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Raincloud plots: a multi-platform tool for robust data visualization.

Micah Allen1,2,3, Davide Poggiali4,5, Kirstie Whitaker2,6

  • 1Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark.

Wellcome Open Research
|May 10, 2019
PubMed
Summary
This summary is machine-generated.

Scientists need better data visualization tools. Raincloud plots offer a statistically robust and transparent method to display raw data and statistical summaries, overcoming limitations of traditional barplots.

Keywords:
MatlabPythonRbarplotsdata visualizationraincloud plots

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Area of Science:

  • Statistics
  • Data Visualization
  • Scientific Computing

Background:

  • Traditional data visualization methods like barplots with error bars can distort effect sizes and obscure underlying data patterns.
  • There is a growing demand for statistically robust and transparent data visualization techniques across scientific disciplines.
  • Existing plotting tools often fail to accurately convey statistical effects and raw data without distortion.

Purpose of the Study:

  • To introduce and demonstrate "raincloud plots," a novel data visualization approach.
  • To provide a method that maximizes statistical information while retaining the intuitive nature of traditional plots.
  • To offer open-source code for implementing raincloud plots in R, Python, and Matlab.

Main Methods:

  • Description of the raincloud plot methodology, integrating raw data, probability density, and key summary statistics.
  • Demonstration of the versatility and appeal of raincloud plots for data representation.
  • Provision of open-source code for implementation in R, Python, and Matlab, with interactive tutorials available via Binder.

Main Results:

  • Raincloud plots effectively visualize raw data, probability density, and summary statistics (median, mean, confidence intervals).
  • This approach minimizes redundancy and distortion, offering maximal statistical information.
  • The plots maintain an 'inference at a glance' quality, similar to traditional barplots.

Conclusions:

  • Raincloud plots represent a significant advancement in statistically robust and transparent data visualization.
  • This method addresses the limitations of traditional plotting techniques, enhancing the accurate representation of statistical effects and raw data.
  • The availability of open-source code and interactive tutorials facilitates the adoption of raincloud plots in scientific research.