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**ISBN:**
9789395654296

**Bind:**
Paperback

**Year: **
2023

**Pages:**
168

**Size:**
6 x 9 Inch

**Publisher:**
Viva Books Originals

**Sales Territory:**
Worldwide

**Description:**

Designed for self-study, this book follows a logical progression from basic concepts to more advanced topics. It can serve as an introduction to programming and introductory R for beginners with no previous experience, or a self-study guide for those with some programming background. The book is also ideal for gaining practical, real-world programming skills that can be applied to data analysis using R.

**Target Audience:**

Useful for all computer science students.

It serves as an introduction to programming and introductory R for beginners with no previous experience, or a self-study guide for those with some programming background. If you are a programmer, data analyst, or someone with a quantitative background interested in machine learning and data analytics, this book is for you. The book is perfect for all data science aspirants who want to leverage the power of R for data analytics.

**Contents:**

*Preface*

**Chapter 1. Introduction to R • **Features of R • Evolution of R • R Studio Installation Guide • R Environment/R IDE • R Packages • Summary • *References* • *Exercises*

**Chapter 2.** **Basics of R Programming** • Basic Syntax • Variables • Data Types • Operators • Decision-making Statement • Looping Statements • Functions • String • Summary • *References* • *Exercises*

**Chapter 3. Data Structures in R** • Vectors • List • Data Frame • Factor • Array • Summary • *References* • *Exercises*

**Chapter 4. Working with Data in R **• Directory and File Handling • Handling .CSV File • Handling .XLS File • Handling .XML File • Handling .JSON File • R Database • Summary • *References* • *Exercises*

**Chapter 5. Data Visualization and Graphical Analysis **• R Packages for Visualization • Graphics in R • Pie Chart • Bar Chart • Boxplot • Histogram • Line Graph • Scatter Plot • Summary • *References* • *Exercises*

**Chapter 6. Statistical Analysis using R •** Concepts of Mean, Median and Mode • Variance • Standard Deviation • Min Function • Max Function • Chi-square Test • Anova • Covariance • Binomial Distribution • Normal Distribution • Summary • *References* • *Exercises*

**Chapter 7. Machine Learning using R **• Machine Learning • Types of Machine Learning • Building Machine Learning Model • R Classification • Decision Tree • Naïve Bayes Classifier • K-NN Classifier • Clustering • K-means Clustering • Support Vector Machine • Summary • *References* • *Exercises*

**Chapter 8. Trend Analysis using R** • Regression using R • Linear Regression • Sample Data • lm() Function • Multiple Linear Regression • Time Series Analysis • Summary • *References* • *Exercises*

**Chapter 9**. **Soft Computing with R** • Neural Network • Fuzzy Logic • Genetic Algorithm • Summary • *References* • *Exercises*

**About the Authors**

**About the Authors:**

**Priyanka P. Shinde** is working as Assistant Professor at the Government College of Engineering, Karad

**Varsha P. Desai **is working as Assistant Professor at the V. P. Institute of Management Studies and Research, Sangli.

**Kavita S. Oza **has been working in machine learning applications for over a decade and is a life member of the Computer Society of India.

**Rajanish K. Kamat** is Vice Chancellor of Dr Homi Bhabha State University, Mumbai.

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