Book Image

Tableau 10 Business Intelligence Cookbook

By : Donabel Santos, Paul Banoub
Book Image

Tableau 10 Business Intelligence Cookbook

By: Donabel Santos, Paul Banoub

Overview of this book

Tableau is a software tool that can speed up data analysis through its rich visualization capabilities, and help uncover insights for better and smarter decision making. This book is for the business, technology, data and analytics professionals who use and analyze data and data-driven approaches to support business operations and strategic initiatives in their organizations. This book provides easy-to-follow recipes to get the reader up and running with Tableau 10, and covers basic to advanced use cases and scenarios. The book starts with building basic charts in Tableau and moves on to building more complex charts by incorporating different Tableau features and interactivity components. There is an entire chapter dedicated to dashboard techniques and best practices. A number of recipes specifically for geospatial visualization, analytics, and data preparation are also covered. By the end of this book, you’ll have gained confidence and competence to analyze and communicate data and insights more efficiently and effectively by creating compelling interactive charts, dashboards, and stories in Tableau.
Table of Contents (17 chapters)
Tableau 10 Business Intelligence Cookbook
Credits
About the Author
Acknowledgements
About the Reviewer
www.PacktPub.com
Preface
Index

Performing linear regression with R


Linear regression is a common technique to find the best fit straight line in a scatter plot. The resulting line can help in predictive analysis.

In this recipe, we will add trend lines to CO2 Emission graphs using R:

Getting ready

This recipe has a few prerequisites before it can be followed:

  1. Install R. The R Project for Statistical Computing website is https://www.r-project.org/.

  2. Optionally, install RStudio from https://www.rstudio.com/, which is an integrated environment for R.

  3. Install the Rserve package by running the following in R:

      install.packages("Rserve"); 
      library(Rserve);
    run.Rserve();
  4. You will get a message that says the connection is blocked:

  5. Go to Help, and under Settings and Performance, select Manage External Service Connection...:

  6. Type localhost for the Server:, and leave the Port/API Key: to 6311. Test the connection, and ensure you get a successful connection message before proceeding:

Once R is ready and connected, you may proceed with the...