Lecture 4: Data Visualization

September 23, 2025

Modified

September 12, 2025

Learning Objectives

From this topic, students are anticipated to be able to:

  • Identify the seven components of the grammar of graphics underlying ggplot2.

  • Produce plots with ggplot2 by implementing the components of the grammar of graphics.

  • Customize the look of ggplot2 graphs.

  • Choose an appropriate plot type for analysis, based on an understanding of what makes a graph effective.

Motivating Example

Tables are a convenient way to display information. Oftentimes, we are tempted to include tables in papers and presentations to emphasize relationships between variables in a data set as tables are relatively simple to create.

Let’s look at a table that one could create to show the relationship between the number of cylinders, miles per gallon, and horsepower in the mtcars data set:

Number of Cylinders Miles per Gallon Horsepower
4 21.4 109
21.5 97
22.8 93
22.8 95
24.4 62
26.0 91
27.3 66
30.4 52
30.4 113
32.4 66
33.9 65
6 17.8 123
18.1 105
19.2 123
19.7 175
21.0 110
21.0 110
21.4 110
8 10.4 205
10.4 215
13.3 245
14.3 245
14.7 230
15.0 335
15.2 180
15.2 150
15.5 150
15.8 264
16.4 180
17.3 180
18.7 175
19.2 175

Was it easy to figure out the relationship I was trying to convey? I’m sure you would eventually be able to figure it out, but I’d bet it took some time. What about now?:

You can very clearly see that as the miles per gallon increase, horsepower tends to decrease. Further, we see that cars with higher number of cylinders tend to have higher horse power AND lower miles per gallon than cars with lower numbers of cylinders. That was a lot easier to deduce than when we were reading numbers off of a large table.

Effective Data Visualization

Plots and other forms of data visualization are powerful tools for conveying complex relationships. While tables can be useful, data visualizations are often preferred to aid with identify patterns and relationships and emphasizing important findings in a research projects. This is especially true for presentations, where the audience may not have time to digest a large table of numbers. Jenny Bryan’s Challenger Example (https://speakerdeck.com/jennybc/ggplot2-tutorial) is a great example of why we may want to visualize data.

Now the question is, what visualization should you use to convey an idea? Well, you need to first formulate the question you want the data visualization to answer. That is, you need to figure out what you want to convey before you start visualizing the data. From UBC’s A First Introduction to Data Science [1] book:

“A good visualization will clearly answer your question without distraction; a great visualization will suggest even what the question was itself without additional explanation. Imagine your visualization as part of a poster presentation for a project; even if you aren’t standing at the poster explaining things, an effective visualization will convey your message to the audience.”

We need to convey the message using a data visualization while removing as much unnecessary information as possible. Below is a direct quote from the A First Introduction to Data Science [1] containing their suggestions for effective data visualizations:

“Convey the message”

  • Make sure the visualization answers the question you have asked most simply and plainly as possible.

  • Use legends and labels so that your visualization is understandable without reading the surrounding text.

  • Ensure the text, symbols, lines, etc., on your visualization are big enough to be easily read.

  • Ensure the data are clearly visible; don’t hide the shape/distribution of the data behind other objects (e.g., a bar).

  • Make sure to use color schemes that are understandable by those with colorblindness (a surprisingly large fraction of the overall population—from about 1% to 10%, depending on sex and ancestry [2]). For example, ColorBrewer and the RColorBrewer R package [3] provide the ability to pick such color schemes, and you can check your visualizations after you have created them by uploading to online tools such as a color blindness simulator.

  • Redundancy can be helpful; sometimes conveying the same message in multiple ways reinforces it for the audience.

“Minimize noise”

  • Use colors sparingly. Too many different colors can be distracting, create false patterns, and detract from the message.

  • Be wary of overplotting. Overplotting is when marks that represent the data overlap, and is problematic as it prevents you from seeing how many data points are represented in areas of the visualization where this occurs. If your plot has too many dots or lines and starts to look like a mess, you need to do something different.

  • Only make the plot area (where the dots, lines, bars are) as big as needed. Simple plots can be made small.

  • Don’t adjust the axes to zoom in on small differences. If the difference is small, show that it’s small!

ggplot2 and the Grammar of Graphics

If you’ve learned about data visualization in R before, you’ve likely produced plots using “base R” methods (for example, the boxplot() function in R. It is a simple framework for making plots and is often “enough” for producing basic plots. In this lecture, we are going to dive into ggplot2, a package R users often use to make more sophisticated plots! If you’ve never used R to plot before, don’t worry. We aren’t assuming you have any experience with either method of plotting in R.

We will be utilising the ggplot2 and tidyverse packages throughout this lecture. To load them:

# install.packages("tidyverse") #uncomment if not already installed
# install.packages("ggplot2") #uncomment if not already installed

library(tidyverse)
library(ggplot2)

ggplot2 is based on the grammar of graphics, which is a systematic approach for describing different components or aspects of a graph. It involves seven components (required components are indicated with the *):

  • Data*

    • the data you’re feeding into the plot, perhaps a tibble or dataframe

  • Aesthetic mappings*

    • a specification of how you will connect variables (for example, horizontal or vertical positioning, grouping, size, colour, shape)

  • Geometric objects*

    • a specification of what the object will be drawn as (for example, a scatter plot, line, bar chart)

  • Scales

    • a specification of how a variable is mapped to its aesthetic

  • Statistical transformations

    • a specification of whether and how the data are combined or transformed. For example, is a bar chart plotting the values or a relative frequency?

  • Coordinate system

    • a specification of how the position aesthetics (x and y) are depicted in the plot. We typically use cartesian coordinates, though polar coordinates are also possible.

  • Facet

    • a specification of data variables that partition the data into smaller “sub plots” or panels

It’s okay if you don’t quite understand all of these components yet. We will walk through examples of commonly used plots and discuss which components are necessary!

Example: Scatterplot

To build our first ggplot, we will use the gapminder data from the gapminder package. To install and load it, we use:

# install.packages("gapminder") #uncomment if not already installed
library(gapminder)
data(gapminder) 

head(gapminder) #view first few rows of the tibble
# A tibble: 6 × 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.
4 Afghanistan Asia       1967    34.0 11537966      836.
5 Afghanistan Asia       1972    36.1 13079460      740.
6 Afghanistan Asia       1977    38.4 14880372      786.

The ggplot() function takes two arguments: data (the data frame or tibble containing your data that you’d like to plot) and mapping (aesthetic mappings applied to the entire plot. We use the aes() function inside of this argument.).

Let’s start building the scatterplot! Let’s plot gdpPercap on the x axis and lifeExp on the y axis:

ggplot(gapminder, aes(x = gdpPercap, lifeExp))

Notice that we haven’t actually plotted anything! ggplot doesn’t know what type of plot we want to draw, only that we want gpdPercap on the x axis and lifeExp on the y axis from the gapminder data we provided. To tell ggplot that we want a scatterplot, we are going to add a layer to the plot using the + at the end of the previous line. geom_point() is the geometric object we’d like to add (i.e., a scatterplot):

ggplot(gapminder, aes(x = gdpPercap, lifeExp)) +
  geom_point()

We now have created a scatterplot! However, it’s a bit dense and difficult to see. We can specify an alpha transparency value within the geom_point() function to change the opacity:

ggplot(gapminder, aes(x = gdpPercap, lifeExp)) +
  geom_point(alpha = 0.2)

That’s already looking better! We can really tell now where there are a lot of observations.

Now, let’s transform the scale of the x axis to see if there is a more linear relationship between life expectancy and the log transformation of GDP per capita. The transformation will be of the form scale_AES_TRANSFORMATION() where AES corresponds to which aesthetic value is being transformed, and the TRANSFORMATION is the transformation being completed (here it will be log10. We can also rename the x axis using the first argument. We also can change the labels on the x axis to be dollar format. Let’s add another layer to do so:

ggplot(gapminder, aes(x = gdpPercap, lifeExp)) +
  geom_point(alpha = 0.2) + 
  scale_x_log10("GDP per capita (log-scale)", labels = scales::dollar_format())

The more translucent grey points are still a bit hard to see. Let’s change the background to a more minimalist theme using a theme() layer. While we’re at it, let’s also rename the y axis using a ylab() later:

ggplot(gapminder, aes(x = gdpPercap, lifeExp)) +
  geom_point(alpha = 0.2) + 
  scale_x_log10("GDP per capita (log-scale)", labels = scales::dollar_format()) +
  theme_minimal() +
  ylab("Life Expectancy (Years)")

Tip

Names of axes should have quotations “” around them.

The order of the layers doesn’t matter after the geom_layer() layer.

Common Types of Plots

  • geom_point(): scatterplot

  • geom_line(): line plot

  • geom_bar(): bar chart

  • geom_histogram(): histogram

  • geom_boxplot(): box plot

  • geom_smooth(): adds a smooth trend line (various methods)

Advice for ggplot

Google is absolutely your friend when building ggplots. I don’t think I’ve ever made a plot without googling how to do it. ggplot is extremely powerful, flexible, and a bit scary to learn! If you need to build a plot, search it! Need to plot two histograms, separated by groups (say, gender), side by side? I’d google: “ggplot histogram grouped by variable”. This brings me to this blog post by R Charts that has a TON of different side by side histograms with code! ggplot is very much a “learn by doing” skill.

Worksheet A3

  • It’s time to try ggplot1 yourself! Spend time working through Worksheet 3.

  • Finished attempting all of the questions? Then do the optional R4DS Data Visualization reading, and maybe even do some of the exercises for extra practice.

Post any questions you have on the Slack channel!

Next class: FEV Case Study

We will get a flavour for how you might use ggplot2 in the wild and get in even more practice by working through a continuation of our FEV case study from last week.

By yourself and in small groups, work through the exercises in the case study. We will also discuss instructor answers to each exercise.

Additional Resources

Video lectures for this topic (ignore the episode numbering):

Back to top

References

1.
Timbers T, Campbell T, Lee M (2022) Data science: A first introduction. Chapman; Hall/CRC
2.
Deeb S (2005) The molecular basis of variation in human color vision. Clinical genetics 67(5):369–377
3.
Neuwirth E et al (2014) RColorBrewer: Colorbrewer palettes