Kamarul Imran Musa is an associate professor and medical epidemiologist at the School of Medical Sciences, Universiti Sains Malaysia. He is a registered public health physician with the Malaysia National Specialist Registry and a Fellow of the American College of Epidemiology (FACE). He is also a core member of the Malaysia R User group and conducts regular workshops on data and statistical analysis at the national level.
Wan Nor Arifin is a senior lecturer at the Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia. His research is mainly in validating measurement tools and machine learning, especially for use in clinical and public health settings. He is also a core member of the Malaysia R User group. He uses the R statistical programming language on a daily basis and promotes its use in medical research.
Tengku Muhammad Hanis is a PhD student at the School of Medical Sciences, Universiti Sains Malaysia. He holds a master's degree in medical statistics. His interests lie in the application of medical statistics and machine learning in medicine. He has run several workshops on bibliometric analysis, systematic reviews, and meta-analysis. He is passionate about R and always excited about its potential in medical and health research.
In medicine and health, data are analysed to guide treatment plan, patients care as well as the control and prevention policies. However, in doing so, researchers in medicine and health often lack the understanding of data and statistical concepts and the skills in programming. In addition, there is also increasing demands for data analyses to be reproducible along with more complex data that requires cutting-edge analysis. This book provides readers with both the fundamental concepts of data and statistical analysis and modelling. It also the skills to perform the analysis using the R programming language which is the lingua-franca for statisticians. The topics in the book are presented in a sequence to minimize the time to help readers understand the objectives of data and statistical analysis, learn the concepts of statistical modelling and acquire to skills to perform the analysis. The R codes and datasets used in the book will be made available on GitHub for easy access. The book will also be live on the website bookdown.org, a service provided by RStudio, PBC to host books written using the bookdown package in R programming language.
Data analysis plays a vital role in guiding medical treatment plans, patient care, and the formulation of control and prevention policies in the field of healthcare. In today's era, researchers in these domains require a firm grasp of data, statistical concepts, and programming skills due to the increasing complexity of data. Reproducible analyses and cutting-edge statistical methods are becoming increasingly necessary.
This book, which is both comprehensive and highly practical, addresses these challenges by laying a solid foundation of data and statistical theory for readers. Subsequently, it equips them with practical skills to conduct analyses using the powerful R programming language, widely used by statisticians. The book takes a gentle approach to help readers navigate data and statistical analysis using R, minimizing the learning curve. RStudio is used as the integrated development environment (IDE) for enhanced productivity for readers to run their R codes.
Following a logical sequence commonly applied in medical and health research, the book covers fundamental concepts of data analysis and statistical modeling techniques. It provides readers, including those with limited statistical knowledge and programming skills, with hands-on experience through R programming.
The online version of this book is available on bookdown.org, a publishing platform provided by RStudio, PBC specifically designed to host books written using the "bookdown" package in R. Additionally, all R codes and datasets in this book can be found on the author's GitHub repository.
1. Introduction to R, RStudio and RStudio Cloud 2. Data Visualization 3. Data Wrangling 4. R Markdown 5. Exploratory data analysis (EDA) 6. Linear Regression 7. Binary Logistic Regression 8. Poisson Regression 9. Survival Analysis
1. R, RStudio and RStudio Cloud 2. Rillegalscripts and R Packages 3. RStudio Project 4. Data Visualization 5. Data Wrangling 6. Exploratory Data Analysis 7. Linear Regression 8. Binary Logistic Regression 9. Multinomial Logistic Regression 10. Poisson Regression 11. Survival Analysis: Kaplan–Meier and Cox Proportional Hazard (PH) Regression 12. Parametric Survival Analysis 13. Introduction to Missing Data Analysis 14. Model Building and Variable Selection
In medicine and health, data are analysed to guide treatment plan, patients care as well as the control and prevention policies. However, in doing so, researchers in medicine and health often lack the understanding of data and statistical concepts and the skills in programming.
Inhaltsverzeichnis
1. R, RStudio and RStudio Cloud 2. Rillegalscripts and R Packages 3. RStudio Project 4. Data Visualization 5. Data Wrangling 6. Exploratory Data Analysis 7. Linear Regression 8. Binary Logistic Regression 9. Multinomial Logistic Regression 10. Poisson Regression 11. Survival Analysis: Kaplan¿Meier and Cox Proportional Hazard (PH) Regression 12. Parametric Survival Analysis 13. Introduction to Missing Data Analysis 14. Model Building and Variable Selection
