Discover new ways to visualize market research data
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How to create visualizations that improve storytelling (accompanying slides to the webinar: https://q-researchsoftware.wistia.com/medias/1d95lbkops). This presentation focuses exclusively on visualizations that work with standard market research data. Visualizations explored: stream graphs, waffle charts, violin plots, slope graphs, bump charts, dot plots, palm trees, pictographs, circle packing, moonplots, Sankey diagrams, chord diagrams, choropleth, statebin, and all the standard chart types. Techniques explored: small multiples, greying, squaring, banking, decomposing and recomposing, redundant encodings, removing lookups, creating shapes, forcing symmetry, adding norms, and showing statistical significance.
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Discover new ways to visualize market research data
- 1. Discover New Ways to Visualize Market Research Data Q - R E S E A R C H S O F T W A R E . C O M W E B I N A R
- 2. visualize market research data survey and related data, data stored in frequency tables, crosstabs, and time series the output needs to look good when exported to PowerPoint and usually show values
- 3. Goal of data visualization: allow people to quickly discover and remember the key stories in data Standard Techniques ReshapingFormatting GOOD COLORS Title 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr LABEL AVOID OVERPLOTTING REPRESENT ACCURATELY 1.2 1.4 3.2 8.2 Sales 0 2 4 6 8 10 Sales DE-CLUTTER ATTRACT ATTENTION APPROPRIATE TYPE CREATE MNEMONICS REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 REDUNDANT ENCODING EMPHASIZE FORM RECTANGLES CREATE SYMMETRY SHOW NORMS Series 1 Series 2 SMALL MULTIPLES DECOMPOSE FORCE CONTRASTS ORDER BY CONTEXT SIMPLIFY THE DATA SUPERNORMALIZE DIAGONALIZESORT DE-COLOR BANK TO 45°
- 4. $0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 1978 1979 1980 1981 1982 AVERAGEPRICEOFAONE-CARAT D-FLAWLESS ATTRACT ATTENTION 1.2 1.4 3.2 8.2 Sales 0 2 4 6 8 10 Sales DE-CLUTTER
- 5. You have 5 seconds to remember what is on the next slide
- 7. What did it say?
- 8. You have 5 seconds to remember what is on the next slide
- 10. What was the word?
- 11. create devices for aiding recall mnemonics
- 12. ATTRACT ATTENTION CREATE MNEMONICS
- 13. Repeated Icon Pictograph Q: Create > Charts > Visualization > Pictograph > Repeated Icon Displayr: Insert > Charts > Visualization > Pictograph > Repeated Icon ATTRACT ATTENTION CREATE MNEMONICS Is worth $13,000,000 today $1,000 invested with Warren Buffet in 1966 Data from https://www.businessinsider.com.au/warren-buffett-berkshire-hathaway-historical-returns-2017-5?r=US&IR=T
- 14. how the data is represented in the visualization encode the key information in multiple different ways redundant encoding
- 15. Heat Map Q: Create > Charts > Visualization > Heap Map Displayr: Insert > Visualization > Heat Map % Aldi Costco Coles Woolworths Harris Farm Foodland IGA Price 78% 46% 49% 40% 29% 29% 23% Access 40% 15% 63% 61% 31% 37% 41% Range 25% 38% 68% 70% 31% 47% 30% Fresh 36% 25% 60% 64% 54% 41% 35% Quality 45% 47% 71% 69% 60% 51% 43% REDUNDANT ENCODING
- 16. Donut Chart Q: Create > Charts > Visualization > Donut Displayr: Insert > Visualization > Donut REDUNDANT ENCODING SORT GOOD COLORS AVOID OVERPLOTTING
- 17. Donut Chart Q: Create > Charts > Visualization > Donut Displayr: Insert > Visualization > Donut + R Code for the coloring REDUNDANT ENCODING
- 18. Labeled Scatterplot Q: Create > Charts > Visualization > Scatter Displayr: Insert > Visualization > Scatter REDUNDANT ENCODING ATTRACT ATTENTION
- 19. Bar Pictograph Q: Create > Charts > Visualization > Pictograph > Bar Chart Displayr: Insert > Charts > Visualization > Pictograph > Bar Chart ATTRACT ATTENTION CREATE MNEMONICS REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 REDUNDANT ENCODING
- 20. reduce movements of our eyes across a visualization, trying to work out what it means minimize the distance between things, by moving them closer together (e.g., removing legends, shrinking the visualization) eye movement
- 21. Bar chart Q: Show Data as: Bar Chart Displayr: Chart > Bar Chart Dot plot Q: R code Displayr: R code SORT SORT REDUNDANT ENCODING REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 Sun Mon Tue Wed Thu Fri Sat 2018-03-25 730.0 1326.0 1888.0 1937.0 2013.0 1682.0 1030.0 2018-04-22 943.0 1797.0 2282.0 1705.0 1715.0 1696.0 976.0
- 22. Pictograph Bar Chart Q: Create > Charts > Visualization > Pictograph > Bar Chart Displayr: Insert > Charts > Visualization > Pictograph > Repeated Icon ATTRACT ATTENTION REDUNDANT ENCODING
- 23. Treemap Q: R code Displayr: R code Mosaic/Marimekko Chart Q: Show Data as: Mosaic Plot Displayr: Chart > Mosaic Plot SORT REDUNDANT ENCODING REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2
- 24. Single Icon Pictograph Q: Create > Charts > Visualization > Pictograph > Single Icon Displayr: Insert > Charts > Visualization > Pictograph > Single Icon ATTRACT ATTENTION CREATE MNEMONICS REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2
- 25. Repeated Icon Pictograph Q: Create > Charts > Visualization > Pictograph > Repeated Icon Displayr: Insert > Charts > Visualization > Pictograph > Repeated Icon ATTRACT ATTENTION CREATE MNEMONICS REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 REDUNDANT ENCODING
- 26. Lean back. What do you see? Colin Ware (2012), Information Visualization: Perception for Design, 3rd Edition, Morgan Kaufmann, Kindle Edition, p. 294.
- 27. Lean in. What do you see? Colin Ware (2012), Information Visualization: Perception for Design, 3rd Edition, Morgan Kaufmann, Kindle Edition, p. 294.
- 28. If we are 50 cm from the image, the image should be less than 5.2cm high/wide! 50cm 5.2cm
- 29. typical, average, median show norms
- 30. Column Chart with a smoothed line of best fit Q: Create > Charts > Visualization > Column Chart Displayr: Insert > Charts > Visualization > Column Chart SHOW NORM(S)
- 31. Heated Density Plot Q: R code Displayr: R code SHOW NORM(S)REDUNDANT ENCODING Density Plot Q: R code Displayr: R code
- 32. Choropleth Q: Create > Charts > Visualization > Geographic Map Displayr: Insert > Charts > Visualization > Geographic Map SHOW NORM(S) Choropleth with diverging color scale Q: Create > Charts > Visualization > Geographic Map Displayr: Insert > Charts > Visualization > Geographic Map
- 33. Decision Tree Q: Create > Machine Learning/Classifier > Classification and Regression Tree (CART) Displayr: Insert > Machine Learning/Classifier > Classification and Regression Tree (CART) + set Output to Tree
- 34. Sankey Tree Q: Create > Machine Learning/Classifier > Classification and Regression Tree (CART) Displayr: Insert > Machine Learning/Classifier > Classification and Regression Tree (CART) SHOW NORM(S) REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 REDUNDANT ENCODING
- 35. Remove as much color as you can. Where possible, make everything gray (exceptions to this rule to follow). Anything you would ordinarily do in black, do in dark gray – it will look better. Any peripheral information should be in a light grey so that it does not detract. Any unnecessary color should be made gray (or a single color) de-color
- 36. Alabama Wyoming Mississippi West Virginia Louisiana Kentucky Tennessee South Dakota North Dakota Oklahoma South Carolina Alaska Nebraska Arkansas Idaho Georgia Texas Missouri Montana Arizona Kansas North Carolina Florida Ohio Indiana Utah Nevada Pennsylvania Iowa Virginia New Hampshire New Mexico Wisconsin Colorado Michigan Minnesota Maine Delaware New Jersey Connecticut Oregon Washington Maryland New York Rhode Island Illinois California Hawaii Massachusetts Vermont District of Columbia Circle Packing Q: R code Displayr: R code Word Cloud Q: Show Data as: Word Cloud Displayr: Chart > Word Cloud REPRESENT ACCURATELY SHOW NORM(S)REDUNDANT ENCODING DE-COLOR
- 37. use visual cues and comments to emphasize what the viewer should focus on (e.g., summaries, callout boxes, highlighting, arrows, color) EMPHASIZE
- 38. Count the 3s 756395068473 658663037576 860372658602 846589107830 Cole Nussbaumer Knaflic (2015): Storytelling with data, Wiley, p. 103
- 39. Count the 3s 756395068473 658663037576 860372658602 846589107830 Cole Nussbaumer Knaflic (2015: Storytelling with data, Wiley, p. 103
- 40. Aldi Clustered Column Chart Q: Show Data as: Column Chart Displayr: Chart > Column Chart Supermarket Associations merged SUMMARY sample size = from 343 to 450; total sample size = 500; 157 missing Aldi is better on price, but much worse on everything else than Coles and Woolworths EMPHASIZE
- 41. Clustered Column Chart Q: Show Data as: Column Chart Displayr: Chart > Column Chart Supermarket Associations merged SUMMARY sample size = from 343 to 450; total sample size = 500; 157 missing EMPHASIZE
- 42. Clustered Column Chart Q: Show Data as: Column Chart Displayr: Chart > Column Chart Supermarket Associations merged SUMMARY sample size = from 343 to 450; total sample size = 500; 157 missing EMPHASIZE
- 43. Clustered Column Chart Q: Show Data as: Column Chart Displayr: Chart > Column Chart Supermarket Associations merged SUMMARY sample size = from 343 to 450; total sample size = 500; 157 missing EMPHASIZE
- 44. Supermarket Associations merged SUMMARY sample size = from 343 to 450; total sample size = 500; 157 missing; 95% confidence level Clustered Column Chart Q: Show Data as: Column Chart + Show significance: Arrows and Font Colors Displayr: Chart > Column Chart + Appearance > Highlight Results > Arrows and Font Colors EMPHASIZE
- 45. Clustered Column Chart Q: Show Data as: Column Chart + Show significance: Compare columns Displayr: Chart > Column Chart + Appearance > Highlight Results > Compare columns Supermarket Associations merged SUMMARY sample size = from 343 to 450; total sample size = 500; 157 missing; 95% confidence level EMPHASIZE
- 46. A visualization can often be improved by placing subjectively-located elements, and spaces, on a visualization so that they form rectangles. While this is a useful hack, a professional designer will be able to improve on this, as they have a much richer repertoire of principles. FORM RECTANGLES
- 47. 0% 20%40%60%80% Colleague recommendation Previous work together Affordability of services National reputation Local knowledge Content expertise Demonstration of results % selecting given attribute Demonstration effectiveness is most important consideration when selecting a provider In general, what attributes are the most important to you in selecting a service provider? Survey shows that demonstration of results is the single most important dimension when choosing a service provider. Affordability and experience working together previously, which were hypothesized to be very important in the decision making process, were both cited less frequently as important attributes. Adapted from cole Nussbaumer Knaflic (2015): Storytelling with data, Wiley, p. 81-82 FORM RECTANGLES 0% 20% 40% 60% 80% Demonstration of results Content expertise Local knowledge National reputation Affordability of services Previous work together Colleague recommendation % selecting given attribute In general, what attributes are the most important to you in selecting a service provider? (Choose up to 3) Demonstration of results is the single most important dimension when choosing a service provider. Affordability and experience working together previously, which were hypothesized to be important in the decision-making process, were both cited less frequently as important attributes. Demonstration effectiveness is most important consideration when selecting a provider REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 EMPHASIZE
- 48. Labeled Scatterplot Q: Create > Charts > Visualization > Scatter Displayr: Insert > Visualization > Scatter AVOID OVERPLOTTING
- 49. CORRESPONDENCE ANALYSIS DIMENSION2(31%) DIMENSION 1 (63%) BRAND ATTRIBUTE IMPORTANC E (SHAPLEY) HORIZONTAL AND VERTICAL DISTANCES ARE ON THE SAME SCALE (ASPECT RATIO = 1) NORMALIZATION: PRINCIPAL 9% 4% 1% Labeled Scatterplot Q: Create > Charts > Visualization > Scatter Displayr: Insert > Visualization > Scatter + Manual layout in PowerPoint or Displayr AVOID OVERPLOTTING FORM RECTANGLESDE-COLOR
- 50. can be surprisingly effective create symmetry forcing visualizations to have
- 51. Promoters Detractors Definitely recommend Definitely not recommend Stacked Column Chart Q: Show Data as: Stacked column chart with negatives Displayr: Insert > Chart > Stacked column chart with negatives CREATE SYMMETRY
- 52. Walking Dead Stranger Things Game of Thrones Walking Dead Stranger Things Game of Thrones Stream Graph Q: Create > Charts > Visualization > Stream Graph Displayr: Insert > Visualization > Stream Graph Area Chart Q: Create > Charts > Visualization > Area Chart Displayr: Insert > Visualization > Area Chart CREATE SYMMETRY
- 53. Violin Plot Q: Create > Charts > Visualization > Violin Plot Displayr: Insert > Visualization > Violin Plot Density Plot overlaid with Box Plot Q: R code Displayr: R Code CREATE SYMMETRY
- 54. lots of charts, so you can see their differences charts that are small and very easy to digest, and easy to compare small multiples
- 55. Series 1 Series 2 SMALL MULTIPLES Small Multiples of Area Charts Q: Create > Charts > Visualization > Area Chart Displayr: Insert > Visualization > Area Chart + Set Chart > Appearance > Show as small multiples
- 56. Adapted from Figure 6 Green, Marc. 1998. Toward a Perceptual Science of Multidimensional Data Visualization: Bertin and Beyond. http://graphics.stanford.edu/courses/cs448b-06-winter/papers/Green_Towards.pdf. Spot the odd one out
- 57. Adapted from Figure 6 Green, Marc. 1998. Toward a Perceptual Science of Multidimensional Data Visualization: Bertin and Beyond. http://graphics.stanford.edu/courses/cs448b-06-winter/papers/Green_Towards.pdf. Spot the odd one out
- 58. Adapted from Figure 6 Green, Marc. 1998. Toward a Perceptual Science of Multidimensional Data Visualization: Bertin and Beyond. http://graphics.stanford.edu/courses/cs448b-06-winter/papers/Green_Towards.pdf. Spot the odd one out
- 59. Adapted from Figure 6 Green, Marc. 1998. Toward a Perceptual Science of Multidimensional Data Visualization: Bertin and Beyond. http://graphics.stanford.edu/courses/cs448b-06-winter/papers/Green_Towards.pdf. Spot the odd one out
- 60. Line Chart Q: Create > Charts > Visualization > Line Chart Displayr: Insert > Visualization > Line Chart Small Multiples of Area Charts Q: Create > Charts > Visualization > Area Chart Displayr: Insert > Visualization > Area Chart + Set Chart > Appearance > Show as small multiples EMPHASIZE Series 1 Series 2 SMALL MULTIPLESREDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 DE-COLOR
- 61. Costco’s price and cost advantage is only relative. It has yet to establish a strong positioning in the market. Small Multiples of Radar Chart Q: Create > Charts > Visualization > Radar Chart Displayr: Insert > Visualization > Radar Chart + Set Chart > Appearance > Show as small multiples EMPHASIZE Series 1 Series 2 SMALL MULTIPLESREDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 SHOW NORM(S) Radar Chart / Spider Chart Q: Create > Charts > Visualization > Radar Chart Displayr: Insert > Visualization > Radar Chart DE-COLOR
- 62. Series 1 Series 2 SMALL MULTIPLES Small Multiples of Histograms Q: Create > Marketing > MaxDiff > Hierarchical Bayes Displayr: Insert > More > Marketing > MaxDiff > Hierarchical Bayes
- 63. resize plots so that lines have an average slope of 45 degrees (i.e., by changing the aspect ratio, which is the ratio of the horizontal and vertical dimensions) bank to 45°
- 64. Line Chart Q: Create > Charts > Visualization > Line Chart Displayr: Insert > Visualization > Line Chart BANK TO 45°
- 65. 45° 39° 31° 75° 56° 82° 65° Banking to 45° means changing the size of the slopes that we want the viewer to look at have an average value of around 45°. Near enough is good enough. The science is rubbery. Doing it by eye is fine. 45°
- 66. Line Chart Q: Show Data as: Line Chart Displayr: Insert > Chart > Line Chart EMPHASIZEREDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 European Social Survey, Data file edition 2.1. NSD - Norwegian Centre for Research Data DE-COLOR BANK TO 45°
- 67. break the visualization down into parts, where each part is easier to understand decompose
- 68. Original data = Seasonal + Trend + Remainder/ Random/Noise Small Multiples of Line Charts Q: Create > Charts > Visualization > Line Chart Displayr: Insert > Visualization > Line Chart + Set Chart > Appearance > Show as small multiples Decomposition performed using R code DECOMPOSE
- 69. Column Chart with Trend Q: Show Data as: Column chart with trend Displayr: Insert > Chart > Column chart with trend NET PROMOTER SCORE (NPS) DECOMPOSE EMPHASIZE
- 70. Attitude to Immigration Country Profile 2003 2015 2003 2015 2003 2015 2003 2015 Combination of existing charts and code Can be assembled in either Displayr or PowerPoint DECOMPOSE ATTRACT ATTENTION
- 71. the key differences relevant to the viewer make contrasts unmissable force contrasts
- 72. Line Chart Q: Create > Charts > Visualization > Line Chart Displayr: Insert > Charts > Visualization > Line Chart FORCE CONTRASTS
- 73. Palm Trees Q: Create > Charts > Visualization > Palm Trees Displayr: Insert > Charts > Visualization > Palm Trees FORCE CONTRASTS
- 74. Dumbbell Plot Q: R code Displayr: R code Clustered Bar Chart Q: Create > Charts > Visualization > Bar Chart Displayr: Insert > Charts > Visualization > Bar Chart FORCE CONTRASTS Greenbook, GRIT, 2012
- 75. Slope Chart Q: R code Displayr: R Code Greenbook, GRIT, 2012 FORCE CONTRASTSREDUNDANT ENCODING
- 76. Slope Chart Q: R code Displayr: R Code
- 77. Bump Chart Q: R code Displayr: R Code EMPHASIZEFORCE CONTRASTSREDUNDANT ENCODING
- 78. Desired traits in the US President Bump Chart / Ranking Plot Q: Show Data as: Ranking Plot Displayr: Insert > Chart > Ranking Plot FORCE CONTRASTSREDUNDANT ENCODING
- 79. additional variables that explain or contextualize rearrange the values or series of data order by context
- 80. Maps with overlaid charts Q: R code Displayr: R Code REPRESENT ACCURATELY ORDER BY CONTEXTREDUNDANT ENCODING
- 81. State Bin with diverging colors Q: R code Displayr: R Code REPRESENT ACCURATELY ORDER BY CONTEXTREDUNDANT ENCODING SHOW NORM(S)
- 82. LOYAL VISITED LAST TRIP VISITED LAST MONTH VISITED LAST 12 MONTHS AWARE Funnel Displayr or Q + PowerPoint ORDER BY CONTEXT
- 83. Confection Displayr or Q + PowerPoint ORDER BY CONTEXT SHOW NORM(S)
- 84. Nested Pie Chart Q: Create > Charts > Visualization > Pie Chart Displayr: Insert > Visualization > Pie Chart ORDER BY CONTEXT
- 85. Series 1 Series 2 SMALL MULTIPLES President Trump Approval March 2018: 41% January 2017: 49% September 2017: 43% Choropleth with diverging color scale Q: Create > Charts > Visualization > Geographic Map Displayr: Insert > Charts > Visualization > Geographic Map
- 86. ORDER BY CONTEXT From http://andrewgelman.com/2011/04/04/irritating_pseu/; some rows removed Series 1 Series 2 SMALL MULTIPLES
- 87. reorder the series in visualizations, so that key patterns appear as diagonal lines across the visualization diagonalize
- 88. Sleepy Innocent Feminine Weight- conscious Health- conscious Open to new experiences Rebellious Older Traditional Diet Pepsi Diet Coke Coke Zero Pepsi Max Pepsi Coke % Feminine Health- conscious Innocent Older Open to new experiences Rebellious Sleepy Traditional Weight- conscious Coke Diet Coke Coke Zero Pepsi Diet Pepsi Pepsi Max % Grid of Bars Chart Q: Show Data as: Grid of Bars + Automate > Data Bars DIAGONALIZEREDUNDANT ENCODING DE-COLOR
- 89. Correlation Matrix Heatmap Q: Create > Correlation > Correlation Matrix Displayr: Insert > More > Correlation > Correlation Matrix DIAGONALIZE
- 90. REDUNDANT ENCODING Principal Components Analysis Loadings Table Q: Create > Dimension Reduction > Principal Component Analysis Displayr: Insert > Dimension Reduction > Principal Component Analysis DE-COLOR DIAGONALIZE
- 91. reduce the quantity of information in the data to be displayed (e.g., aggregate, smooth, filter) simplify the data
- 92. Heat Map Q: Create > Charts > Visualization > Heap Map Displayr: Insert > Visualization > Heat Map Moon Plot Q: Create > Dimension Reduction Correspondence Analysis of a Table Displayr: Insert > Dimension Reduction Correspondence Analysis of a Table + Output: Moonplot SIMPLIFY THE DATA
- 93. 20 30 40 50 60 70 17-Nov-85 13-Aug-88 10-May-91 3-Feb-94 30-Oct-96 27-Jul-99 22-Apr-02 16-Jan-05 13-Oct-07 9-Jul-10 Satisfaction withPM(%) Hawke Keating Howard Rudd Gillard 1.2 1.4 3.2 8.2 Sales 0 2 4 6 8 10 Sales DE-CLUTTER BANK TO 45° SIMPLIFY THE DATA
- 94. normalize we want to try and force visualizations to look like “normal” things, where “normal” means things our brains have evolved to interpret supernormalize we want to exaggerate the key features
- 96. A thin red rod with three white bands provides a stronger stimuli than an accurate three dimensional plaster head
- 98. Michelangelo | The Creation of Adam, c. 1512
- 99. Piet Mondrian | Composition with Gray and Light Brown, 1918
- 100. -100 -80 -60 -40 -20 0 20 40 60 October 2010 November 2010 December 2010 January 2011 February 2011 March 2011 NPS Brand A Brand B Brand C Brand D Brand E Brand F Brand G Brand H Brand I Brand J Brand K Other
- 106. Which is why we misinterpret correspondence analyses Correspondence analysis biplot of American’s concerns when travelling
- 107. Mexico France Great Britain Egypt Australia China Correspondence analysis moon plot of American’s concerns when travelling And we correctly interpret moon plots
- 109. We instinctively understand height and size
- 110. We instinctively line and area charts
- 111. 32% 29% 35% 32% 12% 23% 29% 32% JANUARY FEBRUARY MARCH APRIL NetPromoterScore Bank A Bank B Bank B Bank A 32 12 32 January February March April NET PROMOTER SCORE Over the first four months of the year, Bank B’s NPS score steadily rose. It is now at parity with Bank A. 1.2 1.4 3.2 8.2 Sales 0 2 4 6 8 10 Sales DE-CLUTTER APPROPRIATE TYPE EMPHASIZEFORCE CONTRASTSREDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2
- 114. Tree map with heat map shading
- 118. Chord Diagram Q: R code Displayr: R Code
- 120. The greatest visualization of all time?
- 121. Fluctuations in industry size: 2007 to 2011
- 122. 0 400 1000 1300 1700 1804 USA RUSSIA ENGLAND FRANCE GERMANY DENMARK PORTUGAL HOLLAND FLANDERS HANS TOWNS VENICE & GENOA SPAIN CONSTANTINOPLE ALEXANDRIA ROME CARTHAGE ISRAEL & JUDEA GREECE TYRA ETC. ASSYRIA ETC. EGYPT
- 123. Standard Techniques Reshaping Goal when creating a visualization: allow people to quickly discover and remember the key stories in data We do this by creating supernormal shapes, using the techniques below Formatting GOOD COLORS Title 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr LABEL AVOID OVERPLOTTING REPRESENT ACCURATELY 1.2 1.4 3.2 8.2 Sales 0 2 4 6 8 10 Sales DE-CLUTTER ATTRACT ATTENTION APPROPRIATE TYPE EMPHASIZE FORM RECTANGLES CREATE SYMMETRY SHOW NORMS Series 1 Series 2 SMALL MULTIPLES DECOMPOSE FORCE CONTRASTS ORDER BY CONTEXT SIMPLIFY THE DATA SUPERNORMALIZE DIAGONALIZE CREATE MNEMONICS REDUCE EYE MOVEMENT Series 1 Series 2 Series 1 Series 2 REDUNDANT ENCODINGSORT DE-COLOR BANK TO 45°
- 124. Next steps: Excel = Bland; embrace high resolution images, which can be interactively explored online
- 125. Next steps – Create your own visualizations (with some help from us) 1. Go to q-researchsoftware.com 2. Choose BOOK A DEMO Or if you want to go it alone, choose FREE TRIAL
- 126. QUESTIONS Tim Bock
Editor's Notes
- Welcome to our webinar on visualizing market research data. Questions at the end in GoToWebinar In a couple of days we will send you a link to slides
- In this webinar I will present 93 visualizations. All but a few are from market research. The few that aren’t are ones that do a good job of illustrating important ideas. I have put the constraint on that I am focusing on visualizations that work well in PowerPoint, so wont’ be spending time on interactivity.
- When we create a data visualization, our goal should be to help people to quickly discover, and remember, the key stories in the data. I’m going to take you through 24 techniques for creating visualizations I will very briefly callout the standard techniques which most market researchers are well familiar with Read NEXT I will spend more time on various useful formatting techniques that can be used to improve a visualization NEXT Then, I will cover eight great techniques for improving visualizations by reshaping the underlying data data and the visualization
- There’s been a war of sorts in data visualization. On the one hand there are designers trying to make visualization that attract attention. On the other side are statisticians, who argue that the data should be left to do the talking. They see the lady in the visualization on the left as Chart junk. Best thrown out, as it distracts the viewer. Often is true, but not in this case. To see why let’s do a little thought experiment
- Elephant Elephant Elephant Elephant Elephant
- Let’s try again.
- Elephant Elephant Elephant Elephant Elephant
- If you are of a certain age, you found the second word easy to remember, as it was a word from childhood. Even if you don’t know your mary poppins, there’s a good chance you can remember some of the word.
- Great visualizations contain mnemonics. They are designed to aid our recall.
- This is a great visualization. The diamond prices trace a line starting at the buttock of a reclining person, going up to the knee cap, and then down to the toe. We all know the shape of a bent leg. This visualization aids our memory by associating something we don’t know, the trajectory of diamond prices, with something we do know, which aids are ability to recall it later. Even cleverer, is that our associative memory bonds grow stronger when we use sexual images. This is a really smart visualization. It is no accident that it is discussed so often. Sadly, most of us don’t have the skill to create something like that.
- Here is more of a paint by numbers visualization which I created in Displayr. PAUSE I’ve focused on two things when I designed it with the goal of making it more memorable. I tried to create relevant associations. In this case money and wallets. I used a pictograph as they are great for emphasizing scale. In this case I modified a similar waffle chart I had seen before. A waffle chart is just a pictography that uses squareas as the icons. I tried to make it a bit intriguing, so that somebody wants to squint to work out that there are little wallets.
- Now for a less controversial technique. Redundant encoding. Encoding refers to how data is represented in a visualization. Sometimes redundant implies the opposite of simplicity. Not here. The idea behind redundant encoding is we want to encode the data in multiple ways, so that the patterns in the data are unmissable.
- The heat map here improves on the table, as it shows both the numbers and blueness representing the numbers. The blueness shouts out the patterns, then we verify using the numbers. This would look prettier without the numbers, but in most market research reporting situations we do need to show the numbers, as it is difficult if all a client can quote is that Aldi was bluest on price.
- Pie charts are often derided. This one is a mess. But, it is still easy to work out the top 3 browsers, because pie charts encode the information in three ways: 1. The area of the slices 2. The angle of the slices at the center 3. The labels. This donut chart improves by better choice of colors, no overplotting of labels, and two more encodings: 4. Order 5. The size of the labels
- Now there is an additional level of encoding, with the degree of greenness proportional to the data. The primacy of Chome 48 is now unmissable.
- Most branding is filled with redundant coding. There is the name, the color, the design. It’s pretty hard to miss Coca-Cola on the visualization on the right. It is not just more engaging. It is easier on the brain, as it requires less effort to read.
- And, a bit more controversially here we encode the 7 point scale into the visualization, to make it easier for people to read.
- The more our eyes move, the more work we have to perform to decode a visualization. So, a simple principle when designing a visualization is to try to reduce the amount of eye movement.
- One of the basic techniques for improving a visualization is to reduce clutter. Statisticians sometimes try to quantify this as the Data-ink ratio, which is the proportion of the visualization that contains data that cannot be erased. This can be further simplied to the % of white space. The visualization on the left is a Dot Plot. Statisticians love them, as they maximize the amount of white space. They also have the top of logic that appeals to people that have been trained in maths. We interpret them by reading the horizontal and vertical coordinates. This feels very mathematical, but it involves a lot of eye movement. The bar chart on the right is much better, as the eye has to move much less, by putting the values in the bars and making the visualization smaller. Further, it is better because it encodes the data in 7 different ways. The position of the end of the bars in xy space (as with a dotplot) Length of the bar Areas of the bar Order Color The value lables Position of the labels
- And, there are still more ways of encoding data into bar charts, using images.
- When I started in consulting, the Mosaic Plot, also known as a Marimekko chart, was considered very sophisticated. But, it always had a problem: many clients struggled to understand it. There is a reason for this. Interpretation requires quite a lot of work. Consider the yellow square on the left. To work out what it means, we have to figure it that it is a part of a column, and move our eyes to the top to work out that it refers to the at home accaion. Then, we need to scan for a label next to a yellow square to work out that it is Diet Pepsi. As no value is shown, we need to guestimate. Contrast that with the treemap on the right. It has been drawn so there is room to see Diet Pepsi, so we save a lot of eye movement. It also has more redundant encoding. At home is now represented by both a column and a color. The value of each brand is now shown by size, color, order, and the label.
- Here we do not even need a title, so the eye movement is removed!
- Here we do not need an axis, so more eye movement has been saved. NEXT The one standard technique that you can do to reduce eye movement is to reduce the size of a visualization. It may sound counterintuitive, but the smaller visualizations are less work to read, as they require less eye movement.
- Lean back. What do you see?
- Lean in. What do you see? A gremlin, and a toucan with a big claw!
- We want an image to be in an angle from our eyes of about 4 to 6 degrees from our retina. This means that if we are viewing from about 50cm away, our ideal image is only about 5cm high. To calibrate, when you are about 30cm from image the gremlin takes about about 4 degrees
- Many visualizations can be improved by emphasizing the typical, average, or median result
- I’m sure you are familiar with using lines of best fit to show trend, and also departures from trend. We can employ the same type of idea to many other types of visualizations.
- The visualization on the left is the standard density plot, used to summarize the distribution of a variable. In this case it shows the time taken to purchase after trial of a product. It clearly shows that the peak number of days is about 30. But what is the median? On the right I have overlain heatmap shading. Yellow shows the median. We can now see that the median is around 50 days, which was not very easy to see on the traditional chart.
- Geogrpahic plots like this are called choropleths, form the gree works for region and multitude. The choropleth on the left looks a bit cooler than the on the right. But the one on the right is better, even with all its pastel. It uses a diverging color scale. Gray shows 50% approval for President Trump. Blue states have lower levels of approval. Blue is also the color of the democratic party. We can see that President Trump’s approval is weakest on the west coast and North East. A choropleth map (from Greek χῶρος ("area/region") + πλῆθος ("multitude")) is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. Choropleth map - Wikipedia
- This decision tree shows drivers of trust in the EU Parliament. The only way to interpret it is to read everything, so it is not in any meaningful sense much of a visualization.
- Here is the same data, but shown as a Sankey diagram. We’ve colored blue for above average trust in EU parliament, and red for below average. The thickness of the branches shows the proportion of people. So, we can see that more than half the same are aged 45 or more, and they are below average in their trust. Who is unhappiest? We can see that people aged 45+ who are unhappy in general are particular untrustworthy of the EU. Looking at the younger people are there any subsegment that don’t trust the EU? Yes, if they are unhappy with life they don’t trust the EU. And, if the watch a lot of TV their trust is average.
- We have just seen how useful color can be. Paradoxically, a generally useful technique is to try and design away as much color as you can.
- The word cloud here is showing approval of President Trump by state. Color is used to allow us to separate out the words. The resulting riot of color makes it difficult to see much other than the color. The circle packing visualization on the right has been largely decolored. The diverging Color showing us the states where Trump has higher and lower approval. This visualization is also more accurate, as circle size is more easily interpreted than font size, and it is added in some redundancy, with approval shown by Color Area The order in the spiral.
- This technique is one that everybody knows, but many people forget to do in practice.
- Count the 3s
- It’s much easier now!
- We can focus the viewer’s attention using commentary.
- By formatting.
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- If we have a more technical reader and are using Q or Displayr, we can show statistical test results using arrows and font colors.
- Or letters.
- Often when people do really bad visualizations they give themselves an excuse. “I am not a designer” they say. I too am not a designer, and you can think of this presentation as my set of hacks. One, which I am very fond of, is to form rectangles.
- On the left we have a standard PowerPoint slide. It’s redrawn on the right, but with things lined up to make rectangles more evident. This has the effect of both making it look neater, and, avoiding distracting the viewer user with irregular and irrelevant shapes.
- One of my colleagues, Po, has spent more than a year working on the algorithm for scatterplots. It is a challenging engineering problem due to overplotting. First, we need to allow the bubbles to overlap and be transparent. But the real challenge is to place the labels on the map so that they do not overlap but are close their bubbles. This is the default way they look in Q and Displayr, and the black around the edge, and all the centering of labels detracts us from the bubbles.
- Here I have greyed and formed rectangles with peripheral information, making it less obtrusive. It makes the data of the bubbles our focus.
- Symmetrical visualizations are easier on the eyes and the brain.
- Most market researchers are well versed in the idea of putting positive scale points above the axis and negative below it.
- The stream graph on the right looks cool. But, it is also a better visualization from a functional perspective. The stacked area chart on the left is only good for Game of Thrones. When we have a spike for Game of Thrones, everything else is obscured. By contrast, with the Stream Graph on the right, due to it being reflected around the horizontal axis, the size of the spikes is halved, which makes the degree of distortion smaller, as does the rounding. The consequence is that we can see, for example, that Walking Dead is perennially popular, which isn’t at all clear from the area chart on the left.
- As discussed earlier, density plots aren’t so good for looking at medians. In the density plots on the left I’ve added the medians as a bar. Perhaps its an improvement. The visualizations on the right, are very similar, except that I have mirrored the density making it symmetrical, and flipped it around. Somehow, these visualizations are much easier to digest. Just by being made symmetrical.
- Perhaps the most powerful of all the techniques is small multiples. The idea is to replace a visualization with lots of smaller visualizations.
- We are looking at the TV data again. Even thought Game of Thrones is reduced to being 5% of the area from the previous visualizations, we can still see everything. And, much more jumps out at us now. For example, we can see that the Crown’s spike when the series 2 started 6 months ago was trivial compared to the Game of Thrones and Stranger Things spikes, and that House of Cards got only a tiny blip from Kevin Spacy’s Me Too moment. A little thought experiment will help explain why Small Multiples work so well. NEXT
- Spot the odd one out.
- And again.
- Which one sticks out here?
- I’ve highlighted the odd one here, as while most people get it, some people can’t see it at all. For everybody it is a bit harder. Why? The image on the left encodes data in three visual dimensions: horizontal and vertical space + color The middle image uses rotation of the line as the third dimension. The right image uses four dimension to encode, and adding an extra dimensions adds considerably to our cognitive load.
- On the bottom left we have a standard line chart. The small multiples is not encoding an extra dimension via color, so is easier on our brains. Note that it separates out two things: trend, from overall level. Thus, we can see that Bank H is typically the highest, and Bank A is worst and in decline. Yes, all this can be found in the line chart, but it is much harder. This visualization also reveals the main weakness with Small Multiples: making the labels neat and visible.
- Note here that using Small multiples has allowed us to: Add a norm, the grey background image, which is the average across the different supermarkets. Emphasize the key results Add labels.
- And, we can use small multiples for more technical visualizations as well. Here we are showing a Hierarchical Bayes model from a MaxDiff experiment. It shows us that Apple is the most divisive brand, with some haters, some lovers, various points in between. Google, by contrast, mainly just has likers.
- Bank to 34 degrees. This is a very cool technique. I will illustrate it with a classic example.
- This chart shows sun spot activity for the last 250 years. What can you see? NEXT Now, despite the new visualization being much smaller, we can see a pattern that is difficult to spot in the earlier visualization. We can see that sun spots tend to spike quickly, and then slowly decline. All that banking involves is changing the height and width of visualizations to make patterns easier to see.
- It turns out that if we make it so the trends we want the viewer to see average around 45 degrees, they are more likely to find patterns.
- Now for some market research data. I have replace the legend with labels to remove eye movement. It is de-colored, banked to 45 degrees, with the interesting result emphasized.
- Decomposing data involves breaking data into small chunks to make patterns clearer.
- If you have ever studied business statistics you will be familiar with the simple math of the seasonal decopositoin. The chart at the top shows the original data. The three underneath show the seasonal, trend, and random components broken out, so we can focus on whichever is most interesting to us.
- Here is the same idea applied to the bank NPS data. Each column now shows the most recent month, as this is what the stakeholders for this project cared most about, as it was linked to bonuses. I’ve shown the trend as separate sparklines at the bottom. Statistically significant results have been shown by color, so we can see that although Bank C is poor, it has gone up in the last month, whereas Bank A is both the worst and in long term decline.
- And, you can make your decompositions pretty if you want.
- A contrast is the difference that we want the viewer to spot.
- What pattern can you see here? NEXT I’ve stacked the years. I’ve used different shades of blue for the years, and we can see the most of the growth is is in the first tow year, as the lightest blue is at the bottom. We an also see clear seasonality in the data, with a spike at Easter, a bump at August September, and one for Christmas.
- These are on of my favorite visualizations. They are called Palm Trees. They work better online, but they are good for making the point about contrasts. Looking at the Palm Tree for Cost, we can see that there is a really small leaf for Mexico, which tells us that among American travellers they are not so concerned about prices and travel to Mexico. By contrast, when we look at Safety we see that Mexico, Egypt, and to a lessor extent China suffer in this area. To get value from this visualization we are having to work. NEXT Now, here is the same visualization where trees are by country. If we look at the bottom right, for example, we can quickly see that Great Britain and Australia have roughly the same shape, and the only real concern is Cost. The conclusion jumps out at us, because the visualization has been created to make it easy to see the key contrasts.
- With clustered bar charts, our main interest is on the difference between the bars, but from a purely visual perspective, the chart instead just emphasizes the bars themselves. NEXT A better visualization is often the dumbbell plot, which is a variant of the dot plot, and focuses the user just on the differences.
- This is a slop chart. It is essentially a line chart with redundant encoding of the labels. On the slope chart, the differences are shown by the slopes. We can see, for example, that consultants consider understanding clients to be more important than do market research fieldwork agencies.
- While slope charts are cool, often the labels overlap.
- A simple solution is to show ranks instead. This is called a Bump Chart. Here we are looking at desired traits in a US president by approval of President Trump. I’ve used Red to emphasize some of the key differences. For example, we can see that those that Approve of President Trump place a much lower level of import on concern for global warming, poverty, and minorities, than do those that disapprove.
- And here is a sexier version. We call this a Ranking Plot in our software.
- Another great technique is to order information by context.
- If we have geography data, it is often best viewed when superimposed on a map.
- Here I have reordered the packed circles I showed earlier as squares by geography. This is called a State Bin. This corrects a bias in the earlier choropleths, which overemphasized states with a large land mass.
- Another type of context is the hierarchy of effects or decision making process.
- Here I have placed circles showing the performance of different departments of a supermarket over an image, which provides a frame of reference for the viewer.
- Here I have added NETs to the earlier market share data.
- We can also add additional variables in the analysis to add context. Now we are looking at approval by state over time. The combination of the small multiples with the diverging color allows us to see that six months ago a few more states turned to being disapprovers, but there has been little change since then. That is, there is more blue on the September 2017 choropleth. Its worth reflecting on how efficient this visualization is. We are showing 156 different percentages, and they are easy to absorb.
- Now we are representing more than 1000 values on a visualization.
- Where there is no context, we can still create a pattern for the viewer to assist them. The trick is to try and create a diagonal pattern.
- What can you deduce about Coke on this visualization. NEXT What about now? I have diagonalized the data and used redundant encoding.
- Here I have rearranged the rows of a heatmap of correlations. This has the effect of forming steps. Each of the steps is a factor. So, the step here shows 7 variables that make up the first factor.
- And here I have an actual factor analysis, where I have used redundant coding to make it easy to see which variables load onto which factors.
- We can also improve visualizations by simplifying the underlying data.
- The moon plot of a correspondence analysis, shown on the right is much quicker to digest than the whole heat map.
- The smoothed polling data is much easier to understand that the city skyline of the original visualization.
- Everything I have shown so far is really a part of a single basic ideas: we want to force data and visualizations to correspond to shapes that people can recognize. NEXT And, we want to exaggerate the shape if at all possible. We want to supernormalize the visualization.
- Dutch Nobel Laurette Niko Tinbergen did a cruel but fascinating study. He noticed that birds tended to instinctively peck at their mothers beaks as soon as they hatched. So, he took away their mothers to see what they would do, and showed them a cardboard cut-out. They pecked at it. NEXT He then showed them a two beaked moster. They pecked at it.
- The fascinating finding was that they pecked more ferociously at a red rod with three white lines than a plaster model of their mother’s head. Why? Because the instincts in the brain have evolved to having a simplified concept of what to peck at, and the supernormal stimulus of the red bar better taps into this.
- Of course, we all know this idea. It is why children’s toys and cartoons are not realistic.
- I think it is why people appreciate works like this.
- But tend to dismiss modern art. To most people they instinctively can’t work out what it means. It llooks like bricks, but we do not look for meaning in bricks… http://www.artchive.com/artchive/P/pollock/pollock_blue_poles.jpg.html
- When we see a line chart like this, it is not something we have evolved instincts to interpret. It just looks like spaghetti
- We have not evolved to look for patterns in spaghetti.
- A radar chart looks like a witches hat to me. NEXT I don’t expect to see a pattern.
- But, my forebearers were hunter gathers, and I am good at disambiguating shapes in leaves.
- We instincitvely understand shape. Nabta Playa http://upload.wikimedia.org/wikipedia/commons/c/c9/Calendar_aswan.JPG
- This is why no matter how many times you tell some people that it is wrong to look at a correspondence plot lilke this and conclude that China is strong in Food, many people just do not believe you. Such an interpretatoin goes against their instincts, and the instincts win. Linear projections
- This is why a moonplot is better. People instiinctivley see htat China and Egypt are not differentiated. Correspondence Analysis (Traditional) Inertia(s): Canonical Correlation Inertia Proportion Dimension 1 .462 .213 .715 Dimension 2 .255 .065 .218 Dimension 3 .110 .012 .041 Dimension 4 .069 .005 .016 Dimension 5 .054 .003 .010 Standard Coordinates: Columns Dimension 1 Dimension 2 Dimension 3 Dimension 4 Dimension 5 Mexico -1.38 -.75 -.53 .57 .98 France 1.11 1.56 -.88 .58 .93 Great Britain 1.42 -1.47 .56 2.29 -1.20 Egypt -.38 .29 -.70 -.58 -1.39 Australia 1.73 -1.90 -.45 -2.12 .95 China -.18 .45 1.66 -.35 .17 Principal Coordinates: Columns Dimension 1 Dimension 2 Dimension 3 Dimension 4 Dimension 5 Mexico -.64 -.19 -.06 .04 .05 France .51 .40 -.10 .04 .05 Great Britain .65 -.37 .06 .16 -.06 Egypt -.18 .07 -.08 -.04 -.08 Australia .80 -.48 -.05 -.15 .05 China -.08 .11 .18 -.02 .01 Standard Coordinates: Rows Dimension 1 Dimension 2 Dimension 3 Dimension 4 Dimension 5 Cleanliness -1.02 -.41 .13 .81 1.60 Health -1.00 -.37 .89 -.12 .87 Safety -.89 -.47 -1.26 -.54 -.84 Cost 1.58 -.87 .03 -.37 .33 Food -.28 -.50 1.33 .18 -2.13 Not being understood .09 1.80 1.04 -.74 -.12 Friendliness of the people .58 1.63 -1.66 .81 .24 Boredom .76 -.19 .87 6.00 -1.72 Principal Coordinates: Rows Dimension 1 Dimension 2 Dimension 3 Dimension 4 Dimension 5 Cleanliness -.47 -.11 .01 .06 .09 Health -.46 -.09 .10 -.01 .05 Safety -.41 -.12 -.14 -.04 -.05 Cost .73 -.22 .00 -.03 .02 Food -.13 -.13 .15 .01 -.11 Not being understood .04 .46 .11 -.05 -.01 Friendliness of the people .27 .42 -.18 .06 .01 Boredom .35 -.05 .10 .42 -.09
- We cna all tell how much of a cake has been eaten. This is why no matter how many people tell us that pie charts are rubbish, we still use them anyway. We know they work.
- We have evolved to compare the height of things. This is why column charts are so good.
- Through our history we have needed to judge the slopes of mountains and hills, so it is no surprise we find line and area charts easy to interpret.
- We do not expect to see “trends” in piles We anticipate trends in lines, which is why line charts are so much better for trend data, and one of many reasons why the chart on the right is so much better.
- We instinctively understand heat.
- Which is why typhoid jumps out at us on this visualization.
- But, this grey, red, green scale is not natural, which makes this visualization hard to process. http://blogs.hbr.org/2013/04/how-p-and-g-presents-data/
- “I love this graph because it shows that while the number of people dying from communicable diseases is still far too high, those numbers continue to come down.” Bill Gates “This is an important message and a noble goal. But how well does the graph above tell this story? Not very well, actually.” Stephen Few I’m with Bill gates on this one. I think it is awesome.
- Stephen Few created an improved version. He has misunderstood entirely the previous visualization. This is not really a visualization. It has no shape. No pattern. It is something that you need to read like a table.
- This is a chord diagram. Oh so cool. But, not so natural to read.
- It looks like the Great Pit of Carkoon from Return of the Jedi. Fascinating Yes, but not somewhere we expect to find a pattern.
- This is often described as the greatest visualization of all time. But, it needs to be explained to you before you can understand it. How many people bother?
- This is awesome. Not pretty. But awesome. The small multiples have been lined up to make patterns easy. If I say to you, which industries first declined and then picked themselves up, you can quickly work it out http://wfive.files.wordpress.com/2012/03/linkf.png
- This is my favorite visualization of all time. Each area shows the size of an economy, from 2000BC through to 1804. Note the middle of the map. The dark ages, when only Constantinople was around.
- To summzrize: we can use these 24 techniques to help us create supernormal shapes, which is at the heart of creating good visualizations.
- If you want to create great visualizations you need to escape from Excel. It is an either or situation. You can have Excel charts with the data underneath. Or, you can have good visualizations. If you want the client to access the data, you can do so by putting it online. This means you can get them to self-service, so that saves time too.
- If you are a Q Customer, you can do all of this from the Create menu, go to Charts and Visualizations. If you need some help, just reach out to the support team. If you aren’t yet a customer, go to q-researchsoftware.com and click Book a Demo.