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Mastering Tableau 2023 - Fourth Edition

You're reading from Mastering Tableau 2023 - Fourth Edition

Product type Book

Published in Aug 2023

Publisher Packt

ISBN-13 9781803233765

Pages 684 pages

Edition 4th Edition

Languages

Concepts

Business Intelligence

Author (1):

Marleen Meier

Table of Contents (19) Chapters

Preface

1. Reviewing the Basics

2. Getting Your Data Ready

3. Using Tableau Prep Builder

4. Learning about Joins, Blends, and Data Structures

5. Introducing Table Calculations

6. Utilizing OData, Data Densification, Big Data, and Google BigQuery

7. Practicing Level of Detail Calculations

8. Going Beyond the Basics

9. Working with Maps

10. Presenting with Tableau

11. Designing Dashboards and Best Practices for Visualizations

12. Leveraging Advanced Analytics

13. Improving Performance

14. Exploring Tableau Server and Tableau Cloud

15. Integrating Programming Languages

16. Developing Data Governance Practices

17. Other Books You May Enjoy

18. Index

Creating custom polygons

Geographic areas for which Tableau natively provides polygons include worldwide airport codes, cities, countries, regions, territories, states, provinces, and some postcodes and second-level administrative districts (county-equivalents): U.S. area codes, Core-Based Statistical Areas (CBSAs), Metropolitan Statistical Areas (MSAs), congressional districts, and ZIP codes. This means, for example, that a filled map can easily be created for the countries of the world. Simply copy a list of the world’s countries (Ctrl + C) and paste that list into Tableau by pressing Ctrl + V while your mouse is located on an empty worksheet in Tableau Desktop. A new data source will be added to the top right under Data. Next, set the View type in Tableau to Filled Map and place the country list on the Detail shelf. Tableau will automatically draw polygons for each of those data points:

Figure 9.9: Native polygons

There are some geographic types for which Tableau...

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Authors (1)

Marleen Meier

Marleen Meier is an accomplished analyst and author with a passion for statistics and data. By using traditional methodologies and approaches such as Machine Learning and AI, Marleen is dedicated to driving meaningful insights. Currently working as the APAC Data CoE Lead for ABN AMRO Clearing, Marleen is at the forefront of innovation and implementing data-driven strategies in a global financial environment. She has lived and worked in multiple countries, including Germany, the Netherlands, the USA, and Singapore, allowing her to bring a diverse and global perspective to her work. Through her writing and speaking engagements, she aims to empower individuals and organizations to unlock the full potential of their data assets.

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