How to Make a Thousand Plots Look Good: Data Viz Tips for Parameterized Reporting
Regular talk, 11:10-11:25
Data visualization is complicated enough when you are making one plot. Now imagine you’re making multiple plots in multiple reports. How can you design your data viz so that it will be legible, attractive, and compelling? This is a challenge that we often have at R for the Rest of Us. We regularly work with clients to make parameterized reports. There may be dozens of plots in each report, and dozens of reports. It’s a lot of plots! We’ve learned to use techniques like selectively hiding text labels where they would be hard to read, using packages like ggrepel to ensure labels do not overlap, employing shadows to make things visible, and more. In this talk, I will give examples, complete with detailed code, of how to make data viz shine when using parameterized reporting. Developing data visualization for multiple reports requires a unique set of considerations. Join me for this talk, where I will discuss the lessons we have learned over the years and show how you can make high-quality data visualization in your own parameterized reports.
 | Pronouns: he/himPortland, ORDavid Keyes is the CEO and founder of R for the Rest of Us. Through online courses and trainings for organizations, R for the Rest of Us helps people learn to use R. In addition to its education work, R for the Rest of Us does consulting, developing reports, websites, and more to help organizations use R to improve their workflow, and much more. |