Feb 2017 - Introduction to R for Health Researchers

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General information about the course

R is a powerful and flexible software environment for statistical analysis, high quality graphics and decision modelling. R is freely available, facilitating analysis and model transparency and reproducibility. Last few years it has been increasingly popular among health researchers. 


Biostatistics Research Unit (BRU) at UHN, THETA Collaborative and the Hospital for Sick Children will be providing a 1-day "Introduction to R for health researchers" course on Monday, February 6th, 2017, 9am to 5pm..

Participant experience

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


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


Program Overview

Please find below a list of the topics that will be covered  during the day 

  • Getting to know the R interface
  • Getting help in R
  • Types of data
  • Importing/exporting data
  • Basic data handling
  • Loading packages
  • Simple mathematics in R
  • Scalars, Vectors and Matrices in R
  • Advanced data handling
  • Handling missing values / imputations
  • Plotting in R
  • Generating values from distributions
  • “For” and “if” statements ; the apply function
  • Building functions in R
  • Installing R studios

Past R Course poster