Here’s a preprint of our paper on selecting tick labels for axes which will appear in this year’s InfoVis! Source code of the implementation will be made available before the conference. We’re hoping to get this implemented in a number of common plotting libraries. I already have a partial matplotlib version working. I would also like to have one for ggplot. Other suggestions are welcome.
The non-data components of a visualization, such as axes and legends, can often be just as important as the data itself. They provide contextual information essential to interpreting the data. In this paper, we describe an automated system for choosing positions and labels for axis tick marks. Our system extends Wilkinson’s optimization-based labeling approach to create a more robust, full-featured axis labeler. We define an expanded space of axis labelings by automatically generating additional nice numbers as needed and by permitting the extreme labels to occur inside the data range. These changes provide flexibility in problematic cases, without degrading quality elsewhere. We also propose an additional optimization criterion, legibility, which allows us to simultaneously optimize over label formatting, font size, and orientation. To solve this revised optimization problem, we describe the optimization function and an efficient search algorithm. Finally, we compare our method to previous work using both quantitative and qualitative metrics. This paper is a good example of how ideas from automated graphic design can be applied to information visualization.
Update: We’ve released a preliminary R package implementing the three labeling algorithms we compared in the paper. Feedback is appreciated. The final version should be released by InfoVis (in October).