Programming with R, Monash University, July 2015

The best way to learn how to program is to do something useful, so this introduction to R is built around a common scientific task: data analysis.

Our real goal isn’t to teach you R, but to teach you the basic concepts that all programming depends on. We use R in our lessons because:

1. we have to use something for examples;
2. it’s free, well-documented, and runs almost everywhere;
3. it has a large (and growing) user base among scientists; and
4. it has a large library of external packages available for performing diverse tasks.
   • Statistics focus.
   • Bioconductor for bioinformatics tasks. Several popular Bioconductor packages were developed in Melbourne at the Walter & Eliza Hall Institute, so local expertise is available.

But the two most important things are to use whatever language your colleagues are using, so you can share your work with them easily, and to use that language well.

Prerequisites

An understanding of the concepts of files and directories (including working directory) is required.

Though not required, we often use RStudio when teaching this lesson. You can find our introduction to RStudio here.

Data

• data.zip

Topics

1. Analyzing patient data
2. Creating functions
3. Analyzing multiple data sets
4. Making choices
5. Command-Line Programs
6. Best practices for using R and designing programs
7. Dynamic reports with knitr
8. Making packages in R

Other Resources

• Reference
• Discussion
• Instructor’s Guide

Supplemental lessons

• Introduction to RStudio
• Addressing data
• Understanding factors
• Data types and structures