Raincloud plots: a multi-platform tool for robust data visualization.
Raincloud plots: a multi-platform tool for robust data visualization.
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DOI：
10.12688/wellcomeopenres.15191.1
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发表时间：
2019-01-01
影响因子：
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通讯作者：
Kievit, Rogier A
中科院分区：
文献类型：
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作者：
Allen, Micah;Poggiali, Davide;Kievit, Rogier A
在各个科学学科中，人们越来越认识到需要更强大、更透明的数据可视化方法。作为补充，许多科学家呼吁使用绘图工具，以最小的失真准确、透明地传达统计效果和原始数据的关键方面。以前常见的方法，如绘制条件平均值或中位数条形图与误差条一起被批评为扭曲效应大小，隐藏原始数据中的潜在模式，并模糊了最常用的统计检验所依据的假设。在这里，我们描述了一种数据可视化方法，克服了这些问题，提供最大的统计信息，同时保留所需的“一目了然的推断”的条形图和其他类似的可视化设备的性质。这些“雨云图”可以可视化原始数据、概率密度和关键汇总统计量，如中位数、平均值和相关置信区间，以吸引人的灵活格式呈现，冗余度最小。在本教程中，我们提供了雨云图和类似方法的基本演示，概述了为优化使用而可能进行的修改，并提供了在R，Python和Matlab中简化实现的开源代码（https：//github.com/RainCloudPlots/RainCloudPlots）。读者可以在浏览器中使用Binder by Project Pandyter交互式地研究R和Python教程。
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.