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  • TIBCO Spotfire: A Comprehensive Primer
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TIBCO Spotfire: A Comprehensive Primer

TIBCO Spotfire: A Comprehensive Primer

By : Phillips
4.1 (15)
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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)
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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
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
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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

Visualizing statistical measures using box and whisker plots

The Box Plot visualization is a convenient graphical tool for displaying descriptive statistical measures such as median, mean, and quartile. We have to look at some statistical theory now because the main reason for using a box plot is to show statistical measures, and it's important that you understand the basic principles. In broad terms, statistical analysis may be divided between parametric and nonparametric approaches.

Parametric statistics are based on an assumption that the data falls into some known probability distribution, and they make inferences about the parameters of that distribution. They can provide good accuracy and precision and, therefore, good statistical power, but they are not robust because they depend on assumptions about distribution. The mean (what people commonly refer to as the average) and standard deviation are parametric statistical measures.

Nonparametric statistics make no assumptions about the...

End of Section 12
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