Computing Setup

These notes will provide some thoughts on setting up your computing environment.

Operating System

We will support almost any operating system (OS), within reason, however we assume you will be using one of:

  • Windows
  • macOS
  • Linux

For each, we assume that your OS is reasonably up-to-date.

Install R

R is a freely available language and environment for statistical computing and graphics. Use the appropriate link from the following1 to download and install R:

Many students already have R installed from previous courses. Even if that is the case, we highly recommend re-installing R to insure you have the most recent version, which as of this writing if 4.1.22.

M1 Mac Users: The above direct download link will work through emulation using Rosetta2 in order for R to run properly. There is also an arm64 build available, but course content has not been tested using this setup. If you would like to test and report back if it works, please do!

Install RStudio

RStudio is a free and open-source integrated development environment (IDE) for R. Use the appropriate link from the following3 to download and install RStudio:

macOS Users: Be sure to actually install RStudio. Do not simply run RStudio from the disk image you downloaded. To install RStudio, drag RStudio from the disk image to your Applications folder. Once you have done so, eject the disk image, then open RStudio from the Applications folder.

RStudio is not R and R is not RStudio. You will need to have both installed. RStudio is an integrated development environment (IDE) that will assist you in writing R code and developing software.

Install Packages

The wonderful package system is a larger part of what makes R a great language. To install several packages that we will need later, run the following:

install.packages("tidyverse")

To verify that this has worked, run the following:

library("tidyverse")
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.6     ✓ dplyr   1.0.8
✓ tidyr   1.2.0     ✓ stringr 1.4.0
✓ readr   2.1.1     ✓ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

If you do so, and your output looks relatively similar, you have successfully installed all of the packages of the tidyverse.

We will further discuss installing and using packages later.

RStudio Setup

To see the wealth of ways that you can customize RStudio, take a look at Preferences. For example, many users prefer a dark theme. To change RStudio’s theme, see Appearance in the Preferences window.

In general, we advocate for keeping things as default as possible, however there are some noteworthy exceptions.4

Do Not Restore Old Workspaces

Due to some odd default settings in RStudio, some students never clear their R environment. This is a problem for a number of reasons.5 To avoid these issues, do the following:

  1. Clear your current environment.
  2. Change some RStudio defaults.
    • Deselect “Restore .RData into workspace at startup.”
    • Set “Save workspace .RData on exit:” to “Never”

Footnotes

  1. Linux users: We assume you can figure out how to do this without us providing the links.↩︎

  2. Codename: “Bird Hippie”↩︎

  3. Linux users: Again, you’re on your own.↩︎

  4. We’ll likely add to theses throughout the semester as I re-discover the few changes I’ve made by comparing with students.↩︎

  5. It could prevent your results from being reproducible. It could cause R to run very, very slowly.↩︎