The Art of Data Visualization with ggplot2
Chapman & Hall/CRC (Verlag)
978-1-032-76624-9 (ISBN)
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The visualizations in this book are not necessarily always the most effective choice of visualization for the data and relationship shown. Rather, this book aims to show you examples of the end-to-end process of creating data visualizations, with a focus on the technical details of building them in R.
This book is primarily aimed at those who wish to develop their data visualization skills in R. Readers of this book may find a basic knowledge of R, more specifically of the tidyverse ecosystem, useful - although all code used in examples is fully explained. Readers do not need to be experienced in ggplot2, though this book will also be of interest to those who are. This book will also be of interest to those who are already familiar with R (including ggplot2), and wish to develop their skills in designing data visualizations further. It will also interest those who already design data visualizations using other tools, and want to learn how to do the equivalent in R.
Nicola Rennie is a data visualization specialist and enthusiast, with an academic background in mathematics and statistics. She holds a PhD in Statistics and Operational Research, which focused on analysing and visualising transport demand. She is particularly interested in how we use data visualization to communicate complex, quantitative information in an accessible way, as well as the intersection of visualization and art. She has experience of teaching at both undergraduate and postgraduate level, in courses covering fundamentals of data science, statistical methods, and programming. Nicola has also previously worked in data science consultancy, and delivered training courses covering topics including advanced data visualization with R, statistical modelling, and reproducible reporting. She is the author and maintainer of several R packages, including multiple ggplot2 extension packages. Nicola is a regular speaker at R and data science meetups, has been the chapter organizer of R-Ladies Lancaster, and part of the R-Ladies Global Team. She is co-author of the Royal Statistical Society’s Best Practices for Data Visualisation Guide, and is an active member of the Royal Statistical Society. Several of her data visualizations have been long-listed at the Information is Beautiful Awards.
Preface Acknowledgements Author 1 Introduction 1.1 Who is this book aimed at? 1.2 What do you need to know before reading this book? 1.3 Code style used in this book 1.4 Code evolves over time 1.5 The structure of this book I Common charts don’t need to be boring! 2 Programming languages: dumbbell charts with ggplot2 2.1 Data 2.2 Exploratory work 2.2.1 Data exploration 2.2.2 Exploratory sketches 2.3 Preparing a plot 2.3.1 Data wrangling 2.3.2 The first plot 2.4 Advanced styling 2.4.1 Colors 2.4.2 Text and fonts 2.4.3 Adjusting themes 2.4.4 Saving to an image file 2.5 Reflection 2.6 Exercises 3 UK museums: highlighting line charts with gghighlight 3.1 Data 3.2 Exploratory work 3.2.1 Data exploration 3.2.2 Exploratory sketches 3.3 Preparing a plot 3.3.1 Data wrangling 3.3.2 The first plot 3.3.3 Highlighting with gghighlight 3.4 Advanced styling 3.4.1 Colors 3.4.2 Text and fonts 3.4.3 Adjusting themes 3.5 Reflection 3.6 Exercises 4 Bee colony losses: visualizing quantities with Poisson disk sampling 4.1 Data 4.2 Exploratory work 4.2.1 Data exploration 4.2.2 Exploratory sketches 4.3 Preparing a plot 4.3.1 Data wrangling 4.3.2 Poisson disk sampling with poissoned 4.3.3 The first plot 4.4 Advanced styling 4.4.1 Colors 4.4.2 Fonts 4.4.3 Adding text 4.4.4 Adjusting themes 4.5 Reflection 4.6 Exercises 5 Animal shelter intakes: making gauge charts with ggforce 5.1 Data 5.2 Exploratory work 5.2.1 Data exploration 5.2.2 Exploratory sketches 5.3 Preparing a plot 5.3.1 Data wrangling 5.3.2 The ggforce extension package 5.3.3 Gauge charts with ggforce 5.3.4 Computing aesthetics 5.3.5 The first plot 5.4 Advanced styling 5.4.1 Colors 5.4.2 Text and fonts 5.4.3 Adjusting themes 5.4.4 Alternatives to a traditional legend 5.5 Reflection 5.6 Exercises II Making use of icons fonts and text 6 Canadian wind turbines: waffle plots and pictograms 6.1 Data 6.1.1 Reading data with openxlsx 6.2 Exploratory work 6.2.1 Data exploration 6.2.2 Exploratory sketches 6.3 Preparing a plot 6.3.1 Data wrangling 6.3.2 Installing Font Awesome fonts 6.3.3 The first plot with waffle 6.4 Advanced styling 6.4.1 Colors with rcartocolor 6.4.2 Adding styled text with marquee 6.4.3 Adjusting scales and themes 6.5 Reflection 6.6 Exercises 7 Cats: data-driven annotations with ggtext 7.1 Data 7.2 Exploratory work 7.2.1 Data exploration 7.2.2 Exploratory sketches 7.3 Preparing a plot 7.3.1 Data wrangling 7.3.2 The first plot 7.4 Advanced styling 7.4.1 Colors 7.4.2 Text and fonts 7.4.3 Adding annotations 7.4.4 Adding text 7.4.5 Custom caption functions 7.4.6 Adjusting themes 7.5 Reflection 7.6 Exercises 8 Nobel Prize laureates: positioning text and parameterizing plots 8.1 Data 8.2 Exploratory work 8.2.1 Data exploration 8.2.2 Exploratory sketches 8.3 Preparing a plot 8.3.1 Data wrangling 8.3.2 The first plot 8.4 Advanced styling 8.4.1 Fonts 8.4.2 Colors 8.4.3 Adding text 8.4.4 Adjusting scales and themes 8.4.5 Parameterized plots 8.5 Reflection 8.6 Exercises III Working with images 9 Lemurs: manipulating images in R 9.1 Data 9.2 Exploratory work 9.2.1 Data exploration 9.2.2 Exploratory sketches 9.3 Preparing a plot 9.3.1 Data wrangling 9.3.2 The first plot 9.4 Advanced styling 9.4.1 Colors 9.4.2 Working with systemfonts 9.4.3 Adding text 9.4.4 Adjusting themes 9.5 Working with images 9.5.1 Manipulating images with magick and imager 9.5.2 Adding images to plots with cowplot 9.6 Reflection 9.7 Exercises 10 R packages: using images for custom facet labels 10.1 Data 10.2 Exploratory work 10.2.1 Data exploration 10.2.2 Exploratory sketches 10.3 Preparing a plot 10.3.1 Data wrangling 10.3.2 The first plot 10.4 Advanced styling 10.4.1 Colors 10.4.2 Text and fonts 10.4.3 Adjusting scales and themes 10.4.4 Using images as facet labels 10.5 Reflection 10.6 Exercises IV Visualizing spatial data 11 Doctors across the world: making maps with ggplot2 11.1 Data 11.2 Exploratory work 11.2.1 Data exploration 11.2.2 Exploratory sketches 11.3 Preparing a plot 11.3.1 Data wrangling 11.3.2 The first plot 11.4 Advanced styling 11.4.1 Colors 11.4.2 Text and fonts 11.4.3 Adjusting themes 11.5 Reflection 11.6 Exercises 12 Time zones: spatial data and mapping with sf 12.1 Data 12.2 Exploratory work 12.2.1 Data exploration 12.2.2 Exploratory sketches 12.3 Preparing a plot 12.3.1 Maps with rnaturalearth 12.3.2 Spatial objects with sf 12.3.3 The first plot 12.3.4 Colors 12.3.5 Fonts 12.3.6 Creating a custom bar chart legend 12.4 Advanced styling 12.4.1 Applying colors 12.4.2 Editing the axes 12.4.3 Adding text 12.4.4 Adjusting themes 12.4.5 Joining plots with patchwork 12.5 Reflection 12.6 Exercises 13 US House elections: geography on a grid with geofacet 13.1 Data 13.2 Exploratory work 13.2.1 Data exploration 13.2.2 Exploratory sketches 13.3 Preparing a plot 13.3.1 Data wrangling 13.3.2 The first plot 13.3.3 Faceting with geofacet 13.4 Advanced styling 13.4.1 Colors 13.4.2 Text and fonts 13.4.3 Adjusting themes 13.4.4 Developing a custom legend 13.4.5 Joining with patchwork 13.5 Reflection 13.6 Exercises 14 Other tips and tricks 14.1 Recording gifs with camcorder 14.1.1 Setting the size resolution of images 14.2 Extracting information from plots 14.2.1 Extracting co-ordinates 14.2.2 Plotting with ggpattern 14.3 Code formatting with lintr and styler 14.4 Template files for TidyTuesday 14.5 Writing your own helper functions 14.6 Exercises Bibliography Appendix Frequently asked questions (FAQs) Software requirements Data Images Index
| Erscheint lt. Verlag | 1.4.2026 |
|---|---|
| Zusatzinfo | 1 Tables, black and white; 66 Line drawings, color; 41 Line drawings, black and white; 5 Halftones, color; 20 Halftones, black and white; 71 Illustrations, color; 61 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Mathematik | |
| ISBN-10 | 1-032-76624-7 / 1032766247 |
| ISBN-13 | 978-1-032-76624-9 / 9781032766249 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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