Deep dive: coordinates + facets

Lecture 6

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Spring 2025

February 6, 2025

Announcements

Announcements

• Homework 2 collected
• Team projects begin tomorrow – look for team assignments in Canvas

Visualization critique

Where did the school buses go?

• What is the story?
• How does the design account for the time gaps?

Coordinate systems

Coordinate systems: purpose

• Combine the two position aesthetics (x and y) to produce a two-dimension position on the plot
  • Linear coordinate system: horizontal and vertical coordinates
  • Polar coordinate system: angle and radius
  • Maps: latitude and longitude
• Draw axes and panel backgrounds in coordination with the coordinate systems

Linear coordinate systems

Preserve the shape of geoms

• coord_cartesian(): the default Cartesian coordinate system, where the 2D position of an element is given by the combination of the x and y positions.
• coord_flip(): Cartesian coordinate system with x and y axes flipped.
• coord_fixed(): Cartesian coordinate system with a fixed aspect ratio.

Non-linear coordinate systems

Can change the shapes – a straight line may no longer be straight. The closest distance between two points may no longer be a straight line.

• coord_trans(): Apply arbitrary transformations to x and y positions, after the data has been processed by the stat
• coord_polar() / coord_radial(): Polar coordinates
• coord_sf(): Map projections

Setting limits: what the plots say

base_plot <- ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  geom_point(alpha = 0.25) +
  geom_smooth()
base_plot

Setting limits: what the plots say

Identify the differences between each plot. Focus on the range of the x and y axes as well as the contents of the plots.

02:00

base_plot +
  labs(title = "Plot 1")

base_plot +
  scale_x_continuous(limits = c(190, 220)) +
  scale_y_continuous(limits = c(4000, 5000)) +
  labs(title = "Plot 2")

base_plot +
  xlim(190, 220) +
  ylim(4000, 5000) +
  labs(title = "Plot 3")

base_plot +
  coord_cartesian(xlim = c(190, 220),
                  ylim = c(4000, 5000)) +
  labs(title = "Plot 4")

Setting limits: what the warnings say

base_plot +
  labs(title = "Plot 1")
## `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()`).

base_plot +
  scale_x_continuous(limits = c(190, 220)) +
  scale_y_continuous(limits = c(4000, 5000)) +
  labs(title = "Plot 2")
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 235 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 235 rows containing missing values or values outside the scale range
## (`geom_point()`).

base_plot +
  xlim(190, 220) +
  ylim(4000, 5000) +
  labs(title = "Plot 3")
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 235 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 235 rows containing missing values or values outside the scale range
## (`geom_point()`).

base_plot +
  coord_cartesian(xlim = c(190, 220),
                  ylim = c(4000, 5000)) +
  labs(title = "Plot 4")
## `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()`).

Setting limits

• Setting scale limits: Any data outside the limits is thrown away
  • scale_*_continuous(limits = ...)
  • xlim() and ylim()
• Setting coordinate system limits: Use all the data, but only display a small region of the plot (zooming in)
  • coord_cartesian(xlim = ..., ylim = ...)

Cropping scatterplot

Fixing aspect ratio with coord_fixed()

Useful when having an aspect ratio of 1 makes sense, e.g. scores on two tests (reading and writing) on the same scale (0 to 100 points)

Transformations

• Raw axes
• log()
• scale_*_log10()
• coord_trans()

ggplot(penguins, aes(x = bill_depth_mm, y = body_mass_g)) +
  geom_point() +
  geom_smooth(method = "lm")

ggplot(
  penguins,
  aes(
    x = log10(bill_depth_mm),
    y = log10(body_mass_g)
  )
) +
  geom_point() +
  geom_smooth(method = "lm")

ggplot(penguins, aes(x = bill_depth_mm, y = body_mass_g)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10() +
  scale_y_log10()

ggplot(penguins, aes(x = bill_depth_mm, y = body_mass_g)) +
  geom_point() +
  geom_smooth(method = "lm") +
  coord_trans(x = "log10", y = "log10")

Polar coordinate systems

Radial charts with coord_polar()/coord_radial()

Circular bar charts

Authentic pie chart

Pie charts

What do you know about pie charts and data visualization best practices? Love ’em or lose ’em?

Pie charts

For categorical variables with few levels, pie charts can work well

For categorical variables with many levels, pie charts are difficult to read

Waffle charts

• Like with pie charts, work best when the number of levels represented is low
• Unlike pie charts, easier to compare proportions that represent non-simple fractions

Application exercise

ae-05

Instructions

• Go to the course GitHub org and find your ae-05 (repo name will be suffixed with your GitHub name).
• Clone the repo in RStudio, run renv::restore() to install the required packages, open the Quarto document in the repo, and follow along and complete the exercises.
• Render, commit, and push your edits by the AE deadline – end of the day

12:00

Facets

facet_*()

• facet_wrap()
  • “wraps” a 1d ribbon of panels into 2d
  • generally for faceting by a single variable
• facet_grid() for faceting
  • produces a 2d grid of panels defined by variables which form the rows and columns
  • generally for faceting by two variables
• facet_null(): a single plot, the default

Free the scales!

p <- ggplot(penguins, aes(
  x = flipper_length_mm,
  y = body_mass_g
)) +
  geom_point()

p +
  facet_wrap(facets = vars(species)) +
  labs(title = "Same scales")

p +
  facet_wrap(
    facets = vars(species),
    scales = "free"
  ) +
  labs(title = "Free scales")

Free some scales

p +
  facet_wrap(
    facets = vars(species),
    scales = "free_x"
  ) +
  labs(title = "Free x scale")

p +
  facet_wrap(
    facets = vars(species),
    scales = "free_y"
  ) +
  labs(title = "Free x scale")

Freeing the y scale improves the display, but it’s still not satisfying. What’s wrong with it?

• Plots
• Code

ggplot(penguins, aes(y = species, x = body_mass_g, fill = species)) +
  geom_boxplot(show.legend = FALSE) +
  facet_grid(rows = vars(island)) +
  labs(title = "Same scale and spacing")

ggplot(penguins, aes(y = species, x = body_mass_g, fill = species)) +
  geom_boxplot(show.legend = FALSE) +
  facet_grid(rows = vars(island), scales = "free_y") +
  labs(title = "Free y scale, same spacing")

Free spaces

ggplot(penguins, aes(y = species, x = body_mass_g, fill = species)) +
  geom_boxplot(show.legend = FALSE) +
  facet_grid(rows = vars(island), scales = "free_y", space = "free") +
  labs(title = "Free y scale and spacing")

Highlighting across facets

penguins_sans_species <- penguins |> select(-species)

ggplot(data = penguins, mapping = aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(data = penguins_sans_species, color = "gray") +
  geom_point(mapping = aes(color = species)) +
  facet_wrap(facets = vars(species))

Themes

Complete themes

Themes from {ggthemes}

Themes and color scales from {ggthemes}

p +
  aes(color = species) +
  scale_color_wsj() +
  theme_wsj() +
  labs(title = "Wall Street Journal")

Modifying theme elements

p +
  labs(title = "Palmer penguins") +
  theme(
    plot.title = element_text(color = "red", face = "bold", family = "Comic Sans MS"),
    plot.background = element_rect(color = "red", fill = "mistyrose")
  )

Project 01

Project 01

• Initial proposal
• Develop as a team
• Take chances, make mistakes, get messy!

Wrap up

Recap

• Coordinate systems define how position aesthetics are drawn on the plot
• Limits and transformations work differently when applied to scales vs. coordinate systems
• Waffle charts are an alternative to pie charts for visualizing proportions
• Faceting generates small multiple window plots

Acknowledgements

• Slides derived in part from STA 313: Advanced Data Visualization

Stay in your lane – don’t text and drive!