Book Image

Hands-On Data Science with R

By : Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias
Book Image

Hands-On Data Science with R

By: Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias

Overview of this book

R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.
Table of Contents (16 chapters)

Going to Production with R

"Data is the new oil."
– Kevin Plank

In this chapter, we're going to introduce you to the package used to build an interactive app, or the Shiny app, which is how it is named. The first section explains what the shiny package is and the second section shows how to build a simple kind of Shiny app. The third section describes how reactive works and presents some reactive functions. In the next section, we create a Shiny app using data. And there is the last section, where we give a little advice about the shiny package.

In this chapter, we'll be cover the following topics:

  • What is R shiny package
  • Creating an R Shiny app for statistical modeling
  • Best practices for Shiny