AE 18: Visualizing increased polarization of baby names

Application exercise

Modified

April 7, 2025

library(tidyverse)
library(gganimate)
library(ggbeeswarm)
library(scales)

theme_set(theme_minimal())

# colors for the Democratic and Republican parties
dem <- "#00AEF3"
rep <- "#E81B23"

In this application exercise we will use animation and the {gganimate} package to visualize the increasing polarization of baby names in the United States.

Import data

Our data comes from the Social Security Administration which publishes detailed annual data on all babies born in the United States. We have prepared it by matching state-level births to the results of the 2024 U.S. presidential election so we can distinguish “red states” (those won by the Republican candidate Donald Trump) from “blue states” (those won by the Democratic candidate Kamala Harris). The data is stored in data/partisan-names.csv.

partisan_names <- read_csv(file = "data/partisan-names.csv") |>
  # convert year to a factor column for visualizations
  mutate(year = factor(year))

Rows: 200 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): outcome, sex, name
dbl (4): year, Trump, Harris, part_diff

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

partisan_names

# A tibble: 200 × 7
   outcome sex   year  name     Trump Harris part_diff
   <chr>   <chr> <fct> <chr>    <dbl>  <dbl>     <dbl>
 1 Trump   M     1983  Kendrick 0.942 0.0580     0.884
 2 Trump   M     1983  Trey     0.932 0.0682     0.864
 3 Trump   M     1983  Rodrick  0.927 0.0732     0.854
 4 Trump   F     1983  Ashlea   0.917 0.0833     0.833
 5 Trump   F     1983  Tosha    0.890 0.110      0.780
 6 Trump   F     1983  Misti    0.886 0.114      0.773
 7 Trump   F     1983  Latoria  0.883 0.117      0.767
 8 Trump   M     1983  Jackie   0.876 0.124      0.753
 9 Trump   M     1983  Demarcus 0.870 0.130      0.740
10 Trump   F     1983  Angelia  0.869 0.131      0.737
# ℹ 190 more rows

We have aggregated the data to show the percentage of babies with a given name born in states that voted for Trump and Harris. The data contains the following columns:

outcome - winner of the state-level vote in the 2024 U.S. presidential election
sex - sex assigned at birth to the baby
year - year of birth
name - name of the baby
Trump - percentage of the babies with the given name born in states that voted for Donald Trump in 2024
Harris - percentage of the babies with the given name born in states that voted for Kamala Harris in 2024
part_diff - difference between the percentage of babies with the given name born in Trump states and Harris states. Positive values indicate the name is more common in Trump states, whereas negative values indicate the name is more common in Harris states.

We have filtered the data to focus on the years 1983, 1993, 2003, 2013, and 2023 (the most recent year of births for which data is available), and also limited the data to the 20 most-highly partisan names for red states and blue states (i.e. each year contains 40 rows - the top 20 most “Republican” names and the top 20 most “Democratic” names).

Create a static plot

Before we attempt to animate a chart, let’s first create a single static visualization that communicates the change in polarization over time.

Your turn: Implement a jittered plot that shows the distribution of partisanship for names in red states and blue states for each year. Use the geom_quasirandom() function from the {ggbeeswarm} package to create the plot.¹

# add code here

Define plot components for the animated chart

Before we attempt to animate the chart, let’s first define the components of the chart that will remain static throughout the animation.

Your turn: Modify your plot above to have a single category on the \(y\)-axis. This is what will change throughout the animation.

Helpful hint

When we animate the chart, we will define each frame based on the year variable. When testing the static portions of the chart, you can facet the graph on the year variable to see how the chart will look for each year. When we animate it, each facet panel will essentially become the frames of the animation.

# add code here

Implement basic animation

Now that we have the static components of the chart, we can animate it. The {gganimate} package provides a simple way to animate a plot by defining the transition_*() function.

Your turn: Implement the animation using the appropriate transition_*() function.

# add code here

Your turn: We need to know what years the points are transitioning between. Add this using an appropriate label to the plot.

How do we know the current year?

Look at the documentation for your transition_*() function. It should provide information on how to add labels to the animation based on the transitioning states.

# add code here

Your turn: Adjust the animation to make it smoother. Consider adjusting appropriate parameters in the transition_*() function as well as the easing used for interpolation via ease_aes().

# add code here

Add shadows

To make the animation easier to interpret, it’s helpful to add reference marks during the transition to show where the points are moving from and to. This can be done using the shadow_*() functions.

Your turn: Implement a shadow_*() function to show the transition more smoothly.

# add code here

Rendering

We can control the rendering process using the animate() function.

Your turn: Improve the animated chart through it’s rendering. Some suggestions include:

Increase the number of frames to make the animation smoother.
Increase the length of the animation to give the reader more time to interpret each frame.
Add a pause at the start and end of the animation to give the reader time to interpret the chart.

# add code here

Your turn: Implement the same settings using a Quarto code chunk option. Adjust the aspect ratio of the plot to make it more compact vertically.

Implementing the gganimate code chunk option

Quarto can pass arbitrary R expressions through code chunk options using the syntax !expr. For example, if we wanted to render the animation with 300 frames at 15 frames per second, we could use the following code chunk option:

#| gganimate: !expr list(nframes = 300, fps = 15)

# add code here

Acknowledgments

Application exercise inspired by Philip Bump’s How to Read This Chart March 29th newsletter

Footnotes

Unlike geom_jitter(), this will ensure there are no overlapping points in the plot. You can also use geom_beeswarm() but this introduces unnecessary curvatures into the jittering.↩︎