Mapping Data to Graphics

Dr. Nathaniel Cline

Agenda

1

Grammar of Graphics

2

Grammatical Layers

3

Review and to do

Exploratory Project

Stories about the Census


  1. Factoid

  2. Interaction

  3. Comparison

Warm-up

Data Story Critique


Make your own mobility animation

Grammar of Graphics

History

Before walking through the modern grammar of graphics, let’s look at some historical examples of creative ways people have mapped data to aesthetics to tell a story.

W.E.B. Dubois at the Paris Exhibition

Nice project using Dubois’ style

Famed sociologist, writer, and Black rights activist W. E. B. Du Bois fundamentally changed the representation of Black Americans with his exhibition of data visualizations at the 1900 Paris Exposition. Beautiful in design and powerful in content, these data portraits make visible a wide spectrum of African American culture, from advances in education to the lingering effects of slavery. They convey a literal and figurative representation of what he famously referred to as the color line, collected here in full color for the first time.

Mapping data to aesthetics

Aesthetic

Visual property of a graph

Position, shape, color, etc.


Data

A column in a dataset

Describing Minard

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

Describing Minard in R

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

Recall the basic ggplot framework

ggplot(data = DATA) +
  GEOM_FUNCTION(mapping = aes(AESTHETIC MAPPINGS))

Minard

ggplot(data = troops) +
  geom_path(mapping = aes(x = longitude,
                          y = latitude,
                          color = direction,
                          size = survivors))

This is a dataset named troops:

# A tibble: 3 × 4
  longitude latitude direction survivors
  <chr>     <chr>    <chr>     <chr>    
1 24        54.9     A         340000   
2 24.5      55       A         340000   
3 …         …        …         …        
ggplot(data = troops) +
  geom_path(mapping = aes(x = longitude,
                          y = latitude,
                          color = direction,
                          size = survivors))

Code 
ggplot(data = troops,
       mapping = aes(x = longitude,
                     y = latitude,
                     color = direction,
                     size = survivors)) +
  geom_path(lineend = "round", linejoin = "mitre") + 
  scale_size_continuous(range = c(1, 20),
                        labels = scales::comma) +
  theme_gray(base_size = 20)

Remember this?

Data aes() geom
Wealth (GDP/capita) x geom_point()
Health (Life expectancy) y geom_point()
Continent color geom_point()
Population size geom_point()

This is a dataset named gapminder_2007

# A tibble: 3 × 5
  country     continent gdpPercap   lifeExp pop     
  <chr>       <chr>     <chr>       <chr>   <chr>   
1 Afghanistan Asia      974.5803384 43.828  31889923
2 Albania     Europe    5937.029526 76.423  3600523 
3 …           …         …           …       …       
ggplot(data = gapminder_2007,
       mapping = aes(x = gdpPercap,
                     y = lifeExp,
                     color = continent,
                     size = pop)) +
  geom_point() +
  scale_x_log10()

Code 
ggplot(data = gapminder_2007,
       mapping = aes(x = gdpPercap,
                     y = lifeExp,
                     color = continent,
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_size_continuous(range = c(1, 15),
                        labels = scales::comma) +
  theme_gray(base_size = 20)

Grammatical Layers

Layers


We can layer data, aesthetics, and mappings on to the base ggplot() with the ggplot specific pipe operator: +

Aesthetics: color(discrete)

Aesthetics: color(continuous)

Aesthetics: size

Aesthetics: fill

Aesthetics: shape

Aesthetics: alpha

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

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

scale_x_log10()

Code 
ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) +
  geom_point() +
  scale_x_log10()

scale_color_viridis_d()

Code 
ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d()

Data Viz Catalog

Review and To-do

Review

  • We explored the translation of a visual graphic to a grammar (ggplot)

  • We will keep practicing this translation process, and the process of adding layers to a ggplot graphic

  • Next time we will talk about some other dimensions (like time) and we will explore some more creative approaches to data story telling

Code 
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')

To-Do

Before next class:

  • Make a graph using the county census data set. You can use any geom you like, but you must map a variable to color.

    • note pay attention to whether you are mapping color to a discrete variable or a continuous variable

    • Use ggsave("yourfilename.png") after your ggplot code and post it to our Teams site

  • Pick a data viz from Data Viz Catalog and be ready to summarize what it is, and when it might be useful in class.

Econ 255 - Data Storytelling

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Mapping Data to Graphics Dr. Nathaniel Cline

  1. Slides

  2. Tools

  3. Close
  • Mapping Data to Graphics
  • Agenda
  • Exploratory Project
  • Stories about the Census
  • Warm-up
  • Data Story Critique
  • Grammar of Graphics
  • History
  • W.E.B. Dubois at the Paris Exhibition
  • Nice project using...
  • Slide 11
  • Mapping data to aesthetics
  • Describing Minard
  • Describing Minard in R
  • Recall the basic ggplot framework
  • Minard
  • This is a dataset...
  • Code ggplot(data...
  • Remember this?
  • Data aes() geom ...
  • This is a dataset...
  • Code ggplot(data...
  • Grammatical Layers
  • Layers
  • Aesthetics: color(discrete)
  • Aesthetics: color(continuous)
  • Aesthetics: size
  • Aesthetics: fill
  • Aesthetics: shape
  • Aesthetics: alpha
  • Example geom What...
  • Scales
  • scale_x_log10()
  • scale_color_viridis_d()
  • Data Viz Catalog
  • Review and To-do
  • Review
  • Code ggplot(gapminder,...
  • To-Do
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