This course is part of the PhD course program of the Amsterdam UMC-AMC Graduate School.

- Location: Academic Medical Center, Amsterdam.
- 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.

### Schedule (September 2020 edition)

Location | Day | Time |
---|---|---|

L0-211/229/227 | September 21, 2020 | 9-12am |

L0-211/229/227 | September 22, 2020 | 9-12am |

L0-211/229/227 | September 24, 2020 | 9-12am |

L0-211/229/227 | September 25, 2020 | 9-12am |

### Course material (September 2020 edition)

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

### Datasets for exercises

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

### 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

- Learn R interactively using

*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
- A preprint of the book ggplot2: elegant graphics for data analysis

*Further pointers*- Editors
- Miscellaneous
- Examples of beautiful figures and corresponding R code