What this book covers
Chapter 1, Taking Off with Tableau, introduces the foundational principles of Tableau. We'll go through a series of examples that will introduce the basics of connecting to data, exploring and analyzing the data visually, and finally putting it all together in a fully interactive dashboard.
Chapter 2, Working with Data in Tableau, focuses on essential concepts of how Tableau works with data. You will look at multiple examples of different connections to different data sources, consider the benefits and potential drawbacks of using data extracts, consider how to manage metadata, dive into details on joins and blends, and finally, take a look at options for filtering data.
Chapter 3, Venturing on to Advanced Visualizations, explores how to create the various types of views and how to extend basic visualizations using a variety of advanced techniques such as simple calculations, jittering, multiple mark types, and dual axis. Along the way, we will also cover some details on how dates work in Tableau.
Chapter 4, Starting an Adventure with Calculations, focuses on laying a foundation and also gives a number of practical examples, by means of which you will understand the key concepts behind how calculations work in Tableau.
Chapter 5, Diving Deep with Table Calculations, explores the final main type of calculations: table calculations. These are some of the most powerful calculations in terms of their ability to solve problems and open up incredible possibilities for in-depth analysis. In practice, they range from very easy to exceptionally complex.
Chapter 6, Making Visualizations that Look Great and Work Well, explains how formatting works in Tableau, giving you the ability to refine the visualizations you created in discovery and analysis into incredibly effective communication of your data story.
Chapter 7, Telling a Data Story with Dashboards, demonstrates how Tableau allows you to bring together related data visualizations in a single dashboard. This dashboard could be a static view of various aspects of the data, or a fully interactive environment, allowing users to dynamically filter, drill down, and interact with the data visualizations. In this chapter, you will take a look at most of these concepts within the context of several in-depth examples, where you will walk through the dashboard design process step by step.
Chapter 8, Digging Deeper – Trends, Clustering, Distributions, and Forecasting, explains how Tableau enables you to quickly enhance your data visualizations with statistical analysis. Built-in features, such as trend models, clustering, distributions, and forecasting, allow you to quickly add value to your visual analysis. You will take a look at these concepts in the context of a few practical examples using some sample datasets.
Chapter 9, Cleaning and Structuring Messy Data, focuses on a number of principles for structuring data to work well with Tableau, as well as some specific examples of how to address common data issues.
Chapter 10, Introducing Tableau Prep, works through a practical example as we explore the paradigm of Tableau Prep, enabling the reader to understand the fundamental transformations and see many of the features and functions of Tableau Prep.
Chapter 11, Advanced Visualizations, Techniques, Tips, and Tricks, explains a number of advanced techniques in a practical context. You'll learn things such as creating advanced visualizations, dynamically swapping views on a dashboard, using custom images, and advanced geographic visualizations.
Chapter 12, Sharing Your Data Story, explains how Tableau enables you to share your work using a variety of methods. In this chapter, we'll take a look at the various ways to share visualizations and dashboards, alongwith what to consider when deciding how you will share them.
CONTENTS IN DETAIL
The cycle of analytics
Connecting to data
Foundations for building visualizations
Measures and dimensions
Discrete and continuous fields
Discrete fields
Continuous fields
Visualizing data
Bar charts
Iterations of bar charts for deeper analysis
Line charts
Iterations of line charts for deeper analysis
Geographic visualizations
Filled maps
Symbol maps
Density maps
Using Show Me
Putting everything together in a dashboard
The Dashboard interface
Building your dashboard
Summary
The Tableau paradigm
A simple example
Connecting to data
Connecting to data in a file
Connecting to data on a server
Connecting to data in the cloud
Shortcuts for connecting to data
Managing data source metadata
Working with extracts instead of live connections
Creating extracts
Using extracts
Performance
Portability and security
When to use an extract
Tableau file types
Joins and blends
Joining tables
Cross database joins
Blending data sources
A blending example
Filtering data
Filtering discrete (blue) fields
Filtering continuous (green) fields
Filtering dates
Other filtering options
Summary
Comparing values
Bar charts
Bar chart variations
Bullet chart – comparing to a goal, targ
et, or threshold
Bar-in-bar chart
Highlighting categories of interest
Visualizing dates and times
Date parts, date values, and exact dates
Variations of date and time visualizations
Gantt Charts
Relating parts of the data to the whole
Stacked bars
Treemaps
Area charts
Pie charts
Visualizing distributions
Circle charts
Jittering
Box and whisker plots
Histograms
Visualizing multiple axes to compare different measures
Scatterplot
Dual axis and combination charts
Summary
Introduction to calculations
Creating and editing calculations
Additional functions and operators
Four main types of calculations
Example data
Row-level calculations
Aggregate-level calculations
Why the row-level/aggregate-level difference matters
Level of detail calculations
Level of detail syntax
Level of detail types
FIXED
INCLUDE
EXCLUDE
Level of detail example
Parameters
Creating parameters
Practical examples of calculations and parameters
Fixing data issues
Extending the data
Enhancing user experience, analysis, and visualizations
Ad hoc calculations
Performance considerations
Summary
An overview of Table Calculations
Creating and editing Table Calculations
Quick Table Calculations
Relative versus fixed
Scope and direction
Working with scope and direction
Addressing and partitioning
Advanced addressing and partitioning
Custom Table Calculations
Meta table functions
Lookup and previous value
Running functions
Window functions
Rank functions
Script functions
The Total function
Practical examples
Year over Year Growth
Dynamic titles with totals
Late filtering
Data densification
When and where data densification occurs
An example of leveraging data densification
Summary
Visualization considerations
Leveraging formatting in Tableau
Workbook-level formatting
Worksheet-level formatting
Field-level formatting
Custom number formatting
Custom date formatting
Null formatting
Additional formatting options
Adding value to visualizations
Tooltips
Viz in Tooltip
Summary
Key concepts for dashboards
Dashboard definition
Dashboard objectives
Dashboard approaches
Designing dashboards in Tableau
Objects
Tiled versus floating
Manipulating objects on the dashboard
Dashboard example – is least profitable always unprofitable?
Building the views
Creating the dashboard framework
Implementing actions to guide the story
Interlude – context filtering
Designing for different displays and devices
How actions work
Filter actions
Highlight actions
URL actions
Set actions
Sets
A set action example
Dashboard example – regional scorecard
Stories
Summary
Customizing Trend Lines
Trend models
Linear
Logarithmic
Exponential
Power
Polynomial
Analyzing trend models
Exporting statistical model details
Advanced statistics (and more!) with R and Pyth
on
Clustering
Distributions
Forecasting
Summary
Structuring data for Tableau
Good structure – tall and narrow instead of short
and wide
Wide data
Tall data
Wide and tall in Tableau
Good structure – star schemas (Data Mart/Dat
a Warehouse)
Dealing with data structure issues
Restructuring data in Tableau connections
Union files together
Cross database joins
A practical example – filling out missin
g/sparse dates
Working with different levels of detail
Overview of advanced fixes for data problems
Summary
Getting prepped to explore Tableau Prep
Understanding the Tableau Prep Builder Interface
Flowing with the fundamental paradigm
Connecting to data
Cleaning the data
Union, merging mismatched fields, and removing
unnecessary fields
Grouping and cleaning
Calculations and aggregations in Tableau Prep
Filtering in Tableau Prep
Transforming the data for analysis
Options for automating flows
Summary
Advanced visualizations
Slope Charts
Lollipop Charts
Waterfall Charts
Step Lines and Jump Lines
Spark Lines
Dumbbell Charts
Unit chart/symbol charts
Marimekko Charts
Sheet swapping and dynamic dashboards
Dynamically showing and hiding other controls
Mapping techniques
Supplementing the standard in geographic data
Manually assigning geographic locations
Creating custom territories
Ad hoc custom territories
Field-defined custom territories
Leveraging spatial objects
Some final map tips
Using background images
Animation
Transparency
Summary
Presenting, printing, and exporting
Presenting
Printing
Exporting
Sharing with users of Tableau Desktop or Tableau Reader
Sharing with Tableau Desktop users
Sharing with Tableau Reader users
Sharing with users of Tableau Server, Tableau Online, and Tableau
Public
Publishing to Tableau Public
Publishing to Tableau Server and Tableau Online
Interacting with Tableau Server
Additional distribution options using Tableau Server
Summary
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Preface
What is it about Tableau that inspires an ever growing community to hold up signs that read I ♥ Tableau and excitedly share data visualizations on social media? Why do so many organizations turn to Tableau as the gold standard for visual analytics? How can an analytics platform be so fun, engaging, and useful all at once?
Tableau disrupted the paradigm for visually interacting with data. It made it easy and intuitive (and fun!) to be hands-on with the data, to receive instant visual feedback with every action, and to ask questions and uncover insights in a natural flow of thought and interaction. And Tableau continues to expand and evolve in ways that make seeing and understanding data easier and more powerful. New features such as Set Actions, geospatial support, and new statistical models expand the analysis that's possible. Transparency, density maps, new color palettes, and formatting options greatly enhance the visual story you can tell. The introduction of Tableau Prep brings the same intuitive instant feedback to data prep and cleansing that Tableau Desktop brought to data visualization. We'll cover these new features (and more) in the chapters of this book!
We'll look at Tableau through the lens of understanding the underlyingparadigm of how and why Tableau works in the context of practical examples. And then we'll build on this solid foundation of understanding so that you will have the requisite tools and skills to tackle even the toughest data challenges!
Who this book is for
This book is for anyone who needs to see and understand their data! From the casual business user to the hardcore data analyst, everyone needs to have the ability to ask and answer questions of data. Having a bit of background with data will definitely help, but you don't need to know scripting, SQL, or database structures. Whether you're new to Tableau or have been using it for months or even years, you'll gain a solid foundation for understanding Tableau and possess the tools and skills to build toward advanced mastery of the tool.
Taking Off with Tableau
When you first encounter a dataset, often the first thing you see is the raw data—numbers, dates, text, field names, and data types. Almost certainly, there are insights and stories that need to be uncovered and told, decisions to make, and actions to take. But how do you find the significance? How do you uncover the meaning and tell the stories that are hidden in the data? Tableau is an amazing platform for seeing, understanding, and making key decisions based on your data! With it, you will be able to achieve incredible data discovery, data analysis, and data storytelling. You'll accomplish these tasks and goals visually using an interface that is designed for a natural and seamless flow of thought and work. To leverage the power of Tableau, you don't need to write complex scripts or queries. Instead, you will be interacting with your data in a visual environment where everything that you drag and drop will be translated into the necessary queries for you and then displayed visually. You'll be working in real time, so you will see results immediately, get answers as quickly as you can ask questions, and be able to iterate through potentially dozens of ways to visualize the data to find a key insight or tell a piece of the story.
This chapter introduces the foundational principles of Tableau. We'll go through a series of examples that will introduce the basics of connecting to data, exploring and analyzing the data visually, and finally putting it all together in a fully interactive dashboard. These concepts will be developed far more extensively in subsequent chapters. But don't skip this chapter, as it introduces key terminology and key concepts, including the following:
The cycle of analytics
Connecting to data
Foundations for building visualizations
Creating bar charts
Creating line charts
Creating geographic visualizations
Using Show Me
Bringing everything together via a dashboard
The cycle of analytics
As someone who works with and seeks to understand data, you will find yourself working within the cycle of analytics. This cycle might be illustrated as follows:
Tableau allows you to jump to any step of the cycle, move freely between steps, and iterate through the cycle very rapidly. With Tableau, you have the ability to do the following:
Data discovery: You can very easily explore a dataset using Tableau and begin to understand what data you have visually.
Data preparation: Tableau allows you to connect to data from many different sources and, if necessary, create a structure that works best for your analysis. Most of the time, this is as easy as pointing Tableau to a database or opening a file, but Tableau gives you the tools to bring together even complex and messy data from multiple sources.
Data analysis: Tableau makes it easy to visualize the data, so you can see and understand trends, outliers, and relationships. In addition to this, Tableau has an ever-growing set of analytical functions that allow you dive deep into understanding complex relationships, patterns, and correlations in the data.
Data storytelling: Tableau allows you to build fully interactive dashboards and stories with your visualizations and insights so that you can share the data story with others.
All of this is done visually. Data visualization is the heart of Tableau. You can iterate through countless ways of visualizing the data to ask and answer questions, raise new questions, and gain new insights. And you'll accomplish this as a flow of thought.
