May 25, 2017

ggplot2 tutorial: A cheat sheet of essential functions


A ggplot2 tutorial: a cheat sheet. 

The ggplot2 package, created by Hadley Wickham, provides a fast and efficient way to produce good-looking data visualizations that you can use to derive and communicate insights from your data sets. The package was designed to help you create all different types of data graphics in R, including histograms, scatter plots, bar charts, box plots, and density plots. This textbook has numerous examples of visualizations you can build in ggplot2. 

The ggplot2 package offers a powerful graphics language for creating elegant and complex plots. Originally based on Leland Wilkinson’s The Grammar of Graphics, ggplot2 allows you to create graphs that represent both univariate and multivariate numerical and categorical data in a straightforward manner. Grouping can be represented by color, symbol, size, and transparency. The creation of trellis plots (i.e., conditioning), graphs that show relationships between different variables, is relatively simple.

In recent years, ggplot2’s popularity has grown exponentially. Due to its popularity, the functionalities built into this package have increased — which might be overwhelming for someone getting started with ggplot2. So I created this ggplot2 tutorial and cheatsheet to help you learn the basic functionalities of ggplot2. 

This is a quick ggplot2 tutorial through the basics of ggplot2 — enough so that you can create beautiful visualizations in R. 

You can use it as an extremely handy reference, or cheat-sheet, if you have just started your data science journey with ggplot2 in R, you can use it to help guide you to what you need to get done if you’re looking to create a specific data visualization in R. 

Don’t worry if you haven’t even started yet. You can simply use it as a guide to make learning about it and using it easier. Here it is embedded: 



Here is a downloadable version of the PDF in case you want to have it handy with you as you navigate ggplot2 and data visualization in R.