How to Create Interactive Plots in R

In this part of the R workshop series, explore how to create dynamic data visualizations to better communicate clear insights from complex data and highlight key takeaways. Learn how to go from a static plot to a customizable interactive data visualization for your next big project. 

Follow along by downloading the HTML file from our GitHub.

1. Outline project

As with most data analysis projects, start by defining the scope of the project and question to be addressed within it, and next overview data to get a sense of the types of visualization options available and any data organization that might be needed. 

In this tutorial, explore a fun, real-world example with interactive data visualizations created using English Women’s Football league data (The English Women’s Football Database, 2025). This example shows in action how interactive data visualizations can be used to display complex data more clearly.

2. Make common interactive data visualizations

In the main component of the workshop, learn how to create a few common data visualization options – line plots, bar plots, rank plots, and scatter plots– first statically, and then with interactive options. Discover multiple strategies to customize and highlight takeaways of interest for your next big project when making interactive plots in R and explore how interactivity can facilitate interpretation of complex data visualizations.

3. Create deliverable-ready multi-paneled interactive plot

Lastly, as the workshop wraps up, learn how to combine multiple interactive plots into one, deliverable-ready finished product that depicts both key takeaways and background context in a single multi-paneled plot. 

Conclusion

With a range of options varying in complexity and customizable options, walk away with the tools you need to create beautiful interactive data visualizations in R for your next big project. 

In other R workshop series videos, learn about 3 things you didn’t know you could do in R, basic text analysis in R, and how to create a data dashboard in R using Quarto.

By Ginny Ulichney, Research Analyst, Wharton AI & Analytics Initiative

Pro tips

A few tricks can take interactive data visualizations to the next level:

• Go back to basics: be sure to follow data visualization principles, such as highlighting key takeaways in an accurate way, including any needed context for your audience, using consistent color schemes, and employing descriptive axis titles and labels. 
• Put your audience first: When preparing interactive plots for project deliverables, keep the audience understanding top-of-mind. Interactive data visualizations are strong when they are designed for the understanding of viewers, and simplicity can be sophisticated.
• Prioritize clear storytelling: This workshop aims to show a variety of options, but in real-world workflows, it is valuable to prioritize analyses and hone respective interactive data visualizations that best address questions of interest to tell a clear story.