ggplot2 is a system for declaratively creating graphics, based on The
You provide the data, tell ggplot2 how to map variables to aesthetics,
what graphical primitives to use, and it takes care of the details.
# The easiest way to get ggplot2 is to install the whole tidyverse:
# Alternatively, install just ggplot2:
# Or the development version from GitHub:
It’s hard to succinctly describe how ggplot2 works because it embodies a
deep philosophy of visualisation. However, in most cases you start with
ggplot(), supply a dataset and aesthetic mapping (with aes()). You
then add on layers (like geom_point() or geom_histogram()), scales
(like scale_colour_brewer()), faceting specifications (like
facet_wrap()) and coordinate systems (like coord_flip()).
ggplot2 is now over 10 years old and is used by hundreds of thousands of
people to make millions of plots. That means, by-and-large, ggplot2
itself changes relatively little. When we do make changes, they will be
generally to add new functions or arguments rather than changing the
behaviour of existing functions, and if we do make changes to existing
behaviour we will do them for compelling reasons.
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. Currently, there are three good places to start:
If you want to dive into making common graphics as quickly as
possible, I recommend The R Graphics
Cookbook by Winston Chang. It provides a
set of recipes to solve common graphics problems.
If you’ve mastered the basics and want to learn more, read ggplot2:
Elegant Graphics for Data Analysis. It
describes the theoretical underpinnings of ggplot2 and shows you how all
the pieces fit together. This book helps you understand the theory that
underpins ggplot2, and will help you create new types of graphics
specifically tailored to your needs.
There are two main places to get help with ggplot2: