Du Bois Wrapped Bar Chart: Visualizing categorical data with disproportionate values
This work addresses a visualization challenge for data analysts and researchers dealing with skewed datasets, offering an incremental improvement over standard bar charts.
The paper tackles the problem of visualizing categorical data with disproportionate values by proposing Du Bois wrapped bar charts, which wrap large bars over a threshold to improve comparison. Results from crowdsourcing experiments show that wrapped bar charts lead to higher accuracy in identifying and estimating ratios, with participants being consistently more accurate when category values are disproportionate.
We propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two crowdsourcing experiments comparing wrapped and standard bar charts to evaluate (1) the benefit of wrapped bars in helping participants identify and compare values; (2) the characteristics of data most suitable for wrapped bars. In the first study (n=98) using real-world datasets, we find that wrapped bar charts lead to higher accuracy in identifying and estimating ratios between bars. In a follow-up study (n=190) with 13 simulated datasets, we find participants were consistently more accurate with wrapped bar charts when certain category values are disproportionate as measured by entropy and H-spread. Finally, in an in-lab study, we investigate participants' experience and strategies, leading to guidelines for when and how to use wrapped bar charts.