Visualization for Data Science with R
2022-03-12
About This Book
Note: This book is a work in progress, with a full draft expected in April of 2022.
Proposal
This book combines instruction on writing R code with building basic graphic design skills in a way that is unusual in data science literature. The book will guide readers through a series of projects, each designed to cover both how visualizations work in R and how visualizations can be designed to have the greatest impact. Far more than a “do this, then this” checklist, this book will focus on building understanding, confidence, and the ability to transfer skills to other tools and design contexts. It will avoid technical jargon that our target audience is unlikely to have encountered before. To accommodate learners who don’t have time to work through an entire book, each chapter will operate independently, covering a specific set of tasks that all make sense together as part of a visualization project. For those who would like extra practice, there will be several types of hands-on exercises, from those that are entirely prescribed to those that allow readers to apply new techniques to problems in their own areas.
The book will have solutions (in the form of completed code and sample output) for all exercises. While not a textbook, the book will also include a brief teacher’s guide for courses that might want to use one or more chapters to structure lessons in a course. The book will also have a website, including links to Open Access content, solutions, and related resources like video tutorials.
The target audience of this book would be professionals who are having to learn data science techniques on the job, likely at an under-resourced organization or company. These newly minted data professionals may feel comfortable in Excel but have only just started to learn R for processing data. They have never used a programming language to build a visualization before, and even creating charts in Excel has often been a frustrating and mystifying process. They appreciate that R is freely available and are able to get started on a data science project, but the idea of creating publication-quality visualizations using only code is daunting.
Increasingly, programs of study with a focus on preparing students for professional careers in under-resourced fields, like public policy and even management, include courses on data analysis and communication using freely available software. This book, while not a textbook, could easily be used for a semester-long course, titled something like “Practical data visualization for the modern workforce.” A chapter could be covered each week, and larger projects could help learners synthesize chapters into a complete set of analyses and communication materials.
Why read this book
This book will be:
- Written for non-academics, beginning programmers
- Each chapter stands alone
- Covers pressing modern issues, like accessibility and ethics
- Focuses on freely available software
- Combines hands-on exercises with basic graphic design principles
Structure of the book
- Chapter 1: Overview of common visualizations and how to read them
- Chapter 2: Building basic visualizations with ggplot2
- Chapter 3: Working with textual data in ggplot2
- Chapter 4: Customizing the design of ggplot2 visualizations
- Chapter 5: Avoiding unethical design practices
- Chapter 6: Building ggplot2 visualizations into print publications
- Chapter 7: Basic accessibility for static visualizations
- Chapter 8: Exploring interactivity in visualizations with plotly and crosstalk
- Chapter 9: Using RMarkdown to build websites for projects
- Chapter 10: Using RMarkdown to build dashboards for projects
- Chapter 11: Basic usability for interactive visualizations
- Chapter 12: Teacher’s guide
Software information and conventions
I used the knitr package (Xie 2015) and the bookdown package (Xie 2021) to compile my book. My R session information is shown below:
xfun::session_info()## R version 4.1.2 (2021-11-01)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.2.1
##
## Locale: en_US.UTF-8 / en_US.UTF-8 / en_US.UTF-8 / C / en_US.UTF-8 / en_US.UTF-8
##
## Package version:
## base64enc_0.1.3 bookdown_0.24 bslib_0.3.1
## cli_3.2.0 compiler_4.1.2 digest_0.6.29
## evaluate_0.15 fastmap_1.1.0 fs_1.5.2
## glue_1.6.2 graphics_4.1.2 grDevices_4.1.2
## highr_0.9 htmltools_0.5.2 jquerylib_0.1.4
## jsonlite_1.8.0 knitr_1.37 magrittr_2.0.2
## methods_4.1.2 R6_2.5.1 rappdirs_0.3.3
## rlang_1.0.2 rmarkdown_2.12 rstudioapi_0.13
## sass_0.4.0 stats_4.1.2 stringi_1.7.6
## stringr_1.4.0 tinytex_0.37 tools_4.1.2
## utils_4.1.2 xfun_0.30 yaml_2.3.5Package names are in bold text (e.g., rmarkdown), and inline code and filenames are formatted in a typewriter font (e.g., knitr::knit('foo.Rmd')). Function names are followed by parentheses (e.g., bookdown::render_book()).
Angela Zoss
About the Author
FIGURE 0.1: Angela M. Zoss, Ph.D.
Angela is the Assessment & Data Visualization Analyst in the Assessment & User Experience Department in the Duke University Libraries. She has many years of experience in teaching and training, predominantly focusing on teaching data visualization to university students, faculty, and staff. She is also active in several open source development projects, including FOLIO and Wax.