Explorable explanations (a.k.a. ‘explorables’) enable readers to learn concepts in domains such as math, physics, and the social sciences by interacting with live visualizations. Despite their popularity, there is currently a high barrier to creating explorables since one must be adept at UI and visualization programming. To learn about these challenges, we interviewed 6 educators who were interested in explorables but lacked the skills to create them from scratch. These interviews gave us design insights to lower some of these implementation barriers. We used these insights to create a live programming system called Data Theater that enables programmers to prototype explorables by writing their simulation logic in Python and mapping Python values to visualization elements using a declarative JSON grammar. To demonstrate the capabilities of Data Theater, we used it to recreate two of Bret Victor’s original physics simulation explorables and found that our approach can lower the barriers to prototyping explorables.