R is a free, open-source, adaptable, extensible language with tremendous applications in the field of statistical computation and data science. It offers basic programming and has very strong built-in functions for statistical analysis. It is also a perfect fit for big data solutions and supports graphical techniques to visualise and present data effectively. In this book, students will learn about R programming, from its fundamentals to advanced concepts relating to data science and machine learning. This book covers traditional programing concepts in R, such as its features, data types, categorisation, operators, vectors, matrics, data frames, functions and R profiler. Explains basic and advanced statistical concepts such as measures of central values, dispersion and shape, sampling distribution, correlation coefficient, and regression analysis, inference, ANOVA, machine learning concepts and text mining, and how to implement them in R, includes numerous examples and program code. This book provides multiple-choice programming and concept based questions at the end of every chapter.