Ooh, Shiny!: R Shiny apps as a teaching tool

Contributing author Dan Adrian is an Associate Professor of Statistics at Grand Valley State University.

Interactive web applications (or apps), such as the Rossman-Chance collection, are popular tools for teaching statistics because they help illustrate fundamental concepts such as randomness, sampling, and variability through dynamic visualizations. The StatKey collection of apps created for the Lock5 textbook series to demonstrate and perform simulation-based inference is another example1. Historically, despite the utility of the web apps and the ease of their use, it was difficult for most stat educators to create or modify them because of the requisite coding knowledge in HTML, CSS, and Java/Javascript. Thankfully, RStudio created the R package {shiny}, which allows web apps (i.e., Shiny apps) to be created using R code alone, and the HTML/CSS/Javascript work is done by the package “behind the scenes”.

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Teaching Programming vs. Training Programmers: Where the Means Justify the Means

Contributing author Jonathan Duggins is a Teaching Assistant Professor in the Department of Statistics at North Carolina State University.

Introduction

Most of us statistics (and data science!) educators understand that knowing how to use statistical software is integral to student successes, both in their coursework and in their careers, for our statistics and data science majors. However, in many degree programs, software usage is seen as a means to an end – getting an analysis – rather than an end goal in its own right. How did this come about, why does it matter, and what can we do to change our software-related instruction? These are the questions I discuss below, first by looking at some history of programming in these contexts, then by presenting two current philosophies on how to incorporate programming.

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