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)

Measuring accuracy with score functions

Now that we have checked our model's assumptions, we turn toward measuring it's predictive power. To measure our predictive accuracy, we will use two methods, one for numerical data (Proportion) and the other for categorical data (Vote). We know that the Vote variable is a transformation from the Proportion variable, meaning that we are measuring the same information in two different ways. However, both numerical and categorical data are frequently encountered in data analysis, and thus we wanted to show both approaches here. Both functions, score_proportions() (numerical) and score_votes() (categorical) receive the data we use for testing and the predictions for each of the observations in the testing data, which come from the model we built in previous sections.

In the numerical case, score_proportions() computes a score using...