Setup and Data Viz Considerations

Math 241: Problem Set 0

Due: Tuesday, Sep 15th, noon PT.

Problem 1: Setup

In this class, we’ll encounter a variety of technologies. This problem aims to help you navigate setting up some of those technologies.

part a: GitHub

We’ll use GitHub for collaboration and portfolio building, so each of you need to have a GitHub profile. If you don’t have a GitHub profile already, go to https://github.com/ and sign up for one. Once you’ve signed up for an account, please add it to the Google Sheet so that I can add you to the stat-241-f26 organization. Note: you’ll need to be logged in to your Reed Google account to edit the Google Sheet.

part b: R and RStudio (or Positron!)

We’ll use R and RStudio/Positron for computing in this class. R is a statistical programming language, while RStudio and Positron are Integrated Development Environments (IDEs) that allow you to run R, view files, view R code output, and much more! Reed has an RStudio Server where you can run R and RStudio in the cloud, but for this class I would recommend a local installation of both R and RStudio. Positron is a new IDE from Posit (the company that makes RStudio) you are welcomed and encouraged to try out Positron, although I may not be able to provide as good of problem solving for any issues related to your IDE if you use Positron. Positron seems to be the hot new thing, and I imagine in the next ~5 years we will see a steep decline of RStudio use in favor of Positron for R users and data scientists.

To install R and RStudio, follow the directions from Posit’s website.

To install Positron, see this link from Posit’s website.

Note: always make sure to have R installed before installing your IDE.

part c: Connect git, GitHub and RStudio (or Positron)

These instructions are based on pieces of Happy git with R, in particular chapters 6, 7, and 9. Depending on your computer, setup, or just luck, this step can become difficult/frustrating. If you are having trouble with this step, please come to office hours for help!

In order to have your computer talk with GitHub, you’ll need to install git on your computer. Sometimes, git is already installed on your computer, and you can check it by running

which git

in a Terminal or shell window. If you have git installed, you should see something like “/usr/bin/git” returned to you. If you don’t have git installed, you might see something like “git: command not found” or nothing at all. If you have git installed, congrats! If not, follow the steps in Happy git with R, Chapter 6 to install git.

Next, up you have to introduce yourself to git. Again, following Happy git with R, in Chapter 7 we can run the following in an R session/console

## install if needed (do this exactly once):
## install.packages("usethis")

library(usethis)
use_git_config(user.name = "Jane Doe", user.email = "jane@example.org")

where Jane Doe is replaced with your full name and jane@example.org is replaced with the email you used to sign up for GitHub.

Next, you’ll have to authenticate yourself (i.e., give your computer permission to make changes to GitHub projects when you ask it to). To do this, run the following in an R session/console:

usethis::create_github_token()

This should open a new browser window where you can create your token. I often set no expiration date and check all of the boxes, but that is up to you. Note that when your token expires you’ll have to make a new one if you’re still using the same computer. Now, your token is created (it will start with “ghp_”)! Copy it down somewhere so that you don’t lose it.

The final step is to run

## install if needed (do this exactly once):
## install.packages("gitcreds")

gitcreds::gitcreds_set()

and then enter the token when prompted. If also this has gone well, you are set!

Unless… you’re running Linux. If you’re running Linux, I am happy to help you get git working correctly, but I’d say the first step is to read and follow Danielle Navarro’s blog post, in addition to the steps above.

part d: testing

To make sure everything is working correctly, run

gitcreds::gitcreds_get()

in an R session/console. You should see something like

<gitcreds>
  protocol: https
  host    : github.com
  username: PersonalAccessToken
  password: <-- hidden -->

but if you see

Error in throw(new_error("gitcreds_no_credentials", url = url)) : 
  Could not find any credentials

something has gone wrong.

part e: congrats!

If you’ve made it this far, congrats! You’ve successfully set up git, GitHub, and your IDE on your local machine! We’ll spend time in class during week 2 talking about how to use all of these tools, but for now you are good to go.

If you’ve had trouble with any of these steps, please come to office hours so that we can debug and get you set up.

Problem 2: (Someone Else’s) Data Visualization

On Wednesday in class we talked about considerations for data visualization. In this problem, I’d like you to find two data visualizations “in the wild”. In particular, I’d like you to find one data visualization that you think is very good at accurately portraying the story/answering the research question at hand, and another that is quite bad at the task. For both data visualizations, make sure they are portraying real data, and not simulated or fake data. Once you’ve found your visualizations, post an image (or link to, if interactive) in our datasci-in-the-wild Slack channel, along with a few sentences about why you believe the data visualization to be quite good/bad. In your discussion of the graphs, make sure to mention the aesthetic mappings and other attributes of the graphs. Also, make sure to respond to and engage with at least one data viz from two other people in the channel.

If you’re having trouble finding a data visualization, some places to consider might be:

The New York Times, in particular their What’s going on in this graph? column.
Information is beautiful
r/dataisbeautiful or its other half, r/dataisugly

Problem 3: (Your Own) Data Visualization

Step 1

Collect data on some aspect of your life.
- In deciding what to investigate, think about aspects of your world you might want to better understand using a quantitative lens.
- Brainstorm several variables that you can collect data on; i.e., more than you might end up using in the visualization. You don’t yet know what interesting data story might be revealed in the data! I also recommend collecting both quantitative and categorical variables, if feasible.
- Pick a time span (e.g., day, week, evening) that aligns well with the data you are collecting.
- Plan ahead so that you can collect a good number of observations on your chosen variables.

Step 2

Find a story in your data and determine your audience (i.e., the person who will be receiving the postcard).
- The story could be an interesting trend or pattern. Or, maybe the story is that there is surprisingly no pattern to the aspect of your life that you captured.
- You can pick anyone for the recipient. If you’d like, I will stamp and mail your postcard for you.
Figure out how you want to visualize the story.
- We strongly encourage creating practice sketches.
- Be creative. Don’t constrain yourself to data visualizations you may already know (e.g., scatterplots, bar graphs, pie charts).
- Regardless of the background of the intended recipient, plan a visualization that requires no background statistical knowledge.
- Feel free to check out the “Dear Data” website for inspiration but your postcard must be unique.
Keep in mind that the postcards will be assessed in terms of
- The appropriateness and accuracy of the representation
- How engaging and creative the visualization is
- How clear, readable, and complete the visualization and legends are

Step 3

Create practice sketches. Only use the postcard once you are ready to produce the final visualization.
On the postcard:
- Be sure to create any necessary legends and succinct captions or helper text. (These may go on the front or the back, depending on how you want to use the space.)
- On the back of the postcard, feel free to briefly explain the assignment to the recipient and include their address.

Step 4

Write a reflection that addresses the following questions:
- What was the inspiration behind your visualization? In other words, how did you decide what question to investigate or what data to collect?
- Describe a story that your visualization communicates. Be sure to reference specific details of your visualization/data that support the story.
- Did the story you were planning to tell change during the data collection process? What surprised you most about your findings?

Step 5

Turn in your postcard and reflection to the box outside Grayson’s office. If the postcard is addressed to a recipient, I will add a stamp and mail it for you!