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| <!-- README.md is generated from README.Rmd. Please edit that file --> | ||
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| # ggplot2 <a href="https://ggplot2.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" alt="ggplot2 website" /></a> | ||
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| <!-- badges: start --> | ||
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| [](https://github.com/tidyverse/ggplot2/actions/workflows/R-CMD-check.yaml) | ||
| [](https://cran.r-project.org/package=ggplot2) | ||
| [](https://app.codecov.io/gh/tidyverse/ggplot2) | ||
| <!-- badges: end --> | ||
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| ## Overview | ||
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| ggplot2 is a system for declaratively creating graphics, based on [The | ||
| Grammar of | ||
| Graphics](https://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448/ref=as_li_ss_tl). | ||
| You provide the data, tell ggplot2 how to map variables to aesthetics, | ||
| what graphical primitives to use, and it takes care of the details. | ||
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| ## Installation | ||
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| ``` r | ||
| # The easiest way to get ggplot2 is to install the whole tidyverse: | ||
| install.packages("tidyverse") | ||
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| # Alternatively, install just ggplot2: | ||
| install.packages("ggplot2") | ||
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| # Or the development version from GitHub: | ||
| # install.packages("pak") | ||
| pak::pak("tidyverse/ggplot2") | ||
| ``` | ||
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| ## Cheatsheet | ||
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| <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> | ||
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| ## Usage | ||
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| 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()`). | ||
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| ``` r | ||
| library(ggplot2) | ||
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| ggplot(mpg, aes(displ, hwy, colour = class)) + | ||
| geom_point() | ||
| ``` | ||
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| <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." /> | ||
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| ## Lifecycle | ||
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| [](https://lifecycle.r-lib.org/articles/stages.html) | ||
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| 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. | ||
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| 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/>. | ||
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| ## Learning ggplot2 | ||
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| 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: | ||
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| 1. The [Data Visualization](https://r4ds.hadley.nz/data-visualize) and | ||
| [Communication](https://r4ds.hadley.nz/communication) chapters in [R | ||
| for Data Science](https://r4ds.hadley.nz). R for Data Science is | ||
| designed to give you a comprehensive introduction to the | ||
| [tidyverse](https://www.tidyverse.org), and these two chapters will | ||
| get you up to speed with the essentials of ggplot2 as quickly as | ||
| possible. | ||
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| 2. If you’d like to take an online course, try [Data Visualization in R | ||
| With | ||
| ggplot2](https://learning.oreilly.com/videos/data-visualization-in/9781491963661/) | ||
| by Kara Woo. | ||
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| 3. If you’d like to follow a webinar, try [Plotting Anything with | ||
| ggplot2](https://youtu.be/h29g21z0a68) by Thomas Lin Pedersen. | ||
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| 4. If you want to dive into making common graphics as quickly as | ||
| possible, I recommend [The R Graphics | ||
| Cookbook](https://r-graphics.org) by Winston Chang. It provides a | ||
| set of recipes to solve common graphics problems. | ||
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| 5. If you’ve mastered the basics and want to learn more, read [ggplot2: | ||
| Elegant Graphics for Data Analysis](https://ggplot2-book.org). 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. | ||
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| 6. For articles about announcements and deep-dives you can visit the | ||
| [tidyverse blog](https://www.tidyverse.org/tags/ggplot2/). | ||
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| ## Getting help | ||
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| There are two main places to get help with ggplot2: | ||
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| 1. The [RStudio community](https://forum.posit.co/) is a friendly place | ||
| to ask any questions about ggplot2. | ||
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| 2. [Stack | ||
| Overflow](https://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. | ||
| # TDs dataviz ENSAI |