Axes, ticks and grids

Abstract

nature methods | VOL.10 NO.3 | MARCH 2013 | 183 this month such as axis ranges, can be easily overlooked (Fig. 2a). Make sure that outliers do not compress the dynamic range of the bulk of your data— use bar or axis breaks, but always be sensitive to the journal's policies on breaks and the fact that these elements can disguise important pat-terns. When they are densely labeled, axis ticks burden the figure with repetition. This applies specifically to views of data across large genomes, which are filled with repeating nonsignificant zeros. Innovative strategies exist 2 to keep tick label complexity low while maintaining usability (Fig. 2b). Grids are used to establish sight lines to compare proportions and relate positions to axis ticks. The number of grids powerfully suggests the scale at which differences are important. Faced with a dense grid, the readers will conclude that they should pay close attention to minor fluctuations in the data and infer that the degree of uncertainty is low. Do not send this message falsely. Furthermore, dense grids impede accurate judgment, as tracing them to their axis labels is confounded by increased density (Fig. 3a). Ultimately, no grid may be better than a badly chosen one—use a grid when needed rather than by default. Patterns in data can be quickly obscured if too much ink is used for grids. The grid should be dark enough to be seen clearly (anticipate that LCD projectors will wash out light colors) but not so dark as to appear as a fence in front of data. A useful guideline is to use 15% as the minimum grid opacity. Maximum opacity should be 25%–45%, in proportion to the figure's data density (Fig. 3b). A common example of functional layering of information modali-ties is found in maps. The next time you navigate a map, take note of the strategies used by the cartographer to generate a hierarchy of meaning between place names, terrain type, elevation and landmark annotations, as you relate these features to elements in your figures.