Degree Type

Creative Component

Semester of Graduation

Fall 2020

Department

Statistics

First Major Professor

Heike Hofmann

Degree(s)

Master of Science (MS)

Major(s)

Statistics

Abstract

Parallel coordinate plots (PCP) are a useful tool in exploratory data analysis of high-dimensional numerical data. The use of PCPs is limited when working with categorical variables or a mix of categorical and continuous variables. In this paper, we propose generalized parallel coordinate plots (GPCP) to extend the ability of PCPs from just numeric variables to dealing seamlessly with a mix of categorical and numeric variables in a single plot. In this process we find that existing solutions for categorical values only, such as hammock plots or parsets become edge cases in the new framework. By focusing on individual observation rather a marginal frequency we gain additional flexibility. The resulting approach is implemented in the R package ggpcp.

Comments

Implemented in R package ggpcp

Copyright Owner

Ge, Yawei

File Format

PDF

Embargo Period (admin only)

11-14-2020

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