The grammar of graphics

Lecture 2

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Spring 2026

January 22, 2026

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Number of restaurants by cuisine type

• What is the story?
• How does the design of the chart make you think that is the story?

Learning objectives

• Utilize the grammar of graphics to conceptually define Minard’s graph of Napoleon’s invasion of Russia
• Map variables to aesthetics
• Create small-multiples plots using faceting

“The best statistical graphic ever drawn”

Building Minard’s map in R

troops

# A tibble: 51 × 4
    long   lat survivors direction
   <dbl> <dbl>     <dbl> <chr>    
 1  24    54.9    340000 A        
 2  24.5  55      340000 A        
 3  25.5  54.5    340000 A        
 4  26    54.7    320000 A        
 5  27    54.8    300000 A        
 6  28    54.9    280000 A        
 7  28.5  55      240000 A        
 8  29    55.1    210000 A        
 9  30    55.2    180000 A        
10  30.3  55.3    175000 A        
# ℹ 41 more rows

cities

# A tibble: 20 × 3
    long   lat city          
   <dbl> <dbl> <chr>         
 1  24    55   Kowno         
 2  25.3  54.7 Wilna         
 3  26.4  54.4 Smorgoni      
 4  26.8  54.3 Moiodexno     
 5  27.7  55.2 Gloubokoe     
 6  27.6  53.9 Minsk         
 7  28.5  54.3 Studienska    
 8  28.7  55.5 Polotzk       
 9  29.2  54.4 Bobr          
10  30.2  55.3 Witebsk       
11  30.4  54.5 Orscha        
12  30.4  53.9 Mohilow       
13  32    54.8 Smolensk      
14  33.2  54.9 Dorogobouge   
15  34.3  55.2 Wixma         
16  34.4  55.5 Chjat         
17  36    55.5 Mojaisk       
18  37.6  55.8 Moscou        
19  36.6  55.3 Tarantino     
20  36.5  55   Malo-Jarosewii

Application exercise

ae-01

Define the conceptual grammar of graphics for Minard’s visualization

Data

• Troops
  • Latitude
  • Longitude
  • Survivors
  • Advance/retreat
• Cities
  • Latitude
  • Longitude
  • City name

Aesthetics

Aesthetics options

Commonly used characteristics of plotting characters that can be mapped to a specific variable in the data are

• color
• shape
• size
• alpha (transparency)

Color

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len,
    color = species
  )
) +
  geom_point() +
  scale_color_viridis_d()

Shape

Mapped to a different variable than color

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len,
    color = species,
    shape = island
  )
) +
  geom_point() +
  scale_color_viridis_d()

Shape

Mapped to same variable as color

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len,
    color = species,
    shape = species
  )
) +
  geom_point() +
  scale_color_viridis_d()

Size

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len,
    color = species,
    shape = species,
    size = body_mass
  )
) +
  geom_point() +
  scale_color_viridis_d()

Alpha

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len,
    color = species,
    shape = species,
    size = body_mass,
    alpha = flipper_len
  )
) +
  geom_point() +
  scale_color_viridis_d()

Mapping

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len,
    size = body_mass,
    alpha = flipper_len
  )
) +
  geom_point()

Setting

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len
  )
) +
  geom_point(size = 2, alpha = 0.5)

Mapping vs. setting

• Mapping: Determine the size, alpha, etc. of points based on the values of a variable in the data
  • goes into aes()
• Setting: Determine the size, alpha, etc. of points not based on the values of a variable in the data
  • goes into geom_*()

Faceting

Faceting

• Smaller plots that display different subsets of the data
• Useful for exploring conditional relationships and large data

• Plot
• Code

ggplot(data = penguins, mapping = aes(x = bill_dep, y = bill_len)) + 
  geom_point() +
  facet_grid(rows = vars(species), cols = vars(island))

Various ways to facet

In the next few slides describe what each plot displays. Think about how the code relates to the output.

Note: The plots in the next few slides do not have proper titles, axis labels, etc. because we want you to figure out what’s happening in the plots. But you should always label your plots!

ggplot(data = penguins, mapping = aes(x = bill_dep, y = bill_len)) + 
  geom_point() +
  facet_grid(rows = vars(species), cols = vars(sex))

ggplot(data = penguins, mapping = aes(x = bill_dep, y = bill_len)) + 
  geom_point() +
  facet_grid(rows = vars(sex), cols = vars(species))

ggplot(data = penguins, mapping = aes(x = bill_dep, y = bill_len)) + 
  geom_point() +
  facet_wrap(facets = vars(species))

ggplot(data = penguins, mapping = aes(x = bill_dep, y = bill_len)) + 
  geom_point() +
  facet_grid(rows = NULL, cols = vars(species))

ggplot(data = penguins, mapping = aes(x = bill_dep, y = bill_len)) + 
  geom_point() +
  facet_wrap(facets = vars(species), ncol = 2)

Faceting summary

• facet_grid():
  • 2 dimensional grid
  • rows = vars(<VARIABLE>), cols = vars(<VARIABLE>)
  • Alternative: rows ~ cols
• facet_wrap(): 1 dimensional ribbon wrapped according to number of rows and columns specified or available plotting area

Facet and color

• Plot
• Code

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len,
    color = species
  )
) +
  geom_point() +
  facet_grid(species ~ sex) +
  scale_color_viridis_d()

Facet and color, no legend

• Plot
• Code

ggplot(
  data = penguins,
  mapping = aes(
    x = bill_dep,
    y = bill_len,
    color = species
  )
) +
  geom_point() +
  facet_grid(species ~ sex) +
  scale_color_viridis_d(guide = "none")

Wrap up

Recap

• {ggplot2} is based on the grammar of graphics
• Use the ggplot() function to initialize a plot
• aes() maps variables to aesthetics
• Use geom_*() to add geoms to a plot
• Use facet_*() to facet a plot

Acknowledgements

• Slides derived in part from Data Science in a Box