HW 1 - Data visualization

Homework
Important

This homework is due [some date in the future].

Getting Started

  • Go to the data-sci-101 organization on GitHub. Click on the repo with the prefix hw-1. It contains the starter documents you need to complete the homework assignment.

  • Go to Posit Cloud and click on New Project > New RStudio Project from Git. Make sure you’ve connected your Posit Cloud and GitHub accounts.

Hello GitHub

  • Once you’re in the project, go to the Console and run

    usethis::create_github_token()

  • On the page that pops open in your browser, create a new GitHub Personal Access Token (i.e., sort of like a password you store):

    • Describe the token’s use case under Note, e.g., hw-1

    • Extend expiration date to 90 days

    • Scroll down and click on Generate Token

  • Copy the token by clicking on .

  • Go back to Posit Cloud, to your Console, and run

    gitcreds::gitcreds_set()

    and paste the token you just copied.

Hello Git

Introduce yourself to Git with

usethis::use_git_config(
  user.name = "Mine Çetinkaya-Rundel",
  user.email = "cetinkaya.mine@gmail.com"
)

replacing your name and email (that you used to create your GitHub account) with mine.

YAML

The top portion of your R Markdown file (between the three dashed lines) is called YAML. It stands for “YAML Ain’t Markup Language”. It is a human friendly data serialization standard for all programming languages. All you need to know is that this area is called the YAML (we will refer to it as such) and that it contains meta information about your document.

Important

Open the Quarto (.qmd) file in your project, change the author name to your name, and render the document. Examine the rendered document.

Committing changes

Now, go to the Git pane in your RStudio instance. This will be in the top right hand corner in a separate tab.

If you have made changes to your Quarto (.qmd) file, you should see it listed here. Click on it to select it in this list and then click on Diff. This shows you the difference between the last committed state of the document and its current state including changes. You should see deletions in red and additions in green.

If you’re happy with these changes, we’ll prepare the changes to be pushed to your remote repository. First, stage your changes by checking the appropriate box on the files you want to prepare. Next, write a meaningful commit message (for instance, “updated author name”) in the Commit message box. Finally, click Commit. Note that every commit needs to have a commit message associated with it.

You don’t have to commit after every change, as this would get quite tedious. You should commit states that are meaningful to you for inspection, comparison, or restoration.

In the first few assignments we will tell you exactly when to commit and in some cases, what commit message to use. As the semester progresses we will let you make these decisions.

Now let’s make sure all the changes went to GitHub. Go to your GitHub repo and refresh the page. You should see your commit message next to the updated files. If you see this, all your changes are on GitHub and you’re good to go!

Push changes

Now that you have made an update and committed this change, it’s time to push these changes to your repo on GitHub.

In order to push your changes to GitHub, you must have staged your commit to be pushed. click on Push.

Packages

library(tidyverse)
library(openintro)

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.

Workflow + formatting

Make sure to

  • Update author name on your document.
  • Label all code chunks informatively and concisely.
  • Follow the Tidyverse code style guidelines.
  • Make at least 3 commits.
  • Resize figures where needed, avoid tiny or huge plots.
  • Turn in an organized, well formatted document.

Exercises

Data 1: Duke Forest houses

Note

Use this 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 proceeding.

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 proceeding.

Data 2: BRFSS

Note

Use this dataset for Exercises 3 to 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 proceeding.