In this article we discuss our attempt to incorporate research-informed learning and teaching activities into a final year undergraduate Statistics course. We make use of the Shiny web-based application framework for R to develop "Shiny apps" designed to help facilitate student interaction with methods from recently published papers in the author's primary research field (extreme value theory and applications). We also replace some lectures with dedicated "reading group tutorials." Here, students work in small groups to discuss and critique carefully selected papers from the field. They are also encouraged to use our Shiny apps to implement some of the methods discussed in the papers with their own data, for use in project work. We attempt to evaluate our innovation by comparing students' responses in open-ended data analysis work, and work requiring the interpretation of methods in a recently published paper, to those of students who took the same course two years earlier when our Shiny apps were not available and when research tutorials were not used. This comparison, along with results from a student questionnaire, gives us some confidence that our methods have benefited students, not only in terms of their ability to understand and implement advanced techniques from the recent literature but also in terms of their confidence and overall satisfaction with the course.