Raincloud plots are a multi-platform tool for robust data visualization. This revised version includes a fully functional R package, raincloudplots, which allows an easier way to create raincloud plots for common research designs. The plots combine raw data, probability density, and key summary statistics such as median, mean, and confidence intervals in an appealing and flexible format. The plots provide a balance between statistical information and visual clarity, allowing for inference at a glance. The plots can be created in R, Python, and Matlab. The tutorial provides code and documentation for the step-by-step creation and customization of raincloud plots in these languages. The plots are useful for visualizing data distributions, identifying outliers, and showing patterns in the data. They are particularly useful for repeated measures data and factorial designs. The plots can be customized with different color palettes, orientations, and additional elements such as boxplots and line plots. The plots are designed to be transparent and informative, providing a comprehensive view of the data.Raincloud plots are a multi-platform tool for robust data visualization. This revised version includes a fully functional R package, raincloudplots, which allows an easier way to create raincloud plots for common research designs. The plots combine raw data, probability density, and key summary statistics such as median, mean, and confidence intervals in an appealing and flexible format. The plots provide a balance between statistical information and visual clarity, allowing for inference at a glance. The plots can be created in R, Python, and Matlab. The tutorial provides code and documentation for the step-by-step creation and customization of raincloud plots in these languages. The plots are useful for visualizing data distributions, identifying outliers, and showing patterns in the data. They are particularly useful for repeated measures data and factorial designs. The plots can be customized with different color palettes, orientations, and additional elements such as boxplots and line plots. The plots are designed to be transparent and informative, providing a comprehensive view of the data.