Modern R Programming Cookbook

Key Features

• Develop strategies to speed up your R code
• Tackle programming problems and explore both functional and object-oriented programming techniques
• Learn how to address the core problems of programming in R with the most popular R packages for common tasks

Book Description

R is a high-level statistical language and is widely used among statisticians and data miners for developing statistical applications. The objective of this book is to show how to work with different programming aspects of R. The emerging R developers and data science could have very good programming knowledge but might have limited understanding about R syntax and semantics. Our book will be a platform develop practical solution out of real world problem in scalable fashion and with very good understanding. You will work with various versions of R libraries that are essential for scalable data science solutions. You will learn to work with Input / Output issues when working with relatively larger dataset. At the end of this book readers will also learn how to work with databases from within R and also what and how meta programming helps in developing applications.

What you will learn

• Install R and its various IDE for a given platform along with installing libraries from different repositories and version control
• Learn about basic data structures in R and how to work with them
• Write customized R functions and handle recursions, exceptions in R environments
• Create the data processing task as a step by step computer program and execute using dplyr
• Extract and process unstructured text data
• Interact with database management system to develop statistical applications
• Formulate and implement parallel processing in R

Table of Contents

Chapter 1. Installing And Configuring R And Its Libraries
Chapter 2. Data Structures In R
Chapter 3. Writing Customized Functions
Chapter 4. Conditional And Iterative Operations
Chapter 5. R Objects And Classes
Chapter 6. Querying, Filtering, And Summarizing
Chapter 7. R For Text Processing
Chapter 8. R And Databases
Chapter 9. Parallel Processing In R