Shiny in Production: Tools and Techniques
Description
Shiny brings tremendous possibilities to share innovative data science workflows with others inside an intuitive web interface. Many in the Shiny community have shared effective development techniques for building a robust application. Even with the best intentions during application development, a myriad of issues can arise once it leaves the confines of your machine. In this one-day workshop, you will implement core techniques to account for common scenarios that arise once your application is used in production, such as accounting for thousands of simultaneous users, how effective profiling can address performance bottlenecks, and ensuring your application is doing as little as possible to ensure a smooth and responsive experience.
Audience
This course assumes intermediate knowledge of building Shiny applications in R and prior experience deploying an application to a platform such as the shinyapps.io service or products like Posit Connect.
This course is for you if you:
- had a Shiny application work just fine on your machine, but encounters critical issues after deployment,
- are eager to prospectively apply techniques before deployment to plan for the unexpected, and
- want to know the benefits and trade-offs between various ways of hosting Shiny applications.
Instructors
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Eric Nantz is a director within the statistical innovation center at Eli Lilly and Company, creating analytical pipelines and capabilities of advanced statistical methodologies for clinical design used in multiple phases of development. Outside of his day job, Eric is passionate about connecting with and showcasing the brilliant R community in multiple ways. You may recognize his voice from the R-Podcast that he launched in 2012. Eric is also the creator of the Shiny Developer Series where he interviews authors of Shiny-related packages and practitioners developing applications, as well as sharing his own R and Shiny adventures via live streams on his Twitch channel. In addition, Eric is a curator for the RWeekly project and co-host of the RWeekly Highlights podcast which accompanies every issue. | |
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Mike Thomas is the Chief Data Scientist at Ketchbrook Analytics. He holds a B.S. in Accounting and Mathematics from Stonehill College, and an M.S. in Business Intelligence & Analytics from Saint Joseph’s University. He specializes in architecting and executing end-to-end data science solutions, which often culminate in the development of a Shiny web application. Michael is a Posit-certified trainer in both Tidyverse and Shiny, and he co-hosts the R Weekly Highlights podcast. He enjoys developing Shiny apps that benefit his local community and beyond. |