Biostatistics in R: a practical guide

A curated guide to doing biostatistics in R — choosing a test, comparing groups, association and risk, survival analysis, regression, diagnostic tests, sample size, and reproducible reporting. Tutorials, free calculators and interactive demos, all in one place.

A single map of everything we’ve written about doing statistics and biostatistics in R — organised by the question you’re trying to answer. Each topic links the plain-English tutorial, the free calculator, and the interactive demo where we have one. New to R? Start at the top; looking for something specific? Jump to the section.

1. Getting started with R

If R itself is new, learn it by running it — no install required.

2. Choosing the right test

Before any analysis, match the method to your data.

3. Comparing groups

Is one group’s average different from another’s?

4. Association & risk

How are two variables related — and by how much?

5. Survival analysis

Time-to-event data — the backbone of clinical outcomes research.

6. Regression & prediction

Model an outcome from one or more predictors.

7. Diagnostic tests & agreement

Evaluating a test, a classifier, or two raters.

8. Sample size & power

Plan the study before you collect the data.

9. Tables & reproducible reporting

Turn results into a publication-ready document.

Explore it interactively


This guide is the free layer. When a real dataset, real assumptions and a real deadline are involved, that’s where we come in.