Data Visualization Work Flow in Tableau
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The document defines data visualization and describes the basic workflow for creating data visualizations using Tableau. It discusses acquiring, parsing, filtering, analyzing, and representing data. It also covers developing views and dashboards in Tableau, sharing results, and Schneiderman's mantra of providing an overview, zooming and filtering, and details on demand for effective data visualization.
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Data Visualization Work Flow in Tableau
- 1. Data Visualization Wil O’Shea, Guest MEDA 319, Information Design 2015/03/16
- 2. Goals • Define data visualization and variations • Identify basic data types and how to present them • Describe a basic work flow for data visualization • Use Tableau to open and explore a novel data set 3/16/2015 MEDA 319 2
- 3. Data Visualization 3/16/2015 MEDA 319 3
- 4. What is data visualization? • A general term • Information visualization • “the use of computer supported, interactive, visual representations of abstract data to amplify cognition” • Scientific visualization • Visual representation of generally physical data (e.g., MRI, x- ray) • Infographic • Manually drawn • Present a beautiful and engaging story about complex information quickly and clearly using words and graphics to reveal information, patterns or trends more easily than with words alone Card, Mackinlay, & Schneiderman (1999) from Few (2009) http://visual.ly/what-is-an-infographic 3/16/2015 MEDA 319 4
- 9. Why visualize data? • “Statistics can reduce large, complex data sets to a few numbers, but the reductive approach can also sheer away much of the richness and subtlety in data.” Few (2009) http://visual.ly/what-is-an-infographic 3/16/2015 MEDA 319 9
- 12. How to visualization data • Acquire • Parse • Filter • Mine • Represent • Refine • Interact Fry (2008) http://guides.library.duke.edu/datavis/ 3/16/2015 MEDA 319 12
- 13. Visualization work flow: Acquire • Obtain the data • Spreadsheets • Databases • Websites • Scraping tools • Data entry • Data exhaust Fry (2008) https://import.io/ 3/16/2015 MEDA 319 13
- 14. Visualization work flow: Parse • Structured and formatted for use • Parsing data formats • String – characters such as a word or sentence • Float – number with decimal • Character – single letter or symbol • Integer – number with no decimal • Index – a type of id that can relate data across tables Fry (2008) 3/16/2015 MEDA 319 14
- 15. Visualization work flow: Filter • Narrow data to elements of interest • Reduce unneeded variables • Reduce unneeded observations • Be aware not to introduce bias or loose context Fry (2008) 3/16/2015 MEDA 319 15
- 16. Visualization work flow: Mine • Analyze data for statistical properties • Central tendency • Mean • Median • Mode • Variation • Range • Frequency Fry (2008) 3/16/2015 MEDA 319 16
- 17. Visualization work flow: Represent • Determining the form of the data (key) • Map • Bar graph • Scatterplot • Histogram Fry (2008) 3/16/2015 MEDA 319 17
- 18. Visualization work flow: Refine • Iterate to clarify the representation • Use graphic design methods to further clarify the representation • Call out attributes • Increase readability Fry (2008) 3/16/2015 MEDA 319 18
- 19. Visualization work flow: Interact • Enable users to “control or explore” the data • User controlled filters • Drill down • Mouse over • Painting Fry (2008) 3/16/2015 MEDA 319 19
- 20. Data 3/16/2015 MEDA 319 20
- 21. Types of data • Categorical • Nominal • Can be string or number • Quantitative or numeric • Interval/ratio • Continuous or discrete • Ordinal • Rank • Order • Time • Date / time • Location • Geographic data • Encoded as other type • Relationship • Grouping • Hierarchy http://bit.ly/successfulvis 3/16/2015 MEDA 319 21
- 22. How to visualize the data 3/16/2015 MEDA 319 22
- 23. Tableau 3/16/2015 MEDA 319 23
- 24. Tableau Workspace Data Window View Cards View Sheet Tabs Workspace Controls Column and Row Shelves 3/16/2015 MEDA 319 24
- 25. Connecting to data: Source Types • Desktop Personal & Professional • Excel • CSV • Access • Windows Azure/OData • Tableau Extract • Desktop Professional • Databases (e.g., SQL Server, Oracle) • Cloud (e.g., Google Analytics) • Online * • Tableau Extract • Google Big Query 3/16/2015 MEDA 319 25
- 26. Connecting to data: Methods • Excel Connection * • Menu • Drag and drop • Cut and paste • CSV Connection * • Select table • Single Table • Multiple Table • Custom SQL • Extracts • Faster • Enables unique counts 3/16/2015 MEDA 319 26
- 27. Working with data • Data Connections • SQL • Calculations • Merge tables • Transpose • Data Manipulations • Groups • Calculations • Hierarchies • Edit aliases 3/16/2015 MEDA 319 27
- 28. Developing views • Drag and drop • Show Me • Annotation • Tooltips • Formatting • Maps
- 29. Developing dashboards • Layouts • Annotation • Filters • Actions
- 30. Ways to share results • Copy image • Print to PDF • Formatting • Tableau Reader • Careful of data • Server • Online • Public 3/16/2015 MEDA 319 30
- 31. Interactive dashboard 3/16/2015 MEDA 319 7
- 32. Schneiderman Mantra There are many visual design guidelines but the basic principle might be: summarized as the Visual Information Seeking Mantra: Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Overview first, zoom and filter, then details-on-demand Each line represents one project in which I found myself rediscovering this principle and therefore wrote it down it as a reminder. Schneiderman (1996)
- 33. Schneiderman Mantra • Overview: Gain an overview of the entire collection. • Zoom: Zoom in on items of interest • Filter: filter out uninteresting items. • Details-on-demand: Select an item or group and get details when needed. • Relate: View relationships among items. • History: Keep a history of actions to support undo, replay, and progressive refinement. • Extract: Allow extraction of sub-collections and of the query parameters. Schneiderman (1996)