Rstudio Ggplot2 Cheat Sheet

Today’s take home messages: (1) ggplot2 is the way to go for graphics (2) ggplot2 is a little intimidating, but there’s lots of support from R Graphic Gallery and the RStudio cheat sheet (3) I really love well visualized data! RStudio Cheat Sheets. Contribute to rstudio/cheatsheets development by creating an account on GitHub.

Ggplot2 Elegant Graphics For Data Analysis

Plotting with ggplot2. Ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. The cheat sheet article includes downloadable ggplot2 RStudio code snippets, offering ready-to-use, fill-in-the-placeholder code for a variety of ggplot2 tasks. 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 docs.ggplot2.org for detailed examples. Updated November 2016.

5.1 Plotting with ggplot2

ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatter plot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

Rstudio Ggplot2 Cheat Sheet 2017

ggplot2 functions like data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with ggplot2

ggplot graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

To build a ggplot, we will use the following basic template that can be used for different types of plots:

• use the ggplot() function and bind the plot to a specific data frame using the data argument

• define a mapping (using the aesthetic (aes) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.

• add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars). ggplot2 offers many different geoms; we will use some common ones today, including:

• geom_point() for scatter plots, dot plots, etc.
• geom_boxplot() for, well, boxplots!

• geom_line() for trend lines, time series, etc.

To add a geom to the plot use the + operator. Because we have two continuous variables, let’s use geom_point() first:

Ggplot2 Tutorial

The + in the ggplot2 package is particularly useful because it allows you to modify existing ggplot objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this:

Notes

• Anything you put in the ggplot() function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis mapping you set up in aes().
• You can also specify mappings for a given geom independently of the mappings defined globally in the ggplot() function.
• The + sign used to add new layers must be placed at the end of the line containing the previous layer. If, instead, the + sign is added at the beginning of the line containing the new layer, ggplot2 will not add the new layer and will return an error message.