Session 5: How can I make data visualizations with ggplot2?

A Slower Introduction to R

Yea-Hung Chen, PhD, MS

UCSF Library

Thursday, November 6, 2025

Why

Gallery

Gallery

Gallery

Gallery

Gallery

Components

Aesthetics and geometries

• to make a ggplot2 data visualization, specify 2 components:
  • aesthetics
  • geometries

Aesthetics

• aesthetics = visual dimensions
• 3 common aesthetics are:
  • x
  • y
  • color
• to make a ggplot2 data visualization, you must assign variables to dimensions
• you might hear me refer to this as a mapping

Aesthetics: x and y

Aesthetics: x, y, and color

Geometries

• geometries define the type of data visualization
• common geometries are:
  • density and histogram
  • point
  • line
  • box plot and violin

Geometries: histogram

Geometries: density

Geometries: point

Geometries: line

Geometries: box plot

Geometries: violin plot

Exercise 1

Exercise 1

• for each of the following data visualizations, identify:
  • the aesthetics
  • the geometry

Exercise 1

Exercise 1

Exercise 1

Loading packages

Loading packages

• using a cooking analogy:
  • packages = cookbooks
  • loading the package = taking the cookbook off the shelf

Loading packages

• to load a package, use library()
• for example, to load the ggplot2 package:

library(ggplot2)

• you need to do this once per session

Loading versus installing

action	analogy	frequency
installing	buying a cookbook	once ever
loading	taking the cookbook off the shelf	once per session

Put it in your script

• I suggest that you put library() code near the top of your script
• for example:

# load packages
library(ggplot2) 

# import data
nhanes<-read.csv('nhanes_l.csv')

Scatter plots

Systolic versus height

Systolic versus height: aesthetics

• to specify the aesthetics component, use the aes() function, inside the ggplot(nhanes,) function:

ggplot(data=nhanes,aes(x=bmxht,y=bpxosy1))

• aes() function stands for aesthetic
• notice the data argument inside ggplot()
• data is the first argument so the argument name is optional

Systolic versus height: geometry

• the geometry for scatter plots is points
• to create scatter plots, use geom_point()

Systolic versus height: aesthetics + geometry

• to connect the 2 components, use the plus sign:

ggplot(nhanes,aes(x=bmxht,y=bpxosy1))+
  geom_point()

Systolic versus height: aesthetics + geometry

Systolic versus height: labels

• what about the labels on the x and y axes?
• to specify labels, use the labs() function:

labs(x='Height (cm)',y='Systolic blood pressure (mmHg)')

Systolic versus height: aesthetics + geometry + labels

• putting it all together:

ggplot(nhanes,aes(x=bmxht,y=bpxosy1))+
  geom_point()+
  labs(x='Height (cm)',y='Systolic blood pressure (mmHg)')

• notice that there are 3 components now

Systolic versus height: aesthetics + geometry + labels

Systolic versus height: color

• to use color to represent a variable, add the color argument to aes()
• for example, to use color to represent gender:

ggplot(nhanes,aes(x=bmxht,y=bpxosy1,color=gender))+
  geom_point()+
  labs(x='Height (cm)',y='Systolic blood pressure (mmHg)')

Systolic versus height: color

Systolic versus height: color

• to modify the legend label, add the color argument to labs():

labs(x='Height (cm)',
     y='Systolic blood pressure (mmHg)',
     color='Gender')

Systolic versus height: color

• putting it all together:

ggplot(nhanes,aes(x=bmxht,y=bpxosy1,color=gender))+
  geom_point()+
  labs(x='Height (cm)',
       y='Systolic blood pressure (mmHg)',
       color='Gender')

Systolic versus height: color

Systolic versus height: smoothers

• to add a smoother to the original scatter plot, add the geom_smooth() geometry
• for example, to add a regression line:

geom_smooth(method='lm',se=FALSE)

• 'lm' stands for linear model
• se=FALSE removes the default confidence band

Systolic versus height: smoothers

• putting it all together:

ggplot(nhanes,aes(x=bmxht,y=bpxosy1))+
  geom_point()+
  geom_smooth(method='lm',se=FALSE)+
  labs(x='Height (cm)',
       y='Systolic blood pressure (mmHg)',
       color='Gender')

• the order of the geometries does matter

Systolic versus height: smoothers

Exercise 2

Exercise 2

1. At the top of a new script, include code for loading the ggplot2 package and importing the data. Run your code.

2. In your script, write code for creating a scatter plot of systolic blood pressure (bpxosy1) versus age (ridageyr), with systolic blood pressure on the y axis. Add labels to the axes. Run your code.

Exercise 2

3. Modify the above code to add a mapping of color to smoking status (smoking). Optionally, modify the label for the legend so that the first letter of smoking is capitalized. Run your code.

Exercise 2

4. Copy and paste the code from the second problem. Add a LOESS smoother (you can find more information about LOESS smoothers here). To do this, add geom_smooth() with the method='loess' argument. Also add a color argument to geom_smooth(), using some color of your choice (see this list). For example, you could use color='springgreen3'. Finally, add the se=FALSE argument to geom_smooth() to remove the default confidence band. Run your code.

Exercise 2

5. Create a box plot of systolic blood pressure (bpxosy1) versus hypertension (hypertension), with systolic blood pressure on the y axis. You can define the aesthetics component in a similar way as you did above. For the geometry component, use geom_boxplot() instead of geom_point(). Add labels to the axes using labs(). Run your code.

We will talk in Session 6 about how to remove missing values of hypertension from the plot. We will talk in Session 7 about how to reorder the groups in hypertension.

Exercise 2

6. Create a violin plot of systolic blood pressure (bpxosy1) versus hypertension (hypertension), with systolic blood pressure on the y axis. You can replace geom_boxplot() above with geom_violin(). Add labels to the axes using labs(). Run your code.

We will talk in Session 6 about how to remove missing values of hypertension from the plot. We will talk in Session 7 about how to reorder the groups in hypertension.

Histograms

Histograms

ggplot(nhanes,aes(x=bpxopls1))+
  geom_histogram()+
  labs(x='Pulse (per minute)',y='Count')

• notice that you do not need to include a y argument in aes()

Histograms

Density plots

• to make a density plot, use geom_density() instead of geom_histogram():

ggplot(nhanes,aes(x=bpxopls1))+
  geom_density()+
  labs(x='Pulse (per minute)',y='Density')

Density plots

Overlying the density on a histogram

• by default, the y axis of geom_histogram() is counts
• to get histograms on the density scale, add a y argument to aes():

ggplot(nhanes,aes(x=bpxopls1,y=after_stat(density)))+
  geom_histogram()+
  geom_density(color='deeppink2')+
  labs(x='Pulse (per minute)',y='Density')

• the order of the geometries does matter!

See this page for a brief description of the after_stat() function.

Overlying the density on a histogram

Overlying the density on a histogram

• to change the thickness of the density line, add the size argument to geom_density():

ggplot(nhanes,aes(x=bpxopls1,y=after_stat(density)))+
  geom_histogram()+
  geom_density(color='deeppink2',size=3)+
  labs(x='Pulse (per minute)',y='Density')

Overlying the density on a histogram

Faceting

Faceting

Faceting

• the prior data visualization stratifies the data by smoking status and gender
• it creates a scatter plot for each stratum

Faceting

• to facet, add the facet_grid() component:

ggplot(subset(nhanes,!is.na(smoking)),aes(x=bpxodi1,y=bpxosy1))+
  geom_point()+
  geom_smooth(method='loess',color='tomato',se=FALSE)+
  labs(x='Diastolic blood pressure (mmHg)',
       y='Systolic blood pressure (mmHg)')+
  facet_grid(smoking~gender)

• facet_grid() uses formula notation
• here, smoking is listed first, meaning that it will define the rows

The above includes a subsetting of the data in the data argument, to remove missing values. We’ll talk more about such code in Session 6!

Exercise 3

Exercise 3

1. In your script, write code for creating a density plot of diastolic blood pressure (bpxodi1), with color defining hypertension hypertension. Add labels to the axes. Optionally, change the size of the density line. Run your code.

2. Copy and paste the above code. Add faceting by gender (gender), with gender defining the columns. For the row dimension you can just type a period (.).

Saving visualizations

How can I save my data visualizations?

• as I’ve mentioned, you can save data visualizations using RStudio’s buttons, but this is not easily reproducible
• so, I suggest that you use scripts to save data visualizations
• when using code (in scripts) to save data visualizations, your computer will, by default, put the files in something called the working directory

What is a working directory?

• working directory = the folder that your computer will default to when looking for, and saving, files
• using the cooking analogy, the working directory is kind of like the physical address of whatever kitchen you are working in

How should I be using working directories?

• you can pick (set) the working directory
• and, I recommend that you set the working directory to be the folder that corresponds to the project you are working on at the moment

How can I figure out what the current working directory is?

• to figure out what the current working directory is, examine the top of the Console pane in RStudio:

How can I figure out what the current working directory is?

• the default working directory on a Mac computer might be ~/, which is your home directory

How can I figure out what the current working directory is?

• equivalently, use getwd():

getwd()
#|  [1] "/home/yea-hung/Academia/dsos/workshops and discussions/a slower introduction to r/session_5"

How can I set the working directory? Method 1

• if you have already a script in your project folder, and you want to set that folder as the working directory:
  1. close RStudio if it’s open
  2. from your Finder (MacOS) or File Explorer (Windows), open your script

How can I set the working directory? Method 2

• use RStudio’s menu: Session > Set Working Directory > Choose Directory…

Should I put code for setting the working directory in my script?

• some people do that, and I think that’s fine
• I do not, partly because:
  • I may later reorganize my files or folders
  • I may share my code with other people

Saving data visualizations

# define the data visualization
gg<-ggplot(nhanes,aes(x=bmxht,y=bpxosy1,color=gender))+
  geom_point(color='gray')+
  geom_smooth(method='lm',se=FALSE)+
  labs(x='Height (cm)',
       y='Systolic blood pressure (mmHg)',
       color='Gender')

# output the data visualization
cairo_pdf('my figure.pdf',width=6,height=4)
print(gg)
dev.off()

Saving data visualizations

1. start recording ⏺ using one of these functions:
   • cairo_pdf() or pdf()
   • png()
   • jpeg() or tiff()
2. display your data visualization, possibly by using print()
3. stop the recording ⏹ using dev.off()

Saving data visualizations

• for cairo_pdf and pdf(), the width and height are, by default, in inches
• for most other functions, like png(), the width and height are, by default in pixels

Exercise 4

Exercise 4

1. Create a folder on your computer for this workshop series (or today’s session), if you haven’t already.

2. Set the working directory to that folder, using either method I mentioned.

Exercise 4

3. In your script, copy and paste code for one of your data visualizations. Modify the code so that the data visualization will be output to a file in your working directory. Run your code. Verify that the data visualization was in fact saved.