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Advanced Analytics with R and Tableau

Advanced Analytics with R and Tableau

By : Jen Stirrup, Roberto Rösler, Ruben Oliva Ramos
1.3 (7)
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Advanced Analytics with R and Tableau

Advanced Analytics with R and Tableau

1.3 (7)
By: Jen Stirrup, Roberto Rösler, Ruben Oliva Ramos

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 (10 chapters)
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9
Index

Statistics for Clustering


The Describe Clusters dialog box provides information about the models that Tableau computed for clustering. You can use these statistics to assess the quality of the clustering.

When the view includes clustering, you can open the Describe Clusters dialog box by right-clicking Clusters on the Marks card (Control-clicking on a Mac) and choosing Describe Clusters. The information in the Describe Clusters dialog box is read-only, though you can click Copy to Clipboard and then paste the screen contents into a writeable document.

The Describe Clusters dialog box has two tabs: a Summary tab and a Models tab.

Describing Clusters – Summary tab

These are described in the following table:

Number of Clusters

The number of individual clusters in the clustering.

Number of Points

The number of marks in the view.

Between-group sum of squares

A metric quantifying the separation between clusters as a sum of squared distances between each cluster's centre (average value)...

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Advanced Analytics with R and Tableau
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