Showing the ratios is fine: your meaningful total is their total. For instance, showing revenue breakdown while trying to explain that some department pulls mo

Showing the ratios is fine: your meaningful total is their total. For instance, showing revenue breakdown while trying to explain that some department pulls more than the rest. Here, without normalization (via pie charts, percentages, stacked bars) it becomes more difficult to compare the ratios. This perfectly mirrors the idea of sufficient statistics for a particular inference. I'll also argue that pie charts are still terrible ways to display "data of bounded measure". The primary arguments are (a) people are quite terrible at doing accurate comparisons with area or, worse, arglength and (b) it tends to destroy the consistency of you labels since there's not a clear preferred ordering in a pie chart. In this case, I'd say that stacked bar charts or empirical empirical CDFs do a much better job displaying bounded data, and, if you can suffer removing the bound, then simple bar charts or dot plots do it best. I'd be amazed to hear of a time that a pie chart is actually optimal. I think perhaps the best argument going forward is if ratios are sufficient for your story even with the labeling obscured (it just adds noise). Here, the ambiguity of the ordering on a pie chart might provide exactly the right vehicle. I still wouldn't know whether it should be computed by arclength or area, though.