Course README

Applied R

Applied R is part of the UoN Digital Skills programme. It provides a practical route into R programming for research and data analysis. Start with the Course Overview for the lesson sequence and intended audience.

What This Course Contains

This course section of the site contains:

  • eight lesson pages covering foundations, data, defensive programming, iteration, wrangling, visualisation, notebooks and dashboards
  • the established variables URL, now the active lesson on importing and understanding research data
  • learner setup, reference, discussion and additional practice material
  • instructor notes and optional teaching extensions
  • course-level information about contribution, conduct, licensing, and learner profiles

Course Scope

The course supports participants to:

  • build confidence with core R programming concepts
  • work more effectively with objects, functions, loops, and tidyverse workflows
  • develop more reproducible analytical habits
  • connect programming practice to real research workflows

Adaptation and Attribution

This local course is substantially adapted from Software Carpentry’s r-novice-inflammation lesson.

The UoN version reorganises, extends, and contextualises that material into eight lessons for local delivery in the Methods for Data Science pathway. Each lesson has a 100-minute plan: 60 minutes teaching, 30 minutes activities including debriefs, and a separate 10-minute break. The foundations and Gapminder examples also draw on Software Carpentry’s r-novice-gapminder materials. Lessons 5–8 share a checked project leading to a computational notebook and Shiny dashboard. The inherited examples and optional reference banks remain available.

Contributing

Contribution guidance is available in CONTRIBUTING.qmd.