Ggplot Cheatsheet

Learning ggplot2

GGplot 101: The Ultimate CheatSheet R notebook using data from multiple data sources 5,023 views 9mo ago.
Invoking the ggplot function returns an object that serves as the base of a ggplot2 visualization. Viz ggplot viz # renders blank plot. Data is bound to a ggplot2 visualization by passing a data frame as the first argument in the ggplot function call. Layers can be added to the plot object by adding function calls after ggplot.
Ggplot2 Bar Chart The geombar layer adds a bar chart to a ggplot2 canvas. Typically when creating a bar chart, an aes aesthetic mapping with a single categorical value on the x axes and the aes function will compute the count for each category and display the count values on the y axis.

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:

Ggplot-cheatsheet cheatsheet for ggplot2, compiled mostly from the book, ggplot2: Elegant Graphics for Data Analysis Documentation for ggplot is available here. The ggplot2 library is a phenomenal tool for creating graphics in R but even after many years of near-daily use we still need to refer to our Cheat Sheet. Up until now, we’ve kept these key tidbits on a local PDF. But for our own benefit (and hopefully yours) we decided to post the most useful bits of code.

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.
Benq others driver. 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.

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.

Solution
Color charts

Problem

You want to use colors in a graph with ggplot2.

Solution

The default colors in ggplot2 can be difficult to distinguish from one another because they have equal luminance. They are also not friendly for colorblind viewers.

A good general-purpose solution is to just use the colorblind-friendly palette below.

Sample data

These two data sets will be used to generate the graphs below.

Ggplot2 R

Simple color assignment

The colors of lines and points can be set directly using colour='red', replacing “red” with a color name. The colors of filled objects, like bars, can be set using fill='red'.

If you want to use anything other than very basic colors, it may be easier to use hexadecimal codes for colors, like '#FF6699'. (See the hexadecimal color chart below.)

Mapping variable values to colors

Instead of changing colors globally, you can map variables to colors – in other words, make the color conditional on a variable, by putting it inside an aes() statement.

A colorblind-friendly palette

These are color-blind-friendly palettes, one with gray, and one with black.

To use with ggplot2, it is possible to store the palette in a variable, then use it later.

This palette is from http://jfly.iam.u-tokyo.ac.jp/color/:

Color selection

By default, the colors for discrete scales are evenly spaced around a HSL color circle. For example, if there are two colors, then they will be selected from opposite points on the circle; if there are three colors, they will be 120° apart on the color circle; and so on.The colors used for different numbers of levels are shown here:

Ggplot2 Documentation

The default color selection uses scale_fill_hue() and scale_colour_hue(). For example, adding those commands is redundant in these cases:

Setting luminance and saturation (chromaticity)

Although scale_fill_hue() and scale_colour_hue() were redundant above, they can be used when you want to make changes from the default, like changing the luminance or chromaticity.

This is a chart of colors with luminance=45:

Palettes: Color Brewer

You can also use other color scales, such as ones taken from the RColorBrewer package. See the chart of RColorBrewer palettes below. See the scale section here for more information.

Palettes: manually-defined

Finally, you can define your own set of colors with scale_fill_manual(). See the hexadecimal code chart below for help choosing specific colors.

Continuous colors

[Not complete]

See the scale section here for more information.

Color charts

Hexadecimal color code chart

Colors can specified as a hexadecimal RGB triplet, such as '#0066CC'. The first two digits are the level of red, the next two green, and the last two blue. The value for each ranges from 00 to FF in hexadecimal (base-16) notation, which is equivalent to 0 and 255 in base-10. For example, in the table below, “#FFFFFF” is white and “#990000” is a deep red.

Download avance logic driver. (Color chart is from http://www.visibone.com)

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