Advanced Descriptive Analysis of Tabular Data

Methods and Tools for Exploratory Analysis

Preface

Descriptive statistics are often treated as a preliminary step (a routine box to check before moving on to inference, prediction, or causal analysis). Yet in practice, understanding the structure, associations, and patterns within complex tabular data is neither trivial nor purely mechanical. It requires sophisticated methods, thoughtful visualization, and clear communication.

This book synthesizes advanced techniques for descriptive analysis of tabular data, drawing on recent developments in machine learning, network analysis, and interactive visualization. We aim to equip researchers, analysts, policymakers, and data journalists with tools that go beyond means and standard deviations, enabling them to extract actionable insights from multivariate datasets.

The methods and tools we present here are not intended as a repackaging of standard exploratory data analysis. They reflect methodological syntheses, implementation choices, and applied workflows developed through research and field practice. In that sense, this book also serves as a portfolio: a concrete, citable body of work that documents contributions to descriptive analytics and foregrounds substantive methodological originality.

The material emerged from postdoctoral research at the intersection of applied statistics, machine learning, and data visualization. It reflects a pragmatic philosophy: methods can be interpretable, visual, and suitable for communicating findings to statistically literate but non-technical audiences.

Who This Book Is For

This book is intended for readers who already possess a solid foundation in statistics (including regression analysis, hypothesis testing, and basic multivariate methods). We assume familiarity with concepts like correlation, variance decomposition, and model evaluation.

Our intended audience includes:

• Researchers and applied scientists seeking exploratory tools for complex datasets
• Policymakers and analysts in government and public institutions
• Data journalists investigating patterns in social, economic, or health data
• Consultants and analytical teams in private firms
• Graduate students in statistics, data science, public policy, or related fields

The material is suitable for a Master-level university course and may serve as a foundation for doctoral-level methodological training.

Philosophy and Approach

The unifying thread throughout this book is: How do we move beyond standard descriptive statistics to extract, visualize, and communicate structure in complex tabular data?

We emphasize:

• Interpretability: Methods that produce understandable results
• Visual analytics: Graphs and interactive tools as primary analytical instruments
• Methodological transparency: Explicit discussion of assumptions, trade-offs, and limitations
• Communication: Presenting results to diverse audiences, from technical peers to policy stakeholders

Rather than offering purely theoretical exposition, we ground each method in real applied use cases, showing how techniques perform on actual data challenges.

Structure of the Book

The book is organized into seven parts:

Part I establishes the conceptual framework, revisiting what it means to “describe” data and introducing the challenge of mixed-type variables and multivariate associations.

Part II focuses on association analysis—measuring relationships between pairs of variables of different types and representing these associations as networks.

Part III introduces interactive visual analytics, including the AssociationExplorer Shiny application, which operationalizes association-focused methods in a unified exploratory interface.

Parts IV-VI present three families of advanced methods for higher-dimensional structure: tree-based models for segmentation and description, interpretable machine learning techniques for understanding complex patterns, and AutoML approaches that automate exploration.

Part VII presents extended applied case studies from policy analysis, public health, and business analytics, demonstrating how these methods solve real-world problems.

Acknowledgments

This work has been shaped by collaborations with colleagues, conversations with practitioners, and feedback from students and the open-source community. We are particularly grateful to researchers from UCLouvain Saint-Louis Brussels involved in the Beamm research project for their insights and support.

We also acknowledge the open-source software communities whose tools make this work possible, including R, Python, Shiny, Quarto, and countless contributed packages.

How to Use This Book

You can read chapters sequentially or selectively, depending on your background and interests. Readers already familiar with data preprocessing and basic descriptive statistics might skip Part I, while those primarily interested in tree-based methods could jump directly to Part IV.

Throughout the book, we provide code examples in R and references to accompanying interactive tools. All datasets, code and source files are available on GitHub.

We encourage readers to experiment with the methods on their own data. Descriptive analysis is learned through practice, and one of the best ways to internalize these techniques is to apply them to problems you care about.