Hadley Wickham has a couple of books which teach a lot about ggplot2. The first book I’d recommend is his and Garrett Grolemund’s R For Data Science book, which is available for free online:

To map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside

`aes()`

. ggplot2 will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable, a process known asscaling. ggplot2 will also add a legend that explains which levels correspond to which values.The colors reveal that many of the unusual points are two-seater cars. These cars don’t seem like hybrids, and are, in fact, sports cars! Sports cars have large engines like SUVs and pickup trucks, but small bodies like midsize and compact cars, which improves their gas mileage. In hindsight, these cars were unlikely to be hybrids since they have large engines.

Wickham also has the source to build his ggplot2 book online. If you don’t want to build the source, you also have the option of buying the book.

Hadley Wickham describes some of the decisions he made when putting together ggplot2:

In the examples above, we have seen some of the components that make up a plot:

• data and aesthetic mappings,

• geometric objects,

• scales, and

• facet specification.

We have also touched on two other components:

• statistical transformations, and

• the coordinate system.

Together, the data, mappings, statistical transformation, and geometric object form a layer. A plot may have multiple layers, for example, when we overlay a scatterplot with a smoothed line.

This isn’t an article about how to use ggplot2; rather, it’s an article about implementation decisions. To that end, I think it’s useful to see some of the logic behind ggplot2’s decisions.

Leland Wilkinson has written the book on how we should write systems which visualize data:

This book was written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data. It presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. This foundation was designed for a distributed computing environment (Internet, Intranet, client-server), with special attention given to conserving computer code and system resources.

There’s no free copy of this book, and it’s a very expensive textbook. For most people, you’ll get more from derivative works, but if you’ve thought about putting together a graphics library, this is a must-read.

I’ve started reading Kieran Healy’s book, Data Visualization For Social Science. He has a free draft available online, and it automatically builds nightly so you’re seeing the latest version. From the preface:

This book is a hands-on introduction to the principles and practice of looking at and presenting data using R and ggplot. R is a powerful, widely used, and freely available programming language for data analysis. You may be interested in exploring ggplot after having used R before, or be entirely new to both R and ggplot and just want to graph your data. I do not assume you have any prior knowledge of R.

After installing the software we need, we begin with an overview of some basic principles of visualization. We focus not just on the aesthetic aspects of good plots, but on how their effectiveness is rooted in the way we perceive properties like length, absolute and relative size, orientation, shape, and color. We then learn how to produce and refine plots using ggplot2, a powerful, versatile, and widely-used visualization library for R (Wickham 2016a). The ggplot2 library implements a “grammar of graphics” (Wilkinson 2005). This approach gives us a coherent way to produce visualizations by expressing relationships between the attributes of data and their graphical representation.

Through a series of worked examples, you will learn how to build plots piece by piece, beginning with scatterplots and summaries of single variables, then moving on to more complex graphics. Topics covered include plotting continuous and categorical variables, layering information on graphics; faceting grouped data to produce effective “small multiple” plots; transforming data to easily produce visual summaries on the graph such as trend lines, linear fits, error ranges, and boxplots; creating maps, and also some alternatives to maps worth considering when presenting country- or state-level data. We will also cover cases where we are not working directly with a dataset, but rather with estimates from a statistical model. From there, we will explore the process of refining plots to accomplish common tasks such as highlighting key features of the data, labeling particular items of interest, annotating plots, and changing their overall appearance. Finally we will examine some strategies for presenting graphical results in different formats, and to different sorts of audiences.

I’m less than halfway through the book so far, but it is quite an approachable look at the ggplot2 library with a bit of discussion on what makes for quality graphics.

Kevin Feasel

2017-12-26

R, Visualization