Tutorial: Analysing Categorical Data in R
Marine
Cadoret, Applied Mathematics Department,
Agrocampus Ouest, France
Sébastien
Lê, Applied Mathematics Department,
Agrocampus Ouest, France
Abstract
In many applications, people are interested in the
relationship between categorical variables. The aim of this
tutorial is to propose an overview of both descriptive and
inferential methods for categorical data.
Outline
Topics will include:

Introduction to categorical data via genomic, sensory and
ecological data and their specific problems.

Describing, analysing, and visualizing one categorical
variable: graphical outputs, confidence interval for a
proportion.

Describing, analysing, and visualizing two categorical
variables: contingency table, Correspondence Analysis,
Chisquare test. Application to textual data and open ended
questions.

Describing, analysing, and visualizing several categorical
variables: from Correspondence Analysis to Multiple
Correspondence Analysis. Application to the analysis of
multiple choice questionnaires: introducing and visualizing
external information in the analysis.

Getting a typology of individuals described by several
categorical variables: automatic description of groups of
individuals obtained from a Hierarchical Ascending
Classification. Introduction to Logistic Regression and
Logit Models for multinomial responses.

Introduction to Logistic Regression and Logit Models for
multinomial responses.
Intended Audience
Teachers in data mining and data analysis, researchers in
applied fields, statisticians whose topic of interest is
multivariate analysis of categorical data.
Prerequisites
No prior knowledge is required.
Related Links
More information will be available (scripts and datasets) at http://factominer.free.fr. The information
for the participants will be available on the day of the tutorial both on the webpage or
at the tutorial itself (but unfortunately not before the day itself).
References
[1] R (and SPLUS) Manual to Accompany Agresti's Categorical
Data Analysis (2002) 2nd edition, Laura A. Thompson, 2009.