Welcome to R Café

Duration

3 weeks of study + 1 week of extra content (if any) and closing remarks

Structure

• 3-hour face-to-face sessions per day, 1 day per week
• Take home exercises given between face-to-face sessions

R Café helpful shortcuts

• Teams Tech support channel
• Main GitHub repository
• Theoretical slides
• Additional handouts and instructions
• Current instructor roster:
  • Thinh Ong Phuc (thinhop@oucru.org)
  • Anh Phan Truong Quynh (anhptq@oucru.org)
  • Hung Tran Thai (hungtt@oucru.org)
  • Tuyen Huynh (tuyenhn@oucru.org)
  • Hai Ho Bich (haihb@oucru.org)

Agenda

1. Getting started with R and Git (day 1)
   • Part 1: Setup (45’) - Tuyen & QA
     • What is R Café & Agenda (5’)
     • Brief introduction to Git, GitHub, and GitHub Desktop
       • Goal: Publish your own branch on R Café’s GitHub repository
     • Rstudio (20’)
       • Goal: Create RStudio project
       • Goal: Everyone create and push their first commit onto their own branch
     • Navigate GitHub
   • Part 2: Foundations of R (60’) - QA
     • Variables & Functions & Packages (with outline)
     • Exercises
       • Goal: Create a function and push to GitHub
       • Goal: Create issues and comment on each other’s issues on GitHub
   • Break (15’)
   • Part 3: Data manipulation in R (45’) - QA
     • Data management
     • Take-home exercises setup
2. R programming with tidyverse (day 2)
   • Into the tidyverse
     • What is tidyverse?
     • Why should we use it?
     • How do we use it?
     • Piping with %>%
   • Exercise
   • Vectorise R functions
     • Vectors and vectorisation
     • Vectorisation with map()
     • tidyverse style vs. base R style
   • ggplot2 for data visualisation
     • Grammar of graphics
   • Exercise
3. Quarto & Markdown (and related) (day 3)

Take-home exercises

1. Initial Data Analysis - IDA (with skimr)
   • Generate questions around your data
   • Customize your summary table output
   • Simple visualization data, e.g. histogram
   • Calculate statistics, e.g. correlation, collinearity
2. Data transformation
   • Data cleaning and filtering
   • Data transformation
   • Factor your data (with forcats)
   • Data visualization (with ggplot2)
3. Analysis with gtsummary
   • Demographic summary
   • Statistical tests, e.g. t-test, chi-sq, fisher-exact, OR, etc.
4. Generate report with Quarto
   • Citation (inline footnote and reference)