Book Image

Modern R Programming Cookbook

By : Jaynal Abedin
Book Image

Modern R Programming Cookbook

By: Jaynal Abedin

Overview of this book

R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 2,000 available add-ons, and R is a serious rival to all commercial statistical packages. 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.
Table of Contents (10 chapters)

What this book covers

Chapter 1, Installing and Configuring R and its Libraries, covers the recipes on how to install and configure R and its libraries on Windows and Linux platforms.

Chapter 2, Data Structures in R, covers the data structures of R and how to create and access their properties and various operations related to a specific data structure.

Chapter 3, Writing Customized Functions, guides you to create your own customized functions and understand how to work with various data types within a function and access an output of a function.

Chapter 4, Conditional and Iterative Operations, covers the use of conditional and repetition operators in R.

Chapter 5, R Objects and Classes, guides you in creating the S3 and S4 objects and how to use them in a variety of applications.

Chapter 6, Querying, Filtering, and Summarizing, introduces you to the dplyr library for data processing. This is one of the most popular libraries in R for data processing.

Chapter 7, R for Text Processing, covers the recipes related to working with unstructured text data.

Chapter 8, R and Databases, helps you learn how to interact with a database management system to develop statistical applications.

Chapter 9, Parallel Processing in R, uses the parallel processing approach to solve memory problems with a larger dataset and uses the XDF file for processing.