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<p>Proper data visualization helps researchers draw correct conclusions from their data and facilitates a more complete and transparent report of the results. In factorial designs, so-called <i>raincloud plots</i> have recently attracted attention as a particularly informative data visualization technique; raincloud plots can simultaneously show summary statistics (i.e., a box plot), a density estimate (i.e., the cloud), and the individual data points (i.e., the raindrops). Here we first present a ‘raincloud quartet’ that underscores the added value of raincloud plots over the traditional presentation of means and confidence intervals. The added value of raincloud plots appears to be increasingly recognized: a focused literature review of plots in <i>Psychonomic Bulletin &amp; Review</i> shows that 9% of plots in 2023 were raincloud plots. Another 29% of plots (vs. 2% in 2013) contained individual data points (i.e., raindrops), indicating a strong trend towards transparent and informative data visualization. To further encourage this trend and make raincloud plotting easy and practical for a broader group of researchers and students, we implemented a comprehensive suite of raincloud plots in JASP, an open-source statistics program with an intuitive graphical user interface. Examples from two factorial research designs illustrate how the JASP raincloud plots support a correct and comprehensive interpretation of the data.</p>

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Informative data visualization with raincloud plots in JASP

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月18日

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  • Vincent L. Ott,
  • Don van den Bergh,
  • Bruno Boutin,
  • Johnny van Doorn,
  • František Bartoš,
  • Nicholas Judd,
  • Jordy van Langen,
  • Luke Korthals,
  • Rogier Kievit,
  • Laura Groot,
  • Eric-Jan Wagenmakers

摘要

Proper data visualization helps researchers draw correct conclusions from their data and facilitates a more complete and transparent report of the results. In factorial designs, so-called raincloud plots have recently attracted attention as a particularly informative data visualization technique; raincloud plots can simultaneously show summary statistics (i.e., a box plot), a density estimate (i.e., the cloud), and the individual data points (i.e., the raindrops). Here we first present a ‘raincloud quartet’ that underscores the added value of raincloud plots over the traditional presentation of means and confidence intervals. The added value of raincloud plots appears to be increasingly recognized: a focused literature review of plots in Psychonomic Bulletin & Review shows that 9% of plots in 2023 were raincloud plots. Another 29% of plots (vs. 2% in 2013) contained individual data points (i.e., raindrops), indicating a strong trend towards transparent and informative data visualization. To further encourage this trend and make raincloud plotting easy and practical for a broader group of researchers and students, we implemented a comprehensive suite of raincloud plots in JASP, an open-source statistics program with an intuitive graphical user interface. Examples from two factorial research designs illustrate how the JASP raincloud plots support a correct and comprehensive interpretation of the data.