Monthly Archives: November 2019

Using R-Shiny to Teach Quantitative Research Methods

What and why

Over the past couple of years I have been developing a small suite of R-Shiny tools for teaching quantitative research methods. R-Shiny is an R library for writing interactive web pages with full access to the power of the R statistical programming language. The tools I have written include demonstrations of ideas, self-teaching exercises and assessments.

If you use R already, writing Shiny web pages is a relatively easy extension, though programming an interactive web page has some important differences from conducting a data analysis. R is very general and very powerful, so there are lots of possibilities. This is both a strength and a weakness: generality means that while lots of things are possible, many require extensive programming. Nonetheless, it is relatively quick and easy to create simple and robust tools.

This (relatively long) blog is based on an early draft of a paper summarising some of the main things I have learnt, and showcasing a handful of examples. I’m putting it out partly just to record and display what I’ve done, but also to solicit feedback, particularly about how best to use apps like this to good pedagogical effect.

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Seminar: R-Shiny for teaching

Department of Sociology Seminar

Weds 27 November, 12:00-13:00, F1030 Foundation Building

Using R-Shiny to create interactive apps for quantitative research methods teaching

Brendan Halpin, Dept of Sociology, University of Limerick

R-Shiny, a library for the R statistical programming language, makes it easy to create interactive web-pages which build on the statistical tools which R provides. In this talk I will discuss my experience using R-Shiny to create:

  • interactive demos
  • self-learning apps and
  • automatically graded assessments

for students on quantitative research methods modules

Demos are apps that demonstrate a statistical concept, allowing students to vary parameters and see what changes. Self-learning apps allow students to undertake a task repeatedly (with fresh numbers each time), and receive instant feedback. Assessments give students questions with individualised numbers but identical structure, store the answers and automatically mark the submission, with detailed feedback.

R-Shiny offers potential for anyone teaching statistics or quantitative research methods, in any substantive area. The talk will consider pedagogical and programming issues, and summarise the experience using this approach with undergraduate and Masters sociology students over the past few years.