🤖 AI Summary
This study addresses the lack of a unified computational, management, and visualization framework for characterizing diverse pairwise associations—such as linear correlation, nonlinear dependence, and Simpson’s paradox—between numeric and categorical variables. Methodologically, we propose an end-to-end analytical framework: (1) a unified R interface integrating 12 heterogeneous association measures; (2) a standardized tidy data structure enabling consistent storage, retrieval, and cross-type comparison of results; and (3) an enhanced multidimensional heatmap (implemented in the *bullseye* package, built upon *ggplot2*) supporting grouped comparisons, metric overlay, and automated paradox detection. Our key contribution is the first standardized,全流程 implementation of association analysis in R, significantly improving exploratory efficiency, reproducibility, and interpretability. The framework uniquely enhances detection of nonlinear relationships, mixed-variable dependencies, and structural biases—filling a critical gap in the R ecosystem for out-of-the-box, principled association analysis.
📝 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.