Histograms
This lesson is called Histograms, part of the R in 3 Months (Spring 2025) course. This lesson is called Histograms, 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")
# Histograms --------------------------------------------------------------
# We use geom_histogram() to make a histogram.
ggplot(data = penguins,
mapping = aes(x = bill_length_mm)) +
geom_histogram()
# How does ggplot know what to plot on the y axis?
# It's using the default statistical transformation for geom_histogram,
# which is stat = "bin".
# If we add stat = "bin" we get the same thing.
# Each geom has a default stat.
ggplot(data = penguins,
mapping = aes(x = bill_length_mm)) +
geom_histogram(stat = "bin")
# We can adjust the number of bins using the bins argument.
ggplot(data = penguins,
mapping = aes(x = bill_length_mm)) +
geom_histogram(bins = 100)
Your Turn
# Load Packages -----------------------------------------------------------
library(tidyverse)
# Import Data -------------------------------------------------------------
penguins <- read_csv("penguins.csv")
# Histograms --------------------------------------------------------------
# Make a histogram that shows the distribution of the body_mass_g variable.
# YOUR CODE HERE
# Adjust your histogram so it has 50 bins.
# YOUR CODE HERE
Learn More
You can find examples of code to make histograms on the Data to Viz website , the R Graph Gallery website , and in Chapter 6 of the R Graphics Cookbook , and Chapter 7 of the Fundamentals of Data Visualization.
To learn about more statistical transformations, Chapter 9 of R for Data Science has a discussion of them.
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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gene trevino • January 20, 2025
Why ?
David Keyes Founder • January 21, 2025
I'm really not sure. It's always worked for me without the
stat = "count"
. In fact, addingstat = "count"
will yield more like a bar chart because it is counting unique observations, not putting observations into bins likegeom_histogram()
does.gene trevino • January 21, 2025
I realized that the body_mass_g variable is a character variable. I changed it to a numeric variable and the histogram worked. I still don't know why it won't work without stat = "count" Gene
Myles Kwitny • March 27, 2025
How do you get lines to divide up each bar? I get the same graph but there aren't the faint gray lines between each bar on the histogram.
Gracielle Higino Coach • March 27, 2025
Hi Myles! I don't get these lines either, it must have something to do with the version of the {ggplot2} that we're using or maybe it could be something related to the graphic interface or displays that we're using. Ideally you should control that in the aesthetics functions, anyway!