Various R packages and best practices have played a pivotal role to promote the Findability, Accessibility, Interoperability, and Reuse (FAIR) principles of open science. For example, (1) well-documented R scripts and notebooks with rich narratives are deposited at a trusted data centre, (2) R Markdown interactive notebooks can be run on-demand as a web service, and (3) R Shiny web apps provide nice user interfaces to explore research outputs. However, notebooks require users to go through the entire analysis, while Shiny apps do not expose the underlying code and require extra work for UI design. We propose using the learnr package to expose certain code chunks in R Markdown so that users can readily experiment with them in guided, editable, isolated, executable, and resettable code sandboxes. Our approach does not replace the existing use of notebooks and Shiny apps, but it adds another level of abstraction between them to promote reproducible science.