Year: 2024
Type: preprint
Abstract: We introduce OrigamiPlot, an open-source R package and Shiny web application designed to enhance the visualization of multivariate data. This package implements the origami plot, a novel visualization technique proposed by Duan et al. in 2023, which improves upon traditional radar charts by ensuring that the area of the connected region is invariant to the ordering of attributes, addressing a key limitation of radar charts. The software facilitates multivariate decision-making by supporting comparisons across multiple objects and attributes, offering customizable features such as auxiliary axes and weighted attributes for enhanced clarity. Through the R package and user-friendly Shiny interface, researchers can efficiently create and customize plots without requiring extensive programming knowledge. Demonstrated using network meta-analysis as a real-world example, OrigamiPlot proves to be a versatile tool for visualizing multivariate data across various fields. This package opens new opportunities for simplifying decision-making processes with complex data.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Advancing Drug Repositioning for Alzheimer’s Disease using Real-world Data, G2797749627, Dynamic learning for post-vaccine event prediction using temporal information in VAERS, Methods for Estimating Disease Burden of Seasonal Influenza, G4890456917, AI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methods, Surrogate Augmented Deep Predictive Learning for Retinopathy of Prematurity, G9000027382, G9000053926, G9000115331, G9000219834, G9000264030, PANDA-MSD: Predictive Analytics via Networked Distributed Algorithms for Multi-System Diseases, TRiPOD: Toward Reusable Phenotypes in Observational Data for AD/ADRD - managing definitions and correcting bias
OpenAlex ID: W4404574106