#Raincloudplots – the preprint!

Today we’re extremely excited to bring you our latest project – the raincloud plots preprint! Working on this project has been an absolute pleasure – I’ve learned so much about open science and data visualization. Better yet I can now tick ‘write a paper through twitter DMs’ off my bucket list!

For those of you who missed it, a few months ago I wrote a blog post showing off some plots I’d hacked together in ggplot. To my surprise, these ‘raincloud plots’ generated a great deal of excitement, and people from a variety of disciplines started asking if there was a paper they could cite. Things really started to take off when Davide Poggiali and Tom Rhys Marshall unveiled their own raincloudplot functions in Python and Matlab. Together with Davide and Tom, I reached out to Rogier Kievit and Kirstie Whitaker, two shining stars of the open neuroscience community, and asked if they would be interested in helping us put together a multi-platform tutorial so we could help as many people as possible ‘make it rain’. Together with this all-star team, I’m very happy to say that version 1.0 of the Rainclouds Paper is now published at PeerJ!

Raincloud plots: a multiplatform tool for robust data visualization

https://peerj.com/preprints/27137v1

The paper is accompanied by a GitHub repository where you can find custom functions to create your own raincloud plots in R, Python, and Matlab. Thanks to the magic of Binder and Rmarkdown, you can even run the R and Python tutorials right in your browser! You can also follow these tutorials within the paper itself.

https://github.com/RainCloudPlots/RainCloudPlots#read-the-preprint

Now, at this junction it is important to emphasize this is version 1.0 of this project. We have a long list of revisions to make for our next preprint – and we invite you to contribute your own tweaks, modules, and excellent plots at our github repo! You can find instructions on making your own contributions here:

https://github.com/RainCloudPlots/RainCloudPlots/blob/master/CONTRIBUTING.md

We look forward to your comments, feedback, and contributions to the project! For example, we’re considering adding an empirical aspect to the paper before submitting it for peer review. One idea we’ve had is to try to run an online experiment in a large sample of scientists, to probe whether raincloud plots improve the guesstimation of statistical differences and uncertainty. Do get in touch if that is something you would be interested in contributing to!

Of course, this project wouldn’t be possible without the amazing contributions of the many developers and scientists who make such amazing tools like ggplot, matplotlib, seaborn, and many more possible. As we point out in the paper, raincloud plots themselves are just one extension of a rich history of better plotting alternatives. We hope you’ll find our code and tutorials useful so you can continue to make the most kick-ass, robust data visualizations possible!

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