizes-

Type Test CI?
Parametric Pearson’s correlation coefficient Yes
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 $\chi^2$ test for between-subjects design and McNemar’s $\chi^2$ 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 $\chi^2$ 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,
main = 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,
main = am,
condition = 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 (main)
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 $\chi^2$ 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,
main = 1st survey,
condition = 2nd survey,
counts = Counts,
paired = TRUE, # within-subjects design
conf.level = 0.99, # confidence interval for effect size measure
stat.title = "McNemar's Test: ",
package = "wesanderson",
palette = "Royal1"
)
#> Note: 99% CI for effect size estimate was computed with 100 bootstrap samples.
#> Note: Results from one-sample proportion tests for each level of the variable
#> 2nd survey testing for equal proportions of the variable 1st survey.
#> # A tibble: 2 x 8
#>   condition N     Approve Disapprove Chi-squared    df p-value
#>   <fct>     <chr> <chr>   <chr>              <dbl> <dbl>     <dbl>
#> 1 Approve   (n =~ 90.23%  9.77%               570.     1         0
#> 2 Disappro~ (n =~ 20.83%  79.17%              245      1         0
#> # ... with 1 more variable: significance <chr>


Note that when a two-way table is present (i.e., when both main and condition arguments are specified), p-values for results from one-sample proportion tests are displayed in each facet in the form of asterisks with the following convention:

• $***$: $p < 0.001$
• $**$: $p < 0.01$
• $*$: $p < 0.05$
• $ns$: $p > 0.05$

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")
),
main = 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 $n \times p$ contingency table Pearson’s $\chi^{2}$ test
Paired $n \times p$ contingency table McNemar’s $\chi^{2}$ test
Frequency $n \times 1$ contingency table Goodness of fit ($\chi^{2}$)

Following effect sizes (and confidence intervals/CI) are available for each type of test-

Type Effect size CI?
Pearson’s chi-squared test Cramer’s V Yes
McNemar’s test g Yes
Goodness of fit 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,
main = mpaa,
condition = 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
)


And, needless to say, there is also a grouped_ variant of this function-

# setup
library(ggstatsplot)
set.seed(123)

# let's create a smaller dataframe
diamonds_short <- ggplot2::diamonds %>%
dplyr::filter(.data = ., cut %in% c("Very Good", "Ideal")) %>%
dplyr::filter(.data = ., clarity %in% c("SI1", "SI2", "VS1", "VS2", "VVS1")) %>%
dplyr::sample_frac(tbl = ., size = 0.05)

# plot
ggstatsplot::grouped_ggbarstats(
data = diamonds_short,
main = color,
condition = clarity,
grouping.var = cut,
sampling.plan = "poisson",
title.prefix = "Quality",
data.label = "both",
label.text.size = 3,
perc.k = 1,
package = "palettetown",
palette = "charizard",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
messages = FALSE,
title.text = "Diamond quality and color combination",
nrow = 2
)


### Summary of tests

This is identical to the ggpiestats function summary of tests.

## gghistostats

In case you would like to see the distribution of a single variable and check if it is significantly different from a specified value with a one sample test, this function will let you do that.

ggstatsplot::gghistostats(
data = ToothGrowth, # dataframe from which variable is to be taken
x = len, # numeric variable whose distribution is of interest
title = "Distribution of Sepal.Length", # title for the plot
test.value = 10, # the comparison value for t-test
test.value.line = TRUE, # display a vertical line at test value
type = "bf", # 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

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 = "np", # 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
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).

 r

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

#>

#> 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

#>

#> 1 sleep_to~ 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_cy~ 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 correlat

## Vignettes of ggstatsplot

 Name web_only/combine_plots.Rmd web_only/effsize_interpretation.Rmd web_only/faq.Rmd web_only/gallery.Rmd web_only/ggbetweenstats.Rmd web_only/ggcoefstats.Rmd web_only/ggcorrmat.Rmd web_only/gghistostats.Rmd web_only/ggpiestats.Rmd web_only/ggscatterstats.Rmd web_only/ggstatsplot_paper.Rmd web_only/ggwithinstats.Rmd web_only/purrr_examples.Rmd web_only/session_info.Rmd web_only/theme_ggstatsplot.Rmd additional.Rmd No Results!
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