Course Description

This course provides an introduction to the R programming language and software environment for statistical computing and graphics. A variety of examples with a biological theme will be presented.

Course Objectives

After this course you should be able to:

• Import data and plot graphs
• Install and use R packages
• Create a documented and reproducible piece of R code

Topics Covered

Week 1

• Introduction to R basics and environment
• Understanding data types

Week 2

• Reading and writing data tables
• Basic R graphics

Week 3

• dplyr package for data manipulation
• ggplot2 package for plotting

Week 4

• Automating the generation of multiple plots
• Applying conditions to the data to be plotted

Course Materials

Week 1

• Introduction
• Data Structures

Week 2

• Analysis Walkthrough
• Introduction to Plotting

Week 3

• dplyr and ggplot2

Week 4

• Automating Plotting

Course Presenters

(current and previous)

• Maria Doyle, Research Computing Facility
• Roxane Legaie, Pathology Department
• Lucy Liu, Victorian Centre for Functional Genomics
• Miriam Yeung, Dawson Labs
• Andrew Bakshi, Goode Lab
• James Morgan, Parkville Familial Cancer Centre

Course Helpers

(current and previous, includes all above)

• Liz Christie, Bowtell Lab
• Anna Trigos, Goode Lab
• David Ma, Pathology Department
• Yamuna Kankanige, Pathology Department

Acknowledgements

This course is run by the Peter Mac Data Science Training team and is based on the R course run regularly at the University of Cambridge

Useful Resources

• The R Project for Statistical Computing
• Bioconductor