henhaobio
Sign in

Abstract

Clear data visualization is essential to effectively communicate empirical findings across various research fields. Raincloud plots fill this need by offering a transparent and statistically robust approach to data visualization. This is achieved by combining three plots in an aesthetically pleasing fashion. First, a dot plot displays raw data with minimal distortion, allowing a fast glance at the sample size and outlier identification. Next, a box plot displays key distributional summary statistics such as the median and interquartile range. Lastly, a violin plot transparently displays the underlying distribution of the data. Despite the widespread use of raincloud plots, an R-package in alignment with the grammar of graphics was lacking. `ggrain` fills this need by offering one easy-to-use function (`geom_rain`) allowing the quick and seamless plotting of rainclouds in the R ecosystem. Further, it enables more complex plotting features such as factorial grouping, mapping with a secondary (continuous) covariate, and connecting observations longitudinally across multiple waves.

Citations
1

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISHu '25Postma '18Goedhart '20Kramer '22Sidiropoulo '18Léonard '22
64% semantic matchBioinformatics
Ignophi Hu·bioRxiv·Dec 22, 2025