Preparing for Regression and Modeling in R
Before the course starts, students not already familiar with R should practice data importing and simple programming with R and RStudio (both available as free downloads).
An introduction to R that many previous students found helpful can be found at https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf (Venables, Smith, and the R Core Team). See also DataCamp's Quick-R tutorial and Frank E. Harrell Jr.'s Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis.
Preparing for Introduction to the All of Us Research Program
Case studies for this course, also known as “featured workspaces,” can be found on the All of Us Researcher Workbench. View a publicly available table of contents of these materials at support.researchallofus.org.
The three software programs listed below will be utilized throughout the course. These programs are accessible within the Workbench for all registered researchers. Gaining a fundamental understanding of these software programs will enhance your comprehension of the case studies covered in this course.
Please also look over this course preparation worksheet and complete all of the necessary steps.
Preparing for Big Data in Biomedical Research
These three software programs will be used throughout the course. These programs are free and should be downloaded prior to July 24:
In addition, these online tools will be used. Please bookmark them in advance:
If you have little or no experience in R and Linux, please review these materials prior to the start of the course:
- R tutorial
- ACCRE training - particularly the "Introduction to Unix" and "Introduction to the ACCRE Cluster" slides
Participants who would like a primer on biomedical terminology are encouraged to consult the Beginner’s Guide to RNA-seq or similar resources.
Preparing for Introduction to Causal Inference
We will be using R software for demonstrations, activities, and (optional) homework.
For a free, gentle introduction to many causal inference concepts, you may wish to consult an online book authored by Miguel Hernán and Jamie Robins.
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