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  • Book Overview & Buying TIBCO Spotfire: A Comprehensive Primer
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TIBCO Spotfire: A Comprehensive Primer

TIBCO Spotfire: A Comprehensive Primer

By : Phillips
4.1 (15)
Buy this Book
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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)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Show Me the Data
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1. Show Me the Data
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Adding data to Spotfire from files
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Key data concept – basic row/column structure of a data table
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Key data concept – data types
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Using the inbuilt filters in Spotfire
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Key data concept – data relationships
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How to link data in Spotfire
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Connecting to a database
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Key data concept – data queries
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Summary
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2. Visualize This!
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2. Visualize This!
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Displaying information quickly in tabular form
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Enriching your visualizations with color categorization
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Visualizing categorical information using bar charts
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Dividing a visualization across a trellis grid
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Key Spotfire concept – marking
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Visualizing trends using line charts
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Visualizing proportions using pie charts
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Visualizing relationships using scatter plots
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Visualizing hierarchical relationships using treemaps
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Key Spotfire concept – filters
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Enhancing tabular presentations using graphical tables
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Summary
3
3. Analyze That!
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3. Analyze That!
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Framing your analysis using text areas
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Key Spotfire concept – document properties
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Increasing interactivity using property controls
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Providing summary information at a glance
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Customizing the filter panel
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Getting details of marked items
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Annotating data using tags and lists
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Creating analysis snapshots using bookmarks
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Summary
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4. The Big Wide World of Spotfire
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4. The Big Wide World of Spotfire
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An overview of Spotfire components and architecture
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A quick guide to Administration Manager
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Using the Library Administration interface
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A quick tour of Information Designer
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An overview of Spotfire Analyst (formerly Professional Client)
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An overview of Spotfire Consumer (formerly web player)
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An overview of Spotfire Business Author (new with Version 6.5)
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Automating tasks using Automation Services
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An overview of system monitoring tools
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Summary
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5. Source Data is Never Enough
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5. Source Data is Never Enough
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Creating metrics using calculated columns
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Using the Data Panel tool
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Key data concept – dimensional hierarchies
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Adding dimensionality to your data by defining hierarchies
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Categorizing continuous numerical data using binning functions
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Slicing and dicing data using hierarchy nodes
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Merging data from multiple sources
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Key data concept – narrow tables versus wide tables
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Transforming data structure through pivots and unpivots
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Using Spotfire's Information Designer
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Optimizing complex data manipulations using in-database analytics
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Summary
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6. The World is Your Visualization
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6. The World is Your Visualization
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Applying custom expressions directly to visualization properties
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Key Spotfire concept – difference between calculated columns and custom expressions
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Annotating visualizations with reference lines, fitted curves, and error bars
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Defining color rules and organizing document color schemes
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Slicing and dicing visualizations using hierarchy nodes
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Mashing up data from different tables in a single visualization
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How to create dynamic pivots using cross tables
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Visualizing categorical information and trends together in combination charts
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Visualizing statistical measures using box and whisker plots
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Summarizing statistical measures using summary tables
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Visualizing complex multidimensional data using heat maps
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Profiling your data using parallel coordinate plots
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Exporting and publishing data and visualizations
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Summary
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7. What's Your Location?
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7. What's Your Location?
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Creating background map layers
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Key Spotfire concept: map chart layers
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Key mapping concept – coordinate reference systems
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Using automatic geocoding to accurately position locations from your data on a map
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Incorporating and using a feature layer
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Adding Web Map Service data to a map chart
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Using the map chart for nongeographic spatial analysis
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Summary
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8. The Secret Life of Python
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8. The Secret Life of Python
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Introduction to IronPython
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Overview of the Spotfire API
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Some useful external libraries
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Creating scripts
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Referencing and manipulating the primary elements in an analysis file
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How to manipulate visualization properties
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How to manipulate color
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How to manipulate filters
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Manipulating data already loaded into Spotfire
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How to add data to an analysis
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Summary
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9. It's All About Self-service These Days
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9. It's All About Self-service These Days
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Building a guided analysis
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Key data concept – metadata
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Incorporating configurable business rules into your analysis
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A metadata-driven self-service analytics case study
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Summary
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10. Beyond the Horizon
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10. Beyond the Horizon
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TIBCO Enterprise Runtime for R (TERR)
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JavaScript
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Mobile Metrics
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Event analytics
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Spotfire data connectors
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TIBCO Spotfire Advanced Data Services
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Summary
11
Index
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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...

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