Telling Stories With Data: Class 1
0 likes451 views
This document outlines the key topics that were covered in a class on data visualization, including a brief history of the field, theories of visualization, concepts like dimensions and scope, exploring sample data, mapping data from domains to ranges, and finishing touches like labels, axes, and legends. The instructor provided examples and prompts for students to try applying the concepts to a data exploration exercise of their choosing.
![Data
3, 7, 10, 6, 2
Position of the item in the group.
Domain
[0-10]
[1-5]
Range
X: 400px
Y: 800px
Mapping
X: item position
Y: numeric value
David Newbury — @workergnome 59](https://image.slidesharecdn.com/class1-170322151516/85/Telling-Stories-With-Data-Class-1-59-320.jpg)
Telling Stories With Data: Class 1
- 1. Telling Stories With Data Class #1 March 20th, 2017 David Newbury — @workergnome 1
- 2. What We're Doing Today: — Syllabus Review — (Brief) History of Data Visualization — (Tiny) Theory of Visualization — (Nerdy) Overview of Concepts — (Fake) Data Exploration David Newbury — @workergnome 2
- 3. Course Website: datastories.davidnewbury.com David Newbury — @workergnome 3
- 4. Which is biggest? 15012, 8271, 30193, 1189, 9913, 16000, 92481, 49801, 100407, 2910, 3809, 8018, 61528, 18083, 38691, 1800 David Newbury — @workergnome 4
- 5. Which is biggest? David Newbury — @workergnome 5
- 6. Which is biggest? David Newbury — @workergnome 6
- 7. Why do we visualize? David Newbury — @workergnome 7
- 8. (Brief) History of Data Visualization David Newbury — @workergnome 8
- 9. Tabula Peutingeriana, 5th century CE David Newbury — @workergnome 9
- 10. David Newbury — @workergnome 10
- 11. Rene Descartes, 1600s David Newbury — @workergnome 11
- 12. Joseph Priestly, New Chart of History (1769) David Newbury — @workergnome 12
- 13. William Playfair, (1786 & 1801) David Newbury — @workergnome 13
- 14. David Newbury — @workergnome 14
- 15. John Snow, London Cholera Map (1854) David Newbury — @workergnome 15
- 16. Cholera Map David Newbury — @workergnome 16
- 17. Florence Nightingale, War Deaths (1855) David Newbury — @workergnome 17
- 18. Charles Minard, March on Moscow (1862) David Newbury — @workergnome 18
- 19. More recent history. David Newbury — @workergnome 19
- 20. David Newbury — @workergnome 20
- 21. New York Times David Newbury — @workergnome 21
- 22. (tiny) Theory of Visualization David Newbury — @workergnome 22
- 23. Dataviz is constructed reality. You are telling a story, not (just) stating facts. David Newbury — @workergnome 23
- 24. data art as opposed to data visualization as opposed to statistical graphics David Newbury — @workergnome 24
- 25. Statistical Graphics How do I create Statistical Graphs in SAS 9.1.3 without Proc Gplot. UCLA: Statistical Consulting Group. http://www.ats.ucla.edu/stat/sas/ notes2/ David Newbury — @workergnome 25
- 26. Data Art Dear Data Giorgia Lupi & Stefanie Posavec. http://www.dear-data.com David Newbury — @workergnome 26
- 27. Two Uses1). help people grasp things outside their reach David Newbury — @workergnome 27
- 28. Two Uses1). help people grasp things outside their reach 2.) tell stories David Newbury — @workergnome 28
- 29. explanatory visualization work as opposed to exploratory visualizations David Newbury — @workergnome 29
- 30. Dataviz is constructed reality. Do you care how true your story is? Do you care how accurate your story is? Are you trying to teach, entertain, or convince? David Newbury — @workergnome 30
- 31. (Nerdy) Overview of Concepts David Newbury — @workergnome 31
- 32. What can you visualise? David Newbury — @workergnome 32
- 33. Potential Subjects. subways, sheep, the solar system, shoes, sleep, skyline, snow, supermarket, sausages, school,the sea, spiders, staircases, syrup, soap, sawmills, stereos... David Newbury — @workergnome 33
- 34. Potential Subjects. subways, sheep, the solar system, shoes, sleep, skyline, snow, supermarket, sausages, school,the sea, spiders, staircases, syrup, soap, sawmills, stereos... ...and other things that begin with S. David Newbury — @workergnome 34
- 35. What are you interested in? I'm interested in subways. David Newbury — @workergnome 35
- 36. Data Visualization starts with... A Question. David Newbury — @workergnome 36
- 37. What question about your subject are you interested in? — Are subways more efficient than owning a car? — How often do I ride the subway in a year? — What's locations have the best access to subways? — What's the average subway commute in Pittsburgh? David Newbury — @workergnome 37
- 38. Dimension and Scope are about choosing what to focus on. David Newbury — @workergnome 38
- 39. Dimension Which bits of information about a subject are you going to focus on? David Newbury — @workergnome 39
- 40. Possible Dimensions number of cars duration of ride date of a ride different lines number of stops cost per ride number of stops per day time between stops cleanliness David Newbury — @workergnome 40
- 41. Scope Out of the infinite ways to look at your subject, how are you going to choose one? David Newbury — @workergnome 41
- 42. Possible Scopes All trains in a day All the rides that I've been on this year My train this morning All of the stops in the city Each line Every train stop in the past 50 years David Newbury — @workergnome 42
- 43. (Fake) Data Exploration David Newbury — @workergnome 43
- 44. Choose one. subways, sheep, the solar system, shoes, sleep, skyline, snow, supermarket, sausages, school,the sea, spiders, staircases, syrup, soap, sawmills, stereos... ...and other things that begin with S. David Newbury — @workergnome 44
- 45. TRY IT.1. Write down your subject 2. Write down your question 3. Write down as many dimensions as you can 4. Write down possible scopes for your data David Newbury — @workergnome 45
- 46. What does your data look like? David Newbury — @workergnome 46
- 47. Types of Data Dates Numbers Geo Coordinate Strings Categories David Newbury — @workergnome 47
- 48. Types of Data number of cars - Numeric duration of ride - Numeric date of a ride - Date different lines - Category number of stops - Numeric cost per ride - Category number of stops per day - Numeric time between stops - Numeric cleanliness - String David Newbury — @workergnome 48
- 49. Two (related ides): Categories & measures David Newbury — @workergnome 49
- 50. Categories are Discrete Things Measures are for Counting David Newbury — @workergnome 50
- 51. number of cars - Measure duration of ride - Measure date of a ride - Measure different lines - Categories number of stops - Measure cost per ride - Categories number of stops per day - Measure time between stops - Measure cleanliness - Categories David Newbury — @workergnome 51
- 52. A hidden dimension: David, Daniel, Dawn, Danique David Newbury — @workergnome 52
- 53. A hidden dimension: David (1), Daniel (2), Dawn (3), Danique (4) Position of the item in the group. David Newbury — @workergnome 53
- 54. TRY IT.1. Choose a scope for your data. 2. Identify which dimensions are relevant. 3. Is the dimension is a category or a measure? David Newbury — @workergnome 54
- 55. Now What?David Newbury — @workergnome 55
- 56. We need to map our data from a domain to a range. David Newbury — @workergnome 56
- 57. Domain number of cars - 1...8 duration of ride - 30 sec...2 hours date of a ride - - 24ft...200ft different lines - Red line, Blue line, Green line, Silver Line, Yellow Line number of stops - **2..20 cost per ride - "$2.50, $1.75, $3.00, $0.00" number of stops per day - ??...??? time between stops - 30 sec..5 minutes David Newbury — @workergnome 57
- 58. Range Domain is the possible input values Range is the possible output values David Newbury — @workergnome 58
- 59. Data 3, 7, 10, 6, 2 Position of the item in the group. Domain [0-10] [1-5] Range X: 400px Y: 800px Mapping X: item position Y: numeric value David Newbury — @workergnome 59
- 60. Data 3, 7, 10, 6, 2 Position of the item in the group. Area David Newbury — @workergnome 60
- 61. Data 3, 7, 10, 6, 2 Position of the item in the group. Color David Newbury — @workergnome 61
- 62. Data val1: 3, 7, 10, 6, 2 val2: 5, 8, 1, 8, 3 val3: Cat, Dog, Cat, Cat, Dog Position of the item in the group. Mapping X: item position Y: val1 Size: val2 Color: val3 David Newbury — @workergnome 62
- 63. Dimensions beyond X and Y. Color Size Shape Labels Patterns Icons Anything Else You Can Imagine David Newbury — @workergnome 63
- 64. TRY IT.1. Identify your domains 2. For each domain, choose a range 3. Draw it! David Newbury — @workergnome 64
- 65. Finishing Touches David Newbury — @workergnome 65
- 66. Measures get Axis Categories get Headers David Newbury — @workergnome 66
- 67. Labels David Newbury — @workergnome 67
- 68. Axis Category Axis Number Axis Date Axis Log axis David Newbury — @workergnome 68
- 69. Legends David Newbury — @workergnome 69
- 70. TRY IT.1. Add a title to your chart 2. Label your axis 3. Add legends and labels as needed David Newbury — @workergnome 70
- 71. Review Dimensions Scope Domain Range Categories Measures David Newbury — @workergnome 71
- 72. Thank You. David Newbury — @workergnome 72