Upcoming in this April and May - R-cafe Short course

https://oucru-modelling.github.io/R-cafe-materials/

Welcome to the OUCRU R training course - 2025

Course structure

• The training will be 09:00 – 17:00 (UTC +7) for everyday
• There will be a lunch break between 12:30 – 13:30 (UTC +7)
• Lunch will be provided for in-person attendees

Contributors

• Prof. Ronald Geskus, Biostatistics group, OUCRU HCM
• Dr. Thinh Ong Phuc, Mathematical Modeling group, OUCRU HCM
• Mr. Tuyen Huynh, Mathematical Modeling group, OUCRU HCM
• Dr. Tran Thai Hung, Biostatistics group, OUCRU HCM
• Ms. Anh Phan Truong Quynh, Mathematical Modeling group, OUCRU HCM
• Dr. Hai Ho Bich, Emerging Infections group, OUCRU HCM
• Dr. Duc Du Hong, Biostatistics group, OUCRU HCM
• Mr. Manh Nguyen Duc, Mathematical Modeling group, OUCRU HCM
• Mr. Nguyen Pham Nguyen The, Mathematical Modeling group, OUCRU HCM
• Dr. Marc Choisy, Mathematical Modeling group, OUCRU HCM
• Dr. Dung Vu Tien Viet, Biostatistician, OUCRU Hanoi
• Dr. Trinh Dong Huu Khanh, formerly: Biostatistics group, OUCRU HCM

1 Pre-course setups and requirements

Please follow this handout before joining the course.

2 Course topics

2.1 Introduction to R

🧑‍🏫️ Lead instructors: Ronald and Thinh

Day 1

• Lecture slides
• Contents:
  • Open and inspect datasets, understand their structure
  • Make selections and extract relevant data
  • Perform basic data exploration, including calculating summaries and identifying missing values
  • Navigate RStudio with confidence
• Exercises:
  • Handout file
  • Exercise answer key
  • Exercise answer script
• Example R code scripts - Day 1

Day 2

• Lecture slides
• Contents:
  • Write simple functions to automate tasks
  • Create visualisations using Base R graphics
  • Understand the R working environment, including file paths and workspace management
  • Find and use R documentation and resources to solve problems on your own
• No exercises given
• Example R code scripts - Day 2

2.2 Day 3: Principles of data visualisation

🧑‍🏫️ Lead instructors: Ronald and Thinh

• Lecture slides
• Contents:
  • The step-by-step process of creating graphs in R
  • The grammar of graphics—understanding how plots are built
  • How to design clear and impactful visualisations
• Handout file
• Exercises:
  • Exercise file
  • Exercise answer key
  • Exercise answer script
  • Datasets:
    • NightingaleRose.csv
    • ICUCost.csv

2.3 Day 4: Advanced R topics

🧑‍🏫️ Lead instructors: Tuyen and Hai

• Lecture slides
• Contents:
  • Logics with if-else statements and for-loops
  • Piping in R
  • Data manipulation with mutate()
  • Joining dataframes
  • Pivotting dataframes into long and wide formats
  • Vectorisation in R with map()

2.4 Day 5: Using Quarto for reproducible research

🧑‍🏫️ Lead instructor: Thinh

• Lecture slides
• Contents:
  • Write papers with reproducible tables and figures using Quarto
  • Generate reports in multiple formats: Word, PDF, slides, web pages…
  • Easily update analyses when new data becomes available
• Exercises
• Exercise answer keys

3 Appendix

3.1 Introduction to R and RStudio

Good introduction, with explanatory videos:

• RStudio provides several tutorials for beginners and more advanced users: https://education.rstudio.com/
• Datacamp provides many courses at different levels (OUCRU HCMC has a license). See http://www.statmethods.net. The introduction is free
• A basic course, interactive design: https://www.quantargo.com/courses/course-r-introduction
• Introduction from Pasteur institute: https://hub-courses.pages.pasteur.fr/R_pasteur_phd/First_steps_RStudio.html
• Good summary of basic concepts: http://www.burns-stat.com/documents/tutorials/impatient-r
• A comprehensive introduction: https://github.com/matloff/fasteR
• Introduction to R programming: https://jjallaire.github.io/hopr/

3.2 DeepeR into R

• The book R for Data Science (2e)
• “What They Forgot to Teach You About R”
• https://www.r-project.org/doc/bib/R-books.html
• The official R manuals: https://cran.r-project.org/manuals.html

3.3 R and statistics

• A simple introduction to biomedical statistics using R: https://a-little-book-of-r-for-biomedical-statistics.readthedocs.io/en/latest/
• A website with detailed examples of all types of statistical analysis: https://stats.oarc.ucla.edu/other/dae/
• R by example: http://www.mayin.org/ajayshah/KB/R/index.html
• R course, with focus machine learning

3.4 R Markdown and Quarto

• The main RStudio webpage on R Markdown, with links to two freely available books: https://rmarkdown.rstudio.com/docs/
• Introduction from RStudio
• Introduction to knitr
• First steps in Markdown
• Writing academic papers with R Markdown (focus on bibiliographies): https://ikashnitsky.github.io/2019/zotero/
• R Markdown tips: https://appsilon.com/r-markdown-tips/
• Powerpoint presentations using R Markdown: https://appsilon.com/r-markdown-powerpoint-presentation/
• https://codingclubuc3m.rbind.io/post/2019-09-24/ focus on creating presentations
• R Quarto tutorial: https://appsilon.com/r-quarto-tutorial/

3.5 R GUIs