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R Programming By Example

R Programming By Example

By : Trejo Navarro, Omar Trejo Navarro
3 (4)
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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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Easily achieving high benefit - cost improvements

In this section, we will show how the efficiency of R can be drastically improved without resorting to advanced techniques such as delegating to other programming languages or implementing parallelization. Those techniques will be shown in the later sections.

Using the simple data structure for the job

Many R users would agree that data frame as a data structure is a basic tool for data analysis. It provides an intuitive way to represent a typical structured dataset with rows and columns representing observations and variables, respectively, but it provides more flexibility than a matrix by allowing variables of different types (such as character and numeric variables in a...

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