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  • ÀúÀÚMicah Allen, Davide Poggiali, Kirstie Whitaker,Tom Rhys Marshall, Rogier A. Kievit Àú
  • ÃâÆÇ»ç¾ÆÁø
  • ÃâÆÇÀÏ2020-07-10
  • µî·ÏÀÏ2020-12-21
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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.

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Á¦ 2Æí : ¿¬±¸³í¹®
1. Introduction 53
2. Code tutorials: how to make it rain 56
3. Conclusion 84
4. References 85

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