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.
Salient Features
Sandhya Arora is Professor at the Department of Computer Engineering, MKSSS's Cummins College of Engineering for Women, Pune. A Ph.D. from Jadavpur University, Kolkata, she has more than 22 years of teaching experience and has published papers in acclaimed international journals.
Latesh Malik is Associate Professor and Head of the Department of Computer Science & Engineering, Government College of Engineering, Nagpur. A Ph.D. from Visvesvaraya National Institute of Technology, Nagpur, and a gold medalist in M.Tech. and B.E, she has more than 22 years of teaching experience and has published more than 100 papers in international journals.
Preface
Acknowledgements
Chapter 1: Introduction to R Programming
Objectives
1.1 Overview of R
1.2 Installation of R
1.3 Installation and Loading of R Packages
1.4 R – Basic Syntax
1.5 Data Types and Objects
1.6 Variables
1.7 Constants
1.8 Comments
1.9 Debugging in R
Exercises
Chapter 2: Data Definitions and Categorisation
2.1 Overview of Data
2.2 Sources of Data
2.3 Big Data
2.4 Data Categorisation
2.5 Data Cube
Chapter 3: Operators
3.1 Introduction to Operators
3.2 Arithmetic Operators
3.3 Relational Operators
3.4 Logical Operators
3.5 Miscellaneous Operators
3.6 Precedence and Associativity of Operators
Chapter 4: Control Statements and Functions
4.1 Introduction
4.2 The if Statement
4.3 The for Statement
4.4 The while Loop
4.5 The repeat and break Statements
4.6 The next Statement
4.7 The switch Statement
4.8 Functions
4.9 Some Solved Examples
Chapter 5: Interfacing with R
5.1 Introduction to Extending R
5.2 Interfacing R with C/C++
5.3 Interfacing R with Python
Chapter 6: Vectors
6.1 Overview of Vectors
6.2 Creating a Vector
6.3 Accessing the Elements of a Vector
6.4 Vector Manipulation and Vector Arithmetic
6.5 Deleting a Vector
6.6 Vector Element Sorting
Chapter 7: Matrices
7.1 Creating a Matrix
7.2 Coercion of Matrix Elements
7.3 Matrix Subsetting
7.4 Matrix Operations
7.5 Combining Matrices
7.6 Special Matrices
7.7 Eigenvectors and Eigenvalues
7.8 Arrays
Chapter 8: Lists
8.1 Introduction to Lists
8.2 Creating a List
8.3 General List Operations
8.4 Accessing the Elements of a List
8.5 Manipulating the Elements of a List
8.6 Merging Lists
8.7 Applying Functions to a List
8.8 Recursive List
8.9 Sorting and Searching
Chapter 9: Data Frames
9.1 Introduction to Data Frames
9.2 Creating a Data Frame
9.3 General Operations on Data Frames
9.4 Expanding a Data Frame
9.5 Applying Functions to Data Frames
Chapter 10: Factors and Tables
10.1 Introduction to Factors
10.2 Creating a Factor
10.3 Factor Levels
10.4 Summarising a Factor
10.5 Ordered Factors
10.6 Converting Factors
10.7 Common Functions Used with Factors
10.8 Introduction to Tables and Creating Tables
10.9 Table-related Functions
10.10 Cross-tabulation
Chapter 11: Regular Expressions and String Manipulation in R
11.1 Introduction to Regular Expressions
11.2 Regular Expressions and Pattern Matching
11.3 String Manipulation
11.4 Solved Examples of Regular Expressions
Chapter 12: S3 and S4 Classes and Objects
12.1 Introduction to S3 and S4 Classes and Objects
12.2 S3 Classes
12.3 S4 Classes
Chapter 13: Accessing Input and Output
13.1 Introduction to Files and Input/Output
13.2 Accessing the Keyboard and Monitor
13.3 File Functions
Chapter 14: Graphs in R Programming
14.1 Introduction to Graphs
14.2 Creating Graphs
14.3 Histograms and Density Plots
14.4 Dot Plots
14.5 Bar Plots
14.6 Line Charts
14.7 Pie Charts
14.8 Box Plots
14.9 Scatter Plots
14.10 Saving Graphs to a File
14.11 Creating Three-Dimensional Plots
Chapter 15: R Apply Family
15.1 Introduction to the Apply Family
15.2 The apply() Function
15.3 The lapply() Function
15.4 The sapply() Function
15.5 Slicing a Vector
15.6 The tapply() Function
15.7 The rep() Function
15.8 The mapply() Function
15.9 The vapply() Function
Chapter 16: The R Profiler
16.1 Introduction
16.2 Using the system.time() Function
16.3 Timing Longer Expressions
16.4 Using the R Profiler
16.5 Using the summaryRprof() Function
Chapter 17: Descriptive Statistics using R
17.1 Introduction to Statistical Analysis in R
17.2 Measures of Central Tendency or Location
17.3 Measures of Dispersion
17.4 Measures of Shape
Chapter 18: Probability
18.1 Introduction to Probability
18.2 Probability and Statistics
18.3 Random Variables
18.4 Probability Distribution
Chapter 19: Sampling Distributions
19.1 Introduction to Sampling Distributions
19.2 Central Limit Theorem
19.3 Sampling Distribution of X2
19.4 Student’s T Distribution
19.5 F Distribution
Chapter 20: Correlation and Regression Analysis
20.1 Introduction to Correlation and Regression Analysis
20.2 Correlation Analysis
20.3 Regression Analysis
Chapter 21: Statistical Inference
21.1 Introduction to Statistical Inference
21.2 Hypothesis Testing
Chapter 22: Analysis of Variance
22.1 Introduction to Analysis of Variance
22.2 Implementing Analysis of Variance
22.3 Variants of ANOVA
22.4 ANOVA in R
Chapter 23: Machine Learning Algorithms in R
23.1 Introduction to Machine Learning Algorithms
23.2 Naive Bayes Classifier
23.3 Decision Tree Classifier
23.4 The k-Nearest Neighbour Method
23.5 Clustering Techniques: K-means Clustering
23.6 Association Rule Mining
Chapter 24: Text Mining in R: Sentiment Analysis
24.1 Introduction to Text Mining
24.2 Text Preprocessing
24.3 Sentiment Analysis
24.4 N-grams
Index
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