Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying TIBCO Spotfire: A Comprehensive Primer
  • Table Of Contents Toc
  • Feedback & Rating feedback
TIBCO Spotfire: A Comprehensive Primer

TIBCO Spotfire: A Comprehensive Primer

By : Phillips
4.1 (15)
Buy this Book
close
close
TIBCO Spotfire: A Comprehensive Primer

TIBCO Spotfire: A Comprehensive Primer

4.1 (15)
By: Phillips
Buy this Book

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.
Table of Contents (12 chapters)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Free Chapter
1
1. Show Me the Data
Icon
1. Show Me the Data
Icon
Adding data to Spotfire from files
Icon
Key data concept – basic row/column structure of a data table
Icon
Key data concept – data types
Icon
Using the inbuilt filters in Spotfire
Icon
Key data concept – data relationships
Icon
How to link data in Spotfire
Icon
Connecting to a database
Icon
Key data concept – data queries
Icon
Summary
2
2. Visualize This!
Icon
2. Visualize This!
Icon
Displaying information quickly in tabular form
Icon
Enriching your visualizations with color categorization
Icon
Visualizing categorical information using bar charts
Icon
Dividing a visualization across a trellis grid
Icon
Key Spotfire concept – marking
Icon
Visualizing trends using line charts
Icon
Visualizing proportions using pie charts
Icon
Visualizing relationships using scatter plots
Icon
Visualizing hierarchical relationships using treemaps
Icon
Key Spotfire concept – filters
Icon
Enhancing tabular presentations using graphical tables
Icon
Summary
3
3. Analyze That!
Icon
3. Analyze That!
Icon
Framing your analysis using text areas
Icon
Key Spotfire concept – document properties
Icon
Increasing interactivity using property controls
Icon
Providing summary information at a glance
Icon
Customizing the filter panel
Icon
Getting details of marked items
Icon
Annotating data using tags and lists
Icon
Creating analysis snapshots using bookmarks
Icon
Summary
4
4. The Big Wide World of Spotfire
Icon
4. The Big Wide World of Spotfire
Icon
An overview of Spotfire components and architecture
Icon
A quick guide to Administration Manager
Icon
Using the Library Administration interface
Icon
A quick tour of Information Designer
Icon
An overview of Spotfire Analyst (formerly Professional Client)
Icon
An overview of Spotfire Consumer (formerly web player)
Icon
An overview of Spotfire Business Author (new with Version 6.5)
Icon
Automating tasks using Automation Services
Icon
An overview of system monitoring tools
Icon
Summary
5
5. Source Data is Never Enough
chevron up
Icon
5. Source Data is Never Enough
Icon
Creating metrics using calculated columns
Icon
Using the Data Panel tool
Icon
Key data concept – dimensional hierarchies
Icon
Adding dimensionality to your data by defining hierarchies
Icon
Categorizing continuous numerical data using binning functions
Icon
Slicing and dicing data using hierarchy nodes
Icon
Merging data from multiple sources
Icon
Key data concept – narrow tables versus wide tables
Icon
Transforming data structure through pivots and unpivots
Icon
Using Spotfire's Information Designer
Icon
Optimizing complex data manipulations using in-database analytics
Icon
Summary
6
6. The World is Your Visualization
Icon
6. The World is Your Visualization
Icon
Applying custom expressions directly to visualization properties
Icon
Key Spotfire concept – difference between calculated columns and custom expressions
Icon
Annotating visualizations with reference lines, fitted curves, and error bars
Icon
Defining color rules and organizing document color schemes
Icon
Slicing and dicing visualizations using hierarchy nodes
Icon
Mashing up data from different tables in a single visualization
Icon
How to create dynamic pivots using cross tables
Icon
Visualizing categorical information and trends together in combination charts
Icon
Visualizing statistical measures using box and whisker plots
Icon
Summarizing statistical measures using summary tables
Icon
Visualizing complex multidimensional data using heat maps
Icon
Profiling your data using parallel coordinate plots
Icon
Exporting and publishing data and visualizations
Icon
Summary
7
7. What's Your Location?
Icon
7. What's Your Location?
Icon
Creating background map layers
Icon
Key Spotfire concept: map chart layers
Icon
Key mapping concept – coordinate reference systems
Icon
Using automatic geocoding to accurately position locations from your data on a map
Icon
Incorporating and using a feature layer
Icon
Adding Web Map Service data to a map chart
Icon
Using the map chart for nongeographic spatial analysis
Icon
Summary
8
8. The Secret Life of Python
Icon
8. The Secret Life of Python
Icon
Introduction to IronPython
Icon
Overview of the Spotfire API
Icon
Some useful external libraries
Icon
Creating scripts
Icon
Referencing and manipulating the primary elements in an analysis file
Icon
How to manipulate visualization properties
Icon
How to manipulate color
Icon
How to manipulate filters
Icon
Manipulating data already loaded into Spotfire
Icon
How to add data to an analysis
Icon
Summary
9
9. It's All About Self-service These Days
Icon
9. It's All About Self-service These Days
Icon
Building a guided analysis
Icon
Key data concept – metadata
Icon
Incorporating configurable business rules into your analysis
Icon
A metadata-driven self-service analytics case study
Icon
Summary
10
10. Beyond the Horizon
Icon
10. Beyond the Horizon
Icon
TIBCO Enterprise Runtime for R (TERR)
Icon
JavaScript
Icon
Mobile Metrics
Icon
Event analytics
Icon
Spotfire data connectors
Icon
TIBCO Spotfire Advanced Data Services
Icon
Summary
11
Index
Icon
Index

Key data concept – dimensional hierarchies

Data will often contain intrinsic dimensionality. The most common example is a date. Dates are usually entered as single values, such as 12-May-2005, but if you think about it, there is an intrinsic dimensional hierarchy you might find useful when building visualizations: year > month > day, for example, but also year > quarter > month > day. Include time and you have additional dimensions.

Independent data attributes, or columns, can also be arranged in a hierarchy. A common example is geography (region > country > province > city). The order of the hierarchy can vary depending on your exact needs for a given situation. Do you want to arrange your music collection as artist > year > album > song or year > artist > album > song?

You won't always need to use dimensional hierarchies, but they can be a useful and powerful way to navigate your data.

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 7
Next Section

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY