From Basics to Advanced Health Analytics: Exploring Diabetes Data

In this session, we will walk through a full end-to-end workflow of health data analysis in R using a simulated database based of diabetes.

We will begin with exploratory data analysis (EDA) to identify initial patterns, distributions, and data quality issues. Then, we’ll apply data wrangling techniques to prepare the dataset for robust statistical investigation. Building on this foundation, we will create effective and elegant visualizations with ggplot2 to communicate insights clearly and intuitively.

Next, we will introduce inferential modeling for qualitative outcomes, discussing how to estimate associations, interpret the results, and translate findings into meaningful epidemiological insights.

Finally, we will explore cluster analysis to reveal underlying subgroups within the population and highlight the heterogeneity of diabetes profiles.

The session is designed to transition smoothly from basic to more advanced methods, offering a practical and reproducible framework for health data analysis.

No previous experience with medical datasets is required—all code will be shown step by step and shared afterwards.