R Workshop for Bioscience
CAF Jan 13, 2023
Welcome
- Welcome to the R workshop for Bioscience!
- The workshop is intended for any Bioscience researchers with limited prior experience in R who is interested in learning how to use R to do data wrangling, data visualization, common statistical analyses.
- Workshop materials in the github repository Rworkshop
Learning objectives
At the end of the class, students will be able to:
- navigate R and R studio interface
- set up project and coding environment for data analysis
- efficiently handle datasets
- perform common data visualizations used in Bioscience
- conduct descriptive analysis in R
- conduct regression analysis in R (if time permits)
Schedule
Time | Topic |
---|---|
9:00 - 9:10 | Introduction |
9:10 - 9:45 | Session 1: Get Started with R |
9:45 - 10:00 | Questions + Coffee break |
10:00 - 10:45 | Session 2: Working with Data |
10:45 - 11:00 | Questions + Coffee break |
11:00 - 11:45 | Session 3: Statistical Analysis in R |
11:45 - 12:00 | Conclusions and Questions |
In preparation for the workshop
Participants are required to follow the next steps before the day of the workshop:
Install R and R Studio
- Windows operating system
- install R, https://cran.r-project.org/bin/windows/base/
- install RStudio, https://posit.co/download/rstudio-desktop/#download
- macOS operating system
- install R, https://cran.r-project.org/bin/macosx/
- install RStudio, https://posit.co/download/rstudio-desktop/#download
- Windows operating system
Verify access to the course page, https://kuan-liu.github.io/Rworkshop/
Clone or download the workshop repository: https://github.com/Kuan-Liu/Rworkshop
Reference and resource
R for Data Science | ggplot2: elegant graphics for data analysis |
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About me
- I am an Assistant Professor in Biostatistics and Health Services Research at the University of Toronto
- My primary research focuses on developing methodology for statistical inference with complex longitudinal data in comparative effectiveness research
- causal inference
- Bayesian statistics
- longitudinal data analysis
- joint modelling
- bias analysis
- My ties to forestry and ecology 🌲