CONTRIBUTING Copy Section

Contributing to ggplot2 development

The goal of this guide is to help you get up and contributing to ggplot2 as
quickly as possible. The guide is divided into two main pieces:

1. Filing a bug report or feature request in an issue.
1. Suggesting a change via a pull request.

Please note that ggplot2 is released with a Contributor Code of Conduct. By contributing to this project,
you agree to abide by its terms.

Issues Copy Chapter

When filing an issue, the most important thing is to include a minimal
reproducible example so that we can quickly verify the problem, and then figure
out how to fix it. There are three things you need to include to make your
example reproducible: required packages, data, code.

1. Packages should be loaded at the top of the script, so it's easy to
see which ones the example needs.

1. The easiest way to include data is to use dput() to generate the R code
to recreate it. For example, to recreate the mtcars dataset in R,
I'd perform the following steps:

1. Run dput(mtcars) in R
2. Copy the output
3. In my reproducible script, type mtcars <- then paste.

But even better is if you can create a data.frame() with just a handful
of rows and columns that still illustrates the problem.

1. Spend a little bit of time ensuring that your code is easy for others to
read:

* make sure you've used spaces and your variable names are concise, but
informative

* use comments to indicate where your problem lies

* do your best to remove everything that is not related to the problem.
The shorter your code is, the easier it is to understand.

You can check you have actually made a reproducible example by starting up a
fresh R session and pasting your script in.

(Unless you've been specifically asked for it, please don't include the output
of sessionInfo().)

Pull requests Copy Chapter

To contribute a change to ggplot2, you follow these steps:

1. Create a branch in git and make your changes.
1. Push branch to github and issue pull request (PR).
1. Discuss the pull request.
1. Iterate until either we accept the PR or decide that it's not
a good fit for ggplot2.

Each of these steps are described in more detail below. This might feel
overwhelming the first time you get set up, but it gets easier with practice.
If you get stuck at any point, please reach out for help on the ggplot2-dev mailing list.

If you're not familiar with git or github, please start by reading <https://r-pkgs.org/software-development-practices.html>

Pull requests will be evaluated against a seven point checklist:

1. __Motivation__. Your pull request should clearly and concisely motivate the
need for change. Unfortunately neither Winston nor I have much time to
work on ggplot2 these days, so you need to describe the problem and show
how your pull request solves it as concisely as possible.

Also include this motivation in NEWS so that when a new release of
ggplot2 comes out it's easy for users to see what's changed. Add your
item at the top of the file and use markdown for formatting. The
news item should end with (@yourGithubUsername, #the_issue_number).

1. __Only related changes__. Before you submit your pull request, please
check to make sure that you haven't accidentally included any unrelated
changes. These make it harder to see exactly what's changed, and to
evaluate any unexpected side effects.

Each PR corresponds to a git branch, so if you expect to submit
multiple changes make sure to create multiple branches. If you have
multiple changes that depend on each other, start with the first one
and don't submit any others until the first one has been processed.

1. __Use ggplot2 coding style__. Please follow the
official tidyverse style. Maintaining
a consistent style across the whole code base makes it much easier to
jump into the code. If you're modifying existing ggplot2 code that
doesn't follow the style guide, a separate pull request to fix the
style would be greatly appreciated.

1. If you're adding new parameters or a new function, you'll also need
to document them with roxygen2.
Make sure to re-run devtools::document() on the code before submitting.

1. If fixing a bug or adding a new feature to a non-graphical function,
please add a testthat unit test.

1. If fixing a bug in the visual output, please add a visual test.
(Instructions to follow soon)

1. If you're adding a new graphical feature, please add a short example
to the appropriate function.

This seems like a lot of work but don't worry if your pull request isn't perfect.
It's a learning process and members of the ggplot2 team will be on hand to help you
out. A pull request ("PR") is a process, and unless you've submitted a few in the
past it's unlikely that your pull request will be accepted as is. All PRs require
review and approval from at least one member of the ggplot2 development team
before merge.

Finally, remember that ggplot2 is a mature package used by thousands of people.
This means that it's extremely difficult (i.e. impossible) to change any existing
functionality without breaking someone's code (or another package on CRAN).
Please don't submit pull requests that change existing behaviour. Instead,
think about how you can add a new feature in a minimally invasive way.

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README Copy Section

ggplot2 <a href="https://ggplot2.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" alt="ggplot2 website" /></a>

[](https://github.com/tidyverse/ggplot2/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=ggplot2)
![Codecov test
coverage](https://app.codecov.io/gh/tidyverse/ggplot2)

Overview Copy Chapter

ggplot2 is a system for declaratively creating graphics, based on The
Grammar of
Graphics. 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 Copy Chapter

`` r


The easiest way to get ggplot2 is to install the whole tidyverse:

Cheatsheet  Copy Chapter
<a href="https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf"><img src="https://raw.githubusercontent.com/rstudio/cheatsheets/main/pngs/thumbnails/data-visualization-cheatsheet-thumbs.png" width="630" height="252" alt="ggplot2 cheatsheet" /></a>
Usage  Copy Chapter
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()).

ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point()
``

<img src="man/figures/README-example-1.png" alt="Scatterplot of engine displacement versus highway miles per gallon, for 234 cars coloured by 7 'types' of car. The displacement and miles per gallon are inversely correlated." />

Lifecycle Copy Chapter

[](https://lifecycle.r-lib.org/articles/stages.html)

ggplot2 is now 18 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
<https://exts.ggplot2.tidyverse.org/gallery/>.

Learning ggplot2 Copy Chapter

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 several good places to start:

1. The Data Visualization and
Communication chapters in R
for Data Science. 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.

2. If you’d like to take an online course, try Data Visualization in R
With
ggplot2
by Kara Woo.

3. If you’d like to follow a webinar, try Plotting Anything with
ggplot2 by Thomas Lin Pedersen.

4. 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.

5. 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.

6. For articles about announcements and deep-dives you can visit the
tidyverse blog.

Getting help Copy Chapter

There are two main places to get help with ggplot2:

1. The Posit Community (formerly RStudio
Community) is a friendly place to ask any questions about ggplot2.

2. Stack
Overflow
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.

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