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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/html4/loose.dtd">
<html>
<title>raincloudplots</title>
<h3>What are raincloudplots?</h3>
<p>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. We created and shared our open-source code for raincloudplots implementation in R, Python and Matlab <a href="https://github.com/RainCloudPlots/RainCloudPlots">https://github.com/RainCloudPlots/RainCloudPlots</a>.
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For more information about the creation and examples of raincloudplots, please read our paper:
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<pre>
- Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., van Langen, J., & Kievit, R.A.
Raincloud plots: a multi-platform tool for robust data visualization [version 2; peer review: 2 approved]
Wellcome Open Research 2021, 4:63. <a href="https://doi.org/10.12688/wellcomeopenres.15191.2">https://doi.org/10.12688/wellcomeopenres.15191.2</a>
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<h3>Citation</h3>
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<p>If you use this Shiny App for your work, please cite our package:</p>
<pre>
- Judd, N., van Langen, J., Allen, M., & Kievit, R.A.
ggrain: A Rainclouds Geom for 'ggplot2'.
R package version 0.0.3.
CRAN 2023, https://CRAN.R-project.org/package=ggrain
</pre>
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<h3>ggrain: R-package</h3>
<img src="package_sticker.png" width="150" height="160" align="right"/>
<p>IIn 2022 we have created the <a href="https://github.com/njudd/ggrain">'ggrain' R-package.</a> as an update from the popular <a href="https://github.com/jorvlan/raincloudplots">'raincloudplots' R-package.</a>. This package is tailored towards easy visualization of grouped and repeated measures data. Moreover, it also provides individually linked repeated measures visualizations, which add detail and richness to a multitude of between/within-subject designs. </p>
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<h3>raincloudplots: the ShinyApp</h3>
<p>The creation of this ShinyApp is part of our larger project called: <a href="https://nwo.nl/en/projects/203001011">"Raincloud plots 2.0"</a>. Team members Nicholas Judd, Rogier Kievit and Jordy van Langen were awarded the inaugural <a href="https://www.nwo.nl/en/news/26-projects-stimulate-open-science">NWO Open Science Fund 2021</a>, which allowed us to further develop our raincloudplots tools. One of the tools that we have created is this ShinyApp, which will allow users to easily drag & drop their data to create raincloudplots. </p>
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<h3>Questions/Bugs/Contact</h3>
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<p>For (technical) questions, bugs or any problem using our ShinyApp, please open an issue on the designated GitHub page: <a href="https://github.com/jorvlan/raincloudplots-shiny">https://github.com/jorvlan/raincloudplots-shiny</a></br></p>
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<h3>Credits</h3>
<p> This Shiny application was written by: Jordy van Langen <a href="https://github.com/jorvlan">https://github.com/jorvlan</a> (personal GitHub) & Nicholas Judd <a href="https://github.com/njudd">https://github.com/njudd</a> (personal GitHub)</p>
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