HW 1 - Data visualization

Homework
Important

This homework is due Tuesday, Jan 31 at 11:59pm.

Getting Started

  • Go to the sta199-s23-2 organization on GitHub. Click on the repo with the prefix hw-01. It contains the starter documents you need to complete the homework assignment.

  • Clone the repo and start a new project in RStudio. See the Lab 0 instructions for details on cloning a repo and starting a new R project.

Packages

Warning: package 'ggplot2' was built under R version 4.2.2
Warning: package 'tidyr' was built under R version 4.2.2
Warning: package 'readr' was built under R version 4.2.2
Warning: package 'purrr' was built under R version 4.2.2

Guidelines + tips

As we’ve discussed in lecture, your plots should include an informative title, axes should be labeled, and careful consideration should be given to aesthetic choices.

Remember that continuing to develop a sound workflow for reproducible data analysis is important as you complete this homework and other assignments in this course. There will be periodic reminders in this assignment to remind you to knit, commit, and push your changes to GithHub. You should have at least 3 commits with meaningful commit messages by the end of the assignment.

Note

Note: Do not let R output answer the question for you unless the question specifically asks for just a plot. For example, if the question asks for the number of columns in the data set, please type out the number of columns. You are subject to lose points if you do not.

Exercises

Data 1: Duke Forest houses

Note

Use the duke_forest dataset for Exercises 1 and 2.

For the following two exercises you will work with data on houses that were sold in the Duke Forest neighborhood of Durham, NC in November 2020. The duke_forest dataset comes from the openintro package. You can see a list of the variables on the package website or by running ?duke_forest in your console.

Exercise 1

Suppose you’re helping some family friends who are looking to buy a house in Duke Forest. As they browse Zillow listings, they realize some houses have garages and others don’t, and they wonder: Does having a garage make a difference?

Luckily, you can help them answer this question with data visualization!

  • Make histograms of the prices of houses in Duke Forest based on whether they have a garage.
    • In order to do this, you will first need to create a new variable called garage (with levels "Garage" and "No garage").
    • Below is the code for creating this new variable. Here, we mutate() the duke_forest data frame to add a new variable called garage which takes the value "Garage" if the text string "Garage" is detected in the parking variable and takes the test string "No garage" if not.
duke_forest |>
  mutate(garage = if_else(str_detect(parking, "Garage"),   "Garage", "No garage"))
  • Then, facet by garage and use different colors for the two facets.
  • Choose an appropriate binwidth and decide whether a legend is needed, and turn it off if not.
  • Include informative title and axis labels.
  • Finally, include a brief (2-3 sentence) narrative comparing the distributions of prices of Duke Forest houses that do and don’t have garages. Your narrative should touch on whether having a garage “makes a difference” in terms of the price of the house.

Now is a good time to render, commit, and push. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceding.

Exercise 2

It’s expected that within any given marker larger houses will be priced higher. It’s also expected that the age of the house will have an effect on the price. However in some markets new houses might be more expensive while in others new construction might mean “no character” and hence be less expensive. So your family friends ask: “In Duke Forest, do houses that are bigger and more expensive tend to be newer ones than those that are smaller and cheaper?”

Once again, data visualization skills to the rescue!

  • Create a scatter plot to exploring the relationship between price and area, conditioning for year_built.
  • Use geom_smooth() with the argument se = FALSE to add a smooth curve fit to the data and color the points by year_built.
  • Include informative title, axis, and legend labels.
  • Discuss each of the following claims (1-2 sentences per claim). Your discussion should touch on specific things you observe in your plot as evidence for or against the claims.
    • Claim 1: Larger houses are priced higher.
    • Claim 2: Newer houses are priced higher.
    • Claim 3: Bigger and more expensive houses tend to be newer ones than smaller and cheaper ones.

Now is a good time to render, commit, and push.

Make sure that you commit and push ALL changed documents and your Git pane is completely empty before proceding.

Data 2: BRFSS

Note

Use this dataset for Exercises 3 through 5.

The Behavioral Risk Factor Surveillance System (BRFSS) is the nation’s premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

Source: cdc.gov/brfss

In the following exercises we will work with data from the 2020 BRFSS survey. The originally come from here, though we will work with a random sample of responses and a small number of variables from the data provided. These have already been sampled for you and the dataset you’ll use can be found in the data folder of your repo. It’s called brfss.csv.

brfss <- read_csv("data/brfss.csv")

Exercise 3

  • How many rows are in the brfss dataset? What does each row represent?
  • How many columns are in the brfss dataset? Indicate the type of each variable.
  • Include the code and resulting output used to support your answer.


Now is a good time to render, commit, and push.

Exercise 4

Do people who smoke more tend to have worse health conditions?

  • Use a segmented bar chart to visualize the relationship between smoking (smoke_freq) and general health (general_health). Decide on which variable to represent with bars and which variable to fill the color of the bars by.
  • Pay attention to the order of the bars and, if need be, use the fct_relevel function to reorder the levels of the variables.
    • Below is sample code for releveling general_health. Here we first convert general_health to a factor (how R stores categorical data) and then order the levels from Excellent to Poor.
brfss |>
  mutate(
    general_health = as.factor(general_health),
    general_health = fct_relevel(general_health, "Excellent", "Very good", "Good", "Fair", "Poor")
  )
  • Include informative title, axis, and legend labels.
  • Comment on the motivating question based on evidence from the visualization: Do people who smoke more tend to have worse health conditions?


Now is a good time to render, commit, and push.

Exercise 5

How are sleep and general health associated?

  • Create a visualization displaying the relationship between sleep and general_health.
  • Include informative title and axis labels.
  • Modify your plot to use a different theme than the default.
  • Comment on the motivating question based on evidence from the visualization: How are sleep and general health associated?


Now is a good time to render, commit, and push.

Exercise 6

  1. Fill in the blanks:
    • The gg in the name of the package ggplot2 stands for ___.
    • If you map the same continuous variable to both x and y aesthetics in a scatterplot, you get a straight ___ line. (Choose between “vertical”, “horizontal”, or “diagonal”.)
  2. Code style: Fix up the code style by spaces and line breaks where needed. Briefly describe your fixes. (Hint: You can refer to the Tidyverse style guide.)
ggplot(data=mpg,mapping=aes(x=drv,fill=class))+geom_bar() +scale_fill_viridis_d()
  1. Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol arguments?


Render, commit, and push one last time.

Make sure that you commit and push all changed documents and your Git pane is completely empty before proceding.

Wrap up

Submission

  • Go to http://www.gradescope.com and click Log in in the top right corner.
  • Click School Credentials Duke Net ID and log in using your Net ID credentials.
  • Click on your STA 199 course.
  • Click on the assignment, and you’ll be prompted to submit it.
  • Mark all the pages associated with exercise. All the pages of your homework should be associated with at least one question (i.e., should be “checked”). If you do not do this, you will be subject to lose points on the assignment.
  • Do not select any pages of your PDF submission to be associated with the “Workflow & formatting” question.

Grading

  • Exercise 1: 7 points
  • Exercise 2: 9 points
  • Exercise 3: 5 points
  • Exercise 4: 9 points
  • Exercise 5: 7 points
  • Exercise 6: 8 points
  • Workflow + formatting: 5 points
  • Total: 50 points
Note

The “Workflow & formatting” grade is to assess the reproducible workflow. This includes:

  • linking all pages appropriately on Gradescope
  • putting your name in the YAML at the top of the document
  • committing the submitted version of your .qmd to GitHub
  • Are you under the 80 character code limit? (You shouldn’t have to scroll to see all your code). Pipes %>%, |> and ggplot layers + should be followed by a new line
  • You should be consistent with stylistic choices, e.g. only use 1 of = vs <- and %>% vs |>
  • All binary operators should be surrounded by space. For example x + y is appropriate. x+y is not.