Statistical Programming in R is a textbook designed to explain the theory, syntax and scripting of this powerful language that helps build robust statistical models, analyse huge data with ease and visualize and draw meaningful inferences. This book is designed for the first course on the subject taught for the students of undergraduate engineering in computer science and computer applications. It would also be useful for people who are beginners in data science and statistical analysis and those who want to begin with a hands-on approach to using R.The book begins with the basics, followed by chapters on factors, data frames and lists. A discussion on conditionals and control flow, loops and data structures follows. Applications to data sets are discussed in the succeeding chapters on the apply family and R charts and graphics. The last chapter of the book is dedicated to a detailed discussion of probability and statistical examples from various domains.Interspersed with various programming examples throughout, the book provides multiple-choice questions, programming exercises and simple concept application exercises at the end of relevant chapters.
Key Features
Ȣ Addresses topics such as bar charts and pie charts to perform real data analysis ranging from reading data stored in various file formats to plotting the results of the analysis
Ȣ Illustrates examples such as binary search tree implementation and accessing keyboard and monitor for general input and output
Ȣ Explains the various constructs in R and the nuances among them
Ȣ Explains how R can interface with CSV, Excel, XML and JSON files
Ȣ Provides lucid examples covering ANOVA, advanced statistics, splines and also covers data visualization through R
Online Resources
For faculty
Ȣ Solutions manual (for select exercises)
Ȣ Lecture PPTs
For students
Ȣ Useful web links
Table of Contents
1. Basics of R
2. Factors and Data Frames
3. Lists
4. Conditionals and Control Flow
5. Iterative Programming in R
6. Functions in R
7. Apply Family in R
8. Charts and Graphs
9. Data Interfaces
1. Statistical Applications.
About the Author
K.G. Srinivasa is Associate Professor and Head, Dept of Information Technology, CBP Govt Engineering College, Jaffarpur, New Delhi. He has over 20 years of combined teaching and research experience.
G.M. Siddesh is Associate Professor, Dept of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru. He has around a decade and a half years of experience in teaching and administration.
Chetan Shetty is Assistant Professor, Dept of Computer Science and Engineering, Ramaiah Institute of Technology, Bengaluru.
Sowmya B.J. is Assistant Professor, Dept of Computer Science and Engineering, Ramaiah Institute of Technology, Bengaluru.
