5. Source Data is Never Enough | TIBCO Spotfire: A Comprehensive Primer

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TIBCO Spotfire: A Comprehensive Primer

By : Michael Phillips

4.1 (15)

TIBCO Spotfire: A Comprehensive Primer

4.1 (15)

By: Michael Phillips

Overview of this book

If you are a business user or data professional, this book will give you a solid grounding in the use of TIBCO Spotfire. This book assumes no prior knowledge of Spotfire or even basic data and visualization concepts.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Show Me the Data

1. Show Me the Data

Adding data to Spotfire from files

Key data concept – basic row/column structure of a data table

Key data concept – data types

Using the inbuilt filters in Spotfire

Key data concept – data relationships

How to link data in Spotfire

Connecting to a database

Key data concept – data queries

Summary

2. Visualize This!

2. Visualize This!

Displaying information quickly in tabular form

Enriching your visualizations with color categorization

Visualizing categorical information using bar charts

Dividing a visualization across a trellis grid

Key Spotfire concept – marking

Visualizing trends using line charts

Visualizing proportions using pie charts

Visualizing relationships using scatter plots

Visualizing hierarchical relationships using treemaps

Key Spotfire concept – filters

Enhancing tabular presentations using graphical tables

Summary

3. Analyze That!

3. Analyze That!

Framing your analysis using text areas

Key Spotfire concept – document properties

Increasing interactivity using property controls

Providing summary information at a glance

Customizing the filter panel

Getting details of marked items

Annotating data using tags and lists

Creating analysis snapshots using bookmarks

Summary

4. The Big Wide World of Spotfire

4. The Big Wide World of Spotfire

An overview of Spotfire components and architecture

A quick guide to Administration Manager

Using the Library Administration interface

A quick tour of Information Designer

An overview of Spotfire Analyst (formerly Professional Client)

An overview of Spotfire Consumer (formerly web player)

An overview of Spotfire Business Author (new with Version 6.5)

Automating tasks using Automation Services

An overview of system monitoring tools

Summary

5. Source Data is Never Enough

5. Source Data is Never Enough

Creating metrics using calculated columns

Using the Data Panel tool

Key data concept – dimensional hierarchies

Adding dimensionality to your data by defining hierarchies

Categorizing continuous numerical data using binning functions

Slicing and dicing data using hierarchy nodes

Merging data from multiple sources

Key data concept – narrow tables versus wide tables

Transforming data structure through pivots and unpivots

Using Spotfire's Information Designer

Optimizing complex data manipulations using in-database analytics

Summary

6. The World is Your Visualization

6. The World is Your Visualization

Applying custom expressions directly to visualization properties

Key Spotfire concept – difference between calculated columns and custom expressions

Annotating visualizations with reference lines, fitted curves, and error bars

Defining color rules and organizing document color schemes

Slicing and dicing visualizations using hierarchy nodes

Mashing up data from different tables in a single visualization

How to create dynamic pivots using cross tables

Visualizing categorical information and trends together in combination charts

Visualizing statistical measures using box and whisker plots

Summarizing statistical measures using summary tables

Visualizing complex multidimensional data using heat maps

Profiling your data using parallel coordinate plots

Exporting and publishing data and visualizations

Summary

7. What's Your Location?

7. What's Your Location?

Creating background map layers

Key Spotfire concept: map chart layers

Key mapping concept – coordinate reference systems

Using automatic geocoding to accurately position locations from your data on a map

Incorporating and using a feature layer

Adding Web Map Service data to a map chart

Using the map chart for nongeographic spatial analysis

Summary

8. The Secret Life of Python

8. The Secret Life of Python

Introduction to IronPython

Overview of the Spotfire API

Some useful external libraries

Creating scripts

Referencing and manipulating the primary elements in an analysis file

How to manipulate visualization properties

How to manipulate color

How to manipulate filters

Manipulating data already loaded into Spotfire

How to add data to an analysis

Summary

9. It's All About Self-service These Days

9. It's All About Self-service These Days

Building a guided analysis

Key data concept – metadata

Incorporating configurable business rules into your analysis

A metadata-driven self-service analytics case study

Summary

10. Beyond the Horizon

10. Beyond the Horizon

TIBCO Enterprise Runtime for R (TERR)

JavaScript

Mobile Metrics

Event analytics

Spotfire data connectors

TIBCO Spotfire Advanced Data Services

Summary

Index

Index

Transforming data structure through pivots and unpivots

You can use pivot and unpivot transformations to change narrow tables into wide tables and vice versa. You can also use a pivot to permanently reduce the granularity of a data table by summing up to a higher level.

Take a look at the baseball player data. What sort of table do you think it is? It has columns for player name and team and then individual columns for a range of standard baseball statistics, with each statistic getting its own column. This was very convenient when we wanted to calculate the walk-to-strikeout ratio.

Unpivot

Let's see what happens when we unpivot the baseball player data table.

The data used here is BaseballPlayerData.xls, which you can download from http://www.insidespotfire.com.

Load the baseball data into an analysis file.
Open the Add Data Tables dialog and using the Add dropdown, select the baseball player data From Current Analysis. So far, we've been adding data from external sources, but it's also possible...

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