AE 01: GSS + data visualization

Application exercise

In this mini-analysis, we use data from the 2024 General Social Survey (GSS) to get practice with creating and interpreting data visualizations.

Getting started

Packages

We’ll use the tidyverse package for this analysis.

Data

The data are stored as a CSV (comma-separated values) file in your repository’s data folder. Let’s read the file and save it as an object called gss.

gss <- read_csv("data/gss-2024.csv")

Get to know the data

We can use the glimpse() function to get an overview (or “glimpse”) of the data.

glimpse(gss)
Rows: 3,294
Columns: 9
$ year              <dbl> 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024…
$ marital_status    <chr> "Never married", "Never married", "Married", "Never …
$ age               <dbl> 33, 64, 69, 19, 70, 53, 48, 30, 60, 25, 23, 68, 43, …
$ education_years   <dbl> 16, 16, 14, 12, 13, 14, 13, 14, 14, 12, 18, 5, 14, 1…
$ children          <dbl> 2, 0, 0, 0, 3, 2, 6, 1, 2, 0, 0, 1, 2, 1, 3, 1, 2, N…
$ happiness         <chr> "Not too happy", "Pretty happy", "Pretty happy", "Pr…
$ health            <chr> "Good", "Good", "Good", "Good", "Good", "Good", "Goo…
$ employment_status <chr> "Working full time", "Retired", "Retired", "Working …
$ party_id          <chr> "Not very strong democrat", "Strong democrat", "Stro…
  • What does each observation (row) in the data set represent?

Add your response here.

  • How many observations (rows) are in the data set?

Add your response here.

  • How many variables (columns) are in the data set?

Add your response here.

Variables of interest

The variables we’ll focus on are the following:

  • marital_status: Respondent’s current marital status.
  • happiness: Respondent’s self-reported general happiness.
  • health: Respondent’s self-reported general health.

Demo: One categorical variable

We begin with the question:

What self-rated health levels are most common among respondents in the 2024 GSS?

  • What type of plot is a good starting point for this question?

Both the question and the variable type should guide our choice of visualization. Since health is a categorical variable, a bar chart is a useful place to start.

  • ggplot() creates the initial base coordinate system. The first argument is the data you’re plotting, gss.
# add code here
  • aes() tells how variables in the data map to visual properties of the graph.
# add code here
  • A geom_ function specifies how observations will be represented. geom_bar() creates a bar chart and counts the rows in each category for us.
# add code here
  • Which recorded health response is most common? Which is least common?

Add your response here.

  • The labs() function allows us to give the plot a meaningful title, axis labels, and a source.
# add code here
Preview, commit, push

Now that we’re done with our first task, it’s time to take a snapshot.

  1. Preview your Quarto document and review the output. If all looks good…
  2. Go to the Source Control pane and stage your changes to each file listed. Then commit your staged changes with a simple, informative message.
  3. Click Sync to push your changes to your application exercise repo on GitHub.
  4. Go to your repo on GitHub and confirm that you can see the updated files – you may need to refresh your browser window. Once your updated files are in your GitHub repo, you’re good to go!

Two categorical variables

Now we ask:

How does self-reported happiness vary across marital-status groups?

Both marital_status and happiness are categorical variables.

Option 1 - Demo: Stacked bars

Map happiness to the fill color. By default, geom_bar() stacks the happiness categories within each marital-status bar.

# add code here

This graph displays counts. It also shows that the marital-status groups have different numbers of respondents, which makes the colored segments difficult to compare directly.

Option 2 - Your turn: Dodged bars

Modify the stacked bar chart by setting position = "dodge" inside geom_bar(). This places the happiness categories side by side.

# add code here

Dodging makes it easier to compare the counts in each combination, but larger marital-status groups still tend to have taller bars.

Option 3 - Demo: Filled bars

Suppose our question is specifically about the distribution of happiness within each marital-status group. Setting position = "fill" gives every marital-status bar the same total height and displays proportions instead of counts.

# add code here
  • What is the denominator used to calculate the proportions in each bar?

Add your response here.

  • What patterns do you see?

Add your response here.

  • How do these plots answer the question about how happiness varies across marital-status groups? Which plot is the best?

Add your response here.

Preview, commit, push

Now that we’re done with our second task, it’s time to take another snapshot.

  1. Preview your Quarto document.
  2. Stage and commit your changes with a simple and informative commit message.
  3. Push your changes to GitHub and confirm that the updated files appear in your repository.

On your own

Do the following at the end of class or, if you run out of time, some before the next class.

Use gss to answer one of the following questions with an appropriate plot:

  • Is self-reported happiness distributed differently across self-rated health groups?
  • How does employment status vary across marital-status groups?
  • What is the relationship between education years and age? Does it differ by marital status?

State your question, identify the variable type or types, and explain why your plot answers the question. Give the chart a meaningful title and labels, then describe the patterns using non-causal language.

Add response here. Insert code cells as needed.

Preview, commit, push

Once you’re done, take one last snapshot:

  1. Preview your Quarto document.
  2. Stage and commit your changes with a simple and informative commit message.
  3. Push your changes to GitHub and confirm that the updated files appear in your repository.