# ggplot2
**Repository Path**: caixunbao2019/ggplot2
## Basic Information
- **Project Name**: ggplot2
- **Description**: An implementation of the Grammar of Graphics in R
- **Primary Language**: R
- **License**: GPL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-06-26
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# ggplot2 
[](https://travis-ci.org/tidyverse/ggplot2)
[](https://ci.appveyor.com/project/tidyverse/ggplot2)
[](https://codecov.io/github/tidyverse/ggplot2?branch=master)
[](https://cran.r-project.org/package=ggplot2)
## Overview
ggplot2 is a system for declaratively creating graphics, based on [The
Grammar of Graphics](http://amzn.to/2ef1eWp). You provide the data, tell
ggplot2 how to map variables to aesthetics, what graphical primitives to
use, and it takes care of the details.
## Installation
``` r
# The easiest way to get ggplot2 is to install the whole tidyverse:
install.packages("tidyverse")
# Alternatively, install just ggplot2:
install.packages("ggplot2")
# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("tidyverse/ggplot2")
```
## Cheatsheet
## Usage
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()`).
``` r
library(ggplot2)
ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point()
```
## Lifecycle
[](https://www.tidyverse.org/lifecycle/#stable)
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 looking for innovation, look to ggplot2’s rich ecosystem of
extensions. See a community maintained list at
.
## Learning ggplot2
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:
1. The [data
visualisation](http://r4ds.had.co.nz/data-visualisation.html) and
[graphics for
communication](http://r4ds.had.co.nz/graphics-for-communication.html)
chapters in [R for data science](http://r4ds.had.co.nz). R for data
science is designed to give you a comprehensive introduction to the
[tidyverse](http://tidyverse.org), and these two chapters will you
get up to speed with the essentials of ggplot2 as quickly as
possible.
2. If you’d like to take an interactive online course, try [Data
visualisation with
ggplot2](https://www.datacamp.com/courses/data-visualization-with-ggplot2-1)
by Rick Scavetta on DataCamp.
3. If you want to dive into making common graphics as quickly as
possible, I recommend [The R Graphics
Cookbook](http://amzn.to/2dVfMfn) by Winston Chang. It provides a
set of recipes to solve common graphics problems. A 2nd edition is
due out in 2018.
If you’ve mastered the basics and want to learn more, read [ggplot2:
Elegant Graphics for Data Analysis](http://amzn.to/2fncG50). 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. The book is not available for free,
but you can find the complete source for the book at
.
## Getting help
There are two main places to get help with ggplot2:
1. The [RStudio community](https://community.rstudio.com/) is a
friendly place to ask any questions about ggplot2.
2. [Stack
Overflow](http://stackoverflow.com/questions/tagged/ggplot2?sort=frequent&pageSize=50)
is a great source of answers to common ggplot2 questions. It is also
a great place to get help, once you have created a reproducible
example that illustrates your problem.