Chapter 3 Getting set up

Learning goals

By the end of this chapter you should be able to:

  • Install R and RStudio on your own computer.
  • Explain the difference between R and RStudio.
  • Recognise the main parts of the RStudio window.
  • Download the course data and put it where R can find it.

Prerequisites

  • A laptop or desktop computer with an internet connection. That is all — this is the very first step.

A tiny motivating example

By the end of this short chapter you will be able to open RStudio, type a line like this into the console, press Enter, and see an answer:

1 + 1
## [1] 2

If you can do that and import one of the course data files, you are ready to start.

3.1 R and RStudio are two different things

It is worth being clear about this from the start, because it confuses almost everyone at first:

  • R is the programming language and the engine that does the actual computing. On its own it is not much to look at.
  • RStudio is a separate, much friendlier program — an “integrated development environment” (IDE) — that sits on top of R and makes it pleasant to use: somewhere to write scripts, run code, and see your results and plots side by side.

You install both, but once they are installed you only ever open RStudio — it starts R for you behind the scenes.

Takeaway: Install R and RStudio; work in RStudio.

3.2 Installing R and RStudio

What we’re about to do: Get both programs onto your computer, in the right order.

The easiest route is the Posit download page, which lays the process out as two numbered steps:

https://posit.co/download/rstudio-desktop/

  1. Install R first. Follow the “Install R” link on that page (it takes you to CRAN, the official R website at https://cran.r-project.org). Choose the version for your operating system (Windows or macOS), download it, and run the installer, accepting the default options.
  2. Then install RStudio Desktop (the free version). Download the installer from the same page, run it, and again accept the defaults.

Install R before RStudio, so that RStudio can find it.

Do this now: Install R, then RStudio, then open RStudio (not R). You should see a window divided into panes, described next.

Common mistake: Opening “R” (a very plain window) instead of “RStudio”. If the program you opened looks bare and old-fashioned, close it and open RStudio instead.

Takeaway: R first, then RStudio; then only ever open RStudio.

3.3 A quick tour of RStudio

What we’re about to do: Get oriented so the window is not intimidating.

When you open RStudio you will see up to four panes. (You may see only three at first — the top-left one appears as soon as you open or create a script.)

  • Source / editor (top-left): where you write and save your scripts. This is where most of your work happens.
  • Console (bottom-left): where code actually runs and results appear. You can also type here directly for quick, throwaway commands.
  • Environment / History (top-right): lists the objects (data sets, variables) currently loaded in R’s memory.
  • Files / Plots / Packages / Help (bottom-right): browse files on your computer, view plots, manage installed packages, and read help pages.

Try this: Click in the Console, type 1 + 1, and press Enter. You should see the answer appear. Congratulations — R is working.

Takeaway: Four panes — editor, console, environment, and files/plots/help. You will use all of them.

3.4 Get the course data

What we’re about to do: Download the data used throughout the book and put it somewhere R can find it.

Almost every example in this book reads a data file from a folder called CourseData. Here is how to set that up:

  1. Download the data from the course Dropbox folder: https://www.dropbox.com/scl/fo/5tdl9dtflv79lkvq86vuj/h?rlkey=spw81m08re1ufef5uvxcopgla&dl=0. Dropbox lets you download the whole folder as a single .zip file.
  2. Unzip it. This gives you a folder called CourseData containing many .csv files.
  3. Put it in your project folder. Move the CourseData folder inside the course project folder you will create in the Paths and projects chapter (the folder that contains your .Rproj file).

Once it is there, and you are working inside your RStudio Project, a command like this will just work — no long file paths required:

carni <- read.csv("CourseData/carnivora.csv")

Common mistake: an error such as cannot open file 'CourseData/...': No such file or directory. This almost always means the CourseData folder is not inside your project folder, or that you are not working inside your RStudio Project. The Paths and projects chapter explains how to fix this properly.

Takeaway: Keep CourseData inside your project folder and refer to files with short, relative paths.

3.5 Key takeaways

  • R is the engine; RStudio is the friendly interface. Install both, open RStudio.
  • The RStudio window has four panes: editor, console, environment, and files/plots/help.
  • Put the CourseData folder inside your project and refer to files with relative paths.

3.6 Common pitfalls recap

  • Opening R instead of RStudio.
  • Installing RStudio before R.
  • Downloading the data but leaving it in your Downloads folder, so R cannot find it.

3.7 Mini-project

  1. Install R and RStudio, and open RStudio.
  2. In the Console, type 1 + 1 and then R.version.string, pressing Enter after each.
  3. Download and unzip the course data, ready to move into your project folder in the next chapters.

You are now ready to set up a tidy project (see the Paths and projects chapter) and start learning R (see An R refresher).