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Data Exploration and Visualisation with R: From Data Quality to Credible Evidence
This book is 100% completeLast updated on 2026-09-27
Explore data before you trust it. Data Exploration and Visualisation with R shows how to uncover structure, diagnose data-quality problems, challenge misleading patterns, and turn imperfect observations into credible evidence through thoughtful, reproducible visual analysis.
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About the Book
Good data lead to better questions, deeper understanding, and more credible evidence.
Data Exploration and Visualisation with R: From Data Quality to Credible Evidence is a practical guide to understanding data before drawing conclusions from them. Rather than treating visualisation as a final decorative step, the book places it at the centre of statistical reasoning—using plots to investigate data quality, reveal structure, challenge assumptions, and communicate findings responsibly.
Beginning with the processes through which data are generated, measured, recorded, and prepared, the book develops a thoughtful approach to exploratory analysis. It moves from distributions and relationships to missing data, outliers and robust methods, causal reasoning with DAGs, geospatial exploration, dimensionality reduction, and temporal data. Throughout, R is used not merely to produce graphics, but to support reproducible investigation and critical interpretation.
The emphasis is always on asking the right questions: What does this pattern show? What might explain it? What could mislead us? What does the evidence actually support? Connections to data science, machine learning, and AI show why these habits matter well beyond traditional statistical analysis.
Written for students, researchers, and data professionals, this book aims to develop the judgment needed to move from raw and imperfect observations to visualisations—and conclusions—that can withstand scrutiny.
Better data. Clearer questions. More credible evidence.
Author
About the Author
Osama Abdelhay
Osama Abdelhay, Ph.D., is an Assistant Professor of Data Science at Princess Sumaya University for Technology in Amman, Jordan. He is a senior data scientist and biostatistician with more than 16 years of experience in statistical modelling, predictive analytics, machine learning, biostatistics, epidemiology, and humanitarian data science.
Dr. Abdelhay holds a Ph.D. in Applied Statistics and Biostatistics and a Master’s degree in Biometry from the University of Reading, United Kingdom, in addition to a Bachelor’s degree in Applied Statistics from Yarmouk University, Jordan. His academic work includes teaching undergraduate and postgraduate courses in computational statistics, data visualisation, categorical data analysis, probability, and statistics for data science. He has supervised numerous master’s theses and student projects, particularly in humanitarian, medical, and public-health contexts.
Alongside his academic role, Dr. Abdelhay has served as a consultant data scientist, biostatistician, and statistician for several international organisations, including UNICEF, UNHCR, UN-ESCWA, and Data-Pop Alliance. His work has covered female genital mutilation, child marriage, refugee vulnerability, accountability to affected populations, migration data, performance scorecards, and the use of non-traditional data sources for socio-economic estimation during crises.
His research interests include statistical modelling, interpretable machine learning, epidemiology, predictive modelling, systematic reviews, meta-analysis, and the application of data science to health and humanitarian decision-making. He has published in several peer-reviewed journals.
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