Online Program

Building Shiny Apps: With Great Power Comes Great Responsibility (303556)

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Keywords: r, shiny, data visualization, data analysis, interactivity, software development, collaborations

R Shiny revolutionized the way statisticians distribute analytic results and research methods. We can easily build interactive web tools that empower non-statisticians to interrogate and visualize their data or perform analyses with methods we develop. However, ensuring an enjoyable user experience while guaranteeing analysis options are statistically sound is a difficult balance to achieve. Through a case study of building an app for omics data visualization and analysis, I will discuss how to overcome challenges faced when building and deploying apps. By allowing users to explore and analyze data we can make our job easier and improve collaborative relationships, but success of this goal requires analytical foresight and software development skills. We may need to consider such issues as data security, open source collaborative code development, error handling and testing, user education, maintenance due to advancing methods and packages, and responsibility for downstream analyses and decisions based on app results. With Shiny we do not want to eliminate the “statistician middle man” but instead need to stay relevant and in control of all types of statistical products we create.