R PROGRAMMING
MTSPL
R PROGRAMMING
Authors:
Dr. Prashanth M.C
Publication Date:
8th March, 2026
No.of Pages: 208
Price: ₹ 400/-
ISBN:
978-93-92090-45-5
DOI:
Publisher:
Magestic Technology Solutions (P) Ltd.
Place of Publication:
R Programming: A Practical and Applied Approach
Master R Programming for Statistical Computing, Data Analysis, and Visualization
Preface
The R programming language has become one of the most powerful and widely used tools for statistical computing, data analysis, and visualization. With the increasing importance of data-driven decision-making in fields such as business, research, healthcare, and technology, learning R has become essential for students and professionals alike.
This book has been prepared to provide a comprehensive understanding of R programming, covering fundamental concepts, statistical techniques, graphical representation, and practical applications. It is designed especially for students to help them understand the practical implementation of R programming through examples, case studies, and sample programs.
The book includes various built-in functions, datasets, statistical methods, and visualization techniques that are essential for real-world data analysis. Each chapter is structured in a simple and systematic manner to make learning easy and effective.
I hope this book will serve as a valuable resource for students, researchers, and beginners who wish to learn and apply R programming in their academic and professional careers.
About the Book
R Programming: A Practical and Applied Approach is a structured learning resource designed to help students, beginners, and researchers build practical skills in R. The book introduces foundational programming concepts, data structures, control structures, data handling, visualization, and statistical analysis, with examples, case studies, datasets, and sample programs throughout.
In today’s data-driven environment, R has become an important language for statistical computing, data analysis, and visualization. This book is positioned as a practical guide that helps learners understand both the theory and implementation of R through a systematic, student-friendly approach.
The preface states that the text is intended to support learners with fundamental concepts, statistical techniques, graphical representation, and practical applications. It also emphasizes examples, case studies, sample programs, built-in functions, datasets, and visualization techniques as core learning tools.
The foreword further describes the work as a clear and practical introduction to R programming and its applications, especially useful for students and beginners seeking to apply R in academic and research settings.
Who This Book Is For
Students
Ideal for learners studying programming, statistics, or data analysis in R.
Beginners
A simple and organized introduction to R and its practical applications.
Researchers
Useful for academic learners who need exposure to statistical methods and visualization.
What You Will Learn
The book covers the R ecosystem progressively, starting from setup and syntax, then moving into data structures, control flow, data handling, visualization, and statistical analysis.
Introduction to R Programming
Statistical computing, evolution of R, features and advantages, applications in data science and research, installation of R and RStudio, the RStudio interface, and basic syntax, scripts, workspace, and working directory concepts.
Data Types, Variables, and Operators
Numeric, character, and logical data types, variables, constants, assignment operators, type conversion, type coercion, type checking functions, arithmetic operators, relational operators, logical operators, and operator precedence.
Data Structures in R
Vectors, matrices, arrays, lists, data frames, and factors, along with methods for accessing and modifying data elements.
Control Structures and Functions
Conditional statements, loops, break and next statements, user-defined functions, function arguments, variable scope, recursion, and the apply family of functions.
Data Handling and Visualization
Reading data from files, data cleaning, manipulation using dplyr, base R graphics, ggplot2, and exploratory data analysis.
Statistical Analysis and Advanced Concepts
Descriptive statistics, probability distributions, sampling techniques, hypothesis testing, correlation, simple and multiple regression, time series analysis, and an introduction to machine learning in R.
Special Features
- Clear and systematic chapter-wise structure
- Sample programs for practical understanding
- Case studies and real-world learning support
- Built-in datasets for hands-on analysis
- Appendices for quick reference
- Coverage of functions, packages, datasets, and glossary
About the Author
Dr. Prashanth M.C is described in the book as an academician and researcher in Computer Science with more than a decade of experience. He is noted as a temporary faculty member at Bengaluru City University, holding MSc and PhD degrees in Computer Science, along with KSET qualification.
The book also notes that he has published in Scopus-indexed journals, presented at international and national conferences, participated in Faculty Development Programs, and holds an Indian patent.
How to Cite
APA 7th
Prashanth, M. C. (2026). R programming: A practical and applied approach (1st ed.). Magestic Technology Solutions (P) Ltd. DOI: 10.47716/978-93-92090-45-5
MLA 9th
Prashanth, M. C. R Programming: A Practical and Applied Approach. 1st ed., Magestic Technology Solutions (P) Ltd., 2026. DOI: 10.47716/978-93-92090-45-5.
Chicago
Prashanth, M. C. R Programming: A Practical and Applied Approach. 1st ed. Chennai: Magestic Technology Solutions (P) Ltd., 2026. DOI: 10.47716/978-93-92090-45-5.
IEEE
M. C. Prashanth, R Programming: A Practical and Applied Approach, 1st ed. Chennai, India: Magestic Technology Solutions (P) Ltd., 2026. DOI: 10.47716/978-93-92090-45-5.
Remarks
Whether you are beginning your journey in R or looking for a structured academic guide to statistical computing and data analysis, R Programming: A Practical and Applied Approach offers a practical path from basics to advanced analytical topics. With clear organization, applied examples, and reference appendices, it is designed to support classroom learning, self-study, and research-oriented use.