Lecture 4: Data Visualization with ggplot2

STAT 545 - Fall 2025

Today’s Goals

  • create effective data visualizations

  • explore the grammar of ggplot2

Quick links:

Required Packages

Install (if necessary) and load in the following packages:

library(dplyr)
library(knitr)
library(ggplot2)
library(RColorBrewer)

Motivating Example

We want to investigate if there is a relationship between the number of cylinders (cyl), fuel efficiency (mpg), and horsepower (hp) in the mtcars data set.

Motivating Example

  • Was it easy to figure out the relationship I was trying to convey?
  • What about through a plot?

Motivating Example

Motivating Example

  • We can clearly see that cars with higher horsepower tend to be less fuel efficient (have lower miles per gallon).

  • Cars with higher horsepower also tend to have a higher number of cylinders.

  • That was a lot easier to deduce than when we were reading numbers off of a large table. We will show how to build this exact plot in this lecture!

Effective Data Visualization

  • 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

  • So, what’s the best visualization?

    • Depends on what you need the data visualization to answer!

  • Once you know what you want to show, we need to convey the message as clearly and simply as possible

Effective Data Visualizations: Considerations

  • Make it clear as a “standalone”

    • Use labels, titles, and legends and make them informative and clear

  • Minimize noise

    • Don’t use colours randomly - it may cause us to see false patterns

    • Don’t overlap plots

    • Use as few plots as possible, but don’t be afraid to separate data to plot if it makes things clearer

  • Consider the accessibility of your chart

    • up to 10% of the population has colourblindness

    • make it low-vision friendly and provide descriptions where possible

ggplot2 and the Grammar of Graphics

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)

    * = required component

ggplot2 and the Grammar of Graphics

  • 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 on mtcars

Let’s say you’d like to see if there is a relationship between miles per gallon and horsepower of cars in the mtcars data set. As both of these variables are numeric, we could build a scatterplot as we are comparing two quantitative/numeric variables.

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