These are the course notes and slides from the first class of Telling Stories with Data.

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