Technologies for Interactive and Dynamic Data Visualization

Description: R and Python provide a variety of ways to create visualizations that are static images, but what if you want to add interactivity, animation, or other dynamic behavior to a visualization? This intermediate workshop is a primer on creating dynamic visualizations. We'll discuss what it means for a visualization to be "dynamic" and the advantages and disadvantages of dynamic visualizations compared to static visualizations. We'll also explore the ecosystem of packages for creating dynamic visualizations with R and Python, as well as the JavaScript libraries that underpin these. To make the ideas concrete and get you started on building your own dynamic visualizations, we'll implement a simple dynamic visualization in R and Python.

This workshop is NOT an introduction to R/Python and is intended for motivated intermediate to advanced learners from all domains at UC Davis who want to hone their data visualization skills. Please make sure you meet the prerequisites before registering as we will be unable to answer introductory R/Python questions during this session. This workshop builds upon your existing knowledge of working in R/Python. Learners should also attend the Principles of Data Visualization from Perception to Statistical Graphics workshop in advance of this session to ensure a solid foundation in design principles. (Want to brush up on R/Python? Check out our R/Python Basics 4-part introductory series.)

Learning Objectives

After completing this workshop, learners should be able to:

• Explain the difference between static and dynamic visualizations
• Explain the difference between client-side and server-side code
• Describe the advantages and disadvantages of dynamic visualizations
• Judge whether and what kinds of dynamic visualizations are appropriate for a given task
• Recognize popular R or Python packages for creating dynamic visualizations
• Explain what JavaScript is and how it's relevant to dynamic visualizations
• Recognize popular JavaScript libraries for creating dynamic visualizations
• Construct a simple dynamic visualization in R or Python
• Identify where to go to learn more!

Prerequisites: Participants must have taken DataLab’s “R Basics” or "Python Basics" workshop series and/or have prior experience using Python or R. Learners should also take the Principles of Data Visualization from Perception to Statistical Graphics workshop in advance of this session.

Software: This workshop requires at least one of:

• Recent versions of R and RStudio
• Recent versions of Python 3 with packages `jupyterlab` and `pandas`

Instructor: Nick Ulle

Instructor Bio: Nick Ulle is a statistician and computer scientist. Prior to DataLab he was a visiting assistant professor of Statistics at UC Berkeley, where he designed and taught courses in data science. During his PhD in Statistics at UC Davis, he developed source code analysis techniques for the R programming language. His research interests include statistical computing, programming languages, data visualization, and pedagogy.