Monday 10 February 2020
Introduction to R will cover the use of the open source programming language R (and RStudio) for data manipulation, simple data analysis, and graphing. All practical exercises are illustrated using data from health sciences studies.
Topics covered
- R basics and RStudio
- Installation and set up
- Data importing, organising and manipulating
- Exploratory data analysis
- Graphics and data visualisation
- Specifying formulae and simple regression models
Style of course
Computer lab - hands-on course combining taught overview, hints on programming practices, and practical exercises. Course attendance is limited to 18 participants.
Who should attend?
The course is intended for individuals who would like to become more comfortable with the R environment for research/study purposes. Basic knowledge of biostatistics is required for the course (e.g. confidence intervals, hypothesis testing) as the focus is on R rather than statistics. Some brief experience in R or another statistics package using programming commands (e.g. Stata, SAS) may be useful but is not presumed. The practicals will be completed using Windows but the material also applies to people running R on a Mac or Linux system.
Draft timetable
| Time | Content | Presenter |
|---|---|---|
| 8.30am | Registration and Coffee | |
| 9:.00am | Part 1: Basics, installation and set up | James Stanley |
| 10:30am | Morning break | |
| 11am | Part 2: Data manipulation | James Stanley |
| 12:30pm | Lunch break | |
| 1:15pm | Part 3: Exploratory data analysis and hypothesis testing | James Stanley |
| 3pm | Afternoon break | James Stanley |
| 3:30pm | Part 4: Graphics and data visualisation Part 5: Regression |
James Stanley |
| 4:50pm | Summary of course and evaluation | James Stanley |
| 5pm | Finish |
Teaching staff
- Dr James Stanley is a Research Associate Professor and consulting biostatistician at the University of Otago, Wellington. He started using R in 2009 to draw nicer graphs than he could draw elsewhere, and now uses it for nicer everything-else-analysis.
- Maddie White works as a Kairuruku Tuarua (Assistant Research Fellow) with He Kāinga Oranga at the University of Otago, Wellington. Although R wasn't the first statistical programming language she studied, it quickly became her favourite. She now uses R in her day-to-day work understanding large, linked datasets in the Integrated Data Infrastructure (IDI). Maddie enjoys improving her R skills for data management, graphing and other exciting things, and especially likes encouraging others to do the same through helping teach the PHSS R courses.
Course cost and registration
$300 early bird, $400 after 19 December 2019.
A 50% discount is available to full-time students, those unwaged and University of Otago staff.
