Continue your learning in R programming and data analysis. You will learn how to make use of complex data structures, use custom functions built by other R users, creating your own functions, and how to iterate repeated tasks that scales naturally. You will also learn how to clean messy data to a Tidy form for analysis, and conduct an end-to-end data science workflow.
Dates: Winter 2026: Jan 20, 27, Feb 3, 10, Feb 24, March 3
Time: Tuesdays Noon - 1:30 pm PT
Time commitment: 6 weeks of 1.5 hour classes and 1-2 hours of practice outside class.
Audience: The course is intended for researchers who want to continue learning the fundamentals of R programming and how to deal with messy datasets. The audience should know how to subset dataframes and vectors and conduct basic analysis, and/or have taken our Intro to R course.
Prerequisites: Intro to R
Followed by: Bioconductor for Genomics
Apply tools for Tidying data to get a messy dataset into analysis-ready form, via data recoding, data transformations, and data subsetting.
Describe the data science workflow and how a data analysis project fits into the workflow.
Create simple, custom functions that can be reused throughout an analysis.
Understand the need for using iteration in programming to reduce repeated code and implement simple repeated tasks.
This class is offered once a year (currently winter quarter). Check the schedule to see when it will next be offered.
Yes, you can access the course materials on your own any time.
You need to complete the live class to get a badge.
The class is open to members of the Cancer Consortium including Fred Hutch, the University of Washington, and Seattle Children’s Hospital.
You can, but we recommend you don’t mix R and Python at the same time, because it can be confusing to switch between the two.
You can sign up more than once, but be aware that there is tyspically a waitlist. We will prioritize students who have not taken the class. If there is room you are welcome to take it again.