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R Programming By Example

R Programming By Example

By : Trejo Navarro, Omar Trejo Navarro
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R Programming By Example

R Programming By Example

3 (4)
By: Trejo Navarro, 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)
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The examples in this book

To end this introductory chapter, I want to introduce you to the three examples we will develop throughout the rest of the book. The first one is the Brexit Votes example, in which we are going to use real Brexit votes data, and, with descriptive statistics and linear models, we will attempt to understand the population dynamics at play behind the results. If you're not familiar with Brexit, it is the popular term for the prospective withdrawal of the United Kingdom from the European Union after a referendum which took place on June 23, 2016 (https://en.wikipedia.org/wiki/Brexit). This example will be developed through Chapter 2, Understanding Votes with Descriptive Statistics, and Chapter 3, Predicting Votes with Linear Models.

The second one is The Food Factory example, in which you will learn how to simulate various kinds of data for a hypothetical company called The Food Factory, as well as integrate real data from other sources (customer reviews in this case) to complement our simulations. The data will be used to develop various kinds of visualizations, text analysis, and presentations that are updated automatically. This example will be developed through; Chapter 4, Simulating Sales Data and Working with Databases; Chapter 5, Communicating Sales with Visualizations; Chapter 6, Understanding Reviews with Text Analysis; and Chapter 7, Developing Automatic Presentations.

The third and final one is the Cryptocurrencies Tracking System example, in which we will develop an object-oriented system that will be used to retrieve real-time price data from cryptocurrency markets and the amount of cryptocurrencies assets we hold. We will then show how to compute a simple moving average efficiently using performance optimization techniques, and finally we will show how to build interactive web applications using only R. This example will be developed through Chapter 8, Object-Oriented System to Track Cryptocurrencies; Chapter 9, Implementing an Efficient Simple Moving Average; and Chapter 10, Adding Interactivity with Dashboards.

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