Book Image

R Programming By Example

By : Omar Trejo Navarro
Book Image

R Programming By Example

By: Omar Trejo Navarro

Overview of this book

R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.
Table of Contents (12 chapters)

Who this book is for

This book is for those who wish to develop software in R. You don't need to be an expert or professional programmer to follow this book, but you do need to be interested in learning how R works. My hope is that this book is useful for people ranging from beginners to advanced by providing hands-on examples that may help you understand R in ways you previously did not.

I assume basic programming, mathematical, and statistical knowledge, because there are various parts in the book where concepts from these disciplines will be used, and they will not be explained in detail. If you have programmed something yourself in any programming language, know basic linear algebra and statistics, and know what linear regression is, you have everything you need to understand this book.

This book was written for people in a variety of contexts and with diverse profiles. For example, if you are an analyst employed by an organization that requires you to do frequent data processing to produce reports on a regular basis, and you need to develop programs to automate such tasks, this book is for you. If you are an academic researcher who wants to use current techniques, combine them, and develop tools to test them automatically, this book is for you. If you're a professional programmer looking for ways to take advantage of advanced R features, this book is for you. Finally, if you're preparing for a future in which data will be of paramount importance (it already is), this book is for you.