- Introduction
- Scoping
- element_rect() arguments
Ggplot2 Cheat Sheet 2019
Continuous X, Continuous Y f Cheat Sheet Data Visualization Basics. I + geombin2d(binwidth = c(5, 0.5)) f + geomblank a + geomarea(stat = 'bin') Data Visualization. Continuous Bivariate Distribution.
Ggplot-cheatsheet cheatsheet for ggplot2, compiled mostly from the book, ggplot2: Elegant Graphics for Data Analysis Documentation for ggplot is available here. Data Visualization Cheatsheet The ggplot2 package lets you make beautiful and customizable plots of your data. It implements the grammar of graphics, an easy to use system for building plots. See ggplot2.tidyverse.org for more. With ggplot2 Cheat Sheet RStudio® is a trademark of RStudio, Inc. CC BY RStudio. info@rstudio.com. 844-448-1212. rstudio.com Learn more at docs.ggplot2.org. ggplot2 0.9.3.1. Updated: 3/15 Geoms - Use a geom to represent data points, use the geom's aesthetic properties to.
ggplot2 is an extremely popular library for data visualization.
Using the theme() function from the ggplot2 library, you can customize every element of your visualization, ranging from the margins around various elements to the font family, size and color.
You can both go granular by specifying the style of each and every element, or address them in bulk by using global line, rect, text, and title arguments passed to the theme() function.
In this post, I'll show you how you can customize your plots step by step using the theme() function and the element_text(), element_rect(), and element_line() arguments. I'll be using an example plot based on the data from library(palmerpenguins) (credits to Allison Horst for creating the dataset).
Setting up
Let's start by downloading and loading the package.
Looking at the summary of our data, we have some missing values, particularly when it comes to the penguins' sex (As with many species of birds, it isn't always easy to determine a penguin's sex).
For the purpose of this article, let's just omit all the rows where there are missing data.
A basic plot
Now, let's look at a simple plot showing how bill and flipper lengths vary among specimens of the three penguin species in the dataset.
This plot is definitely readable and informative. But since you're here, you probably want to learn how to make your ggplot2 plots more custom.
Mosfet 300a power supply. Before I get into styling, I should mention that there are two ways of styling your plots: global styling, and scoped styling.
Global styling
You can style all your text, rect, or line elements globally by passing an argument that will define all of them to the theme() function.In the following example, I'm styling all my rect elements.
There's one thing you should know about globally styling your text elements, though.
Although the documentation claims that the text argument defines all text elements, technically this is not true - there's one exception to this rule.
axis.text arguments inherit some properties from the default theme, instead of the text argument. This is a known issue and it's unlikely to be fixed anytime soon. In any case, you need to define your axis text elements separately from the text argument.
Scoped styling
You can also style rect elements separately. In the following example I'm styling the legend and panel backgrounds separately.
I can similarly style text elements. In the following example I'm defining the plot caption, subtitle, and title separately.
familyYour desired font family. To browse available fonts, use windowsFonts().
faceYou can pick the font face from among 'plain', 'italic', 'bold', or 'bold.italic'. Keep in mind that in some cases, font face may already be specified in the family argument.
color: The color of your text. You can use both R's predefined colors and hexadecimal values.
Loupedeck ct capture one 20. size: Your desired text size in points.
hjust: Horizontal justification that takes a value between 0 and 1 (0: left-justified, 1: right-justified). hjust and vjust are brilliantly explained in detail here.
vjust: Vertical justification, see above.
angle: The angle of your text elements, ranging from 0-360 and applied counterclockwise.
lineheight: Line height that applies to text elements which occupy more than one line.
Guitar rig 5 pro free. margin: The margin around your elements. The margin argument is used together with the margin() function. margin() takes 5 arguments: four margins specified in the direction of top, right, bottom, left, and the unit argument, such as 'pt' for points, or 'cm' for centimeters.
debug: Setting the debug to a logical TRUE outputs a plot with all the text elements marked by colored rectangles, so that you can better see the effects of your changes.
inherit_blank: If set to TRUE, this argument makes an element inherit the blank state from its parent.
fill: Determines the fill color of your rect elements.
colour (or color): The color of your borders.
size: The size of your borders in points.
linetype: The linetype of your border. You can choose from blank, solid, dashed, dotted, dotdash, longdash, and twodash. You can also use an integer from 0-6, 0 being blank and 6 being twodash. Alternatively, you can pass a string of an even number of hexadecimal digits which give the lengths in consecutive positions in the string.
element_line() arguments
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:
Ggplot2 Cheat Sheet 2021
The Data Visualisation and Graphics for 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.
If you'd like to take an online course, try Data Visualization in R With ggplot2 by Kara Woo.
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.
You can style all your text, rect, or line elements globally by passing an argument that will define all of them to the theme() function.In the following example, I'm styling all my rect elements.
There's one thing you should know about globally styling your text elements, though.
Although the documentation claims that the text argument defines all text elements, technically this is not true - there's one exception to this rule.
axis.text arguments inherit some properties from the default theme, instead of the text argument. This is a known issue and it's unlikely to be fixed anytime soon. In any case, you need to define your axis text elements separately from the text argument.
Scoped styling
You can also style rect elements separately. In the following example I'm styling the legend and panel backgrounds separately.
I can similarly style text elements. In the following example I'm defining the plot caption, subtitle, and title separately.
familyYour desired font family. To browse available fonts, use windowsFonts().
faceYou can pick the font face from among 'plain', 'italic', 'bold', or 'bold.italic'. Keep in mind that in some cases, font face may already be specified in the family argument.
color: The color of your text. You can use both R's predefined colors and hexadecimal values.
Loupedeck ct capture one 20. size: Your desired text size in points.
hjust: Horizontal justification that takes a value between 0 and 1 (0: left-justified, 1: right-justified). hjust and vjust are brilliantly explained in detail here.
vjust: Vertical justification, see above.
angle: The angle of your text elements, ranging from 0-360 and applied counterclockwise.
lineheight: Line height that applies to text elements which occupy more than one line.
Guitar rig 5 pro free. margin: The margin around your elements. The margin argument is used together with the margin() function. margin() takes 5 arguments: four margins specified in the direction of top, right, bottom, left, and the unit argument, such as 'pt' for points, or 'cm' for centimeters.
debug: Setting the debug to a logical TRUE outputs a plot with all the text elements marked by colored rectangles, so that you can better see the effects of your changes.
inherit_blank: If set to TRUE, this argument makes an element inherit the blank state from its parent.
fill: Determines the fill color of your rect elements.
colour (or color): The color of your borders.
size: The size of your borders in points.
linetype: The linetype of your border. You can choose from blank, solid, dashed, dotted, dotdash, longdash, and twodash. You can also use an integer from 0-6, 0 being blank and 6 being twodash. Alternatively, you can pass a string of an even number of hexadecimal digits which give the lengths in consecutive positions in the string.
element_line() arguments
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:
Ggplot2 Cheat Sheet 2021
The Data Visualisation and Graphics for 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.
If you'd like to take an online course, try Data Visualization in R With ggplot2 by Kara Woo.
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.
Ggplot2 Gallery
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. The book is not available for free, but you can find the complete source for the book at https://github.com/hadley/ggplot2-book.
