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)

Approach for creating a data product from statistical modeling and web UI

In this section, we are going to build an app with a dataset. Before we start with the construction of the architecture of our app, we need some open data to work with. I'm going to use the computer dataset that can be found in the Ecdat package, so make sure to install it by running install.packages("Ecdat"). A documentation about its variables is found at https://vincentarelbundock.github.io/Rdatasets/doc/Ecdat/Computers.html.

Once it was installed, if you type class(Ecdat::Computers), you will see that it is a DataFrame. A lot of information is hidden inside this data, and our goal here is to present a couple of them in a Shiny application, publishing it in a web page. We are going to rearrange and group our dataset, so you'll need the dplyr package; make sure it is installed and run...