Resources
This page contains a non-exhaustive list of resources for learning R with a focus on epidemiology in humanitarian settings.
Want to add some other resources? Suggestions and contributions are welcome! Please open an issue or get in touch.
R Programming
DataCamp — Introduction to R. Interactive and beginner-friendly introduction to R.
Posit Cloud Primers. Browser-based interactive R tutorials covering the basics through to data visualisation and iteration.
R for Data Science. A good introduction to the tidyverse, covering data import, transformation, visualisation, and communication.
Advanced R. Deep dive into R’s internals and programming patterns.
Focus: Epidemiology and Statistics
The Epidemiologist R Handbook. An excellent resource for applied epidemiology in R. Written with field epidemiologists in mind, it covers outbreak investigation, survey analysis, GIS mapping, automated reporting, and more. Available in multiple languages.
Introduction to R for Health Data Science. Focused on health data, includes survival analysis and logistic regression.
Statistical Inference via Data Science. Gentle introduction to statistical thinking with R and the tidyverse.
The MSF-Epicentre Github. Provides a number of publicly available packages tailored to the specific needs of of MSF epidemiologists.
{sitrep}. Developed by R4EPIs / MSF. Templates and functions for situation reports in outbreak and survey contexts.R4EPIs Project. MSF & RECON initiative providing standardised R templates and tutorials for field epidemiology: outbreak investigation, mortality surveys, and vaccination coverage surveys.
Focus: Surveys and Needs Assessments
{srvyr}. Tidyverse-style analysis tools for complex survey data (cluster sampling, stratification, weights). Particularly useful for SMART nutrition surveys, mortality surveys, and other humanitarian assessments. This package is based off of{survey}.Exploring Complex Survey Data Analysis Using R. A useful text providing an in depth look at using
{srvyr}and{survey}
Focus: GIS & Mapping
- Geocomputation with R. Full guide to spatial data analysis and mapping in R.
Focus: Reporting & Reproducibility
- Quarto — A rich text format well suited for reproducible and automated reporting. Quarto supports reports, websites, and even simple dashboards.