Statistical package R helps you teach statistics, data analysis, coding and reproducible workflows in one environment. You can use it to work with real datasets, create visualisations, write and run code, and combine analysis with written interpretation in the same workflow. In teaching practice, R is often used through environments such as RStudio and related Posit tools, which make it easier to organise code, output and teaching materials. This makes R especially useful when you want students not only to produce results, but also to understand and document how those results were produced.
- Functions
- Data analysis
- Access
- Standalone
- Difficulty of use
- Needs guidance
Examples of use in your education
Use R when you want students to develop statistical reasoning by seeing how analytical choices, data and output connect to a research question.
You can apply this tool in your teaching in several concrete ways:
Step-by-step modelling - Demonstrate a short analysis from importing data to selecting a method and interpreting the output, so students can see the full analytical sequence before working independently.
Visual explanation - Use plots and summary output in class when you want to explain distributions, relationships or model results and connect them to the underlying statistical concept.
Controlled comparison - Change one element at a time, such as the sample size, variable coding or model specification, and ask students to compare how the output changes.
Real-data application - Ask students to apply the same analytical sequence to an authentic dataset and connect the results to a substantive research question.
Tip 1
Start with small datasets and short scripts, so students can focus on the logic of the analysis before they have to manage more complex code or data structures.
Tip 2
Show students both the code and the resulting output, and discuss them together, so R supports statistical reasoning instead of becoming a black box.
Use R when the learning goal extends beyond obtaining statistical results to documenting, structuring and reproducing the analytical process.
You can apply this tool in your teaching in several concrete ways:
Workflow modelling - Demonstrate your own analytical workflow live, showing how data preparation, coding, analysis and interpretation fit together in one reproducible process.
Guided coding - Model a short script line by line, then ask students to adapt one element at a time, such as a variable name, function or filter, before they write a similar section independently.
Reproducible reports - Ask students to combine code, output and written interpretation in one document, so their submission shows both the analytical result and how it was produced.
Tip 1
Make clear whether the learning goal is mainly statistical reasoning, coding or reproducible workflow, so students understand what they are expected to focus on when working in R.
Tip 2
Use one shared starter script or report template across the course, with clearly named sections for data preparation, analysis and interpretation, so students can recognise the workflow and focus on the analytical choices.
Use R-based tutorial tools when you want students to practise coding or analysis independently with structured guidance and immediate feedback. A browser-based or pre-configured environment can make these activities easier to run by giving all students the same technical setup.
You can apply this tool in your teaching in several concrete ways:
Interactive tutorials - Use guided tutorials that combine short explanations, code exercises and checks when students need to practise a new analytical or programming step at their own pace.
Automated feedback - Use auto-checked coding exercises to give students immediate feedback on errors or incomplete solutions, so they can revise their code before the next class or assessment.
Targeted revision - Assign different tutorial sections or practice exercises based on common errors you observed in class, so students revisit the specific coding or analytical steps they have not yet mastered.
Tip 1
Use Posit Cloud or another pre-configured environment when you want all students to work with the same packages and setup, especially in introductory courses or short workshops.
Tip 2
Keep guided practice tasks focused on one coding or analytical step at a time, so students can use the feedback to revise their work and arrive in class with concrete questions or results to discuss.
How-to videos
RStudio for the Total Beginner
How to Teach and Learn with RStudio Cloud
Teaching data courses and workshops
Getting started
Support
In case of educational questions, please contact your learning innovator or web team from your faculty.
In case of technical difficulty, please consult the the official R Project and Posit support resources first. If you are using R in teaching through RStudio, Posit Cloud or tutorial-based workflows, it can also help to start with Posit Academy or the official Posit Cloud teaching documentation.
