Although R is not new, its popularity has increased rapidly over the last 10 years or so (see here for some interesting data). It was originally created and developed by Ross Ihaka and Robert Gentleman during the 1990’s with the first stable version released in 2000. Nowadays R is maintained by the R Development Core Team. So, why has R become so popular and why should you learn how to use it? Some reasons include:
- R is open source and freely available.
- R is available for Windows, Mac and Linux operating systems.
- R has an extensive and coherent set of tools for statistical analysis.
- R has an extensive and highly flexible graphical facility capable of producing publication quality figures.
- R has an expanding set of freely available ‘packages’ to extend R’s capabilities.
- R has an extensive support network with numerous online and freely available documents.
All of the reasons above are great reasons to use R. However, in our opinion, the single biggest reason to use R is that it facilitates robust and reproducible research practices. In contrast to more traditional ‘point and click’ software, writing code to perform your analysis ensures you have a permanent and accurate record of all the methods you used (and decisions you made) whilst analysing your data. You are then able to share this code (and your data) with other researchers / colleagues / journal reviewers who will be able to reproduce your analysis exactly. This is one of the tenets of open science. We will cover other topics to facilitate open science throughout this book, including creating reproducible reports and version control.
In this Chapter we’ll show you how to download and install R and RStudio on your computer, give you a brief RStudio orientation including working with RStudio Projects, installing and working with R packages to extend R’s capabilities, some good habits to get into when working on projects and finally some advice on documenting your workflow and writing nice readable R code.