Introduction to Data Visualisation
This first topic covers a general introduction to data visualisation. This document points to specific readings that you should complete. The lab for this topic will encourage you to attempt to put the information in the readings into practice.
Introduction
One of the greatest benefits of data visualization is the sheer quantity of information that can be rapidly interpreted if it is presented well.
The Introduction to Data Visualisation reading provides a broad discussion of what we mean by a (statistical) data visualisation, why data visualisations are useful, and how to decide on a good data visualisation. This provides a taste of the issues that we will deal with in more detail throughout the course.
The reading from Healey provides some examples of why data visualisation is an important and useful way to display information, plus a discussion of things that can go wrong. The concepts of aesthetic problems, substantive problems, and perceptual problems will be relevant to us. We will pursue the issues of perceptual problems and, to a lesser extent, aesthetic problems later in the course.
The reading from Wilke provides a gallery of common types of data visualisations for common tasks. We will use the terminology in this reading to identify data visualisation tasks and types of plots (where possible).
The reading from Broman is there to make sure that we understand the value of working with R Markdown documents. This is a technology that we will work with for all of the labs in this course.
Readings:
An Introduction to Data Visualisation
This provides an overview of the data visualisation topics that will be covered in this course.
Sections 1.1 and 1.2 of “Data Visualization: A practical introduction” by Kieran Healey.
These sections discuss why data visualisations are useful, but also what limitations to look out for.
Chapter 5 of “Fundamentals of Data Visualization” by Claus O. Wilke.
This provides an overview of common types of plots and what they are used for.
It will also be useful to read Chapter 1 for the book overview as we will be working through much of this book over time.
Knitr overview by Karl Broman.
This provides a bit of history of literate documents as well as a brief introduction to R Markdown.
References:
Chapters 6 to 13 of “Fundamentals of Data Visualization” by Claus O. Wilke.
A more in-depth and wider ranging discussion of different types of plots.
R Markdown Introduction by R Studio.
This is a bit “advertisey”, but is still a useful summary of the key points if you have never used R Markdown before.
“R Markdown: The Definitive Guide” by Yihui Xie, J. J. Allaire, and Garrett Grolemund.
Everything you need to know, and more, about R Markdown.
“knitr: Elegant, flexible, and fast dynamic report generation with R”, by Yihui Xie.
This is useful for looking up the options that can be applied to individual R markdown code chunks, like turning off evaluation (so that the code is shown, but not run) and turning off “echo” (so that code is run, but not shown).
Bibliography:
“Fundamentals of Data Visualization” by Claus O. Wilke
“Data Visualization: A practical introduction” by Kieran Healey.
“The Functional Art” by Alberto Cairo.
“knitr in a knutshell” by Karl Broman.
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