Book Image

Mastering Tableau 2023 - Fourth Edition

By : Marleen Meier
Book Image

Mastering Tableau 2023 - Fourth Edition

By: Marleen Meier

Overview of this book

This edition of the bestselling Tableau guide will teach you how to leverage Tableau's newest features and offerings in various paradigms of the BI domain. Updated with fresh topics, including the newest features in Tableau Server, Prep, and Desktop, as well as up-to-date examples, this book will take you from mastering essential Tableau concepts to advance functionalities. A chapter on data governance has also been added. Throughout this book, you'll learn how to use Tableau Hyper files and Prep Builder to easily perform data preparation and handling, as well as complex joins, spatial joins, unions, and data blending tasks using practical examples. You'll also get to grips with executing data densification and explore other expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. Later chapters will teach you all about improving dashboard performance, connecting to Tableau Server, and understanding data visualization with examples. Finally, you'll cover advanced use cases, such as self-service analysis, time series analysis, geo-spatial analysis, and how to connect Tableau to Python and R to implement programming functionalities within Tableau. By the end of this book, you'll have mastered Tableau 2023 and be able to tackle common and advanced challenges in the BI domain.
Table of Contents (19 chapters)
17
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18
Index

Unions

Sometimes you might want to analyze data with the same metadata structure that is stored in different files – for example, sales data from multiple years, different months, or countries. Instead of copying and pasting the data, you can union it. We already touched upon this topic in Chapter 3, Using Tableau Prep Builder, but a union is basically where Tableau will append new rows of data to existing columns with the same header. For the following exercise, we will use FIFA data (from the PlayStation game, not the World Cup). The data is from Kaggle (https://www.kaggle.com/datasets/stefanoleone992/fifa-22-complete-player-dataset?resource=download) and ships in multiple CSVs; each CSV contains data for one year and male/female are split too.

For our analysis, we want to combine all the files into one. Hence, we need to union, by taking the following steps:

  1. Download the CSV files from GitHub (https://github.com/PacktPublishing/Mastering-Tableau-2023-Fourth...