Statistics, data analytics, and machine learning are becoming increasingly important in today's data-driven world. With the explosion of data in various fields such as business, healthcare, and social sciences, the ability to analyze and make sense of this data has become a crucial skill for professionals in these fields.

This book is designed to provide an introduction to statistics, data analytics, and machine learning using the R programming language. R is a powerful tool for statistical computing and graphics, and it has become one of the most popular languages for data analysis.

The book is divided into three parts. Part I covers introductory statistics topics such as descriptive statistics. Part II focuses on data analytics techniques such as data wrangling, visualization, and exploratory data analysis using R. Part III & IV covers matrix algebra and machine learning algorithms such as decision trees, random forests, and support vector machines using R software. 

Each chapter includes numerous examples and exercises to help readers develop their skills in R programming and statistical analysis. The book also includes a comprehensive R code snippets.

This book is intended for students and professionals who want to learn how to use R for statistical analysis, data analytics, and machine learning. It assumes no prior knowledge of R or statistics.