Grammar of data visualization

Lecture 2

Published

May 16, 2025

Reminders

I have office hours today! 1:00-3:00 PM in Old Chemistry 203/203B.
We will start grading your ae repositories next week - make sure you have them ready to go.
First ‘real’ lab is on Monday; the topic will be data visualization (what we are starting today).

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 commited and pushed your changes back.

. . .

Today:
- 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

`ae-01-meet-the-penguins`

Go to RStudio, confirm that you’re in the ae project, and open the document ae-01-meet-the-penguins.qmd.

Common problems:

The environment used by Quarto when rendering starts EMPTY - it does not see what you see in your environment.
Using functions that cause a popup (like View() ) are not going to work when you render a document. Either use a comment (with #) to remove them, or just delete before rendering!
Make sure you commit and then PUSH! Just committing is not enough!

Data visualization

Thoughts on this plot?

More Penguins

Start plotting!

How can you create something like this???

The ggplot2 package has the plotting functions you need!
ggplot2 is a part of the tidyverse package - when you load tidyverse, you also load ggplot2

Load Packages

library(palmerpenguins)
library(tidyverse)
library(ggthemes)

Look at the data

Visualize the data

What are some steps you can take to visualize a data set?

What do you want on the x-axis?
What do you want on the y-axis?

Step 1. Prepare a canvas for plotting

ggplot(data = penguins)

Step 2. Map variables to aesthetics

Map year to the x aesthetic

ggplot(data = penguins, mapping = aes(x = bill_length_mm))

Step 3. Map variables to aesthetics

Map percent_yes to the y aesthetic

ggplot(data = penguins, mapping = aes(x = bill_length_mm, y = body_mass_g))

Argument names

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

. . .

Instead of

ggplot(data = your_data, mapping = aes(x = x_variable, y = y_variable))

ggplot(your_data, aes(x = x_variable, y = y_variable))

Step 3. Map variables to aesthetics

Map percent_yes to the y aesthetic

ggplot(data = penguins, mapping = aes(x = bill_length_mm, y = body_mass_g))

Step 3. Map variables to aesthetics

Map percent_yes to the y aesthetic

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g))

Step 4. Represent data on your canvas

with a geom

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale
range (`geom_point()`).

Step 4. Represent data on your canvas

Adding geom_point() resulted in the following warning:

Warning: Removed 2 rows containing missing values or values outside the scale
range (`geom_point()`)

Step 4. Represent data on your canvas

with a geom

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g)) +
  geom_point()

Step 5. Map variables to aesthetics

Map species to the color aesthetic

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point()

Step 5. Map variables to aesthetics

Map species to the color aesthetic

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point()

What exactly are aesthetics? They map from a variable to a plot feature.

x and y axes
color, shape, size of points

Step 6. Represent data on your canvas

with another geom

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth()

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

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 2 rows containing missing values or values outside the scale
range (`geom_point()`).

Warnings and messages

Adding geom_smooth() resulted in the following warning:

`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 species.

. . .

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

Step 6. Represent data on your canvas

with another geom

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth()

Step 7. Split plot into facets

Use facet_wrap to make sub-plots

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(~island)

Step 7. Split plot into facets

We can facet by other variables!

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(~species)

A note on facets:

Which plot do you think made it easier to compare between penguin species?

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth()

Step 8. Use a different color scale

With a scale_color_ function

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  scale_color_grey()

Step 8. Use a different color scale

With another scale_color_ function

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  scale_color_colorblind() #this is from ggthemes

Step 9. Apply a different theme

With a theme_ function

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  scale_color_colorblind() +
  theme_minimal()

Step 9. Apply a different theme

With a theme_ function

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  scale_color_colorblind() +
  theme_classic()

Step 9. Apply a different theme

With a theme_ function

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  scale_color_colorblind() +
  theme_solarized() #this is from ggthemes

Step 9. Apply a different theme

With a theme_ function

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  scale_color_colorblind() +
  theme_minimal()

Step 10. Add labels

With labs() function

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() +
  geom_smooth() +
  scale_color_colorblind() +
  theme_minimal() +
  labs(x = "Bill Length (mm)", y = "Body Mass (g)", color = "Species", title = "Penguin Body Mass vs. Bill Length")

Step 11. Set transparency of points

with alpha

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point(alpha = 0.1) +
  geom_smooth() +
  scale_color_colorblind() +
  theme_minimal() +
  labs(x = "Bill Length (mm)", y = "Body Mass (g)", color = "Species")

Step 11. Set transparency of points

with alpha

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point(alpha = 0.7) +
  geom_smooth() +
  scale_color_colorblind() +
  theme_minimal() +
  labs(x = "Bill Length (mm)", y = "Body Mass (g)", color = "Species")

Step 12. Hide standard errors of curves

with se = FALSE

ggplot(penguins, mapping = aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point(alpha = 0.5) +
  geom_smooth(se = FALSE) +
  scale_color_colorblind() +
  theme_minimal() +
  labs(x = "Bill Length (mm)", y = "Body Mass (g)", color = "Species")

How am I supposed to remember all of this?!

You aren’t!!!

It’s important to (eventually) know and remember the key ideas: what does changing a theme do? What are aesthetics? What is a geom?
You do not need to memorize a comprehensive list of all of the different geoms, themes, color scales, etc.
There will be a few fundamentals we expect you to know – more on that later!
https://ggplot2.tidyverse.org is super helpful!

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.

Application exercise

What if we want to use our own data?

read_csv("data_file.csv") (assuming the data is in a CSV format)

`ae-02-bechdel-dataviz`

We will be looking at data on movies and the Bechdel test.

`ae-02-bechdel-dataviz`

Go to your ae project in RStudio.
Make sure all of your changes up to this point are committed and pushed, i.e., there’s nothing left in your Git pane.
If you haven’t yet done so, click Pull to get today’s application exercise file.
Work through the application exercise in class, and render, commit, and push your edits by the end of class.

Recap

Construct plots with ggplot().
Layers of ggplots are separated by +s.
The formula is (almost) always as follows:

ggplot(DATA, aes(x = X-VAR, y = Y-VAR, ...)) +
  geom_XXX()

Coming Up…

What are some other types of plots you can make?
How can you talk about the information conveyed by plots?