Flipbooks - Evangeline Reynolds

January 30, 2020 Evangeline Gina Reynolds
Good examples facilitate accomplishing new or unpracticed tasks in a programmatic workflow. Tools for communicating examples have improved in recent years. Especially embraced are tools that show code and its resultant output immediately thereafter --- the case of `Jupytr` notebooks and `Rmarkdown` documents. But creators using these tools often must choose between big-picture or narrow-focus demonstration; creators tend to either demo a complete code pipeline that accomplishes a realistic task or instead demonstrate a minimal example which makes clear the behavior of a particular function, but how it might be used in a larger project isn't clear. Flipbooks help address this problem, allowing the creator to present a full demonstration which accomplishes a real task, and gives the viewer the opportunity to focus on unfamiliar steps. A set of flipbook building functions parse code in a data manipulation or visualization pipeline and then build it back up incrementally. Aligned superimposition of new code and output atop previous code and output makes it easy to identify how each code change triggers changes in output. The presentation will guide attendees in creating their own Flipbooks (with Xaringan slides) or mini Flipbooks (gif output).

About the Author

Evangeline Gina Reynolds

I am a Visiting Teaching Assistant Professor at the University of Denver’s Josef Korbel School of International Studies where I teach research methodology. I use R in my classes.

Follow on Twitter Follow on Linkedin More Content by Evangeline Gina Reynolds
Previous Video
Deploying End-To-End Data Science with Shiny, Plumber, and Pins - Alex Gold
Deploying End-To-End Data Science with Shiny, Plumber, and Pins - Alex Gold

It’s easier than ever to craft a complete R-centric data science pipeline thanks to packages like Shiny, Pl...

Next Video
Professional Case Studies - Katie Masiello
Professional Case Studies - Katie Masiello

The path to becoming a world-class, data-driven organization is daunting.