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)

Putting it all together into high-quality code

Now that we have the fundamentals about analyzing data with descriptive statistics, we're going to improve our code's structure and flexibility by breaking it up into functions. Even though this is common knowledge among efficient programmers, it's not a common practice among data analysts. Many data analysts would simply paste the code we have developed all together, as-is, into a single file, and run it every time they wanted to perform the analysis. We won't be adding new features to the analysis. All we'll do is reorder code into functions to encapsulate their inner-workings and communicate intention with function names (this substantially reduces the need for comments).

We'll focus on producing high-quality code that is easy to read, reuse, modify, and fix (in case of bugs). The way we actually do...