Table of contents
  1. Story
  2. Slides
  3. Spotfire Dashboard
  4. Research Notes
  5. Table of Contents
    1. Introduction
      1. Introduction
      2. The User Interface
        1. 1. Visualizations
        2. 2. Text areas
        3. 3. Filters
        4. 4. Details-on-Demand
      3. Logging In
        1. Login Dialog
        2. Connecting via Proxy Server
        3. Downloading Updates
        4. Working Offline
        5. Updates and Working Offline
      4. Logging In Details
        1. 4 Subtopics
    2. Data
      1. Data Overview
        1. Introduction
        2. In-Memory Data
        3. In-Database Data
      2. Data in Spotfire
        1. Working With Large Data Volumes
          1. Introduction
          2. Visualizations and Analyses
          3. Hardware
          4. Loading Data
          5. Data Export
          6. Web Player
          7. Preferences
          8. API
        2. Working With In-Database Data
        3. Working With Microsoft SQL Server Analysis Services
      3. Loading Data
        1. Loading Data Overview
        2. Open File
          1. Opening an Analysis File
          2. Opening a Text File
          3. Opening an Excel File
          4. Opening a SAS File
          5. Details
          6. 4 Subtopics
        3. Open From Library
          1. Opening Files from the Library
          2. Opening an Information Link
          3. Searching the Library
          4. Edit Properties
          5. 3 Subtopics
        4. Add Data Table
          1. How to Add Data Tables
          2. Details
          3. 6 Subtopics
          4. On-Demand Data Tables
          5. 4 Subtopics
          6. Details
          7. 6 Subtopics
          8. Load Data from Active Spaces
          9. 2 Subtopics
          10. Open Database
          11. 3 Subtopics
        5. Add Data Connection
          1. What is a Data Connection?
          2. What is a Connection Data Source?
          3. Adding Data Connections to an Analysis
        6. Available Connections
          1. Connectors Overview
          2. 8 Subtopics
        7. Details on General Dialogs
          1. 5 Subtopics
        8. Mapping of External Data Types
          1. 7 Subtypes
        9. Replace Data
          1. Replacing Data
          2. Details
          3. 3 Subtopics
        10. Transform Data
          1. Transforming Data
          2. Pivoting Data
          3. Unpivoting Data
          4. Normalizing Data
          5. Normalizing Columns
          6. Details
          7. 11 Subtopics
        11. Details
          1. 12 Subtopics
        12. Missing File
          1. Details on Missing File – Local File
          2. 3 Subtopics
      4. Inserting More Data
        1. Insert Calculated Column
          1. What is a Calculated Column?
          2. How to Insert a Calculated Column
          3. Details on Insert Calculated Column
          4. Expression and Script Editor Keyboard Shortcuts
          5. Expression Language
          6. General Syntax
          7. Operations
          8. 3 Subtopics
          9. Functions
          10. 13 Subtopics
          11. Invalid Values
          12. Details on Formatting
          13. Format String
          14. Properties
          15. 2 Subtopics
        2. Insert Binned Column
          1. What is Binning?
          2. How to Use Binning
          3. Details on Insert Binned Column
          4. The Binning Slider
        3. Insert Columns
          1. How to Insert Columns
          2. 4 Subtopics
        4. Insert Rows
          1. How to Insert Rows
          2. 3 Subtopics
      5. Working with Data Tables
        1. Data Tables Overview
          1. Introduction
          2. On-Demand Data Tables
          3. In-Database Data Tables
          4. Related Data Tables
          5. Column Matches in Data Tables
        2. How to Add Data Tables
        3. Working with Data Tables
        4. Multiple Data Tables in One Visualization
        5. Recommended Workflow
          1. 1. Choose the main data table
          2. 2. Set up the visualization with only the main data table
          3. 3. Add the aggregating measures
        6. Examples
          1. Independent Data Tables
          2. Multiple Related Data Tables
          3. Master-Detail Visualizations
          4. Insert Columns – Example
      6. Copy/Paste Data
        1. Copying Data
        2. Pasting Data
      7. Data Panel
        1. What is the Data Panel?
        2. Data Panel Pop-up Menu
        3. Details on Rename Column
      8. Data Connections Properties
        1. What is a Data Connection?
        2. What is a Connection Data Source?
        3. 9 Subtopics
      9. Data Table Properties
        1. How to Edit Data Table Properties
        2. 6 Subtopics
        3. Details
          1. Details on Select Key Columns
          2. 5 Subtopics
      10. Column Properties
        1. How to Edit Column Properties
          1. 5 Subtopics
          2. Details
          3. Details on Insert Hierarchy
          4. 4 Subtopics
    3. Visualizations
      1. Table
        1. What is a Table?
        2. 3 Subtopics
        3. Table Properties
          1. 12 Subtopics
      2. Cross Table
        1. What is a Cross Table?
        2. 2 Subtopics
        3. Cross Table Properties
          1. 13 Subtopics
      3. Graphical Table
        1. What is a Graphical Table?
        2. How to Use the Graphical Table
        3. Graphical Table Properties
          1. Graphical Table Properties
          2. 8 Subtopics
        4. Sparklines
          1. What are Sparklines?
          2. How to Use Sparklines
          3. 8 Subtopics
        5. Calculated Value
          1. What are Calculated Values?
          2. 9 Subtopics
        6. Icons
          1. What are Icons?
          2. How to Use Icons
          3. 7 Subtopics
        7. Bullet Graph
          1. What are Bullet Graphs?
          2. How to Use Bullet Graphs
          3. 8 Subtopics
      4. Bar Chart
        1. What is a Bar Chart?
        2. How to Use the Bar Chart
        3. Bar Chart Properties
          1. Bar Chart Properties
          2. 16 Subtopics
      5. Line Chart
        1. What is a Line Chart?
          1. Step lines
        2. How to Use the Line Chart
        3. Line Chart Properties
          1. Line Chart Properties
          2. 17 Subtopics
      6. Combination Chart
        1. What is a Combination Chart?
          1. Series
        2. How to Use the Combination Chart
        3. Combination Chart Properties
          1. Combination Chart Properties
          2. 15 Subtopics
      7. Pie Chart
        1. What is a Pie Chart?
        2. How to Use the Pie Chart
        3. Pie Chart Properties
          1. Pie Chart Properties
          2. 12 Subtopics
      8. Scatter Plot
        1. What is a Scatter Plot?
        2. How to Use the Scatter Plot
        3. Scatter Plot Properties
          1. Scatter Plot Properties
          2. 20 Subtopics
      9. 3D Scatter Plot
        1. What is a 3D Scatter Plot?
        2. How to Use the 3D Scatter Plot
        3. 3D Scatter Plot Properties
          1. 3D Scatter Plot Properties
          2. 18 Subtopics
      10. Map Chart
        1. What is a Map Chart?
          1. Introduction
          2. Feature Layers
          3. Marker Layers
          4. Map Layers
          5. Image Layers
        2. How to Use the Map Chart
        3. Zooming and Navigating in the Map Chart
        4. Geocoding
          1. What is Geocoding?
          2. Setting Up New Geocoding Tables
        5. Configuration of Geographical Data for Map Charts
        6. Map Chart Properties
          1. Map Chart Properties
          2. 7 Subtopics
        7. Marker Layer Settings
          1. Marker Layer Settings
          2. 13 Subtopics
        8. Feature Layer Settings
          1. Feature Layer Settings
          2. 9 Subtopics
        9. Image Layer Settings
          1. Image Layer Settings
          2. 3 Subtopics
        10. Map Layer Settings
          1. Map Layer Settings
          2. 2 Subtopics
        11. 5.5 Map Chart
          1. What is a 5.5 Map Chart?
          2. How to Use the 5.5 Map Chart
          3. Map Chart Properties
          4. Map Chart Properties (5.5)
          5. 19 Subtopics
          6. Configuration of Geographical Data for Map Charts (5.5)
      11. Treemap
        1. What is a Treemap?
        2. How to Use the Treemap
        3. Treemap Properties
          1. Treemap Properties
          2. 13 Subtopics
      12. Heat Map
        1. What is a Heat Map?
          1. Dendrograms
        2. How to Use the Heat Map
        3. Dendrograms and Clustering
        4. Importing/Exporting Dendrograms
        5. Heat Map Properties
          1. Heat Map Properties
          2. 17 Subtopics
      13. Parallel Coordinate Plot
        1. What is a Parallel Coordinate Plot?
        2. How to Use the Parallel Coordinate Plot
        3. Parallel Coordinate Plot Properties
          1. Parallel Coordinate Plot Properties
          2. 15 Subtopics
      14. Summary Table
        1. What is a Summary Table?
        2. How to Use the Summary Table
        3. Summary Table Properties
          1. Summary Table Properties
          2. 9 Subtopics
        4. Statistical Measures Overview
      15. Box Plot
        1. What is a Box Plot?
        2. How to Use the Box Plot
        3. What are Comparison Circles?
        4. Box Plot Properties
          1. Box Plot Properties
          2. 16 Subtopics
        5. Aggregations Overview
        6. Comparison Circles Algorithm
          1. References
      16. Text Area
        1. How to Use the Text Area
        2. Using Properties in the Analysis
        3. 5 Subtopics
        4. Details
          1. Details on Action Control
        5. 3.0 Text Area
          1. 5 Subtopics
      17. Details on General Dialogs
        1. Details on Add/Edit Tooltip Value
        2. 12 Subtopics
        3. Subsets
          1. Details on Edit Subset
          2. 4 Subtopics
    4. Using Visualizations
      1. Interacting with Visualizations
        1. Marking in Visualizations
        2. Highlighting in Visualizations
        3. Drag-and-Drop
        4. Zoom Sliders
      2. Column Selectors
        1. Column Selectors
        2. Non-Column Selections
        3. What is Column from Marked?
        4. Details on Column from Marked
        5. Details on Set from Property
        6. Aggregation
      3. Legend
      4. Multiple Tables in One Visualization
        1. Multiple Data Tables in One Visualization
        2. Column Matching
        3. Working with Multiple Data Tables in One Visualization
        4. More Examples
      5. Shortcuts
      6. Hierarchies
        1. Hierarchies
        2. Hierarchy Slider
        3. Working with Time Hierarchies
      7. Trellis Visualizations
      8. Information and Warnings
      9. Aggregations
        1. Aggregations Overview
        2. Statistical Measures
          1. 18 Subtopics
        3. Expression Shortcuts
          1. 13 Subtopics
    5. Enhancing Visualizations
      1. Coloring
        1. Coloring Overview
        2. Color Schemes
          1. Color Schemes Overview
          2. Opening a Color Scheme
          3. Predefined Color Schemes
          4. Applying a Color Scheme to Another Visualization
          5. Saving a Color Scheme
          6. Details
          7. 5 Subtopics
        3. Color Modes
          1. Color Modes Overview
          2. Fixed Color Mode
          3. Categorical Color Mode
          4. Gradient Color Mode
          5. Segments Color Mode
          6. Unique Values Color Mode
          7. Details on Point/Value Menu
        4. Rules in Color Schemes
          1. Color Rules Overview
          2. Details on Add/Edit Rule
        5. Coloring in Tables, Cross Tables, and Heat Maps
          1. Coloring in Tables, Cross Tables and Heat Maps
          2. Coloring in Tables
          3. Coloring in Cross Tables and Heat Maps
          4. Details on Add/Edit Color Scheme Grouping
        6. Custom Expressions for Coloring
      2. Limiting What is Shown in Visualizations
      3. Details Visualizations
        1. What is a Details Visualization?
        2. Details on Create Details Visualization
      4. Subsets
        1. What are Subsets?
        2. How to Work with Subsets
        3. Details
          1. 5 Subtopics
      5. Show/Hide Items
        1. What is Show/Hide Items?
        2. How to Work with Show/Hide Items
        3. Details on Add/Edit Rule
      6. Titles and Descriptions
        1. Overview
        2. Working with Dynamic Titles and Descriptions
        3. Visualization Properties in Expressions
        4. Details
          1. 2 Subtopics
      7. Custom Expressions
        1. Custom Expressions Introduction
        2. Custom Expressions Overview
        3. Basic Custom Expressions
        4. OVER in Custom Expressions
        5. Using Expressions on Aggregated Data (the THEN Keyword)
        6. Axes in Expressions
        7. Advanced Custom Expressions
        8. How to Insert a Custom Expression
        9. Details
          1. 3 Subtopics
      8. Lines and Curves
        1. Lines and Curves
        2. Curve Fit Models
          1. Holt-Winters Forecast
          2. References for Holt-Winters Forecast
        3. Curve Fit Theory
        4. Details
          1. 12 Subtopics
      9. Formatting
        1. Formatting Overview
        2. Formatting Settings
        3. Format String
        4. Short Number Format
      10. Error Bars
    6. Pages and Layout
      1. Visualization Layout
      2. Arranging Visualizations
      3. Pages
        1. Titled Tabs
        2. Step-by-Step
        3. History arrows
      4. Cover Page
        1. Cover Page
        2. Text Area Edit Mode
      5. Details-on-Demand
        1. What is the Details-on-Demand?
        2. Details-on-Demand Properties
          1. 6 Subtopics
      6. Document Properties
        1. How to Edit Document Properties
        2. 7 Subtopics
      7. Setting Defaults
        1. How to Specify Default Values
        2. Options
          1. 22 Subtopics
          2. Details
          3. 10 Subtopics
      8. Panels and Popovers
    7. Filters
      1. What is a Filter?
      2. Filter Types
        1. Range Filter
        2. Item Filter
        3. Radio Buttons
        4. Check Boxes
        5. Text Filter
        6. List Box Filter
        7. Hierarchy Filter
          1. What is a Hierarchy Filter?
          2. Creating a Hierarchy Filter
      3. Filters Panel
        1. 5 Subtopics
        2. Filters Panel Properties
          1. 7 Subtopics
      4. Filtering Schemes
      5. Filtering in Related Data Tables
        1. Include Filtered Rows Only
        2. Exclude Filtered Out Rows
        3. Ignore Filtering
    8. Tags
      1. What are Tags?
      2. How to Work with Tags
      3. Details
        1. 5 Subtopics
    9. Bookmarks
      1. What are Bookmarks?
      2. How to Use Bookmarks
      3. Bookmarks Example Scenarios
      4. Bookmarks Pop-up Menu
      5. Details on Add Bookmark Special
    10. Lists
      1. What Are Lists?
      2. How to Use Lists
        1. Creating Lists
        2. Working with Annotations
        3. 7 Subtopics
      3. Details
        1. 4 Subtopics
    11. Collaboration
      1. Collaboration Panel
        1. What is the Collaboration Panel?
        2. How to Use the Collaboration Panel
        3. Details on Configure Collaboration Panel
          1. Integrating with tibbr®
      2. Share
        1. What is the Share Menu?
        2. Details on Log into tibbr®
        3. Details on Share to tibbr®
    12. Tools
      1. Find
        1. Find
        2. Searching in TIBCO Spotfire
      2. Data Relationships
        1. What is the Data Relationships Tool?
        2. How to Use Data Relationships
        3. Details on Data Relationships
        4. Data Relationships Column Descriptions
        5. Data Relationships Error Codes
        6. Theory and Models
          1. Overview of Data Relationships Theory
          2. Data Relationships Linear Regression Algorithm 
          3. Data Relationships Spearman R algorithm
          4. Data Relationships Anova Algorithm
          5. Data Relationships Kruskal-Wallis Algorithm
          6. Data Relationships Chi-square Independence Test Algorithm
          7. Requirements on Input Data for Data Relationships
      3. K-means Clustering
        1. How to Perform a K-means Clustering
        2. Details on K-means Clustering
          1. References
      4. Line Similarity
        1. How to Perform a Line Similarity Comparison
        2. Details on Line Similarity
      5. Hierarchical Clustering
        1. What is the Hierarchical Clustering Tool?
        2. Details on Hierarchical Clustering
        3. Theory and Modeling
          1. Overview of Hierarchical Clustering Theory
          2. Algorithm
          3. Distance Measures
          4. 7 Subtopics
          5. Clustering Methods
          6. 6 Subtopics
          7. Ordering Weight
          8. Hierarchical Clustering References
      6. Predictive Modeling
        1. What is Predictive Modeling?
        2. Regression Modeling
          1. Linear Regression Method
          2. Regression Tree Method
          3. Details on Regression Modeling – General
          4. Details on Regression Modeling – Options
        3. Classification Modeling
          1. Logistic Regression Method
          2. Classification Tree Method
          3. Details on Classification Modeling – General
          4. Details on Classification Modeling – Options
        4. How to Use the Model Page
          1. The Model Page
          2. Using a Model Summary
          3. Using a Table of Coefficients
          4. Available Diagnostic Visualizations
        5. How to Use the Evaluation Page
          1. The Evaluation Page
          2. Using an Evaluation Summary
          3. Using a Confusion Matrix
          4. Available Diagnostic Visualizations
        6. What is the Analytic Models Panel?
        7. Details on Duplicate Model
        8. Details on Evaluate Model
        9. Details on Insert Predicted Columns
      7. Data Functions
        1. What are Data Functions?
        2. How to Use Data Functions
        3. Details
          1. 8 Subtopics
        4. Data Type Mapping
        5. Name Encoding for Column Names Sent to Spotfire Statistics Services
      8. Information Designer
        1. What is the Information Designer?
        2. General Workflow
          1. 1. Set up the data sources
          2. 2. Create folders for storing elements and set permissions
          3. 3. Combine tables by creating joins
          4. 4. Define column elements from available data sources
          5. 5. Create filter elements to limit the data retrieved
          6. 6. Create information links
        3. General Guidelines for Setting Up an Information Model
          1. Purpose
          2. Who are the end users?
          3. What data do they need?
          4. Will users build their own information links?
          5. Tip
        4. Icon Explanations
        5. Fundamental Concepts
        6. Information Links
          1. Information Links
          2. Editing Information Links
          3. 12 Subtopics
          4. Opening Information Links
          5. 4 Subtopics
          6. Transforming the Data
        7. Data Sources
          1. 5 Subtopics
        8. Folders
          1. 6 Subtopics
        9. Joins
          1. 6 Subtopics
        10. Column Elements
          1. 8 Subtopics
          2. Working with Aggregation
        11. Filter Elements
          1. 5 Subtopics
        12. Procedures
          1. 6 Subtopics
        13. User Interface Details
          1. 5 Subtopics
        14. Tips and Examples
          1. 8 Subtopics
      9. Library Administration
        1. 13 Subtopics
      10. Manage Data Connections
        1. 10 Subtopics
    13. Saving and Exporting
      1. Creating a Guided Analysis
        1. What is a Guided Analysis?
          1. Introduction
          2. Create a cover page
          3. Write instructions in text areas
          4. Place links or buttons to relevant tools or views in the text areas
          5. Use step-by-step or history arrows page navigation
          6. Use customized filtering schemes
          7. Keep in mind the intended end users’ level of data access
      2. Saving
        1. Save Overview
        2. Saving an Analysis File
        3. Details on Save
        4. Saving an Analysis File in the Library
        5. Embedded or Linked Data?
          1. Reloading Data
        6. Preparing Analyses for TIBCO Spotfire Web Clients
          1. Introduction
          2. Tips when preparing analyses for TIBCO Spotfire web clients:
          3. Design for the intended platform
          4. To use bookmarks for adapting an analysis to different screen sizes:
          5. Mobile clients (Spotfire Analytics for iPad)
        7. Links to Analyses in the Library
        8. Details on Save to Library
          1. 6 Subtopics
      3. Export Image
        1. Exporting an Image
      4. Export Data to File
        1. Exporting Data to File
        2. Details on Export Data to File
      5. Export Data to Library
        1. Exporting Data to Library
        2. 2 Subtopics
      6. Export to HTML
        1. Exporting to HTML
        2. 1 Subtopic
      7. Export to PowerPoint
        1. Exporting to Microsoft PowerPoint
        2. 1 Subtopic
      8. Export to PDF
        1. Exporting to PDF
        2. 6 Subtopics
      9. Printing
        1. Printing
        2. 1 Subtopic
    14. Appendix
      1. Software License
      2. Support
      3. Details on Support Diagnostics and Logging
        1. Dump File
  6. Glossary
    1. 3D Scatter Plot
    2. Analysis File
    3. Axis
    4. Axis Selector
    5. Bar
    6. Bar Chart
    7. Bar Labels
    8. Bar Segment
    9. Bar Segment Labels
    10. Binning
    11. Bookmark
    12. Box Plot
    13. Bullet Graph
    14. Calculated Column
    15. Calculated Value
    16. Categorical Axis
    17. Category Axis
    18. Categorical Scale
    19. Cell
    20. Check Box Filter
    21. Collaboration Panel
    22. Color Mode
    23. Color Palette
    24. Color Scheme
    25. Color Scheme Grouping
    26. Column
    27. Column from Marked
    28. Column Name
    29. (Column Names)
    30. Column Selector
    31. Combination Chart
    32. Comparison Circles
    33. Continuous Axis
    34. Continuous Scale
    35. Cover Page
    36. Cross Table
    37. Curve Fit
    38. Custom Expression
    39. Data Relationships
    40. Data Source
    41. Data Table
    42. Dendrogram
    43. Details-on-Demand
    44. Details Visualization
    45. Drop Targets
    46. DXP File
    47. Dynamic Items
    48. Empty Values
    49. Error Bars
    50. Escape characters
    51. External Column ID
    52. External Column Name
    53. Filter
    54. Filtering Scheme
    55. Filtered Out Rows
    56. Filtered Rows
    57. Filters Panel
    58. Find
    59. Formatting
    60. Graphical Table
    61. Gridlines
    62. GUID
    63. Heat Map
    64. Hierarchical Clustering
    65. Hierarchy
    66. Hierarchy Filter
    67. Horizontal Bars
    68. Hyperlink
    69. Icon
    70. Information Link
    71. Item Filter
    72. Jittering
    73. K-means Clustering
    74. Label
    75. Legend
    76. Library
    77. Line By
    78. Line Connection
    79. Line Chart
    80. Line Labels
    81. Line Similarity
    82. Lines & Curves
    83. List Box Filter
    84. Lists
    85. Map Chart
    86. Marked Row
    87. Marking
    88. Marker
    89. Marker Labels
    90. Page
    91. Parallel Coordinate Plot
    92. Parameterized Information Link
    93. Personalized Information Link
    94. Pie
    95. Pie Chart
    96. Pie Labels
    97. Pie Sector
    98. Pie Sector Labels
    99. Pivot
    100. Primary Key
    101. Properties
    102. Radio Button Filter
    103. Range Filter
    104. Range Filter Data Range
    105. Range Filter Lower Value
    106. Range Filter Upper Value
    107. Renderer
    108. Root View
    109. Row
    110. Scale
    111. Scale Labels
    112. Scatter Plot
    113. Series By
    114. Share
    115. Short Number Format
    116. Short Number Symbol
    117. Sparkline
    118. Spotfire Server
    119. Spotfire Text Data Format
    120. Stacked Bar
    121. Summary Table
    122. Symbol Set
    123. Table
    124. Table Cell
    125. Table Column
    126. Table Column Header
    127. Table Row
    128. Table Row Header
    129. Tags Panel
    130. Tags
    131. Text Area
    132. Tick Marks
    133. Time Scale
    134. Tooltip
    135. Tree Filter (Hierarchy Filter)
    136. Treemap
    137. Trellis
    138. Unpivot
    139. URL
    140. Value Axis
    141. Value Columns
    142. Vertical Bars
    143. Virtual Column
    144. Visualization
    145. Visualization Item
    146. Visualization Title
    147. Web Player
    148. X-Axis
    149. Y-Axis
    150. Z-Axis

TIBCO Spotfire 6 for Data Science

Last modified
Table of contents
  1. Story
  2. Slides
  3. Spotfire Dashboard
  4. Research Notes
  5. Table of Contents
    1. Introduction
      1. Introduction
      2. The User Interface
        1. 1. Visualizations
        2. 2. Text areas
        3. 3. Filters
        4. 4. Details-on-Demand
      3. Logging In
        1. Login Dialog
        2. Connecting via Proxy Server
        3. Downloading Updates
        4. Working Offline
        5. Updates and Working Offline
      4. Logging In Details
        1. 4 Subtopics
    2. Data
      1. Data Overview
        1. Introduction
        2. In-Memory Data
        3. In-Database Data
      2. Data in Spotfire
        1. Working With Large Data Volumes
          1. Introduction
          2. Visualizations and Analyses
          3. Hardware
          4. Loading Data
          5. Data Export
          6. Web Player
          7. Preferences
          8. API
        2. Working With In-Database Data
        3. Working With Microsoft SQL Server Analysis Services
      3. Loading Data
        1. Loading Data Overview
        2. Open File
          1. Opening an Analysis File
          2. Opening a Text File
          3. Opening an Excel File
          4. Opening a SAS File
          5. Details
          6. 4 Subtopics
        3. Open From Library
          1. Opening Files from the Library
          2. Opening an Information Link
          3. Searching the Library
          4. Edit Properties
          5. 3 Subtopics
        4. Add Data Table
          1. How to Add Data Tables
          2. Details
          3. 6 Subtopics
          4. On-Demand Data Tables
          5. 4 Subtopics
          6. Details
          7. 6 Subtopics
          8. Load Data from Active Spaces
          9. 2 Subtopics
          10. Open Database
          11. 3 Subtopics
        5. Add Data Connection
          1. What is a Data Connection?
          2. What is a Connection Data Source?
          3. Adding Data Connections to an Analysis
        6. Available Connections
          1. Connectors Overview
          2. 8 Subtopics
        7. Details on General Dialogs
          1. 5 Subtopics
        8. Mapping of External Data Types
          1. 7 Subtypes
        9. Replace Data
          1. Replacing Data
          2. Details
          3. 3 Subtopics
        10. Transform Data
          1. Transforming Data
          2. Pivoting Data
          3. Unpivoting Data
          4. Normalizing Data
          5. Normalizing Columns
          6. Details
          7. 11 Subtopics
        11. Details
          1. 12 Subtopics
        12. Missing File
          1. Details on Missing File – Local File
          2. 3 Subtopics
      4. Inserting More Data
        1. Insert Calculated Column
          1. What is a Calculated Column?
          2. How to Insert a Calculated Column
          3. Details on Insert Calculated Column
          4. Expression and Script Editor Keyboard Shortcuts
          5. Expression Language
          6. General Syntax
          7. Operations
          8. 3 Subtopics
          9. Functions
          10. 13 Subtopics
          11. Invalid Values
          12. Details on Formatting
          13. Format String
          14. Properties
          15. 2 Subtopics
        2. Insert Binned Column
          1. What is Binning?
          2. How to Use Binning
          3. Details on Insert Binned Column
          4. The Binning Slider
        3. Insert Columns
          1. How to Insert Columns
          2. 4 Subtopics
        4. Insert Rows
          1. How to Insert Rows
          2. 3 Subtopics
      5. Working with Data Tables
        1. Data Tables Overview
          1. Introduction
          2. On-Demand Data Tables
          3. In-Database Data Tables
          4. Related Data Tables
          5. Column Matches in Data Tables
        2. How to Add Data Tables
        3. Working with Data Tables
        4. Multiple Data Tables in One Visualization
        5. Recommended Workflow
          1. 1. Choose the main data table
          2. 2. Set up the visualization with only the main data table
          3. 3. Add the aggregating measures
        6. Examples
          1. Independent Data Tables
          2. Multiple Related Data Tables
          3. Master-Detail Visualizations
          4. Insert Columns – Example
      6. Copy/Paste Data
        1. Copying Data
        2. Pasting Data
      7. Data Panel
        1. What is the Data Panel?
        2. Data Panel Pop-up Menu
        3. Details on Rename Column
      8. Data Connections Properties
        1. What is a Data Connection?
        2. What is a Connection Data Source?
        3. 9 Subtopics
      9. Data Table Properties
        1. How to Edit Data Table Properties
        2. 6 Subtopics
        3. Details
          1. Details on Select Key Columns
          2. 5 Subtopics
      10. Column Properties
        1. How to Edit Column Properties
          1. 5 Subtopics
          2. Details
          3. Details on Insert Hierarchy
          4. 4 Subtopics
    3. Visualizations
      1. Table
        1. What is a Table?
        2. 3 Subtopics
        3. Table Properties
          1. 12 Subtopics
      2. Cross Table
        1. What is a Cross Table?
        2. 2 Subtopics
        3. Cross Table Properties
          1. 13 Subtopics
      3. Graphical Table
        1. What is a Graphical Table?
        2. How to Use the Graphical Table
        3. Graphical Table Properties
          1. Graphical Table Properties
          2. 8 Subtopics
        4. Sparklines
          1. What are Sparklines?
          2. How to Use Sparklines
          3. 8 Subtopics
        5. Calculated Value
          1. What are Calculated Values?
          2. 9 Subtopics
        6. Icons
          1. What are Icons?
          2. How to Use Icons
          3. 7 Subtopics
        7. Bullet Graph
          1. What are Bullet Graphs?
          2. How to Use Bullet Graphs
          3. 8 Subtopics
      4. Bar Chart
        1. What is a Bar Chart?
        2. How to Use the Bar Chart
        3. Bar Chart Properties
          1. Bar Chart Properties
          2. 16 Subtopics
      5. Line Chart
        1. What is a Line Chart?
          1. Step lines
        2. How to Use the Line Chart
        3. Line Chart Properties
          1. Line Chart Properties
          2. 17 Subtopics
      6. Combination Chart
        1. What is a Combination Chart?
          1. Series
        2. How to Use the Combination Chart
        3. Combination Chart Properties
          1. Combination Chart Properties
          2. 15 Subtopics
      7. Pie Chart
        1. What is a Pie Chart?
        2. How to Use the Pie Chart
        3. Pie Chart Properties
          1. Pie Chart Properties
          2. 12 Subtopics
      8. Scatter Plot
        1. What is a Scatter Plot?
        2. How to Use the Scatter Plot
        3. Scatter Plot Properties
          1. Scatter Plot Properties
          2. 20 Subtopics
      9. 3D Scatter Plot
        1. What is a 3D Scatter Plot?
        2. How to Use the 3D Scatter Plot
        3. 3D Scatter Plot Properties
          1. 3D Scatter Plot Properties
          2. 18 Subtopics
      10. Map Chart
        1. What is a Map Chart?
          1. Introduction
          2. Feature Layers
          3. Marker Layers
          4. Map Layers
          5. Image Layers
        2. How to Use the Map Chart
        3. Zooming and Navigating in the Map Chart
        4. Geocoding
          1. What is Geocoding?
          2. Setting Up New Geocoding Tables
        5. Configuration of Geographical Data for Map Charts
        6. Map Chart Properties
          1. Map Chart Properties
          2. 7 Subtopics
        7. Marker Layer Settings
          1. Marker Layer Settings
          2. 13 Subtopics
        8. Feature Layer Settings
          1. Feature Layer Settings
          2. 9 Subtopics
        9. Image Layer Settings
          1. Image Layer Settings
          2. 3 Subtopics
        10. Map Layer Settings
          1. Map Layer Settings
          2. 2 Subtopics
        11. 5.5 Map Chart
          1. What is a 5.5 Map Chart?
          2. How to Use the 5.5 Map Chart
          3. Map Chart Properties
          4. Map Chart Properties (5.5)
          5. 19 Subtopics
          6. Configuration of Geographical Data for Map Charts (5.5)
      11. Treemap
        1. What is a Treemap?
        2. How to Use the Treemap
        3. Treemap Properties
          1. Treemap Properties
          2. 13 Subtopics
      12. Heat Map
        1. What is a Heat Map?
          1. Dendrograms
        2. How to Use the Heat Map
        3. Dendrograms and Clustering
        4. Importing/Exporting Dendrograms
        5. Heat Map Properties
          1. Heat Map Properties
          2. 17 Subtopics
      13. Parallel Coordinate Plot
        1. What is a Parallel Coordinate Plot?
        2. How to Use the Parallel Coordinate Plot
        3. Parallel Coordinate Plot Properties
          1. Parallel Coordinate Plot Properties
          2. 15 Subtopics
      14. Summary Table
        1. What is a Summary Table?
        2. How to Use the Summary Table
        3. Summary Table Properties
          1. Summary Table Properties
          2. 9 Subtopics
        4. Statistical Measures Overview
      15. Box Plot
        1. What is a Box Plot?
        2. How to Use the Box Plot
        3. What are Comparison Circles?
        4. Box Plot Properties
          1. Box Plot Properties
          2. 16 Subtopics
        5. Aggregations Overview
        6. Comparison Circles Algorithm
          1. References
      16. Text Area
        1. How to Use the Text Area
        2. Using Properties in the Analysis
        3. 5 Subtopics
        4. Details
          1. Details on Action Control
        5. 3.0 Text Area
          1. 5 Subtopics
      17. Details on General Dialogs
        1. Details on Add/Edit Tooltip Value
        2. 12 Subtopics
        3. Subsets
          1. Details on Edit Subset
          2. 4 Subtopics
    4. Using Visualizations
      1. Interacting with Visualizations
        1. Marking in Visualizations
        2. Highlighting in Visualizations
        3. Drag-and-Drop
        4. Zoom Sliders
      2. Column Selectors
        1. Column Selectors
        2. Non-Column Selections
        3. What is Column from Marked?
        4. Details on Column from Marked
        5. Details on Set from Property
        6. Aggregation
      3. Legend
      4. Multiple Tables in One Visualization
        1. Multiple Data Tables in One Visualization
        2. Column Matching
        3. Working with Multiple Data Tables in One Visualization
        4. More Examples
      5. Shortcuts
      6. Hierarchies
        1. Hierarchies
        2. Hierarchy Slider
        3. Working with Time Hierarchies
      7. Trellis Visualizations
      8. Information and Warnings
      9. Aggregations
        1. Aggregations Overview
        2. Statistical Measures
          1. 18 Subtopics
        3. Expression Shortcuts
          1. 13 Subtopics
    5. Enhancing Visualizations
      1. Coloring
        1. Coloring Overview
        2. Color Schemes
          1. Color Schemes Overview
          2. Opening a Color Scheme
          3. Predefined Color Schemes
          4. Applying a Color Scheme to Another Visualization
          5. Saving a Color Scheme
          6. Details
          7. 5 Subtopics
        3. Color Modes
          1. Color Modes Overview
          2. Fixed Color Mode
          3. Categorical Color Mode
          4. Gradient Color Mode
          5. Segments Color Mode
          6. Unique Values Color Mode
          7. Details on Point/Value Menu
        4. Rules in Color Schemes
          1. Color Rules Overview
          2. Details on Add/Edit Rule
        5. Coloring in Tables, Cross Tables, and Heat Maps
          1. Coloring in Tables, Cross Tables and Heat Maps
          2. Coloring in Tables
          3. Coloring in Cross Tables and Heat Maps
          4. Details on Add/Edit Color Scheme Grouping
        6. Custom Expressions for Coloring
      2. Limiting What is Shown in Visualizations
      3. Details Visualizations
        1. What is a Details Visualization?
        2. Details on Create Details Visualization
      4. Subsets
        1. What are Subsets?
        2. How to Work with Subsets
        3. Details
          1. 5 Subtopics
      5. Show/Hide Items
        1. What is Show/Hide Items?
        2. How to Work with Show/Hide Items
        3. Details on Add/Edit Rule
      6. Titles and Descriptions
        1. Overview
        2. Working with Dynamic Titles and Descriptions
        3. Visualization Properties in Expressions
        4. Details
          1. 2 Subtopics
      7. Custom Expressions
        1. Custom Expressions Introduction
        2. Custom Expressions Overview
        3. Basic Custom Expressions
        4. OVER in Custom Expressions
        5. Using Expressions on Aggregated Data (the THEN Keyword)
        6. Axes in Expressions
        7. Advanced Custom Expressions
        8. How to Insert a Custom Expression
        9. Details
          1. 3 Subtopics
      8. Lines and Curves
        1. Lines and Curves
        2. Curve Fit Models
          1. Holt-Winters Forecast
          2. References for Holt-Winters Forecast
        3. Curve Fit Theory
        4. Details
          1. 12 Subtopics
      9. Formatting
        1. Formatting Overview
        2. Formatting Settings
        3. Format String
        4. Short Number Format
      10. Error Bars
    6. Pages and Layout
      1. Visualization Layout
      2. Arranging Visualizations
      3. Pages
        1. Titled Tabs
        2. Step-by-Step
        3. History arrows
      4. Cover Page
        1. Cover Page
        2. Text Area Edit Mode
      5. Details-on-Demand
        1. What is the Details-on-Demand?
        2. Details-on-Demand Properties
          1. 6 Subtopics
      6. Document Properties
        1. How to Edit Document Properties
        2. 7 Subtopics
      7. Setting Defaults
        1. How to Specify Default Values
        2. Options
          1. 22 Subtopics
          2. Details
          3. 10 Subtopics
      8. Panels and Popovers
    7. Filters
      1. What is a Filter?
      2. Filter Types
        1. Range Filter
        2. Item Filter
        3. Radio Buttons
        4. Check Boxes
        5. Text Filter
        6. List Box Filter
        7. Hierarchy Filter
          1. What is a Hierarchy Filter?
          2. Creating a Hierarchy Filter
      3. Filters Panel
        1. 5 Subtopics
        2. Filters Panel Properties
          1. 7 Subtopics
      4. Filtering Schemes
      5. Filtering in Related Data Tables
        1. Include Filtered Rows Only
        2. Exclude Filtered Out Rows
        3. Ignore Filtering
    8. Tags
      1. What are Tags?
      2. How to Work with Tags
      3. Details
        1. 5 Subtopics
    9. Bookmarks
      1. What are Bookmarks?
      2. How to Use Bookmarks
      3. Bookmarks Example Scenarios
      4. Bookmarks Pop-up Menu
      5. Details on Add Bookmark Special
    10. Lists
      1. What Are Lists?
      2. How to Use Lists
        1. Creating Lists
        2. Working with Annotations
        3. 7 Subtopics
      3. Details
        1. 4 Subtopics
    11. Collaboration
      1. Collaboration Panel
        1. What is the Collaboration Panel?
        2. How to Use the Collaboration Panel
        3. Details on Configure Collaboration Panel
          1. Integrating with tibbr®
      2. Share
        1. What is the Share Menu?
        2. Details on Log into tibbr®
        3. Details on Share to tibbr®
    12. Tools
      1. Find
        1. Find
        2. Searching in TIBCO Spotfire
      2. Data Relationships
        1. What is the Data Relationships Tool?
        2. How to Use Data Relationships
        3. Details on Data Relationships
        4. Data Relationships Column Descriptions
        5. Data Relationships Error Codes
        6. Theory and Models
          1. Overview of Data Relationships Theory
          2. Data Relationships Linear Regression Algorithm 
          3. Data Relationships Spearman R algorithm
          4. Data Relationships Anova Algorithm
          5. Data Relationships Kruskal-Wallis Algorithm
          6. Data Relationships Chi-square Independence Test Algorithm
          7. Requirements on Input Data for Data Relationships
      3. K-means Clustering
        1. How to Perform a K-means Clustering
        2. Details on K-means Clustering
          1. References
      4. Line Similarity
        1. How to Perform a Line Similarity Comparison
        2. Details on Line Similarity
      5. Hierarchical Clustering
        1. What is the Hierarchical Clustering Tool?
        2. Details on Hierarchical Clustering
        3. Theory and Modeling
          1. Overview of Hierarchical Clustering Theory
          2. Algorithm
          3. Distance Measures
          4. 7 Subtopics
          5. Clustering Methods
          6. 6 Subtopics
          7. Ordering Weight
          8. Hierarchical Clustering References
      6. Predictive Modeling
        1. What is Predictive Modeling?
        2. Regression Modeling
          1. Linear Regression Method
          2. Regression Tree Method
          3. Details on Regression Modeling – General
          4. Details on Regression Modeling – Options
        3. Classification Modeling
          1. Logistic Regression Method
          2. Classification Tree Method
          3. Details on Classification Modeling – General
          4. Details on Classification Modeling – Options
        4. How to Use the Model Page
          1. The Model Page
          2. Using a Model Summary
          3. Using a Table of Coefficients
          4. Available Diagnostic Visualizations
        5. How to Use the Evaluation Page
          1. The Evaluation Page
          2. Using an Evaluation Summary
          3. Using a Confusion Matrix
          4. Available Diagnostic Visualizations
        6. What is the Analytic Models Panel?
        7. Details on Duplicate Model
        8. Details on Evaluate Model
        9. Details on Insert Predicted Columns
      7. Data Functions
        1. What are Data Functions?
        2. How to Use Data Functions
        3. Details
          1. 8 Subtopics
        4. Data Type Mapping
        5. Name Encoding for Column Names Sent to Spotfire Statistics Services
      8. Information Designer
        1. What is the Information Designer?
        2. General Workflow
          1. 1. Set up the data sources
          2. 2. Create folders for storing elements and set permissions
          3. 3. Combine tables by creating joins
          4. 4. Define column elements from available data sources
          5. 5. Create filter elements to limit the data retrieved
          6. 6. Create information links
        3. General Guidelines for Setting Up an Information Model
          1. Purpose
          2. Who are the end users?
          3. What data do they need?
          4. Will users build their own information links?
          5. Tip
        4. Icon Explanations
        5. Fundamental Concepts
        6. Information Links
          1. Information Links
          2. Editing Information Links
          3. 12 Subtopics
          4. Opening Information Links
          5. 4 Subtopics
          6. Transforming the Data
        7. Data Sources
          1. 5 Subtopics
        8. Folders
          1. 6 Subtopics
        9. Joins
          1. 6 Subtopics
        10. Column Elements
          1. 8 Subtopics
          2. Working with Aggregation
        11. Filter Elements
          1. 5 Subtopics
        12. Procedures
          1. 6 Subtopics
        13. User Interface Details
          1. 5 Subtopics
        14. Tips and Examples
          1. 8 Subtopics
      9. Library Administration
        1. 13 Subtopics
      10. Manage Data Connections
        1. 10 Subtopics
    13. Saving and Exporting
      1. Creating a Guided Analysis
        1. What is a Guided Analysis?
          1. Introduction
          2. Create a cover page
          3. Write instructions in text areas
          4. Place links or buttons to relevant tools or views in the text areas
          5. Use step-by-step or history arrows page navigation
          6. Use customized filtering schemes
          7. Keep in mind the intended end users’ level of data access
      2. Saving
        1. Save Overview
        2. Saving an Analysis File
        3. Details on Save
        4. Saving an Analysis File in the Library
        5. Embedded or Linked Data?
          1. Reloading Data
        6. Preparing Analyses for TIBCO Spotfire Web Clients
          1. Introduction
          2. Tips when preparing analyses for TIBCO Spotfire web clients:
          3. Design for the intended platform
          4. To use bookmarks for adapting an analysis to different screen sizes:
          5. Mobile clients (Spotfire Analytics for iPad)
        7. Links to Analyses in the Library
        8. Details on Save to Library
          1. 6 Subtopics
      3. Export Image
        1. Exporting an Image
      4. Export Data to File
        1. Exporting Data to File
        2. Details on Export Data to File
      5. Export Data to Library
        1. Exporting Data to Library
        2. 2 Subtopics
      6. Export to HTML
        1. Exporting to HTML
        2. 1 Subtopic
      7. Export to PowerPoint
        1. Exporting to Microsoft PowerPoint
        2. 1 Subtopic
      8. Export to PDF
        1. Exporting to PDF
        2. 6 Subtopics
      9. Printing
        1. Printing
        2. 1 Subtopic
    14. Appendix
      1. Software License
      2. Support
      3. Details on Support Diagnostics and Logging
        1. Dump File
  6. Glossary
    1. 3D Scatter Plot
    2. Analysis File
    3. Axis
    4. Axis Selector
    5. Bar
    6. Bar Chart
    7. Bar Labels
    8. Bar Segment
    9. Bar Segment Labels
    10. Binning
    11. Bookmark
    12. Box Plot
    13. Bullet Graph
    14. Calculated Column
    15. Calculated Value
    16. Categorical Axis
    17. Category Axis
    18. Categorical Scale
    19. Cell
    20. Check Box Filter
    21. Collaboration Panel
    22. Color Mode
    23. Color Palette
    24. Color Scheme
    25. Color Scheme Grouping
    26. Column
    27. Column from Marked
    28. Column Name
    29. (Column Names)
    30. Column Selector
    31. Combination Chart
    32. Comparison Circles
    33. Continuous Axis
    34. Continuous Scale
    35. Cover Page
    36. Cross Table
    37. Curve Fit
    38. Custom Expression
    39. Data Relationships
    40. Data Source
    41. Data Table
    42. Dendrogram
    43. Details-on-Demand
    44. Details Visualization
    45. Drop Targets
    46. DXP File
    47. Dynamic Items
    48. Empty Values
    49. Error Bars
    50. Escape characters
    51. External Column ID
    52. External Column Name
    53. Filter
    54. Filtering Scheme
    55. Filtered Out Rows
    56. Filtered Rows
    57. Filters Panel
    58. Find
    59. Formatting
    60. Graphical Table
    61. Gridlines
    62. GUID
    63. Heat Map
    64. Hierarchical Clustering
    65. Hierarchy
    66. Hierarchy Filter
    67. Horizontal Bars
    68. Hyperlink
    69. Icon
    70. Information Link
    71. Item Filter
    72. Jittering
    73. K-means Clustering
    74. Label
    75. Legend
    76. Library
    77. Line By
    78. Line Connection
    79. Line Chart
    80. Line Labels
    81. Line Similarity
    82. Lines & Curves
    83. List Box Filter
    84. Lists
    85. Map Chart
    86. Marked Row
    87. Marking
    88. Marker
    89. Marker Labels
    90. Page
    91. Parallel Coordinate Plot
    92. Parameterized Information Link
    93. Personalized Information Link
    94. Pie
    95. Pie Chart
    96. Pie Labels
    97. Pie Sector
    98. Pie Sector Labels
    99. Pivot
    100. Primary Key
    101. Properties
    102. Radio Button Filter
    103. Range Filter
    104. Range Filter Data Range
    105. Range Filter Lower Value
    106. Range Filter Upper Value
    107. Renderer
    108. Root View
    109. Row
    110. Scale
    111. Scale Labels
    112. Scatter Plot
    113. Series By
    114. Share
    115. Short Number Format
    116. Short Number Symbol
    117. Sparkline
    118. Spotfire Server
    119. Spotfire Text Data Format
    120. Stacked Bar
    121. Summary Table
    122. Symbol Set
    123. Table
    124. Table Cell
    125. Table Column
    126. Table Column Header
    127. Table Row
    128. Table Row Header
    129. Tags Panel
    130. Tags
    131. Text Area
    132. Tick Marks
    133. Time Scale
    134. Tooltip
    135. Tree Filter (Hierarchy Filter)
    136. Treemap
    137. Trellis
    138. Unpivot
    139. URL
    140. Value Axis
    141. Value Columns
    142. Vertical Bars
    143. Virtual Column
    144. Visualization
    145. Visualization Item
    146. Visualization Title
    147. Web Player
    148. X-Axis
    149. Y-Axis
    150. Z-Axis

  1. Story
  2. Slides
  3. Spotfire Dashboard
  4. Research Notes
  5. Table of Contents
    1. Introduction
      1. Introduction
      2. The User Interface
        1. 1. Visualizations
        2. 2. Text areas
        3. 3. Filters
        4. 4. Details-on-Demand
      3. Logging In
        1. Login Dialog
        2. Connecting via Proxy Server
        3. Downloading Updates
        4. Working Offline
        5. Updates and Working Offline
      4. Logging In Details
        1. 4 Subtopics
    2. Data
      1. Data Overview
        1. Introduction
        2. In-Memory Data
        3. In-Database Data
      2. Data in Spotfire
        1. Working With Large Data Volumes
          1. Introduction
          2. Visualizations and Analyses
          3. Hardware
          4. Loading Data
          5. Data Export
          6. Web Player
          7. Preferences
          8. API
        2. Working With In-Database Data
        3. Working With Microsoft SQL Server Analysis Services
      3. Loading Data
        1. Loading Data Overview
        2. Open File
          1. Opening an Analysis File
          2. Opening a Text File
          3. Opening an Excel File
          4. Opening a SAS File
          5. Details
          6. 4 Subtopics
        3. Open From Library
          1. Opening Files from the Library
          2. Opening an Information Link
          3. Searching the Library
          4. Edit Properties
          5. 3 Subtopics
        4. Add Data Table
          1. How to Add Data Tables
          2. Details
          3. 6 Subtopics
          4. On-Demand Data Tables
          5. 4 Subtopics
          6. Details
          7. 6 Subtopics
          8. Load Data from Active Spaces
          9. 2 Subtopics
          10. Open Database
          11. 3 Subtopics
        5. Add Data Connection
          1. What is a Data Connection?
          2. What is a Connection Data Source?
          3. Adding Data Connections to an Analysis
        6. Available Connections
          1. Connectors Overview
          2. 8 Subtopics
        7. Details on General Dialogs
          1. 5 Subtopics
        8. Mapping of External Data Types
          1. 7 Subtypes
        9. Replace Data
          1. Replacing Data
          2. Details
          3. 3 Subtopics
        10. Transform Data
          1. Transforming Data
          2. Pivoting Data
          3. Unpivoting Data
          4. Normalizing Data
          5. Normalizing Columns
          6. Details
          7. 11 Subtopics
        11. Details
          1. 12 Subtopics
        12. Missing File
          1. Details on Missing File – Local File
          2. 3 Subtopics
      4. Inserting More Data
        1. Insert Calculated Column
          1. What is a Calculated Column?
          2. How to Insert a Calculated Column
          3. Details on Insert Calculated Column
          4. Expression and Script Editor Keyboard Shortcuts
          5. Expression Language
          6. General Syntax
          7. Operations
          8. 3 Subtopics
          9. Functions
          10. 13 Subtopics
          11. Invalid Values
          12. Details on Formatting
          13. Format String
          14. Properties
          15. 2 Subtopics
        2. Insert Binned Column
          1. What is Binning?
          2. How to Use Binning
          3. Details on Insert Binned Column
          4. The Binning Slider
        3. Insert Columns
          1. How to Insert Columns
          2. 4 Subtopics
        4. Insert Rows
          1. How to Insert Rows
          2. 3 Subtopics
      5. Working with Data Tables
        1. Data Tables Overview
          1. Introduction
          2. On-Demand Data Tables
          3. In-Database Data Tables
          4. Related Data Tables
          5. Column Matches in Data Tables
        2. How to Add Data Tables
        3. Working with Data Tables
        4. Multiple Data Tables in One Visualization
        5. Recommended Workflow
          1. 1. Choose the main data table
          2. 2. Set up the visualization with only the main data table
          3. 3. Add the aggregating measures
        6. Examples
          1. Independent Data Tables
          2. Multiple Related Data Tables
          3. Master-Detail Visualizations
          4. Insert Columns – Example
      6. Copy/Paste Data
        1. Copying Data
        2. Pasting Data
      7. Data Panel
        1. What is the Data Panel?
        2. Data Panel Pop-up Menu
        3. Details on Rename Column
      8. Data Connections Properties
        1. What is a Data Connection?
        2. What is a Connection Data Source?
        3. 9 Subtopics
      9. Data Table Properties
        1. How to Edit Data Table Properties
        2. 6 Subtopics
        3. Details
          1. Details on Select Key Columns
          2. 5 Subtopics
      10. Column Properties
        1. How to Edit Column Properties
          1. 5 Subtopics
          2. Details
          3. Details on Insert Hierarchy
          4. 4 Subtopics
    3. Visualizations
      1. Table
        1. What is a Table?
        2. 3 Subtopics
        3. Table Properties
          1. 12 Subtopics
      2. Cross Table
        1. What is a Cross Table?
        2. 2 Subtopics
        3. Cross Table Properties
          1. 13 Subtopics
      3. Graphical Table
        1. What is a Graphical Table?
        2. How to Use the Graphical Table
        3. Graphical Table Properties
          1. Graphical Table Properties
          2. 8 Subtopics
        4. Sparklines
          1. What are Sparklines?
          2. How to Use Sparklines
          3. 8 Subtopics
        5. Calculated Value
          1. What are Calculated Values?
          2. 9 Subtopics
        6. Icons
          1. What are Icons?
          2. How to Use Icons
          3. 7 Subtopics
        7. Bullet Graph
          1. What are Bullet Graphs?
          2. How to Use Bullet Graphs
          3. 8 Subtopics
      4. Bar Chart
        1. What is a Bar Chart?
        2. How to Use the Bar Chart
        3. Bar Chart Properties
          1. Bar Chart Properties
          2. 16 Subtopics
      5. Line Chart
        1. What is a Line Chart?
          1. Step lines
        2. How to Use the Line Chart
        3. Line Chart Properties
          1. Line Chart Properties
          2. 17 Subtopics
      6. Combination Chart
        1. What is a Combination Chart?
          1. Series
        2. How to Use the Combination Chart
        3. Combination Chart Properties
          1. Combination Chart Properties
          2. 15 Subtopics
      7. Pie Chart
        1. What is a Pie Chart?
        2. How to Use the Pie Chart
        3. Pie Chart Properties
          1. Pie Chart Properties
          2. 12 Subtopics
      8. Scatter Plot
        1. What is a Scatter Plot?
        2. How to Use the Scatter Plot
        3. Scatter Plot Properties
          1. Scatter Plot Properties
          2. 20 Subtopics
      9. 3D Scatter Plot
        1. What is a 3D Scatter Plot?
        2. How to Use the 3D Scatter Plot
        3. 3D Scatter Plot Properties
          1. 3D Scatter Plot Properties
          2. 18 Subtopics
      10. Map Chart
        1. What is a Map Chart?
          1. Introduction
          2. Feature Layers
          3. Marker Layers
          4. Map Layers
          5. Image Layers
        2. How to Use the Map Chart
        3. Zooming and Navigating in the Map Chart
        4. Geocoding
          1. What is Geocoding?
          2. Setting Up New Geocoding Tables
        5. Configuration of Geographical Data for Map Charts
        6. Map Chart Properties
          1. Map Chart Properties
          2. 7 Subtopics
        7. Marker Layer Settings
          1. Marker Layer Settings
          2. 13 Subtopics
        8. Feature Layer Settings
          1. Feature Layer Settings
          2. 9 Subtopics
        9. Image Layer Settings
          1. Image Layer Settings
          2. 3 Subtopics
        10. Map Layer Settings
          1. Map Layer Settings
          2. 2 Subtopics
        11. 5.5 Map Chart
          1. What is a 5.5 Map Chart?
          2. How to Use the 5.5 Map Chart
          3. Map Chart Properties
          4. Map Chart Properties (5.5)
          5. 19 Subtopics
          6. Configuration of Geographical Data for Map Charts (5.5)
      11. Treemap
        1. What is a Treemap?
        2. How to Use the Treemap
        3. Treemap Properties
          1. Treemap Properties
          2. 13 Subtopics
      12. Heat Map
        1. What is a Heat Map?
          1. Dendrograms
        2. How to Use the Heat Map
        3. Dendrograms and Clustering
        4. Importing/Exporting Dendrograms
        5. Heat Map Properties
          1. Heat Map Properties
          2. 17 Subtopics
      13. Parallel Coordinate Plot
        1. What is a Parallel Coordinate Plot?
        2. How to Use the Parallel Coordinate Plot
        3. Parallel Coordinate Plot Properties
          1. Parallel Coordinate Plot Properties
          2. 15 Subtopics
      14. Summary Table
        1. What is a Summary Table?
        2. How to Use the Summary Table
        3. Summary Table Properties
          1. Summary Table Properties
          2. 9 Subtopics
        4. Statistical Measures Overview
      15. Box Plot
        1. What is a Box Plot?
        2. How to Use the Box Plot
        3. What are Comparison Circles?
        4. Box Plot Properties
          1. Box Plot Properties
          2. 16 Subtopics
        5. Aggregations Overview
        6. Comparison Circles Algorithm
          1. References
      16. Text Area
        1. How to Use the Text Area
        2. Using Properties in the Analysis
        3. 5 Subtopics
        4. Details
          1. Details on Action Control
        5. 3.0 Text Area
          1. 5 Subtopics
      17. Details on General Dialogs
        1. Details on Add/Edit Tooltip Value
        2. 12 Subtopics
        3. Subsets
          1. Details on Edit Subset
          2. 4 Subtopics
    4. Using Visualizations
      1. Interacting with Visualizations
        1. Marking in Visualizations
        2. Highlighting in Visualizations
        3. Drag-and-Drop
        4. Zoom Sliders
      2. Column Selectors
        1. Column Selectors
        2. Non-Column Selections
        3. What is Column from Marked?
        4. Details on Column from Marked
        5. Details on Set from Property
        6. Aggregation
      3. Legend
      4. Multiple Tables in One Visualization
        1. Multiple Data Tables in One Visualization
        2. Column Matching
        3. Working with Multiple Data Tables in One Visualization
        4. More Examples
      5. Shortcuts
      6. Hierarchies
        1. Hierarchies
        2. Hierarchy Slider
        3. Working with Time Hierarchies
      7. Trellis Visualizations
      8. Information and Warnings
      9. Aggregations
        1. Aggregations Overview
        2. Statistical Measures
          1. 18 Subtopics
        3. Expression Shortcuts
          1. 13 Subtopics
    5. Enhancing Visualizations
      1. Coloring
        1. Coloring Overview
        2. Color Schemes
          1. Color Schemes Overview
          2. Opening a Color Scheme
          3. Predefined Color Schemes
          4. Applying a Color Scheme to Another Visualization
          5. Saving a Color Scheme
          6. Details
          7. 5 Subtopics
        3. Color Modes
          1. Color Modes Overview
          2. Fixed Color Mode
          3. Categorical Color Mode
          4. Gradient Color Mode
          5. Segments Color Mode
          6. Unique Values Color Mode
          7. Details on Point/Value Menu
        4. Rules in Color Schemes
          1. Color Rules Overview
          2. Details on Add/Edit Rule
        5. Coloring in Tables, Cross Tables, and Heat Maps
          1. Coloring in Tables, Cross Tables and Heat Maps
          2. Coloring in Tables
          3. Coloring in Cross Tables and Heat Maps
          4. Details on Add/Edit Color Scheme Grouping
        6. Custom Expressions for Coloring
      2. Limiting What is Shown in Visualizations
      3. Details Visualizations
        1. What is a Details Visualization?
        2. Details on Create Details Visualization
      4. Subsets
        1. What are Subsets?
        2. How to Work with Subsets
        3. Details
          1. 5 Subtopics
      5. Show/Hide Items
        1. What is Show/Hide Items?
        2. How to Work with Show/Hide Items
        3. Details on Add/Edit Rule
      6. Titles and Descriptions
        1. Overview
        2. Working with Dynamic Titles and Descriptions
        3. Visualization Properties in Expressions
        4. Details
          1. 2 Subtopics
      7. Custom Expressions
        1. Custom Expressions Introduction
        2. Custom Expressions Overview
        3. Basic Custom Expressions
        4. OVER in Custom Expressions
        5. Using Expressions on Aggregated Data (the THEN Keyword)
        6. Axes in Expressions
        7. Advanced Custom Expressions
        8. How to Insert a Custom Expression
        9. Details
          1. 3 Subtopics
      8. Lines and Curves
        1. Lines and Curves
        2. Curve Fit Models
          1. Holt-Winters Forecast
          2. References for Holt-Winters Forecast
        3. Curve Fit Theory
        4. Details
          1. 12 Subtopics
      9. Formatting
        1. Formatting Overview
        2. Formatting Settings
        3. Format String
        4. Short Number Format
      10. Error Bars
    6. Pages and Layout
      1. Visualization Layout
      2. Arranging Visualizations
      3. Pages
        1. Titled Tabs
        2. Step-by-Step
        3. History arrows
      4. Cover Page
        1. Cover Page
        2. Text Area Edit Mode
      5. Details-on-Demand
        1. What is the Details-on-Demand?
        2. Details-on-Demand Properties
          1. 6 Subtopics
      6. Document Properties
        1. How to Edit Document Properties
        2. 7 Subtopics
      7. Setting Defaults
        1. How to Specify Default Values
        2. Options
          1. 22 Subtopics
          2. Details
          3. 10 Subtopics
      8. Panels and Popovers
    7. Filters
      1. What is a Filter?
      2. Filter Types
        1. Range Filter
        2. Item Filter
        3. Radio Buttons
        4. Check Boxes
        5. Text Filter
        6. List Box Filter
        7. Hierarchy Filter
          1. What is a Hierarchy Filter?
          2. Creating a Hierarchy Filter
      3. Filters Panel
        1. 5 Subtopics
        2. Filters Panel Properties
          1. 7 Subtopics
      4. Filtering Schemes
      5. Filtering in Related Data Tables
        1. Include Filtered Rows Only
        2. Exclude Filtered Out Rows
        3. Ignore Filtering
    8. Tags
      1. What are Tags?
      2. How to Work with Tags
      3. Details
        1. 5 Subtopics
    9. Bookmarks
      1. What are Bookmarks?
      2. How to Use Bookmarks
      3. Bookmarks Example Scenarios
      4. Bookmarks Pop-up Menu
      5. Details on Add Bookmark Special
    10. Lists
      1. What Are Lists?
      2. How to Use Lists
        1. Creating Lists
        2. Working with Annotations
        3. 7 Subtopics
      3. Details
        1. 4 Subtopics
    11. Collaboration
      1. Collaboration Panel
        1. What is the Collaboration Panel?
        2. How to Use the Collaboration Panel
        3. Details on Configure Collaboration Panel
          1. Integrating with tibbr®
      2. Share
        1. What is the Share Menu?
        2. Details on Log into tibbr®
        3. Details on Share to tibbr®
    12. Tools
      1. Find
        1. Find
        2. Searching in TIBCO Spotfire
      2. Data Relationships
        1. What is the Data Relationships Tool?
        2. How to Use Data Relationships
        3. Details on Data Relationships
        4. Data Relationships Column Descriptions
        5. Data Relationships Error Codes
        6. Theory and Models
          1. Overview of Data Relationships Theory
          2. Data Relationships Linear Regression Algorithm 
          3. Data Relationships Spearman R algorithm
          4. Data Relationships Anova Algorithm
          5. Data Relationships Kruskal-Wallis Algorithm
          6. Data Relationships Chi-square Independence Test Algorithm
          7. Requirements on Input Data for Data Relationships
      3. K-means Clustering
        1. How to Perform a K-means Clustering
        2. Details on K-means Clustering
          1. References
      4. Line Similarity
        1. How to Perform a Line Similarity Comparison
        2. Details on Line Similarity
      5. Hierarchical Clustering
        1. What is the Hierarchical Clustering Tool?
        2. Details on Hierarchical Clustering
        3. Theory and Modeling
          1. Overview of Hierarchical Clustering Theory
          2. Algorithm
          3. Distance Measures
          4. 7 Subtopics
          5. Clustering Methods
          6. 6 Subtopics
          7. Ordering Weight
          8. Hierarchical Clustering References
      6. Predictive Modeling
        1. What is Predictive Modeling?
        2. Regression Modeling
          1. Linear Regression Method
          2. Regression Tree Method
          3. Details on Regression Modeling – General
          4. Details on Regression Modeling – Options
        3. Classification Modeling
          1. Logistic Regression Method
          2. Classification Tree Method
          3. Details on Classification Modeling – General
          4. Details on Classification Modeling – Options
        4. How to Use the Model Page
          1. The Model Page
          2. Using a Model Summary
          3. Using a Table of Coefficients
          4. Available Diagnostic Visualizations
        5. How to Use the Evaluation Page
          1. The Evaluation Page
          2. Using an Evaluation Summary
          3. Using a Confusion Matrix
          4. Available Diagnostic Visualizations
        6. What is the Analytic Models Panel?
        7. Details on Duplicate Model
        8. Details on Evaluate Model
        9. Details on Insert Predicted Columns
      7. Data Functions
        1. What are Data Functions?
        2. How to Use Data Functions
        3. Details
          1. 8 Subtopics
        4. Data Type Mapping
        5. Name Encoding for Column Names Sent to Spotfire Statistics Services
      8. Information Designer
        1. What is the Information Designer?
        2. General Workflow
          1. 1. Set up the data sources
          2. 2. Create folders for storing elements and set permissions
          3. 3. Combine tables by creating joins
          4. 4. Define column elements from available data sources
          5. 5. Create filter elements to limit the data retrieved
          6. 6. Create information links
        3. General Guidelines for Setting Up an Information Model
          1. Purpose
          2. Who are the end users?
          3. What data do they need?
          4. Will users build their own information links?
          5. Tip
        4. Icon Explanations
        5. Fundamental Concepts
        6. Information Links
          1. Information Links
          2. Editing Information Links
          3. 12 Subtopics
          4. Opening Information Links
          5. 4 Subtopics
          6. Transforming the Data
        7. Data Sources
          1. 5 Subtopics
        8. Folders
          1. 6 Subtopics
        9. Joins
          1. 6 Subtopics
        10. Column Elements
          1. 8 Subtopics
          2. Working with Aggregation
        11. Filter Elements
          1. 5 Subtopics
        12. Procedures
          1. 6 Subtopics
        13. User Interface Details
          1. 5 Subtopics
        14. Tips and Examples
          1. 8 Subtopics
      9. Library Administration
        1. 13 Subtopics
      10. Manage Data Connections
        1. 10 Subtopics
    13. Saving and Exporting
      1. Creating a Guided Analysis
        1. What is a Guided Analysis?
          1. Introduction
          2. Create a cover page
          3. Write instructions in text areas
          4. Place links or buttons to relevant tools or views in the text areas
          5. Use step-by-step or history arrows page navigation
          6. Use customized filtering schemes
          7. Keep in mind the intended end users’ level of data access
      2. Saving
        1. Save Overview
        2. Saving an Analysis File
        3. Details on Save
        4. Saving an Analysis File in the Library
        5. Embedded or Linked Data?
          1. Reloading Data
        6. Preparing Analyses for TIBCO Spotfire Web Clients
          1. Introduction
          2. Tips when preparing analyses for TIBCO Spotfire web clients:
          3. Design for the intended platform
          4. To use bookmarks for adapting an analysis to different screen sizes:
          5. Mobile clients (Spotfire Analytics for iPad)
        7. Links to Analyses in the Library
        8. Details on Save to Library
          1. 6 Subtopics
      3. Export Image
        1. Exporting an Image
      4. Export Data to File
        1. Exporting Data to File
        2. Details on Export Data to File
      5. Export Data to Library
        1. Exporting Data to Library
        2. 2 Subtopics
      6. Export to HTML
        1. Exporting to HTML
        2. 1 Subtopic
      7. Export to PowerPoint
        1. Exporting to Microsoft PowerPoint
        2. 1 Subtopic
      8. Export to PDF
        1. Exporting to PDF
        2. 6 Subtopics
      9. Printing
        1. Printing
        2. 1 Subtopic
    14. Appendix
      1. Software License
      2. Support
      3. Details on Support Diagnostics and Logging
        1. Dump File
  6. Glossary
    1. 3D Scatter Plot
    2. Analysis File
    3. Axis
    4. Axis Selector
    5. Bar
    6. Bar Chart
    7. Bar Labels
    8. Bar Segment
    9. Bar Segment Labels
    10. Binning
    11. Bookmark
    12. Box Plot
    13. Bullet Graph
    14. Calculated Column
    15. Calculated Value
    16. Categorical Axis
    17. Category Axis
    18. Categorical Scale
    19. Cell
    20. Check Box Filter
    21. Collaboration Panel
    22. Color Mode
    23. Color Palette
    24. Color Scheme
    25. Color Scheme Grouping
    26. Column
    27. Column from Marked
    28. Column Name
    29. (Column Names)
    30. Column Selector
    31. Combination Chart
    32. Comparison Circles
    33. Continuous Axis
    34. Continuous Scale
    35. Cover Page
    36. Cross Table
    37. Curve Fit
    38. Custom Expression
    39. Data Relationships
    40. Data Source
    41. Data Table
    42. Dendrogram
    43. Details-on-Demand
    44. Details Visualization
    45. Drop Targets
    46. DXP File
    47. Dynamic Items
    48. Empty Values
    49. Error Bars
    50. Escape characters
    51. External Column ID
    52. External Column Name
    53. Filter
    54. Filtering Scheme
    55. Filtered Out Rows
    56. Filtered Rows
    57. Filters Panel
    58. Find
    59. Formatting
    60. Graphical Table
    61. Gridlines
    62. GUID
    63. Heat Map
    64. Hierarchical Clustering
    65. Hierarchy
    66. Hierarchy Filter
    67. Horizontal Bars
    68. Hyperlink
    69. Icon
    70. Information Link
    71. Item Filter
    72. Jittering
    73. K-means Clustering
    74. Label
    75. Legend
    76. Library
    77. Line By
    78. Line Connection
    79. Line Chart
    80. Line Labels
    81. Line Similarity
    82. Lines & Curves
    83. List Box Filter
    84. Lists
    85. Map Chart
    86. Marked Row
    87. Marking
    88. Marker
    89. Marker Labels
    90. Page
    91. Parallel Coordinate Plot
    92. Parameterized Information Link
    93. Personalized Information Link
    94. Pie
    95. Pie Chart
    96. Pie Labels
    97. Pie Sector
    98. Pie Sector Labels
    99. Pivot
    100. Primary Key
    101. Properties
    102. Radio Button Filter
    103. Range Filter
    104. Range Filter Data Range
    105. Range Filter Lower Value
    106. Range Filter Upper Value
    107. Renderer
    108. Root View
    109. Row
    110. Scale
    111. Scale Labels
    112. Scatter Plot
    113. Series By
    114. Share
    115. Short Number Format
    116. Short Number Symbol
    117. Sparkline
    118. Spotfire Server
    119. Spotfire Text Data Format
    120. Stacked Bar
    121. Summary Table
    122. Symbol Set
    123. Table
    124. Table Cell
    125. Table Column
    126. Table Column Header
    127. Table Row
    128. Table Row Header
    129. Tags Panel
    130. Tags
    131. Text Area
    132. Tick Marks
    133. Time Scale
    134. Tooltip
    135. Tree Filter (Hierarchy Filter)
    136. Treemap
    137. Trellis
    138. Unpivot
    139. URL
    140. Value Axis
    141. Value Columns
    142. Vertical Bars
    143. Virtual Column
    144. Visualization
    145. Visualization Item
    146. Visualization Title
    147. Web Player
    148. X-Axis
    149. Y-Axis
    150. Z-Axis

Story

TIBCO Spotfire 6 for Data Science

TIBCO Spotfire 6 was released in early December 2013 along with new documentation dated 11/13/2013. As with any new software version, one wonders about the new features and if they change the features one is used to, etc. One is also interested in the broader context of all the Spotfire 6 features for data science so lets construct a Model of Spotfire 6 based on the Spotfire User's Guide using the Glossary as the Vocabulary and the Table of Contents as the Taxonomy. The Model is shown in this Story and the detailed Glossary (150 terms) and Table of Contents (14 topics) are shown below. The purpose of the Model is to make Spotfire 6 easier to use and document for Data Science Products. The data science product in Spotfire links to the sections in the User's Guide that help explain the data science product itself!

The steps are:

  • Copy the entire Glossary and give it structure. Done
  • Copy the Table of Content Topics and Subtopics (at least down to five sub-levels initially). Done
  • Find (Google Chrome Browser) the 150 Glossary terms to there place in the User's Guide. Done
  • Excerpt the information from the Table of Content Topics and Subtopics that seems especially useful to my work. Done
  • Highlight the features that I find most help in my Data Science Products. See below.
  • This is then the Model!

Highlight the features that I find most help in my data science product in Spotfire below:

Under Introduction:

  • After importing data, your Spotfire file needs 4 basic things:

1. Visualizations

2. Text areas

3. Filters

4. Details-on-Demand

  1.  

The User Interface.png

Under Visualizations:

  • Map Chart has new features that I really like using map layers that allow you to display your data on a tile based web map from TIBCO GeoAnalytics.

Under Text Areas:

Under Filters:

  • One of the main strengths of TIBCO Spotfire is the ability it gives you to filter your data, hence, to control what data shall be visible and used in some calculations.
  • I usually use List and Radio Button Filters, but there are several other types that I should try.

Under Details-on-Demand:

  • GlossaryThe concept of expanding a small set of items to reveal more data behind it.

Under Saving and Exporting:

Under Appendix:

  • Dump FileThe chances of getting your problems fixed greatly increases if you can attach a dump file to your support issue.

​​Under Collaboration:

  • This is not enabled in my version. I use MindTouch for this purpose.​

Under TagsBookmarks, and Lists:

  • I have used Bookmarks to 'play back' steps in my analyses, but not Tags and List, which I need to try.

Under Pages and Layout:

Under Tools

In summary, the three most important sections in the Users Guide to read first for design are:

See Spreadsheet for more details.

I did need to add some more subtopics under Predictive Modeling to have reference links to Logistic Regression Method and Logistic Regression Method. The Knowledge Base index needs to be updated to include those additional rows.

MORE IN PROCESS

Slides

Spotfire Dashboard

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire iPad App

Research Notes

Table of Contents

Introduction

Introduction

Welcome to TIBCO Spotfire®!

TIBCO Spotfire makes it easy for you to access, analyze and create dynamic reports on your data. It delivers immediate value whether you are a market researcher, a sales representative, a scientist or a process engineer by letting you quickly identify trends and patterns in your critical business data.

Spotfire can access data in a number of places such as on your desktop or in a network file system. It can even access your data if it is located in remote databases, without you having to involve your IT department each time you wish to ask a new question.

Spotfire lets you filter your data interactively, and gives you answers instantly. It also lets you rapidly create clear and concise, yet sleek and colorful visualizations in the form of bar charts, cross tables, scatter plots and many more valuable tools that will help you respond to events that affect your business.  

And finally, Spotfire lets you share your results. Static reports can be limiting to good business in this fast-paced world of data, and Spotfire allows you to create dynamic reports that help you to ask new questions, as well as be able to quickly turn your reports into instant presentations to show to your colleagues and customers.

Note: This user's manual contains information about all functionality that can be used within the Spotfire end user environment. If you do not have access to all licenses, some tools described in this help will be unavailable. For more information on how to get access to the full range of functionality, please visit the website http://support.spotfire.com/support.asp.

Last update: 2013-11-13

The User Interface

The image below shows some of the main parts of the TIBCO Spotfire® user interface.

The User Interface.png

1. Visualizations

Visualizations are the key to analyzing data in Spotfire. A variety of visualization types can be used to provide the best view of the data:

  • Tables
  • Cross Tables
  • Graphical Tables
  • Bar Charts
  • Line Charts
  • Combination Charts
  • Pie Charts
  • Scatter Plots
  • 3D Scatter Plots
  • Map Charts
  • Treemaps
  • Heat Maps
  • Parallel Coordinate Plots
  • Summary Tables
  • Box Plots

Different types of visualizations can be shown simultaneously. They can be linked to each other, and may or may not be updated dynamically when the corresponding filters on the page are manipulated (see below).

Visualizations can be made to reflect many dimensions of data by letting values control visual attributes such as size, color, shape, etc.

2. Text areas

You can type text in text areas, explaining what is seen in the different visualizations. This can be particularly useful if you are creating analytic applications for other users. Text areas can also include several different types of controls, allowing you to filter, perform actions or make selections to view particular types of data, etc.

3. Filters

By adjusting filters, you can reduce the data seen in the visualizations to "drill down" to the things that interest you. Filters are powerful tools that quickly let you see various aspects of your data and make discoveries.

Filters appear in several forms, and you can select the type of filter device that best suits your needs (for example, check boxes, sliders, etc). When you manipulate a filter by moving a slider or by selecting a check box, all linked visualizations are immediately updated to reflect the new selection of data. By default, all new visualizations on a page will be limited by the filtering scheme used on the page. However, the filtering scheme can be changed for each visualization separately.

4. Details-on-Demand

The Details-on-Demand window can be used to show the exact values of a row or a group of rows. By clicking an item in a visualization, or marking several items by clicking and dragging with the mouse around them, you can see the numerical values and textual data they represent directly in the Details-on-Demand window.

Logging In

Login Dialog

When you start TIBCO Spotfire a login dialog appears. Enter your Username and Password, and click on the Login button to start Spotfire. If you select the Save my login information check box, you will automatically be logged in when you start Spotfire in the future. Logging into Spotfire will let you access the joint library and other collaboration features.

If the Save my login information check box has been selected, but you later want to reach this dialog again, you can force it to be shown by using the TIBCO Spotfire (show login dialog) option, reached via the Start menu > All Programs > TIBCO.

Logging In.png

If you are working on a large company with multiple TIBCO Spotfire Servers, you may occasionally also need to change the server you are connecting to via the drop-down list. New servers can be added to the list by clicking on the Manage Servers... link.

Connecting via Proxy Server

If you are connecting via a proxy server, you may need to change your security settings in Internet Explorer prior to logging into Spotfire. See the Microsoft Internet Explorer help for more information. Prior to logging into Spotfire, make sure that the Spotfire Server start page can be accessed by browsing to http://<hostname>/spotfire/.

Downloading Updates

Spotfire will automatically check for updates on your Spotfire Server that apply to you. If you have a network connection to the Spotfire Server, and there are updates available, you will be notified of this and can select whether to install them right away or at a later time.

You can get a look at the contents of the available updates by clicking on the View updates link in the notification dialog.

Working Offline

If you are on a plane or just happen to not be connected to the network where your Spotfire Server is located, you can work with Spotfire offline. Almost all of the functionality of Spotfire works fine without a connection to the server. Library access, however, does not, nor can you access information links to databases. To work offline, simply click the Work Offline button in the login dialog. With some licenses of Spotfire, you do need to connect to your Spotfire Server at least once a month to be able to continue to work offline.

Updates and Working Offline

If you have more than one server, and one of them has provided you with updates, this server must be selected in the login screen for those updates to be available, even if you choose to work offline.

Logging In Details

4 Subtopics

Data

Data Overview

Introduction

You can load data into TIBCO Spotfire from a number of different sources: by pasting from the clipboard, by opening simple text files, Microsoft Excel files, SAS files, a database or an information link (a predefined connection to a shared data source). You may also have access to additional file sources if such have been set up by your company.

You reach the different ways to load data either directly via the File menu or using Add Data Tables. In the Add Data Tables dialog you can add more than one data table to your analysis. TIBCO Spotfire also supports connections to external data sources*, such as Microsoft® SQL Server®, Microsoft®  SQL Server®  Analysis Services, Oracle®  and Oracle® MySQL databases, Teradata® , PostgreSQL®, SAP® Business Information Warehouse (SAP BW) and IBM® Netezza®. These connections allow you to analyze data in-database, see below.

Note: You may not have access to all of the external data sources listed above. See Connectors Overview for more information.

In-Memory Data

When you are working with in-memory data tables (text files, Excel files, information links, etc.) you have access to all the functionality of Spotfire. You have the opportunity to use all columns as filters and perform any number of calculations. You can also use any of the tools within Spotfire to cluster data, calculate new columns, bin columns, make predictions etc. See Working With Large Data Volumes for some tips on how to improve the performance of an analysis with lots of data.

In-Database Data

When a connection to an external source is set up, all calculations are done using the external system and not with the Spotfire data engine. This will allow you to work with data volumes too large to fit into primary memory and take advantage of the power of the external system. When working with external data connections, you access only the current selection of data and all aggregations and calculations are made in-database (in-db). 

When a visualization is configured to use in-db data, the visualization will query the external data source directly. Every time a change is done to the setup of the visualization, e.g., a fact column is set on the Y-axis or a Color by dimension column is added, a new query will be sent to the external data source resulting in a new table of aggregated data. This means that you cannot make any changes to a visualization using an in-db data table when you are not connected to the external data source.

In some cases, the data in a database is modeled as a star schema or snowflake, also known as a multidimensional model. If this is the case, you can reuse this model, the relations and constraints defined in the database, to setup your analysis. If no relations have been set up, it is possible to define those when selecting the data tables to retrieve for a certain connection. By using constraints and relations that have either been predefined in the database or created explicitly by a configurator multiple database tables can appear as a single de-normalized table, i.e., a virtual table. A virtual table can be used in a visualization providing the illusion of a single table. Virtual tables differ from regular Spotfire tables containing imported data in the sense that virtual tables only contain metadata which is used during configuration of a visualization—a virtual table does not actually contain any data.

This has the implication that some of the functionality within TIBCO Spotfire that is available for in-memory data is not applicable when working with in-db data. See Working With In-Database Data for more information.

*  Microsoft SQL Server and Microsoft SQL Server Analysis Services are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. 

* Oracle is a registered trademark of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. 

* Teradata is a trademark or registered trademark of Teradata Corporation in the United States and other countries. 

* SAP® Business Information Warehouse (SAP BW) is the trademark or registered trademark of SAP AG in Germany and in several other countries.

* IBM and Netezza are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide.

Data in Spotfire

Working With Large Data Volumes
Introduction

When you are working with massive amounts of data there may always be certain operations that take time to perform. However, with TIBCO Spotfire you do not have to be afraid to try out different alternatives. You can always cancel an operation if it looks like it is going to take a long time. You can undo an operation, or switch to a different alternative (e.g., switch to a column with fewer unique values on an axis) if you do not want to wait for the calculations to finish.

However, here are a few tips which can be useful when you are working with large data tables and you want to increase the performance of your analysis:

Visualizations and Analyses
  • Use aggregated visualizations as a starting point and use details visualizations for smaller, filtered portions of data only. Many graphical elements in the analysis will take some time to render. This is especially important on the web player which does not allow hardware acceleration.

  • Think about if there are any alternative ways  you can visualize your data in order to see the same thing. Can you use a different visualization type? Or partly aggregate the data? For example, binning can be used to aggregate markers in a scatter plot and still allow you to see a distribution. Using the bin sliders you can increase the number of markers shown until it takes too long time to make changes.

  • Sorting in cross tables, etc., takes time.

  • Analysis files which have previously been saved in version 4.5 or older can be saved in the latest version to shorten the time it takes to load the file.

  • Hide or delete unused filters (or do not create filters for external columns unless you have to).

  • Use the list box filter or the text filter rather than the item filter when working with columns with a lot of unique values. Item filters are costly to display, even when they are not used. If you have old analysis files using item filters for these type of columns it is recommended to manually change the filter type to a list box or text filter and save the file again.

  • Some types of aggregations are more time consuming than others. For example, use average rather than median, if possible.

  • Use the data type real rather than currency. The currency formatter can be applied to the real data type.

  • It is recommended to use the filters panel instead of adding a lot of filters to text areas. Filters in text areas can make the analysis seem unresponsive. The more filters you add to the text area, the less responsive the application becomes.

  • Calculated values (labels) and sparklines in text areas may also give rise to unresponsive analyses.

  • Use post-aggregation expressions for all expressions including OVER since these calculations are faster when done on an already aggregated view.

Hardware
  • Use 64-bit machines rather than 32-bit.

  • Use a fast solid-state drive (SSD) if possible.

  • Do not run other applications on the same machine when working with large data volumes.

Loading Data
  • Use sorted input on categorical columns.

  • Loading data from an SBDF file is much faster than from TXT.

  • If the data is in a tall and skinny format rather than a short and wide you may obtain better performance.

  • Remove invalid values from your data before importing into Spotfire.

Data Export
  • Export from a data table rather than from a table visualization.

  • Export to SBFD rather than to TXT.

Web Player
  • Avoid visualizations with many graphical elements (no hardware acceleration will make the rendering time very long).

  • Use scheduled updates, when possible.

Preferences
  • An administrator can modify the MarkingWhereClauseLimit or the MarkingInQueryLimit preference (under Administration Manager > Preferences > DataOptimization). With lower limits, the allowed complexity of marking queries is reduced. This is important when working with external data sources. See Preferences Descriptions in the Administration Manager help for more information.

  • Switch off the automatic creation of  filters. This can be turned off for a specific data table in the Data Table Properties dialog, and for all new in-memory data tables under Tools > Options – Document.

API
  • Prefer iterator based data access over random access. Use DataRowCursor API:s over GetValue(rowindex) style API:s.

  • Be careful when using custom comparers - depending on usage they may become a bottleneck. Consider if the problem cannot be solved in other ways.​

  • If things are slow and you are using old custom extensions, see if they can be refactored or if some time-consuming steps can be removed. Some API:s are by nature slow and old code might benefit from some refactoring. Try loading without any extensions to see if one of them may be the culprit. 

Working With In-Database Data

When you are working with data from an external data source (in-database or in-db data) there are a number of features that you can use with in-memory data that are unavailable. See below for more information.

One thing to think about when working with in-db data is that changes to the underlying database schema will not automatically be reflected in the Spotfire analysis. This means that if a column is added to a database table you need to perform a Refresh Schema operation in Spotfire in order to see the new column in the analysis. Not all users will have the sufficient database privileges to perform a full schema refresh. However, changes to the actual data can be updated using a simple Reload/Refresh Data by most users.

Note: Before you can work with in-db data in Spotfire there are few prerequisites that need to be met (see the System Requirements at http://support.spotfire.com/sr.asp for details):

  • You may need to have drivers for the data source of interest installed on your machine.

  • Some connectors require additional packages to be deployed on the Spotfire server.

  • You need to have been granted access to the licenses by your Spotfire administrator.

Working With Microsoft SQL Server Analysis Services

When data is located in Microsoft SQL Server Analysis Services cubes it behaves rather different compared to data in the relational databases traditionally accessed via Spotfire. In relational databases the facts are directly available in the database tables. In the cube, all facts are already aggregated by the cube administrator.

A cube is built from several predefined combinations of hierarchies, which could be either attribute hierarchies or user hierarchies defined using dimension attributes, see below.

DataCube.png

In the schematic image above, the sides of the cube could be said to represent different hierarchies. 

As a simplification, if side 1 represents product type, side 2 is a time unit  and 3 is region, then the cube could be queried in several different ways: the yellow plane could mean "Show sales per product for different years.", the pink plane represents "Show sales per product in different regions." and the blue plane "Show sales per region for different years.".

Loading Data

Loading Data Overview

You can load data into the internal TIBCO Spotfire engine from a number of different sources: by pasting from the clipboard, by opening simple text files, Microsoft Excel files, SAS files, a database or an information link​ (a predefined connection to a shared data source). You may also have access to additional file sources if such have been set up by your company.

TIBCO Spotfire also supports connections to external data sources, such as Microsoft SQL Server Analysis Services, Oracle, Teradata, etc. When a connection to an external source is set up with in-database data tables, all calculations are done by the external data source and not by Spotfire. See Working With In-Database Data​ for more information.

You reach the different ways to load data via the File menu or using Add Data Tables. With Add Data Tables, you can add more than one data table to your analysis.

Limiting What Data to Load

When the data source contains large amounts of data, it may take a long time to retrieve all data and the application could also be perceived as less responsive to different actions. You may also want to restrict some data from certain users. When you are working with information links it is possible to limit what data to open in different analyses in a number of different ways (combinations are also possible):

Method

Use when?

Define where?

On-Demand Data Table

When you want the data in your analysis to dynamically change with some predefined condition. For example, when setting up a details visualization dependent on the marking or filtering in another data table.

Another example is when you want one information link to return different data for different analysis files, in which case you could use the on-demand data table as the only data table in the analysis (with a document property as input).

On-demand data tables are added to your analysis in TIBCO Spotfire by selecting an information link or a data table from a data connection in the File > Add Data Tables dialog and then selecting the Load on demand check box. You must also specify  the input conditions that should control loading.

See On-Demand Overview​ for more information.

Note: You can only specify a single fixed value as input to on-demand loading, so if you need to retrieve multiple values from a certain column you will have to make sure that an information link is set up to use a multiple selection prompt rather than using it as an on-demand data table.

Details Visualizations Against External Data Sources

When you are analyzing in-database data using a connection to an external data source you only load the requested data.

By setting up visualizations based on the in-db data as details visualizations limited by the marking or filtering in a master visualization you can make sure that the actual loaded data is limited to a subsection of the available data only.

Make sure that the master data table and the in-db data table are releated.

Right-click on the master visualization and select Create Details Visualization. Set up the new details visualization to use the in-db data table.

Prompted Information Links

When the source data amount is huge, but the end users of the information link are allowed to determine what data to bring in for analysis themselves.

Can in some cases be replaced by an on-demand data table.

Prompts are defined in Information Designer, Information Link tab, Prompts section.

Personalized Information Links

When you want the data source to return only information applicable for a certain user name (via a lookup table) or for a specified group or user domain.

Personalized information links are set up on a filter or column element in Information Designer using the %CURRENT_USER%, %CURRENT_GROUPS% or %CURRENT_USER_DOMAIN% syntax. See Personalized Information Links​ for more information.

Parameterized Information Links

When you want the data source to return only information applicable for a certain user or group in a more flexible way than with personalized information links.

Parameters are created in Information Designer (for example, as a part of an expression set on a column or filter) but their properties and definitions are defined using the API.

By using a parameterized information link and a configuration block, it is possible to create an analysis with different input parameters (e.g., to be used by an On-Demand data table) for different groups of users. See Parameterized Information Links​ for more information.

Open File
Opening an Analysis File

If a colleague has created an analysis file (a DXP file) and either sent it to you in an email, or, given you a link to the Library where the file is located, double-clicking on the file will open it. To open a file from within TIBCO Spotfire, see below.

Note: SFS files created with TIBCO Spotfire DecisionSite, opened in TIBCO Spotfire will not retain any visualizations created in DecisionSite, and the file will be opened as if it was a standard Spotfire Text Data Format file. Note that SFS files cannot be opened from the Library.

Opening a Text File

This option is used when delimited text files, such as CSV or TXT files, are opened in Spotfire.

Note: If the content of a delimited text file is pasted into Spotfire, the Import Settings dialog will not be displayed. The default settings will be used during import.

Note: SFS files created with Spotfire DecisionSite, opened in Spotfire will not retain any visualizations created in DecisionSite, and the file will be opened as if it was a standard Spotfire Text Data Format file. Note that SFS files cannot be opened from the Library.

Opening an Excel File

Microsoft Excel files (XLSX or XLS) stored using Microsoft Office Excel 2000 or later can be opened in Spotfire.

Opening a SAS File

Note: To be able to open SAS data files (*.sas7bdat) directly into TIBCO Spotfire, the SAS Providers for OLE DB 9.1.3 or later must first be installed on the client machine (see http://support.spotfire.com/sr.asp for more information). *.sd7 files can also be opened provided that they first are renamed to *.sas7bdat.

Details
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Open From Library
Opening Files from the Library

The library provides publishing capabilities for your analysis materials, so you can share data with your colleagues. You can save both complete analyses and raw SBDF files in the library. The files in the library can be used directly from Spotfire by anyone who has at least read privileges.

Analysis files published in the library can also be accessed directly by users of Spotfire Web Player by clicking on a link to the analysis in an email or on a website.

Tip: Right-click in the library tree to display a pop-up menu where you can delete or edit the properties of previously added files and folders. You can also copy the URL to an analysis and open the analysis in the Web Player or send the link to a colleague.

Opening an Information Link

Information links are predefined database queries, specifying the columns to be loaded, and any filters needed to reduce the size of the data table prior to visualization. They are organized into different folders in the library. Which folders in the library are available to you depends on how your permissions have been set by the administrator. Information links are defined using Tools > Information Designer.

Note: You can also search for an item in the library by entering its name, or part of the name in the search field in the upper right corner in the dialog, and then pressing enter. All the files, information links and folders matching your search string will then be listed. See Searching the Library for detailed information about library search.

Searching the Library

You can search for library items in the Open from Library dialog, in the Library Administration tool and in Information Designer.

Searching for a text string will by default look for matching text in the title and keywords of the items in the library. You can use wildcards and boolean operators to search for parts and combinations of words. For a listing of the basic search syntax, see Searching in TIBCO Spotfire.

Edit Properties
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Add Data Table
How to Add Data Tables

Data can be added to the analysis in several different ways: as new columns, as new rows or as new data tables. Adding data as separate data tables is useful if the new data is unrelated to the previously opened data table or if the new data is in a different format (pivoted vs. unpivoted).

If you have a visualization made from a particular data table which has filtering and marking that you would like to apply to visualizations made from another data table, then you must define a relation between the two tables. For a relation to be useful, you need to have one or more key columns (identifier columns) available in both data tables, and use these to define which rows in the first data table will correspond to rows in the second data table. If you need more than one key column to set up a unique identifier, you must add one relation for each identifier column.

To combine data from different data tables in one visualization, the data tables are loaded as usual, but at least one column must match between the data tables in order to combine the data from the data tables. Columns match if they have the same data type. If two columns are of the same data type and have the same name, Spotfire will match them automatically during loading. You can view and edit column matches in the Data Table Properties dialog. To learn more about using many data tables in the same visualization, see Multiple Data Tables in One Visualization.

In some cases when you need to bring in-memory data from different data sources together in any other single visualization, it may be more suitable to use the Insert Columns or Insert Rows tools. And with in-database data tables you can often join several database tables into a single virtual data table before adding it to Spotfire. See Details on Views  in Connection for more information.

Tip: For a simple line from a different data table in a scatter plot, see Details on Line from Data Table.

Tip: You can always go back and edit relations as well as create new ones using the Data Table Properties dialog.

Details
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On-Demand Data Tables
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Details
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Load Data from Active Spaces
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Open Database
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Add Data Connection
What is a Data Connection?

A data connection is used when you are analyzing massive amounts of data and you need to keep the underlying data in the database (in-db) rather than bringing it into Spotfire's internal data engine. However, you can also select to import data tables from relational data connections. Both imported and in-db data tables from a relational  connection can be set up to be loaded on demand only. On-demand is not available for cube connections.

The data connection consists of two parts:

  • A connection data source.
  • One or more data tables which may or may not be used by the analysis.

Both the connection data source and the data connection itself can be shared in the library, if desired. 

What is a Connection Data Source?

A connection data source is an important part of a data connection. It can be set up in advance by your administrator, using the Manage Data Connections tool, and shared in the library, but it can also be set up within the context of a data connection, "embedded in connection", if you have access to the login credentials to the underlying database.

The information required to set up a connection data source varies between different types of connections, but it typically includes a server name, port number, database name and credentials information. See the login dialog for the interesting connection type for details.

You cannot use a Data source specified in Information Designer as a connection data source.

Adding Data Connections to an Analysis

Note: Which connectors you have access to is determined by the licenses set up by your Spotfire administrator. Some connectors also require that a driver is installed on the machine running Spotfire. See the system requirements at http://support.spotfire.com/sr.asp for details.

If data connections have been set up in advance you can add them directly from the library.

Note: You can also create relations between different data tables in Spotfire without actually joining them. This will form a looser connection between the tables but it can be used if you want to set up a details visualization using one of the data tables, limited by selections in the other. See Details on Manage Relations for more information.

Available Connections
Connectors Overview

Connectors are used to set up data connections. The connectors can be seen as adapters which can translate data queries between Spotfire and an external system. Which connectors you have access to depends on several different things:

What licenses the groups you belong to have been granted access to by the Spotfire administrator.

Whether or not your computer has the required drivers for a certain connector. See the system requirements at http://support.spotfire.com/sr.asp for details.

  • Spotfire Professional comes with the following connectors automatically:
  • Microsoft SQL Server
  • Microsoft SQL Server Analysis Services
  • Oracle
  • Teradata

Your company may also have deployed additional connectors on the server that are not embedded in the standard Spotfire distribution. If your connector is not listed below, the help for these products will be available under Help > Additional Help Topics:

  • ADS Composite Information Server
  • IBM Netezza
  • Oracle MySQL
  • PostgreSQL
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Details on General Dialogs
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Mapping of External Data Types
7 Subtypes
Replace Data
Replacing Data

In Spotfire it is possible to reuse the visualizations, calculations and setup from a previously created document with new data, as long as the new data is reasonably similar to the old data. This is useful when creating an analysis for, say, sales figures for a certain month. You create a full analysis using the data from January, set up visualizations, calculations, etc., and save the file. When the sales figures for February are available, you can open the same file again, and replace the data from January with the data from February, and the visualizations will be updated. This of course requires that the data table for February is structured in the same way as for January, using the same column names and format.

Details
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Transform Data
Transforming Data

Sometimes the data you want to analyze in Spotfire is not in the most appropriate format, or may contain errors. It can therefore be useful to perform transformations on the data in order to get the best results from the analysis. 

There are several methods that can be used to transform your data:

  • Calculate and replace column allows you to replace a column in the data table with a calculated column.
  • Calculate new column allows you to add a calculated column to the data table.
  • Change column names allows you to change the name of one or more of the columns in the data table.
  • Change data types allows you to change the data type for one or more of the columns in the data table.
  • Data function allows you to use a previously registered data function as a transformation step.
  • Exclude columns allows you to exclude one or more of the columns from the data table.
  • Normalization allows you to normalize the data prior to addition of the data table.
  • Pivot allows you to pivot the data – to change the data table from a tall/skinny format to a short/wide format.
  • Unpivot allows you to unpivot the data – to change the data table from a short/wide format to a tall/skinny format.
  • Additional transformations may be available to you if these have been added locally.

Transformations can be applied either when data is loaded, or later on, when the data has already been loaded into Spotfire. You can perform transformations on most of the "regular" column types that are loaded into Spotfire, but not on certain column types whose content changes depending on selections you make in the analysis. Calculated columns, columns that were created by adding tags to the analysis, and columns created by using tools like K-means Clustering and Line Similarity are some examples of column types that you cannot apply transformations to. Columns that cannot be transformed will not be available for selection in any of the settings dialogs used for transformations. Also, data that is located in an external data source (in-database data) cannot be transformed. However, if you add data from an external data source, and select to import the data table into Spotfire, you can apply transformations to the data after it has been loaded, by using Insert Transformations, as described below.

Pivoting Data

A pivot transformation is one way to transform data from a tall/skinny format to a short/wide format. The data is distributed into columns usually aggregating the values. This means that multiple values from the original data end up in the same place in the new data table.

Unpivoting Data

An unpivot transformation is one way to transform data from a short/wide to a tall/skinny format. When the data types of source columns differ, the varying data is converted to a common data type so the source data can be part of one single column in the new data set.

Normalizing Data
Normalizing Columns

A number of normalization methods can be written as expressions, or used when transforming data. See the links at the end of this topic for a description of the theory behind each method.

Details
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Details
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Missing File
Details on Missing File – Local File

This dialog is shown when you open a linked analysis file in which the file path to one or more of the source files is no longer correct.

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Inserting More Data

Insert Calculated Column
What is a Calculated Column?

Occasionally, the columns included in a data table do not allow you to perform all necessary operations, or to create the visualizations needed to fully explore the data table. However, in many cases the necessary information can be computed from existing columns by using the mathematical and logical expressions provided by the Insert Calculated Column tool.

Note: A calculated column is treated like any other column and its contents are static during all further analysis. If you want to use expressions that change during filtering of your data table, you should instead use custom expressions that are defined where you need them (for example, select Custom Expression... from the right-click menu on the axis selector).

How to Insert a Calculated Column

TIBCO Spotfire supports two different types of expressions: Insert Calculated Column, which creates a new column in the data table, and Custom Expression, which is used to dynamically modify the expression used on an axis or to define a setting. Both types of expressions are created with a similar user interface.

Details on Insert Calculated Column

TIBCO Spotfire supports two different types of expressions: Insert Calculated Column, which creates a new column in the data table, and Custom Expression, which is used to dynamically modify the expression used on an axis or to define a setting. Both types of expressions are created with a similar user interface.

Expression and Script Editor Keyboard Shortcuts

Many of the expression or script editing fields available in Spotfire allow you to use the following keyboard shortcuts:

Expression Language
General Syntax

My Note: I need to study this

Operations
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Functions
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Invalid Values

An expression is considered valid if it is syntactically correct and all function, operator and column references can be resolved. If an expression is not valid, it cannot be evaluated. This will be indicated in the Sample result field of the Insert Calculated Column dialog as "#Error", (Empty), or similar. When generating a result data table from the expression, errors are converted to null. Wrap the expression with a call to the SN(Arg1, Arg2) function to override this behavior. The SN(Arg1, Arg2) function can be used to substitute null with a certain value, for example, 0.

Empty values are generated whenever a column value from the data table is missing, when a calculation involves an invalid value, or by explicitly writing null in the expression. Results that are null, are displayed as "(Empty)" or simply left blank.

When aggregating within a column, the invalid value will be ignored, whereas row-wise calculations between columns will result in invalid values each time one of the involved columns contains an invalid value.

Details on Formatting

This dialog lets you format values on column level. If you change settings for a specific column or hierarchy in this dialog the new settings will be used for that specific column or hierarchy everywhere in the analysis from then on.

For general information about formatting, see Formatting Overview.

Format String

If the format you want to use cannot be created with the given settings, the custom format string allows you to create your own formats using a code explained in the examples below.

The special characters allow you to multiply, divide, separate numbers, etc. Other characters are printed out in the resulting data.

Properties
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Insert Binned Column
What is Binning?

Binning is a way to group a number of more or less continuous values into a smaller number of "bins". For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals. Numeric columns can also be temporarily grouped by right-clicking on a column selector and clicking Auto-bin Column.

How to Use Binning

Tip: Right-click on a column selector and select Auto-bin Column to create a temporary, automatic binning on an axis. Binning functions can also be used in custom expressions.

Details on Insert Binned Column
The Binning Slider

When using a numeric column for the X-axis in a visualization (the category axis in a bar chart), you sometimes may want to bin the values to compare segments of the data to each other. One very handy tool to help you dynamically do this is the binning slider.

Tip: The Auto-bin Column functionality is actually creating a custom expression using the BinByEvenIntervals function. There are more binning functions available in the Custom Expression dialog.

Insert Columns
How to Insert Columns

You can insert columns from many different sources. Below are some examples of how to add columns from some of the most common sources. 

Tip: Type the name of the data source directly at the top of the Select menu to search for a data source. The results are grouped after source origin to help you find the source from the correct location.

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Insert Rows
How to Insert Rows

You can insert rows from many different sources. Below are some examples of how to add rows from some of the most common sources. 

Tip: Type the name of the data source directly at the top of the Select menu to search for a data source. The results are grouped after source origin to help you find the source from the correct location.

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Working with Data Tables

Data Tables Overview
Introduction

With TIBCO Spotfire is possible to work with more than one data table within a single analysis. Below is a short description of the different concepts used when handling multiple data tables.

A data table is either fetched from a data source, or created within the application. Data loaded from a data source can be handled either in-memory or in-database depending on how it is added to the analysis. In-memory data tables have one or more columns and zero or more rows, whereas in-database data tables technically do not contain any data but simply fetch the requested data directly from the source. See Data Overview for more information. 

In-memory data tables can be linked or embedded. Linked data tables can be loaded completely into the application, but if the source is an information link they can also be configured to load data on demand only.

Data tables can be related to each other, using primary and/or foreign keys (key columns), but they can also be unrelated. When data tables are related, any marking or filtering in one data table may be propagated to the other related data tables.

Data tables can have column matches between them if columns are of the same data type. A column match is used to aggregate data correctly when the data from different data tables is combined in a single visualization. Column matches are much looser in nature than the relation mentioned above, because they do not join the data tables together. Marking and filtering is still individual for each data table even if they have columns matching between them. To learn more about column matches, see Matching Columns.

On-Demand Data Tables

On-demand data tables are data tables to which only rows related to a defined input are loaded. The input could be something like the marked rows in another, related, data table, the filtered rows of another data table or a property value selected in a text area. Changing the input means changing the "demand", i.e., that more, fewer or other rows are loaded into the data table. On-demand data tables can be used by Details Visualizations, and only data from information links or data connections can be loaded on demand.

In-Database Data Tables

While data from in-database data tables is retrieved only when needed, the use of an in-database data table as a details visualization may also be seen as a type of on-demand visualization.

Related Data Tables

As a means of helping you keep track of which data tables are related, a stripe of color will be added to the left of the filters in the filters panel when more than one data table is available. Filters from related data tables (which may affect each other when they are manipulated) all have the same color. Also, the visualizations that use related data tables will show the same color in the title bar, if it is displayed.

Note: You can specify whether or not filtering in a data table should affect what is shown in visualizations used by other, related data tables. The default setting is to ignore filtering in related data tables. See Filtering in Related Data Tables for more information.

Column Matches in Data Tables

To see which column matches exist in the visualization, open the Column Matches tab in Data Table Properties. You can also see which column matches are used in a specific visualization by opening the Visualization Properties dialog on the Data page for the visualization of interest.

How to Add Data Tables

Data can be added to the analysis in several different ways: as new columns, as new rows or as new data tables. Adding data as separate data tables is useful if the new data is unrelated to the previously opened data table or if the new data is in a different format (pivoted vs. unpivoted).

If you have a visualization made from a particular data table which has filtering and marking that you would like to apply to visualizations made from another data table, then you must define a relation between the two tables. For a relation to be useful, you need to have one or more key columns (identifier columns) available in both data tables, and use these to define which rows in the first data table will correspond to rows in the second data table. If you need more than one key column to set up a unique identifier, you must add one relation for each identifier column.

To combine data from different data tables in one visualization, the data tables are loaded as usual, but at least one column must match between the data tables in order to combine the data from the data tables. Columns match if they have the same data type. If two columns are of the same data type and have the same name, Spotfire will match them automatically during loading. You can view and edit column matches in the Data Table Properties dialog. To learn more about using many data tables in the same visualization, see Multiple Data Tables in One Visualization.

In some cases when you need to bring in-memory data from different data sources together in any other single visualization, it may be more suitable to use the Insert Columns or Insert Rows tools. And with in-database data tables you can often join several database tables into a single virtual data table before adding it to Spotfire. See Details on Views  in Connection for more information.

Tip: For a simple line from a different data table in a scatter plot, see Details on Line from Data Table.

Working with Data Tables

When you set up an analysis in TIBCO Spotfire, you may want to be able to visualize data from more than one data table. Adding other data tables is fairly easy; just select File > Add Data Tables... and use the Add button to select the data source of interest. See How to Add Data Tables for more information. However, if you choose to bring in a lot of data tables, you may find it difficult to keep track of which data tables are related and which are not. Spotfire will add some extra visual hints when more than one data table is available to help you see which data tables are related. You may also want to view which data tables have matching columns and therefore can be combined in one visualization. Or simply view which data tables have already been combined in a certain visualization.

You can always get a collected view of all data tables in the analysis by selecting Edit > Data Table Properties or View > Data which opens the data panel.

Multiple Data Tables in One Visualization

Sometimes the data you want to analyze in Spotfire is located in different data tables. Working with visualizations combining data from multiple data tables is not very different from working with data from a single data table. You can choose the visualization that best suits your data, you can filter, mark, and drill down in your data, just as with a visualization using data from a single data table. However, a couple of concepts are important to be familiar with when setting up and working with a visualization combining data from different data tables. This topic describes key concepts, and includes a couple of examples.

Recommended Workflow

If you are unsure how to set up a visualization combining columns from different data tables, this recommended workflow can be helpful.

1. Choose the main data table

Start by having a look at the data in the different data tables, and try to answer a couple of questions. What data do they contain? What do you want to visualize based on that data? A data table containing categories you would like to group your visualization by is a good candidate for the main data table. For instance, you may want to group by region, department, salesperson, product type, or similar.

2. Set up the visualization with only the main data table

Create the visualization type you want to use, and then configure as much as you can of that visualization with columns only from the main data table. Select how and by which columns the visualization should be grouped, and if the main data table also contains columns that you want to show as aggregated, add those columns to the appropriate axes as well.

3. Add the aggregating measures

When the visualization has been configured as much as possible with only main data table, you can start adding aggregated columns from other data tables.

Examples
Independent Data Tables

This is an example of independent data tables. These two visualizations are placed on the same page, but they are not related to each other. The visualizations correspond to separate data tables. Marking or filtering in one visualization will not affect the other when they are independent. The Details-on-Demand displays information about the marked item in the active visualization. Color stripes are used to indicate what visualization, filter and Details-on-Demand that are related.

In this example, the bar chart shows the sum of sales for different types of fruits and vegetables. The scatter plot shows the content of fructose and glucose for different types of fruits and vegetables.

Multiple Related Data Tables

This is an example of multiple related data tables. The visualizations are based on different data tables that are related. Marking items in one visualization will mark the corresponding items in the related visualizations. Filtering data in one data table may filter the related data in the other data tables. The relation between the data tables is set up in TIBCO Spotfire. Visualizations that are related share the same color in the color stripe to the left in the visualization. Filters belonging to related data tables also share the same color stripe.

Note: Related visualizations can be placed on different pages in an analysis. This means that markings that are not visible at the moment can affect the analysis that you are looking at.

In this case, two data tables with information about fruit and vegetables are related. The scatter plot shows the amount of glucose and fructose for different types of fruits and vegetables, while the bar chart shows the sum of sales for the same types of fruits and vegetables. Marking an item in the scatter plot, in this case the one with the highest level of fructose (Apples), will mark the Sum(Sales) for Apples in the bar chart.

Master-Detail Visualizations

This is an example of multi-step Master-Detail visualizations. The visualizations in this example are based on the same data table and show different levels of detail. However, the visualizations could just as well be based on data from different data tables. Marking in one visualization defines the data of the next visualization, making it possible to drill down in level of detail.

Note: Related visualizations (as the Master-Detail case) can be placed on different pages in a visualization. This means that markings in a visualization that is not visible at the moment can affect the visualization that you are looking at. If a visualization is empty, it may be because it is based on markings from another visualization. Go to the master visualization and mark an item to display information in the details visualization. When creating the analysis, you can add a message explaining in which visualizations to mark items in order to view details. See What is a Details Visualization? for more information.

Note: The Details-on-Demand displays information about the marked rows from the active visualization; it could be either the master or the details visualization.

Insert Columns – Example

By inserting columns or rows, it is possible to combine data from different sources into a single data table that can be used in a visualization.

Copy/Paste Data

Copying Data

You can copy data from Spotfire for later use within Spotfire, or in other applications. The copying options below are available from the Edit menu.

Pasting Data

You can add data to your analysis by pasting data that is currently available on the clipboard. For example, you may have copied some data from a TXT file, the content in a few cells in a spreadsheet, or an entire file that is located in a folder on your computer. Either way, you can load the copied content into Spotfire by using one of the available pasting options. The pasting options below are available from the Edit menu.

Data Panel

What is the Data Panel?

The Data panel is used to get an overview of the columns in all data tables, in-memory as well as in-database (in-db). When working with in-db data the Data panel is the starting point for configuring both visualizations and the filters panel, since no filters are created automatically for external data. Depending on the data source, there will be different sections available for a selected data table, see below for some examples.

Using the right-click menu in the Columns field, you can easily specify that a column contains geocoding information, that is, information that can be used to position data on a map

Data Panel Pop-up Menu

Right-click in the data panel to bring up the pop-up menu. You will have access to different options depending on where in the data panel you click.

Details on Rename Column

Data Connections Properties

What is a Data Connection?

A data connection is used when you are analyzing massive amounts of data and you need to keep the underlying data in the database (in-db) rather than bringing it into Spotfire's internal data engine. However, you can also select to import data tables from relational data connections. Both imported and in-db data tables from a relational  connection can be set up to be loaded on demand only. On-demand is not available for cube connections.

The data connection consists of two parts:

  • A connection data source.
  • One or more data tables which may or may not be used by the analysis.

Both the connection data source and the data connection itself can be shared in the library, if desired.

What is a Connection Data Source?

A connection data source is an important part of a data connection. It can be set up in advance by your administrator, using the Manage Data Connections tool, and shared in the library, but it can also be set up within the context of a data connection, "embedded in connection", if you have access to the login credentials to the underlying database.

The information required to set up a connection data source varies between different types of connections, but it typically includes a server name, port number, database name and credentials information. See the login dialog for the interesting connection type for details.

You cannot use a Data source specified in Information Designer as a connection data source.

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Data Table Properties

How to Edit Data Table Properties

The dialog found under Edit > Data Table Properties contains settings that apply to the data tables used in the analysis. For example, you can define which data table to use as default when creating new visualizations, set up sharing routines, or define how data should be stored when saving the analysis. To learn more about data tables, see Data Tables Overview.

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Details
Details on Select Key Columns

This dialog is used to define key columns for a data table in an analysis. The key columns are used to uniquely identify all rows in the data table. You should specify key columns if you want to be able to see the markings that were active when saving the file, or if you want any specified tags or bookmarks to be able to be reapplied when reopening the analysis file. However, there is no guarantee that a selection always can be reapplied even if key columns are specified since a selection of a visualization item might include references to other columns than the key columns.

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Column Properties

How to Edit Column Properties

Column properties are any type of metadata available for the columns (and, in some cases, also for hierarchies) in your data table. For example, this could be the name or number of decimals of a column, the data type, an optional description of the column content, or, a customized sort order for a string column. All properties can be viewed, and some can be edited, by selecting Edit > Column Properties.

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Details
Details on Insert Hierarchy

Predefined hierarchies can be set up when two or more columns are somehow related to each other. For example, a hierarchy can add structure to columns containing Country, State and City. The predefined hierarchies allow you to quickly change the level of detail in a visualization by using the hierarchy sliders, or, when you wish to combine two or more filters to a more structured hierarchy filter.

Note: The number of allowed nodes in a hierarchy with more than one level is limited to 100 000. If you try to create a hierarchy with more nodes, you will simply receive a hierarchy with one value, (All). If this should happen, edit the hierarchy and remove the column with too many unique values from the hierarchy.

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Visualizations

Table

See: Table

What is a Table?

The table in TIBCO Spotfire works much like any other table you might be familiar with. It presents the data as a table of rows and columns, and is used to see details and compare values.

By clicking on a row you mark it, and by dragging the mouse pointer over several rows you can mark more than one row.

You can sort the rows in the table according to different columns by clicking on the column headers, or filter out unwanted rows by using the filters.

All visualizations can be set up to show data limited by one or more markings in other visualizations only (details visualizations). Tables can also be limited by one or more filterings. You can also set up a table without any filtering at all. See Limiting What is Shown in Visualizations for more information.

Note: When working with in-db data the table visualization cannot show more than 10 000 rows. If not all data can be shown, you will get a notification about this in the legend. A primary key must be specified in the external database table to enable highlighting and markings in the table visualization.

3 Subtopics
Table Properties
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Cross Table

See: Cross Table

What is a Cross Table?

A cross table is a two-way table consisting of columns and rows. It is also known as a pivot table or a multi-dimensional table. Its greatest strength is its ability to structure, summarize and display large amounts of data. Cross tables can also be used to determine whether there is a relation between the row variable and the column variable or not.

Optionally, the cross table can display grand totals for columns, rows, or for the whole measure. It can also display subtotals for columns,

Note: The aggregated value for subtotals and grand totals is not calculated on the values shown in the cross table, but on the underlying row values. This means that if you hide rows on the Sorting page or if you are using Show/Hide Items rules, then the grand totals or sub totals will be the sum of all values and not the sums of the visible values.

All visualizations can be set up to show data limited by one or more markings in other visualizations only (details visualizations). Cross tables can also be limited by one or more filterings. Another alternative is to set up a cross table without any filtering at all. See Limiting What is Shown in Visualizations for more information.

2 Subtopics
Cross Table Properties
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Graphical Table

See: Graphical Table

What is a Graphical Table?

A graphical table is a summarizing visualization designed to provide a lot of information at one glance. It can be set up to show columns with dynamic items such as sparklines, calculated values, conditional icons, or bullet graphs. One value is shown for each row as specified on the Rows axis. In the example below, the graphical table shows sales performance for different regions.

You can add any number of dynamic items to a graphical table. Each dynamic item column uses its own axis expression and it can also be filtered and limited by markings separately. This way, you can show both the total values for some calculated value and the currently filtered values simultaneously. See Limiting What is Shown in Visualizations for more information.

When a hierarchical structure is used on the Rows axis, the graphical table is grouped into sections and sorting can be performed within each section by clicking on a column header.

How to Use the Graphical Table
Graphical Table Properties
Graphical Table Properties

The Graphical Table Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Fonts
  • Axes
  • Legend
  • Subsets
  • Show/Hide Items
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Sparklines

​See: Sparkline

What are Sparklines?

Sparklines are small, simple line graphs traditionally used for displaying trends or variations of some variable:

They can be displayed in the context of a graphical table or, separately, in a text area. The general idea of sparklines is that they can be included directly where they are needed, in tables or text, in order to provide context to a value. Sparklines can be set up to change with filtering like any traditional Spotfire visualization or they can be locked to show  fixed values, using the Data page in the Sparkline Settings dialog.

How to Use Sparklines

Sparklines can be shown both separately in a text area or be included as a column in a graphical table. The behavior of the sparkline is quite similar in both places, but some differences exist. Therefore, this list of step instructions has been split into three different sections: Graphical table specific information, Text area specific information, and General information applicable to both instances.

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Calculated Value
What are Calculated Values?

Calculated values are values derived from some kind of aggregated expression, similar to the data shown in cross tables. They can be displayed in the context of a graphical table or, separately, in a text area.

The general idea of calculated values is that they can be included directly where they are needed, in tables or text, in order to provide information at a glance. Calculated values can be set up to change with filtering like any traditional Spotfire visualization or they can be locked to show  fixed values, using the Data page in the Calculated Value Settings dialog.

By adding rules that control the color and font style you can make sure that a value stands out when it falls outside the specified limits:

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Icons
What are Icons?

Icons are small, simple images traditionally used for displaying trends or variations of some variable. They can be displayed in the context of a graphical table or, separately, in a text area. In the example below, the icons are used in a graphical table to show the top, bottom and intermediate sales region of some fictive product:

The general idea of icons is that they can be included directly where they are needed, in tables or text, in order to provide information at a glance. Icons can be set up to change with filtering like any traditional Spotfire visualization or they can be locked to show  fixed values, using the Data page in the Icon Settings dialog.

How to Use Icons

Icons can be shown both separately in a text area or be included as a column in a graphical table. The behavior of the icon is quite similar in both places, but some differences exist. Therefore, this list of step instructions has been split into three different sections: Graphical table specific information, Text area specific information, and General information applicable to both instances

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Bullet Graph

See: Bullet Graph

What are Bullet Graphs?

Bullet graphs are used to compare one value, represented by a horizontal bar, to another value, represented by a vertical line, and relate those to qualitative ranges. 

The general idea of bullet graphs is that they can be included directly where they are needed, in tables or text, in order to provide information at a glance. Bullet graphs can be set up to change with filtering like any traditional Spotfire visualization or they can be locked to show fixed values, using the Data page in the Bullet Graph Settings dialog.

How to Use Bullet Graphs

Bullet Graphs can be shown both separately in a text area or be included as a column in a graphical table. The behavior of the bullet graph is quite similar in both places, but some differences exist. Therefore, this list of step instructions has been split into three different sections: Graphical table specific information, Text area specific information, and General information applicable to both instances.

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Bar Chart

See: Bar Chart

What is a Bar Chart?

A bar chart is a way of summarizing a set of categorical data (continuous data can be made categorical by auto-binning). The bar chart displays data using a number of bars, each representing a particular category. The height of each bar is proportional to a specific aggregation (for example the sum of the values in the category it represents). The categories could be something like an age group or a geographical location. It is also possible to color or split each bar into another categorical column in the data, which enables you to see the contribution from different categories to each bar or group of bars in the bar chart.

How to Use the Bar Chart
Bar Chart Properties
Bar Chart Properties

The Bar Chart Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Formatting
  • Fonts
  • Category Axis
  • Value Axis
  • Colors
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Lines & Curves
  • Error Bars
  • Subsets
  • Show/Hide Items
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Line Chart

See: Line Chart

What is a Line Chart?

Line charts are ideal for showing trends over time. A standard example would be how the stock value for a certain company develops over time on the stock market. However, it does not necessarily need to be time along the X-axis. Any data that behaves like a function with respect to the variable on the X-axis can be plotted. Line charts emphasize time flow and rate of change rather than the amount of change.

You can select parts of a line by clicking and dragging with the mouse. If one node in the line is included when you drag, that node will be marked. If two or more adjacent nodes are included, the line between the nodes will also be marked, but if there are nodes in between which are not included, only the separate nodes will be marked. You can select several nodes in different parts of the line by pressing Ctrl and clicking and dragging with the mouse. Press Alt and click and drag to use lasso-marking to encircle the nodes of interest.

Step lines

The line chart can also be used as a step chart, where the lines are drawn in incremental steps rather than as straight lines between each value.

Step charts are especially useful when changes occur at certain times but the values remain more or less stable between changes.

How to Use the Line Chart
Line Chart Properties
Line Chart Properties

The Line Chart Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Formatting
  • Fonts
  • X-axis
  • Y-axis
  • Line By
  • Colors
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Lines & Curves
  • Error Bars
  • Subsets
  • Show/Hide Items
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Combination Chart

What is a Combination Chart?

The combination chart is a visualization that combines the features of the bar chart and the line chart. The combination chart displays the data using a number of bars and/or lines, each of which represent a particular category. A combination of bars and lines in the same visualization can be useful when comparing values in different categories, since the combination gives a clear view of which category is higher or lower. An example of this can be seen when using the combination chart to compare the projected sales with the actual sales for different time periods.

Series

Similarly to the function of Color by in other visualizations, Series by in the combination chart is a way to divide the data into slices. The difference is that the slices in the combination chart, called series, can be defined as bars or lines as well as being colored separately. That is, each series in the combination chart will be represented by a line or a set of bars in the visualization.

How to Use the Combination Chart
Combination Chart Properties
Combination Chart Properties

The Combination Chart Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Formatting
  • Fonts
  • X-axis
  • Y-axis
  • Series
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Lines & Curves
  • Subsets
  • Show/Hide Items
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Pie Chart

See: Pie Chart

What is a Pie Chart?

Pie charts are circle graphs divided into sectors, each pie sector displaying the size of some related piece of information. Pie charts are used to show the relative sizes of the parts of a whole.

How to Use the Pie Chart
Pie Chart Properties
Pie Chart Properties

The Pie Chart Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Fonts
  • Colors
  • Size
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Subsets
  • Show/Hide Items
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Scatter Plot

See: Scatter Plot

What is a Scatter Plot?

Scatter plots are used to plot data points on a horizontal and a vertical axis in the attempt to show how much one variable is affected by another. Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X and Y axes.

A third variable can be set to correspond to the color or size of the markers, thus adding yet another dimension to the plot.  

The relationship between two variables is called their correlation. If the markers are close to making a straight line in the scatter plot, the two variables have a high correlation. If the markers are equally distributed in the scatter plot, the correlation is low, or zero. However, even though a correlation may seem to be present, this might not always be the case. Both variables could be related to some third variable, thus explaining their variation, or, pure coincidence might cause an apparent correlation.

How to Use the Scatter Plot
Scatter Plot Properties
Scatter Plot Properties

The Scatter Plot Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Formatting
  • Fonts
  • X-axis
  • Y-axis
  • Colors
  • Size
  • Shape
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Line Connection
  • Marker By
  • Lines & Curves
  • Error Bars
  • Subsets
  • Show/Hide Items
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3D Scatter Plot

See: 3D Scatter Plot

What is a 3D Scatter Plot?

3D scatter plots are used to plot data points on three axes in the attempt to show the relationship between three variables. Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X, Y, and Z axes.

A fourth variable can be set to correspond to the color or size of the markers, thus adding yet another dimension to the plot.

The relationship between different variables is called correlation. If the markers are close to making a straight line in any direction in the three-dimensional space of the 3D scatter plot, the correlation between the corresponding variables is high. If the markers are equally distributed in the 3D scatter plot, the correlation is low, or zero. However, even though a correlation may seem to be present, this might not always be the case. The variables could be related to some fourth variable, thus explaining their variation, or pure coincidence might cause an apparent correlation.

You can change how the 3D scatter plot is viewed by zooming in and out as well as rotating it by using the navigation controls located in the top right part of the visualization.

Note: The 3D scatter plot is not supported in TIBCO Spotfire Web Player. It is still possible to open an analysis with a 3D scatter plot in the web player, but the 3D scatter plot will not be shown.

How to Use the 3D Scatter Plot
3D Scatter Plot Properties
3D Scatter Plot Properties

The 3D Scatter Plot Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Formatting
  • Fonts
  • X-axis
  • Y-axis
  • Z-axis
  • Colors
  • Size
  • Shape
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Marker By
  • Subsets
  • Show/Hide Items
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Map Chart

See: Map Chart

What is a Map Chart?
Introduction

Map charts allow you to position your data in a context, often geographical, using different layers. The layers can be either data layers, such as marker layers or feature layers, or reference layers such as map layers or image layers. If multiple data layers are included in the map chart, you must always specify which layer should be the interactive layer. The interactive layer is the only layer in which you can mark items, but you can easily switch the interactive layer using the Layers control. The layers control can also be used to hide or show the different layers.

Feature Layers

Feature layers contain map features or shapes of the type polygon, line, or point. A feature layer can be used either as a data layer or as a reference layer only displaying items of visual interest. Below is an example of a map chart with an interactive feature layer showing shapes, where each shape represents a state in the United States. Each shape in the map is a separate item, and you can interact with those items the same way you do with items in any other visualization.

What is a Map Chart1.png

As mentioned above, the shapes in the feature layer can be one of three geometry types: polygons, lines, or points. When polygons are used, as in the example above, the shapes constitute different areas in the map, and these areas will be filled with color. How to color the shapes is defined in the Colors page of the layer settings, or in the legend. When lines or points are used, the interactive shapes are the actual lines or points. The color you define in the Colors will be the color of the lines or points. Examples of when maps with lines as interactive shapes could be useful are maps showing highways or a street grid. Below is an example of a map chart with interactive shapes, where each shape represents a highway.

What is a Map Chart2.png

Which geometry type is used in a map is defined in the map data before you load it into your analysis, and this cannot be changed in Spotfire.

Marker Layers

In a marker layer, markers or pies are positioned in the different areas. In the example below, the map shows the same geographical area as in the first example, and is also divided into states. But instead of the states being interactive, a marker is placed in each of the states, and you can interact with the markers just as you do with markers in other visualizations.

What is a Map Chart3.png

If the data table for markers or pies has columns containing coordinates, you can use these to position the markers or pies in their correct locations on the map, but you can also map a certain hierarchy (e.g., State, County, City) to a corresponding hierarchy in a geocoding data table.

The markers are also well suited to be displayed on an online map using a map layer, see below.

Note: Since the base map is limited to latitudes of +85 to -85, markers with a latitude of 86 to 90 or -86 to -90 will not be rendered.

Map Layers

The map layers allow you to display your data on a tile based web map from TIBCO GeoAnalytics. Map layers are always used as a reference layer and cannot be interacted with directly. The available default maps can either be compound standard maps which include both borders, labels and roads, or you can use separate layers for each type of information and select only the information of interest.

What is a Map Chart4.png

More details may become visible when you zoom in the map. You also have the option to specify at which zoom level a certain layer should become visible. See Map Chart Properties – Zoom Visibility​ for more information.

You can zoom and pan in a map using the navigation controls to the right of the map. Click on the small arrow icon on the map chart title bar (shown on mouse over) to show or hide the navigation controls. The example below shows a map chart with states in North America as in the first example, but it has been zoomed in to show only some of the states. To learn more, see Zooming and Navigating in the Map Chart.

What is a Map Chart5.png

Labels can be used in the map chart to identify and describe markers or interactive shapes. In the example above, a label with the state name has been added to the marked shape in the feature layer. Open the Labels page of the layer settings if you want to modify the labels settings for a certain layer.

Map layers require the projection reference system to be set to Web Mercator. When this projection reference system is selected you can zoom out to view more than one version of the earth, so that you can show data using any location as the center of the earth and view parts of the earth outside the data range. If you want to restrict the shown part of the world to the data range, either use auto-zoom or switch the projection reference system to None.

Image Layers

A third way to set up a map chart is to use a background image and then position markers or pies on top of that image. This works similarly to the map with markers or pies, but with the difference that you do not need to have map data in a data table in order to set it up. However, for the markers to be placed correctly in geographical positions, the data table must contain X and Y coordinates. Below is an example of a map chart where the background is a map image of a part of North America. On top of the background image are markers pointing out cities in the United States.

What is a Map Chart6.png

A map chart can be used to show other than geographical data. The example below displays different types of failures on a wafer, a semi-conductor material used to manufacture microchips.

What is a Map Chart7.png

The background is an image representing the wafer. The markers in the visualization represent the chips on the wafer, and are placed on the background the same way they are placed on the actual wafer. The colors and labels indicate the six different types of manufacturing failures that have occurred on this wafer. Copying the actual layout of the wafer is a way to enhance the readability of the data. To be able to view the data this way, you need to use tiled markers. This means that all the markers have the same size, and are displayed in a grid-like layout. Go to the Shape page in the Marker Layer Settings to change to tiled markers.

All visualizations can be set up to show data limited by one or more markings in other visualizations only (details visualizations). Map charts can also be limited by one or more filterings. Another alternative is to set up a map chart without any filtering at all. See Limiting What is Shown in Visualizations​ for more information.

How to Use the Map Chart

A map chart is normally built by several different layers. Each layer can be configured separately with regards to coloring, labels and appearance. The order of the visible layers (and the transparency of each layer) determines what will be visible in the final map chart. For example, place transparent layers containing labels at the top.

The key to positioning different layers relative to each other is in most cases based on geocoding, and the geocoding is in turn based on a column matching between the data table containing the data of interest and a geocoding hierarchy. If any issues should arise, it is often the column matching that needs to be reviewed.

Since the column matching allows you to view data from multiple data tables in one visualization, you can use data from one data table to display information in a layer based on a different data table. For example, if you create a feature layer based on a geocoding data table to show different regions in your country, you can use your own sales data table to color the regions.

For some information about converting 5.5 map charts into 6.0 map charts, see How to Use the 5.5 Map Chart.

Zooming and Navigating in the Map Chart

The navigation controls for zooming and panning are located at the top right of the visualization:

The interaction mode control is normally located below the navigation controls:

Geocoding
What is Geocoding?

In order to display data on a map, the data needs to be geocoded. Geocoding in TIBCO Spotfire is the process of using some type of identifiers in a data table and matching those to similar identifiers in another set of data tables (a geocoding hierarchy) which contains latitude/longitude coordinates or geographic features. These coordinates or features are then used for correctly positioning the data in a map context.

If your data contains simple geographic elements such as country names, states, or similar, then Spotfire will attempt to automatically geocode your data. If no automatic geocoding can be performed, you can set up the geocoding manually instead.

Many parts of the user interface allow you to specify that a column in a data table should be used to match against a specific geocoding hierarchy. This will make the process of setting up a map chart with that data easier.

Setting Up New Geocoding Tables

TIBCO Spotfire comes with a selection of geocoding hierarchies that normally should be added to the library, but you can also define your own geocoding tables and save them in the library. This is accomplished by setting a few document and column properties on the geocoding table in TIBCO Spotfire and then exporting the file to the library.

Configuration of Geographical Data for Map Charts

When Shape files are opened in TIBCO Spotfire they are automatically configured so that they can be used as feature layers in map charts. However, there may be times when some manual work is needed before the data can be used in a feature layer.

Map Chart Properties
Map Chart Properties

The Map Chart Properties dialog consists of several pages:

  • General
  • Appearance
  • Fonts
  • Layers
  • Zoom Visibility
  • Legend
  • Trellis
7 Subtopics
Marker Layer Settings
Marker Layer Settings

The Marker Layer Settings dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Positioning
  • Colors
  • Size
  • Shape
  • Labels
  • Tooltip
  • Line Connection
  • Marker By
  • Subsets
  • Show/Hide Items
13 Subtopics
Feature Layer Settings
Feature Layer Settings

The Feature Layer Settings dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Geocoding
  • Colors
  • Labels
  • Tooltip
  • Subsets
  • Show/Hide Items
9 Subtopics
Image Layer Settings
Image Layer Settings

The Image Layer Settings dialog consists of several pages:

  • General
  • Data
3 Subtopics
Map Layer Settings
Map Layer Settings

The Map Layer Settings dialog consists of several pages:

  • General
  • Map
2 Subtopics
5.5 Map Chart
What is a 5.5 Map Chart?

In TIBCO Spotfire 6.0, a new version of the map chart was created in order to allow for web map backgrounds to be shown. See What is a Map Chart? for information about the new map chart. However, for compatibility reasons, you can go back to use the 5.5 version of the map chart instead.

How to Use the 5.5 Map Chart

In TIBCO Spotfire 6.0, a new version of the map chart was created in order to allow for web map backgrounds to be shown. See What is a Map Chart? for information about the new map chart. However, for compatibility reasons, you can go back to use the 5.5 version of the map chart instead.

Map Chart Properties
Map Chart Properties (5.5)

The Map Chart Properties (5.5) dialog consists of several pages:

  • General
  • Data – Map with Interactive Shapes
  • Data – Map with Markers or Pies
  • Data – Background Image with Markers or Pies
  • Appearance
  • Fonts
  • Colors
  • Size
  • Shape
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Line Connection
  • Marker By
  • Subsets
  • Show/Hide Items
19 Subtopics
Configuration of Geographical Data for Map Charts (5.5)

When Shape files are opened in TIBCO Spotfire they are automatically configured so that maps with interactive shapes can be displayed. However, there may be times when some manual work is needed before the data can be used in a map with interactive shapes.

Treemap

​See: Treemap

What is a Treemap?

Treemaps are ideal for displaying large amounts of hierarchically structured (tree-structured) data. The space in the visualization is split up into rectangles that are sized and ordered by a quantitative variable.

The levels in the hierarchy of the treemap are visualized as rectangles containing other rectangles. Each set of rectangles on the same level in the hierarchy represents a column or an expression in a data table. Each individual rectangle on a level in the hierarchy represents a category in a column. For example, a rectangle representing a continent may contain several rectangles representing countries in that continent. Each rectangle representing a country may in turn contain rectangles representing cities in these countries. You can create a treemap hierarchy directly in the visualization, or use an already defined hierarchy. To learn more, see the section To Create a Treemap Hierarchy.

A number of different algorithms can be used to determine how the rectangles in a treemap should be sized and ordered. The treemap in Spotfire uses a squarified algorithm.

The rectangles in the treemap range in size from the top left corner of the visualization to the bottom right corner, with the largest rectangle positioned in the top left corner and the smallest rectangle in the bottom right corner. For hierarchies, that is, when the rectangles are nested, the same ordering of the rectangles is repeated for each rectangle in the treemap. This means that the size, and thereby also position, of a rectangle that contains other rectangles is decided by the sum of the areas of the contained rectangles.

How to Use the Treemap
Treemap Properties
Treemap Properties

The Treemap Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Fonts
  • Colors
  • Size
  • Hierarchy
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Subsets
  • Show/Hide Items
13 Subtopics

Heat Map

See: Heat Map

What is a Heat Map?

The easiest way to understand a heat map is to think of a cross table or spreadsheet which contains colors instead of numbers. The default color gradient sets the lowest value in the heat map to dark blue, the highest value to a bright red, and mid-range values to light gray, with a corresponding transition (or gradient) between these extremes. Heat maps are well-suited for visualizing large amounts of multi-dimensional data and can be used to identify clusters of rows with similar values, as these are displayed as areas of similar color.

Dendrograms

​See: Dendrogram

It is often useful to combine heat maps with hierarchical clustering, which is a way of arranging items in a hierarchy based on the distance or similarity between them. The result of a clustering calculation is presented either as the distance or the similarity between the clustered items depending on the selected distance measure. See Distance Measures Overview and the detailed description for each measure for further information. To learn more about hierarchical clustering in general, see Overview of Hierarchical Clustering Theory. You can cluster both rows and columns in the heat map. The result of a hierarchical clustering calculation is displayed in a heat map as a dendrogram, which is a tree-structure of the hierarchy. Row dendrograms show the distance (or similarity) between rows and which nodes each row belongs to as a result of the clustering calculation. Column dendrograms show the distance (or similarity) between the variables (the selected cell value columns). The example below shows a heat map with a row dendrogram where the distance between the rows were calculated.

How to Use the Heat Map
Dendrograms and Clustering

A dendrogram is a tree-structured graph used in heat maps to visualize the result of a hierarchical clustering calculation. The result of a clustering is presented either as the distance or the similarity between the clustered rows or columns depending on the selected distance measure. See Distance Measures Overview and the detailed description for each measure for further information about the available distance measures. You can perform hierarchical clustering on an existing heat map by opening the Dendrograms page of the Visualization Properties. You can also use the Hierarchical Clustering tool to cluster with a data table as the input. To learn more about hierarchical clustering and heat maps, see Overview of Hierarchical Clustering Theory and What is a Heat Map? respectively. Note that only numeric columns will be included when clustering.

Importing/Exporting Dendrograms

All dendrograms in TIBCO Spotfire can be represented by a data table. This makes it possible to use various clustering methods and statistical calculations, other than those included in the Edit Clustering Settings dialog. For example, you can use TIBCO Spotfire Statistics Services to execute a custom made S-PLUS or R script, which performs a clustering with a method of your choice. More specifically, you can make use of any calculation that can order leaves in a hierarchical fashion. The result from such a procedure would be a data table, which you can add to the analysis, and then import to the heat map and use for displaying a dendrogram.

You can also export a dendrogram from a heat map, view the resulting data table, make modifications, and import it back to the heat map - in effect modifying the dendrogram.

Another reason for exporting a dendrogram to a data table, and later importing it again, is performance. If you have a really large data set, and perform a clustering method on it, the calculations could take some time. If you have run a clustering method once, which is used in a dendrogram, you can export it and later import it without having to run the clustering again.

The data table representation of a dendrogram used in TIBCO Spotfire must adhere to a certain format. This format is described below.

Heat Map Properties
Heat Map Properties

The Heat Map Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Fonts
  • Cell Values
  • X-axis
  • Y-axis
  • Colors
  • Sorting
  • Tooltip
  • Legend
  • Trellis
  • Dendrograms
  • Subsets
  • Show/Hide Items
17 Subtopics

Parallel Coordinate Plot

See: Parallel Coordinate Plot

What is a Parallel Coordinate Plot?

A parallel coordinate plot maps each row in the data table as a line, or profile. Each attribute of a row is represented by a point on the line. This makes parallel coordinate plots similar in appearance to line charts, but the way data is translated into a plot is substantially different.

How to Use the Parallel Coordinate Plot
Parallel Coordinate Plot Properties
Parallel Coordinate Plot Properties

The Parallel Coordinate Plot Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Formatting
  • Fonts
  • Scale Labels
  • Columns
  • Colors
  • Labels
  • Tooltip
  • Legend
  • Trellis
  • Line By
  • Subsets
  • Show/Hide Items
15 Subtopics

Summary Table

See: Summary Table

What is a Summary Table?

The summary table is a visualization that summarizes statistical information about data in table form. The information is based on one data table in TIBCO Spotfire. You can, at any time, choose which measures you want to see (such as mean, median, etc.), as well as the columns on which to base these measures. As you change the set of filtered rows, the Summary Table automatically updates the values displayed to reflect the current selection.

How to Use the Summary Table
Summary Table Properties
Summary Table Properties

The Summary Table Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Fonts
  • Columns
  • Sorting
  • Statistical Measures
  • Legend
  • Subsets
9 Subtopics
Statistical Measures Overview

TIBCO Spotfire contains several visualizations and tools which calculate various aggregation measures or statistical measures. For a description of each measure, see the corresponding section.

Note: For measures where a large statistical selection is needed, the result from a calculation may vary with the number of available values.

Box Plot

See: Box Plot

What is a Box Plot?

Box plots are graphical tools to visualize key statistical measures, such as median, mean and quartiles.

A single box plot can be used to represent all the data. It is also possible to visualize separate statistics for subsets by selecting a column for the X-axis.

The individual box plot is a visual aid to examining key statistical properties of a variable. The diagram below shows how the shape of a box plot encodes these properties. The range of the vertical scale is from the minimum to the maximum value of the selected column, or, to the highest or lowest of the displayed reference points.

What is a Box Plot1.png

Which reference points should be shown in the box plot is selected in the Properties dialog. There, it is also possible to change the shape and color of each reference point. The shape of outside values cannot be changed. For details of each measure, see Statistical measures.

The axis selectors control which column is mapped to which axis. See Column Selectors for more information about how they work.

The Y-axis should be set to the column or columns on which the statistical measures should be based.

The X-axis can be set to any column. However, since a separate plot will be drawn for each unique value, the column or hierarchy should not contain too many unique values. To summarize the data in a single plot, select (None) on the X-axis. If multiple columns are used on the Y-axis, '(Column Names)' must be used either on the X-axis or in one of the trellis options.

What is a Box Plot2.png

All visualizations can be set up to show data limited by one or more markings in other visualizations only (details visualizations). Box plots  can also be limited by one or more filterings. Another alternative is to set up a box plot without any filtering at all. See Limiting What is Shown in Visualizations for more information.

How to Use the Box Plot
What are Comparison Circles?

The drawing of comparison circles is a way to display whether or not the mean values for various categories (boxes in the box plot) are significantly different from each other. The circles are drawn with their centers at the mean value for the box to which it corresponds.

If the circles for different groups do not overlap, the means of the two groups are generally significantly different. If the circles have a large overlap, the means are not significantly different.

What are Comparison Circles1.png

In the example above, the sum of sales is shown for a number of different fruits and vegetables. The box for Pears has been marked, which is also indicated in the corresponding comparison circle. The marked comparison circle is shown with a darker border and a transparent fill. By looking at the comparison circles or the little relation indicator at the bottom of the visualization area (circled in red on the image), one can see that the sum of sales for Lettuce does not display a significantly different group mean, whereas the group means for all other fruits and vegetables are significantly different from that of Pears. The square in the relation indicator indicates the marked box and the lines in the relation indicator extend to any boxes that are not significantly different from the marked one.

You can also highlight a comparison circle by highlighting its box plot or vice versa. The highlighted comparison circle is drawn with a double lined border:

What are Comparison Circles2.png

Box Plot Properties
Box Plot Properties

The Box Plot Properties dialog consists of several pages:

  • General
  • Data
  • Appearance
  • Formatting
  • Fonts
  • X-axis
  • Y-axis
  • Reference Points
  • Colors
  • Tooltip
  • Legend
  • Trellis
  • Statistics Table
  • Lines & Curves
  • Subsets
16 Subtopics
Aggregations Overview

TIBCO Spotfire contains several visualizations and tools which calculate various aggregation measures or statistical measures. For a description of each measure, see the corresponding section.

Note: For measures where a large statistical selection is needed, the result from a calculation may vary with the number of available values.

Comparison Circles Algorithm

The drawing of comparison circles is a way to display whether or not the group means for all pairs are significantly different from each other. The Tukey-Kramer method is used for the calculation. Each group (each box plot) gets a circle where the center of the circle is aligned with the group mean value.

References

http://lib.stat.cmu.edu/general/qprob

Sall, J. (1992), "Graphical Comparison of Means" Statistical Computing and Statistical Graphics Newsletter, 3, pages 27-32.

Text Area

See: Text Area

How to Use the Text Area

The text area is not a visualization as such, but it can be placed within a page just like a bar chart or scatter plot. The text area is where you can provide text, images and links or buttons that you think are helpful for other users opening your analysis. You can provide information on the purpose of a page, or maybe state the observations you have made so that other people can verify or comment on your findings. See Visualization Layout for more information on how to position the text area in a page.

There are several different types of content you can add to a text area:

  • Text - text can be formatted to your liking, by changing the font, color, alignment, etc. You can also add links leading to an external web page.
  • Images - images can be inserted into the text area in GIF, BMP, PNG or JPG format. Regular images are added using Insert Image, but you can also add images that behave like action controls when clicking on them, see below.
  • Action controls - you can add links, buttons or images that perform a certain action or series of actions to the text area. For example, an action link can switch to a different page or apply a bookmark. It can also refresh a data function calculation or run a script. This could be very handy if you intend to share your analysis with other people. You could, for example, write instructions in a text area, and include links to any operations you want them to perform, such as: "...and when you are done filtering, refresh the calculation." And clicking on the link would launch a predefined data function calculation.
  • Property controls - you can add a number of different items that control the values of selected properties. This could be drop-down lists, list boxes or sliders with predefined values. You can also add manual input fields where anything can be entered, or you could simply add a label displaying the value of a specified property.
  • Filters - if you only want to display a few filters in your analysis, you can add those filters to a text area and save screen estate by closing the filters panel. Filters in the text area can also be set up to use a different filtering scheme than the one used on the rest of the page. This could be useful if you have selected to limit one or more visualizations on a page by some other filtering scheme than the one used on the page.
  • Dynamic items - dynamic items are small "visualizations" that can live within a text area or in a graphical table. When included in a text area they represent an aggregated view of some data. For example, this could be a calculated value displaying the total sum of sales. The dynamic items can be set up to respond to the page filtering, some other filtering or no filtering. They can also be limited by markings in other visualizations, similar to details visualizations. Currently, the available dynamic items are sparklines, calculated values, icons and bullet graphs.
Using Properties in the Analysis

Properties can control one or many settings by being applied in custom expressions. For example, a document property can be used to specify a column name. This property can then be used to define what is shown on one or more visualization axes, either directly or as a part of a custom expression. By using a property instead of simply placing the column name directly on the axes, you only need to change a single value (the property value) in order to change all axes where the property is used. Adding a property control that can change the property value further simplifies the update process.

Since the property controls are available in Spotfire Web Player, this also gives the Web Player users a possibility to change the axes of visualizations. Property expressions can also be used to define a line or a curve. If a property used in expressions is updated, the property will be updated in all currently used locations.

5 Subtopics
Details
Details on Action Control

This dialog is used for adding or editing action links or buttons in a text area. The action controls can open TIBCO Spotfire tools that work on the range of filtered or marked data, apply bookmarks, or navigate to a certain page or visualization in the analysis. Multiple actions can be performed in just one click. It is also possible to add your own custom actions using the IronPython scripting functionality, or to refresh data function calculations.

15 Subtopics

3.0 Text Area
5 Subtopics

Details on General Dialogs

Details on Add/Edit Tooltip Value

Use these dialogs to choose additional values to be displayed in the tooltips for the active visualization.

12 Subtopics
Subsets
Details on Edit Subset

This dialog is used to change the settings for the subsets available by default; All data, Current filtering, and Not in current filtering.

4 Subtopics

Using Visualizations

Interacting with Visualizations

Marking in Visualizations

The purpose of marking items (for example, bar segments, pie sectors, lines, or markers) or rows in a visualization may be to view details for the item, or to distinguish rows in order to tag, copy, delete, or in other ways manipulate them. Marked rows can either be given an identifying color in the visualizations, or, they can be kept as they were while all unmarked items are faded out:

Highlighting in Visualizations

When you move the mouse pointer over an item (for example, a bar segment, a pie sector, a line, or a marker) in a visualization, a tooltip appears displaying details about the highlighted item. The tooltip displays a few items by default, but it can also be configured to show information from additional columns or expressions.

Drag-and-Drop

TIBCO Spotfire contains rich possibilities of using drag-and-drop operations for setting up the visualizations. You can drag filters from the filters panel or columns from the data panel to the axes, or filters or column selectors to drop targets in the middle of the visualizations. These drop targets control coloring, trellising, size or shape, etc. All operations are undoable, so that you can try different layouts without being afraid of destroying anything.

Zoom Sliders

By clicking on the small arrow icon in the title bar of a visualization you can turn on or off zoom sliders for the axes in a visualization. (The icons in the title bar are only shown when hovering with the mouse pointer over the title bar area.)

Column Selectors

Column Selectors

When you open data in TIBCO Spotfire and create a visualization, you can select what will be shown on each axis in the visualization using column selectors:

Non-Column Selections

For many visualizations, there are up to four special options available on the column selector menu: (Column Names), (Subsets), (Row Number), and (Row Count). The use of these options is best described with examples.

What is Column from Marked?

The Column from Marked function is a way to look up which column to assign to a column selector by fetching the cell value in a data table. The cell would then contain the name of the column you want to assign to a property in a visualization, such as what to use on the X-axis or what to color by.

Use the Column from Marked function when you want to update the visualization iteratively and quickly set another column for the property. Once Column from Marked has been configured, pointing to a new cell by marking a row updates the property. If you only want to configure a visualization to use a certain column, you should not use the Column from Marked function.

Details on Column from Marked

The Column from Marked functionality allows you to create a visualization which changes what column is used on an axis depending on what you mark in a different visualization. This makes it possible to create a visualization that is connected to, say, a table visualization for example, in such a way that when you click on the table, the axes of the new visualization will change to show the values of a specified cell in the table. The cell should contain a column name available in the current data table. For an example of how it works, see the automatically created visualizations from a Data Relationships calculation.

Details on Set from Property

This dialog is used to specify that a value should be picked from a document property. See Using Properties in the Analysis for more information.

Aggregation

Aggregation is the grouping of data using statistical measures or other calculations. For example, you could select to show the Sum of all Sales for a year or the Average Sale for each month.

To use aggregation, your visualization must meet the following requirements:

  • The underlying data table must contain at least one numeric column. For example, an Integer, Real, or Currency column.
  • The visualization type must support aggregation. Some visualizations, like Tables, do not support any aggregation. Others can use aggregation on the coloring axis or some other axis only.

In visualizations which support aggregation  you can aggregate the data using one or more column selectors, for example, the axis selectors or the color selector.

Click on an axis selector and look in the aggregation menu to show a list of all available measures you can use for aggregation. In some visualizations you may also choose (None), which will remove aggregation from this axis. Type in the search field to look for a particular aggregation method.

Legend

The legend can be displayed either as a temporary popover which is visible until you click somewhere outside it, or as a docked part of the visualization. It can be docked either to the left or to the right in a visualization. If the legend is hidden you can click on the Legend button in the visualization features menu to show it (the icons in the title bar are only shown when hovering with the mouse pointer over the title bar area.):

Multiple Tables in One Visualization

Multiple Data Tables in One Visualization

Sometimes the data you want to analyze in Spotfire is located in different data tables. Working with visualizations combining data from multiple data tables is not very different from working with data from a single data table. You can choose the visualization that best suits your data, you can filter, mark, and drill down in your data, just as with a visualization using data from a single data table. However, a couple of concepts are important to be familiar with when setting up and working with a visualization combining data from different data tables. This topic describes key concepts, and includes a couple of examples.

Column Matching

When you combine data from different data tables in one visualization, you need to consider how the columns in the data tables match. A rule of thumb is that all the columns you are planning to group the visualization by, should exist in all the data tables. For example, columns that should define what a marker is in a scatter plot, columns you wish to set on the category axis of a bar chart, or columns you wish to trellis or color the visualization by. If your data is structured that way, setting up the visualization and matching columns will be easy. However, there are cases when it is in fact OK that some columns in the main data table do not have matches in all the data tables. And, even if the data is set up the recommended way, you may sometimes need to make a few manual adjustments. This will be explained further down in this topic.

Working with Multiple Data Tables in One Visualization

Loading many data tables into Spotfire works the same way no matter if you are going to combine the data tables in one visualization or not. See How to Add Data Tables to learn more about loading data. After the data has been loaded you may need to match columns in order to combine them in a visualization. You can read more about this in Column Matching and below.

More Examples

As mentioned in other topics about working with multiple data tables in one visualization, the recommended setup is that you base your visualization on a main data table where all the categories you want to group the visualization by are included. Preferably the categories you want to group by are also located in all the data tables. However, this may not always be the case, and this topic gives two examples of how you can set up useful visualizations when your data is not arranged that way.

Shortcuts

If you have set up a good layout of visualizations, but then want to change one of the visualizations to a different type, you can right-click on the visualization and select Switch Visualization To > [desired visualization].

Hierarchies

Hierarchies

By adding more than one column to the axis selectors, you can create a hierarchy in the visualization. In the example below the column Year and the column Category have been added to the category axis. The bar chart automatically displays a bar for each combination in the hierarchy - in this case the sale of fruit and vegetables per year.

Hierarchy Slider

As explained in the Hierarchies chapter, adding multiple columns to the column selectors creates a hierarchy in the visualization. However, you can also add "predefined" hierarchies to your column selectors. A predefined hierarchy is a hierarchy that has been set up while working with the data itself, or while creating a hierarchy filter in the filters panel. These hierarchies are even more powerful, and provide an additional feature in the visualization - the hierarchy slider.

Working with Time Hierarchies

Sometimes you may work with data where some category values are missing. If the visualizations make use of a Date, Time or DateTime column and you like to present the data in an aggregated form, missing data may have strange effects on your calculations. For example, below is a cumulative sum of sales for a few years, where data for three quarters are missing:

Trellis Visualizations

See: Trellis

Trellised visualizations enable you to quickly recognize similarities or differences between different categories in the data. Each individual panel in a trellis visualization displays a subset of the original data table, where the subsets are defined by the categories available in a column or hierarchy.

For example, if you choose to trellis a visualization based on the two variables "Gender" and "Political affiliation", this will result in four separate panels representing the combinations Female-Republican, Female-Democrat, Male-Republican, and Male-Democrat. If the "Gender" variable is used in conjunction with another variable that has five different values, this will yield ten panels. From this follows that variables with a continuous distribution and a wide range of values (for example, Real values) should be binned before they are used to form a trellis visualization. Otherwise the number of panels quickly becomes unmanageable.  

Information and Warnings

If there are items in a visualization that cannot be shown or might be misinterpreted because of some settings, an icon will appear in the title bar of that visualization. If something cannot be shown, a notification icon, Notification Icon.png, will appear. If there is a risk that something might be misinterpreted due to lack of data to calculate a curve or similar, a warning icon, Warning Icon.png, will appear. On mouseover, a tooltip will appear, and if you click the icon a longer description will appear.

Aggregations

Aggregations Overview

TIBCO Spotfire contains several visualizations and tools which calculate various aggregation measures or statistical measures. For a description of each measure, see the corresponding section.

Note: For measures where a large statistical selection is needed, the result from a calculation may vary with the number of available values.

  • Sum
  • Average (Avg)
  • Count
  • Unique Count
  • Min
  • Max
  • Median
  • Standard Deviation (StdDev)
  • Standard Error (StdErr)
  • Variance (Var)
  • Lower endpoint of 95% Confidence Interval (L95)
  • Upper endpoint of 95% Confidence Interval (U95)
  • First Quartile (Q1)
  • Third Quartile (Q3)
  • Lower Adjacent Value (LAV)
  • Upper Adjacent Value (UAV)
  • CountBig
  • Unique Concatenate
  • Concatenate
  • First
  • GeometricMean
  • Interquartile Range (IQR)
  • Last
  • Lower Inner Fence (LIF)
  • Lower Outer Fence (LOF)
  • Mean Deviation
  • Median Absolute Deviation (MAD)
  • Most Common
  • Outlier Count (Outliers)
  • 10th Percentile (P10)
  • 90th Percentile (P90)
  • Outlier Percentage (PctOutliers)
  • Product
  • Range
  • Upper Inner Fence (UIF)
  • Upper Outer Fence (UOF)

In the column selectors of some visualizations there are also a number of aggregation measures available that are in fact shortcuts to expressions. See below for a description:

  • Cumulative Sum
  • Moving Average
  • Difference
  • Difference %
  • Difference Year Over Year
  • Difference % Year over Year
  • % of Total
  • Year to Date Total
  • Year to Date Growth
  • Change Relative to Start
  • Change Relative to Fixed Point
  • Compound Annual Growth Rate

See Advanced Custom Expressions and Using Expressions on Aggregated Data (the THEN Keyword) for more general information about writing custom expressions with OVER and THEN.

Statistical Measures
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Expression Shortcuts
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Enhancing Visualizations

Coloring

Coloring Overview

By coloring the items in a visualization you can add an extra dimension to your data. For instance, you can use colors to identify outliers in the data, or to distinguish different categories. In TIBCO Spotfire there are many ways to customize the coloring in your visualizations. Most of the coloring settings can be found on the Colors page of the Visualization Properties for each visualization type. Configuration of the coloring settings works in a similar way for most of the visualization types. However, for the table, cross table, and heat map it works in a slightly different way. Coloring for those visualizations is therefore described separately.

To learn more about coloring in Spotfire, see the following sections:

  • Color Schemes Overview
  • Color Modes Overview
  • Color Rules Overview
  • Coloring in Tables, Cross Tables and Heat Maps
Color Schemes
Color Schemes Overview

The entire setup of colors for a visualization is referred to as its color scheme. Which colors and threshold values you choose, as well as the color mode you select, are all part of a visualization's color scheme. The current color scheme of a visualization can be viewed in the legend, and on the Colors page of the Visualization Properties. This is also where you edit a color scheme. In the example below two scatter plots are shown. Their respective color schemes are displayed in the legend.

Opening a Color Scheme

You can open a color scheme that has previously been saved to disk or to the library. You can also apply a color scheme from another visualization in the analysis, or open a document color scheme. The color scheme must be in the same scale mode as the visualization in which you want to use it.

Predefined Color Schemes

To facilitate setting up color schemes you can open one of the predefined ones and then adjust it according to your preferences. Predefined color schemes are only available for color schemes based on a column in continuous scale mode. Which predefined schemes are available differs slightly depending on the data type of the column.

Applying a Color Scheme to Another Visualization

When you have set up a color scheme for a visualization, you can apply it to another visualization, provided that the two visualizations are colored by a column in the same scale mode.

Saving a Color Scheme

You can save a color scheme for later reuse or to share it with others. If you save the color scheme to disk or in the library, you can use it in other analyses. If you save it as a document color scheme, you can use it again within the analysis. A document color scheme can be selected for a specific visualization in the analysis from the Colors page of the visualization properties. You can also select it as the default color scheme to use for a specific column as well as for new visualizations in the analysis. See Column Properties – Properties, Column Properties Descriptions, and Options – Visualization to learn more about using default color schemes.

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Color Modes
Color Modes Overview

A color mode can be described as the way the colors in a color scheme are assigned to the values in the data. For example, you can determine whether you want the items in your visualization to get distinct colors representing different categories, or if you want to see a gradient color transition between two or more anchor points in a range of values.

The following color modes are available in TIBCO Spotfire:

  • Fixed
  • Categorical
  • Gradient
  • Segments
  • Unique values

Which color modes are available depends on the characteristics of the column or hierarchy you have selected to color the visualization by. For a column in categorical scale mode you can select Categorical or Fixed color mode. For a column in continuous scale mode you can choose one of the following color modes: Gradient, Segments, Fixed, or Unique values. The data type in the selected column decides which scale mode the column will be set to by default, as well as if it is possible to change the scale mode.

Note: In tables, cross tables, and heat maps coloring is always continuous. This means that the color mode Categorical is not applicable. However, you can use the color mode Unique values to get a categorical appearance in those visualizations. To learn more about coloring in these visualization types, see Coloring in Tables, Cross Tables and Heat Maps.

Fixed Color Mode

Fixed color mode is available as an option for columns in both categorical scale mode and continuous scale mode. In fixed mode, all items in the visualization will get a single, fixed color as seen in the example below.

Categorical Color Mode

Categorical color mode is available for columns in categorical scale mode, and gives each value in the column a separate color, as seen in the example below.

Gradient Color Mode

Gradient color mode is available for columns in continuous scale mode. In this mode the items will show a color gradient between two or more anchor points as seen in the example below.

Segments Color Mode

Segments color mode is available for columns in continuous scale mode. This will color the items representing values between two or more anchor points in fixed colors, as seen in the example below.

Unique Values Color Mode

Unique values color mode is available for columns in continuous scale mode. It is similar to the Categorical color mode, and gives each unique value in the column a separate color, as seen in the example below.

Details on Point/Value Menu

This menu is available for points in the color scheme area in the two color modes Segments and Gradient, as seen below. It is also available in the Add/Edit Rule dialog. It is used to define the value at which a color change or rule should kick in.

Rules in Color Schemes
Color Rules Overview

Rules can be very useful when you are setting up color schemes for your visualizations. Rules function as exceptions to the rest of the color scheme. You can use them in all kinds of visualizations, and in any color mode. The collection of available rules is different for categorical columns than for continuous columns. For instance, for categorical color schemes you can add a rule saying that all values starting with a certain letter should have a specific color. For continuous color schemes, you can, among many other things, define a rule stating that all items representing values below a certain value should have a certain color. Or, if you use a date column to color by, you can set up a rule stating that items representing values between two specific dates should get a certain color. See Details on Add/Edit Rule for descriptions of all the rule types.

Details on Add/Edit Rule

Use these dialogs to define or edit a color rule. See also Color Rules Overview to get an introduction to rules in color schemes. Note that the set of rule types available in these dialogs is different depending on whether the color scheme is categorical or continuous. Both sets of rules are described below.

Coloring in Tables, Cross Tables, and Heat Maps
Coloring in Tables, Cross Tables and Heat Maps

Coloring is applied to tables, cross tables, and heat maps in a different way than other visualizations. The following two sections describe how to apply coloring to these visualizations.

  • Coloring in Tables
  • Coloring in Cross Tables and Heat Maps
Coloring in Tables

For a table visualization, a color scheme is always applied to a color scheme grouping. A color scheme grouping functions as a container for one or more columns on which you want to apply the same color scheme. You can add many color schemes to the same table. In fact, you can add as many as one color scheme per column. When you create a new table it has no coloring applied to it. You must add color scheme groupings with the columns you want to color, and set up color schemes for each of the groupings. To learn more about color schemes and how to set them up in different color modes, see Color Schemes Overview and Color Modes Overview respectively.

Coloring in Cross Tables and Heat Maps

For cross tables and heat maps, a color scheme is always applied to a color scheme grouping. A color scheme grouping functions as a container for one or more axis values on which you want to apply the same color scheme. You can add many color schemes to the same cross table or heat map, and it is possible to color by the columns on any of the axes. However, you can only color a cross table and a heat map by one column at a time, and if the cell values axis contains more than one column, you can only color by the cell values axis. To learn more about color schemes and how to set them up in different color modes, see Color Schemes Overview and Color Modes Overview respectively. The examples below illustrate how coloring can be applied to cross tables and heat maps.

Details on Add/Edit Color Scheme Grouping

Use these dialogs when you want to add a new color scheme grouping or edit an existing one.

Custom Expressions for Coloring

You can use custom expressions in color schemes in a few different ways and some examples are described below. You can define an anchor point with a custom expression, or you can use expressions in rules. To get a basic understanding of custom expressions, see Custom Expressions Introduction. To learn more about using rules in color schemes, see Color Rules Overview.

Limiting What is Shown in Visualizations

When a new visualization is created, the default limitation setting is that the visualization is affected by the current filtering on the page where it resides. However, there are many alternatives for you to set up the visualization available on the Data page of the Visualization Properties dialog.

Details Visualizations

What is a Details Visualization?

A details visualization is a special case of a limited visualization, slightly related to the Details-on-Demand. The information shown in a details visualization depends on what is marked in one or more other visualizations (the master visualizations). Details visualizations can be used to set up analyses where you can drill down into your data in multiple steps. The marking you perform in one visualization, determines what you will see in the next visualization, and so on.

In contrast to the Details-on-Demand, which is always a table, the details visualization can be any type of visualization and it can be placed anywhere within the page, just like any other visualization. You can create a details visualization in two different ways, as described below.

Details on Create Details Visualization

Details visualizations are limited to showing data marked in another visualization (the master visualization) only. See What is a Details Visualization? to learn more. The Create Details Visualization dialog is shown if the master visualization is based on a data table that is related to one or more other data tables in the analysis. You must then select which data table the details visualization should be based on.

Subsets

What are Subsets?

With subsets you can compare collections of your data within the same visualization. For example, you can compare all data to the current filtering, or the filtered data to the data that has been filtered out. Subsets are defined in the Subsets page of visualization properties, and can be added to all visualization types except the table. There are three subsets available by default on the Subsets page; All data, Current filtering, and Not in current filtering. 

How to Work with Subsets
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Show/Hide Items

What is Show/Hide Items?

When you filter out values in the Filters panel you change the underlying data used to calculate the items available in a visualization. However, sometimes you may want the calculations to be exactly the way they are, but you are only interested in some of the values in the resulting visualization. This is when you can use Show/Hide Items. In the example below, a simple rule is applied, so that only the top three highest bars are shown:

How to Work with Show/Hide Items

You can apply a number of different rules to determine which items to show or hide. Which rules are available in a certain situation depends on the data type and aggregation method of the currently selected column to apply the rule on. Below are few examples of how different rules can be set up.

Details on Add/Edit Rule

Use these dialogs to define or edit a rule that is used to show or hide items in a visualization.

Titles and Descriptions

Overview

The titles of pages and visualizations as well as the visualization descriptions can be set to change with the selections done on the visualization axes. This can be accomplished either using the built-in "axis visualization properties" or by setting up custom document properties. Document properties can, in turn, be controlled via property controls in the text area.

Note: If you have specified a dynamic title or description and then change the setup of the visualization by removing all columns from an axis included in the title or description, you may end up with unexpected blank sections in the title or description. Try to consider how you and other users of the analysis intend to modify the visualization before you specify what to include.

Working with Dynamic Titles and Descriptions

Titles and descriptions of visualizations can be configured so that parts of the text are showing values that are updated somewhere else in the document. You can let the title or description of a visualization contain a value specified using either the built-in "axis visualization properties" or custom document properties. The value of a property can in turn be modified using a property control in a text area. See To add a property control to the text area for more information on how to do this.

Page titles do not have access to any predefined properties, but can use custom document properties. Double-click on a page tab and click Edit... to incorporate a property into the page name.

Visualization Properties in Expressions

In some cases, you may want to use the expression or the display name currently set on an axis as a dynamic part of another expression, so that it changes with your selections. For example, this may be interesting in a visualization title or in a tooltip or label. You may also want one axis to be automatically set to use the same expression as the one set on another axis. 

There are a number of different "axis expressions working like properties" available in the visualizations. These properties exist only in the context of a visualization and they have values that are defined by the currently used settings in the visualization. For example, a scatter plot can expose the display name of its X and Y-axis expressions as properties and these can in turn be used to set the visualization title.

The syntax to use is ${Axis.Axis Name.DisplayName} and ${Axis.Axis Name.Expression}, for the display name and the underlying expression, respectively. For example, if the Axis Name is "X", the expression should refer to ${Axis.X.DisplayName} or ${Axis.X.Expression}. Note that the actual axis names to use may be different in two similar looking visualizations, see below. The ${DataTable.DisplayName} property can be used in all visualizations to retrieve the name of the main data table used in the visualization.

These properties can be used the same way as other types of properties. See Properties in Expressions for more information. All of the display name-properties listed below also have a corresponding property for the actual expression.

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Custom Expressions

Custom Expressions Introduction

The ability to create custom expressions is a powerful feature of TIBCO Spotfire. Custom expressions allow you to create your own aggregation methods for the visualizations.

The custom expression functionality can be accessed by right-clicking on the column selector in the visualization (or in the Visualization Properties dialog) and selecting the Custom Expression… option from the pop-up menu.

Custom Expressions Overview

Custom expressions are powerful tools which can be used to set up your data the way you want it. The purpose of this overview is to introduce you to some basic concepts regarding custom expressions. What are they? How can they be used? Why do they affect the visualizations the way they do? This overview includes some examples of how to use custom expressions, and more examples can be found in the following help sections: Basic Custom Expressions, OVER in Custom Expressions, and Advanced Custom Expressions.

Basic Custom Expressions

Now that you know what custom expressions are, it is time to look at some basic examples of how they can be used.

OVER in Custom Expressions

The OVER statement and the node navigation methods are used in many of the more advanced custom expressions, especially when time periods are to be compared to or combined with each other. To understand how it works, consider again how markers represent slices of your data, and that the visualization properties, such as color or aggregations, determine how the data is sliced. Custom expressions work on each of the already defined slices in the visualization. This is the fundamental difference between node navigation methods in custom expressions and in calculated columns. When you add a calculated column with node navigation methods, they define how data is to be sliced. Since custom expressions work on the individual slices, node navigation methods in custom expressions actually does quite the opposite. When used in custom expressions these methods are telling the visualizations to ignore specific slices that are built into the visualization. A description of the OVER expressions and node navigation methods is available under  Advanced Custom Expressions.

Note: When working with in-db data you must always apply OVER expressions to the already aggregated data using the THEN keyword, since there is no row-level data available in that case. This expression structure can also improve the performance when working with in-memory data. See Using Expressions on Aggregated Data (the THEN Keyword) for more information.

Using Expressions on Aggregated Data (the THEN Keyword)

In many cases, you can gain performance by adding a part of an expression to an already aggregated set of data, namely the data used in the current visualization only. For example, when using OVER expressions there is always a gain to be done by first allowing the data to be aggregated and then applying the OVER part of the expression. This is required when working with in-db data since there is no row-level data available in that case, but the performance can be improved when working with in-memory data as well.

The part of the expression after the keyword THEN will operate on the part of the expression before THEN. You can include multiple THEN parts within a single expression. In this case, the following THEN expressions will operate on the entire part of the expression before the THEN. The temporary column "[Value]" in a THEN expression refers to the result of the previous aggregation, before the THEN.

Axes in Expressions

When creating custom expressions, you may need to refer to an axis from the visualization in the expression. Below is a list of the available axes for different visualization types. Some of the axes can be useful in OVER expressions, whereas others are only used when defining rules or reference lines.

The term axis, in this context, does not only refer to the X and Y axes, but is a general term for the mapping of data to a dimension represented in the visualization: coloring is controlled by the Color axis, marker shape by the Shape axis and so on.

The syntax when referring to axes in an expression is [Axis.Axis Name]. For example, if the Axis Name is "X", the expression should refer to [Axis.X]. Note that the actual names to use may be different in two similar looking visualizations. For example, in the cross table you would refer to Axis.Columns whereas in a heat map you would use Axis.X for similarly set up visualizations.

It does not make any sense to include the axis you are creating the expression for in an OVER expression. Instead, you would probably want to use the categorization on the X-axis or the Color axis when defining an OVER expression on the Y-axis.

Advanced Custom Expressions

The OVER statement is used in many of the more advanced custom expressions. In this section, several node and time period methods are explained.

In the examples below, a bar chart with a Year/Month hierarchy on the category axis is used, and in most cases it is colored by Product.

Note: When working with in-db data you must always apply OVER expressions to the already aggregated data using the THEN keyword, since there is no row-level data available in that case. This expression structure can also improve the performance when working with in-memory data. See Using Expressions on Aggregated Data (the THEN Keyword) for more information.

How to Insert a Custom Expression

TIBCO Spotfire supports two different types of expressions: Insert Calculated Column, which creates a new column in the data table, and Custom Expression, which is used to dynamically modify the expression used on an axis or to define a setting. Both types of expressions are created with a similar user interface, but custom expressions can also be typed directly into the column selector.

Tip: For simple expressions it is often faster to type expressions directly in the expanded column selector. If the expression is longer and involves lots of properties and functions, the Custom Expression dialog can provide more help, as described below.

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Lines and Curves

Lines and Curves

Some of the visualization types can display additional information in reference lines or several different types of curves. This is set up in the properties dialog of each appropriate visualization type. Here, the Lines and Curves page of the scatter plot properties is shown:

Curve Fit Models

There are several different models available for curve fitting. See Lines and Curves for information about how to apply the various curves.

Holt-Winters Forecast

The Holt-Winters Forecast uses TIBCO Spotfire Enterprise Runtime for R to compute the Holt-Winters filtering of a time series or anything that can be coerced to a time series. This is an exponentially weighted moving average filter of the level, trend, and seasonal components of a time series. The smoothing parameters are chosen to minimize the sum of the squared one-step ahead prediction errors.

The output of a Holt-Winters Forecast is three different curves: a fitted curve showing the general variation of the measure of interest, a forecast curve predicting the future trend and a confidence interval showing how the insecurity increases the further away from the known values the prediction reaches.

TIBCO Enterprise Runtime for R and open-source R return different prediction intervals for multiplicative seasonal models. TIBCO Enterprise Runtime for R assumes that the seasonal and error components are multiplicative in effect and it uses the formula for prediction variance found in section 6.4.2 of Hyndman, et al, 2008. See the references listed in the References section.

References for Holt-Winters Forecast

Rob J Hyndman and George Athanasopoulos (2013), Forecasting: principles and practice. http://otests.com/fpp/7/1.

Rob J. Hyndman, Anne B Koehler, J. Keith Ord, and Ralph D. Snyder (2008), Forecasting with Exponential Smoothing: the state space approach, Springer.

Curve Fit Theory

Generally, curve fit algorithms determine the best-fit parameters by minimizing a chosen merit function. In order to optimize the merit function, it is necessary to select a set of initial parameter estimates and then iteratively refine the merit parameters until the merit function does not change significantly between iterations. The Levenberg-Marquardt algorithm has been used for nonlinear least squares calculations in the current implementation.

The goodness of fit is shown as an R2-value. A value of R2=1.0 indicates a perfect fit, whereas R2=0.0 indicates that the regression model might be unsuitable for this type of data.

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Formatting

Formatting Overview

Formatting is giving a value meaning by adding units of measurements, thousands and decimal separators, and other information. Locale settings, determined by Windows Regional Settings, are used to determine formatting, but you can also make certain changes yourself, such as whether to show thousands separators or how many decimals to show.

Formatting does not include visual properties, such as color, font, or size.

Example: If you have Windows Regional Settings set to Swedish and select to show thousands separators and the numbers are in US currency, you will get formatted values such as $1.000.000,00. If you change your locale settings to US English, the value will be re-formatted into $1,000,000.00.

Which formatting options are available depends on the data type of the value. Text cannot be formatted at all, while an integer has several different possibilities. An integer can, among other things, be formatted as a number, currency and as percentage. You can set the number of decimals to be displayed, as well as whether or not to use a thousands separator. Another possibility is to use short number format, which is a way to shorten values to take up less space by replacing powers of tens with symbols. See Short Number Format to learn more. You can apply formatting to your data on different levels and you can access the settings in different ways as described below.

Formatting Settings

The image above shows the Formatting page of a Scatter Plot Properties dialog. The lower part of the dialog contains the formatting settings and is the same for all the dialogs where you can change formatting settings. The Category list displays the available categories for the selected axis, column or data type depending on whether you are changing formatting settings on visualization level, column level, or default settings level respectively. Each category in this list has separate settings, as shown below. What categories are available depends on the data type of the selected column. For general information about formatting, see Formatting Overview.

Format String

If the format you want to use cannot be created with the given settings, the custom format string allows you to create your own formats using a code explained in the examples below.

The special characters allow you to multiply, divide, separate numbers, etc. Other characters are printed out in the resulting data.

Short Number Format

If the values on an axis or a column are numerical, you can choose to display them in short number format. This means that values with many digits can be shortened to take up less space. For example, by using short number format you can set the number 1,000 to be displayed as 1k.

A standard symbol set is already defined and available to use. The defined symbols in the standard symbol set are M for 10^6, and k for 10^3. You cannot delete or change the standard symbol set. However, you can add your own symbol sets and define symbols of your choice.

As with other formatting settings, you can apply short number formatting in different ways and on different levels. For general information about formatting, see Formatting Overview.

Error Bars

Error bars are used to indicate the estimated error in a measurement. In other words, an error bar indicates the uncertainty in a value.

In Spotfire, you can use error bars in bar charts, line charts, and scatter plots. Bar charts and line charts can display vertical errors. Scatter plots can display both vertical and horizontal errors. The image below shows all four possible error bars on a scatter plot marker. However, upper and lower errors refer to the underlying data. This means that if you use reversed scales in a visualization, or change orientation of the bars in a bar chart, the error bars will also be reversed or change orientation respectively. For example, for a scatter plot with a reversed Y-axis, an upper vertical error will be displayed below the marker instead of above the marker. For a bar chart with horizontal bars and non-reversed scale, an upper horizontal error will be displayed to the right of the bar.

Error Bars.png

Note: When you are working with error bars in bar charts, make sure that the bar chart is displayed using Side-by-side bars. Open the Appearance page in the Bar Chart Properties dialog if you need to change the bar chart layout.

Pages and Layout

Visualization Layout

You can insert several visualizations on a page. Each new visualization will be inserted at the top of the page.

Often you will want to adjust the layout of the visualizations, as you might want some to be larger than others, or some visualization to be placed beside another instead of above it.

Tip: If the title bar of a visualization has been hidden, it can be shown by right-clicking on the visualization and selecting Properties. On the General page, select the Show title bar check box.

Arranging Visualizations

Apart from positioning the visualizations on a page using a drag-and-drop operation, there are some shortcuts to apply a basic layout.

Tip: Use Maximize Active and drag to switch visualizations in order to view one visualization at a time.

Pages

You can work with several pages in your analysis if you want to keep information about different topics separated, or if you simply need more space. Navigation between pages can be done using three different modes; titled tabs, step-by-step, or history arrows, as described below.

Titled Tabs

In tab mode, pages are indicated by tabs at the top of the screen, and clicking on these tabs switches page. You can also click on the small arrow icon to the right of the tabs to open a drop-down list where you can switch between all pages in the analysis. Tab mode is useful when you want to see the titles of each page and choose in what order to look at the pages.

On each page, you can insert one or many visualizations, and arrange these as you like. Perhaps you want to see both a table and a bar chart next to each other on one page to compare details, and then be able to switch pages to see a pie chart. When in tab mode, you can change the order of your pages using drag-and-drop.

Step-by-Step

You can also select to show the pages as numeric links instead of tabs. This is called Step-by-Step mode. Step-by-Step mode is useful when the order of the pages is important and you want to present your analysis as a guided flow.

The pages are now reached by clicking on the numbered links or the Previous/Next links. This is useful if you want to create a guided analysis and share it with other colleagues, and you intend for your colleagues to go through the analysis in a certain order.

History arrows

If you want to set up your own navigation in the analysis you can select Page Navigation > History Arrows. This will give you a similar look as in the step-by-step case, but all links will be hidden.

When this option is selected you can keep pages in the analysis that are not shown to Web Player users at all. All connections between the available pages must then be separately specified using actions in the text area or on dynamic items in graphical tables. The page history arrows to the left of the page name makes it possible to return to a previously visited page.

Cover Page

Cover Page

The cover page is a page meant to serve as an introduction to your analysis. It contains a text area in which you can enter information about the purpose of your analysis as well as other useful information, before you share your analysis with your colleagues. If desired, it can be automatically created each time you make a new document, see below. If you are creating a guided analysis in step-by-step mode, and have selected to use a cover page, this should be the first page in the sequence of links.

Text Area Edit Mode

The text area can only be edited when the Toggle Edit Mode button, in the visualization title bar has been clicked, or, when Edit Text Area has been selected from the pop-up menu.

When in edit mode, you will see a toolbar at the top of the text area where a number of options are available:

Details-on-Demand

What is the Details-on-Demand?

The Details-on-Demand display the actual values of marked items in the active visualization. If you mark a bar in a bar chart, all the rows of data included in that bar are presented. Marking a record in a scatter plot might only display information about a single row of data.

You can view and manage Details-on-Demand in a popover, in a docked panel, or as a floating window. Click on the Details-on-Demand button, , on the toolbar, or select View > Details-on-Demand to open Details-on-Demand. They will open in the mode they were opened last time you had them open.

The Details-on-Demand works just like any other table—you can click on column headings to sort the presented details, drag and drop column headings to change the order, and change the width of the columns by moving the mouse over the column separator line and dragging.

  

If more than one data table is available within the analysis, a color stripe showing the relations color for the active data table is shown on the right hand side of the Details-on-Demand. 

Note that when a visualization combines data from more than one visualization, the Details-on-demand will show data from the main data table only.

Note: For analyses with multiple data tables, settings for the Details-on-Demand must be defined in the Details-on-Demand properties dialog for each data table.

Details-on-Demand Properties
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Document Properties

How to Edit Document Properties

The dialog found under Edit > Document Properties contains settings that apply to the entire document. However, settings that affect the visualizations are found in the Properties dialog for a specific visualization. These dialogs are reached by clicking on the visualization of interest to make it active, and then selecting Edit > Visualization Properties.

The Document Properties dialog is divided into a number of different tabs. A number of common procedures are listed for each tab below.

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Setting Defaults

How to Specify Default Values

Sometimes, you may want to reuse settings from one time to another. For example, you may want to specify that the marked items color should always be red when you start TIBCO Spotfire, or that the default visualization should be a table. This is done in the Options dialog. The default values you set are also saved for your profile on the server, so your default settings will be available even if you are using a different computer.

An administrator can also configure which settings should be default for certain user groups on the server. If you are a member of such a group, these defaults will take effect for you, unless you have made an active choice and set your own default value in the Options dialog.

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Panels and Popovers

In TIBCO Spotfire, some functionality can be reached through panels that can be shown either at all times or on demand only. The state of a panel is remembered per page, so you can use one state on one page and another on the next page.

By default, the Filters panel and Details-on-Demand are shown as docked panels to the right and the Tags panel, the Lists panel and the Collaboration panel will be shown to the left in the main window, if displayed. Bookmarks are shown in a popover by default. However, all panels can be shown in three different states: as docked panels,as popovers or as floating windows. The legend of all visualizations is a special case which can be shown in a docked state or as a popover only; it cannot be unattached to a floating window like the other panels and popovers. See Legend for more information.

No matter what state is used to display the panel, the content will always be remembered.

Filters

What is a Filter?

Filters are used to narrow down the selection of data shown in the visualizations. For example, a filter could be adjusted so that data is only shown for a certain range of dates or for a certain number of food products. When you manipulate a filter, you can instantly see how the current setting affects the visible data in the visualizations.

When you load data into Spotfire, each column in the data table is automatically represented by a filter. The initial type of filter depends on the type of data in the column, but you can right-click on any filter and change the type if you like.

You can view and manage filters in a popover, in a docked panel, or as a floating window.

Filter Types

Range Filter

The range filter lets you narrow down the data shown to a range of values.

An important feature of the range filter is that the values are distributed on a linear scale according to the values of the data. Thus, if values are unevenly distributed, this will be reflected in the range filter. Note that this is not the case with item filters, where values are distributed at even intervals along the range of the slider, regardless of the actual numeric values.

Item Filter

The item filter is used to select a single item at a time, and lets you easily step between nearby items.

Radio Buttons

In a radio button filter, each value is represented by a radio button.

The radio buttons are mutually exclusive, that is, only one of the alternatives in the filter can be set at a time. However, an (All) option is always present, letting you select all values. A (None) option is also available, letting you filter out all the values, showing nothing. If there are empty values present, a radio button named (Empty) will be available. Selecting this radio button will filter to the empty values.

Values that have been filtered out by other filters are indicated with gray text. If you select a radio button that is grayed out, nothing will be shown in the visualizations since that value has been filtered out already.

When the filter is active, you can use the arrow keys on the keyboard to change the selected radio button.

For columns containing more than 500 values, radio buttons cannot be used as the filter type.

Check Boxes

In a check box filter, each unique value in the column is represented by a check box. One or several check boxes may be selected or cleared to determine which values are to appear in the visualizations.

If there are empty values present, a check box called "Empty" will be available, letting you filter to those values.

Values that have been filtered out by other filters are indicated with gray text.

When the filter is active, you can use the arrow buttons and the spacebar on the keyboard to select and clear check boxes.

For quick selecting or clearing of all the values, right-click on the filter, and select Reset Filter to select all the check boxes, or Deselect All Values from the pop-up menu.

For columns containing more than 500 values, check boxes cannot be used as the filter type.

Text Filter

The text filter lets you type a string of text, and any values that do not match the entered string are filtered out. As you type the string, the visualization is continuously updated with the values that begin with the current substring.

List Box Filter

The list box filter is used to select a few values from a very long list of values present in the column.

Select items in the list to filter to those items. Press Ctrl or Shift and click to select multiple values. If you click on the first alternative in the list, (All), no filter is applied, and all values are shown.

You can narrow down the list of values by typing a string of text in the search field above the list. The values that do not match the string are removed from the list. As you type the string, the list is continuously updated with the values that match the current substring. For list box filters in the text area, you need to press enter to search. By narrowing down the list, you can more easily find the values of interest, but this does not affect the applied filtering in any way. To apply filtering you must select values from the list. You can also enter an expression in the field to narrow down the list, using the rules described in Searching in TIBCO Spotfire. Remove the text string from the search field to make all the values reappear in the list.

The gray indicator on the right hand side of the filter shows where your selected values are located in the whole list. On mouseover, a tooltip appears, showing how many values have been selected in the list. If fewer than six values are selected, you also see those values in the tooltip.

Hierarchy Filter
What is a Hierarchy Filter?

A hierarchy filter is a filter type that shows hierarchical data in an expandable tree view. Good examples of data that can be used in a hierarchy filter are date and time information, or geographical data such as continents/countries/cities. The examples below illustrate those two uses of hierarchy filters.

Note: The hierarchy filter is not designed to handle more than 500 nodes per level.

In this case, you go from a linear series of dates that you can only manipulate on the most granular level, to a view that groups the dates from the column into a hierarchy you decide. You can select to show the dates as Year/Quarter/Month or Year/Month/Day or Year/Month/Week/Day or any other configuration you want.

Creating a Hierarchy Filter

In this case, you go from a linear series of dates that you can only manipulate on the most granular level, to a view that groups the dates from the column into a hierarchy you decide. You can select to show the dates as Year/Quarter/Month or Year/Month/Day or Year/Month/Week/Day or any other configuration you want.

Note: Creating a hierarchy with a very large number of nodes may take a long time. It may also result in a hierarchy filter with too many check boxes to be practically useful. Use the Filtering Scheme Properties or the Column Properties to edit the hierarchy and remove the column with too many unique values if this should happen.

Filters Panel

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Filters Panel Properties
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Filtering Schemes

One of the main strengths of TIBCO Spotfire is the ability it gives you to filter your data, hence, to control what data shall be visible and used in some calculations. This means that you can show/hide data for specific categories, change the time range to look at, step through a sequence of values one at a time, etc.

You have the possibility to add your own filtering schemes, which can be applied to the analysis per page or per visualization. This gives you the complete freedom to control which pages and visualizations will affect each other. You can set up the filtering schemes to work on any combination of visualizations and/or pages in your analysis. For example, you can keep all the visualizations on all the pages related by using the same filtering scheme for all of them, or, you can choose to specify different filtering schemes for all the visualizations in an analysis, or any combination in between these two extremes. See Limiting What is Shown in Visualizations to learn how to set up a visualization to use a different filtering scheme than the filtering scheme used on the page.

The filter type is a part of the filter settings in a filtering scheme. This and other filter settings are controlled via the Filtering Scheme Properties. It is reached via the right-click menu in the filters panel. The selection of which filters are visible on a page is, in contrast, specified per page, via the Organize Filters option on the right-click menu.

Filtering in Related Data Tables

When you have multiple data tables that are related to each other in your analysis, and the data tables do not include exactly the same rows, you may want to handle filtering in the related data tables in different ways, depending on whether you are interested in the filtered rows or the filtered out rows. To help show the difference between the three options available, we use an example with two related data tables, DT1 and DT2. Both DT1 and DT2 contain rows that are not available in the other data table (pink and yellow), but they also contain common rows (blue):

Filtering in Related Data Tables.png

A = Rows in DT1 that are not available in DT2.

B = Rows in DT1 that are available in DT2, but have been filtered out.

C = Rows in DT1 that are available in DT2 and included in the currently filtered rows of DT2.

F = The filtered rows (rows remaining after filtering) in DT2.

When the filtering management for DT2 is specified (from the DT1 data table header) the different options will give the following results:

When the filtering management for DT2 is specified (from the DT1 data table header) the different options will give the following results:

Include Filtered Rows Only

The first option will make all rows that are only present in DT1 disappear from the visualizations using DT1, since only the rows that are currently filtered in DT2 will be included. Hence, this option keeps only those rows that are present in both data tables (and have not been filtered out).

In the example above, this means that only the rows in C will remain after filtering in DT2.

Exclude Filtered Out Rows

The second option will remove those rows that have been filtered out from DT2 from all visualizations using DT1. Hence, this option keeps those rows that are filtered in DT2 as well as the additional rows from DT1.

In the example above, this means that A and C will remain after filtering in DT2.

Ignore Filtering

The third option is to ignore any filtering done in the related data table completely. This way, all rows that are available in the current data table will remain available.

In the example above, this means that A, B and C will all remain after filtering in DT2.

 

Note that you need to specify how each table should respond to filtering in all other related tables separately, to be certain of what will be shown in the visualizations after filtering.

Tags

What are Tags?

Tags are annotations that can be attached to marked rows. Each row can only contain a single tag from each tag collection, but the document can contain many tag collections simultaneously. A tag collection is basically a column containing a set of different tags, or annotations. Each tag collection is represented by a new column in the data table and can be used for filtering the data, just like any other column. Tags can only be attached to rows from a single data table, but the same tag collection and tag names can be used for multiple data tables.

Tags are similar to Lists, but Tags are specific to the current analysis, while with Lists you work with the same collection of lists all the time, from one session to the next. Combining the functionality from Tags and Lists can be very useful. You can create lists from tag collections, and you can create tag collections from lists. This means that Lists can be a way to transfer knowledge from one analysis to another, while Tags can be a way to use lists within an analysis. See What are Lists? to learn more.

You can view and manage Tags in a popover, in a docked panel, or as a floating window.

How to Work with Tags

Note: If tags are to be reapplied after reloading linked data, you need to specify key columns that can be used to uniquely identify the rows in each data table. See Details on Select Key Columns for more information.

Details

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Bookmarks

My Note: I omitted 3.2 Bookmarks with 3 Subtopics

What are Bookmarks?

Bookmarks are snapshots of the state of an analysis. Add a bookmark to your analysis to be able to return to a state where you found something interesting when you marked or filtered out certain items. A bookmark can be applied at any time, allowing you to quickly return to a previously created view of the data. You can also share your insights with others by making your bookmarks available to other users, or by sending links to the bookmarks.

One of the most important uses of bookmarks is that they can be included as links in a text area. This helps you create guided analyses where the recipient of your analysis can click on action links or buttons to quickly move through several different views of the analysis.

A bookmark can capture one or more of the following: specific rows you have marked, active pages and visualizations, and even specific filtering that you have applied. A bookmark also contains information about visualization properties such as what column was used on an axis, what column was used to color by, etc., as well as any custom property values you have used on the active page. You decide what should be included in a captured state, but a bookmark that is added without making any adjustments will automatically include all those parts in the bookmark. It can be worth noting that a bookmark never recreates any removed visualizations or pages. Neither will any added pages or visualizations be removed when a bookmark is applied.

You can view and manage bookmarks in a popover, in a docked panel, or as a floating window.

How to Use Bookmarks

Note: If bookmarks are to be reapplied after reloading linked data, you need to specify key columns that can be used to uniquely identify the rows in each data table. See Details on Select Key Columns for more information.

Bookmarks Example Scenarios

The concept of Bookmarks has many potential usages and usage goals. The list below presents some possible scenarios where bookmarks can be useful.

Bookmarks Pop-up Menu

Right-click on a bookmark to bring up the pop-up menu. You can always apply any bookmarks that are visible to you in an analysis, but whether or not you can add new bookmarks, or update, delete and otherwise modify existing ones depends on permissions defined by the creator of the analysis as well as your licenses. See Permissions to learn more.

Details on Add Bookmark Special

This dialog allows you to select exactly which bookmark parts to include in your bookmark.

Lists

What Are Lists?

Lists represent captured knowledge from one or many data sources. For example, you may have identified a number of transactions where the sum of cost was high. You can then create a list containing the rows representing these transactions. This will make it easier to access the interesting items for further analysis based on the findings. See Creating Lists to learn more about adding new lists.

Lists are available from one session to the next and are automatically saved when you modify them. Lists are similar to Tags, but with Lists you work with the same collection of lists all the time, while Tags are specific to the current analysis. Combining the functionality from Lists and Tags can be very useful. You can create tag collections from lists, and you can create lists from tag collections. This means that Lists can be a way to transfer knowledge from one analysis to another, while Tags can be a way to use lists within an analysis. See What are Tags? and Creating Tag Collections from Lists to learn more.

You can view and manage lists in a popover, in a docked panel, or as a floating window.

How to Use Lists

Creating Lists

You can create new lists in two ways. One way is to mark items in a visualization and make a new list based on these. Another way is to add a list from list logic, which means that you create a new list by comparing existing lists using boolean operators. This lets you determine which list items are present or not in a certain combination of lists. The result of the comparison is then added as a new list in the Lists panel.

Working with Annotations

You can add annotations to both lists and list items. To view annotations, hover with the mouse pointer over the list or list item of interest. A tooltip with the annotations will appear, as seen in the example below.
The tooltip shows the name of the list, "High Cost Transactions", followed by the annotation text. "Sum of Cost" is the name of the annotation, but specifying a name is optional. If you add many annotations to the same list or list item, the annotations will be listed vertically after each other in the tooltip. It is possible to search for lists and list items with a particular annotation.

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Details

4 Subtopics

Collaboration

My Note: This is not enabled in my version. I use MindTouch for this purpose.

Collaboration Panel

What is the Collaboration Panel?

The collaboration panel is a tool that allows you to view web pages in Spotfire. This is useful, for instance, if you use some kind of web based collaboration tool, such as tibbr®, a communication and collaboration tool designed for the workplace, enabling users to collaborate by participating in communication threads about different subjects.

You can configure the Collaboration to go to different URLs on different pages in the analysis, thus making it possible to follow several different subjects within a single analysis.

The Collaboration panel can also be viewed in a popover or as a floating window. 

How to Use the Collaboration Panel
Details on Configure Collaboration Panel

To configure the Collaboration Panel, you can enter a URL to a web page that TIBCO Spotfire will show in the Collaboration Panel.

In TIBCO Spotfire, the Collaboration Panel will run an instance of the Internet Explorer rendering engine installed on the machine to parse content.

In TIBCO Spotfire Web Player, the Collaboration Panel will open in an iframe and use the same rendering function available to the rest of the Web Player. If the Collaboration Panel is used in both TIBCO Spotfire and the Web Player, this could potentially lead to differences in html rendering between different web browser versions.

Note: The Collaboration Panel is configured per page in the analysis, so you can link the Collaboration Panel on different pages to different URLs.

Integrating with tibbr®

You can use the collaboration panel to integrate with tibbr®. For example, you may want to follow and post to a certain tibbr subject thread and show that thread in the collaboration panel.

To show a tibbr subject thread in the collaboration panel, the URL should be in the following format:
http(s)://<tibbr server>/a/gadgets/subject_messages.html?id=<subject>&name=<subject>
where <tibbr server> and <subject> should be replaced by your tibbr server and subject of choice. Also note that you need to use http or https depending on what the tibbr server configuration requires. 

For example, if your tibbr server is called mytibbrserver, and you have a subject called SpotfireTibbrDemo, the URL will appear as below:
https://mytibbrserver.com/a/gadgets/...tfireTibbrDemo

See the tibbr®, tibbr Service, tibbr Community, and tibbr Community Service – Installation and Configuration manual for a list of other tibbr gadgets.

Share

What is the Share Menu?

The Share menu allows you to quickly share your analyses with other people you are collaborating with. If you are using tibbr in your organization, you can post messages with images of the analysis you are working with to the tibbr® flow. Your company may also have added other collaboration tools, besides tibbr®, to the Share menu.

Right click on a visualization, on a bookmark, or on a page title to access the Share menu. Depending on what you have clicked upon you will see different options on the menu.

Details on Log into tibbr®

To be able to log into a tibbr® server, the Spotfire tibbr® host preference must be set by a Spotfire Administrator. This preference is found under Application > tibbr® in the Preferences tab in the Spotfire Administration Manager. Enter the hostname without http://, for instance tibbrserver.

Details on Share to tibbr®

tibbr® is a communication and collaboration tool designed for the workplace, enabling users to collaborate by participating in communication threads about different subjects.

If you use tibbr® to collaborate with your colleagues, you may want to share your Spotfire analyses with them. Specifically, you can share pages, visualizations, and bookmarks.

Tools

Find

Find

The find tool is a fast way to find contents in your data, navigate in the analysis, and to perform actions found in the menus of Spotfire. It consists of a text field where you enter a search string and a list of results for the search.

To reach the Find dialog: Press Ctrl+F. OR Select Tools > Find....

Searching in TIBCO Spotfire

There are many places in TIBCO Spotfire where you can search for different items. For example, you can search for filters, analyses in the library or elements used to build information links in the Information Designer. All of the available search fields use the same basic search syntax, which is presented below. For more information regarding search of a specific item, see the links at the bottom of this page.

Tip: If you cannot find what you are looking for, try adding more wildcards. For example, to locate a filter called "Sales ($)" , enter the search expression "Sales ($*", to avoid interpreting the text within the parenthesis as a Boolean expression.

Data Relationships

What is the Data Relationships Tool?

The Data Relationships tool is used for investigating the relationships between different column pairs. The tool always works on the currently filtered data. The Linear regression and the Spearman R options allow you to compare numerical columns, the Anova option will help you determine how well a category column categorizes values in a (numerical) value column, the Kruskal-Wallis option is used to compare sortable columns to categorical columns, and the Chi-square option helps you to compare categorical columns.

For each combination of columns, the tool calculates a p-value, representing the degree to which the first column predicts values in the second column. A low p-value indicates a probable strong connection between two columns.

The resulting table displays the p-value for each combination of Y and X columns. The table is sorted by p-value. Clicking on a column heading will sort the rows according to that column.

How to Use Data Relationships
Details on Data Relationships
Data Relationships Column Descriptions

The Data Relationships table displays a number of different measures for the different types of calculations. A description of the statistics available is found below:

Data Relationships Error Codes

If your data contain empty values or errors, or if filtering has reduced the number of valid rows too much, the data relationships calculation may result in errors for specific cells in the table. The available error codes are described below:

Theory and Models
Overview of Data Relationships Theory

The Data Relationships tool calculates a probability value (p-value) for any combination of columns. This p-value can be used to determine whether or not the association between the columns is statistically significant.

  • Linear regression
  • Spearman R
  • Anova
  • Kruskal-Wallis
  • Chi-square
Data Relationships Linear Regression Algorithm 

The Linear Regression option calculates the p-value under the assumption that there are no empty values in the data table.

Note: If there are empty values in the data table, the data table will first be reduced to the rows containing values for both the first and the second column.

Let n be the total number of values and denote by (xi, yi), i = 1, ..., n the set of data points to fit a straight line

References:

Arnold, Steven F., The Theory of Linear Models and Multivariate Analysis.

Rice, John A., Mathematical Statistics and Data Analysis, 2nd ed. pp 509.

Data Relationships Spearman R algorithm

The Spearman R option calculates the p-value under the assumption that there are no empty values in the data table.

Note: If there are empty values in the data table, the data table will first be reduced to the rows containing values for both the first and the second column.

The Spearman R calculation is a nonparametric comparison based on the ranks of the observations, rather than on the values themselves. This test can be used as an alternative to the Linear Regression, when the assumption of normality or equality of variance is not met. For example, this is useful on occasions where outliers contribute too much to the calculations in a parametric test.

Spearman R can be calculated in several different ways depending on whether or not ties are common in the data table , that is, if several values are identical and thus have the same rank. Since it is quite common with ties in general data analysis, TIBCO Spotfire uses an algorithm where these can be handled. When ties occur, they are all given the mean of the ranks that they would have had if they had not been exactly identical (see Ranking Functions, "ties.method=average").

Data Relationships Anova Algorithm

The Anova option computes the difference between groups by comparing the mean values of the data in each group. The results are obtained by testing the null hypothesis; the hypothesis that there is no difference between the means of the groups. More formally, the p-value is the probability of the actual or a more extreme outcome under the null-hypothesis.

Note: If there are empty values in the data table, the data table will first be reduced to the rows containing values for both the first and the second column.

Data Relationships Kruskal-Wallis Algorithm

The Kruskal-Wallis option calculates the p-value under the assumption that there are no empty values in the data table.

Note: If there are empty values in the data table, the data table will first be reduced to the rows containing values for both the first and the second column.

The Kruskal-Wallis test can be seen as the nonparametric version of a one-way Anova. The test uses the ranks of the data rather than their actual values to calculate the test statistic. This test can be used as an alternative to the Anova, when the assumption of normality or equality of variance is not met.

For k groups of observations, all N observations are combined into one large sample, the result is sorted from smallest to largest values and ranks are assigned, assigning ties (when values occur more than once) the same rank.

Data Relationships Chi-square Independence Test Algorithm

The Chi-square option calculates the p-value under the assumption that there are no empty values in the data table.

Note: If there are empty values in the data table, the data table will first be reduced to the rows containing values for both the first and the second column.

Let n be the total number of values and denote by I the number of unique values in the first column and by J the number of unique values in the second column. Also for i = 1, ..., I let ni be the number of occurrences of the ith unique value and for j = 1, ..., J, let nj be the number of occurrences of the jth unique value.

Requirements on Input Data for Data Relationships

Experimental design

In this tool, a one-way layout of Anovas has been employed. This means that the experimental design should be of the type where the outcome of a single continuous variable is compared between different groups. The tool cannot be used to analyze experiments where two or more variables vary together.

Tip: You can create a new column using the Concatenate function (or '&') of the Insert Calculated Column tool (Insert > Calculated Column...) if you want to analyze two or more variables together.

Distribution of data

The Anova and Linear regression comparisons assume the following:

The data is approximately normally distributed.

The variances of the separate groups, or the variances of the errors in the case of linear regression, are approximately equal.

If the data do not fulfill these conditions, the Anova and Linear Regression comparisons may produce unreliable results. In this case, it may be more valid to use a Kruskal-Wallis or Spearman R comparison instead.

Note: If more than one test is performed at the same time, then it is more likely that there will be at least one p-value less than 0.05 than in the case where only one test is performed. A guideline of when to reject the hypothesis is then "Reject the hypothesis if the p-value is less than 0.05 divided by the number of tests". This is called the Bonferroni method for multiple tests.

K-means Clustering

How to Perform a K-means Clustering

The K-means Clustering tool cannot be used unless you have created a suitable line chart to base the calculation on. For example, you cannot use multiple Y-axes scales or an X-axis which is both continuous and binned when performing a K-means clustering. See below for more information about how to set up the line chart.

Details on K-means Clustering

K-means clustering is an algorithm for partitioning a data table into subsets (clusters), in such a way that the members of each cluster are relatively similar.

The K-means clustering in TIBCO Spotfire is based on a line chart visualization which has been set up either so that each line corresponds to one row in the root view of the data table, or, if the line chart is aggregated, so that there is a one to many mapping between lines and rows in the root view. The clustering is initialized using data centroid based search, using unit weights, and correlation or Euclidean distance as the distance measure. The clustering is always performed on filtered rows. If you wish all rows to be included in the clustering you need to reset all filters prior to clustering. The columns the clustering operation should be based on are specified in the line chart that is used as starting point.

If "break on empty" is not active, empty values will be replaced using row (line) interpolation, similar to what is shown in the visualization. If "break on empty" is active, any rows (lines) containing empty values shall be excluded from the clustering operation.

Note: If the input line chart is trellised, the column or expression used to trellis by will be moved to the Line By setting upon running a K-means clustering. This is done in order to keep the original lines in the line chart after presenting the K-means result in trellis panels.

References

Mirkin, B. (1996) Mathematical Classification and Clustering, Nonconvex Optimization and Its Applications Volume 11, Pardalos, P. and Horst, R., editors, Kluwer Academic Publishers, The Netherlands.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Le Cam, L. M. and Neyman, J., editors, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Volume I: Statistics, pages 281-297. University of California Press, Berkeley and Los Angeles, CA.

Hair, J.F.Jr., Anderson, R.E., Tatham, R.L., Black, W.C. (1995) Multivariate Data Analysis, Fourth Edition, Prentice Hall, Englewood Cliffs, New Jersey.

Line Similarity

How to Perform a Line Similarity Comparison

Note: The Line Similarity tool cannot be used unless you have created a suitable line chart to base the calculation on. For example, you cannot use multiple Y-axes scales or an X-axis which is both continuous and binned when performing a line similarity comparison. See below for more information about how to set up the line chart.

Tip: If you do not want to be able to overwrite the result columns by consequent calculations, or when saving an analysis file with linked data, you can turn them into static columns by performing the following: Select Edit > Column Properties. Click on a result column to select it, and then click on the Freeze Column button in the lower part of the General tab.

Details on Line Similarity

The Line Similarity tool is used to compare the lines in a line chart to a selected master line. As a result, two new columns are generated. The first is a similarity column, where the similarity to the master line is presented for each individual row (line). The second is a rank column, where the line most similar to the master line receives the rank 1. Correlation or Euclidean distance is used as the distance measure.

If "break on empty" is not active, empty values will be replaced using row (line) interpolation, similar to what is shown in the visualization. If "break on empty" is active, any rows (lines) containing empty values shall be excluded from the line similarity calculation.

Hierarchical Clustering

What is the Hierarchical Clustering Tool?

The Hierarchical Clustering tool groups rows and/or columns in a data table and arranges them in a heat map visualization with a dendrogram (a tree graph) based on the distance or similarity between them. When using the hierarchical clustering tool, the input is a data table, and the result is a heat map with dendrograms. You can also initiate hierarchical clustering on an existing heat map from the Dendrograms page of the Heat Map Properties. See How to Use the Heat Map to learn more.

Details on Hierarchical Clustering
Theory and Modeling
Overview of Hierarchical Clustering Theory

Hierarchical clustering arranges items in a hierarchy with a treelike structure based on the distance or similarity between them. The graphical representation of the resulting hierarchy is a tree-structured graph called a dendrogram. In Spotfire, hierarchical clustering and dendrograms are strongly connected to heat map visualizations. You can cluster both rows and columns in the heat map. Row dendrograms show the distance or similarity between rows, and which nodes each row belongs to as a result of clustering. Column dendrograms show the distance or similarity between the variables (the selected cell value columns). The example below shows a heat map with a row dendrogram.

Heat Map with a Row Dendrogram.png

You can perform hierarchical clustering in two different ways: by using the Hierarchical Clustering tool, or by performing hierarchical clustering on an existing heat map visualization. If you use the Hierarchical clustering tool, a heat map with a dendrogram will be created. To learn more about heat maps and dendrograms, see What is a Heat Map? and Dendrograms and Clustering.

Algorithm

The algorithm used for hierarchical clustering in Spotfire is a hierarchical agglomerative method. For row clustering, the cluster analysis begins with each row placed in a separate cluster. Then the distance between all possible combinations of two rows is calculated using a selected distance measure. The two most similar clusters are then grouped together and form a new cluster. In subsequent steps, the distance between the new cluster and all remaining clusters is recalculated using a selected clustering method. The number of clusters is thereby reduced by one in each iteration step. Eventually, all rows are grouped into one large cluster. The order of the rows in a dendrogram are defined by the selected ordering weight. The cluster analysis works the same way for column clustering.

Note: Only numeric columns will be included when clustering.

Distance Measures
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Clustering Methods
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Ordering Weight

The ordering weight controls in what vertical order the rows are displayed in the row dendrogram. For column dendrograms it controls the horizontal order of the columns. The two subclusters within a cluster (there are always exactly two subclusters) are weighted and the cluster with the lower weight is placed above (to the left of) the other cluster.

Hierarchical Clustering References

Hierarchical clustering

Mirkin, B. (1996) Mathematical Classification and Clustering, Nonconvex Optimization and Its Applications Volume 11, Pardalos, P. and Horst, R., editors, Kluwer Academic Publishers, The Netherlands.

Sneath, P., Sokal, R. R. (1973) Numerical taxonomy, Second Edition, W. H. Freeman, San Francisco.

General information about clustering

Hair, J.F.Jr., Anderson, R.E., Tatham, R.L., Black, W.C. (1995) Multivariate Data Analysis, Fourth Edition, Prentice Hall, Englewood Cliffs, New Jersey.

Predictive Modeling

What is Predictive Modeling?

TIBCO Spotfire provides you with the tools to incorporate predictive models into your analysis using either regression modeling or classification modeling.

  • Regression modeling is useful for making numeric predictions, such as profit and expenses.
  • Classification modeling is useful for making predictions for typically two nodes or classes, such as whether a business transaction is fraudulent or legitimate.

The three tasks of predictive modeling include:

  • Fitting the model.
  • Evaluating the model.
  • Predicting from the model.

To fit the model, in the Regression Modeling or Classification Modeling dialog, select the model options and click OK. TIBCO Enterprise Runtime for R for Spotfire creates the model and returns it to the analysis. Each model creates a number of new data tables that you can use for further analysis.

A model page is created (see The Model Page) and the model is added to the Analytic Models panel. You can include more than one model in your analysis, and then you can iterate and evaluate through all models with new data. 

After you have completed your evaluation, optionally you can predict from the model. When you include a model in the analysis, you can use the model to insert predicted columns into your data table and share the result with others.

Note: Columns of the data type Currency are not supported by the predictive modeling tools. To use data from such columns in predictions you must first cast the columns to another numeric data type, e.g., Real.

Regression Modeling

My Note: I might add these later when I use them. I did on 2/14/2014!

Linear Regression Method

Linear regression models the numeric response column as a weighted sum of the predictor columns. The weights, also known as the regression coefficients, are selected by the method of least squares, which minimizes the sum of the squared differences between the observed response and the predictions based on the weighted sum.

Any predictor column with character data is expanded into a set of indicator columns, one column for each unique value in the character column. The indicator column for a character value is 1 if the corresponding entry in the original column contains that character value, otherwise it is zero. Character data columns used as predictors should each have small numbers of unique values relative to the total number of rows in the data set.

Regression Tree Method

Regression trees are a nonparametric regression method that creates a binary tree by recursively splitting the data on the predictor values.

The splits are selected so that the two child nodes have smaller variability around their average value than the parent node. Various options are used to control how deep the tree is grown. Regression predictions for an observation are based on the mean value of all the responses in the terminal node.

The Predictor columns can be either numeric or character (provided there are not more then 31 unique character values in any one character column). There is no need for making transformations of the response or predictor columns; the same tree is grown for any monotone transformations of the data.

Details on Regression Modeling – General

This tool allows you to create regression models using the TIBCO Enterprise Runtime for R engine, without the need of writing any scripts yourself. A model page will be created (see The Model Page) and the model will be added to the Analytic Models panel.

Details on Regression Modeling – Options

This tool allows you to create regression models using the TIBCO Enterprise Runtime for R engine, without the need of writing any scripts yourself. A model page will be created (see The Model Page) and the model will be added to the Analytic Models panel.

Classification Modeling

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Logistic Regression Method

Logistic regression is a classification method used when the Response column is categorical with only two possible values. The probability of the possible outcomes is modeled with a logistic transformation as a weighted sum of the Predictor columns. The weights or regression coefficients are selected to maximize the likelihood of the observed data.

Any Predictor column with character data is expanded into a set of indicator columns: one column for each unique value in the character column. The indicator column for a character value is one if the corresponding entry in the original column contains that character value; otherwise, it is zero.  Character data columns used as predictors should each have small numbers of unique values relative to the total number of rows in the data set.

Classification Tree Method

Classification trees are a nonparametric classification method that creates a binary tree by recursively splitting the data on the predictor values. The splits are selected so that the two child nodes are purer in terms of the levels of the Response column than the parent node. Various options are used to control how deep the tree is grown. Class predictions for an observation are based on the majority class in the terminal node for the observation.

Classification trees can handle response variables with more than two classes. The Predictor columns can be either numeric or character (provided there are not more then 31 unique character values in any one character column). There is no need to make transformations of the Predictor columns; the same tree is grown for any monotone transformations of the data.

Details on Classification Modeling – General

This tool allows you to create classification models using the TIBCO Enterprise Runtime for R engine, without the need of writing any scripts yourself. A model page will be created (see The Model Page) and the model will be added to the Analytic Models panel.

Details on Classification Modeling – Options

This tool allows you to create classification models using the TIBCO Enterprise Runtime for R engine, without the need of writing any scripts yourself. A model page will be created (see The Model Page) and the model will be added to the Analytic Models panel.

How to Use the Model Page

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The Model Page

Each time a new model is created, a new page, the model page, is added to the analysis. It consists of up to four different sections:

  1. The Model Summary.
  2. The Table of Coefficient.
  3. The Available Diagnostic Visualizations.
  4. The visualization area where the diagnostic visualizations can be displayed.

The Model Page.png

Using a Model Summary

A model summary is automatically created when running a regression modeling or a classification modeling. The model summary displays the name of the model, the model type, and the model formula.

For parametric models (Linear Regression and Logistic Regression), additional summary statistics, appropriate for the particular model type are also shown. These statistics can give an indication of how well the model fits the data and can also be used to compare one model with another model of the same type.

For tree models, a text description of the tree structure is displayed, followed by table showing the model improvement at each split. Finally, a summary of each individual split, starting at the root node, is shown.

Using a Table of Coefficients

The table of coefficients provides the model coefficients for the parametric models (linear and logistic regression). In addition to the estimates of the coefficients, the table includes a measure of the variability or error of each estimate and a test statistic (t.value or z.value) of the null hypothesis that the coefficients is zero (in other words, not needed in the model). A p-value for the statistical test is also provided. A small p-value (typically less that 0.05) indicates that the null hypothesis can be rejected--that is, that the coefficient is significant or important in the model.

Note that the test for each coefficient is based on the model with all the other coefficients included in the model.

The absolute value of the test statistic can provide a measure of variable importance for that term in the model. The larger the absolute value, the greater the importance.

The table of coefficients is added as a separate data table in Spotfire and can be used for further analysis.

Available Diagnostic Visualizations

This section lists the available diagnostic plots for the model. They can be an aid to help determining the validity of a predictive model. Different model methods display different lists of diagnostic plots. Click on an option to display the visualization in the model page.

Residuals vs. Fitted

The residuals vs. fitted visualization is a scatter plot showing the residuals on the Y-axis and the fitted values on the X-axis. You can compare it to doing a linear fit and then flipping the fitted line so that it becomes horizontal. Values that have the residual 0 are those that would end up directly on the estimated regression line. The residuals vs fit plot is commonly used to detect non-linearity, unequal error variances and outliers.

Shape (exaggerated)
Conclusion: When a linear regression model is suitable for a data set, then the residuals are more or less randomly distributed around the 0 line. When residuals form a pattern in the visualization, then the current model might be less suitable for the data.

Normal Quantile-Quantile

The normal quantile-quantile visualization calculates the normal quantiles of all values in a column. The values (Y-axis) are then plotted against the normal quantiles (X-axis).

Things to look for:

Shape (exaggerated)
Conclusion: Approximately normal distribution.

Less variance than expected. While this distribution differs from the normal, it seldom presents any problems in statistical calculations.

More variance than you would expect in a normal distribution.

Left skew in the distribution.

Right skew in the distribution.

Outlier. Outliers can disturb statistical analyses and should always be thoroughly investigated. If the outliers are due to known errors, they should be removed from the data before a more detailed analysis is performed.

Note: Plateaus will occur in the plot if there are only a few discrete values that the variable may take on. However, clustering in the plot may also be due to a second variable that has not been considered in the analysis.

Scale – Location

The scale – location plot is similar to the residuals vs fit plot, but instead of linear residuals it uses the square root of the residuals. It is used to reveal trends in the magnitudes of residuals. For a good model, the values should be more or less randomly distributed.

Cook's Distance

Cook's distance is a statistic which tries to identify those values which have more influence than others on the estimated coefficients. High peaks in the bar chart might represent values that should be investigated further, since they have a larger effect on the coefficients.

Response vs. Fitted or Predicted

The Response vs. Fitted or Response vs. Predicted visualization is a scatter plot of the response variable versus the fitted values for the model or the predicted values computed from new data using a previously computed model. The ideal shape for this plot is all points on a line with an intercept of 0 and a slope of 1 (about a 45 degrees angle). This would indicate that the response values and values computed from the model match up perfectly. In reality, the points will be in a diagonal band around the (0,1) line.  Points that deviate greatly from this band can indicate outliers or deficiencies in the model.

Generally, the Residuals vs. Fitted or Predicted scatter plot is a better visualization to diagnose model deficiencies, since the deviations are centered around the horizontal line, y=0, instead of around the (0,1) line.

Predicted Probability Histograms

The Predicted Probability is a histogram of the predicted probabilities for a particular level of the response variable. For a two level response, you would like to have all the values in one histogram close to one and, in the other histogram, all the values should be close to zero.

ROC Curve

An ROC, or receiver operating characteristic curve, shows the performance of the classifier as the threshold for class prediction is varied. It is a plot of the sensitivity, or true positive rate of the classifier, versus one minus the specificity, or false positive rate. The true positive rate is the number of the predicted positives out of true positives and the true negative rate is the number of the predicted negatives out of the number of false positives. The predicted positives and negatives varies as the threshold for class prediction varies.

For example, with classes A and B, if the threshold is set very low for class A (close to zero) then all the tree class A observations will be classified as A (sensitivity is one). However, many class B observations will also be incorrectly classified as A leading to a large false positive rate. The ideal ROC curve starts at (0,0) goes up to (0,1) and then over to (1,1).

Randomly assigning predicted classes leads to an ROC curve that is a line with a slope of 1 from (0,0) to (1,1).

How to Use the Evaluation Page
The Evaluation Page

Each time a model is evaluated, a new page, the evaluation page, is added to the analysis. It consists of up to four different sections:

  1. The Evaluation Summary.
  2. The Confusion Matrix (only available for the classification modeling methods).
  3. The Available Diagnostic Visualizations.
  4. The visualization area where the diagnostic visualizations can be displayed.

The Evaluation Page.png

Using an Evaluation Summary

An evaluation summary is automatically created when you are evaluating a regression model or a classification model. 

The evaluation summary displays the name of the model, including the model type, the data table used in the model evaluation, and the model formula.

Additional summary statistics, appropriate for the particular model type are also shown. These statistics can give an indication of how good the model predicts the data and can also be used to compare model predictions against another model of the same type.

Using a Confusion Matrix

The confusion matrix for a classification model compares the counts of the predicted class values with the observed or true class values.

It is a k x k table where k is the number of classes in the response. A good classifier has most of the counts on the diagonal from upper left to lower right (correct classifications) and few values on the off diagonal.

Available Diagnostic Visualizations

This section lists the available diagnostic plots for the model. They can be an aid to help determining the validity of a predictive model. Different model methods display different lists of diagnostic plots. Click on an option to display the visualization in the model page.

My Note: More Details Same as Above

What is the Analytic Models Panel?

The Analytic Models panel is used to manage all models within your analysis. Click on the corresponding icon to perform a task with the selected model.

Details on Duplicate Model

You can use an old model as the base for a new one by clicking Duplicate model icon,Duplicate Model Icon.png , on the model page for the base model.

Details on Evaluate Model

This dialog allows you to compare the model to another data table that includes the values you are trying to predict using the model. For example, a model can be created using confirmed sales data for the month of January, evaluated by using confirmed sales data for February and then used to predict future sales.

When evaluating a model, you can select a data table to evaluate the model on and match the response and predictor columns in the model data and the evaluation data.

Details on Insert Predicted Columns

The Insert Predicted Column dialog allows you to embed model predictions in your data. You must create an analytic model using Regression Modeling or Classification Modeling before you can insert any predicted columns.

Data Functions

What are Data Functions?

Data functions are calculations based on S-PLUS, open-source R, SAS®,  MATLAB® scripts*, or R scripts running under TIBCO Enterprise Runtime for R for Spotfire, which you make available in the TIBCO Spotfire environment. Once a data function has been defined and saved in the Spotfire library using the Register Data Functions dialog, it can be applied at many different places in an analysis. For example, it can be used as a transformation step when you add or replace data tables. It can also be a separate tool that is run from the Insert menu.

S-PLUS or open-source R data functions can be defined from either an existing function in the corresponding Spotfire Statistics Services package repository, or by writing a script directly in the Register Data Functions dialog, and then running in the appropriate engine (either in the S-PLUS engine for S-PLUS functions, or in the TIBCO Enterprise Runtime for R engine or open-source R engine for R functions).  SAS or MATLAB engines can only be accessed using scripts, and only when you have a working installation of the selected software available. See http://support.spotfire.com/sr.asp for information about the TIBCO Spotfire Statistics Services system requirements.

During the set-up of a data function, a number of input and output parameters can be specified.

It is a best practice to avoid sending very large data sets from Spotfire to a statistical engine, or to invoke complex, long-running calculations. This ensures a rapid response and a good user experience.

* SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

MATLAB is a trademark or registered trademark of The MathWorks, Inc.

How to Use Data Functions

Data functions can be used to enhance the functionality of TIBCO Spotfire in many ways. Below are a few examples of where and how data functions can be defined and applied. Note that you or your admin must first set the address to TIBCO Spotfire Statistics Services or TIBCO Spotfire Statistics Services Local Adapter as explained below. See also What are Data Functions?

Details

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Data Type Mapping

Concerning R:

R is available under separate open source software license terms and is not part of TIBCO Spotfire.  As such, R is not within the scope of your license for TIBCO Spotfire. R is not supported, maintained, or warranted in any way by TIBCO Software Inc.  Download and use of R is solely at your own discretion and subject to the free open source license terms applicable to R.

Name Encoding for Column Names Sent to Spotfire Statistics Services

Column names in TIBCO Spotfire are stored as UTF-16 encoded strings, while variable names in TIBCO Spotfire Statistics Services are built from 8-bit ASCII characters matching [.0-9a-zA-Z]. Thus, the column names that are sent to TIBCO Spotfire Statistics Services must be encoded. This is done automatically when sending data to TIBCO Spotfire Statistics Services via the built-in data functions input handlers. If you need to provide column name input by some other means (e.g., via a document property) you may need to use the NameEncode function manually, in order to encode the column names prior to applying the data function.

Decoding may be necessary to interpret column names when the result from a data function is a text report about the columns. Use the NameDecode function to decode results that have not been automatically decoded by Spotfire output handlers.

NameDecode (and NameEncode) can be written as an S-PLUS script for ASCII and ISO-8859-1 characters.

Information Designer

What is the Information Designer?

The Information Designer is a tool for setting up data sources and creating and opening information links. An information link is a database query specifying the columns to be loaded and any filters needed to narrow down the data table prior to creating visualizations in TIBCO Spotfire. In Information Designer, information links are created from building blocks such as columns and filters using joins, calculations and aggregations.

The Elements tree in Information Designer is a representation of the folder structure in the library. The permissions for each folder specify which databases and elements should be available for different users or groups when creating information links. Folder permissions can be specified in the Information Designer, but the main permission handling is done with the Library Administration tool.

Once information links have been created in Information Designer, they can be opened by any user who has the appropriate licenses, allowing users who may not have knowledge of SQL or the underlying database structures to be able to execute advanced database queries.

Information links are opened by selecting File > Open From > Library....

Note that neither the Information Designer, nor the resulting information links are available when you are working offline.

General Workflow

This is the general workflow for using Information Designer:

1. Set up the data sources

Enter the information required to connect to the databases which will be accessed through Information Designer.

More

2. Create folders for storing elements and set permissions

The library is a hierarchical structure where data sources, elements and information links can be organized into folders. Given that the library is also used for storing analysis files, it could be worthwhile to spend some time thinking over a suitable structure.  More

Different groups of users are given different levels of access to data. More

3. Combine tables by creating joins

If you want to work with data from different tables, you first need to create joins.

More

4. Define column elements from available data sources

Define the column elements to be shown when creating information links. These columns can be taken directly from tables in one or more databases. They can also be calculated, filtered or otherwise modified.

More

5. Create filter elements to limit the data retrieved

Create filter elements with descriptive names to be applied when creating information links.

More

6. Create information links

Create information links that retrieve data from one or more databases and share them with your colleagues.

More

General Guidelines for Setting Up an Information Model
Purpose

The Information Model (IM) concept aims to supply each end user with the data they need, with a minimum of effort and confusion.  Consequently, when building an IM, it is important to understand who the end users are and what data they require for their work.

Who are the end users?

Permissions are set on the folder level. Finding groups of users who work on related data will give you a good clue about the folder structure you should implement. Do not give all users access to everything - this will only cause confusion.

What data do they need?

What information is needed? How much data can users handle in a single request? Are there any commonly used threshold values? Answering these questions will guide you in setting up the correct joins, columns and filters.

Will users build their own information links?

Some end users will want to use the column and filter elements that you design, and assemble their own information links using Information Designer. Others will be less experienced, or may perform repetitive tasks. For these you should consider preparing complete information links in advance.

Tip

If your data source contains well-ordered data you can right-click on the data source and select Create Default Information Model... in order to quickly set up a simple information model.

Icon Explanations

In the Elements tree, the following icons may appear. Click on a link in the table below to find out more about each element type. In the Data sources tree only the items belonging to a database are visible.

Fundamental Concepts

The Information Designer in TIBCO Spotfire requires no prior knowledge of query languages such as SQL. However, it is important to understand a few terms and concepts as they are used in this product:

  • Information links
  • Column elements
  • Filter elements
  • Folders
  • Procedures
Information Links
Information Links

An information link is a structured request for data which can be sent to the database. These specifications include one or more columns, and may include one or more filters.

Stated in plain English, an information link could be: "Fetch the Name, Address and Phone_number for employees that pass the filter High_Income."

Information links can also be used to limit what data to open in an analysis in a number of different ways. See Loading Data Overview for a summary of the various methods.

Editing Information Links

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Opening Information Links

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Transforming the Data

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Data Sources

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Folders

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Joins

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Column Elements

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Working with Aggregation

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Filter Elements

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Procedures

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User Interface Details

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Tips and Examples

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Library Administration

13 Subtopics

Manage Data Connections

10 Subtopics

Saving and Exporting

Creating a Guided Analysis

What is a Guided Analysis?
Introduction

There may be times when you want to create and share an analysis file with other people and have them perform their own analysis on it. You might want to set up the analysis file to load particular data and show certain visualizations, but also provide instructions for other people on how to use the document. There might be a specific order a person should go through the pages, and detailed instructions on what to look for and which filters are relevant to manipulate on each page. To aid in this, there are a number of things you can do in TIBCO Spotfire to set up a guided flow through your document.

When the recipients of your analysis file open it, they will be guided through the analysis as per your instructions, but be able to do their own filtering and look closer at any noteworthy aspects they find interesting. This allows you to set up a generic analysis covering a subject such as sales over the entire United States, but instruct the recipients to filter down to the state they work in.

Some methods you can use to make your analysis guided are:

  • Create a cover page.
  • Write instructions in text areas.
  • Place links or buttons leading to relevant tools, pages or views in the text areas.
  • Switch to step-by-step mode or define your own page navigation through actions using history arrows navigation mode.
  • Use customized filtering schemes.
  • Keep in mind the intended end users’ level of data access.
Create a cover page

Show the cover page for your document and explain the purpose of the analysis on this page. Tell the recipients what kind of data are included in the analysis, and the possible results to look for. When you save the analysis file, before sharing it with your colleagues, make sure the cover page is active so that the analysis file will open showing that page first.

Write instructions in text areas

You might want several pages in your document. The first page might display a map chart of the United States showing overall sales results. The second page might have a bar chart comparing the sales and expenditures across the different states. The third page might show sales figures for each individual salesperson.

It is recommended that each page include a text area in which you provide some explanatory text about what the page shows and its purpose. Give the reader some instructions on what filters are relevant to manipulate, and perhaps mention that they can mark interesting items in the visualization and see more information about those items in the Details-on-Demand window. For example, on the second page with the bar chart comparing states' sales and expenditures – you could ask the recipients to select the radio button that filters down to the state they work in.

Tip: To more easily indicate which filter you want users to manipulate, right-click on the filter, select Copy, then open the text area, and Paste the image of the filter there along with your instructions.

The use of property controls in the text area may be a great help when creating analyses for other people. Just remember to add instructions regarding any constrictions for the control and inform about the purpose of the control using regular text.

Place links or buttons to relevant tools or views in the text areas

In some cases, it might be relevant for the recipients to perform an action; to filter out values, to go to another page, to apply a bookmark or to open a panel from the menu bar, etc. Instead of writing a long instruction, you can create a link or a button in the text area that performs this action when clicked. The instruction you do write in the text area can be simpler, like "When you have marked the items of interest, click here to filter to filter out all other items from the visualization." Links and buttons are a very powerful way to allow even casual users of TIBCO Spotfire to perform analysis of data in a fast and easy manner.

Links or buttons can also include bookmarks that show a specific view of the data, thus explaining the steps that have led you to a particular conclusion. Using custom properties to define the visualizations, and property controls that allow the end users to easily change the property values can further simplify the analysis procedure for many people.

Use step-by-step or history arrows page navigation

When you want to emphasize that the recipients of your analysis file should step through the pages in a certain order, you should change the page navigation from titled tabs to step-by-step navigation. This means that the pages will instead be shown as numeric links, together with a Previous and Next link, above the visualization area. The recipient of the analysis file will then start on the cover page, if you have selected to show it, or on the first real page. By clicking Next, the user will step through each page in order, performing the analysis described in the text areas along the way.

This can be very powerful since, by default, the filtering done on one page affects all other pages as well. You can therefore create a procedural flow wherein the first page allows the recipient to filter out unwanted data by looking at one visualization. Then he can proceed to the next page, where he continues to drill down into the data, filtering out more unwanted rows which he might see using another visualization, and so on.

You can also select Page Navigation > History Arrows and define your own navigation flow using actions in text areas or in graphical tables. This way, clicking on an item directly on the pages will be the only way to move forward within the analysis. However, the page history arrows located above the visualizations will always make it possible to return to a previously visited page.

Use customized filtering schemes

By default, the filter settings are the same for every page in the document, and they will stay the same until you start changing the filtering schemes. The filtering schemes give you complete control and complete freedom to decide which pages of your guided analysis should affect one another and which should not. You can keep the same filtering scheme for all pages, create a different one for each page, or assign the same filtering scheme to two or more pages.

Applying different filtering schemes can be useful if your guided analysis consists of several separate analyses originating from the same data table. When two pages use the same filtering scheme, the filtering performed on one page is propagated to the second page and vice versa. If they use different filtering schemes the filtering on each page only affects that very page.

For example, you can create one page where State is the only activated filter, permitting the recipients to click through and compare sales for the states without being bothered with any other filters. (If you want to, you can also use organize filters to hide all unused filters.) On the next page, you can apply a different filtering scheme relevant to another analysis of the sales data and so on.

Keep in mind the intended end users’ level of data access

Always make sure the end users of your guided analysis have access to the same data sources as you do. Permissions to analyses and information links are handled using the Library Administration tool. See also Preparing Analyses for the TIBCO Spotfire Web Clients. You may also consider adding prompt steps that could limit the available data for each end user. If prompts should be shown each time the analysis is loaded, then this should be specified in the Data Table Properties dialog before saving the analysis to the library.

Saving

Save Overview

When an analysis has been set up (or when you are in the process of creating an analysis), you need to determine how to save your document. If the analysis is intended to be used by a larger audience you probably want to save it as a library item. Once in the library your colleagues can easily access the analysis. When more than one person is going to use the analysis, you always need to make sure that any linked data sources are available to all end users. See Preparing Analyses for TIBCO Spotfire Web Clients for some useful tips.

If the analysis is to be used by a smaller number of people you could instead save it as a regular file.

If you are working with an analysis in the library and you make some changes that you do not want to save in the original file, you can use one of the Save Copy As alternatives instead.

Saving an Analysis File

You can save your analysis as a DXP file. All visualizations, filter settings and other work you have done will be saved in the file so you can continue working with the analysis just as you left off.

Depending on from where you opened the data in the analysis, you may encounter some different options when saving an analysis file. These options are described in the procedure below.

Details on Save
Saving an Analysis File in the Library

Using the Library, you and your colleagues can collaborate on the same analysis, keeping everyone up to date. When publishing your document, your current analysis is stored as a DXP file in the library. Files in the library can also be opened by your colleagues who run TIBCO Spotfire Web Player. If this is the intended use of the file, see the chapter Preparing Analyses for TIBCO Spotfire Web Clients to be aware of the limitations on the files available to Web Player users.

The Open from Library functionality is not available if you are working offline (without a connection to a server).

Embedded or Linked Data?

When you are saving an analysis you should decide how data will be saved. For in-memory data there are two different options available: Linked or Embedded. Which to choose depends on the data you use and who the end users of the analysis are, and also what data sources they have access to.

  • Use Linked data to always retrieve the latest data from the data source and keep the file size to a minimum.

  • Use Embedded data to include a snapshot of the data in the analysis file so that the analysis file is self-contained with data and possible to use offline. The snapshot can be updated manually, see below.

In-database data can never be embedded since it is always fetched directly from the database.

Reloading Data

When you save data embedded, the Reload/Refresh options can be used to reload data from the original data source. This means that the embedded data may not necessarily be fixed and unchanged during the lifetime of an analysis. If a reload is done, then the latest data from the original source is fetched into the analysis. Hence, the analysis will contain a snapshot of the data from the latest time of refresh and save. Simply opening an analysis with embedded data will not cause a reload of the data.

Preparing Analyses for TIBCO Spotfire Web Clients
Introduction

When an analysis file is saved to the library, it becomes available to use in Spotfire web clients. Web browser-based versions of Spotfire can be used for viewing and exploring prepared analyses, but also, in some cases, for creating and editing analyses.  

You can choose to copy the link to the analysis in the library from the final step of the Save to Library wizard and paste the link, in an email or similar, to share the information with colleagues and give access to the analysis.

Any analysis can be published to the library and opened with a Spotfire web client, but there are a few things to keep in mind that can make things easier for the web client users. First, consider how familiar your target audience is with the visualized data. The familiarity of the target audience may affect the instructions required as well as the setup of the analysis. For example, you may need to change the visibility of some filters for a certain audience. Second, bear in mind that all users of the analysis may not have access to the same data sources.

Tips when preparing analyses for TIBCO Spotfire web clients:
  1. Use text areas to write instructions about the analysis and how it is set up. For instance, if master-detail visualizations are used, try to explain their relationship.
  2. Use informative titles for the visualizations.
  3. The overview of data will be improved if you hide filters that are not relevant for the visualizations in the analysis. For an explanation of how to hide filters, see Showing and Hiding Filters.
  4. When using links or buttons in a text area, try to write instructions so that the information is helpful, even if the links should happen to become temporarily unavailable.
  5. Consider whether all users of the analysis have access to all of the original data sources. If not, you may need to save some data tables embedded in the analysis, rather than linked to the original data sources. See Permissions for information about how permissions are handled in the library. There may also be user authentication limitations directly on the data source itself. Talk to your Spotfire Administrator or data manager to understand how the permissions for different user groups have been set up within your company.
  6. It is not possible to view the 3D Scatter Plot on the web. Also note that some custom visualizations may not be shown. The analysis can still be opened in web clients, but the visualizations that are not supported will not be shown.
  7. Consider whether or not web client users should be allowed to export data from tables. Make the appropriate settings on the Appearance page of the Table Properties, Cross Table Properties, Summary Table Properties, and/or Details-on-Demand Properties.
  8. Consider whether or not web client users should be able to open a personalized view of the analysis and/or be allowed to add bookmarks. Open the Document Properties dialog to change these settings.
  9. Consider whether or not web client users with sufficient permissions should be allowed to edit the analysis. Open the Document Properties dialog to change these settings.
Design for the intended platform

If you know that your end users will view an analysis using a particular equipment or with a specific browser you can look up the current visualization area size on that device and then design your analysis so that it is optimized for that particular screen size. Note that the current visualization area size is dependent on what browser and which toolbars and items are shown in the browser, so make sure that you are using the same settings when creating the analysis.

Tips when designing for a smaller screen size:

  • Remove all things that do not  fit the small screen size.
  • Use Tools > Options > Fonts to globally resize the important font categories to fit the intended display.
  • Resize and move around the contents of the analysis.
  • Remove legends, axis selectors, etc., if they are not needed.
  • Use the popover mode rather than panels for filters, bookmarks, lists, etc..

If you want to set up an analysis that works both on a desktop computer with a large screen and also on a projector, you can use bookmarks to switch between the different sizes and layouts. The sizes for the projector and the large screen desktop computer can be stored by an administrator in the preferences.

To use bookmarks for adapting an analysis to different screen sizes:
  1. Set up an analysis that works well on your large screen with the intended fixed size preset. Configure all text areas to Include configuration in bookmark.
  2. When the document is finished, add a bookmark named something like "Large screen desktop computer".
  3. Switch to the fixed size preset for the projector, and make updates to make the analysis work on a projector:
    • Change the size of text areas and fonts.
    • Change font sizes for titles, axis labels, etc., using Tools > Options, Fonts page and Apply to Document.
    • Update the layout, remove unnecessary legends, etc.
  4. When the projector version of the analysis is finished, add a bookmark named "Projector".
  5. Save the analysis.

The analysis is now adapted both to a projector and to a large screen desktop computer.

Mobile clients (Spotfire Analytics for iPad)

There are also some additional features that you may take advantage of when designing analyses to be used on mobile clients, see below.

Barcode scanning

The existence of a String document property named AppMachineReadableCode indicates to the iPad app that the opened analysis is configured to work with machine readable codes. If the document property does not exist in the analysis there will be no "barcode"-icon available in the app.

The value of the AppMachineReadableCode document property is set by scanning bar codes. It is set to the empty string when no bar code is scanned.

The following machine readable code types are supported:

UPC-E

Code 39

Code 39 mod 43

EAN-13 (including UPC-A)

EAN-8

Code 93

Code 128

PDF417

QR

Aztec

Positioning

The existence of two Real document properties named AppGeoLatitude and AppGeoLongitude indicates to the iPad app that the opened analysis is configured to work with geo-positioning. If the document properties do not exist in the analysis there will be no "positioning"-icon available in the app.

The values of the document properties are set using the device location. They are both set to 0.0 when no location is activated.

Tip for usage of the device location properties:

If you have a data table with columns containing latitude and longitude you can use the Spotfire expression method GreatCircleDistance() to create a calculated column containing the distance between the device location and all locations in the data.

You multiply the resulting value by the radius of the planet; For kilometers use 6371, for miles use 3959.

For example, the expression below, gives the distance in km:

6371 * GreatCircleDistance(${AppGeoLatitude}, ${AppGeoLongitude}, [latitude_column_name], [longitude-column_name])

Links to Analyses in the Library

Once an analysis has been published to the library, you can reach it or share it using a number of different link types. A bookmark URL can be included to guide other people to an interesting aspect or starting point in an analysis. See the table below for an overview of the link types available.

Details on Save to Library
6 Subtopics

Export Image

Exporting an Image

You can export any visualization as an image and save it to disk.

Tip: You can also select Edit > Copy Special > Visualization Image... to copy the active visualization.

Export Data to File

Exporting Data to File

You can export data from TIBCO Spotfire and save as a text file, a TIBCO Spotfire Binary Data Format file (see below) or a Microsoft Excel file. The text file can be either a regular tab separated text file, or a Spotfire Text Data Format file. The Excel file can be either an XLS file or an XLSX file. Data from a visualization can only be exported to an Excel file if the visualization is a table.

Tip: You can use the Export Data from Visualization option when you want to export from in-db data tables. Note that for all visualizations other than the cross table you need to mark the items of interest before opening the Export Data dialog.

Details on Export Data to File

Tip: Use the Export data from Visualization option when you want to export from in-database data tables. See Working With In-Database Data for more information.

Note: For all visualizations other than tables, cross tables and summary tables you need to mark the items of interest to be able to export from the visualization.

The TIBCO Spotfire Binary Data Format stores the data and metadata from your analysis in binary form. It can be used to greatly increase the performance when working with linked data in TIBCO Spotfire.

The TIBCO Spotfire Text Data Format also includes metadata. For example, the data types of the columns are saved, so you do not need to specify any import settings when opening files of this data format in TIBCO Spotfire. If you select the TIBCO Spotfire Text Data Format (*.txt) you will be able to open the file in older versions of TIBCO Spotfire (before 3.1) and in TIBCO Spotfire DecisionSite, but you will not get all functionality of the Spotfire Text Data Format.

Export Data to Library

Exporting Data to Library

My Note: I should try this

You can export data from TIBCO Spotfire to the library and save as an SBDF file (TIBCO Spotfire Binary Data Format file). The TIBCO Spotfire Binary Data Format stores the data and metadata from your analysis in binary form.

2 Subtopics

Export to HTML

Exporting to HTML

My Note: I prefer to use screen captures

Export to HTML can be seen as an alternative to printing a paper report of your analysis. By exporting to HTML instead of printing, the report can be viewed in a web browser. The settings for export to HTML are similar to the print settings. For example, you can choose paper size and orientation. Exporting to HTML always results in one single HTML document. For example, if you export all the pages in an analysis, the result will be one single HTML document where the pages from the analysis are placed after each other vertically. To learn more about the various export options, see Details on Export to HTML.

1 Subtopic

Export to PowerPoint

Exporting to Microsoft PowerPoint

My Note: I prefer to use screen captures

In order to export to PowerPoint, you need to have Microsoft® PowerPoint® installed on your computer.

If you chose to export to a new presentation, PowerPoint is started and the selected visualizations are shown in a new presentation. If you chose to export to an open presentation, the selected visualizations are appended as new slides in an open presentation.

1 Subtopic

Export to PDF

Exporting to PDF

My Note: I prefer to use screen captures

You can export to PDF in three different ways. With regular export, you can export selected visualizations or pages as they currently appear in the analysis. With bookmarks export and filter export, selected bookmarks or filters are applied during the export. Regular export is described below, bookmarks export and filter export are described in Exporting Bookmarks to PDF and Exporting Filter Values to PDF, respectively. Note that you need to have a PDF reader installed on your computer in order to view an exported PDF document.

6 Subtopics

Printing

Printing

You can print a single visualization, an entire page, all pages in an analysis, or the Details-on-Demand. For more information about the various print options, see Details on Print Layout Options.

1 Subtopic

Appendix

Software License

Support

If you have any questions about a TIBCO Spotfire® product, please do the following:

Use the built-in help function or consult the relevant installation manuals.

Consult the README files and Release Notes which are included with the product.

Visit the Spotfire support website, http://support.spotfire.com/support.asp, where you can send questions to our support department.

On the same website, you can also get personal contact from TIBCO Spotfire Support.

TIBCO Spotfire offers a variety of support agreements. Contact TIBCO Spotfire for more information about support, training, consulting agreements, and our Product Subscription Programs.

Details on Support Diagnostics and Logging

This dialog contains information that may be useful in contact with TIBCO Spotfire customer support, as well as possibilities for specifying client side logging and error reporting.

Dump File

The dump file tab allows you to create a dump file which can be sent to TIBCO Spotfire support and be of use when investigating problems. The chances of getting your problems fixed greatly increases if you can attach a dump file to your support issue.

Click on the Create Dump File button to create a TIBCO Spotfire dump file. You will be prompted to specify the name and location of the file.

The dump file contains all information about your analysis, including the current data and all analysis steps performed so far.

Glossary

3D Scatter Plot

A visualization that represents quantitative information as a point along three axes. Used to explore relationships.

See: 3D Scatter Plot

Analysis File

The file type used for analysis files created with TIBCO Spotfire. Can be saved to disk and to the Library. Defines what data to include and how to present it. Can include linked or embedded data based on user settings when saving the file. Includes one or more pages. There can only be one analysis file open at a time, but it is possible to run several instances of TIBCO Spotfire simultaneously, and one analysis file can also contain several data tables. See also DXP File.

See: Opening an Analysis File and Saving an Analysis File and Saving an Analysis File in the Library

Axis

A line that provides a reference from which coordinates in a visualization are measured; it orients the visualizations and provides a frame around it. Each axis is associated with at least one scale.  

 

Axis Selector

A control used to change what will be shown on each axis in the visualization. The axis selector is what tells you which column is assigned to the axis. See also Column Selectors.

 

Bar

A vertical or horizontal rectangle whose height represents a numerical value.

 

Bar Chart

A visualization that represents quantitative information by means of a series of vertical or horizontal rectangles known as bars.

See: Bar Chart

Bar Labels

Words or numbers used to identify and describe a bar and data associated with it.

 

Bar Segment

A section of a bar that represents a subset of data that the whole bar is made up of.

 

Bar Segment Labels

Words or numbers used to identify and describe a bar segment and data associated with it.

 

Binning

A way to create a categorical column from a column with continuous data. Binning is used to create a new column where the values of a specified column are grouped into different bins using a binning method.

See: What is Binning?

Bookmark

A Bookmark is a snapshot of the state of an analysis, which can be applied at any time, allowing you to return to a previously created view of the data.

See: Bookmarks

Box Plot

A visualization used for examining key statistical properties of a variable. Measures, such as median, mean and quartiles are displayed using boxes, whiskers and other symbols.

See: Box Plot

Bullet Graph

A dynamic item that can be included in a graphical table or in a text area. It is used to compare one value, represented by a horizontal bar, to another value, represented by a vertical line. Those values can also be related to qualitative color ranges. 

See; Bullet Graph

Calculated Column

A column that has been created from different expressions where calculations on other columns could be included.

See: Insert Calculated Column

Calculated Value

A dynamic item that can be included in a graphical table or in a text area. It displays the result of an aggregated expression.

See: Calculated Value

Categorical Axis

An axis that is associated with a categorical scale.

 

Category Axis

The category axis is the horizontal axis in a bar chart where the bars are displayed vertically. When bars are displayed horizontally, the category axis is the vertical axis.

 

Categorical Scale

A scale on which the values have interruptions, such as values representing names, places, things or events.

 

Cell

A box or other unit on a spreadsheet or a table at the intersection of a column and a row.

 

Check Box Filter

A Filter used to filter out rows based on whether or not a column value is a member of a set of values.

See: Check Boxes

Collaboration Panel

A tool that can be used to view web pages in Spotfire. For instance, a web based collaboration tool such as tibbr®.

See: Collaboration Panel

Color Mode

The way colors in a color scheme are assigned to the values in the data. There are five color modes in Spotfire: Fixed, Categorical, Gradient, Segments, and Unique Values.

See; Color Modes

Color Palette

A group of default colors you could choose from when changing a color in a color scheme.

 

Color Scheme

A set of colors and a definition of how they are applied to values. The definition includes whether the scheme is categorical or continuous, and whether colors are simply applied by index or by specific values.

See: Color Schemes

Color Scheme Grouping

A color scheme grouping works as a container for one or more columns or axis values on which you want to apply the same color scheme when working with coloring in tables, cross tables, and heat maps.

See: Details on Add/Edit Color Scheme Grouping

Column

A vertical list of values in a data table.

See: Normalizing Columns

Column from Marked

Column from marked is a way to make a visualization axis dependent on the cell value of a marked row of a data table related to, or unrelated to, the data table used by the visualization.

See: What is Column from Marked?

Column Name

The name of a column as displayed in the user interface. It is a normalized, trimmed, and unique text string. It is initially set to a tidied form of the external name, but it may be modified through a Rename Column operation.

See: Transforming Data

(Column Names)

When more than one column is used to define the values on an axis in a visualization (for example, on the value axis in a bar chart), the option (Column Names) becomes available for selection on other axes, in the Color by column selector, in the trellising column selectors, etc. This option will treat the column names of the selected value columns as separate categories, so that if Sales and Cost are selected on the value axis, (Column Names) can be used to apply different colors on bars or bar segments showing Sales and Cost.

See: Non-Column Selections

Column Selector

A control used to change the column or hierarchy used to define a certain property (axis column, color, size, etc.) in a visualization. Column selectors can be dragged and dropped to change the order of dynamic hierarchies, or, to apply or remove columns from a specific property using drop targets in the visualizations.

See: Column Selectors

Combination Chart

A visualization that combines the features of the bar chart and the line chart. It represents quantitative information by means of a number of bars and/or lines.

See; Combination Chart

Comparison Circles

Circles in the box plot used to show whether or not the mean values for various categories are significantly different from each other.

See: What are Comparison Circles?

Continuous Axis

An axis that is associated with a continuous scale.

 

Continuous Scale

A scale on which there are no interruptions between values, for example if the values are real numbers.

See: Predefined Color Schemes

Cover Page

The cover page is the first page in an analysis. It normally contains information about the analysis and the person who created the analysis. If desired, a cover page can automatically be added when creating new analyses.

See: Cover Page

Cross Table

A cross table is a two-way table consisting of rows and columns. It is typically used to determine whether there is a relation between the row variable and the column variable. Each row and column can be summarized to a grand total. If the vertical axis is a hierarchy, subtotals can be displayed in the columns of the cross table.

See: Cross Table

Curve Fit

Curve fit or regression analysis allows you to summarize a collection of sample data points by fitting them to a model that will describe the data and display a curve or a line on top of a visualization. There are several different models available in Spotfire.

See: Curve Fit Models

Custom Expression

The column selectors can, in addition to specifying one or more columns to use on an axis or to define a property, also be set to use a custom expression. This means that a dynamic calculation can be performed using more or less complex expressions, involving one or more columns, aggregation measures, or mathematical expressions. When a custom expression is used on an axis, the values on that axis are dynamically recalculated using filtered rows only. To create a new column which is always based on the values of all rows, you should instead use the Insert Calculated Column tool.

See: Custom Expressions for Coloring

Data Relationships

The Data Relationships tool is used for investigating the relationships between different column pairs, using comparison methods such as Linear regression, Spearman R, Anova, Kruskal-Wallis or Chi-square.

See: Data Relationships

Data Source

A handle to a data source, such as a file or information link. A data source produces a single table of data.

See: What is a Connection Data Source?

Data Table

A data table in TIBCO Spotfire is defined as either data loaded from a data source, or new data created within the application. Data loaded from a data source can be handled either in-memory or in-database depending on how it is added to the analysis. In-memory data tables have one or more columns and zero or more rows, whereas in-database data tables technically do not contain any data but simply fetch the requested data directly from the source. A visualization can be based on one or more data tables.

In-memory data tables can be linked or embedded. Linked data tables can be loaded completely into the application, but if the source is an information link or a data connection they can also be configured to load data on demand only.

Data tables can be related to each other, using primary and/or foreign keys (key columns), but they can also be unrelated.

See: Add Data Table

Dendrogram

A dendrogram is a branching diagram which represents a hierarchy of categories based on the degree of similarity or number of shared characteristics.

See: Dendrograms

Details-on-Demand

The concept of expanding a small set of items to reveal more data behind it.  

See: Details-on-Demand

Details Visualization

A visualization where the data is limited by one or more markings.

See: Details Visualizations

Drop Targets

Specifically, icons in the middle of a visualization onto which filters or column selectors can be dropped in order to define a specific property.

See: Drag-and-Drop

DXP File

DXP is the file extension for an Analysis File. See Analysis File.

See: Opening an Analysis File

Dynamic Items

Small visualizations that can be included in a graphical table or in a text area. For example, sparklines, calculated values and icons.

See: History arrows

Empty Values

Empty values, or null values, are values that are missing in your data table.

See: Details on Line Similarity

Error Bars

Used in bar charts, line charts and/or scatter plots to indicate the estimated error in a measurement.

See: Error Bars

Escape characters

Since certain characters have a special meaning in the Spotfire expression language, you need to perform some actions if you are going to use those characters for other purposes, such as including them in column names.

An escape character is a special character used to inform the expression language that the following character in a character sequence should be seen as a standard character rather than as an item performing its special purpose.

 

External Column ID

A GUID string, retrieved from an information link. May or may not be present, and may or may not be well-formed.

 

External Column Name

The original column name, exactly as it came from the data source. It is a non-null, but possibly empty text string. It is not trimmed or normalized, and duplicates may exist among the columns of a data view.

 

Filter

Used to reduce the amount of data to work on in TIBCO Spotfire. The same as Query Devices in TIBCO Spotfire DecisionSite. Filters can be either column filters, directly related to a column, or hierarchy filters (tree filters) which represent a hierarchy. Filters can be grouped into folders in the Filters panel.

See: Filters

Filtering Scheme

A filtering scheme is a data selection that points out what data shall be filtered (visible). Each analysis can hold several filtering schemes. What filtering scheme to use can be specified separately for each page and/or each visualization. Each filtering scheme can be used by several pages and visualizations.

See: Filtering Schemes

Filtered Out Rows

The rows that have been removed after one or more filtering operations.

See: Exclude Filtered Out Rows

Filtered Rows

The rows that remain after one or more filtering operations.

See: Include Filtered Rows Only

Filters Panel

The area where filters are shown. Adjusting the filters modifies the filtering that is used by the page.

The filters panel visibility is set per page, just like the visibility of the individual filters. When the filtering scheme is changed for the page, the filtering showed by the filters and visualizations are changed, but the visibility of the filters panel and the filters therein are kept.

See: Filters Panel

Find

A tool in TIBCO Spotfire that lets you search for data content, perform menu actions, and much more, by entering words or parts of words in a text field.

See: Find

Formatting

Describes how different values in a data set should be presented, for example as text or currency, or how many decimals should be shown.

See:  Formatting

Graphical Table

A summarizing table visualization designed to provide a lot of information at one glance. It can display dynamic items such as sparklines, calculated values and icons.

See: Graphical Table

Gridlines

Lines that form a grid inside a visualization to serve as a reference for the viewer.

 

GUID

A unique identifier for an information link that remains the same if the name of the information link is changed.

 

Heat Map

A visualization that presents data in the form of a table which contains colors instead of numbers. It can be used to identify clusters of similar values, as these are displayed as "areas" of similar color.

See: Heat Map

Hierarchical Clustering

Hierarchical clustering arranges objects in a hierarchy with a treelike structure based on the similarity between them.

See: Hierarchical Clustering

Hierarchy

A hierarchy is defined as A) a set of ordered columns where the order defines the hierarchy, B) a timestamp column where it is possible to derive a natural hierarchy or C) an external hierarchical structure where every node maps to one or more rows in the data table through an identifier column.

See: Hierarchies

Hierarchy Filter

A filter based on a predefined hierarchy. The same thing as a tree filter.

See: Hierarchy Filter

Horizontal Bars

Bars displayed horizontally in a bar chart. You can change the orientation of the bars by right-clicking in the visualization and selecting Horizontal Bars or Vertical Bars.

See: Error Bars

Hyperlink

A link to that will launch your default web browser when clicked on.

 

Icon

A dynamic item that can be included in a graphical table or in a text area. Icons are defined using rules.

See: Icons

Information Link

Information links are predefined database queries, specifying the columns to be loaded, and any filters needed to reduce the size of the data table prior to visualization.

See: Opening an Information Link

Item Filter

A filter used to filter out rows based on whether or not a column value is equal to a specified value. The behavior is similar to a radio button filter, but the appearance is different, and it is more suited to selecting a value from a large number of unique values.

See: Item Filter

Jittering

An option that displaces the visualization items randomly in the display window, thereby making overlapping markers visible. Attention can be brought to areas where many markers overlap. Such regions can then be investigated further, by zooming, changing axes, etc.

 

K-means Clustering

A tool that helps you group rows into a defined number of clusters based on their similarity. A line chart is needed in order to use the tool.

See: K-means Clustering

Label

In a visualization, labels come in three varieties: Marker labels, showing the value of the marker. Scale labels, showing the scale of an axis. Line & Curve labels, showing the name and description of a line or curve.


Legend

Information that helps the viewer identify what the graphics in the visualization represent.

See: Legend

Library

The Library is a space on the server where you can publish or open shared analysis files.

See: Open From Library

Line By

In a line chart, the line itself can visualize a column or a hierarchy. This is set up in the properties of the visualization.

See: What is a Line Chart?

Line Connection

In a scatter plot or a map chart, markers may be connected with a line to show trend or other relation. This is set up in the properties of the visualization.

 

Line Chart

A visualization that represents quantitative information by means of one or more lines.

See: Line Chart

 

Line Labels

Words or numbers used to identify and describe a line and the data associated with it.

 

Line Similarity

A tool where you can compare the similarity of different lines in a line chart against each other.

See: Line Similarity

Lines & Curves

To connect or otherwise show relationship between Markers, Lines and Curves may be drawn on top of the visualization according to a number of models and functions.

 

List Box Filter

A Filter used to filter out rows based on which rows you mark in a list of all rows in a certain column.

See: List Box Filter

Lists

Lists consist of sets of rows in your data representing captured knowledge from one or many data sources. You work with the same list collection from one session to the next.

See: Lists

Map Chart

A map chart is used to organize information visually in relation to an image or a shape file map.

See: Map Chart

Marked Row

An item in a visualization becomes marked when you click on it, or, when it is captured using the rectangle method (left mouse button pressed while moving pointer). Marked rows are given a definable color to distinguish them from the rest of the data.

See: Marking in Visualizations

Marking

A marking identifies marked rows in the data tables of an analysis. If the data tables are related, the marked rows are propagated using the specified key relation between the data tables. Setting a marking in one data table does not affect the marking of unrelated data tables.

Each analysis can hold multiple markings and each marking has its own marking color. One or more markings can be used to limit what data are displayed in a visualization.

See: Marking in Visualizations

Marker

A graphical object that represents a category.

See: Marker Layers

Marker Labels

Words or numbers used to identify and describe a marker and the data associated with it.

 

Page

A page can be thought of as a "container" for visualizations, filters, a Details-on-Demand, etc. Pages make it possible to set up several sheets of visualizations that you can switch between in an analysis. Pages can contain visualizations and text areas that guide you through the analysis. Visualizations can only exist inside a page (they cannot be dragged outside even partly). 

All visualizations in an analysis can be linked, both within and between pages, but they do not have to be. The visualizations on a page use one or more filtering schemes, and the filtering schemes determine whether visualizations are linked or not. The visualizations in a page can use one or several data tables.

See: Pages

Parallel Coordinate Plot

A parallel coordinate plot is used to compare the values within a multitude of columns for a number of rows in the analysis.

See: Parallel Coordinate Plot

Parameterized Information Link

The data an information link returns on different occasions or by different users may be parameterized, which means it depends upon for instance user input or other factors. A Personalized Information Link is a special case of this, where the identity of the user is used as a parameter in deciding which data to be returned.

See: Loading Data Overview

Personalized Information Link

A personalized information link returns a subset of data depending on the identity of the user.

See: Loading Data Overview

Pie

A circular graphic divided into sectors used to show the relative values of entities compared to each other and to the whole.  

See: What is a Pie Chart?

Pie Chart

A visualization composed of one or more pies.

See: Pie Chart

Pie Labels

Words or numbers used to describe a pie.

 

Pie Sector

A part of a pie that represents a subset of data of which the whole pie is made up.

See: What is a Pie Chart?

Pie Sector Labels

Words or numbers used to describe a pie sector.

 

Pivot

A transformation used to transform data from a tall/skinny format to a short/wide format.

See: Pivoting Data

Primary Key

This is a set of one or more columns whose values uniquely identify every data row. By saving value tuples for the primary key, the application can support persistent masks and annotations for linked data tables. The primary key is a setting on the root view.

See: What is a Table?

Properties

Properties can be compared to variables. All changeable settings in visualizations, data tables or documents are properties in some sense, but you can also create your own properties and use them to control the configuration of visualizations, calculations, or on-demand data loading. You can create property controls in a text area to simplify the process of changing a property value. Using Properties in the Analysis

See: Properties

Radio Button Filter

A filter used to filter out rows based on whether or not a column value is equal to a specified value. The behavior is similar to an item filter, but the appearance is different, and it is more suited to selecting a value from a small number of values.

See: Radio Buttons

Range Filter

A filter used to filter out rows based on whether or not the values in a column fall between a certain lower and upper value.

See: Range Filter

Range Filter Data Range

The data range that the filter is operating on.

 

Range Filter Lower Value

All values below this limit are excluded from the filtered rows by the range filter.

 

Range Filter Upper Value

All values above this limit are excluded from the filtered rows by the range filter.

 

Renderer

In a Table Visualization, how values are presented in cells depends on which renderer is used.

 

Root View

The default view of a data table, as it is first opened, with no modifications such as binned columns or filtered out data.

See: Details on K-means Clustering

Row

A horizontal list of values in a data table.

See: Insert Rows

Scale

A line with tick marks and labels used as a reference along an axis in a visualization.

See: What is a Box Plot?

Scale Labels

Words or numbers along a scale.

 

Scatter Plot

A visualization that represents quantitative information as a point along two axes. Used to explore relationships.

See: Scatter Plot

Series By

In a combination chart, it is possible to divide the data into slices, called series. Each series will be represented by a line or a set of bars in the visualization.

See: Series

Share

A tool that can be used to quickly share parts of an analysis, such as bookmarks, visualizations, and pages, with others. The Share tool is reached from the right-click menu.

See: Share

Short Number Format

The method of writing multiples of 10 using non-numerical characters, such as k for 1,000 and M for 1,000,000.

See: Short Number Format

Short Number Symbol

The non-numerical character used instead of a multiple of 10, such as k or M.

 

Sparkline

A dynamic item that can be included in a graphical table or in a text area. It is a small simple line graph used for displaying trends or variations of some variable.

See: Sparklines

Spotfire Server

TIBCO Spotfire Server is the server that a user of TIBCO Spotfire logs into and is able to save data to.

See: Login Dialog

Spotfire Text Data Format

A well-defined text data format that does not require type guessing when read by TIBCO Spotfire or TIBCO Spotfire DecisionSite.

See: Opening an Analysis File

Stacked Bar

A set of vertical rectangles (bars) stacked on top of each other to represent a numerical value and how different components contributed to that numerical value.

 

Summary Table

A visualization that summarizes statistical information about data in table form.

See: Summary Table

Symbol Set

A collection of Short Number Symbols.

See: Short Number Format

Table

A visualization with information arranged in rows and columns.

See: Working with Data Tables

Table Cell

The intersection of a table row and a table column, where values are located.

 

Table Column

A vertical list of cells in a table.

See: What is the Data Relationships Tool?

Table Column Header

The title of a table column.

 

Table Row

A horizontal list of cells in a table.

 

Table Row Header

The title of a table row.

 

Tags Panel

The area where tags are being defined and handled.

See: Tags

Tags

Tags are annotations which can be added to different sets of marked rows and included in an annotation column.

See: Tags

Text Area

A text area can contain information about a visualization, instructions on how to perform the analysis, or links which are shortcuts to specific tools in TIBCO Spotfire. In regards to the layout of a page, a text area is treated the same way as a visualization.

See: Text Area

Tick Marks

Short lines drawn perpendicular to a scale and used to mark off uniform increments along that scale.

 

Time Scale

A scale consisting of units of time organized in a sequence so that intervals of equal physical size represent equal increments of time.

 

Tooltip

In addition to the ordinary tooltips that give information about buttons and controls in the user interface, TIBCO Spotfire also contains configurable tooltips displaying detailed information about the smallest items in a visualization. For example, when you hover with the mouse pointer over a bar segment in a bar chart, the tooltip will by default show the exact category axis and value axis values, as well as information about the coloring, if any split by color has been applied.

See: Details on Add/Edit Tooltip Value

Tree Filter (Hierarchy Filter)

A filter based on a predefined hierarchy.

See: Hierarchy Filter

Treemap

A visualization that displays hierarchically structured data using nested rectangles.

See: Treemap

Trellis

A Trellis is a split view of a visualization, organized by category in separate panels.

See: Trellis Visualizations

Unpivot

A transformation used to transform data from a short/wide format to a tall/skinny format.

See: Unpivoting Data

URL

A world wide web address.

See: Details on Configure Collaboration Panel

Value Axis

The value axis is the vertical axis in a bar chart where the bars are displayed vertically. When bars are displayed horizontally, the value axis is the horizontal axis.

See: (Column Names)

Value Columns

The columns used to calculate a measure based on the measure method, the dimension and the dimension aggregation level.

See: (Column Names)

Vertical Bars

Bars displayed vertically in a bar chart. You can change the orientation of the bars by right-clicking in the visualization and selecting Horizontal Bars or Vertical Bars.

 

Virtual Column

A column that is added to a table visualization by loading data from a remote source, such as a database.

 

Visualization

A representation of some data in TIBCO Spotfire. For example, a table, a bar chart, a pie chart, etc. A visualization displays data from one data table. The data displayed can be limited by one or more filtering schemes and by zero, one or several markings. A visualization shows and allows modification to one marking.

See: Visualizations

Visualization Item

The smallest building block of a visualization. For example, a pie sector in a pie chart, a line in a line chart or a cell in a table.

 

Visualization Title

Words or text used to identify a visualization.

See: Visualization Properties in Expressions

Web Player

TIBCO Spotfire Web Player is the web client that can be used to view Spotfire data. You can export data for view in Web Player from Spotfire.

See: Web Player

X-Axis

The horizontal axis in most 2D visualizations or the first axis in a 3D visualization.

See: What is a Line Chart?

Y-Axis

The vertical axis in most 2D visualizations or the second axis in a 3D visualization.

See: What is a Box Plot?

Z-Axis

The third axis in a 3D visualization.

See: 3D Scatter Plot Properties

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