View on GitHub

Introduction to R's four-class intro to R statistical programming for biomedical researchers

Introduction to R


This four class course introduces R statistical programming and its broad applications, specifically for data analysis relevant to biomedical researchers. This course assumes attendees have no prior computer coding experience. At the end of this course, you will be able to use R to import, manipulate, and visualize data. Please see each set of class materials for specific learning objectives. These materials are developed by, the data and computational analysis training program at Fred Hutch. Each class in this course includes brief tutorials interspersed with challenge exercises.

Sessions of the course are periodically taught by instructors at Fred Hutch; each of the four classes is scheduled for two hours. The HackMD (interactive page used for sharing links and information) for instructor-led courses is here. The materials are also freely available for self-guided, work-at-your-own-pace study.

Required software: Software requirements for this course include:

The links above reference relevant sections of’s Software page.

This course is adapted from content originally appearing in R for data analysis and visualization of Ecological Data, Copyright (c) Data Carpentry. These materials may be useful for additional review and reinforcement of content included here.

Class material

Materials for all lessons in this course include:

Solutions for exercises can be found in solutions.

Information about use of R and RStudio at Fred Hutch is available on the Fred Hutch Biomedical Data Science Wiki

For curriculum contributors and instructors

Please see the following resources for more information on:

The data used for this course are from the National Cancer Institute’s Genomic Data Commons. extra/ holds the original data files used for download during the activities, as well as the intermediate data files for each cancer type directly downloaded from NCI-GDC, and the script used to derive them: clinical_data.R.