R Intro (Fall 2022)
R is an open-source programming language for statistical computing and graphics. R is extensible, and has a large collection of high-quality user contributed packages that provides easy tools for common data analysis tasks. This 4-session workshop will introduce you the fundamentals of R programming.
What To Prepare
Please install R and RStudio Desktop before the first session.
If you encounter technical difficulties installing the software, you can instead create a free RStudio Cloud account so you can run R in the cloud via your browser.
An alternative to RStudio Cloud is the UofT JupyterHub/RStudio system. Go to its home page, choose the RStudio option, and click Log in to start. You will need your UTORid to login.
Session 1, 2 & 3
- Slides
- Code
- weighted dice (We will code this one together.)
- R basic data structures (R Notebook
) - R basic programming structures (R Notebook )
- Reading list
- Hands-on Programming with R (Session 1 & 2: Chapter 1 to 3; Session 3: Chapter 4 to 7, 9 and 11)
- R for Data Science (Session 3: Chapter 19, 20 and 21)
Session 4
- Slides
- Code
- Linear Regression - Base R (R Notebook )
- Linear Regression - Tidyverse & Others (R Notebook )
- Linear Regression - Base R (R Notebook
- Reading list
- R for Data Science (Chapter 5, 9 to 11, and 18)
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 how to manipulate, analyze and visualize data using R’s Tidyverse packages.)
- Step 3: Advanced R (advanced level; you plan to be really good at R.)
- Other Resources
- 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)
- Programming with R (from software carpentry)
- DoSStoolkit (self-paced interactive modules to help you learn and use R from Uoft Dept. of Statistical Science)
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