Skip to contents

ggstatsplot: 'ggplot2' Based Plots with Statistical Details

Source: R/ggstatsplot-package.R
ggstatsplot-package.Rd

{ggstatsplot} is an extension of {ggplot2} package. It creates graphics with details from statistical tests included in the plots themselves. It provides an easier API 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 the most common types of statistical tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.

Details

ggstatsplot

The main functions are:

  • ggbetweenstats() function to produce information-rich comparison plot between different groups or conditions with {ggplot2} and details from the statistical tests in the subtitle.

  • ggwithinstats() function to produce information-rich comparison plot within different groups or conditions with {ggplot2} and details from the statistical tests in the subtitle.

  • ggscatterstats() function to produce {ggplot2} scatterplots along with a marginal distribution plots from {ggside} package and details from the statistical tests in the subtitle.

  • ggpiestats() function to produce pie chart with details from the statistical tests in the subtitle.

  • ggbarstats() function to produce stacked bar chart with details from the statistical tests in the subtitle.

  • gghistostats() function to produce histogram for a single variable with results from one sample test displayed in the subtitle.

  • ggdotplotstats() function to produce Cleveland-style dot plots/charts for a single variable with labels and results from one sample test displayed in the subtitle.

  • ggcorrmat() function to visualize the correlation matrix.

  • ggcoefstats() function to visualize results from regression analyses.

  • combine_plots() helper function to combine multiple {ggstatsplot} plots using patchwork::wrap_plots().

References: Patil (2021) doi:10.21105/joss.03236 .

For more documentation, see the dedicated Website.

See also

Useful links:

Author

Maintainer: Indrajeet Patil patilindrajeet.science@gmail.com (ORCID) [copyright holder]

Other contributors: