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Learning Tableau

Learning Tableau

By : Joshua N. Milligan
3.8 (22)
Buy this Book
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Learning Tableau

Learning Tableau

3.8 (22)
By: Joshua N. Milligan
Buy this Book

Overview of this book

If you want to understand your data using data visualization and don't know where to start, then this is the book for you. Whether you are a beginner or have years of experience, this book will help you to quickly acquire the skills and techniques used to discover, analyze, and communicate data visually. Some familiarity with databases and data structures is helpful, but not required.
Table of Contents (13 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Creating Your First Visualizations and Dashboard
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1. Creating Your First Visualizations and Dashboard
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Connecting to data in Access
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Foundations for building visualizations
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Visualizing data
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Bringing everything together in a dashboard
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Summary
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2. Working with Data in Tableau
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2. Working with Data in Tableau
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The Tableau paradigm
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Connecting to data
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Working with extracts instead of live connections
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Metadata and sharing data source connections
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Joins and blends
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Filtering data
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Summary
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3. Moving from Foundational to Advanced Visualizations
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3. Moving from Foundational to Advanced Visualizations
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Comparing values across different dimensions
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Visualizing dates and times
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Relating parts of the data to the whole
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Visualizing distributions
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Visualizing multiple axes to compare different measures
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Summary
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4. Using Row-level and Aggregate Calculations
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4. Using Row-level and Aggregate Calculations
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Creating and editing calculations
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Three levels of calculation
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Parameters
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Practical examples of calculations and parameters
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Ad hoc calculations
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Performance considerations
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Summary
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5. Table Calculations
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5. Table Calculations
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An overview of table calculations
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Quick table calculations
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Scope and direction
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Addressing and partitioning
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Advanced table calculations
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Practical examples
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Summary
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6. Formatting a Visualization to Look Great and Work Well
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6. Formatting a Visualization to Look Great and Work Well
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Formatting considerations
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How formatting works in Tableau
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Adding value to visualizations
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Summary
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7. Telling a Data Story with Dashboards
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7. Telling a Data Story with Dashboards
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Dashboard objectives
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Example – is least profitable always unprofitable?
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How actions work
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Example – a regional scorecard
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Story points
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Summary
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8. Adding Value to Analysis – Trends, Distributions, and Forecasting
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8. Adding Value to Analysis – Trends, Distributions, and Forecasting
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Trends
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Distributions
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Forecasting
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Summary
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9. Making Data Work for You
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9. Making Data Work for You
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Structuring data for Tableau
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Dealing with data structure issues
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An overview of advanced fixes for data problems
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Summary
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10. Advanced Techniques, Tips, and Tricks
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10. Advanced Techniques, Tips, and Tricks
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Sheet swapping and dynamic dashboards
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Leveraging sets to answer complex questions
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Mapping techniques
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Using background images
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Summary
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11. Sharing Your Data Story
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11. Sharing Your Data Story
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Presenting, printing, and exporting
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Sharing a workbook with users of Tableau Desktop or Tableau Reader
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Sharing data with users of Tableau Server, Tableau Online, and Tableau Public
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Summary
12
Index
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Index

Comparing values across different dimensions

Often, you will want to compare the differences of measured values across different dimensions. You might find yourself asking questions like these:

  • How much profit did I generate in each department?
  • How many views did each of my websites get?
  • How many cases did each doctor in the hospital treat last year?

In each case, you are looking to make a comparison (among departments, websites, or doctors) in terms of some quantitative measurement (profit, number of views, and the count of cases).

Bar charts

Here is a simple bar chart, created using the Superstore Sales data source, similar to the one we built in Chapter 1, Creating Your First Visualizations and Dashboard:

Bar charts

The sum of sales is easily compared for each category of item sold in the chain of stores. Category is used as a discrete dimension in the view, which defines row headers (because it is discrete) and slices the sum of sales for each category (because it is a dimension). Sales defines an axis...

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