Friendly & Meyer (2016),
Discrete Data Analysis with R: Visualization and Modeling Techniques for
Categorical and Count Data
. This book provides the syllabus and main content for the course.
Use the code ADC22 for a 30% discount from the publisher's web site.
Web site for the book: http://ddar.datavis.ca/ 

Alan Agresti (2019),
Introduction to Categorical Data Analysis, 3rd Ed.
. A somewhat parallel book, offering a different perspective on categorical data analysis.
An ebook version can be purchased from the York Bookstore. See How to purchase your course materials for PSYC 6136 
Agresti (2013)
Categorical Data Analysis
. A much more technical book, that many consider the 'bible' for categorical data analysis methods.
Web site for the book http://www.stat.ufl.edu/~aa/cda/cda.html Solutions manual for R https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf A PDF copy of this book is available to students in this course. 

Fox (2015) Applied Regression Analysis and Generalized Linear Models . An excellent text on linear models; Part IV on Generalized Linear Models provides a clear and comprehensive discussion. 
In lectures and lab sessions I will be using R software exclusively, together with the R Studio user interface for R.
You are welladvised to download and install these to your computer so you can follow along.
Instructions to install R and R Studio for Windows and Mac. There is also a learnr tutorial that guides you through the steps.
My installvcdpkgs.R R script, to install the most useful packages for this course. Download and run this in your R or RStudio console.
R Studio
cheatsheets A handy collection of cheat sheets for R, R Studio and a
number of the most useful R packages. You can also get some of these in
R Studio from the menu Help > Cheat sheets
.
An Introduction to R Graphics Notes from my SCS short course on R Graphics.
The vcdExtra
package contains a number of vignettes, each giving practical
methods for working with categorical data and models.
Short Making tables in R
tutorial. Also: Udi’s Psy3136
Tables tutorial on making tables with the rempsyc
and
apaTables
packages.
The majority of the graphs in DDAR
and in my lectures use custom graphic methods implemented in the
vcd
, vcdExtra
, ca
and other
packages specific to categorical data analysis. Yet it is also helpful
to learn how to make and customize graphs using ggplot2
,
the modern lingua franca for specifying and producing
graphs.
Here are a few links that I find useful:
The ggplot2 Book is the bible of graphing with the Grammar of Graphics framework. It explains the logic of layers, geoms, scales, themes, etc. It has many, many small, easily understood examples.
The R Graphics Cookbook is a great resource of recipes for ggplot2, organized by type of graph (bar charts, line graphs, scatterplots, …) and things you might want to do with them.
My favorite book on general ideas about graphs: Claus Wilke, Fundamentals of Data Visualization. Well thought out, a wide range of topics, good practical advice, lots of examples, but no R code in the book.
My lecture slides from Psy 6135: Psychology of Data Visualization give a reasonable overview:
Friendly (2002). A brief history of mosaic displays, JCGS 11(1), 89107. Traces the origin of visual and conceptual ideas leading to modern mosaic displays.
Slides from a talk at CARME 2011, Advances in Visualizing Categorical Data Using the vcd, gnm and vcdExtra Packages in R
Slides from a talk at CARME 2015, General Models and Graphs for Log Odds and Log Odds Ratios. This reconsiders some standard loglinear models and graphical methods (correspondence analysis, mosaic plots) from the perspective of models and visualization for log odds and log odds ratios.
Copyright © 2018 Michael Friendly. All rights reserved.  lastModified :
friendly AT yorku DOT ca