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)

Summary

In this chapter, you learned how to create various types of data visualizations and how to work with graph functions and graph objects efficiently. Apart from the basic graph types, you learned how to create interactive graphs and maps and how to create our own custom types of graphs. The fundamentals shown in this chapter allow you to create high-quality visualizations using important and popular packages such as ggplot2 and leaflet.

In the next chapter, Chapter 6, Understanding Reviews with Text Analysis, we will analyze the text data we have from client messages as well as data we retrieve from Twitter in real-time. We will show how to generate sentiment analysis given textual data, and we will prepare ourselves to put the graphs in this chapter together with the text analysis in the following chapter into automatic reports in Chapter 7, Developing Automatic Presentations...