Now that I’m back in the US and relaxing, I can take time for one final blather on the [PC Maker Slopegraph](http://rud.is/b/2013/04/11/ugly-tables-vs-slopegraphs-pc-maker-shipments-marketshare/) post from earlier in the week.
Slopegraphs can be quite long depending on the increment between discrete entries (as I’ve [pointed out before](http://rud.is/b/2012/06/07/slopegraphs-in-python-exploring-binningrounding/)). You either need to do binning/rounding, change the scale or add some annotations to the chart to make up for the length. Binning/rounding seems to make the most sense since you can add a table for precision but give the reader a good view of what you’re trying to communicate in as compact a fashion as possible.
I’ll, again, ask the reader, what tells you which PC maker is on top: this table:
or these slopegraphs:
Labeled properly, the rounding makes for a much more compact chart and doesn’t detract from the message, especially when I also include a much prettier, quick precision reference via Google Fusion Tables:
(though the column sort feature seems a bit wonky for some reason…).
Given that the focus was on the top individual maker, the “Other” category is just noise, so excluding it is also not an issue. If we wanted to tell the story of how well individual makers are performing against that bucket of contenders or point-players, then we would include that data and use other visualizations to communicate whatever conclusions we want to lead the reader to.
Remember, data tables and visualizations should be there to help tell your story, not detract from it or require real work/effort to grok (unless you’re breaking new visualization ground, which is most definitely not happening in the Ars story).
