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)

Getting intuition with graphs and correlations

Now that we have some clean data to work with, we will create lots of plots to build intuition about the data. In this chapter, we will work with plots that are easy to create and are used for exploratory purposes. In Chapter 4, Simulating Sales Data and Working with Databases, we will look into publication ready plots that are a little more verbose to create.

Visualizing variable distributions

Our first plot is a simple one and shows the proportion of votes by each RegionName. As you can see in the plot shown below, the London, North West, and West Midlands regions account for around 55 percent of the observations in the data.

Vote Proportion by Region

To create the plot, we...