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Software: Learn R

Resources to learn and use the Open Source Statistical software R (R-Project)

ggplot2 is universally considered the best package for general graphing and is part of the tidyverse (see Data Management).

Note: The function name is ggplot() - without the 2.

Go to the website for

Getting Started
Cheatsheets
Learning Suggestions from the author, including
- Chapters from R for Data Science
- Data Visualization in R with ggplot2 by Kara Woo - Available for Free Through Mason

Note: There is some graphing in base r--e.g., plot()--that can be quick and easy for exploration, but few people use lattice any more.

Packages

Packages using ggplot2:

esquiesse - Point-and-Click interface to create a single graph from cleaned/prepared data and/or to learn ggplot syntax.
- Tableau-like workflow helps build and refine a single graph using drag and drop. Automatically chooses chart types based on data types.
- Produces nice, clean code.
- Allows for specifying variable types on import (e.g., create factors), transforming values (e.g., log), and filtering by value
GGally - Good for bivariate graphing with functions like ggpairs()
ggpubr - Easily create Publication-Ready Plots

Create Interactive Graphics

ggvis – make interactive graphics using ggplot2
shiny – make interactive graphical applications using RStudio
- Shiny Tutorials for Statistics Instructors (Show Me Shiny)

GGPlot2

Other ggplot2 Tutorials

Data Visualization with GGplot2
- From a larger Carpentries R lesson
Albert Rapp YouTube Videos

Data Visualization by Kieran Healy An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective "small multiple" plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the "tidyverse" of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions
Call Number: QA76.9.I52 H43 2018, Preprint available ONLINE through tht eauthor
ISBN: 9780691181615
Publication Date: 2018-12-18

Graph Galleries with Example Code

R Graph Gallery
- Large, user-contributed collection, categorized and searchable
- See also the R Gallery Book by Kyle Brown
How to Choose a Chart by Andy Grogan-Kaylor
- Small collection of charts, organized by data types.
R Graphics Cookbook by Winston Chang
- Book available ONLINE through Mason which has many examples
GGPlot2 Graph Examples by Data Novia
- Examples of how to deal with common issues
BBC Visual and Data Journalism cookbook for R graphics
- Small collection of specific charts and graphic elements with full code