General information about the course
Currently no new sessions of this course are offered. Please visit this page for updates.
R is a powerful and flexible software environment for statistical analysis and graphics. R is freely available, facilitating analysis and model transparency and reproducibility. Over recent years, it has become increasingly popular among health researchers.
The 1-day "Introduction to R for health researchers" course will be provided by the Biostatistics Research Unit (BRU) at UHN, THETA Collaborative and the Hospital for Sick Children.
Participants will be expected to have some basic knowledge of statistics but are not expected to be familiar with R.
There will also be an option for real-time, interactive webinar participation in the course. Webinar participants will have the ability of audiovisual participation and access to a platform that will provide real-time support from the course instructors
Contact information & location details
The course was offered at:
Toronto Health Economics and Technology Assessment (THETA) Collaborative
University Health Network
Toronto General Hospital Eaton Building,
10th Floor, Room 248 200 Elizabeth Street,
Toronto, ON M5G 2C4
Faculty Members 2017
George Tomlinson, MSc, PhD, Biostatistician, Director of the Biostatistics Research Unit, University Health Network, Toronto; Associate Professor at the Institute of Health Policy, Management and Evaluation, University of Toronto.
Nicholas Mitsakakis, MSc, PhD, Biostatistician at Biostatistics Research Unit (BRU) at University Health Network, Assistant Professor at the Institute of Health Policy, Management and Evaluation, University of Toronto.
Petros Pechlivanoglou, MSc, PhD, Health Economist, Scientist at Child Health Evaluative Sciences, Sick Kids Hospital, Toronto.
John Matelski, MSc, Biostatistician at the Biostatistics Research Unit, University Health Network, Toronto.
Jin Ma, MSc, PhD, Biostatistician at the Biostatistics Research Unit, University Health Network, Toronto.
R course summaries for each of the day's four sessions
Session 1: Getting to know R and R Studio
- The R Studio programming environment
- R script files
- Objects, assignment, and functions
- Vectors and data frames
- “Bread and Butter” built in functions
- Saving and importing data
Session 2: Basic R Graphics
- Adding colours and informative labels to figures
Session 3: Common Statistical Tests and Procedures
- Categorical data: Binomial, Chi-squared, and Fisher’s Exact tests
- Continuous data: t-test and non-parametric tests
- Sample size calculations
Session 4: Common regression models
- Linear regression
- Logistic regression
- Statistical interactions
- Model comparisons