Text
Fundamentals of data visualization: a primer on making informative and compelling figures
Data visualization is part art and part science. The challenge is to get the art right without getting the science wrong, and vice versa. A data visualization first and fore‐ most has to accurately convey the data. It must not mislead or distort. If one number is twice as large as another, but in the visualization they look to be about the same, then the visualization is wrong. At the same time, a data visualization should be aesthetically pleasing. Good visual presentations tend to enhance the message of the visualization. If a figure contains jarring colors, imbalanced visual elements, or other features that distract, then the viewer will find it harder to inspect the figure and interpret it correctly.
This book attempts to cover the key principles, methods, and concepts required to visualize data for publications, reports, or presentations. Because data visualization is a vast field, and in its broadest definition could include topics as varied as schematic technical drawings, 3D animations, and user interfaces, I necessarily had to limit my scope. I am specifically covering the case of static visualizations presented in print, online, or as slides. The book does not cover interactive visuals or movies, except in one brief section in Chapter 16. Therefore, throughout this book, I will use the words “visualization” and “figure” somewhat interchangeably. The book also does not provide any instruction on how to make figures with existing visualization software or programming libraries. The annotated bibliography at the end of the book includes pointers to appropriate texts covering these topics.
Tidak tersedia versi lain