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Mapping Data
to Graphics

Session 3

PMAP 8921: Data Visualization with R
Andrew Young School of Policy Studies
Summer 2022

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Plan for today

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Plan for today

Data, aesthetics, & the grammar of graphics

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Plan for today

Data, aesthetics, & the grammar of graphics

Grammatical layers

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Plan for today

Data, aesthetics, & the grammar of graphics

Grammatical layers

Aesthetics in extra dimensions

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Plan for today

Data, aesthetics, & the grammar of graphics

Grammatical layers

Aesthetics in extra dimensions

Tidy data

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Data, aesthetics,
& the grammar of graphics

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Source: Wikipedia

Long distance!

Moscow to Vilnius
Moscow to Vilnius
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Very cold!

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Lots of people died!

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Source: Wikimedia Commons

Mapping data to aesthetics

ZZZ

Aesthetic

Visual property of a graph

Position, shape, color, etc.

Data

A column in a dataset

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Mapping data to aesthetics

Data Aesthetic Graphic/Geometry
Longitude Position (x-axis)  Point
Latitude Position (y-axis) Point
Army size Size Path
Army direction  Color Path
Date Position (x-axis) Line + text
Temperature Position (y-axis) Line + text
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Mapping data to aesthetics

Data aes() geom
Longitude x geom_point()
Latitude y geom_point()
Army size size geom_path()
Army direction  color  geom_path()
Date x geom_line() + geom_text()
Temperature y geom_line() + geom_text()
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ggplot() template

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ggplot() template

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This is a dataset named troops:

longitude latitude direction survivors
24 54.9 A 340000
24.5 55 A 340000
… … … …
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This is a dataset named troops:

longitude latitude direction survivors
24 54.9 A 340000
24.5 55 A 340000
… … … …
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Source: New York Times

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Source: Gapminder

Mapping data to aesthetics

Data aes() geom
Wealth (GDP/capita) x geom_point()
Health (Life expectancy)  y geom_point()
Continent color geom_point()
Population size  geom_point()
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This is a dataset named gapminder_2007:

country continent gdpPercap lifeExp pop
Afghanistan Asia 974.5803384 43.828 31889923
Albania Europe 5937.029526 76.423 3600523
… … … … …
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This is a dataset named gapminder_2007:

country continent gdpPercap lifeExp pop
Afghanistan Asia 974.5803384 43.828 31889923
Albania Europe 5937.029526 76.423 3600523
… … … … …
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Health and wealth

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Grammatical layers

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Grammar components as layers

So far we know about data, aesthetics, and geometries

Think of these
components as layers

Add them to foundational ggplot() with +

 

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Layer analogy borrowed from Thomas Lin Pedersen and his "Drawing Anything with ggplot2" workshop.

Possible aesthetics

color (discrete)

color (continuous)

size

fill

shape

alpha

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Possible geoms

Example geom What it makes
geom_col() Bar charts
geom_text() Text
geom_point() Points
geom_boxplot()  Boxplots
geom_sf() Maps
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Possible geoms

There are dozens of possible geoms and
each class session will cover different ones.

See the ggplot2 documentation for
complete examples of all the different geom layers

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Additional layers

There are many of other grammatical layers we can use to describe graphs!

We sequentially add layers onto the foundational ggplot() plot to create complex figures

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Scales

Scales change the properties of the variable mapping

Example layer What it does
scale_x_continuous() Make the x-axis continuous
scale_x_continuous(breaks = 1:5)  Manually specify axis ticks
scale_x_log10() Log the x-axis
scale_color_gradient() Use a gradient
scale_fill_viridis_d() Fill with discrete viridis colors
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Scales

scale_x_log10()

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Scales

scale_x_log10()

scale_color_viridis_d()

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Facets

Facets show subplots for different subsets of data

Example layer What it does
facet_wrap(vars(continent)) Plot for each continent
facet_wrap(vars(continent, year))  Plot for each continent/year
facet_wrap(..., ncol = 1) Put all facets in one column
facet_wrap(..., nrow = 1) Put all facets in one row
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Facets

facet_wrap(vars(continent))

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Facets

facet_wrap(vars(continent))

facet_wrap(vars(continent, year))

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Coordinates

Change the coordinate system

Example layer What it does
coord_cartesian() Plot for each continent
coord_cartesian(ylim = c(1, 10))  Zoom in where y is 1–10
coord_flip() Switch x and y
coord_polar() Use circular polar system
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Coordinates

coord_cartesian(ylim = c(70, 80), xlim = c(10000, 30000))

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Coordinates

coord_cartesian(ylim = c(70, 80), xlim = c(10000, 30000))

coord_flip()

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Labels

Add labels to the plot with a single labs() layer

Example layer What it does
labs(title = "Neat title") Title
labs(caption = "Something") Caption
labs(y = "Something") y-axis
labs(size = "Population") Title of size legend
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Labels

ggplot(gapminder_2007, 
       aes(x = gdpPercap, y = lifeExp, 
           color = continent, size = pop)) +
  geom_point() +
  scale_x_log10() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from 2007",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population",
       caption = "Source: The Gapminder Project")

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Theme

Change the appearance of anything in the plot

There are many built-in themes

Example layer What it does
theme_grey() Default grey background
theme_bw() Black and white
theme_dark() Dark
theme_minimal() Minimal
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Theme

theme_dark()

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Theme

theme_dark()

theme_minimal()

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Theme

There are collections of pre-built themes online,
like the ggthemes package

ggthemes
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Theme

Organizations often make their own custom themes, like the BBC

ggthemes
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Theme options

Make theme adjustments with theme()

There are a billion options here!
We have a whole class session dedicated to this!

theme_bw() + 
theme(legend.position = "bottom",
      plot.title = element_text(face = "bold"),
      panel.grid = element_blank(),
      axis.title.y = element_text(face = "italic"))
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So many possibilities!

These were just a few examples of layers!

See the ggplot2 documentation for
complete examples of everything you can do

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Putting it all together

We can build a plot sequentially
to see how each grammatical layer
changes the appearance

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Start with data and aesthetics

ggplot(data = mpg,
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv))

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Add a point geom

ggplot(data = mpg,
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point()

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Add a smooth geom

ggplot(data = mpg,
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point() +
  geom_smooth()

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Make it straight

ggplot(data = mpg,
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point() +
  geom_smooth(method = "lm")

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Use a viridis color scale

ggplot(data = mpg,
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_color_viridis_d()

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Facet by drive

ggplot(data = mpg,
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_color_viridis_d() +
  facet_wrap(vars(drv), ncol = 1)

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Add labels

ggplot(data = mpg,
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_color_viridis_d() +
  facet_wrap(vars(drv), ncol = 1) +
  labs(x = "Displacement", y = "Highway MPG",
       color = "Drive",
       title = "Heavier cars get lower mileage",
       subtitle = "Displacement indicates weight(?)",
       caption = "I know nothing about cars")

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Add a theme

ggplot(data = mpg,
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_color_viridis_d() +
  facet_wrap(vars(drv), ncol = 1) +
  labs(x = "Displacement", y = "Highway MPG",
       color = "Drive", 
       title = "Heavier cars get lower mileage",
       subtitle = "Displacement indicates weight(?)",
       caption = "I know nothing about cars") +
  theme_bw()

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Modify the theme

ggplot(data = mpg, 
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_color_viridis_d() +
  facet_wrap(vars(drv), ncol = 1) +
  labs(x = "Displacement", y = "Highway MPG",
       color = "Drive", 
       title = "Heavier cars get lower mileage",
       subtitle = "Displacement indicates weight(?)",
       caption = "I know nothing about cars") +
  theme_bw() +
  theme(legend.position = "bottom",
        plot.title = element_text(face = "bold"))

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Finished!

ggplot(data = mpg, 
       mapping = aes(x = displ,
                     y = hwy,
                     color = drv)) +
  geom_point() +
  geom_smooth(method = "lm") + 
  scale_color_viridis_d() +
  facet_wrap(vars(drv), ncol = 1) +
  labs(x = "Displacement", y = "Highway MPG",
       color = "Drive", 
       title = "Heavier cars get lower mileage",
       subtitle = "Displacement indicates weight(?)",
       caption = "I know nothing about cars") +
  theme_bw() +
  theme(legend.position = "bottom",
        plot.title = element_text(face = "bold"))

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A true grammar

With the grammar of graphics, we don't talk about specific chart types

Hunt through Excel menus for a stacked bar chart and manually reshape your data to work with it

Excel chart types
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A true grammar

With the grammar of graphics, we do talk about specific chart elements

Map a column to the x-axis, fill by a different variable, and geom_col() to get stacked bars

Geoms can be interchangable
(e.g. switch geom_violin() to geom_boxplot())

Grammar of graphics layers
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Describing graphs with the grammar

Map wealth to the x-axis, health to the y-axis, add points, color by continent, size by population, scale the y-axis with a log, and facet by year

ggplot(data = filter(gapminder, year %in% c(2002, 2007)), 
       mapping = aes(x = gdpPercap,
                     y = lifeExp,
                     color = continent,
                     size = pop)) +
  geom_point() +
  scale_x_log10() + 
  facet_wrap(vars(year), ncol = 1)

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Describing graphs with the grammar

Map health to the x-axis, add a histogram with bins for every 5 years, fill and facet by continent

ggplot(data = gapminder_2007, 
       mapping = aes(x = lifeExp,
                     fill = continent)) +
  geom_histogram(binwidth = 5, 
                 color = "white") +
  guides(fill = "none") +  # Turn off legend
  facet_wrap(vars(continent))

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Describing graphs with the grammar

Map continent to the x-axis, health to the y-axis, add violin plots and semi-transparent boxplots, fill by continent

ggplot(data = gapminder, 
       mapping = aes(x = continent,
                     y = lifeExp,
                     fill = continent)) +
  geom_violin() +
  geom_boxplot(alpha = 0.5) +
  guides(fill = "none")  # Turn off legend

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Aesthetics in
extra dimensions

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Time

Use gganimate to map variables to a time aesthetic

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, 
                      size = pop, color = country)) +
  geom_point(alpha = 0.7) +
  scale_size(range = c(2, 12)) +
  scale_x_log10(labels = scales::dollar) +
  guides(size = "none", color = "none") +
  facet_wrap(~continent) +
  # Special gganimate stuff
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

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Sound

Visualize internal rhyming schemes in music

http://graphics.wsj.com/hamilton/

Daveed Diggs in 'Washington On Your Side'
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Daveed Diggs in 'Washington On Your Side'
Kendrick Lamar in 'good kid, m.A.A.d city'
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Animation, time, and sound

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Erika Navarro for the Weather Channel: https://www.pewtrusts.org/en/research-and-analysis/articles/2018/12/03/the-weather-channel-uses-animation-to-show-dangers-of-storm-surge

Tidy data

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Data shapes

For ggplot() to work,
your data needs to be in a tidy format

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Data shapes

For ggplot() to work,
your data needs to be in a tidy format

This doesn't mean that it's clean—
it refers to the structure of the data

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Data shapes

For ggplot() to work,
your data needs to be in a tidy format

This doesn't mean that it's clean—
it refers to the structure of the data

All the packages in the tidyverse work best with
tidy data; that why it's called that!

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Tidy data

Each variable has its own column

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Tidy data

Each variable has its own column

Each observation has its own row

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Tidy data

Each variable has its own column

Each observation has its own row

Each value has its own cell

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Tidy data

Each variable has its own column

Each observation has its own row

Each value has its own cell

Tidy data
From chapter 12 of R for Data Science
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Untidy data example

Real world data is often untidy, like this:

Example of untidy data
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Tidy data example

Here's the tidy version of that same data:

Example of tidy data

This is plottable!

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Wide vs. long

Tidy data is also called "long" data

Example of tidy data
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Figure by Garrick Aden-Buie in tidyexplain

Moving from wide to long

Nowadays, gather() is called pivot_longer() and spread() is called pivot_wider()

Moving from wide to long

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Figure by Garrick Aden-Buie in tidyexplain

Moving from wide to long

Nowadays, gather() is called pivot_longer() and spread() is called pivot_wider()

Moving from wide to long

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Figure by Garrick Aden-Buie in tidyexplain

Plan for today

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