Raincloud plots: a multi-platform tool for robust data visualization

TLDR

Across scientific disciplines, there is a growing need for statistically robust, transparent data visualization tools that accurately convey statistical effects and raw data without distortion, as traditional barplots with error bars can misrepresent effect sizes and obscure underlying patterns. The paper introduces raincloud plots as a robust visualization approach that preserves inference at a glance while providing maximal statistical information and demonstrates their use with open‑source code. Raincloud plots combine raw data, density estimates, and key summary statistics in a single flexible format, with interactive R and Python tutorials available via Binder. The authors demonstrate that raincloud plots effectively convey statistical information, outline modifications for optimal use, and provide open‑source code for implementation.

Abstract

Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired ‘inference at a glance’ nature of barplots and other similar visualization devices. These “raincloud plots” can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.