A Tidy Data Structure and Visualisations for Multiple Variable Correlations and Other Pairwise Scores
Computation
2025-11-17 v2
Abstract
We provide a pipeline for calculating, managing and visualising correlations and other pairwise association scores for numerical and categorical data. We present a uniform interface for calculating a plethora of pairwise scores and propose a tidy data structure for organising the results. We also provide new visualisations which simultaneously show multiple and/or grouped pairwise scores. The visualisations are far richer than a traditional heatmap of correlation scores, as they help identify relationships with categorical variables, numeric variable pairs with non-linear associations or those which exhibit Simpson's paradox. These methods are available in our R package bullseye.
Cite
@article{arxiv.2411.19830,
title = {A Tidy Data Structure and Visualisations for Multiple Variable Correlations and Other Pairwise Scores},
author = {Amit Chinwan and Catherine B. Hurley},
journal= {arXiv preprint arXiv:2411.19830},
year = {2025}
}