Exploring Data

Due: Sep 28 by 11:59pm

Weight: This assignment is worth 1% of your final grade.

Purpose: The purpose of this assignment is to develop some basic strategies for exploring data sets to gain a greater understanding of the variable types as well as key relationships such as centrality, variability, and correlation

Assessment: This assignment is graded using a check system:

• ✔+ (110%): Responses shows phenomenal thought and engagement with the course content. I will not assign these often.
• ✔ (100%): Responses are thoughtful, well-written, and show engagement with the course content. This is the expected level of performance.
• ✔− (50%): Responses are hastily composed, too short, and/or only cursorily engages with the course content. This grade signals that you need to improve next time. I will hopefully not assign these often.

Notice that this is essentially a pass/fail system. I’m not grading your writing ability and I’m not counting the number of words you write - I’m looking for thoughtful engagement. One or two sentences is not enough. Write at least a paragraph and show me that you did the readings assigned.

1. Get Organized

Open your course repo (eda-f26/eda-netID) in Positron using File › Open Folder - open the folder itself, not a file inside it. If you haven’t cloned your course repo yet, follow the steps in HW1 first.

Everything for this assignment lives in the hw4 folder. Open the hw4.qmd file inside it - that’s the only file you need to edit. Take notes and write example code under the “Begin notes here” heading as you go through the readings and exercises below, then write your reflection under the “Reflection” heading at the end.

2. Read & Practice

Before class, install the packages we’ll use in the practice exercises:

install.packages(c('HistData', 'GGally', 'palmerpenguins'))

• R4DS - 10: Exploratory data analysis: Provides some more “hands on” strategies for exploring data using R and the ggplot2 library.
• Wilke - 12: Visualizing associations: An overview of ways to visualize associations between two or more variables.
• “Beware Spurious Correlations”, Harvard Business Review (if you don’t have a subscription to see it, a backed up version can be found here).
• Watch this 20 minute video to learn about some basic data types and descriptive statistics.
• Watch this 15 minute video to learn about the basics of correlation measures.

Choosing the right chart

You will want to choose different chart types depending on the relationship or message you want to convey. Fortunately, there are lots of great guides to help you make those choices.

• View a quick overview of many common plot types in Wilke - 5: Directory of visualizations.
• View the course site help page on choosing the right chart.

Optional readings

The history of statistical correlation is rather ugly and rooted in racism and eugenics. These readings discuss these topics in more detail:

• Francis Galton pioneered scientific advances in many fields – but also founded the racist pseudoscience of eugenics
• Column: The false, racist theory of eugenics once ruled science. Let’s never let that happen again
• Listen to this Radiolab episode on eugenics.

3. Reflect

This week, consider writing about an example of a correlation that you could (incorrectly) assume was causation.

Reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

Write at least a paragraph in your hw4.qmd file, and include at least one question. The teaching team will review the questions we get and will try to answer them either in Slack or in class.

Some thoughts you may want to try in your reflection:

• “I used to think ______, now I think ______ 🤔”
• Discuss some of the key insights or things you found interesting in the readings or recent class periods.
• Connect the course content to your own work or project you’re working on.

4. Submit

To submit your assignment, follow these instructions:

1. Render your .qmd file by either clicking the “Preview” button in Positron or running quarto render in the terminal.
2. Open the rendered html file and make sure it looks good! Is all the formatting as you expected?
3. Commit and push your work using GitHub Desktop: look at the diff, write a short commit message, click Commit, then Push.

NoteRemember

Your work isn’t submitted until you’ve pushed it. Committing only saves the change on your own computer – go look at your repo on GitHub.com and confirm your work is actually there before you call this done.