Computing in R (Doctoral School)

Computing in R (Amsterdam UMC Doctoral School)

This course is part of the PhD course program of the Amsterdam UMC Doctoral School.

• Teachers: Aldo Jongejan, Gaby Lunansky and Jurgen Claesen (Div10-Intro2R@amsterdamumc.nl)
• ECTS: 0.4
• Location AMC: Lecture hall 5 (Mon), lecture hall 2 (Tue), Theatrum Anatomicum (Thu), lecture hall 1 (Fri)

Goal

R is a simple programming language for statistical computing. Due to its flexibility and the large variety of statistical functions available in R, it is a popular alternative for programs like SPSS. However, for a beginner mastering R can be rather difficult. This course helps the student to become familiar with the basics of R. After the course the student will be able to write short programs in R for basic (data) analyses and for plotting figures.

Scheduled dates

Edition: 9-13 March, 2026

Edition: 8-12 June, 2026

Register via the Amsterdam UMC Doctoral School.

Schedule (March 2026 edition)

Course material (March 2026 edition)

• Slides: PDF (part1), PDF (part2)
• Code: CourseMain
• Computer exercises: PDF
• Computer exercises with answers: PDF

Datasets for exercises

• titanic3.dta
• titanic3select.txt (right click followed by ‘save link as’)
• titanic3.xls
• titanic3select_corrected.txt (right click followed by ‘save link as’)

Information on R

• R homepage
• R and its packages can be downloaded from CRAN
• Introductory material
  • Learn R interactively using swirl. Use their ‘R Programming’ course to refresh what you learnt in our course.
  • Datacamp offers several on-line courses at the beginner and intermediate level with lots of exercises
  • Cookbook for R offers many great examples
  • A short introduction to R with a nice list of common error messages (and how to maybe solve them) on the last page
  • Quick-R shows the commands to be used for many aspects of a statistical analysis, and has been useful information for experienced users of some other statistical packages
  • Documentation for R packages organized by topical domain

• Manuals
  • An introduction to R (html): R in 100 pages
  • R reference card: R in 6 pages
  • Useful cheat sheets (for example, for ggplot2 and importing and transforming data via tidyr and dplyr) provided by the people at RStudio
  • An on-line version of the book ggplot2: elegant graphics for data analysis
• Further pointers
  • Editors
    • RStudio provides an intuitive interface to R and powerful coding tools. See this YouTube movie for a nice explanation of the basics of RStudio.
    • Tinn-R
    • WinEdt together with the RWinEdt plug-in
  • Miscellaneous
    • Examples of beautiful figures and corresponding R code