Computing in R (Amsterdam UMC Doctoral School)
This course is part of the PhD course program of the Amsterdam UMC Doctoral School.
- Location: L0-227/230, AMC
- ECTS: 0.4
- Teachers: Perry Moerland (coordinator), Aldo Jongejan, and Michel Hof
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 3: 12, 13, 15 and 16 June, 2023
Register via the Amsterdam UMC Doctoral School.
Schedule (June 2023 edition)
Location | Day | Time |
---|---|---|
L0-227 | Monday June 12, 2023 | 9-12am |
L0-227 | Tuesday June 13, 2023 | 9-12am |
L0-227 | Thursday June 15, 2023 | 9-12am |
L0-230 | Friday June 16, 2023 | 9-12am |
Course material (June 2023 edition)
- Handouts: PDF
- Slides: PDF, Graphics
- Computer exercises: HTML, PDF
- Computer exercises with answers: HTML, PDF, R code, RMD
- Demo code: HTML, RMD, R code
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
- Miscellaneous
- Examples of beautiful figures and corresponding R code