R Training Intensive

Overview

Welcome to our three-day R training intensive! By Thursday afternoon, you’ll have learnt the R skills to manipulate, visualise and present data. We’ll spend roughly half the time learning content, and half the time working on a project in groups.

As we set up, there’s a few things to do, if you haven’t already

  1. Install the software
  2. Introduce yourself to your table
  3. Join our Teams channel
  4. Register your attendance

R + RStudio

The R programming language is a language used for calculations, statistics, visualisations and many more data science tasks.

RStudio is an open source Integrated Development Environment (IDE) for R, which means it provides many features on top of R to make it easier to write and run code.

R’s main strong points are:

  • Open Source: you can install it anywhere and adapt it to your needs;
  • Reproducibility: makes an analysis repeatable by detailing the process in a script;
  • Customisable: being a programming language, you can create your own custom tools;
  • Large datasets: it can handle very large datasets (certainly well beyond the row limitations of Excel, and even further using HPCs and other tricks);
  • Diverse ecosystem: packages allow you to extend R for thousands of different analyses.

The learning curve will be steeper than point-and-click tools, but as far as programming languages go, R is more user-friendly than others.

Installation

For this course, you need to have both R and RStudio installed (installation instructions).

R Projects

Let’s first create a new project:

  • Click the “File” menu button (top left corner), then “New Project”
  • Click “New Directory”
  • Click “New Project”
  • In “Directory name”, type the name of your project, for example “YYYY-MM-DD_rstudio-intro”
  • Browse and select a folder where to locate your project (~ is your home directory). For example, a folder called “r-projects”.
  • Click the “Create Project” button

R Projects make your work with R more straight forward, as they allow you to segregate your different projects in separate folders. You can create a .Rproj file in a new directory or an existing directory that already has R code and data. Everything then happens by default in this directory. The .Rproj file stores information about your project options, and allows you to go straight back to your work.