useR: Learning {ggplot2} with generative art
This talk will highlight a few of the more subtle differences in some aspects of ggplot2 discovered through experimenting with generative art, and show how we might exploit these subtleties to make more informative data science plots.
By Nicola Rennie in Conference
Abstract
Generative art, the practice of creating art with code, is becoming ever more popular. Rtistry, the name often given to generative art when the language of choice is R, might not be the most obvious approach to learning how to use ggplot2. However, there’s a lot we can learn from it and take into our everyday work.
Many of the data visualizations that data scientists produce in practice tend to fall into one of the basic categories–scatter plots, line plots, or histograms–and we tend to spend quite a bit of time preparing our data beforehand, to get it into the “right” format. And so we never discover some of the intricacies beneath the functions we use.
Much of generative art relies on messier data, randomness, disorder, and unusual structures–things that, as data scientists, we often try to remove from our data before we visualize it. But when we think about our data, and therefore the visualization process, from a different perspective, we can learn new things about the tools we rely on every day. This talk will highlight a few of the more subtle differences in some aspects of ggplot2 discovered through experimenting with Rtistry. It will also highlight how we might exploit these subtleties to make more informative “data science” plots. You might even become convinced that being an Rtist could make you a better data scientist.