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)

Understanding Reviews with Text Analysis

It is well known that a very large percentage of relevant information originates in an unstructured form, an important player being text data. Text analysis, Natural Language Processing (NLP), Information Retrieval (IR), and Statistical Learning (SL) are some areas focused on developing techniques and processes to deal with this data. These techniques and processes discover and present knowledge, facts, business rules, relationships, among others, that is otherwise locked in textual form, impenetrable to automated processing.

Given the explosion of textual data we see nowadays, an important skill for analysts such as statisticians and data scientists is to be able to work efficiently with this data and find the insights they are looking for. In this chapter, we will try to predict whether a customer is going to make repeated purchases given...