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.
  • TIBCO Spotfire: A Comprehensive Primer
  • Toc
  • feedback
TIBCO Spotfire: A Comprehensive Primer

TIBCO Spotfire: A Comprehensive Primer

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

TIBCO Spotfire: A Comprehensive Primer

4.1 (15)
By: 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.
Table of Contents (12 chapters)
close
close
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
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
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
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
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
5. Source Data is Never Enough
chevron up
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
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
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
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
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
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
11
Index
Index

Key data concept – narrow tables versus wide tables

Data tables are row-by-column matrices, and obviously they can range in size from one row by one column to many rows by many columns. You might have come across the colloquial expressions narrow table and wide table, or even narrow long table and wide short table. These concepts refer to the number of columns in the table, but not in a literal sense.

They refer to the fundamental way in which the data is structured or categorized in a table. In a basic narrow table, a list of categories is contained under a single column and a second column lists values associated with each of those categories: just two columns, but potentially many rows. In reality, there may be other reference columns, but the basic concept holds: one column containing all values across categories. In a basic wide table, each category gets its own column and the associated values appear under it: potentially a lot of columns and as few as one row.

Consider the GDP data...

End of Section 12
Next Section
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