![]() This book helps you understand the theory that underpins ggplot2, and will help you create new types of graphics specifically tailored to your needs. It describes the theoretical underpinnings of ggplot2 and shows you how all the pieces fit together. ![]() If you’ve mastered the basics and want to learn more, read ggplot2: Elegant Graphics for Data Analysis. It provides a set of recipes to solve common graphics problems. If you want to dive into making common graphics as quickly as possible, I recommend The R Graphics Cookbook by Winston Chang. If you’d like to follow a webinar, try Plotting Anything with ggplot2 by Thomas Lin Pedersen. If you’d like to take an online course, try Data Visualization in R With ggplot2 by Kara Woo. Ubuntu is part of the Linux family of operating systems, which are all open-source and highly customizable. macOS is slightly better in resource efficiency and stability because it has hardware (Macbooks) specially designed to be compatible with it. ![]() R for Data Science is designed to give you a comprehensive introduction to the tidyverse, and these two chapters will get you up to speed with the essentials of ggplot2 as quickly as possible. Ubuntu has better privacy than macOS as an open-source operating system. Otherwise a pc gives just about as much flexibility. The Data Visualisation and Graphics for communication chapters in R for Data Science. If you have a lot of excess cash laying around, get the mac with the option to run Ubuntu in a VM. ![]() Currently, there are three good places to start: ![]() If you are new to ggplot2 you are better off starting with a systematic introduction, rather than trying to learn from reading individual documentation pages. ![]()
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