Learner Profiles

These profiles guide teaching choices; they are not entry requirements. Use the Course Overview to choose a starting point and Setup to prepare.

Profile 1: Researchers New to Programming

Background: New to R and moving from manual or spreadsheet-heavy analysis toward code-based workflows.
Motivation: Build confidence with programming while improving the quality and reproducibility of data analysis.
Needs: Clear explanations, approachable examples, and a practical route into core R concepts.

Background: Comfortable with analysis or programming in another environment, but relatively new to R.
Motivation: Translate existing analytical knowledge into R-based workflows for data manipulation, visualisation, and reporting.
Needs: A clear map of R syntax, common data structures, and reproducible working habits.

Background: Interested in using R not just to get answers, but to structure analysis more transparently and repeatably.
Motivation: Improve code reuse, debugging confidence, and the ability to explain analytical steps clearly.
Needs: Good habits around functions, iteration, tidyverse workflows, and readable analytical code.

For new programmers, start with the foundations and use paired prediction before running code.
For analysts changing language, connect familiar operations to the local reference and inspect differences in indexing and object types.
For reproducibility-focused learners, use Additional Material to check a saved workflow from a fresh session.
For researchers communicating results, use lessons 7/8 to compare a computational notebook and a reactive interface, retaining source checks and limitations in both.