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.

• Location: Lecture hall 5 (Mo, Th), lecture hall 3 (Tu), lecture hall 1 (Fr); AMC
• ECTS: 0.4
• Teachers: Perry Moerland (coordinator, p.d.moerland@amsterdamumc.nl), Aldo Jongejan, Michel Hof and Thanos Angelakis

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 December, 2024

Register via the Amsterdam UMC Doctoral School.

Schedule (December 2024 edition)

Course material (December 2024 edition; will still be updated before the course starts)

• Handouts: PDF
• Slides: PDF, Graphics
• Computer exercises: HTML, PDF
• Computer exercises with answers: HTML, PDF, R code, RMD
• Demo code: R code, RMD, HTML

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
    • Tinn-R
    • WinEdt together with the RWinEdt plug-in
  • Miscellaneous
    • Examples of beautiful figures and corresponding R code