Deep dive: coordinates + facets

Lecture 6

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Spring 2024

February 9, 2024

Announcements

Homework 1 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?

Agenda

Agenda for today

Coordinate systems
Facets
Themes (a little bit)

Coordinate systems

Coordinate systems: purpose

Combine the two position aesthetics (x and y) to produce a 2d 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

Coordinate systems: types

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 (won’t be using much now that geoms can take aesthetic mappings in x and y axes)
coord_fixed(): Cartesian coordinate system with a fixed aspect ratio. (useful only in limited circumstances)

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(): Polar coordinates
coord_map() / coord_quickmap() / 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 values (`stat_smooth()`).
## Warning: Removed 2 rows containing missing values (`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 values (`stat_smooth()`).
## Warning: Removed 235 rows containing missing values (`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 values (`stat_smooth()`).
## Removed 235 rows containing missing values (`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 values (`stat_smooth()`).
## Warning: Removed 2 rows containing missing values (`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)

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 and pie charts

Pie charts and bullseye charts with `coord_polar()`

ggplot(penguins, aes(x = 1, fill = species)) +
  geom_bar() +
  labs(title = "Stacked bar chart")

ggplot(penguins, aes(x = 1, fill = species)) +
  geom_bar() +
  coord_polar(theta = "y") +
  labs(title = "Pie chart")

ggplot(penguins, aes(x = 1, fill = species)) +
  geom_bar() +
  coord_polar(theta = "x") +
  labs(title = "Bullseye chart")

aside: about pie charts…

Pie charts

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

Pie charts: when to love ’em, when to lose ’em

❤️ 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-04`

Go to the course GitHub org and find your ae-04 (repo name will be suffixed with your NetID).
Clone the repo in RStudio Workbench, 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 tomorrow.

12:00

`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(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(data = penguins_sans_species, color = "gray") +
  geom_point(aes(color = species)) +
  facet_wrap(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

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

Deep dive: coordinates + facets

Announcements

Announcements

Visualization critique

Where did the school buses go?

Agenda

Agenda for today

Coordinate systems

Coordinate systems: purpose

Coordinate systems: types

Setting limits: what the plots say

Setting limits: what the plots say

Setting limits: what the warnings say

Setting limits

Cropping scatterplot

Fixing aspect ratio with coord_fixed()

Transformations

Polar coordinate systems and pie charts

Pie charts and bullseye charts with coord_polar()

Pie charts

Pie charts: when to love ’em, when to lose ’em

Waffle charts

Application exercise

ae-04

Facets

facet_*()

Free the scales!

Free some scales

Free spaces

Highlighting across facets

Themes

Complete themes

Themes from ggthemes

Themes and color scales from ggthemes

Modifying theme elements

Project 01

Project 01

Fixing aspect ratio with `coord_fixed()`

Pie charts and bullseye charts with `coord_polar()`

`ae-04`

`facet_*()`