11.8. Summary

Whenever we analyze a dataset, we use visualizations to uncover patterns in the data that are impossible to see otherwise. Data visualization is an iterative process. We create a plot, then decide whether to make adjustments or choose a new type of plot entirely. This chapter covered principles that we use to make these decisions.

We started by covering principles of scale, and saw how adjusting the scale by changing or transforming plot axes can reveal hidden structure in the data. We then discussed smoothing and aggregating techniques that help us work with large datasets that would otherwise result in over-plotting. To facilitate meaningful comparisons, we talked about how to apply principles of perception, like aligning plot marks to make them easier to compare. Taking the data design into account lets us use domain knowledge to improve visualizations. And we saw how adding context to a plot helps future readers understand our analyses. Finally, we covered the basics of creating plots using the plotly Python package, and compared plotly with other plotting tools.

After this chapter, you should be able to create a plot and understand what kinds of adjustments would make the plot more effective. We expect that you might also see examples of plots in the media that might need some of the techniques we discussed in this chapter.

In the next chapter, we’ll walk through an extended case study. It combines everything we’ve learned in the book so far, so we hope that you’ll find yourself surprised by how much you can already do.