The grammar of graphics

Lecture 2

Dr. Benjamin Soltoff

Cornell University
INFO 3312/5312 - Spring 2024

January 25, 2024

Announcements

Announcements

• Waitlist update
• 3312 discussion sections
• Lab meetings tomorrow
• Homework 01 due next week

Visualization critique

Racial, ethnic diversity in Florida political parties 2022

• What is the story?
• Effectiveness of a pie chart
• Effectiveness of color

Pie charts become problematic with too many categories that are evenly distributed. However in this situation some of the pies have a dominant slice. If the story is mostly about differences in White party registration, maybe this works. But if the focus is on the other groups, then this is not a good choice.

Color palettes try to map on to the standard “red-blue” that media decided represents Republicans and Democrats. But this leaves it very challenging to create separate colors for each racial/ethnic group. Might have made more sense to use color only for racial/ethnic groups and not color code party.

Source: USA Today

ggplot2 ❤️ 🐧

ggplot2 \(\in\) tidyverse

• ggplot2 is tidyverse’s data visualization package
• Structure of the code for plots can be summarized as

ggplot(data = [dataset], 
       mapping = aes(x = [x-variable], 
                     y = [y-variable])) +
   geom_xxx() +
   other options

Data: Palmer Penguins

Measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.

library(palmerpenguins)
glimpse(penguins)

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, …
$ island            <fct> Torgersen, Torgersen, Torgersen,…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3…
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6…
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650…
$ sex               <fct> male, female, female, NA, female…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 20…

• Plot
• Code

ggplot(data = penguins, 
       mapping = aes(x = bill_depth_mm, y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species")

Coding out loud

Start with the penguins data frame

ggplot(data = penguins)

Start with the penguins data frame, map bill depth to the x-axis

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm))

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm))

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm)) + 
  geom_point()

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point()

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively, label the legend “Species”

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively, label the legend “Species”, and add a caption for the data source.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively, label the legend “Species”, and add a caption for the data source. Finally, use a discrete color scale that is designed to be perceived by viewers with common forms of color blindness.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_color_viridis_d()

• Plot
• Code
• Narrative

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_color_viridis_d()

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis.

Represent each observation with a point and map species to the color of each point.

Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively, label the legend “Species”, and add a caption for the data source.

Finally, use a discrete color scale that is designed to be perceived by viewers with common forms of color blindness.

Application exercise

Source: Wikipedia

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

Minard’s grammar

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

• Layer
  • Data
  • Mapping
  • Statistical transformation (stat)
  • Geometric object (geom)
  • Position adjustment (position)
• Scale
• Coordinate system
• Faceting

ae-00

• Go to the course website
• Complete the application exercise with 1-3 peers

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(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species)) +
  geom_point() +
  scale_color_viridis_d()

Shape

Mapped to a different variable than color

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = island)) +
  geom_point() +
  scale_color_viridis_d()

Shape

Mapped to same variable as color

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species)) +
  geom_point() +
  scale_color_viridis_d()

Size

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species,
           size = body_mass_g)) +
  geom_point() +
  scale_color_viridis_d()

Alpha

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species,
           size = body_mass_g,
           alpha = flipper_length_mm)) +
  geom_point() +
  scale_color_viridis_d()

Mapping

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           size = body_mass_g,
           alpha = flipper_length_mm)) +
  geom_point()

Setting

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm)) + 
  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_*() (this was geom_point() in the previous example, but we’ll learn about other geoms soon!)

Faceting

Faceting

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

• Plot
• Code

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  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(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(rows = vars(species), cols = vars(sex))

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(rows = vars(sex), cols = vars(species))

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_wrap(facets = vars(species))

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(rows = NULL, cols = vars(species))

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  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(
  penguins, 
  aes(x = bill_depth_mm, 
      y = bill_length_mm, 
      color = species)) +
  geom_point() +
  facet_grid(species ~ sex) +
  scale_color_viridis_d()

Facet and color, no legend

• Plot
• Code

ggplot(
  penguins, 
  aes(x = bill_depth_mm, 
      y = bill_length_mm, 
      color = species)) +
  geom_point() +
  facet_grid(species ~ sex) +
  scale_color_viridis_d(guide = "none")

Wrap-up

Wrap-up

• 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

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