04-ggplot2

Professor Shannon Ellis

2023-10-12

Data Visualization with ggplot2 ❤️ 🐧

Q&A

Q: What is the difference between pull and select?
A: select specifies which columns to display in your resulting dataframe. pull extracts the values from a column and stores them in a vector (not a dataframe)

Q: I am a bit confused on factors and what levels mean.
A: Factors store categorical information. The levels of a factor are all the possible unique values in a variable.

Q: how similar is R to numpy/which scenarios are each used in the industry?
A: Basically, anything data science-y you can do in R, you can also do in python. R has linear algebra/working with matrices built directly into its base installation, so no additional package would be need for numpy-like operations. And, dplyr does very similar things to pandas, but with a more readable and consistent syntax overall.

Q: would we ever load just dplyr instead of the entire tidyverse package? is there a big difference?
A: We’ll always just load tidyverse. The difference is that the tidyverse is quite big, so if you ever wanted to just use dplyr functions, you could load just that. This matters more in development where you’re trying to minimize external dependencies and make code run as fast as possible. For our purposes, there’s no real need to only load dplyr

Q: I found the demos to be the most confusing part, because it’s very different understanding slides and applying that to actual coding. Personally, I would prefer if the lecture content were put into recordings, or just uploaded earlier so we could learn it on our own, and then have classes be more focused on data science best practices, applications, etc.
A: I do really like this idea and would love to run this like this in the. I’m curious what y’all think of this and will add a question like this to the post-course survey to get students’ thoughts.

Course Announcements

Due Dates:

  • Lab 02 due Friday (11:59 PM)
  • HW 01 due Monday (11:59 PM)
  • Lecture Participation survey “due” after class

Suggested Reading

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.

# install.packages("palmerpenguins")
library(palmerpenguins)
glimpse(penguins)
Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

The Data

penguins |>
  datatable()

A Plot

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") +
  scale_color_viridis_d()
Warning: Removed 2 rows containing missing values (`geom_point()`).

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()

Coding out loud

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()
Warning: Removed 2 rows containing missing values (`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”, 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.

Argument names

Tip

You can omit the names of first two arguments when building plots with ggplot().

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

Your Turn

Generate a basic plot in ggplot2 using different variables than those in the last example (last example: bill_depth_mm & bill_depth_mm).

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 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!)

Mapping vs. Setting (example)

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)

Your Turn

Edit the basic plot you created earlier to change something about its aesthetics.

Faceting

Faceting

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

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(species ~ island) 
Warning: Removed 2 rows containing missing values (`geom_point()`).

Various ways to facet

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

Warning

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(species ~ sex) 

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

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

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

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_wrap(~ species, ncol = 2)

Faceting summary

  • facet_grid():
    • 2d grid
    • rows ~ cols
    • use . for no split
  • facet_wrap(): 1d ribbon wrapped according to number of rows and columns specified or available plotting area

Facet and color

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

Face and color, no legend

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

geoms

Common geoms

geom 1 Description 2
geom_point scatterplot
geom_bar barplot
geom_line line plot
geom_density densityplot
geom_histogram histogram
geom_boxplot boxplot

Your Turn

Generate a plot in ggplot2 using a different geom than what you did previously. Customize as much as you can before time is “up.”

Recap

  • Can I explain the overall structure of a call to generate a plot in ggplot2?
  • Can I describe ggplot2 code? Can I create plots using ggplot2?
  • Can I alter the aesthetics of a basic plot? (color, shape, size, transparency)
  • Am I able to facet a plot to generate a grid of figures
  • Can I describe what a geom is and do I know the basic plots available?

https://cogs137.github.io/website/