Adding Text to Plots
This lesson is called Adding Text to Plots, part of the R in 3 Months (Spring 2025) course. This lesson is called Adding Text to Plots, part of the R in 3 Months (Spring 2025) course.
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# Load Packages -----------------------------------------------------------
library(tidyverse)
# Import Data -------------------------------------------------------------
penguins <- read_csv("penguins.csv")
# Adding Text to Plots ---------------------------------------------------------
# Text is just another geom.
# We can use geom_text() to add labels to our figures.
ggplot(data = penguin_bill_length_by_island,
mapping = aes(x = island,
y = mean_bill_length,
fill = island,
label = mean_bill_length)) +
geom_col() +
geom_text()
# Those text labels are too long!
# Let's create a new variable to use for plotting.
# We're using the number() function from the scales package
# to make this variable
library(scales)
penguin_bill_length_by_island_v2 <- penguin_bill_length_by_island |>
mutate(mean_bill_length_one_digit = number(mean_bill_length, accuracy = 0.1))
# Now let's plot using our new data frame
ggplot(data = penguin_bill_length_by_island_v2,
mapping = aes(x = island,
y = mean_bill_length,
fill = island,
label = mean_bill_length_one_digit)) +
geom_col() +
geom_text()
# Note that we use mean_bill_length_one_digit for the label aesthetic property
# and mean_bill_length for y.
# If you use mean_bill_length_one_digit for both, your graph will
# look different.
ggplot(data = penguin_bill_length_by_island_v2,
mapping = aes(x = island,
y = mean_bill_length_one_digit,
fill = island,
label = mean_bill_length_one_digit)) +
geom_col() +
geom_text()
# We can use the hjust and vjust arguments to horizontally and vertically
# adjust text.
# vjust = 0 puts the labels on the outer edge of the bars.
ggplot(data = penguin_bill_length_by_island_v2,
mapping = aes(x = island,
y = mean_bill_length,
fill = island,
label = mean_bill_length_one_digit)) +
geom_col() +
geom_text(vjust = 0)
# vjust = 1 puts the labels at the inner edge of the bars.
ggplot(data = penguin_bill_length_by_island_v2,
mapping = aes(x = island,
y = mean_bill_length,
fill = island,
label = mean_bill_length_one_digit)) +
geom_col() +
geom_text(vjust = 1)
# I often do something like vjust = 1.5 to give a bit more padding.
ggplot(data = penguin_bill_length_by_island_v2,
mapping = aes(x = island,
y = mean_bill_length,
fill = island,
label = mean_bill_length_one_digit)) +
geom_col() +
geom_text(vjust = 1.5)
# We can adjust the color of the text using the color argument.
# We're putting it outside of the aes() because we are setting it
# for the whole layer.
ggplot(data = penguin_bill_length_by_island_v2,
mapping = aes(x = island,
y = mean_bill_length,
fill = island,
label = mean_bill_length_one_digit)) +
geom_col() +
geom_text(vjust = 1.5,
color = "white")
# geom_label() is nearly identical but it adds a background.
# With geom_label() the color argument determines the text and border color
# while the fill is the background color.
ggplot(data = penguin_bill_length_by_island_v2,
mapping = aes(x = island,
y = mean_bill_length,
fill = island,
label = mean_bill_length_one_digit)) +
geom_col() +
geom_label(vjust = 1.5,
color = "white",
fill = "black")
Your Turn
# Load Packages -----------------------------------------------------------
library(tidyverse)
# Import Data -------------------------------------------------------------
penguins <- read_csv("penguins.csv")
# Adding Text to Plots ---------------------------------------------------------
# Copy your last code chunk.
# Then add text labels on the top of each bar that show the number of penguins of each species.
# You'll need to use geom_text() and the vjust argument to do this.
# Make the text labels show up in red.
# YOUR CODE HERE
# Do the same thing, but use geom_label() instead of geom_text().
# This time, make the text itself show up in white.
# YOUR CODE HERE
Learn More
Data Visualization: A Practical Introduction has a section in Chapter 5 on adding text to plots, as does Chapter 11 of R for Data Science.
Information about using vjust and hjust is on the geom_label page of the tidyverse website.
Also, check out the ggrepel package , which automatically adjusts overlapping text and labels.
Have any questions? Put them below and we will help you out!
Course Content
127 Lessons
1
Welcome to Getting Started with R
00:57
2
Install R
02:05
3
Install RStudio
02:14
4
Files in R
04:33
5
Projects
07:54
6
Packages
02:38
7
Import Data
05:24
8
Objects and Functions
03:16
9
Examine our Data
12:50
10
Import Our Data Again
07:11
11
Getting Help
07:46
12
Week 1 Live Session (Spring 2025)
1:03:11
1
Welcome to Fundamentals of R
01:36
2
Update Everything
02:45
3
Start a New Project
02:16
4
The Tidyverse
03:34
5
Pipes
04:15
6
select()
07:25
7
mutate()
04:25
8
filter()
10:05
9
summarize()
05:59
10
group_by() and summarize()
05:54
11
arrange()
02:07
12
Create a New Data Frame
03:58
13
Bring it All Together (Data Wrangling)
07:29
14
Week 2 Project Assignment
09:39
15
Week 2 Coworking Session (Spring 2025)
16
Week 2 Live Session (Spring 2025)
1:03:24
1
The Grammar of Graphics
04:39
2
Scatterplots
03:46
3
Histograms
05:47
4
Bar Charts
06:37
5
Setting color and fill Aesthetic Properties
02:39
6
Setting color and fill Scales
05:40
7
Setting x and y Scales
03:09
8
Adding Text to Plots
07:32
9
Plot Labels
03:57
10
Themes
02:19
11
Facets
03:12
12
Save Plots
02:57
13
Bring it All Together (Data Visualization)
06:42
14
Week 3 Project Assignment
03:30
15
Week 3 Coworking Session (Spring 2025)
16
Week 3 Live Session (Spring 2025)
1:02:31
1
Downloading and Importing Data
10:32
2
Overview of Tidy Data
05:50
3
Tidy Data Rule #1: Every Column is a Variable
07:43
4
Tidy Data Rule #3: Every Cell is a Single Value
10:04
5
Tidy Data Rule #2: Every Row is an Observation
04:42
6
Week 6 Coworking Session (Spring 2025)
7
Week 6 Live Session (Spring 2025)
1:02:38
1
Best Practices in Data Visualization
03:44
2
Tidy Data
02:25
3
Pipe Data into ggplot
09:54
4
Reorder Plots to Highlight Findings
03:37
5
Line Charts
04:17
6
Use Color to Highlight Findings
09:16
7
Declutter
08:29
8
Add Descriptive Labels to Your Plots
09:10
9
Use Titles to Highlight Findings
08:14
10
Use Annotations to Explain
07:09
11
Week 9 Coworking Session (Spring 2025)
12
Week 9 Live Session (Spring 2025)
59:09
1
Advanced Markdown
06:43
2
Tables
18:36
3
Advanced YAML and Code Chunk Options
05:53
4
Inline R Code
04:42
5
Making Your Reports Shine: Word Edition
04:30
6
Making Your Reports Shine: PDF Edition
06:11
7
Making Your Reports Shine: HTML Edition
06:06
8
Presentations
10:12
9
Dashboards
05:38
10
Websites
06:43
11
Publishing Your Work
04:38
12
Quarto Extensions
05:50
13
Parameterized Reporting, Part 1
10:57
14
Parameterized Reporting, Part 2
05:11
15
Parameterized Reporting, Part 3
07:47
16
Week 12 Coworking Session (Spring 2025)
17
Week 12 Live Session (Spring 2025)
57:01
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