InfoGuides: Learn R: Graphics

Choose from the following:

Comparison (StackOverflow)

ggplot2 – based on the book “The Grammar of Graphics” [MOST POPULAR]
ggformula - uses formula notation and ggplot for ease of teaching
lattice – implementation of Trellis graphics in R
base r - use a series of functions to build your graph

Quick and Easy Exploratory Data Analysis

ggquickeda - Provides a Point and Click Interface
esquiesse - Helps build graphs with ggplot2
GGally - Good for bivariate graphing with functions like ggpairs()

Interactive Graphics

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

ggplot2

Chapters from R for Data Science
- by Hadley Wickham & Garrett Grolemnd
- Data Visalization and Graphics for Communication
- A non-ciruclating copy is in the DiSC Lab
FlowingData
- (free and members-only) Tutorials and courses on graphing in R

Graph Galleries

Graph Galleries with Example Code

R Graph Gallery
- Large, user-contributed collection, categorized and searchable with a special section for ggplot2.
- See also the R Gallery Book by Kyle Brown
Tufte in R by Lukasz Piwek
- Code for base R, lattice, and ggplot2 to fulfil Tufte’s principles
A Compendium of Clean Graphs in R by Eric-Jan Wagenmakers and Quentin F. Gronau
- A large collection of complex but good-looking graphs, primarily in base r, but some in ggplot2
How to Choose a Chart by Andy Grogan-Kaylor
- Small collection of charts in ggplot2, organized by data types.
R Graphics Cookbook by Winston Chang
- Book available ONLINE through Mason which has many examples using ggplot2
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 in ggplot2

Interactive Graphics

Ggplot2 Essentials by Donato Teutonico This book is perfect for R programmers who are interested in learning to use ggplot2 for data visualization, from the basics up to using more advanced applications, such as faceting and grouping. Since this book will not cover the basics of R commands and objects, you should have a basic understanding of the R language.
Call Number: Available ONLINE through Mason
ISBN: 9781785283529
Publication Date: 2015-06-25
Ggplot2 by Hadley Wickham; Carson Sievert This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to:  produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R).  ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.
Call Number: Available ONLINE through Mason and an older version at QA90 .W53 2009
ISBN: 9783319242750
Publication Date: 2016-06-16

Packages in R

ggraph plots network graphs using the conventions and power of ggplot2.
igraph is a collection of network analysis tools with the emphasis on efficiency, portability, and ease of use. igraph is open source and free, and can be used in R, Python, and C++.
network manages relational data in R. Networks can be created with any combination of undirected/directed, valued/unvalued, dyadic/hyper, and single/multiple edges.
networkD3 creates D3 JavaScript network, tree, dendrogram, aand Sankey graphs.
tidygraph conceptualizes a network graph as two tidy data frames consisting of node and edge data.

Online courses and tutorials

Alex Brey, Temporal Network Analysis with R (2018)
Ryan Deschamps, Correspondence Analysis for Historical Research with R (2017)
Daniel McFarland, Solomon Messing, Michael Nowak, and Sean J. Westwood, Social Network Analysis Labs in R and SONIA (2010)
Lynda.com, Social Network Analysis Using R
Katherine Ognyanova, Network Analysis and Visualization with R and igraph (2016)
Jesse Sadler, Introduction to Network Analysis with R: Creating Static and Interactive Network Graphs (2017)

Networkx is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Networkx resources

NetworkX tutorials

Aric Hagberg, Dan Schult, and Pieter Swart, Networkx Tutorial (2014)
DataCamp, Graph Optimization with Networkx in Python (2017)

Other Python tutorials

Coursera, Applied Social Network Analysis in Python
DataCamp, Network Analysis in Python: Introduction to Networks (2017)
DataCamp, Network Analysis in Python: Network Visualization (2017)