Shiny App in R: Interactive Logistic Regression Shiny App for Environmental Science Case Studies

This Shiny application provides an interactive, end-to-end environment for exploring logistic regression modelling in the context of environmental science. It allows users to simulate environmental datasets, perform model training, and analyse key predictive relationships across three real-world case studies: air quality and PM2.5 exceedance, river nitrate contamination, and habitat suitability through species presence modelling. Each case study is backed by a tailored simulation engine that produces realistic environmental variables such as temperature, rainfall, wind speed, industrial emissions, soil and water characteristics, vegetation conditions, and human disturbance levels. These synthetic data reflect typical ecological and environmental systems, enabling users to learn modelling techniques without requiring access to sensitive or hard-to-obtain datasets.

The app’s left sidebar provides full control over the simulation workflow, including options to choose the environmental domain, specify sample size, and adjust the random seed for reproducibility. Once the user triggers the “Simulate & Fit Model” button, the app automatically generates data, scales predictors, fits a logistic regression model, and evaluates its predictive performance on a test set. The Summary tab displays essential outputs such as the confusion matrix, classification accuracy, ROC AUC, and fitted model coefficients. To ensure robustness, the app includes built-in safeguards that prevent ROC-curve failures when the test set contains only one outcome class—an issue common in simulated or imbalanced datasets.

Beyond the numerical summary, the application offers rich exploratory and diagnostic visualisations. The Distributions & Boxplots tab helps users examine how environmental predictors vary between event and non-event outcomes. The Correlation & Binned Rate tab provides insights into relationships between features and the event probability using correlation heatmaps and binned-rate visualisations. Finally, the Model Diagnostics tab presents ROC curves, predicted-probability histograms, calibration curves, and coefficient plots, allowing users to deeply evaluate model behaviour and interpret logistic regression outputs. Together, these components form a complete learning and experimentation toolkit for understanding environmental risk modelling through logistic regression.

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