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)

Building blocks for reusable high-quality graphs

To diagnose the business state and find new opportunities, in this chapter, we will use various types of graphs. When it comes to developing static high-quality graphs, you can't go wrong with the ggplot2 package. Standard (built-in) graphs in R are fine for exploratory purposes, but are not as flexible or nice-looking as ggplot2 graphs. Since we want to show how to create high-quality graphs, we will focus on using this package (and others extending it) for static graphs. However, since the vanilla ggplot2 package only works for static graphs, we will use other packages for high-quality interactive graphs.

A downside of having so much flexibility when using ggplot2 is that it's very verbose, thus requiring a lot of code to create graphs (specially when compared to standard R built-in graphing functions). We want to avoid...