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)

Creating a new dataset with what we've learned

What we have learned so far in this chapter is that age, education, and ethnicity are important factors in understanding the way people voted in the Brexit Referendum. Younger people with higher education levels are related with votes in favor of remaining in the EU. Older white people are related with votes in favor of leaving the EU. We can now use this knowledge to make a more succinct data set that incorporates this knowledge. First we add relevant variables, and then we remove non-relevant variables.

Our new relevant variables are two groups of age (adults below and above 45), two groups of ethnicity (whites and non-whites), and two groups of education (high and low education levels):

data$Age_18to44 <- (
    data$Age_18to19 +
    data$Age_20to24 +
    data$Age_25to29 +
    data$Age_30to44
)
data$Age_45plus <- (
 ...