Getting Started

You can never look at the data too much. – Mark Engerman

The world confronts us. Make decisions we must.

We do all of our data science in the cloud, using GitHub Codespaces with Visual Studio Code (VS Code).

In this chapter you will use a Codespace to run the “Getting Started” tutorial from the tutorial.helpers package.

GitHub

Sign up for a GitHub account by following the instructions on the GitHub homepage. Follow this advice when choosing your username.

Use your school email address for this account, if you are a student. The GitHub Student Developer Pack provides all sorts of perks, but you must visit that page to turn on the benefits. It confirms your student status on the basis of you having an email account at your school. Next, add a second email address for this account, a permanent one that is not tied to your current school or job. After all, your school email address will disappear someday but your GitHub account is for life.

On GitHub, your projects are organized into “repositories,” usually called “repos.”

GitHub gives every account 60 free Codespaces hours per month. Free hours are not infinite, however, so it is your responsibility to stop (and delete) Codespaces you are no longer using.

Use either Chrome or Edge

In theory, the below instructions work in any browser. In practice, we have found that the best experience is with either Chrome or Edge. If you are using a different browser, you may find that some of the instructions do not work as expected.

A throwaway Codespace

You will work inside a codespace-starter Codespace. We use it here to learn the platform and run the “Getting Started” tutorial.

Go to https://github.com/PPBDS/codespace-starter.

This Codespace is for learning the platform, not for your permanent work.

Launching the Codespace

Click the green Code button at the top right of the repository page, switch to the Codespaces tab, and click “Create codespace on main.”

This will take a minute or so. Behind the scenes, GitHub is creating a virtual machine in the cloud with all the necessary tools for doing data science. That machine is called a “Codespace.”

You may be asked: “Do you trust the authors of the files in this folder?”

Click to agree.

Once the process is done, a banner message appears announcing “✅ YOUR CODESPACE IS READY.”

The GitHub name, fictional winner, now appears next to the repo name as well as in the quick access window above the editor. Your name will be different, as GitHub assigns a unique name to each Codespace.

Touring the workspace

VS Code is an integrated development environment (IDE) for coding and data science. Highlights:

  • This Codespace is in the cloud. The URL will be a combination of the GitHub-determined human-readable but somewhat nonsensical name — fictional winner in this case — and a bunch of letters and numbers. There is no need to remember this URL. GitHub keeps track of things. You can see all your current Codespaces at https://github.com/codespaces.

  • In the upper right-hand corner are the “Customize Layout …” buttons. These are part of the VS Code “Title Bar.” Since we aren’t using the AI tools right now, it often makes sense to close the “Chat” window, which appears on the right side of the screen. You can close this in two ways: Click the “X” mark or click the “Toggle Secondary Side Bar” button, the furthest right-hand button. You can then bring the Chat window back by clicking the “Toggle Secondary Side Bar” button again. Try it now.

  • The “Activity Bar” is the narrow vertical strip on the far left with icons for Explorer, Search, Source Control, Extensions, etc. By default, the “Explorer” button is selected, showing that the only thing in the project is a folder called .devcontainer. Click on that folder to show its contents.

Click on the STUDENT_WORKFLOW.md file. Doing so opens that file in the Editor window. Your screen should now look like this:

The “Editor” is the large central area where you edit files.

The “Panel” is the horizontal area below the Editor, containing the Problems, Output, Debug Console, Terminal, and Ports views. Our main focus is the Terminal view. This is where we “talk” to both the (cloud) computer itself and to the R program that it provides.

The Terminal view currently shows two terminals. They are listed along the right-hand side. You can move back and forth between them by clicking on them. You can close a terminal by clicking on the trash can icon next to its name, which appears when you hover your cursor over the name.

  • You can start new bash Terminals by clicking on the + sign above the list of terminals. Do so now.

The language can be a little confusing. There is a capital T “Terminal” view, which is one of the tabs across the top of the Panel. In that Terminal view, we can work with many individual small T “terminals” of various sorts. So far, you have only seen bash Terminals, which get a capital T because they are a type of named terminal. The bash shell is a program that lets you “talk” to the computer itself.

In addition to bash Terminals, we can also start an R session under the Terminal. Instead of clicking the + sign, click the small downward-pointing arrow next to it. This will show a variety of options.

Select “R Terminal.” This will start an R session that lets you “talk” to R in the same way that a bash shell allows you to talk to the computer.

Click on the “R Interactive” tab which should appear beneath the other terminals on the right side of the Panel.

Type in 2 + 2 at the R prompt and hit enter (Windows) or return (Mac). (Going forward we will just use Enter to refer to this action. Mac users should hit return.)

An IDE like VS Code is designed to organize all the different work we do as data scientists. We need to talk to the computer via the bash Terminal (a terminal running the bash shell), talk to R, view plots, and so on.

At the R Terminal, run plot(1:10). We use the terms “run” and “execute” interchangeably. They both indicate that you should type the command at the appropriate terminal and hit Enter.

You have created a plot in the “Plots” tab of the Panel.

Running the Getting Started tutorial

If you hover your cursor over the Activity Bar on the far left-hand side, you can see the names of the different options. Near the bottom is one labeled “R Tutorials.” Click on it. This brings up all the R packages with tutorials. Click on the package name tutorial.helpers.

Doing so shows the one tutorial in the tutorial.helpers package. If you hover your cursor over a tutorial, a rightward pointing arrow appears. Clicking that arrow starts the tutorial. Start the Getting Started tutorial from the tutorial.helpers package. Do so now.

Clicking the tutorial arrow starts a new R session, labeled “R Tutorial” on the right side of the Panel. We now have several different terminals. In this case, a “terminal” is any connection to the (cloud) computer itself or to a program running on it, like R. In fact, the bash shell is just another program which runs in the computer.

The R Tutorial session shows the tutorial being built and its current state, which is “listening,” i.e., waiting for you to complete the tutorial. While the tutorial is running, this R session is unavailable for other work.

You should also have been given an option to open the tutorial in the browser.

You should take that option. If it does not appear, or if you missed it, you can also open the tutorial by hand by hovering over the URL and pressing Cmd + Click (on Mac) or Ctrl + Click (on Windows/Linux). Throughout this book, shortcuts are written like Cmd/Ctrl + Click, meaning the command key on Mac or the control key on Windows/Linux.

The http address refers to a file located in your GitHub Codespace but which is still visible on your local machine via the magic of “port forwarding,” meaning that the Codespace is allowing your browser to open it. Opening it in your browser shows:

Read and follow the instructions. At the end of the tutorial, download your answers.

Stopping, restarting and deleting the Codespace

A Codespace is your responsibility in the same way that your laptop is your responsibility. While a Codespace is running it counts against your free hours, and an unused Codespace will be deleted by GitHub after 30 days.

There are three common ways to close a Codespace.

First, just leave it alone. GitHub will close it on its own after 30 minutes of inactivity, though we recommend changing that default to 15 minutes in your Codespaces settings.

Second, type Cmd/Ctrl + Shift + P. The Command Palette provides access to all VS Code commands. Type stop into the search bar.

On some browsers, the keyboard shortcut does not work. You can always access the Command Palette by clicking the search bar at the top of the window and typing > followed by key words from the command you would like to use.

Select “Codespaces: Stop Current Codespace.”

Third, you can go to your personal Codespaces control panel at https://github.com/codespaces. You can also reach this page from any page on GitHub by clicking the menu icon in the upper left and selecting “Codespaces”:

Which brings up this lst of options. Select “Codespaces.”

You main Codespaces page should look something like this:

This shows all your Codespaces, both active and inactive. The ... menu on each row provides several commands, including “Stop Codespace.”

Simply closing the browser window does not stop your Codespace from running. Always stop a Codespace explicitly to preserve your free hours.

Now stop this Codespace using whichever method you prefer.

Once it is stopped, you can restart it from your Codespaces page by clicking the ... menu next to this Codespace and then selecting Open in Browser.

Once you are done with a Codespace, you should delete it. To do so, go to your Codespaces page, click the ... menu next to this Codespace and select Delete.

This Codespace was a sandbox. You do not own PPBDS/codespace-starter, so there is nowhere for your work to go once you stop using the Codespace. That is fine — you have already downloaded your tutorial answers, which is the only thing here worth keeping.

Using your own machine

You can do all of this work on your own laptop, if you prefer. But, in that case, you are responsible for setting everything up. That means installing VS Code, Git and R. You will almost certainly want to install the same VS Code extensions which we use, including:

"reditorsupport.r",
"quarto.quarto",
"PPBDS.vscode-r-tutorials",
"ritwickdey.LiveServer",
"tomoki1207.pdf",
"mechatroner.rainbow-csv"x

This listing is from the .devcontainer/devcontainer.json file from PPBDS/codespace-starter. You may also find it useful to use the same VS settings which are defined there.

You will also need to install, by hand, various R packages. From the R Terminal, you would run commands like:

install.packages("pak")

You may be asked to select a CRAN mirror. It does not matter which you choose.

pak::pak("tidyverse")  
pak::pak("PPBDS/vscode.tutorials")

These steps are not enough to perfectly replicate what we show in the Codespace. See PPBDS/devcontainers for more details.

But this should be enough to get you started, should you decide to go this route. If you have trouble, ask AI, pointing it toward this chapter and to the PPBDS/codespace-starter and PPBDS/devcontainers repos.

Summary

You should have done the following:

  • Signed up for a GitHub account.
  • Used a throwaway Codespace on PPBDS/codespace-starter to complete the Getting Started tutorial from tutorial.helpers, downloaded your answers, then deleted the Codespace.

For doing some tutorials, a throwaway Codespace is all you need: complete the tutorial and download your answers — no repository needed. When a later tutorial asks you to keep your work and build on it, the next chapter, Git and GitHub, shows you how to create a repository you own.

Let’s get started!