Deep dive: stats + scales + guides

Lecture 5

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Spring 2024

February 6, 2024

Announcements

Homework 02 due tomorrow
Project 01

Project 01

Project description
Team assignments on Thursday
Deliverables
- February 15 - proposals for peer review
- February 19 - revised proposals for instructor review
- March 1 - write-up and presentation

Visualization critique

Media bias in the United States

What is the story?
How effective is the design?

Setup

Packages + figures

# load packages
library(tidyverse)
library(scales)

# set default theme for ggplot2
theme_set(theme_minimal(base_size = 14))

YAML options

execute:
  fig-width: 7
  fig-asp: 0.618
  fig-retina: 2
  dpi: 150
  fig-align: center
  out-width: 80%

World Bank indicators

world_bank <- read_rds("data/wb-indicators.rds")
world_bank

# A tibble: 181 × 8
   iso2c country  year gdp_per_cap female_labor_pct life_exp    pop income_level
   <chr> <chr>   <dbl>       <dbl>            <dbl>    <dbl>  <dbl> <fct>       
 1 AO    Angola   2021       1927.             49.9     61.6 3.45e7 Lower middl…
 2 AL    Albania  2021       6377.             44.1     76.5 2.81e6 Upper middl…
 3 AE    United…  2021      44332.             17.7     78.7 9.37e6 High income 
 4 AR    Argent…  2021      10651.             42.5     75.4 4.58e7 Upper middl…
 5 AM    Armenia  2021       4973.             52.7     72.0 2.79e6 Upper middl…
 6 AU    Austra…  2021      60697.             47.2     83.3 2.57e7 High income 
 7 AT    Austria  2021      53518.             46.8     81.2 8.96e6 High income 
 8 AZ    Azerba…  2021       5408.             49.9     69.4 1.01e7 Upper middl…
 9 BI    Burundi  2021        221.             51.9     61.7 1.26e7 Low income  
10 BE    Belgium  2021      51850.             46.8     81.9 1.16e7 High income 
# ℹ 171 more rows

Stats

Stats < > geoms

Statistical transformation (stat) transforms the data, typically by summarizing
Many of ggplot2’s stats are used behind the scenes to generate many important geoms

`stat`	`geom`
`stat_bin()`	`geom_bar()`, `geom_freqpoly()`, `geom_histogram()`
`stat_bin2d()`	`geom_bin2d()`
`stat_bindot()`	`geom_dotplot()`
`stat_binhex()`	`geom_hex()`
`stat_boxplot()`	`geom_boxplot()`
`stat_contour()`	`geom_contour()`
`stat_quantile()`	`geom_quantile()`
`stat_smooth()`	`geom_smooth()`
`stat_sum()`	`geom_count()`

`stat_boxplot()`

Layering with stats

ggplot(world_bank, aes(x = income_level, y = life_exp)) +
  geom_point(alpha = 0.5) +
  stat_summary(geom = "point", fun = "median", color = "red", size = 5, pch = 4, stroke = 2)

Alternate: layering with stats

ggplot(world_bank, aes(x = income_level, y = life_exp)) +
  geom_point(alpha = 0.5) +
  geom_point(stat = "summary", fun = "median", color = "red", size = 5, pch = 4, stroke = 2)

Alternate alternate: do it with dplyr

world_bank |>
  group_by(income_level) |>
  summarize(median_life_exp = median(life_exp)) |>
  ggplot(mapping = aes(x = income_level)) +
  geom_point(data = world_bank, mapping = aes(y = life_exp), alpha = 0.5) +
  geom_point(mapping = aes(y = median_life_exp), color = "red", size = 5, pch = 4, stroke = 2)

Statistical transformations

What can you say about the distribution of average life expectancy from the following QQ plot?

ggplot(world_bank, aes(sample = life_exp)) +
  stat_qq() +
  stat_qq_line() +
  labs(y = "life_exp")

Scales

What is a scale?

Each scale is a function from a region in data space (the domain of the scale) to a region in aesthetic space (the range of the scale)
The axis or legend is the inverse function: it allows you to convert visual properties back to data

Scale specification

Every aesthetic in your plot is associated with exactly one scale:

Code
Plot

# automatic scales
ggplot(world_bank, aes(x = female_labor_pct, y = life_exp, color = income_level)) +
  geom_point(alpha = 0.8)

# manual scales
ggplot(world_bank, aes(x = female_labor_pct, y = life_exp, color = income_level)) +
  geom_point(alpha = 0.8) +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_color_discrete()

Anatomy of a scale function

scale_A_B()

Always starts with scale
A: Name of the primary aesthetic (e.g., color, shape, x)
B: Name of the scale (e.g., continuous, discrete, brewer)

Some scale functions add a fourth component to the function name

scale_color_viridis_b()
scale_color_viridis_c()
scale_color_viridis_d()

1: Binned palette
2: Continuous palette
3: Discrete palette

Guess the output

What will the x-axis label of the following plot say?

ggplot(world_bank, aes(x = female_labor_pct, y = life_exp, color = income_level)) +
  geom_point(alpha = 0.8) +
  scale_x_continuous(name = "female_labor_pct") +
  scale_x_continuous(name = "Female labor (percentage of total workforce)")

00:30

“Address” messages

ggplot(world_bank, aes(x = female_labor_pct, y = life_exp, color = income_level)) +
  geom_point(alpha = 0.8) +
  scale_x_continuous(name = "female_labor_pct") +
  scale_x_continuous(name = "Female labor (percentage of total workforce)")

Scale for x is already present.
Adding another scale for x, which will replace the existing scale.

Guess the output

What happens if you pair a discrete variable with a continuous scale? What happens if you pair a continuous variable with a discrete scale? Answer in the context of the following plots.

Plots
Results

ggplot(
  data = world_bank,
  mapping = aes(
    x = income_level,
    y = life_exp
  )
) +
  geom_point(alpha = 0.5) +
  scale_x_continuous()

ggplot(
  data = world_bank,
  mapping = aes(
    x = income_level,
    y = life_exp
  )
) +
  geom_point(alpha = 0.5) +
  scale_y_discrete()

Error: Discrete value supplied to continuous scale

01:00

Transformations

When working with continuous data, the default is to map linearly from the data space onto the aesthetic space, but this scale can be transformed

Linear
Transformed

ggplot(
  world_bank,
  aes(x = female_labor_pct, y = gdp_per_cap)
) +
  geom_point(alpha = 0.5)

ggplot(
  world_bank,
  aes(x = female_labor_pct, y = gdp_per_cap)
) +
  geom_point(alpha = 0.5) +
  scale_y_continuous(trans = "log10")

Common scale transformations

Box-Cox scale transformations

Continuous scale transformations

Name	Function \(f(x)\)	Inverse \(f^{-1}(y)\)
asn	\(\tanh^{-1}(x)\)	\(\tanh(y)\)
exp	\(e ^ x\)	\(\log(y)\)
identity	\(x\)	\(y\)
log	\(\log(x)\)	\(e ^ y\)
log10	\(\log_{10}(x)\)	\(10 ^ y\)
log2	\(\log_2(x)\)	\(2 ^ y\)
logit	\(\log(\frac{x}{1 - x})\)	\(\frac{1}{1 + e(y)}\)
pow10	\(10^x\)	\(\log_{10}(y)\)
probit	\(\Phi(x)\)	\(\Phi^{-1}(y)\)
reciprocal	\(x^{-1}\)	\(y^{-1}\)
reverse	\(-x\)	\(-y\)
sqrt	\(x^{1/2}\)	\(y ^ 2\)

Convenience functions for transformations

ggplot(
  world_bank,
  aes(x = female_labor_pct, y = life_exp)
) +
  geom_point(alpha = 0.5) +
  scale_y_continuous(trans = "log10")

ggplot(
  world_bank,
  aes(x = female_labor_pct, y = life_exp)
) +
  geom_point(alpha = 0.5) +
  scale_y_log10()

Application exercise

`ae-03` - Part 1

Go to the course GitHub org and find your ae-03 (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.

07:00

Guides

What is a guide?

Guides are legends and axes:

Source: ggplot2: Elegant Graphics for Data Analysis, Chp 14.

Customizing axes

ggplot(world_bank, aes(x = gdp_per_cap, y = life_exp)) +
  geom_point(alpha = 0.5) +
  scale_y_continuous(
    name = "Life expectancy at birth"
  )

Customizing axes

Why do 50 and 90 not appear on the \(y\)-axis?

ggplot(world_bank, aes(x = gdp_per_cap, y = life_exp)) +
  geom_point(alpha = 0.5) +
  scale_y_continuous(
    name = "Life expectancy at birth",
    breaks = seq(from = 50, to = 90, by = 10)
  )

Customizing axes

ggplot(world_bank, aes(x = gdp_per_cap, y = life_exp)) +
  geom_point(alpha = 0.5) +
  scale_y_continuous(
    name = "Life expectancy at birth",
    breaks = seq(from = 50, to = 90, by = 10),
    limits = c(50, 90)
  )

Customizing axes

ggplot(world_bank, aes(x = gdp_per_cap, y = life_exp)) +
  geom_point(alpha = 0.5) +
  scale_y_continuous(
    name = "Life expectancy at birth",
    breaks = seq(from = 50, to = 90, by = 10),
    limits = c(50, 90)
  ) +
  scale_x_continuous(
    name = "GDP per capita",
    breaks = c(0, 5e04, 1e05),
    labels = c("$0", "$50,000", "$100,000")
  )

Customizing axes

library(scales)

ggplot(world_bank, aes(x = gdp_per_cap, y = life_exp)) +
  geom_point(alpha = 0.5) +
  scale_y_continuous(
    name = "Life expectancy at birth",
    breaks = seq(from = 50, to = 90, by = 10),
    limits = c(50, 90)
  ) +
  scale_x_continuous(
    name = "GDP per capita",
    labels = label_dollar()
  )

Customizing axes

ggplot(world_bank, aes(x = gdp_per_cap, y = life_exp)) +
  geom_point(alpha = 0.5) +
  scale_y_continuous(
    name = "Life expectancy at birth",
    breaks = seq(from = 50, to = 90, by = 10),
    limits = c(50, 90)
  ) +
  scale_x_continuous(
    name = "GDP per capita",
    labels = label_dollar(scale_cut = cut_short_scale())
  )

Modifying scale guides

Scale guides

Scale type	Default guide type	Function
Continuous scales for color/fill aesthetics	colorbar	`guide_colorbar()`
Binned scales for color/fill aesthetics	colorsteps	`guide_colorsteps()`
Position scales (continuous, binned and discrete)	axis	`guide_axis()`
Discrete scales (except position scales)	legend	`guide_legend()`
Binned scales (except position/color/fill scales)	bins	`guide_bins()`

Implementation

... +
  guides(color = guide_colorbar(...))

... +
  scale_color_gradient(guide = guide_colorbar(...))

base_plot + guides(color = guide_colorbar(reverse = TRUE))

base_plot + guides(color = guide_colorbar(barheight = unit(2, "cm")))

base_plot + guides(color = guide_colorbar(direction = "horizontal"))

base_plot + guides(color = guide_colorbar(label.position = "left"))

Application exercise

`ae-03` - Part 2

Recreate this plot.

10:00

Wrap up

ggplot2 implements statistical transformations (typically as defaults)
Scales visually encode data to mappings
Guides control the appearance of scales
scales package provides a wide range of transformation and formatting functions
Use guide_*() functions to customize the appearance of guides

Some artwork

A drawing of three bunnies by my daughter, Beverly.

Deep dive: stats + scales + guides

Announcements

Announcements

Project 01

Visualization critique

Media bias in the United States

Setup

Packages + figures

YAML options

World Bank indicators

Stats

Stats < > geoms

stat_boxplot()

Layering with stats

Alternate: layering with stats

Alternate alternate: do it with dplyr

Statistical transformations

Scales

What is a scale?

Scale specification

Anatomy of a scale function

Guess the output

“Address” messages

Guess the output

Transformations

Common scale transformations

Box-Cox scale transformations

Continuous scale transformations

Convenience functions for transformations

Application exercise

ae-03 - Part 1

Guides

What is a guide?

Customizing axes

Customizing axes

Customizing axes

Customizing axes

Customizing axes

Customizing axes

Modifying scale guides

Scale guides

Scale guides

Implementation

Example implementation

Application exercise

ae-03 - Part 2

Wrap up

Wrap up

Some artwork

`stat_boxplot()`

`ae-03` - Part 1

`ae-03` - Part 2