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![Character Co-occurrences
{!
nodes: [!
'valjean',!
'fantine',!
'cosette',!
...!
],!
edges: [!
{character1: 'valjean', character2: 'fantine', 10},!
{character1: 'valjean', character2: 'cosette', 5},!
...!
]!
}!
DATA](https://image.slidesharecdn.com/datatovis2-140409130521-phpapp02/75/From-Data-to-Visualization-what-happens-in-between-44-2048.jpg)
Uploaded byKrist Wongsuphasawat
From Data to Visualization, what happens in between?
Krist Wongsuphasawat discusses the importance and techniques of data visualization, emphasizing the power of visual representations in uncovering insights and telling stories with data. The document explains different types of data visualizations, approaches to selecting appropriate tools based on data types and desired outcomes, and outlines an ideal workflow for creating visualizations. It highlights the significance of understanding the data first before choosing visualization methods.
In this document
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Introduction by Krist Wongsuphasawat, Senior Data Visualization Scientist at Twitter.
Overview of Twitter's analytics tools for internal and external visualizations; includes dashboarding and exploratory tools.
Link to Twitter's interactive visualizations website.
Introduction to various examples of data visualizations.
Explains what visualizations are, focusing on their aesthetic and meaningful representation of data.Illustration of Anscombe’s Quartet to show identical statistics and different data distributions visually.
Examples include Napoleon’s March and the London Cholera Outbreak to illustrate data through visualization.
Benefits and usage of visualizations: quick understanding, revealing hidden facts, storytelling, and exploratory analysis.
Terminologies: Information Visualization (InfoVis), Data Visualization (DataVis), and a mention of infographics.
Questions to determine how to start visualizing data, focusing on tools and data types, including dimensions.
Examples of multi-dimensional data visualizations, using flower species data as a reference.
Different examples of visualization techniques such as scatterplots, scatterplot matrices, and trees (from geographical to stock market data).
Explains network visualizations and provides a character co-occurrences example showing nodes and edges.
Multi-dimensional and temporal combinations, including visual encodings, tooltips, animations, and other types.
Criteria to choose visualization tools based on data type and goals, outlining an ideal workflow for visualization.
Describes real-life challenges faced in data visualization workflows: data quality and transformation issues.
Promotion of interactive visualizations through the linked Twitter resource.
Conclusion on the importance of focusing on data first when creating visualization methodologies.
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From Data to Visualization, what happens in between?
- 1. VISUALIZATION Krist Wongsuphasawat (@kristw) FROMDATA TO Senior Data Visualization Scientist, Twitter
- 2. Twitter Analytics /Visual Insights Internal Dashboarding system Exploratory data visualization tools ! External Public facing visualizations #interactive
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- 6. pretty graphics POWER OFTHE EYES
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- 8. Anscombe’s Quartet X Y 10.08.04 8.0 6.95 13.0 7.58 9.0 8.81 11.0 8.33 14.0 9.96 6.0 7.24 4.0 4.26 12.0 10.84 7.0 4.82 5.0 5.68 X Y 10.0 9.14 8.0 8.14 13.0 8.74 9.0 8.77 11.0 9.26 14.0 8.10 6.0 6.13 4.0 3.10 12.0 9.13 7.0 7.26 5.0 4.74 X Y 10.0 7.46 8.0 6.77 13.0 12.74 9.0 7.11 11.0 7.81 14.0 8.84 6.0 6.08 4.0 5.39 12.0 8.15 7.0 6.42 5.0 5.73 X Y 8.0 6.58 8.0 5.76 8.0 7.71 8.0 8.84 8.0 8.47 8.0 7.04 8.0 5.25 19.0 12.50 8.0 5.56 8.0 7.91 8.0 6.89 #1 #2 #3 #4
- 9. Anscombe’s Quartet Property Value Meanof X 11.0 Variance of X 10.0 Mean of Y 7.5 Variance of Y 3.75 Correlation between X and Y 0.816 Linear regression y = 3.0 +0.5x #1 #2 #3 #4 Identical statistics!
- 10. Anscombe’s Quartet #1 #2#3 #4 0! 2! 4! 6! 8! 10! 12! 0! 5! 10! 15! 0! 1! 2! 3! 4! 5! 6! 7! 8! 9! 10! 0! 5! 10! 15! 0! 2! 4! 6! 8! 10! 12! 14! 0! 5! 10! 15! 0! 2! 4! 6! 8! 10! 12! 14! 0! 10! 20! but very different
- 11. Napoleon’s March geography time course (attack/retreat) quantityof troops temperature direction
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- 14. Visualization • Power • Understanddata quickly • Discover hidden facts • Usage • Storytelling / Reporting • Exploratory data analysis
- 15. “Visualization” • Information Visualization(academia) • InfoVis • Data Visualization (commonly used) • DataVis ! • infographics (...)
- 16. How to start? •What tool should I use? ! ! DATA
- 17. How to start? •What tool should I use? ! ! ! 1. What type of data do I have? DATA
- 18. DATA 1) What typeof data?
- 19. DATA 1) What typeof data? vis7 vis5 vis3 vis2 vis1 vis6 vis4 Many options... Which visualization technique should I use?
- 20. 1) What typeof data? • Visualizations are categorized by data types: • 2,3- dimensional • Multi-dimensional • Temporal • Tree • Network • etc.
- 21. Let’s take atour.
- 22. 2D, 3D data (realworld objects) ! a.k.a. Scientific Visualization (SciVis)
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- 25. Multi-dimensional data abstract dimensions (+real world dimensions)
- 26. Flowers species sepalLength sepalWidthpetalLength petalWidth setosa 5.1 3.5 1.4 0.2 setosa 4.9 3.0 1.4 0.2 setosa 4.7 3.2 1.3 0.2 virginica 4.6 3.1 1.5 0.2 virginica 5.0 3.6 1.4 0.2 virginica 5.4 3.9 1.7 0.4 DATA
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- 29. Cars Name economy (mpg) cylinders power (hp) weight (lb) 0-60 mph (s) Ford Mustang18 6 88 3139 14.5 Honda Accord 31.5 4 68 2045 18.5 Honda Civic 24 4 97 2489 15 Mazda RX-7 23.7 3 100 2420 12.5 DATA
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- 31. The Geography ofTweets @miguelrios
- 32. The Geography ofTweets @miguelrios tweet counts latitude longitude 20,000 27.174526 78.042153 9,000 49.124093 52.201304 1,000 12.2995 31.59592 ... ... ... DATA abstract dimension real world dimensions
- 33. Temporal Data value changesover time events
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- 39. Stock Market Financial All stocks HealthcareTechnology ... Apple Google Canon ... DATA
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- 44. Character Co-occurrences {! nodes: [! 'valjean',! 'fantine',! 'cosette',! ...! ],! edges:[! {character1: 'valjean', character2: 'fantine', 10},! {character1: 'valjean', character2: 'cosette', 5},! ...! ]! }! DATA
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- 47. Combination Multi-D + Temporal Multi-D+ Tree Multi-D + Network Temporal + Tree Temporal + Network ...
- 48. Life Expectancy (Multi-D +Temporal) http://www.gapminder.org/videos/the-river-of-myths/
- 49. VISUALIZATION visual encodings +interactions tooltips animation highlight filter etc. bar chart line chart matrix node-link treemaps etc. or multiple views (data type)
- 50. DATA 1) What typeof data? vis7 vis5 vis3 vis2 vis1 vis6 vis4 Many options... Which visualization technique should I use?
- 51. DATA 1) What typeof data? vis7 vis3 vis4 Less options... Still, which one should I use?
- 52. How to start? •What tool should I use? ! ! ! 1. What type of data do I have? 2. What do I want from the data? DATA
- 53. 2) What doI want from the data? • Many ways to visualize one type of data. • Things to consider: • audience (data scientist, execs, etc.) • goal (storytelling, exploratory analysis) • tasks
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- 56. Photogrid (Treemap +photo) http://twitter.github.io/interactive/sochi
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- 58. State of theUnion http://twitter.github.io/interactive/sotu2014/#p1
- 59. Ok, now tools. 1.What type of data do I have? 2. What do I want from the data?
- 60. Tools Option 1: Programminglibrary Option 2: Packaged software You have to write code. (Mostly) no coding involved
- 61. Programming libraries • d3.js,processing, R, etc. ! • Copy and modify from examples. • Can do custom stuffs (if you can figure out how) • More overhead for common task
- 62. Packaged software • Tableau(multi-dimensional) • Gephi (graph) • NodeXL (graph) • Research projects (contact authors) ! • Just use the software. No hassle of code/debug • Limited functionalities to what the tools can do • Custom designs more difficult
- 63. Ideal workflow 1. Whattype of data do I have? 2. What do I want from the data? 3. Pick appropriate techniques/tools 4. Done!
- 64. Ideal workflow 1. Whattype of data do I have? 2. What do I want from the data? 3. Pick appropriate techniques/tools 4. Done! Not that easy!
- 65. Real-life workflow data aredirty unsatisfied transform What type of data do I have? Pre-process data What do I want from the data? Pick appropriate techniques/tools See results change goal change perspective
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- 75. VISUALIZATION FROM DATA TO @kristw DATAfirst, not tools.
- 76. VISUALIZATION FROM DATA TO @kristw DATAfirst, not tools. visual encodings (by data types) + interactionschoose:
- 77. VISUALIZATION FROM DATA TO visualencodings (by data types) + interactions DATA first, not tools. @kristw choose: twitter.github.io/interactive
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