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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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Starting by using good algorithms

To be able to communicate the ideas contained in this chapter clearly, first I need to provide some simple definitions. When I refer to an algorithm, I mean an abstract specification for a process. When I refer to an implementation, I refer to the way an algorithm is actually programmed. Finally, when I refer to a program or an application, I mean a set of such algorithm implementations working together. Having said that, it's easy to see how an algorithm can be implemented in many different ways (for example, one implementation may be using a list, while another may be using an array). Each of these implementations will have different performances, and they are related, but not equivalent, to an algorithm's time-complexity.

For those unfamiliar with the last term, each algorithm has the following two basic properties

  • Time complexity...

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