Visualising data with ggplot2

wk2-d02-ggplot2

Author
Affiliation

Dr. D

Chico State
DATA 385 - Fall 2026

Published

September 2, 2026

ggplot2 💕🐧

Setup

🎥 ggplot2 (21 min) - code along in wk2-d02

library(tidyverse)
library(palmerpenguins)

ggplot2 is part of the 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, Adelie, Ad…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39…
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19…
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 46…
$ sex               <fct> male, female, female, NA, female, male, fe…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, …

Bill depth and length

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

Coding out loud

Start with the penguins data frame

ggplot(data = penguins)

Map bill depth to the x-axis

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

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

Map bill length to the y-axis

…and map bill length to the y-axis.

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

Represent each observation with a point

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

Map species to the colour of each point

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

Title the plot “Bill depth and length”

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

Label the x and y axes

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

Label the legend “Species”

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

Add a caption for the data source

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = 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)",
       colour = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package")

Use a colour-blind-safe palette

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

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = 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)",
       colour = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_colour_viridis_d()

Putting it all together

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 colour 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 colour scale that is designed to be perceived by viewers with common forms of colour blindness.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = 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)",
       colour = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_colour_viridis_d()

Argument names

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,
                     colour = species)) +
  geom_point() +
  scale_colour_viridis_d()
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point() +
  scale_colour_viridis_d()

Aesthetics

Aesthetics options

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

  • colour
  • shape
  • size
  • alpha (transparency)

Colour

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point() +
  scale_colour_viridis_d()

Shape

Mapped to a different variable than colour

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

Shape

Mapped to the same variable as colour

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species,
           shape = species)) +
  geom_point() +
  scale_colour_viridis_d()

Size

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

Alpha

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

Mapping vs. setting

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: 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
#| out-width: "70%"
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) +
  geom_point() +
  facet_grid(species ~ island)

Various ways to facet

In the next few plots, think about how the code relates to the output. Note: the plots below do not have proper titles, axis labels, etc. because the point is to figure out what’s happening in the plot – 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)

#| fig-asp: 0.5
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) +
  geom_point() +
  facet_wrap(~ species)

#| fig-asp: 0.5
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 = "none")

This page adapts material from Data Science in a Box (Unit 2, Deck 2: “Visualising data with ggplot2”) by Mine Çetinkaya-Rundel, licensed under CC BY-SA 4.0. Source: tidyverse/datascience-box. Modified: reorganized into a single Quarto page; no dataset/package substitutions were needed for this lesson.