Themes
This lesson is called Themes, part of the R in 3 Months (Spring 2026) course. This lesson is called Themes, part of the R in 3 Months (Spring 2026) course.
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# Load Packages -----------------------------------------------------------
library(tidyverse)
# Import Data -------------------------------------------------------------
penguins <- read_csv("penguins.csv")
penguin_bill_length_by_island_and_sex <-
penguins |>
drop_na(sex) |>
group_by(island, sex) |>
summarize(mean_bill_length = mean(bill_length_mm))
# Themes ------------------------------------------------------------------
# To add a theme to a plot, we use the theme_ set of functions.
# There are several built-in themes. For instance, theme_minimal().
ggplot(
data = penguin_bill_length_by_island_and_sex,
mapping = aes(
x = island,
y = mean_bill_length,
fill = sex
)
) +
geom_col(position = "dodge") +
labs(
title = "Males have longer bills than females",
subtitle = "But they're all good penguins",
caption = "Data from the palmerpenguins R package",
x = NULL,
y = "Mean Bill Length in Millimeters",
fill = NULL
) +
theme_minimal()
# There's also theme_light().
ggplot(
data = penguin_bill_length_by_island_and_sex,
mapping = aes(
x = island,
y = mean_bill_length,
fill = sex
)
) +
geom_col(position = "dodge") +
labs(
title = "Males have longer bills than females",
subtitle = "But they're all good penguins",
caption = "Data from the palmerpenguins R package",
x = NULL,
y = "Mean Bill Length in Millimeters",
fill = NULL
) +
theme_light()
# There are also packages that give you themes you can apply to your plots.
# Let's load the ggthemes() package
# Install it if necessary using install.packages("ggthemes")
library(ggthemes)
# We can then use a theme from this package called theme_economist()
# to make our plots look like those in the Economist.
ggplot(
data = penguin_bill_length_by_island_and_sex,
mapping = aes(
x = island,
y = mean_bill_length,
fill = sex
)
) +
geom_col(position = "dodge") +
labs(
title = "Males have longer bills than females",
subtitle = "But they're all good penguins",
caption = "Data from the palmerpenguins R package",
x = NULL,
y = "Mean Bill Length in Millimeters",
fill = NULL
) +
theme_economist()
# You can see a number of other themes from this package at
# https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/
Your Turn
# Load Packages -----------------------------------------------------------
library(tidyverse)
# Import Data -------------------------------------------------------------
penguins <- read_csv("penguins.csv")
# Themes ------------------------------------------------------------------
# Use one of the built-in ggplot2 themes to change the look and feel of your last plot
# https://ggplot2.tidyverse.org/reference/index.html#themes
# YOUR CODE HERE
# Install the ggthemes package
# Load the ggthemes package
# Use one of themes from the package to update your last plot
# The themes can be found here:
# https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/
# YOUR CODE HERE
Learn More
Data Visualization: A Practical Introduction has a section on themes in Chapter 8.
If you’re looking for packages that give you extra themes, check out this roundup.
Note that theme packages often include code that changes to overall look and feel as well as palettes that you can apply to the color and fill scales.
Have any questions? Put them below and we will help you out!
Course Content
144 Lessons
1
Welcome to Fundamentals of R
01:20
2
Update Everything
02:26
3
Start a New Project
02:38
4
The Tidyverse
03:24
5
Pipes
03:52
6
select()
04:43
7
mutate()
03:22
8
filter()
10:18
9
Quiz
10
summarize()
05:38
11
Grouped Summaries
04:24
12
arrange()
02:50
13
Create a New Data Frame
03:30
14
Quiz
15
Bring it All Together (Data Wrangling)
07:09
16
Week 2 Project Assignment
13:10
17
Week 2 Coworking Session (Spring 2026)
18
Week 2 Live Session (Spring 2026)
59:16
1
The Grammar of Graphics
04:36
2
Scatterplots
03:40
3
Histograms
04:51
4
Bar Charts
04:53
5
Quiz
6
Setting color and fill Aesthetic Properties
02:43
7
Setting color and fill Scales
05:12
8
Quiz
9
Setting x and y Scales
02:58
10
Adding Text to Plots
05:50
11
Plot Labels
02:59
12
Themes
02:10
13
Facets
02:56
14
Save Plots
02:49
15
Bring it All Together (Data Visualization)
06:14
16
Week 3 Project Assignment
06:02
17
Week 3 Coworking Session (Spring 2026)
18
Week 3 Live Session (Spring 2026)
1:00:46
1
Downloading and Importing Data
08:13
2
Overview of Tidy Data
05:03
3
Tidy Data Rule #1: Every Column is a Variable
06:26
4
Tidy Data Rule #3: Every Cell is a Single Value
09:27
5
Tidy Data Rule #2: Every Row is an Observation
04:05
6
Quiz
7
Week 6 Coworking Session (Spring 2026)
8
Week 6 Live Session (Spring 2026)
59:31
1
Best Practices in Data Visualization
03:38
2
Tidy Data
02:25
3
Pipe Data in ggplot
08:18
4
Reorder Plots to Highlight Findings
03:50
5
Line Charts
04:13
6
Use Color to Highlight Findings
08:23
7
Declutter
07:53
8
Add Descriptive Labels to Your Plots
09:18
9
Use Titles to Highlight Findings
08:30
10
Use Annotations to Explain
06:35
11
Quiz
12
Week 9 Coworking Session (Spring 2026)
13
Week 9 Live Session (Spring 2026)
1
Advanced Markdown
07:10
2
Tables
15:48
3
Advanced YAML and Code Chunk Options
05:42
4
Inline R Code
03:42
5
Making Your Reports Shine: Word Edition
05:08
6
Making Your Reports Shine: PDF Edition
07:37
7
Making Your Reports Shine: HTML Edition
06:08
8
Presentations
11:12
9
Dashboards
06:20
10
Websites
08:11
11
Publishing Your Work
02:37
12
Quarto Extensions
06:38
13
Parameterized Reporting, Part 1
07:02
14
Parameterized Reporting, Part 2
04:03
15
Parameterized Reporting, Part 3
06:22
16
Quiz
17
Week 12 Coworking Session (Spring 2026)
18
Week 12 Live Session (Spring 2026)
1
All videos from R in 3 Months (Spring 2026)
2
Working with labelled data
05:35
3
Understanding Documentation Pages
05:20
4
Factors in R
11:18
5
Add citations to Quarto documents
10:42
6
Change titles of facet plots
08:16
7
Population pyramid plot
04:24
8
Why use Git - example case
06:13
9
How to access data not on GitHub
13:46
10
Dealing with merge conflicts in GitHub Desktop
03:22
11
Crosstabs
06:58
12
Difference between == and %in%
03:17
13
Quarto - rendering and working directories
13:05
14
Using Function Arguments
13:06
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