Self-Paced R Tutorials

A self-paced introduction to R for students with no programming experience, using simulated data laid out like All of Us Research Program data. Each notebook opens in Google Colab with the R runtime already selected and takes roughly 20 to 40 minutes.

Everyone starts with the Foundations, then follows one of two paths.

How to Use the Notebooks

  1. Click a notebook link below.
  2. Sign in with a Google account.
  3. Choose File > Save a copy in Drive and work in your copy.
  4. Run the cells in order from top to bottom. Each notebook ends with solutions and a link to the next one.

Foundations

Notebook Covers
F1: First Steps in R Running code, arithmetic, variables, functions, reading error messages
F2: Vectors and Data Types Vectors, numbers vs. text, TRUE/FALSE, dates, missing values
F3: Dataframes Building and inspecting a table; one row per person or many
F4: Choosing Rows and Columns Packages, select(), filter(), the pipe
F5: New Columns and Data Files mutate(), if_else(), reading a CSV file, exploring a new dataset
F6: Combining Tables Inner and left joins, missing values from joins, duplicated rows

Path A: Write It Yourself

Students write the code.

Notebook Covers
A1: Cleaning Real Data Recoding categories with if_else() and case_when()
A2: One Row per Person Earliest record per person, merging three tables, long to wide
A3: Dates and New Variables Date logic, exposure before diagnosis, analysis variables, saving
A4: Exploring and Plotting Group summaries, histogram, box plot, scatter plot
A5: Statistical Tests t-test, ANOVA, chi-square, Fisher’s exact test
A6: Regression Linear and logistic regression, odds ratios

Path B: AI-Assisted

Students describe what they want to an AI chat assistant, then read and check the code it returns.

Notebook Covers
B1: Your First Prompts The describe-generate-read-run-check loop; what may be shared with an AI tool
B2: Building a Dataset with AI Prompting for one row per person, merging, date logic; test cases
B3: Exploring and Plotting with AI Summaries and plots by prompt; checking a plot against numbers
B4: Catching Mistakes Debugging errors with AI; four mistakes that run without an error
B5: Statistics with AI Choosing the test, reading output, odds ratios, moving to the Workbench