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R programming for beginners /

]]> By Arora, Sandhya.
Hyderabad University press 2020 .
264p , Table of Contents 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 Objectives 2.1 Overview of Data 2.2 Sources of Data 2.3 Big Data 2.4 Data Categorisation 2.5 Data Cube Exercises Chapter 3: Operators Objectives 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 Exercises Chapter 4: Control Statements and Functions Objectives 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 Exercises Chapter 5: Interfacing with R Objectives 5.1 Introduction to Extending R 5.2 Interfacing R with C/C++ 5.3 Interfacing R with Python Exercises Chapter 6: Vectors Objectives 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 Exercises Chapter 7: Matrices Objectives 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 Exercises Chapter 8: Lists Objectives 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 Exercises Chapter 9: Data Frames Objectives 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 Exercises Chapter 10: Factors and Tables Objectives 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 Exercises Chapter 11: Regular Expressions and String Manipulation in R Objectives 11.1 Introduction to Regular Expressions 11.2 Regular Expressions and Pattern Matching 11.3 String Manipulation 11.4 Solved Examples of Regular Expressions Exercises Chapter 12: S3 and S4 Classes and Objects Objectives 12.1 Introduction to S3 and S4 Classes and Objects 12.2 S3 Classes 12.3 S4 Classes Exercises Chapter 13: Accessing Input and Output Objectives 13.1 Introduction to Files and Input/Output 13.2 Accessing the Keyboard and Monitor 13.3 File Functions Exercises Chapter 14: Graphs in R Programming Objectives 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 Exercises Chapter 15: R Apply Family Objectives 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 Exercises Chapter 16: The R Profiler Objectives 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 Exercises Chapter 17: Descriptive Statistics using R Objectives 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 Exercises Chapter 18: Probability Objectives 18.1 Introduction to Probability 18.2 Probability and Statistics 18.3 Random Variables 18.4 Probability Distribution Exercises Chapter 19: Sampling Distributions Objectives 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 Exercises Chapter 20: Correlation and Regression Analysis Objectives 20.1 Introduction to Correlation and Regression Analysis 20.2 Correlation Analysis 20.3 Regression Analysis Exercises Chapter 21: Statistical Inference Objectives 21.1 Introduction to Statistical Inference 21.2 Hypothesis Testing Exercises Chapter 22: Analysis of Variance Objectives 22.1 Introduction to Analysis of Variance 22.2 Implementing Analysis of Variance 22.3 Variants of ANOVA 22.4 ANOVA in R Exercises Chapter 23: Machine Learning Algorithms in R Objectives 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 Exercises Chapter 24: Text Mining in R: Sentiment Analysis Objectives 24.1 Introduction to Text Mining 24.2 Text Preprocessing 24.3 Sentiment Analysis 24.4 N-grams Exercises Index 9789389211566

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R programming for beginners

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Data science and analytics with Python

]]> By Arora, Sandhya .
Hyderabad Universities Press 2023 .
x, 488 , Includes index 9789393330345

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Data science and analytics with python

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