Grammar of data visualization

Lecture 3

Author
Affiliation

Dr. Mine Çetinkaya-Rundel

Duke University
STA 199 - Fall 2025

Published

September 2, 2025

Warm-up

Announcements

  • Office hours are posted on the course website!

  • If you can follow along with today’s application exercise steps, great! If something doesn’t work as expected, ask me/TA during the exercise. We’ll either:

    • help you on the spot and get you unstuck or
    • identify that you’re missing an earlier step, ask you to follow along with your neighbor, and visit office hours before Thursday for one-on-one help

Participate 📱💻

What is this a visualization of?

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Outline

  • Last time:

    • We introduced you to the course toolkit.

    • You cloned your ae repositories and started making some updates in your Quarto documents.

    • You committed and pushed your changes back – at least most of you did!

. . .

  • Today:

    • You will wrap up that application exercis, and commit and push your final changes.

    • We will introduce data visualization.

    • You will pull to get today’s application exercise file.

    • You will work on the new application exercise on data visualization, commit your changes, and push them.

From last time

Tour: Quarto (and more Git + GitHub)

Option 2:

Go to RStudio and open the document ae-01-income-inequality.qmd.


Tour recap: Quarto

RStudio IDE with a Quarto document, source code on the left and output on the right. Annotated to show the YAML, a link, a header, and a code cell.


Tour recap: Git + GitHub

Once we made changes to our Quarto document, we

  • went to the Git pane in RStudio

  • staged our changes by clicking the checkboxes next to the relevant files

  • committed our changes with an informative commit message

  • pulled from GitHub to make sure we had the latest version of our repo

  • pushed our changes to our application exercise repos

  • confirmed on GitHub that we could see our changes pushed from RStudio

How will we use Quarto?

  • Every application exercise, lab, project, etc. is an Quarto document
  • You’ll always have a template Quarto document to start with
  • The amount of scaffolding in the template will decrease over the semester

What’s with all the hexes?

Hex logos for many packages

We have hexes too!

Grab one before you leave!

STA 199 hex logo

Data visualization

Participate 📱💻

Remember this visualization from the code along video – what was it about?

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Let’ see…

how the sausage is made!

Load packages

Prepare the data

us_uk_tr_votes <- un_votes |>
  inner_join(un_roll_calls, by = "rcid") |>
  inner_join(un_roll_call_issues, by = "rcid", relationship = "many-to-many") |>
  filter(country %in% c("United Kingdom", "United States", "Turkey")) |>
  mutate(year = year(date)) |>
  group_by(country, year, issue) |>
  summarize(percent_yes = mean(vote == "yes"), .groups = "drop")
Note

Let’s leave these details aside for a bit, we’ll revisit this code at a later point in the semester. For now, let’s agree that we need to do some “data wrangling” to get the data into the right format for the plot we want to create. Just note that we called the data frame we’ll visualize us_uk_tr_votes.

View the data

us_uk_tr_votes
# A tibble: 1,212 × 4
   country  year issue                        percent_yes
   <chr>   <dbl> <fct>                              <dbl>
 1 Turkey   1946 Colonialism                        0.8  
 2 Turkey   1946 Economic development               0.6  
 3 Turkey   1946 Human rights                       0    
 4 Turkey   1947 Colonialism                        0.222
 5 Turkey   1947 Economic development               0.5  
 6 Turkey   1947 Palestinian conflict               0.143
 7 Turkey   1948 Colonialism                        0.417
 8 Turkey   1948 Arms control and disarmament       0    
 9 Turkey   1948 Economic development               0.375
10 Turkey   1948 Human rights                       0.167
# ℹ 1,202 more rows

Visualize the data

# code to visualize the data

Step 1. Prepare a canvas for plotting

ggplot(data = us_uk_tr_votes)

Step 2. Map variables to aesthetics

Map year to the x aesthetic

ggplot(data = us_uk_tr_votes, mapping = aes(x = year))

Step 3. Map variables to aesthetics

Map percent_yes to the y aesthetic

ggplot(data = us_uk_tr_votes, mapping = aes(x = year, y = percent_yes))

Mapping and aesthetics

  • Aesthetics are visual properties of a plot

  • In the grammar of graphics, variables from the data frame are mapped to aesthetics

Argument names

It’s common practice in R to omit the names of first two arguments of a function:

. . .

  • Instead of:
ggplot(data = us_uk_tr_votes, mapping = aes(x = year, y = percent_yes))
  • We usually write:
ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes))

Step 4. Represent data on your canvas

with a geom

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes)) +
  geom_point()

Step 5. Map variables to aesthetics

Map country to the color aesthetic

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes, color = country)) +
  geom_point()

Step 6. Represent data on your canvas

with another geom

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes, color = country)) +
  geom_point() +
  geom_smooth()
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Warnings and messages

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

. . .

  • It tells us the type of smoothing ggplot2 does under the hood when drawing the smooth curves that represent trends for each country.

. . .

  • Going forward we’ll suppress this warning to save some space.

Step 7. Split plot into facets

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes, color = country)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(~issue)

Step 8. Use a different color scale

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes, color = country)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(~issue) +
  scale_color_colorblind()

Step 9. Apply a different theme

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes, color = country)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(~issue) +
  scale_color_colorblind() +
  theme_minimal()

Step 10. Add labels

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes, color = country)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(~issue) +
  scale_color_colorblind() +
  theme_minimal() +
  labs(x = "Year", y = "% yes", color = "Country")

Participate 📱💻

Which of the following modifications will change the transparency of the points in the plot?

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Step 11. Set transparency of points

with alpha

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.5) +
  geom_smooth() +
  facet_wrap(~issue) +
  scale_color_colorblind() +
  theme_minimal() +
  labs(x = "Year", y = "% yes", color = "Country")

Step 12. Hide standard errors of curves

with se = FALSE

ggplot(us_uk_tr_votes, aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.5) +
  geom_smooth(se = FALSE) +
  facet_wrap(~issue) +
  scale_color_colorblind() +
  theme_minimal() +
  labs(x = "Year", y = "% yes", color = "Country")

Grammar of graphics

We built a plot layer-by-layer

  • just like described in the book The Grammar of Graphics and
  • implemented in the ggplot2 package, the data visualization package of the tidyverse.