Visualizing Categorical Data

Visualizing and Modeling Categorical Time Series Data

Categorical time series data can not be eeectively visualized and modeled using methods developed for ordinal data. The arbitrary mapping of categorical data to ordinal values can have a number of undesirable consequences. New techniques for visualizing and modeling categorical time series data are described, and examples are presented using computer and communications network traces.

Visualizing Categorical Data: Data, Stories, and Pictures

Categorical data—frequency data, and discrete data—are most often presented in tables, and analyses using loglinear models and logistic regression are most often presented in terms of parameter estimates. Over the past decade, I and others have developed novel visualization methods for categorical data, designed to provide exploratory and confirmatory graphic displays analogous to those used re...

Visualizing Relationships among Categorical Variables

Centuries of chart-making have produced some outstanding charts tailored specifically to the data being visualized. They have also produced a myriad of less-than-outstanding charts in the same vein. I instead present a set of techniques that may be applied to arbitrary datasets with specific properties. In particular, I describe two techniques – Nested Category Maps and Correlation Maps – for v...

“ Visual Exploration of Multivariate Graphs “ Martin Wattenberg

Focuses on visualizing multivariate graphs (discrete categorical dimensions)  Introduces a new technique for visualizing and analyzing graph structures -PivotGraph What is the problem?  Data analysis needs analyzing graphs  Such analysis is difficult because of complex structure and large size of graphs. Solution: Visualization

Visualizing Categorical Time Series Data with Applications to Computer and Communications Network Traces

A Reader’s Guide to Visualizing Categorical Data

Visualizing Categorical Data presents a comprehensive overview of graphical methods for discrete data— count data, cross-tabulated frequency tables, and discrete response data. These methods are designed to complement traditional numerical summaries and statistical models, expose patterns in the data, and to aid in diagnosing model defects. They are illustrated with real data problems, and impl...