The article introduces "raincloud plots" as a robust and transparent data visualization method that combines raw data points, probability density, and key summary statistics such as median, mean, and confidence intervals. This approach addresses the limitations of traditional bar plots, which can distort effect sizes, hide underlying patterns, and obscure statistical assumptions. Raincloud plots are designed to provide maximal statistical information while maintaining the intuitive nature of bar plots. The authors provide a multi-platform tutorial, including R, Python, and Matlab code, to guide users through the creation and customization of raincloud plots. They demonstrate how to transform bar plots into raincloud plots, add boxplots for statistical inference, and customize various aspects of the plots to suit different data types and experimental designs. The tutorial also covers handling repeated measures data and complex factorial designs. The goal is to offer a flexible and widely accessible tool for robust and transparent data visualization across scientific disciplines.The article introduces "raincloud plots" as a robust and transparent data visualization method that combines raw data points, probability density, and key summary statistics such as median, mean, and confidence intervals. This approach addresses the limitations of traditional bar plots, which can distort effect sizes, hide underlying patterns, and obscure statistical assumptions. Raincloud plots are designed to provide maximal statistical information while maintaining the intuitive nature of bar plots. The authors provide a multi-platform tutorial, including R, Python, and Matlab code, to guide users through the creation and customization of raincloud plots. They demonstrate how to transform bar plots into raincloud plots, add boxplots for statistical inference, and customize various aspects of the plots to suit different data types and experimental designs. The tutorial also covers handling repeated measures data and complex factorial designs. The goal is to offer a flexible and widely accessible tool for robust and transparent data visualization across scientific disciplines.