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2 | 2 | <!-- README.md is generated from README.Rmd. Please edit that file --> |
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4 | | -# ggplot2 <a href="https://ggplot2.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" alt="ggplot2 website" /></a> |
5 | | - |
6 | | -<!-- badges: start --> |
7 | | - |
8 | | -[](https://github.com/tidyverse/ggplot2/actions/workflows/R-CMD-check.yaml) |
9 | | -[](https://cran.r-project.org/package=ggplot2) |
10 | | -[](https://app.codecov.io/gh/tidyverse/ggplot2) |
12 | | -<!-- badges: end --> |
13 | | - |
14 | | -## Overview |
15 | | - |
16 | | -ggplot2 is a system for declaratively creating graphics, based on [The |
17 | | -Grammar of |
18 | | -Graphics](https://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448/ref=as_li_ss_tl). |
19 | | -You provide the data, tell ggplot2 how to map variables to aesthetics, |
20 | | -what graphical primitives to use, and it takes care of the details. |
21 | | - |
22 | | -## Installation |
23 | | - |
24 | | -``` r |
25 | | -# The easiest way to get ggplot2 is to install the whole tidyverse: |
26 | | -install.packages("tidyverse") |
27 | | - |
28 | | -# Alternatively, install just ggplot2: |
29 | | -install.packages("ggplot2") |
30 | | - |
31 | | -# Or the development version from GitHub: |
32 | | -# install.packages("pak") |
33 | | -pak::pak("tidyverse/ggplot2") |
34 | | -``` |
35 | | - |
36 | | -## Cheatsheet |
37 | | - |
38 | | -<a href="https://github.com/rstudio/cheatsheets/blob/master/data-visualization.pdf"><img src="https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-visualization-cheatsheet-thumbs.png" width="630" height="252" alt="ggplot2 cheatsheet" /></a> |
39 | | - |
40 | | -## Usage |
41 | | - |
42 | | -It’s hard to succinctly describe how ggplot2 works because it embodies a |
43 | | -deep philosophy of visualisation. However, in most cases you start with |
44 | | -`ggplot()`, supply a dataset and aesthetic mapping (with `aes()`). You |
45 | | -then add on layers (like `geom_point()` or `geom_histogram()`), scales |
46 | | -(like `scale_colour_brewer()`), faceting specifications (like |
47 | | -`facet_wrap()`) and coordinate systems (like `coord_flip()`). |
48 | | - |
49 | | -``` r |
50 | | -library(ggplot2) |
51 | | - |
52 | | -ggplot(mpg, aes(displ, hwy, colour = class)) + |
53 | | - geom_point() |
54 | | -``` |
55 | | - |
56 | | -<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." /> |
57 | | - |
58 | | -## Lifecycle |
59 | | - |
60 | | -[](https://lifecycle.r-lib.org/articles/stages.html) |
61 | | - |
62 | | -ggplot2 is now over 10 years old and is used by hundreds of thousands of |
63 | | -people to make millions of plots. That means, by-and-large, ggplot2 |
64 | | -itself changes relatively little. When we do make changes, they will be |
65 | | -generally to add new functions or arguments rather than changing the |
66 | | -behaviour of existing functions, and if we do make changes to existing |
67 | | -behaviour we will do them for compelling reasons. |
68 | | - |
69 | | -If you are looking for innovation, look to ggplot2’s rich ecosystem of |
70 | | -extensions. See a community maintained list at |
71 | | -<https://exts.ggplot2.tidyverse.org/gallery/>. |
72 | | - |
73 | | -## Learning ggplot2 |
74 | | - |
75 | | -If you are new to ggplot2 you are better off starting with a systematic |
76 | | -introduction, rather than trying to learn from reading individual |
77 | | -documentation pages. Currently, there are several good places to start: |
78 | | - |
79 | | -1. The [Data Visualization](https://r4ds.hadley.nz/data-visualize) and |
80 | | - [Communication](https://r4ds.hadley.nz/communication) chapters in [R |
81 | | - for Data Science](https://r4ds.hadley.nz). R for Data Science is |
82 | | - designed to give you a comprehensive introduction to the |
83 | | - [tidyverse](https://www.tidyverse.org), and these two chapters will |
84 | | - get you up to speed with the essentials of ggplot2 as quickly as |
85 | | - possible. |
86 | | - |
87 | | -2. If you’d like to take an online course, try [Data Visualization in R |
88 | | - With |
89 | | - ggplot2](https://learning.oreilly.com/videos/data-visualization-in/9781491963661/) |
90 | | - by Kara Woo. |
91 | | - |
92 | | -3. If you’d like to follow a webinar, try [Plotting Anything with |
93 | | - ggplot2](https://youtu.be/h29g21z0a68) by Thomas Lin Pedersen. |
94 | | - |
95 | | -4. If you want to dive into making common graphics as quickly as |
96 | | - possible, I recommend [The R Graphics |
97 | | - Cookbook](https://r-graphics.org) by Winston Chang. It provides a |
98 | | - set of recipes to solve common graphics problems. |
99 | | - |
100 | | -5. If you’ve mastered the basics and want to learn more, read [ggplot2: |
101 | | - Elegant Graphics for Data Analysis](https://ggplot2-book.org). It |
102 | | - describes the theoretical underpinnings of ggplot2 and shows you how |
103 | | - all the pieces fit together. This book helps you understand the |
104 | | - theory that underpins ggplot2, and will help you create new types of |
105 | | - graphics specifically tailored to your needs. |
106 | | - |
107 | | -6. For articles about announcements and deep-dives you can visit the |
108 | | - [tidyverse blog](https://www.tidyverse.org/tags/ggplot2/). |
109 | | - |
110 | | -## Getting help |
111 | | - |
112 | | -There are two main places to get help with ggplot2: |
113 | | - |
114 | | -1. The [RStudio community](https://forum.posit.co/) is a friendly place |
115 | | - to ask any questions about ggplot2. |
116 | | - |
117 | | -2. [Stack |
118 | | - Overflow](https://stackoverflow.com/questions/tagged/ggplot2?sort=frequent&pageSize=50) |
119 | | - is a great source of answers to common ggplot2 questions. It is also |
120 | | - a great place to get help, once you have created a reproducible |
121 | | - example that illustrates your problem. |
| 4 | +# TDs dataviz ENSAI |
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