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Data_viz_principles (1).pptx
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- 3. Principles of GraphicalExcellence • Clarity • Precision • Efficiency • Maximize ideas, minimize ink (from Tufte)
- 4. Data Viz ina Nutshell 1. History 2. Graphical Basics 3. Details to Make Your Viz More Understandable
- 5. Data Viz inthe 10th Century From Funkhouser 1936
- 7. Maps as RichInformation Source
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- 10. Sometimes One Variableis Enough
- 11. Adding Space toTime
- 12. Unifying Maps andTimeseries
- 14. Abstraction Can TellWonderful Stories
- 15. Data Viz ina Nutshell 1. History 2. Graphical Basics 3. Details to Make Your Viz More Understandable
- 16. Elements of aPlot Wickham 2010 Geometric Objects Scales & Coordinates Annotations
- 17. Elements of aPlot Wickham 2010
- 18. Aesthetics of aPlot Wilke 2018
- 19. Aesthetics Map Datato Visual Representation Wilke 2018
- 20. Aesthetics Map Datato Visual Representation Wilke 2018
- 21. Aesthetics Map Datato Visual Representation Wilke 2018
- 22. Types of Visualizations:Amounts Wilke 2018
- 23. Types of Visualizations:Amounts Wilke 2018
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- 25. What is WrongHere? Wilke 2018
- 26. What is WrongHere? Wilke 2018
- 27. Bars are NotAlways Right Wilke 2018
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- 30. Types of Visualizations:Multiple Distributions Wilke 2018
- 31. Boxplots to ShowVariation
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- 33. Visualizing Uncertainty isDifficult Wilke 2018
- 34. What is WrongHere if Bars Represent 2SD of Temperature? Wilke 2018
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- 36. How Does thisView Differently? Wilke 2018
- 37. It’s Tempting toPlot Raw Data, but, Be Careful… Wilke 2018
- 38. Lots of Waysto Visualize Data, Distribution, and Uncertainty https://dallasnova.rbind.io/post/efficient-data-visualization-with-faded-raincloud-plots-delete-boxplot/ From rweekly.org 9/5 edition https://rweekly.org/2022-W36.html
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- 40. Relationships Between Variables Witha Lot of Data Wilke 2018
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- 43. Alternate Ways toView Correlations Efficiently https://albert-rapp.de/posts/ggplot2- tips/13_alternative_corrplots/13_alternative_corrplots.html
- 44. Should Your DataBe Connected? Wilke 2018
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- 46. Combining Data Sourcesand Maps • Chlorophyll a • Abundance of taxonomic groups • Temperature • Salinity Hopkinson and Hobbie 2012 http://ecosystems.mbl.edu/PIE/data/EST/EST-PR-ChemTax.html
- 47. Visualizing Uncertainty in Relationshipsis Difficult Wilke 2018
- 48. Data Viz ina Nutshell 1. History 2. Graphical Basics 3. Details to Make Your Viz More Understandable
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- 50. The Polar CoordinateSystem is Useful! Wilke 2018
- 51. The Polar CoordinateSystem is Useful! Wilke 2018
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- 55. Scales to AddDimensions of Data
- 56. Colors Can DistinguishGroups Wilke 2018
- 57. Colors Can ShowData Wilke 2018
- 58. Beware Not ThinkingAbout Color Blindness Wilke 2018 Never use Red-Green! Use redundant coding: shapes, sizes, etc.
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- 61. A Touch ofColor!
- 62. Color Can Bringin Another Dimension
- 63. Statistical Fit toAid Understanding
- 64. Facets to AddFine-Grained Information or New Dimensions
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- 66. Tufte 2002 Finally: MinimalistPresentation 1. Above all else show data. 2. Maximize the data-ink ratio 3. Erase non-data-ink. 4. Erase redundant data-ink.
Editor's Notes
- #2 Reorganize – gets very choppy
- #6 10th century What do you see here. What works. What does not?
- #7 Early maps. Little information beyond intersections – 1546 from Pedrus Apenius What do you get from this?
- #8 Edmond Halley 1686
- #9 How would you make this abstract? John Snow 1854 It’s not all maps – timeseries, particularly as govs started getting into data
- #10 Playfair! A father of modern statistical graphics
- #12 Marey’s train graph What works, what doesn’t?
- #14 Abstract & Statistical Information 1955 Surgeon General’s Report
- #21 What do you see here? What’s the story and how do the aesthetics tell it?
- #22 This is the same data What do you see here? What’s the story and how do the aesthetics tell it?
- #23 Costs and benefits of each
- #24 Costs and benefits of each