Homework 3
For any exercise where you’re writing code, insert a code chunk and make sure to label the chunk. Use a short and informative label. For any exercise where you’re creating a plot, make sure to label all axes, legends, etc. and give it an informative title. For any exercise where you’re including a description and/or interpretation, use full sentences. Make a commit at least after finishing each exercise, or better yet, more frequently. Push your work regularly to GitHub, and make sure all checks pass.
Question 1
Thank you.
Make a plot that says “Thank you” on it. Get creative! You can make it however you like, e.g., annotation, geoms, fake data, etc. There will be points assigned to creativity for this exercise. So just annotating the plot with the letters would earn fewer points than a more creative approach. Also, use as little as defaults as possible, in terms of color scales, themes, etc.
Question 2
A second chance.
Take one of the visualizations from your first project, ideally one you received more feedback on / lost more points on and improve it. First, write one sentence reminding us of your project and a a few sentences on why you chose this plot and how you plan to improve it. Some of these improvements might be “fixes” for things that were pointed out as missing or incorrect. Some of them might be things you hoped to do before the project deadline, but didn’t get a chance to. And some might be things you wanted to do in your project but your teammates didn’t agree so you gave up on at the time. Some notes for completing this assignment:
- You will need to add your data from your project to the `data/` folder in this assignment. You do not need to also add the data dictionary.
- You will need to copy over any code needed for cleaning / preparing your data for this plot. You can reuse code from your project but note that we will re-evaluate your code as part of the grading for this exercise. This means we might catch something wrong with it that we didn't catch before, so if you spot any errors make sure to fix them.
- Don't worry about being critical of your own work. Even if you lost no points on the plot, if you think it can be improved, articulate how / why. We will not go back and penalize for any mistakes you might point out that we didn't catch at the time of grading your project. There's no risk to being critical!Question 3
Median housing prices in the US.
The inspiration and the data for this exercise comes from https://fred.stlouisfed.org/series/MSPUS. The two datasets you’ll use are median_housing and recessions, both of which are in the data folder of your repository.
- Load the two datasets using `read_csv()`.
- Rename the variables as date and price.
- Create the following visualization.
<img src="images/median-housing-1-1.png" width="90%"/>
- Identify recessions that happened during the time frame of the `median_housing` dataset. Do this by adding a new variable to recessions that takes the value TRUE if the recession happened during this time frame and FALSE if not.
- Now recreate the following visualization. The shaded areas are recessions that happened during the time frame of the `median_housing` dataset. Hint: The shaded areas are "behind" the line.
<img src="images/median-housing-2-1.png" width="90%"/>
- Create a subset of the `median_housing` dataset for data from 2019 and 2020 early. Add two columns: `year` and `quarter`. `year` is the year of the `date` and `quarter` takes the values Q1, Q2, Q3, or Q4 based on `date`.
- Create the following visualization.
<img src="images/median-housing-3-1.png" width="90%"/>Question 4
Visualizing uncertainty
Using the openintro::loans_full_schema dataset, fit a linear model predicting interest_rate from loan_purpose. Print out the regression output with broom::tidy(). Then, using half-eye plots, plot the confidence intervals around each of the slope coefficients and the intercept. Based on your plot, (1) comment on how the variability of the slope coefficient for various levels of loan_purpose varies and (2) describe in one or two sentences how the p-values in the regression output correspond to the widths of the confidence intervals. Hints: (1) Take a look at how many loans per each type of loan purpose. Does that help explain why some curves are very wide while others are much narrower? (2) Which 95% confidence intervals include 0 and which don’t? How does that correspond to which p-values are less than 0.05 and which are not?
Question 5
Du Bois challenge.
Recreate the following visualization by W.E.B. Du Bois on family budgets split by income classes for 150 families in Atlanta, Georgia. This visualization was originally created using ink and watercolors.
<img src="images/income-expenditures-orig.jpeg" width="90%"/>
Notes for recreation:
- Use the `data/income.csv` as your starting point.
- You do not need to recreate (1) the plot title, (2) the dollar coin (the circle on top left) and the arched "Annual Income" text above it,
(3) the image and tiny text inside the legend box.
- It is also ok if your y-axis isn't a table like the one shown on the plot, but you want to make sure you do have the income ranges styled as closely as possible to the plot.
- To change the background of the plot to match the background of the original visualization, look for an image of a "parchment paper" on a free image site like Pixabay ([https://pixabay.com](https://pixabay.com/)) and place the image file in your `images/` folder and the image in the background of the plot.