The 'ggstatsplot' package in R provides a one-line syntax to enrich ggplot2-based visualizations with statistical analysis results. This approach combines data visualization and statistical modeling into a single informative graphic, helping researchers adopt a rigorous, reliable, and robust data exploratory and reporting workflow. The package supports common statistical tests and corresponding visualizations, including parametric, non-parametric, robust, and Bayesian tests, as well as box plots, scatter plots, dot-and-whisker plots, pie charts, and bar charts. It is designed to follow best practices in data visualization and statistical reporting, making it easy for users with little to no coding experience to use. The package internally uses the tidyverse for data cleaning and statsExpressions and easystats for statistical analysis. It is licensed under the GNU General Public License (v3.0) and is available on GitHub. The package encourages researchers and readers to evaluate statistical assumptions of a model in the context of the underlying data. The 'ggstatsplot' approach provides benefits such as producing charts with raw data and numerical summary indices, avoiding errors in statistical reporting, highlighting the importance of effect size, and encouraging the evaluation of statistical assumptions. The package is an ongoing, ambitious, and long-term project with a future scope to support an increasing collection of statistical analyses and visualizations.The 'ggstatsplot' package in R provides a one-line syntax to enrich ggplot2-based visualizations with statistical analysis results. This approach combines data visualization and statistical modeling into a single informative graphic, helping researchers adopt a rigorous, reliable, and robust data exploratory and reporting workflow. The package supports common statistical tests and corresponding visualizations, including parametric, non-parametric, robust, and Bayesian tests, as well as box plots, scatter plots, dot-and-whisker plots, pie charts, and bar charts. It is designed to follow best practices in data visualization and statistical reporting, making it easy for users with little to no coding experience to use. The package internally uses the tidyverse for data cleaning and statsExpressions and easystats for statistical analysis. It is licensed under the GNU General Public License (v3.0) and is available on GitHub. The package encourages researchers and readers to evaluate statistical assumptions of a model in the context of the underlying data. The 'ggstatsplot' approach provides benefits such as producing charts with raw data and numerical summary indices, avoiding errors in statistical reporting, highlighting the importance of effect size, and encouraging the evaluation of statistical assumptions. The package is an ongoing, ambitious, and long-term project with a future scope to support an increasing collection of statistical analyses and visualizations.