# ggstatsplot v0.0.4

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## 'ggplot2' Based Plots with Statistical Details

Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests (parametric, non-parametric, or robust) 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) or categorical (pie charts) data.

# ggstatsplot: ggplot2 Based Plots with Statistical Details

## 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. Currently, it supports only the most common types of statistical tests (parametric, nonparametric, and robust versions of t-tets/anova, correlation, and contingency tables analyses).

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

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

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

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

## Installation

To get the latest, stable CRAN release:

utils::install.packages(pkgs = "ggstatsplot")


You can get the development version from GitHub. 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 = "devtools")

devtools::install_github(
repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = FALSE,                # assumes that you already have all packages installed needed for this package to work
quick = TRUE                         # skips docs, demos, and vignettes
)


If time is not a constraint-

devtools::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:

utils::citation(package = "ggstatsplot")


## Help

There is a dedicated website to ggstatplot, which is updated after every new commit: https://indrajeetpatil.github.io/ggstatsplot/.

In R, documentation for any function can be accessed with the standard help command-

?ggbetweenstats
?ggscatterstats
?gghistostats
?ggpiestats
?ggcorrmat
?ggcoefstats
?combine_plots
?grouped_ggbetweenstats
?grouped_ggscatterstats
?grouped_gghistostats
?grouped_ggpiestats
?grouped_ggcorrmat


## Usage

ggstatsplot relies on non-standard evaluation, which means you shouldn’t enter arguments in the following manner: data = NULL, x = data$x, y = data$y. You must always specify the data argument for all functions.

Additionally, ggstatsplot is a very chatty package and will by default output information about references for tests, notes on assumptions about linear models, and warnings. 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.

Here are examples of the main functions currently supported in ggstatsplot. Note: 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: https://cran.r-project.org/web/packages/ggstatsplot/index.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)

# plot
ggstatsplot::ggbetweenstats(
data = datasets::iris,
x = Species,
y = Sepal.Length,
messages = FALSE
)


Number of other arguments can be specified to make this plot even more informative and, additionally, this function returns a ggplot2 object and thus any of the graphics layers can be further modified:

library(ggplot2)

# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggbetweenstats(
data = datasets::iris,
x = Species,
y = Sepal.Length,
notch = TRUE,                                   # show notched box plot
mean.plotting = TRUE,                           # whether mean for each group id to be displayed
type = "parametric",                            # which type of test is to be run
outlier.tagging = TRUE,                         # whether outliers need to be tagged
outlier.label = Sepal.Width,                    # variable to be used for the outlier tag
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
caption = expression(                           # caption text for the plot
paste(italic("Note"), ": this is a demo")
),
ggtheme = ggplot2::theme_grey(),                # choosing a different theme
palette = "Set1",                               # choosing a different color palette
messages = FALSE
) +                                               # further modification outside of ggstatsplot
ggplot2::coord_cartesian(ylim = c(3, 8)) +
ggplot2::scale_y_continuous(breaks = seq(3, 8, by = 1))


The type (of test) argument also accepts the following abbreviations: "p" (for parametric), "np" (for nonparametric), "r" (for robust). Additionally, the type of plot to be displayed can also be modified ("box", "violin", or "boxviolin").

Variant of this function ggwithinstats is currently under work. You can still use this function just to prepare the plot for exploratory data analysis, but the statistical details displayed in the subtitle will be incorrect. You can remove them by adding + ggplot2::labs(subtitle = NULL).

For more, see the ggbetweenstats vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/ggbetweenstats.html

• ggscatterstats

This function creates a scatterplot with marginal histograms/boxplots/density/violin plots from and results from statistical tests in the subtitle:

ggstatsplot::ggscatterstats(
data = datasets::iris,
x = Sepal.Length,
y = Petal.Length,
title = "Dataset: Iris flower data set",
messages = FALSE
)


Number of other arguments can be specified to modify this basic plot-

library(datasets)

# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggscatterstats(
data = subset(datasets::iris, iris$Species == "setosa"), x = Sepal.Length, y = Petal.Length, type = "robust", # type of test that needs to be run xlab = "Attribute: Sepal Length", # label for x axis ylab = "Attribute: Petal Length", # label for y axis line.color = "black", # changing regression line color line title = "Dataset: Iris flower data set", # title text for the plot caption = expression( # caption text for the plot paste(italic("Note"), ": this is a demo") ), marginal.type = "density", # type of marginal distribution to be displayed xfill = "blue", # color fill for x-axis marginal distribution yfill = "red", # color fill for y-axis marginal distribution centrality.para = "median", # which type of central tendency lines are to be displayed width.jitter = 0.2, # amount of horizontal jitter for data points height.jitter = 0.4, # amount of vertical jitter for data points messages = FALSE # turn off messages and notes )  For more, see the ggscatterstats vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/ggscatterstats.html • ggpiestats This function creates a pie chart for categorical variables with results from contingency table analysis included in the subtitle of the plot. If only one categorical variable is entered, results from one-sample proportion test will be displayed as a subtitle. # for reproducibility set.seed(123) # plot ggstatsplot::ggpiestats( data = datasets::iris, main = Species, messages = FALSE )  This function can also be used to study an interaction between two categorical variables. Additionally, as with the other functions in ggstatsplot, this function returns a ggplot2 object and can further be modified with ggplot2 syntax (e.g., we can change the color palette after ggstatsplot has produced the plot)- library(ggplot2) # for reproducibility set.seed(123) # plot ggstatsplot::ggpiestats( data = datasets::mtcars, main = cyl, condition = am, title = "Dataset: Motor Trend Car Road Tests", messages = FALSE ) + # further modification outside of ggstatsplot to change the default palette as an example ggplot2::scale_fill_brewer(palette = "Set1")  As with the other functions, this basic plot can further be modified with additional arguments: # for reproducibility set.seed(123) # plot ggstatsplot::ggpiestats( data = datasets::mtcars, main = am, condition = cyl, title = "Dataset: Motor Trend Car Road Tests", # title for the plot stat.title = "interaction: ", # title for the results from Pearson's chi-squared test legend.title = "Transmission", # title for the legend factor.levels = c("1 = manual", "0 = automatic"), # renaming the factor level names for 'main' variable facet.wrap.name = "No. of cylinders", # name for the facetting variable facet.proptest = FALSE, # turning of facetted proportion test results caption = expression( # text for the caption paste(italic("Note"), ": this is a demo") ), messages = FALSE # turn off messages and notes )  For more, including information about the variant of this function grouped_ggpiestats, see the ggpiestats vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/ggpiestats.html • gghistostats In case you would like to see the distribution of one variable and check if it is significantly different from a specified value with a one sample test, this function will let you do that. library(datasets) ggstatsplot::gghistostats( data = datasets::iris, x = Sepal.Length, title = "Distribution of Iris sepal length", type = "parametric", # one sample t-test test.value = 3, # default value is 0 centrality.para = "mean", # which measure of central tendency is to be plotted centrality.color = "darkred", # decides color of vertical line representing central tendency binwidth = 0.10, # binwidth value (needs to be toyed around with until you find the best one) messages = FALSE # turn off the messages )  The type (of test) argument also accepts the following abbreviations: "p" (for parametric) or "np" (for nonparametric) or "bf" (for Bayes Factor). ggstatsplot::gghistostats( data = NULL, title = "Distribution of variable x", x = stats::rnorm(n = 1000, mean = 0, sd = 1), test.value = 1, test.value.line = TRUE, test.value.color = "black", centrality.para = "mean", type = "bf", bf.prior = 0.8, messages = FALSE, caption = expression( paste(italic("Note"), ": black line - test value; blue line - observed mean") ) )  As seen here, by default, Bayes Factor quantifies the support for the alternative hypothesis (H1) over the null hypothesis (H0) (i.e., BF10 is displayed). For more, including information about the variant of this function grouped_gghistostats, see the gghistostats vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/gghistostats.html • ggcorrmat ggcorrmat makes correlalograms with minimal amount of code. Just sticking to the defaults itself produces publication-ready correlation matrices. (Wrapper around ggcorrplot) # as a default this function outputs a correlalogram plot ggstatsplot::ggcorrmat( data = datasets::iris, corr.method = "spearman", # correlation method sig.level = 0.005, # threshold of significance cor.vars = Sepal.Length:Petal.Width, # a range of variables can be selected cor.vars.names = c("Sepal Length", "Sepal Width", "Petal Length", "Petal Width"), title = "Correlalogram for length measures for Iris species", subtitle = "Iris dataset by Anderson", caption = expression( paste( italic("Note"), ": X denotes correlation non-significant at ", italic("p "), "< 0.005; adjusted alpha" ) ) )  Multiple arguments can be modified to change the appearance of the correlation matrix. Alternatively, you can use it just to get the correlation matrices and their corresponding p-values (in a tibble format). This is especially useful for robust correlation coefficient, which is not currently supported in ggcorrmat plot. # getting the correlation coefficient matrix ggstatsplot::ggcorrmat( data = datasets::iris, cor.vars = Sepal.Length:Petal.Width, corr.method = "robust", output = "correlations", # specifying the needed output 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 = datasets::iris, cor.vars = Sepal.Length:Petal.Width, corr.method = "robust", output = "p-values" ) #> # A tibble: 4 x 5 #> variable Sepal.Length Sepal.Width Petal.Length Petal.Width #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Sepal.Length 0 0.0818 0 0 #> 2 Sepal.Width 0.0818 0 0.0000000529 0.00000252 #> 3 Petal.Length 0 0.0000000529 0 0 #> 4 Petal.Width 0 0.00000252 0 0  For examples and more information, see the ggcorrmat vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/ggcorrmat.html • ggcoefstats ggcoefstats creates a lot with the regression coefficients’ point estimates as dots with confidence interval whiskers. This is a wrapper function around GGally::ggcoef. ggstatsplot::ggcoefstats(x = stats::lm(formula = mpg ~ am * cyl, data = mtcars))  The basic can be further modified to one’s liking with additional arguments: ggstatsplot::ggcoefstats( x = stats::lm(formula = mpg ~ am * cyl, data = mtcars), point.color = "red", vline.color = "#CC79A7", vline.linetype = "dotdash", stats.label.size = 3.5, stats.label.color = c("#0072B2", "#D55E00", "darkgreen"), title = "Car performance predicted by transmission and cylinder count", subtitle = "Source: 1974 Motor Trend US magazine" ) + # 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)  All the regression model classes that are supported in the broom package with tidy and glance methods (https://broom.tidyverse.org/articles/available-methods.html) are also supported by ggcoefstats. Let’s see few examples: library(dplyr) library(lme4) # for reproducibility set.seed(200) # creating dataframes needed for the analysis below d <- as.data.frame(Titanic) # combining plots together ggstatsplot::combine_plots( # generalized linear model ggstatsplot::ggcoefstats( x = stats::glm( formula = Survived ~ Sex + Age, data = d, weights = d$Freq,
family = "binomial"
),
exponentiate = TRUE,
exclude.intercept = FALSE,
title = "generalized linear model"
),
# nonlinear least squares
ggstatsplot::ggcoefstats(
x = stats::nls(
formula = mpg ~ k / wt + b,
data = mtcars,
start = list(k = 1, b = 0)
),
point.color = "darkgreen",
title = "non-linear least squares"
),
# linear mmodel
ggstatsplot::ggcoefstats(
x = lme4::lmer(
formula = Reaction ~ Days + (Days | Subject),
data = lme4::sleepstudy
),
point.color = "red",
exclude.intercept = TRUE,
title = "linear mixed-effects model"
),
# generalized linear mixed-effects model
ggstatsplot::ggcoefstats(
x = lme4::glmer(
formula = cbind(incidence, size - incidence) ~ period + (1 | herd),
data = lme4::cbpp,
family = binomial
),
exclude.intercept = FALSE,
title = "generalized linear mixed-effects model"
),
labels = c("(a)", "(b)", "(c)", "(d)"),
nrow = 2,
ncol = 2
)


This is by no means an exhaustive list of models supported by ggcoefstats. For more, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/ggcoefstats.html

• combine_plots

ggstatsplot also contains a helper function combine_plots to combine multiple plots. This is a wrapper around and lets you combine multiple plots and add combination of title, caption, and annotation texts with suitable default parameters.

The full power of ggstatsplot can be leveraged with a functional programming package like purrr that replaces many for loops with code that is both more succinct and easier to read and, therefore, purrr should be preferrred.

For more, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/combine_plots.html

• theme_mprl

All plots from ggstatsplot have a default theme: theme_mprl. For more on how to modify it, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/theme_mprl.html

## Functions in ggstatsplot

 Name Description check_outlier Finding the outliers in the dataframe using Tukey's interquartile range rule combine_plots Combining multiple plots using cowplot::plot_grid() with a combination of title, caption, and annotation label grouped_ggpiestats Grouped pie charts with statistical tests cor_tets_ci A correlation test with confidence interval for effect size. grouped_ggscatterstats Scatterplot with marginal distributions for all levels of a grouping variable normality_message Display normality test result as a message. %>% Pipe operator lm_effsize_ci Confidence intervals for partial eta-squared and omega-squared for linear models. theme_mprl Default theme used in all ggstatsplot package plots theme_corrmat Default theme used for correlation matrix ggbetweenstats violin plots for group or condition comparisons untable Untable a dataset ggcoefstats Model coefficients for fitted models with the model summary as a caption. grouped_proptest Function to run proportion test on grouped data. ggcorrmat Visualization of a correlalogram (or correlation matrix) using 'ggplot2'/'ggcorrplot' gghistostats Histogram for distribution of a numeric variable intent_morality Moral judgments about third-party moral behavior. robcor_ci Robust correlation coefficient and its confidence interval signif_column Creating a new character type column with significance labels Titanic_full Titanic dataset. bartlett_message Display homogeneity of variance test as a message specify_decimal_p Custom function for getting specified number of decimal places in results for p-value ggpiestats Pie charts with statistical tests t1way_ci A heteroscedastic one-way ANOVA for trimmed means with confidence interval for effect size. ggscatterstats Scatterplot with marginal distributions movies_long Movie information and user ratings from IMDB.com (long format). movies_wide Movie information and user ratings from IMDB.com (wide format). theme_pie Default theme used for pie chart tibble Anticipate use of tibbles chisq_v_ci Chi-squared test of association with confidence interval for effect size (Cramer's V). grouped_gghistostats Grouped histograms for distribution of a numeric variable grouped_ggbetweenstats Violin plots for group or condition comparisons repeated across all levels of a grouping variable. grouped_ggcorrmat Visualization of a correlalogram (or correlation matrix) using 'ggplot2'/'ggcorrplot' for all levels of a grouping variable ggstatsplot-package ggstatsplot legend_title_margin Custom function to set upper and lower margins to legend title in ggplot2 No Results!