Plot Labels
This lesson is called Plot Labels, part of the R in 3 Months (Spring 2025) course. This lesson is called Plot Labels, part of the R in 3 Months (Spring 2025) course.
Transcript
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# Load Packages -----------------------------------------------------------
library(tidyverse)
# Import Data -------------------------------------------------------------
penguins <- read_csv("penguins.csv")
# Plot Labels -------------------------------------------------------------
# To start, let's make a new data frame
penguin_bill_length_by_island_and_sex <- penguins |>
drop_na(sex) |>
group_by(island, sex) |>
summarize(mean_bill_length = mean(bill_length_mm))
# Now let's plot this data frame using a bar chart.
ggplot(data = penguin_bill_length_by_island_and_sex,
mapping = aes(x = island,
y = mean_bill_length,
fill = sex)) +
geom_col()
# The bars are stacked by default.
# To put them side by side, we use the
# position = "dodge" argument within geom_col().
ggplot(data = penguin_bill_length_by_island_and_sex,
mapping = aes(x = island,
y = mean_bill_length,
fill = sex)) +
geom_col(position = "dodge")
# To add labels to our plot, we use labs().
# We can add a title to the plot with the title argument.
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")
# We can also add a subtitle and caption
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")
# We can change the x and y axis labels using the x and y arguments.
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 = "Island",
y = "Mean Bill Length in Millimeters")
# To change the legend title,
# we use the name of the aesthetic that is being shown.
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 = "Island",
y = "Mean Bill Length in Millimeters",
fill = "Sex")
# You can remove plot labels using NULL
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)
Your Turn
# Load Packages -----------------------------------------------------------
library(tidyverse)
# Import Data -------------------------------------------------------------
penguins <- read_csv("penguins.csv")
# Plot Labels -------------------------------------------------------------
# Copy the code for the last plot you made that uses geom_label().
# Then do the following:
# 1. Add a title
# 2. Remove the x and y axis labels
# YOUR CODE HERE
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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Rachel Udow • March 27, 2024
There are a lot of functions involved in data visualization! Are there widely accepted best practices for how to order them?
Libby Heeren Coach • March 27, 2024
ggplot2 (and the grammar of graphics in general) works by layering, so that's the most important concept to keep in mind. The things you add underneath can cover up or overwrite things you put above. We tend to do things in order of foundational (like our ggplot call, plus our geom, which gives us the background of our plot and our line/bar/points), to more mid-grain details like labels in the middle, and then super specific adjustment like removing the legend or moving the plot title at the end.
Here's an example. You'll see the order here is ggplot, geom, axis labels, setting specific limits for the y axis, and then changing a theme setting.
Rachel Udow • March 29, 2024
Thank you, Libby - this is super helpful!