boxenplot area scale calculation

The area method for calculating the width of boxenplot letter-value boxes is:

'area': lambda h, i, k: (1 - 2**(-k + i - 2)) / h}

in https://github.com/mwaskom/seaborn/blob/master/seaborn/categorical.py#L1890

IIUC, in order for the area to be proportional to the percentage of data covered, as documented (https://github.com/mwaskom/seaborn/blob/master/seaborn/categorical.py#L2672), the formula should rather be:

'area': lambda h, i, k: (1 - 2**(-k + i - 1)) / h}

[mwaskom]

Is there a compelling reason to use area? Could we just deprecate it?

[pierreDELANGEN]

Hello, I’m digging up this issue, but when comparing “area” scaling to the original paper’s representation (Figure 3C, https://doi.org/10.1080/10618600.2017.1305277), it seems that the Seaborn implementation is still incorrect. Seaborn (random uniform): Expected result from the paper :

For me the “area” scaling is the “correct” way of doing boxenplots as it is directly representative of the underlying PDF of the studied variable, but it still allows easy reading of quantiles and differences between multiple categories. It is a bit of an hybrid between an histogram and a boxplot.