I’ve recently started re-reading a book that I also recommended to @adamschroeder , and it remains one of my favorite data visualization books for anyone interested: https://www.storytellingwithdata.com/
Here are a few principles from the book that I applied:
- I used color to highlight 2023, while keeping 2022 in grey, to focus on the recent year and the development from 2022 to 2023.
- I tried to reduce cognitive load e.g. by:
- replacing the terms Q1-Q4 with more intuitive names (low, lower-middle, upper-middle and high), as they could be mistaken for quarters otherwise
- adding a reference line for equal gender representation (50%) to visually guide users in the analysis of identifying unequal gender representation
- I removed any visual clutter (can’t take all the credit here, as many of these principles are already incorporated into the Vizro Plotly chart template), e.g. such as removing unnecessary grid lines and legend titles, and making the grid lines more subtle.
In summary, I found that, there has been no significant progress in female representation from 2022 to 2023 if you take the average across all companies. On average, female representation remains lower across all pay quartiles, with the gender gap widening in the upper-middle and highest quartiles, as expected.
Additionally, I created an interactive app that allows you to filter by specific companies. The drill-down into a single company can provide a different picture. Little sneak peak below 
- App: PyCafe - Vizro - Irish Gender Pay Gap
- App/Code side-by-side: PyCafe - Vizro - Irish Gender Pay Gap