Latest: Msuthemes: Building Consistent Analytics Through Thoughtful Tooling
Posted on Jan 13
• Originally published at exeresearch.com
It's always great to give back to a community. I especially appreciate it when someone provides a small thing that has a massive impact on my day-to-day workflow. Like an easy-to-use color palette for the institution I work at.
We've all been there. Your colleague has moved on or is taking a much-needed break, but a previous analysis needs updating. You have the analysis code but not the code to make the plots. Because you are pressed for time, you update the plots to the best of your ability, but they look slightly different from the other plots in the document or presentation.
MSUthemes – for R and Python – removes the need for people to create their own internal MSU colour palettes and reduces the barriers to updating colleagues' analyses. Using these packages enables you to create plots with a consistent aesthetic and focus on the analysis.
These moments of friction add up - not just in lost time, but in the subtle inconsistencies that make collaborative work look less professional than it should. That's why I created the MSUthemes packages for R and Python: to provide consistent, professional colour palettes and themes for Michigan State University and comprehensive colour support for all Big Ten Conference institutions.
The packages include MSU-specific palettes (sequential, diverging, and qualitative) aligned with MSU's branding guidelines, use the Metropolis font in plot construction, and provide primary and secondary colour palettes for all 18 Big Ten institutions - making them ideal for multi-institutional comparisons and collaborative research visualizations.
The idea was sparked by the work of Andreas Krause, Nicola Rennie, and Brian Tarran. While the original RSSthemes package focused on Royal Statistical Society (RSS) branding, the MSUthemes packages adapt the concepts to fit MSU's visual identity using the RSSthemes framework.
Michigan State's primary colors—Spartan Green and white—provide a strong foundation for data visualization. Creating a consistent color palette that works across both R and Python ensures your visualizations maintain brand identity while remaining accessible and informative.
The palette includes not just the primary green, but complementary colours that work well for categorical and continuous data while maintaining accessibility standards.
For those working with ggplot2 (as most R users do), integrating custom themes streamli
Source: Dev.to
