R Intro (Fall 2024 / RSM358)

R is an open-source programming language for statistical computing and graphics. It has a large collection of high-quality user contributed packages that provides powerful tools for data analysis tasks. This 4-session workshop will introduce you the basics of R programming and data analysis so as to prepare you for the RSM358 coding assignments.

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What To Prepare

Please install R and RStudio Desktop before the first session.

If you encounter technical difficulties installing the software, you can instead use R and RStudio in the Cloud at the UofT JupyterHub. Simply go to its landing page and choose the RStudio option. You will need your UTORid to login.

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Session 1

• Slides
• Code
  • Chapter 2 lab from your textbook
• Extra notebooks for R programming basics (optional)
  • Data structures basics (R Notebook )
  • Programming structures basics (R Notebook )
• Reading list
  • Your Textbook (Section 2.3 Lab: Introduction to R)
  • Hands-on Programming with R (Optional; Chapter 1 to 3; Chapter 4 to 7, 9 and 11)

Session 2

• Slides
• Code
  • Chapter 2 & 3 labs from your textbook
• Extra slides and notebook for linear regression (optional)
  • Slides
  • Linear Regression - Base R tooling (R Notebook )
  • Linear Regression - Tidyverse & Others (R Notebook )
• Reading list
  • Your Textbook (Section 3.6 Lab: Linear Regression)

Session 3

• Slides
• Code
  • Chapter 3 & 4 labs from your textbook
• Extra slides and notebook for logistic regression (optional)
  • Slides
  • Linear Regression - Base R tooling (R Notebook )
• Reading list
  • Your Textbook (Section 4.7 Lab: Classification Methods)

Session 4

• Slides
• Code
  • Chapter 4 labs from your textbook
• Reading list
  • Your Textbook (Section 4.7 Lab: Classification Methods)

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Resources

• From Zero to Hero
  • Step 1: Hands-on Programming with R (beginner level; learn R programming basics.)
  • Step 2: R for Data Science (intermediate level; learn to manipulate, analyze and visualize data using R’s Tidyverse packages.)
  • Step 3: Advanced R (advanced level; learn to master R.)
• Other Resources
  • R for Reproducible Scientific Analysis (from software carpentry)
  • Programming with R (from software carpentry)
  • R Graphics Cookbook
  • ggplot2: Elegant Graphics for Data Analysis
  • The R Graph Gallery (R graph samples with code)
  • R Cheat Sheets (cheat sheets for many popular R packages)
  • DoSStoolkit (self-paced interactive modules to help you learn and use R from Uoft Dept. of Statistical Science)

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