You must turn in a knitted file to Gradescope from a Quarto Markdown file in order to receive credit. Be sure to “associate” questions appropriately on Gradescope. As a reminder, late work is not accepted outside of the 24-hour grace period for homework assignments.

The Quarto template for this assignment may be found in the repository at the following link: https://classroom.github.com/a/26xKrKGn

These data contain pocket measurements for 80 pairs of jeans from popular US brands, as mentioned in the Pudding article available here - please read the article prior to starting this assignment (it’s short and pretty interesting!). For a description of the variables, check out the data dictionary here.

Important: Some of your grade on this assignment will also be based on meaningful commit descriptions. For the purposes of this assignment, you must commit and push your changes after Exercise 2 and again after Exercise 4 (of course, you’re welcome to commit/push more often than that!). As well, don’t forget to change the name in the Quarto template.

  1. Create a visualization that summarizes the relationship between the maximum height of the front pocket and the maximum width of the front pocket. In this visualization, color code the observations by whether the jeans are sold as “men’s” or “women’s.” In this plot, make sure you have clear labels for the axis and legend in plain English (e.g., don’t use the defaults). Provide a meaningful title and subtitle that provide interesting data insights.
  2. Create a similar visualization as in Exercise 1, but this time instead of color coding by “men’s vs. women’s jeans,” facet instead to create two side-by-side plots (the graphs should be oriented horizontally - one row, two graphs). Again, make sure the plot is well-labeled with a meaningful title and subtitle.
  3. Given the basic visualizations constructed in Exercises 1 and 2, what can you say about front pockets in jeans marketed to men vs. to women? Do your visualizations support the storyline from the Pudding article?
  4. Create a visualization that plots the maximum width of the back pocket against the maximum height of the back pocket (no need to separate by men’s vs. women’s-marketed jeans, but do label and title the plot meaningfully). How many points appear to be plotted? How many observations are there in the dataset? With these two things in mind, what are the potential dangers of displaying this plot? Suggest a strategy that might mitigate these issues.