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)

Introducing interactivity with user input

The interactivity we saw previously with the dynamic data table works within the web browser itself using JavaScript, and it does not need to go through the server function to provide the interactivity, only to pass the table itself. However, many interesting interactivity features need to go through the server so that we can provide custom responses for them. In this section, we show how to add various types of inputs to our application.

Setting up static user inputs

First, we will show how to filter the timestamps in the data to only show observations that fall within a range defined by the user. To do this, we need to first define four timestamps: the minimum, the initial left limit...