A comprehensive, beginner-friendly walkthrough demonstrating how to implement Bayesian spatial disease mapping using R-INLA (Integrated Nested Laplace Approximations). This tutorial guides users through the entire epidemiological data pipeline: from importing local administrative shapefiles (using Kenya as a study case) to simulating realistic disease data using Poisson distributions and accounting for background populations. It explicitly breaks down how to construct spatial neighborhood graphs and configure the classic Besag-York-Mollié (BYM) model to split geographic variation into smooth regional patterns and localized noise. Finally, it demonstrates how to extract posterior mean relative risks and visualize public health hot-spots using advanced continuous color styling in ggplot2. Ideal for epidemiologists, data scientists, and students transitioning into spatial statistics.

This tutorial provides a clear, step-by-step introduction to calculating and interpreting Relative Risk (RR) using R. Designed for biostatistics beginners and epidemiologists, the guide walks through three distinct methodologies: manual extraction from a 2 × 2 table, automated estimation using the epitools package, and adjusted analysis controlling for clinical confounders using log-binomial regression models (glm).

This document provides a clear, step-by-step introduction to calculating and interpreting Odds Ratios (OR) using R. Using a practical health science scenario (Smoking and Lung Cancer), it demonstrates how to build a 2x2 contingency table, extract individual group odds, and replicate the results using both the epitools package and logistic regression (glm).It is specifically designed for beginners, featuring plain-English explanations of data framing, factor levels, and how to avoid common coding layout traps like inverted risk estimates.