This 5-day course will introduce students to the R statistical programming environment. The main learning objective of the course is to give students a basic skill set and familiarity with several of the most used packages in the biological sciences. It is intended primarily for users who have no experience in R, or who have performed a few basic functions but wish to develop their skills. This course is not aimed at students with extensive experience in R and it will not cover the theory behind the analyses implemented.
The typical day of the course will include step-by-step examination of pre-worked code examples as well as hands-on live-coding, so that students develop their skills through interacting with R directly. Students will learn to read in data, conduct common analyses, and produce publication-quality plots and reports. The students will also work independently on conducting analyses in order to produce a final project using available data provided by the course organisers.
It is intended primarily for users who have no experience in R, or who have performed a few basic functions but wish to develop their skills. This course is not aimed at students with extensive experience in R and it will not cover the theory behind the analyses implemented.
All participants must bring their own personal laptop.
Monday, July 17th, 2017. Intro to R.
- Introduction to the R statistical programming environment.
- Introduction to R Studio.
- Interacting with the console.
- Working with variables.
- Basic plots using qplot.
- Reading data files.
- Writing data files.
- Working with tables, data frames, and lists.
Tuesday, July 18th, 2017. Improving workflow and organization.
- Commenting your code.
- Control flow.
- What to do when your code isn’t working.
Wednesday, July 19th, 2017. Taking R to the next level.
- R packages.
- Project organization.
- Version control.
- R Markdown.
Thursday, July 20th, 2017. Making it pretty.
- Introduction to ggplot2.
- What’s in a ggplot object.
- Basic plot types.
- Customizing your plot.
- Creating and using themes.
- Using extensions.
Friday, July 21st, 2017. Problem solving / final project.
- T-test and linear models.
- Looking for help online.
- Bringing it all together in a final project.