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)

Extending Markdown with R Markdown

As mentioned earlier, R Markdown extends Markdown. It offers many features to enhance it. There are various examples in R Markdown's documentation (http://rmarkdown.rstudio.com/gallery.html) where you may get a sense of what's possible. In this section, we will focus on code chunks, tables, graphs, local and global chunk options, and caching.

Code chunks

Code chunks are simply standard Markdown code blocks, which have a special syntax that uses curly braces ({}) along the top line of the block to send metadata to knitr, about how the block should be treated. The metadata sent is in the form of parameters with the key = value format. We'll cover more on this in the Chunk options...