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Lecture 1: Basic plot ‘types’ with ggplot2

Lecture Preamble

Today’s Lessons

After going over the course syllabus, we’ll go over the following three lessons:

1. Orientation to statistical graphics
2. The grammar of graphics
3. Plotting with x and y aesthetics

Resources

Concepts from today’s class (and next class) are closely mirrored by the following resources, which introduce ggplot2, although are organized in different ways for each.

The following are good walk-throughs that introduce ggplot2:

• r4ds: data-vis chapter.
  • Perhaps the most compact “walk-through” style resource.
• The ggplot2 book, Chapter 2.
  • A bit more comprehensive “walk-through” style resource.
  • Section 1.2 introduces the actual grammar components.

Here are some other resources you might find useful:

• Jenny’s ggplot2 tutorial.
  • Has a lot of examples, but less dialogue.
• R Graphics Cookbook
  • Good as a reference if you want to learn how to make a specific type of plot.
• ggplot2 cheatsheet

Participation

To get participation points for today:

1. Fill out this exercise sheet with me in class.
2. git commit whatever we finish in class to your participation repository.

Lesson 1: Orientation to statistical graphics

Learning Objectives

By the end of this lesson, students are expected to be able to:

• Identify the plotting framework available in R
• Have a sense of why we’re learning the ggplot2 tool (not on quiz)
• Have a sense of the importance of statistical graphics in communicating information

Discussion

There are four main ways you can produce graphics in R. In order of inception, they are

• base R
• lattice
• ggplot2
  • Part of the tidyverse
• plotly

ggplot2 will receive the strongest focus in this course. Why?

• once fluent, can make most plots up to publication quality standard very quickly.
• has theoretical underpinning in the “layered grammar of graphics”, which is described in the book by Leland Wilkinson.

Stackoverflow was my main source of learning. Google what you’re trying to do, and persevere. You can do it.

Jenny Bryan on statistical graphics:

• A picture is worth 1000 words
• More philosophy on graphics
• Comparing base R/lattice/ggplot2
• The learning curve

Lesson 2: The grammar of graphics

Learning Objectives

By the end of this lesson, students are expected to be able to:

• Identify the seven components of the grammar of graphics underlying ggplot2
• Have a sense of what the seven components are
  • You’ll later be expected to be able to explain the main idea behind the components.

Discussion

Leland Wilkinson lays out the grammar of graphics in his book.

They define the “space of statistical graphics”.

The grammar components, adapted to ggplot2 (gg = grammar of graphics), where the bold ones are necessary to specify for every plot:

• Data
• Aesthetic mappings
• Geometric objects
• Scales
• Statistical transformations
• Coordinate system
• Facet specification

Lesson 3: Plotting with x and y aesthetics

Learning Objectives

This live-coding-based lesson focusses on:

• using the x and y aesthetic mappings, while
• using different geometric objects to explore various plot “types”.

By the end of this lesson, students are expected to be able to:

• Create a variety of “plot types” using ggplot2 under the following situations:
  • two numeric variables
  • one numeric variable
  • one numeric, one categorical variable

Demonstration

Let’s fill out as much as we can of the worksheet labelled lec1-worksheet.Rmd.

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