1. Data Visualization

2. Lies, Damn Lies, and Bad Graphs
Notice the bars don’t start at zero, giving you a false perception that the differences are large.

3. Lies, Damn Lies, and Bad Graphs
Here the bars are adjusted and start at zero, and the graphs go to 100 percent, but this crushes the vertical distance, maybe leading you to believe there is NO difference.

4. Lies, Damn Lies, and Bad Graphs
This does a better job of putting spending in perspective. Things don’t get lost in averages, trends are easy to see, and the fact that they use a line graph allows them to start at something besides zero.

5. Visual Medium Reports Presentations (static and dynamic)
Paper (static, with time)
Web (dynamic and interactive)
Presentations (static and dynamic)
The different medium require you to spend time thinking about the audience, the message, and they time they have for digesting the data.

6. “It’s not just about producing graphics for publication,” Aldhous explains. “It’s about playing around and making a bunch of graphics that help you explore your data. This kind of graphical analysis is a really useful way to help you understand what you’re dealing with, because if you can’t see it, you can’t really understand it. But when you start graphing it out, you can really see what you’ve got.”
In an article headlined “Hey, Green Spender,” Aldhous and colleague Phil McKenna examined the gap between consumer perception and environmental realities across multiple industries such as retail, media, travel and leisure, food and beverages, technology, construction and chemicals.
When the data was plotted, the differences between the perceptions and the realities were immediately visible – and the reporters knew they were on the right track. 
“It’s not just about producing graphics for publication,” Aldhous explains. “It’s about playing around and making a bunch of graphics that help you explore your data. This kind of graphical analysis is a really useful way to help you understand what you’re dealing with, because if you can’t see it, you can’t really understand it. But when you start graphing it out, you can really see what you’ve got.”

8. Data vis is sometimes about simple error checking.

10. Four sets of data with the same correlation of 0.816

12. Percent Blue relative to Red?

13. Percent Blue relative to Red?

14. You want to make it as easy as possible to make visual interpretations
You want to make it as easy as possible to make visual interpretations. Positions along a common scale are the easiest. Never require more difficult means when easier ones suffice. Don’t use 3 d bar charts when all you need it the height encoded.

15. More Bad versus good charts
Some bad 3D graphs

18. Bad

19. Better
From

20. Even Better*
The problem remaining is starting Y axis at 0 compresses the differences. This is good and bad. Its bad because there is too much useless whitespace. Its good because it doesn’t distort the data. The other problem is it connects data points across time when in fact there are 4 years intervening and the composition of the groups are different of those time periods as some people move groups, but this is minor.

26. #Introduction
#History of Plots  
#The Explanatory Power of Graphics
#Basic Philosophy of Approach
#Graphical Integrity
#Data Densities
#Data Compression
#Multifunctioning Graphical Elements
#Maximize data-ink; minimize non-data ink
#Small Multiples
#Chartjunk
#Colors
#General Philosophy for Increasing Data Comprehension
#Techniques for Increasing Data Comprehension
#When NOT to Use Graphics
#Aesthetics

28. Chartjunk and Graphics Integrity

29. Types of chartjunk
Chartjunk are non-data-ink or redundant data-ink decoration
Unintended Optical Art (Moiré vibration)
The Grid
The Duck: Self-promoting Graphics

30. Unintended Optical Art
Mainly rely on moiré effects
Distracting appearance of vibration and movement
The most common form of graphical clutter

31. Moiré Vibrations

32. The Grid Dark grid lines are chartjunk
The grid should usually be muted or completely supressed

33. The Grid (cont’d)
Marey’s train schedule

34. The Duck
Self-promoting graphics: when the data measures become design elements

35. Duck Examples

36. "In our excitement to produce what we could only make before with great effort, many of us have lost sight of the real purpose of quantitative displays — to provide the reader with important, meaningful, and useful insight."
— Stephen Few

37. Graphical Integrity
Graphical excellence begins with telling the truth about the data
Some examples of Lie

38. Two Principles
The representation of numbers, as physically measured on the surface of the graphics, should be directly proportional to the numerical quantities represented
Clear, detailed and thorough labeling should be used to defeat distoration

39. Violating rule 1
18 miles/gallon: 0.6 inches; 27.5miles/gallon: 5.3 inches

40. Lie Factor Rule 1 can be measured by Lie factor
size of effect shown in graphics
size of effect in data
Lie Factor equal to one is ideal
The previous slide has a lie factor of 14.8
Lie Factor =

41. Design and Data Variation
Show data variation, not design variation
: one vertical inch
equals to $8.00. In 1979,
One vertical inch equals $3-4
: one horzontal inch
equals 3.7 years, while 1979
equals 0.57 year

42. Example Lie factor: 9.5 The price of oil is
inflated so need to be repaired.

43. Government Spending
Tricks to exaggerate the growth of spending

44. Real Government Spending
Tricks to exaggerate the growth of spending
Tricks to exaggerate the growth of spending

45. Visual Area and Numerical Measure
Tricking the reviewer with design variation is to use areas to show 1D data
Lie factor: 2.8

46. Content is Essential
Graphics must not quote data out of context

47. Content is Essential
Graphics must not quote data out of context

48. On Using Color…
The gray squares in the center are all the same color, but notice the apparent differences.

50. Rule #3: Use color only when needed to serve a particular communication goal.

57. Picking Color Schemes http://colorbrewer2.org/ http://kuler.adobe.com
Colorbrewer

58. Stop Visually Assaulting Me

59. The principles
The representation of numbers, as physically measured on the surface of graphics, should be proportionally to the numerical quantities represented
Use clear and detailed labeling
Show data variation, not design variation
The number of information-carrying dimensions depicted should not exceed the number of dimensions in the data (2 dimensions of data 2 D, 2 dimensions 3 D)
Graphics should not quote data out of context

60. Why do graphics lie?
Lack of quantitative skills of professional artists
The doctrine that statistical data are boring
The doctrine that graphics are only for the unsophisticated readers

61. Design is choice. The theory of the visual display of quantitative information consists of principles that generate design options and that guide choices among options. The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper.
— Edward Tufte, The Visual Display of Quantitative Information

62. Word Cloud
This is similar to the unordered bar charts. But in this case the ordering is sacrificed for some aesthetic value. The hope is that you spend time with the data.

63. Wordle.net
In this case good~times and bad~times appear equally. Notice that phrases are joined by ~

64. Spine Plot / Matrix Chart
This encodes two types of data.

65. Bullet Graph Data dense. Each bar communicates a piece of data.
Overlapping bars. Another data dense visual.
Data dense. Each bar communicates a piece of data.

66. Bullet Graph

67. Bullet Graph

68. Choropleth “Heat Map”
The problem with this type of chart is it makes the entire county appear equally at risk. Tufte doesn’t like these for cancer maps. It might work in this case since it represents the proportion of houses in the county in foreclosure. Since the data is legitimately geographically bounded it is useful.

69. RED STATE BLUE STATE PURPLE STATE
PURPLE STATE

71. Dynamic Charts

72. Encoding three variables and plotting them over time. Dynamic.

75. Problem with this representation?
0 should mean absence of bar.

76. Avoid defaults in Excel

77. Show the data
Data dense

78. Maximize Data Ink Ratio Minimize Non-Data Ink

79. Eliminate Chart Junk

80. Streamline Placement

81.
Napoleon’s Russian offensive was a catastrophe. He started with 422,000 men in June 1812 and returned with less than 10,000 soldiers on October 7, His army had already lost 135,000 men two weeks into the campaign – although there were no major battles at this point. Napoleon wanted his troops to feed off the land during their advance but the enemy left nothing but ‘scorched earth’ during its retreat. Since they had no alcohol to sanitize the water, there was a rapid outbreak of dysentery. Before the battle of Smolensk on August 17th, disease, weakness and desertion had already decimated the troops to 175,000 men. Napoleon arrived in Moscow with 100,000 soldiers. He had already lost two-thirds of his main army – not to mention many horses.
Undoubtedly a graphical milestone: This 1869 visualization drawn by the engineer Charles Joseph Minard shows the data from Napoleon’s disastrous Russian campaign from 1812–1813. Without further explanation, however, it is more of an appeal than an analysis. Click on the image to enlarge.
Napoleon’s army faced many battles during its retreat. It lacked horses to pull the loads. The soldiers torched their wagons and left their dismantled canons behind. When winter arrived, they had no warm clothing. Since the horses had the wrong shoes, the number of accidents rose on the slick paths. They even burned the pontoons that they carried to build bridges just a few days before they reached the Beresina River. Lice thrived in the appalling hygienic conditions and transmitted typhus fever. Napoleon returned with less than 10,000 men.