Feature Engineering for Data Viz

This is not so much an additional technique for adding variables to your plot, but a reminder that feature engineering is a tool that you can leverage as you explore and learn about your data. As you explore a dataset, you might find that two variables are related in some way. Feature engineering is all about creating a new variable with a sum, difference, product, or ratio between those original variables that may lend a better insight into the research questions you seek to answer.

For example, if you have one variable that gives a count of crime incidents, and a second one that gives population totals, then you may want to engineer a new variable by dividing the former by the latter, obtaining an incident rate. This would account for a possible relationship between the original features where if there are more people, there might naturally be more chances for crimes to occur. If we looked at the raw counts rather than the incident rate, we risk just seeing information about population sizes rather than what we might really want.

Another way that you can perform feature engineering is to use the cutfunction to divide a numeric variable into ordered bins. When we split a numeric variable into ordinal bins, it opens it up to more visual encodings. For example, we might facet plots by bins of a numeric variable, or use discrete color bins rather than a continuous color scale. This kind of discretization step might help in storytelling by clearing up noise, allowing the reader to concentrate on major trends in the data. Of course, the bins might also mislead if they're spaced improperly – check out this PolicyViz post if you'd like to see a deeper discussion in the context of map-based visualizations.