Install R

First, install R at this link: https://cran.r-project.org/mirrors.html. Choose a mirror approximately close to you geographically, and then choose the option corresponding to your operating system:

Follow all the operating system-specific instructions.

Congratulations! 🥳 You have R installed! That means you could write an R script and run it. But let’s make things a lot easier by downloading RStudio, which will keep us organized and supply us with lots of shortcuts.

Install R Studio

You can download RStudio at this link: https://rstudio.com/products/rstudio/download/. Choose the RStudio Desktop free version on the left:

And then the blue download button near the top should match your operating system. If not, you can choose from the list below it.

Again follow the instructions for download. Now open up RStudio. It should automatically be connected with R!

Personalize R Studio

There’s a lot you can do to personalize RStudio. Feel free to explore all the options and do what you want with it.¹ You can do a lot of personalization through the “Global Options” menu under “Tools”. Here’s what it looks like on Windows:

🛑 One change that I will request that you make is to change what RStudio does when you restart R. You want your settings to look like this:

(You will have to restart RStudio.)

The reason I want you to set things up this way is because it’s really easy to make mistakes if you let R remember the data and packages you had loaded the last time you used it.

Install the tidyverse package

Open up RStudio. In the console, type in install.packages("tidyverse") and press enter. Then wait a while. Voilà! You have just installed a lot of super useful packages.

You ran that code in the console because it’s not something you’ll need to run again or look back at. When you are loading a package, you’ll want to do that from a script. You’ll want a record of the fact that you loaded the package so that if you or someone else wants to rerun the code, they’ll have the appropriate packages.

If you’re trying to run code that requires a package you haven’t installed, either RStudio will suggest they do so (because RStudio is magical 🧙), or you’ll get an error when you run library(package_name). In fact, here’s what happens when you try to load that package (which I haven’t installed because it doesn’t exist!)

library(package_name)

#>  Error in library(package_name): there is no package called 'package_name'

Load the tidyverse package

To open a new R script, choose “File” -> “New File” -> “R Script”, or simply click on the button with a green plus sign at the upper-left corner of the window. This is a blank document that you can write code in and save to come back to.

In that document, write:

# load packages
library(tidyverse)

The first line is a comment, which won’t run when you run the script. Comments in R start with # and can include whatever they want after that. To comment out multiple lines, simply preface each one with #. (Highlighting them all and using Cmd/Ctrl + Shift + c is faster!)

If you highlight both lines, or just put your cursor on the end of the second, and then press Cmd/Ctrl + Enter, the code will run. You should see some text show up in the console.

# load packages
library(tidyverse)

#>  ── Attaching packages ────────────── tidyverse 1.3.0 ──

#>  ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
#>  ✓ tibble  3.0.3     ✓ dplyr   1.0.0
#>  ✓ tidyr   1.1.0     ✓ stringr 1.4.0
#>  ✓ readr   1.3.1     ✓ forcats 0.5.0

#>  ── Conflicts ───────────────── tidyverse_conflicts() ──
#>  x dplyr::filter() masks stats::filter()
#>  x dplyr::lag()    masks stats::lag()

The first section is telling you which packages are actually being loaded with the “mega-package” tidyverse, and the second section is telling you that there are some functions loaded, specifically from the dplyr package, that have the same names as those in the stats package. The stats package is on that automatically is loaded with R, so you’ll always have these conflicts.

In this class, we’ll be using the dplyr versions of those functions, so this isn’t a problem. In general, if you want to be specific about which package a function comes from, you can write, e.g., stats::filter(x), so that R knows to use the version of the filter() function from the stats package.²

Refresh R and load another package

Now let’s see what happens when we restart R. There are two ways to do this:

1. Literally quit RStudio, and open it back up again.
2. Go to Session -> Restart R in the menu bar. There’s also a keyboard shortcut you can memorize.³

Now, open a new file, and type in and run this code:

ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
  geom_smooth(se = FALSE, method = lm)

You should get this error:

#>  Error in ggplot(mpg, aes(displ, hwy, colour = class)): could not find function "ggplot"

If you didn’t, you didn’t correctly restart R! 🤦 Try again.

Now add the necessary library() call and run the whole thing again:

library(ggplot2)
ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
  geom_smooth(se = FALSE, method = lm, formula = "y ~ x")

You should see this figure pop up in the Plots window!

You can quit RStudio and you don’t have to save this file. We’ll be back to figures like that next week! 📊

Unzip a folder in Windows

1. Save the zip file to Downloads. You should see something like this:

2. Go up to Extract -> Compressed Folder Tools -> Extract All, like this:

3. Just leave the default folder for extraction. We’ll move it later.

4. Now you’ll see that you’re in the Downloads/01-week/ folder, in which there’s another folder called 01-week. We want this one. You can ignore the _MACOSX one (that’s just there because I made it on a Mac). Move this 01-week folder to wherever you’re keeping files for this class, e.g., in a folder called R-course as in the slides. (Here I’m moving it to a folder called R – don’t do this, name it something better!)

5. Then, back in your downloads folder, you can delete both folders called 01-week, because you’ve moved out the stuff you need.

Unzip a folder on a Mac

1. Go to Downloads, or wherever you have files set to download. You’ll see something like this:

2. Double click to unzip. You’ll now have the unzipped folder as well. Move that one to wherever you’re keeping files for this class, e.g., in a folder called R-course as in the slides. You can delete 01-week.zip!

Open up an R project

Inside the 01-week folder, you’ll see a file called 01-week.Rproj. If you’re on Windows, it should look like this:

Double-clicking on it should open up RStudio. If not, follow these instructions:

Folder structure

Check out your files in the Files viewpane (one of the tabs by Plots). You should see all the materials for this week! They include:

• 01-week.Rproj: We’ve covered what this does
• 01-exercise.html: This is essentially what you see on this webpage, but you can open it up in your browser if you don’t have an internet connection
• 01-exercise.Rmd: This is the R Markdown file I wrote to generate the webpage, if you want to see the code behind it. You can open it up and click Knit at the top of the RStudio window if you want to generate it yourself (you might need to install some packages). Learn more about R Markdown here.
• 01-todo.R: This is the R script you’ll be working with in the next session!

Also check to make sure that you’re in the correct working directory. You can find that at the top of your console pane. It should look something like this:⁴

If not, you didn’t open RStudio via the 01-week.Rproj file. 🤦 Close RStudio and try again!