Book Image

Advanced Analytics with R and Tableau

By : Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler
Book Image

Advanced Analytics with R and Tableau

By: Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Overview of this book

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.
Table of Contents (16 chapters)
Advanced Analytics with R and Tableau
About the Authors
About the Reviewers
Customer Feedback

Neural network in R

Let's load up the libraries that we need. We are going to use the neuralnet package. The neuralnet package is a flexible package that is created for the training of neural networks using the backpropagation method. We discussed the backpropagation method previously in this chapter.

Let's install the package using the following command:


Now, let's load the library:


We need to load up some data. We will use the iris quality dataset from the UCI website, which is installed along with your R installation. You can check that you have it, by typing in iris at the Command Prompt. You should get 150 rows of data.

If not, then download the data from the UCI website, and rename the file to iris.csv. Then, use the Import Dataset button on RStudio to import the data.

Now, let's assign the iris data to the data command. Now, let's look at the data to see if it is loaded correctly. It's enough to look at the first few rows of data, and...