Learning resources

Courses, books and references for R, epidemiology and statistics

If you only pick one: Start with DDEA Introductory Course if you are completely new to R, or The Epidemiologist R Handbook if you know a bit of R and want to go straight to health science applications.

Getting started — learn R

Resource	What it is
⭐ DDEA Introductory Course	Introductory R course from the Danish Diabetes and Endocrine Academy — held once a year, materials freely available. Best starting point if you are completely new.
⭐ R for Data Science	Free e-book by Hadley Wickham — the standard reference for modern R with tidyverse. Concept-based, read from start to finish.
Cookbook for R	Task-based reference: “how do I do X?” with direct code examples. Use it when you know what you want but can’t remember the syntax.
Quick-R	Compact reference page for R syntax and statistics. Good for quick lookups mid-script.

Health science and epidemiology

Resource	What it is
⭐ The Epidemiologist R Handbook	Practical reference written for epidemiologists and health researchers — covers data cleaning, analyses and visualisation with health science examples.
⭐ Zheers R Coding Café	Notes and examples specifically for register data — local to Steno Diabetes Center Aarhus. Most relevant for this study.
CRAN Task View: Epidemiology	Curated package list for epidemiology — incidence rates, matching, cohort analysis. Good starting point when looking for the right package.
CRAN Task View: Survival Analysis	Curated package list for survival analysis — Cox regression, Kaplan–Meier and competing risks.

Podcasts:

Resource	What it is
🎧 SERious Epi	Podcast on epidemiological methods in practice — study design, bias and analytical choices, discussed by experienced epidemiologists.
🎧 Causal Inference	Podcast on causal inference — methods, confounding and causal questions, in an informal conversational format.

Methods and statistics

Resource	What it is
Learning Statistics with R	Free e-book by Danielle Navarro — covers statistical theory and R implementation side by side, from basic probability to regression and ANOVA. Written for a non-mathematical audience. Relevant if you want to understand the statistics you are running, not just copy the code.
Causal Inference: What If (Hernán & Robins)	Free PDF — the standard reference on causal inference, confounding and DAGs. Epidemiological methods, not R.