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)

Predicting Votes with Linear Models

This chapter shows how to work with statistical models using R. It shows how to check data assumptions, specify linear models, make predictions, and measure predictive accuracy. It also shows how to find good models programatically to avoid doing analysis by hand, which can potentially save a lot of time. By the end of this chapter, we will have worked with various quantitative tools that are used in many business and research areas nowadays. The packages used in this chapter are the same ones from the previous chapter.

Just like in the previous chapter, the focus here will be on automating the analysis programatically rather than on deeply understanding the statistical techniques used in the chapter. Furthermore, since we have seen in Chapter 2, Understanding Votes With Descriptive Statistics, how to work efficiently with functions, we will...