A grammar of graphics framework for generalized parallel coordinate plots

TL;DR

This paper introduces a generalized framework for parallel coordinate plots that effectively visualizes high-dimensional data with mixed categorical and numerical variables, enhancing exploratory data analysis capabilities.

Contribution

It extends traditional PCPs to handle mixed data types seamlessly, unifying categorical and numerical visualization within a single, flexible framework.

Findings

Unified visualization of mixed data types achieved

Implementation available in R package ggpcp

Enhanced flexibility over existing categorical-only solutions

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.