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

Model deployment

Now that we have created our model, we can reuse it in Tableau. This model will just work in Tableau, as long as you have Rserve running. You will also need to have the relevant packages installed, as per the script. In particular, the rpart package is the workhorse of this example, and it must be installed since it is self-contained as it loads the library, trains the model, and then uses the model to make predictions within the same calculation.

There are many ways to deploy your model for future use, and this part of the process involves the CRISP-DM methodology. Here are a few ways:

  • You can go through the model fitting inside R using RStudio or another IDE and save it. Then, you could simply load the model into Tableau or you can save it to a file directly from within Tableau. The advantage of doing it in this way is that you can reuse your R model in other packages as well. The downside is that you will need to switch between R and Tableau, and then back again.

  • If you...