Journal article
Raincloud plots: a multi-platform tool for robust data visualization
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
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- Files:
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(Preview, Version of record, pdf, 6.9MB, Terms of use)
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- Publisher copy:
- 10.12688/wellcomeopenres.15191.1
Authors
- Publisher:
- F1000Research
- Journal:
- Wellcome Open Research More from this journal
- Volume:
- 4
- Article number:
- 63
- Publication date:
- 2019-04-17
- Acceptance date:
- 2019-04-01
- DOI:
- EISSN:
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2398-502X
- Pmid:
-
31069261
- Language:
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English
- Keywords:
- Pubs id:
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pubs:998176
- UUID:
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uuid:e870716e-0be2-4f7c-9e9f-a05f4aa0dd47
- Local pid:
-
pubs:998176
- Source identifiers:
-
998176
- Deposit date:
-
2019-06-13
Terms of use
- Copyright holder:
- Allen et al
- Copyright date:
- 2019
- Notes:
- © 2019 Allen M et al. This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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