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Raincloud Plots: The Hidden Gem of Data Visualisation
...that very few are talking about.
Visualizing data distributions using box plots and histograms can be misleading at times.
This is because:
It is possible to get the same box plot with entirely different data.
For instance, consider the illustration below from one of my previous posts: Use Box Plots With Caution! They May Be Misleading.
We get the same box plot with three different datasets.
Altering the number of bins changes the shape of a histogram.
Thus, to avoid misleading conclusions, it is recommended to plot the data distribution.
Here, jitter (strip) plots and KDE plots are immensely helpful.
One way is to draw them separately and analyze them together, as shown below. But this is quite tedious.
Instead, try Raincloud plots.
They provide a concise way to combine and visualize three different types of plots together.
Box plots for data statistics.
Strip plots for data overview.
KDE plots for the probability distribution of data.
Overall, Raincloud plots are an excellent choice for data visualization.
With Raincloud plots, you can:
Combine multiple plots to prevent incorrect/misleading conclusions
Reduce clutter and enhance clarity
Improve comparisons between groups
Capture different aspects of the data through a single plot
You can use the PtitPrince library to create Raincloud plots in Python: GitHub.
R users can use Raincloud Plots library: GitHub.
P.S. If the name “Raincloud plot” isn’t obvious yet, it comes from the visual appearance of the plot:
👉 Over to you: What are some other hidden gems of data visualization?
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Find the code for my tips here: GitHub.