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Data Visualisation for Data Science

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La visualisation est un élément important de la compréhension et de la (re)présentation des données dans les (data) sciences. Elle repose sur des principes et des outils que Christophe Bontemps (Toulouse School of Economics) décryptera à la lumière de son expérience et de ses lectures.

Publié dans : Technologie
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Data Visualisation for Data Science

  1. 1. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ Data Visualization for Data Science Principles in action Christophe Bontemps Toulouse School of Economics, INRA
  2. 2. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ MY JOB
  3. 3. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHY I’M HERE ? From Huff (1993)
  4. 4. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHY I’M HERE ? From Huff (1993)
  5. 5. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHY I’M HERE ? From Huff (1993)
  6. 6. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHY I’M HERE ? From Huff (1993)
  7. 7. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ BEFORE WE START Let’s do a simple exercise (from Buja et al. (2009))
  8. 8. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ THE “VISUAL PERCEPTION” OF A GRAPHIC (source : Buja et al. (2009))
  9. 9. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ THE “VISUAL PERCEPTION” OF A GRAPHIC (source : Buja et al. (2009))
  10. 10. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A STATISTICAL TEST
  11. 11. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A STATISTICAL TEST “ The human eye acts is a broad feature detector and general statistical test”. Buja et al. (2009)
  12. 12. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A STATISTICAL TEST “ The human eye acts is a broad feature detector and general statistical test”. Buja et al. (2009) Test : H0 : {There is "nothing" } = {No relation}
  13. 13. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A STATISTICAL TEST “ The human eye acts is a broad feature detector and general statistical test”. Buja et al. (2009) Test : H0 : {There is "nothing" } = {No relation} H1 : { There is "something" } = {There is some relation (Correlation, linearity, heterogeneity, groups..) }
  14. 14. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A COMPARISON
  15. 15. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A COMPARISON What do you see here ?
  16. 16. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A COMPARISON What do you see here ?
  17. 17. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A COMPARISON What do you see here ? Difficult to see the maximum/minimum of each curve...
  18. 18. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “VISUAL PERCEPTION” AS A COMPARISON What do you see here ? Difficult to see the maximum/minimum of each curve... Idea shared by Gelman (2004) and Munzner (2014)
  19. 19. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data
  20. 20. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data A statistic too, is a function or a summary of the data
  21. 21. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data A statistic too, is a function or a summary of the data So, it is a sort of statistic
  22. 22. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data A statistic too, is a function or a summary of the data So, it is a sort of statistic It can be descriptive or inferential
  23. 23. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data A statistic too, is a function or a summary of the data So, it is a sort of statistic It can be descriptive or inferential Two or multi-dimensional
  24. 24. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data A statistic too, is a function or a summary of the data So, it is a sort of statistic It can be descriptive or inferential Two or multi-dimensional Static or dynamic
  25. 25. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data A statistic too, is a function or a summary of the data So, it is a sort of statistic It can be descriptive or inferential Two or multi-dimensional Static or dynamic Informative or not
  26. 26. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data A statistic too, is a function or a summary of the data So, it is a sort of statistic It can be descriptive or inferential Two or multi-dimensional Static or dynamic Informative or not Misleading or accurately representing the data
  27. 27. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? It is a representation, a function of the data A statistic too, is a function or a summary of the data So, it is a sort of statistic It can be descriptive or inferential Two or multi-dimensional Static or dynamic Informative or not Misleading or accurately representing the data Beautiful or ugly....
  28. 28. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? For Tukey (1977) “The greatest value of a picture is when it forces us to notice what we never expected to see”
  29. 29. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? For Tukey (1977) “The greatest value of a picture is when it forces us to notice what we never expected to see” Cleveland (1994) says that “graphical methods and techniques are powerful tools for showing the structure of data. The material is relevant for data analysis, when the analyst wants to study data, and for data communication, when the analyst wants to communicate data to others”
  30. 30. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? For Tukey (1977) “The greatest value of a picture is when it forces us to notice what we never expected to see” Cleveland (1994) says that “graphical methods and techniques are powerful tools for showing the structure of data. The material is relevant for data analysis, when the analyst wants to study data, and for data communication, when the analyst wants to communicate data to others” Bertin (2005) (translated in Bertin (1983)) defines it as a "visual language" and, as such, with a semiology, i.e. with a theory of the functions of signs and symbols.
  31. 31. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT IS DATA VISUALIZATION ? For Tukey (1977) “The greatest value of a picture is when it forces us to notice what we never expected to see” Cleveland (1994) says that “graphical methods and techniques are powerful tools for showing the structure of data. The material is relevant for data analysis, when the analyst wants to study data, and for data communication, when the analyst wants to communicate data to others” Bertin (2005) (translated in Bertin (1983)) defines it as a "visual language" and, as such, with a semiology, i.e. with a theory of the functions of signs and symbols. Tufte (2001) “ Graphics are instruments for reasoning about quantitative information. Often the most effective way to describe , explore and summarize a set of numbers - even a large set - is to look at pictures of those numbers”
  32. 32. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ? Data visualisation serves different purposes : Explanatory data analysis
  33. 33. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ? Data visualisation serves different purposes : Explanatory data analysis Statistical questioning of data patterns
  34. 34. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ? Data visualisation serves different purposes : Explanatory data analysis Statistical questioning of data patterns Visual display of information for communication
  35. 35. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ? Data visualisation serves different purposes : Explanatory data analysis Statistical questioning of data patterns Visual display of information for communication Tool for interacting with data
  36. 36. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ 2 TYPES OF GRAPHICS : THOSE IMMEDIATE TO UNDERSTAND FIGURE – Seen on HK-TV
  37. 37. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ 2 TYPES OF GRAPHICS : THOSE IMMEDIATE TO UNDERSTAND FIGURE – Seen on HK-TV
  38. 38. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ 2 TYPES OF GRAPHICS : THOSE IMMEDIATE TO UNDERSTAND FIGURE – Where do people run in Paris (N. Yau) source : http://flowingdata.com/2014/02/05/where-people-run/
  39. 39. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ 2 TYPES OF GRAPHICS : THOSE IMMEDIATE TO UNDERSTAND FIGURE – Climate forecast uncertainty (S. Planton)
  40. 40. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ ... AND THOSE NOT UNDERSTOOD IMMEDIATELY : FIGURE – (Dynamic) Parallel Coordinates Plot comparing 5 indicators for 3 countries (Sweden, Nigeria and Germany). source : http://ncva.itn.liu.se/education-geovisual-analytics/parallel-c
  41. 41. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ ... AND THOSE NOT UNDERSTOOD IMMEDIATELY : FIGURE – Pagerank Algorithm Reveals World’s All-Time Top Soccer Team (MIT Review, March 2015)
  42. 42. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ ... AND THOSE NOT UNDERSTOOD IMMEDIATELY : FIGURE – How people spend their days (NYT).
  43. 43. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “GOOD” OR “BAD” GRAPHICS ? “There are no “good” nor “bad” graphics (...), there are graphics answering legitimate questions and graphics that do not answer question at all ” Bertin (1981)
  44. 44. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FAMOUS EXAMPLES OF “GOOD” VISUALIZATIONS FIGURE – Charles Minard’s (1869) chart showing the number of men in Napoleon’s 1812 Russian campaign army, their movements, as well as the temperature they encountered on the return path.
  45. 45. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FAMOUS EXAMPLES OF “GOOD” VISUALIZATIONS FIGURE – Charles Minard’s (1869) chart showing the number of men in Napoleon’s 1812 Russian campaign army, their movements, as well as the temperature they encountered on the return path.
  46. 46. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FAMOUS EXAMPLES OF “GOOD” VISUALIZATIONS FIGURE – Charles Minard’s (1869) chart showing the number of men in Napoleon’s 1812 Russian campaign army, their movements, as well as the temperature they encountered on the return path.
  47. 47. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FAMOUS EXAMPLES OF “GOOD” VISUALIZATIONS FIGURE – London Cholera Map - John Snow (1854)
  48. 48. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FAMOUS EXAMPLES OF “GOOD” VISUALIZATIONS
  49. 49. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FAMOUS EXAMPLES OF “GOOD” VISUALIZATIONS FIGURE – War Mortality - Florence Nightingale (1855) found that Zymotic diseases (blue) > wounds injuries.
  50. 50. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FAMOUS EXAMPLES OF “GOOD” VISUALIZATIONS Same data with “modern” visualisation tools. Gelman and Unwin (2011) FIGURE – War Mortality - Florence Nightingale (1855) redrawn by Gelman and Unwin (2011).
  51. 51. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FAMOUS EXAMPLES OF “GOOD” VISUALIZATIONS FIGURE – Visualizing 5 dimensions : Gapminder (Hans Rosling)
  52. 52. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ARE THE RULES ? Can you name some rules for a good (resp. bad) graphic ? Your turn !
  53. 53. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ARE THE RULES ? Can you name some rules for a good (resp. bad) graphic ? Your turn ! Axis and scale (starting at zero !) ?
  54. 54. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ARE THE RULES ? Can you name some rules for a good (resp. bad) graphic ? Your turn ! Axis and scale (starting at zero !) ? Context ?
  55. 55. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ARE THE RULES ? Can you name some rules for a good (resp. bad) graphic ? Your turn ! Axis and scale (starting at zero !) ? Context ? No multiple scales ?
  56. 56. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SO WHAT ARE THE RULES ? Can you name some rules for a good (resp. bad) graphic ? Your turn ! Axis and scale (starting at zero !) ? Context ? No multiple scales ? Colors ?
  57. 57. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ YOUR TURN : WHAT’S WRONG WITH THIS GRAPHIC ?
  58. 58. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ BANANA’S SALES HAVE INCREASED ! FIGURE – from A. Dix example of interactive bar chart
  59. 59. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT’S WRONG WITH THIS GRAPHIC ? FIGURE – Government spending "Skyrocketing".Tufte (2001) from Playfair(1786).
  60. 60. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SCALES ARE MISLEADING ! FIGURE – Governemnt spending "Skyrocketing" (revisited). Tufte (2001) from Playfair(1786).
  61. 61. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT’S WRONG WITH THIS GRAPHIC ? (HARDER) FIGURE – Major Cause of Disability - 1975-2010 (J. Schwabish, 2014).
  62. 62. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT’S WRONG WITH THIS GRAPHIC ? (HARDER) Do you remember a damn thing of this graph ?
  63. 63. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ (SMALL) MULTIPLE GRAPHS, ARE OFTEN BETTER FIGURE – Major Cause of Disability- 1975-2010 (J. Schwabish). Cf. "brushing" (ex : for parallel Coordinates plots)
  64. 64. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT’S WRONG WITH THIS GRAPHIC ? (HARDER)
  65. 65. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ KEEP ALL YOUR AUDIENCE Normal → Color-blind →
  66. 66. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHICH MEANS THAT FOR 5 % OF MEN : See also the ggplot option + scale_colour_colorblind()
  67. 67. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ DATA VISUALISATION IS USED FOR TWO MAIN PURPOSES Data exploration Graphs as visual tests, comparisons (short time to built and to read)
  68. 68. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ DATA VISUALISATION IS USED FOR TWO MAIN PURPOSES Data exploration Graphs as visual tests, comparisons (short time to built and to read) Data representation Summaries, storytelling (long time to build, short time to read)
  69. 69. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ DATA VISUALISATION IS USED FOR TWO MAIN PURPOSES Data exploration Graphs as visual tests, comparisons (short time to built and to read) Data representation Summaries, storytelling (long time to build, short time to read) The problem is that : “ Communicating implies simplification data exploration implies exhaustivity”
  70. 70. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TABLES VS GRAPHICS ? Several papers have discussed the issue : Gelman et al. (2002), Gelman (2011) and Friendly and Kwan (2012). Here, descriptive statistics of continuous variables.
  71. 71. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TABLES VS GRAPHICS ? Graph version of the table. From Gelman (2011)
  72. 72. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ GRAPHICS reveal DATA : ANSCOMBE (1973) QUARTET We use here 4 couples of random variables : (X1, Y1), (X2, Y2) (X3, Y3) and (X4, Y4). All four data sets have the same descriptive statistics. Xs Mean Std. Dev. Ys Mean Std. Dev. corr(Xi, Yi) N X1 9 3.32 Y1 7.5 2.03 0.8164 11 X2 9 3.32 Y2 7.5 2.03 0.8162 11 X3 9 3.32 Y3 7.5 2.03 0.8163 11 X4 9 3.32 Y4 7.5 2.03 0.8165 11
  73. 73. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ ANSCOMBE (1973) QUARTET All four data sets are described by the same linear model (Yi = α + βXi + i), revealing apparently the same relationships : Dependent variable : Y1 Y2 Y3 Y4 Regressed on : Xi, i=1,...,4 0.500 ∗∗∗ 0.500∗∗∗ 0.500∗∗∗ 0.500∗∗∗ Constant 3.000∗∗ 3.001∗∗ 3.002∗∗ 3.002∗∗ R2 0.667 0.666 0.666 0.667 Resid Std. Error 1.237 1.237 1.236 1.236 F Statistic 17.990∗∗∗ 17.966∗∗∗ 17.972∗∗∗ 18.003∗∗∗ Note : Data from Anscombe (1973). ∗ p <0.1 ; ∗∗ p < 0.05 ; ∗∗∗ p < 0.01
  74. 74. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ ANSCOMBE (1973) QUARTET A simple scatter plot (regression overlaid) shows something very different. 4 8 12 5 10 15 x1 y1 Regression of Y1 on X1 (with constant) 4 8 12 5 10 15 x2 y2 Regression of Y2 on X2 (with constant) 4 8 12 5 10 15 x3 y3 Regression of Y3 on X3 (with constant) 4 8 12 5 10 15 x4 y4 Regression of Y4 on X4 (with constant)
  75. 75. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ ANSCOMBE (1973) QUARTET NP : Plots of the residuals shows also same differences −2 −1 0 1 2 5 6 7 8 9 10 Fitted values Residuals Residual vs Fitted Plot −2 −1 0 1 5 6 7 8 9 10 Fitted values Residuals Residual vs Fitted Plot −1 0 1 2 3 5 6 7 8 9 10 Fitted values Residuals Residual vs Fitted Plot −1 0 1 2 7 8 9 10 11 12 Fitted values Residuals Residual vs Fitted Plot
  76. 76. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TABLES AND MATRICES Data with many 0/1 variables (indicators for towns) Bertin (1981)
  77. 77. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TABLES AND MATRICES
  78. 78. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TABLES AND MATRICES Bertin (1981)
  79. 79. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ AND IN MANY DIMENSIONS ?
  80. 80. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TABLES AND MATRICES From Munzner (2014)
  81. 81. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TABLES AND MATRICES From Munzner (2014)
  82. 82. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TABLES AND MATRICES From Munzner (2014)
  83. 83. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ REGRESSION TABLES ARE GRAPHICS ! (Mod. 1) (Mod. 2) Special Special i_under18 -0.0692∗ -0.119∗∗∗ (-2.25) (-3.57) log_income 0.116∗∗∗ 0.102∗∗∗ (4.31) (3.51) i_car 0.00131 -0.112∗ (0.03) (-2.00) b08_locenv_water 0.0624∗∗∗ 0.0583∗∗ (4.99) (4.28) i_can 0.710∗∗∗ (23.27) Constant -1.467∗∗∗ -0.961∗∗ (-5.38) (-3.24) Classical "visualisation" of regressions
  84. 84. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ REGRESSION TABLES ARE GRAPHICS ! (Mod. 1) (Mod. 2) Special Special i_under18 -0.0692 -0.119 (-2.25) (-3.57) log_income 0.116 0.102 (4.31) (3.51) i_car 0.00131 -0.112 (0.03) (-2.00) b08_locenv_water 0.0624 0.0583 (4.99) (4.28) i_can 0.710 (23.27) Constant -1.467 -0.961 (-5.38) (-3.24) Stars are used as preattentive visual variables !
  85. 85. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ REGRESSION AS A GRAPHIC
  86. 86. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ GOOD GRAPHICS ? It the excellent Handbook of data visualisation Chen et al. (2007), we find some good questions : What to Whom, How and Why ? A graphic may be linked to three pieces of text : its caption, a headline and an article it accompanies. Ideally, all three should be consistent and complement each other.
  87. 87. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ GOOD GRAPHICS ? It the excellent Handbook of data visualisation Chen et al. (2007), we find some good questions : What to Whom, How and Why ? A graphic may be linked to three pieces of text : its caption, a headline and an article it accompanies. Ideally, all three should be consistent and complement each other. Present or explore data ? Different purpose, different requirements !
  88. 88. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ GOOD GRAPHICS ? It the excellent Handbook of data visualisation Chen et al. (2007), we find some good questions : What to Whom, How and Why ? A graphic may be linked to three pieces of text : its caption, a headline and an article it accompanies. Ideally, all three should be consistent and complement each other. Present or explore data ? Different purpose, different requirements ! Choice of Graphical form ? Choice depends on the type of data to be displayed (e.g. univariate continuous data, bivariate categorical data, etc..) and on what is to be shown.
  89. 89. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ GOOD GRAPHICS ? It the excellent Handbook of data visualisation Chen et al. (2007), we find some good questions : What to Whom, How and Why ? A graphic may be linked to three pieces of text : its caption, a headline and an article it accompanies. Ideally, all three should be consistent and complement each other. Present or explore data ? Different purpose, different requirements ! Choice of Graphical form ? Choice depends on the type of data to be displayed (e.g. univariate continuous data, bivariate categorical data, etc..) and on what is to be shown. Unique solution ? There is not always a unique optimal choice and alternatives can be equally good or good in different ways, emphasizing different aspects of the same data.
  90. 90. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ EDWARD R. TUFTE’S RULES In his seminal book, Tufte (2001) propose some principles for displaying quantitative information. Data : Above all, show the data
  91. 91. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ EDWARD R. TUFTE’S RULES In his seminal book, Tufte (2001) propose some principles for displaying quantitative information. Data : Above all, show the data Question : Induce the viewer to think about the substance rather than about methodology, graphic design. Encourage the eye to compare different piece of data.
  92. 92. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ EDWARD R. TUFTE’S RULES In his seminal book, Tufte (2001) propose some principles for displaying quantitative information. Data : Above all, show the data Question : Induce the viewer to think about the substance rather than about methodology, graphic design. Encourage the eye to compare different piece of data. Data-ink ratio : Maximize the ink-data ratio. Erase all non data ink, Erase redundant information
  93. 93. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ EDWARD R. TUFTE’S RULES In his seminal book, Tufte (2001) propose some principles for displaying quantitative information. Data : Above all, show the data Question : Induce the viewer to think about the substance rather than about methodology, graphic design. Encourage the eye to compare different piece of data. Data-ink ratio : Maximize the ink-data ratio. Erase all non data ink, Erase redundant information Integrity : Avoid distorting what the data have to say
  94. 94. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ EDWARD R. TUFTE’S RULES In his seminal book, Tufte (2001) propose some principles for displaying quantitative information. Data : Above all, show the data Question : Induce the viewer to think about the substance rather than about methodology, graphic design. Encourage the eye to compare different piece of data. Data-ink ratio : Maximize the ink-data ratio. Erase all non data ink, Erase redundant information Integrity : Avoid distorting what the data have to say General to specific : Reveal the data at different levels of detail (from broad picture to fine structure)
  95. 95. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ EDWARD R. TUFTE’S RULES In his seminal book, Tufte (2001) propose some principles for displaying quantitative information. Data : Above all, show the data Question : Induce the viewer to think about the substance rather than about methodology, graphic design. Encourage the eye to compare different piece of data. Data-ink ratio : Maximize the ink-data ratio. Erase all non data ink, Erase redundant information Integrity : Avoid distorting what the data have to say General to specific : Reveal the data at different levels of detail (from broad picture to fine structure) Context : Graphical display should be closely integrated with the statistical and verbal descriptions of the data set.
  96. 96. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PRACTICAL EXAMPLE : DATA-INK RATIO Let’s start with a classical graph (R default - Boxplot ) g1 g2 g3 g4 g5 98100102104106108110112 Groupe Response FIGURE – Distribution of a continuous variable on 4 groups
  97. 97. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ ERASE ALL NON DATA INK Groupe Response 1 2 3 4 5 98100102104106108110112 FIGURE – Distribution of a continuous variable on 4 groups
  98. 98. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ ERASE ALL REDUNDANT ! Groupe Response 1 2 3 4 5 98100102104106108110112 FIGURE – Distribution of a continuous variable on 4 groups
  99. 99. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ GOING FURTHER... Groupe Response 1 2 3 4 5 98100102104106108110112 FIGURE – Distribution of a continuous variable on 4 groups
  100. 100. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ AND SHOW THE DATA... Groupe Response 101.0 100.0 101.0 103.8 109.1 1 2 3 4 5 FIGURE – Distribution of a continuous variable on 4 groups
  101. 101. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ HAVE WE LOST SOMETHING ? g1 g2 g3 g4 g5 98100102104106108110112 Groupe Response Groupe Response 101.0 100.0 101.0 103.8 109.1 1 2 3 4 5 FIGURE – Distribution of a continuous variable on 4 groups Did you noticed that group 1 and group 3 had the same median (101.0) ? see the ggplot theme + theme_tufte()
  102. 102. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ INTEGRITY : THE LIE FACTOR LieFactor = Size of effect shown in graphic Size of effect in data (1) A Lie Factor = 1 indicates a substantial distortion FIGURE – Fuel economy standards. (E. Tufte - from NY Times 1978)
  103. 103. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FIGURE – Fuel economy standards (revisited) The "18 mpg" line measures 1.5 cm (in 1978) ; the "27,5 mpg" measures 13 cm (in 1985) −→ Lie factor = 14.5% ! ! !
  104. 104. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ BERTIN’S APPROACH : A VISUAL LANGUAGE If graphs are used to communicate, it is a form of language. Any language has a grammar, “words” and logic. Let us study the science that deals with signs or sign language : “The Semiology”. TABLE – Bertin’s definition of 8 visual variables Position (x, y) Size Value Texture Colour Orientation Shape
  105. 105. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ THESE VARIABLES SERVE DIFFERENT GOALS Visual variable syntactics, designating each visual variable as suited or not for levels of measurement : Equivalence, differences, order, proportions. Variable suited for : Position (x, y) = O ∝ Size = O ∝ Value = O ∝ Texture = O Colour = Orientation = Shape ≡ ≡ : Equivalence, = : Differences, O : Order, ∝ : Proportions
  106. 106. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ EXAMPLE : SHAPE IS NOT SUITABLE FOR PROPORTIONALITY Price of land in the East of France Bertin (1970)
  107. 107. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ EXAMPLE : SIZE IS SUITABLE FOR PROPORTIONALITY Price of land in the East of France Bertin (1970)
  108. 108. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ A NOTE ON COLORS “Colors” are not suited for ordering ! Try putting the following hues in order from low to high.
  109. 109. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ A NOTE ON COLORS These colors are easy to order from low to high. Few (2008) provides meaningful solutions for choosing palettes of colours, for example for heatmaps. See also the ggplot theme theme_few()
  110. 110. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ A NOTE ON COLORS (FINAL) Colors are sometimes a graphic puzzle Tufte (2001). Your eyes will go back and forth from the graph to the legend... (source : http://viz.wtf/image/135265269618)
  111. 111. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CONJUNCTION OF COLOURS AND PROPORTIONALITY Productivity of Airlines (Demo with goodleVis)
  112. 112. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ FLASH QUIZZ : If 100% of the US prisoners are represented by the big square...what is the percentage for each group ? FIGURE – Ethic composition of prisoners in Jail in 2008 in the USA. (Le Monde 5/12/2014)
  113. 113. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ NOT SO SIMPLE... If 100% of the US prisoners are represented by the big square...what is the percentage for each group ? FIGURE – Ethic composition of prisoners in Jail in 2008 in the USA. (Le Monde 5/12/2014)
  114. 114. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VERIFICATION If 100% of the US prisoners are represented by the big square...what is the percentage for each group ? →
  115. 115. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ OR... If 100% of the US prisoners are represented by the big square...what is the percentage for each group ?
  116. 116. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ IT MATTERS BECAUSE MANY HIGH DIMENSION VISUALISATION USE AREA.. Spinograms A spinogram is area-proportional just like the histogram, but allows a non-linear x-axis and thus can make all boxes of equal height. Theus and Urbanek (2009)
  117. 117. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ MOSAIC PLOTS Step 1 of the construction of a mosaic plot (Similar to spineplot here). All surviving passengers are highlighted in all plots. Theus and Urbanek (2009)
  118. 118. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ MOSAIC PLOTS Step 2 of the construction of a mosaic plot. Conditioning on Age.Theus and Urbanek (2009)
  119. 119. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ MOSAIC PLOTS Step 3 of the construction of a mosaic plot. Conditioning on Age and Gender.Theus and Urbanek (2009)
  120. 120. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ MOSAIC PLOTS Final step of the construction of a mosaic plot. Explicit mention of Survived as highlighted.Theus and Urbanek (2009)
  121. 121. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SCHWABISH (JEP, 2014) BEFORE-AFTER FIGURE – An Unbalanced Chart - Original
  122. 122. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SCHWABISH (JEP, 2014) BEFORE-AFTER FIGURE – An Unbalanced Chart - Revised
  123. 123. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SCHWABISH (JEP, 2014) BEFORE-AFTER FIGURE – A Clutterplot Example - Original
  124. 124. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ SCHWABISH (JEP, 2014) BEFORE-AFTER FIGURE – A Clutterplot Example - Revised
  125. 125. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “GOOD” OR “BAD” GRAPHICS ? “There are no “good” nor “bad” graphics (...), there are graphics answering legitimate questions and graphics that do not answer question at all ” Bertin (1981) It is easy to criticize ... but are there some rules ?
  126. 126. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ A NOTE ON PERCEPTION A bird (Duck, Toucan ?) on the X axis, a rabbit on the Y axis ! // Source http://flowingdata.com/2014/06/25/duck-vs-rabbit-plot/
  127. 127. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “PREATTENTIVE” VARIABLES How many "3" in that sequence ? (from Ware (2012))
  128. 128. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “PREATTENTIVE” VARIABLES How many "3" in that sequence ? (from Ware (2012))
  129. 129. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ “PREATTENTIVE” VARIABLES How many "3" in that sequence ? (from Ware (2012))
  130. 130. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ AND NOW... Find the red dot !
  131. 131. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  132. 132. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  133. 133. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  134. 134. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  135. 135. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  136. 136. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  137. 137. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  138. 138. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  139. 139. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  140. 140. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  141. 141. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  142. 142. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TEST : FIND THE RED DOT !
  143. 143. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ HARDER : IS THERE A "STRANGER" ?
  144. 144. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ HARDER : IS THERE A "STRANGER" ?
  145. 145. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ HARDER : IS THERE A "STRANGER" ?
  146. 146. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ HARDER : IS THERE A "STRANGER" ?
  147. 147. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ HARDER : IS THERE A "STRANGER" ?
  148. 148. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ HARDER : IS THERE A "STRANGER" ?
  149. 149. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ HARDER : IS THERE A "STRANGER" ?
  150. 150. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ THAT WASN’T EASY Preattentive concept, Treisman (1985) and Healey (2007) Some visual elements or patterns are detected immediately But there may be interferences (colour and form) Very useful (detection, explanatory and presentation) Helpful to highlight a message !
  151. 151. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ TOO MUCH VARIATION DOESN’T HELP From Ware (2012)
  152. 152. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ MOST PREATTENTIVE VISUAL VARIABLES From Ware (2012)
  153. 153. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND PIE CHARTS
  154. 154. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND PIE CHARTS
  155. 155. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND PIE CHARTS https://twitter.com/freakonometrics/status/6127423301609512
  156. 156. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND LINES From Cairo (2012)
  157. 157. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND LINES When was the biggest negative (positive) difference ? From Cairo (2012)
  158. 158. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND LINES
  159. 159. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND LINES When was the biggest negative (positive) difference ?
  160. 160. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND LINES When was the biggest negative (positive) difference ?
  161. 161. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ VISUAL PERCEPTION AND LINES When was the biggest negative (positive) difference ? From Cairo (2012)
  162. 162. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ THE CLEVELAND-MCGILL EFFECT
  163. 163. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ THE CLEVELAND-MCGILL EFFECT
  164. 164. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ THE CLEVELAND-MCGILL EFFECT From Cleveland and McGill (1984)
  165. 165. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WEBER’S LAW AND FRAMED BOXES
  166. 166. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WEBER’S LAW AND FRAMED BOXES
  167. 167. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WEBER’S LAW AND FRAMED BOXES From Cleveland and McGill (1984)
  168. 168. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ THE CLEVELAND-MCGILL SCALE http://hcil2.cs.umd.edu/trs/99-20/99-20.html
  169. 169. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Gordon and Finch (2015) gives some nice principles
  170. 170. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Gordon and Finch (2015) gives some nice principles 1. Show the data clearly
  171. 171. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Gordon and Finch (2015) gives some nice principles 1. Show the data clearly 2. Use simplicity in design
  172. 172. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Gordon and Finch (2015) gives some nice principles 1. Show the data clearly 2. Use simplicity in design 3. Use good alignment on a common scale for quantities to be compared
  173. 173. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Gordon and Finch (2015) gives some nice principles 1. Show the data clearly 2. Use simplicity in design 3. Use good alignment on a common scale for quantities to be compared 4. Keep visual encoding transparent
  174. 174. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Gordon and Finch (2015) gives some nice principles 1. Show the data clearly 2. Use simplicity in design 3. Use good alignment on a common scale for quantities to be compared 4. Keep visual encoding transparent 5. Use graphical forms consistent with those principles
  175. 175. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Gordon and Finch (2015) gives some nice principles 1. Show the data clearly 2. Use simplicity in design 3. Use good alignment on a common scale for quantities to be compared 4. Keep visual encoding transparent 5. Use graphical forms consistent with those principles We may add some others (use preattentive elements, integrity, ...)
  176. 176. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Do not forget the big picture
  177. 177. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ PARTIAL CONCLUSION Do not forget the big picture
  178. 178. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : VISUALIZING THE WHOLE AND THE DETAILS ! 2588 dairy farmers over 11 years. One variable is estimated : risk aversion (AR) 6 region of study Don’t know the results https: //xtophedataviz.shinyapps.io/ShinyParallel/
  179. 179. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : RISK AVERSION Simple plot : Median value over time.
  180. 180. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : RISK AVERSION Simple plot : Median value with dispersion visualized.
  181. 181. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : RISK AVERSION Classical BoxPlot : There are changes over time.
  182. 182. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : HOW TO VISUALIZE FARMS ? Points over time : Too much overlapping
  183. 183. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : HOW TO VISUALIZE FARMS ? Points over time : Jitter helps !
  184. 184. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : HOW TO VISUALIZE FARMS ? Farms over time : Jitter helps !
  185. 185. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : HOW TO VISUALIZE FARMS ? Farms over time : Spaghetti plots !
  186. 186. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : HOW TO VISUALIZE FARMS ? Farms over time : Spaghetti plots with some Brushing !
  187. 187. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : HOW TO VISUALIZE FARMS ? Farms over time by region : Multiple Spaghetti plots !
  188. 188. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : HOW TO VISUALIZE FARMS ? Farms over time : Spaghetti plots with some Brushing !
  189. 189. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CASE STUDY : HOW TO VISUALIZE FARMS ? Farms over time by region : Highlighting Spaghetti plots !
  190. 190. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER Data visualisation serves at least two main purposes Data exploration Graphs as visual tests, comparisons (short time to built and to read)
  191. 191. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER Data visualisation serves at least two main purposes Data exploration Graphs as visual tests, comparisons (short time to built and to read) Data representation Summaries, storytelling (long time to build, short time to read)
  192. 192. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER Data visualisation serves at least two main purposes Data exploration Graphs as visual tests, comparisons (short time to built and to read) Data representation Summaries, storytelling (long time to build, short time to read) The problem is that : “ Communicating implies simplification data exploration implies exhaustivity”
  193. 193. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER From the viewer“data visualisation” are implicitly or explicitly comparisons or even tests (in the statistical sense) Graphics should help questioning
  194. 194. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER From the viewer“data visualisation” are implicitly or explicitly comparisons or even tests (in the statistical sense) Graphics should help questioning They should provide elements, to answer (data at least)
  195. 195. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER From the viewer“data visualisation” are implicitly or explicitly comparisons or even tests (in the statistical sense) Graphics should help questioning They should provide elements, to answer (data at least) If the question implies comparison, they should truthfully show the comparison
  196. 196. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER Many “data visualisation” are useless, meaningless or stupid ! Some are simply poor : Graphs as visual tests, comparisons (short time to built and to read)
  197. 197. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER Many “data visualisation” are useless, meaningless or stupid ! Some are simply poor : Graphs as visual tests, comparisons (short time to built and to read) Some are funny :
  198. 198. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER Many “data visualisation” are useless, meaningless or stupid ! Some are simply poor : Graphs as visual tests, comparisons (short time to built and to read) Some are funny : Many are ridiculous :
  199. 199. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ CHALLENGES : NETWORKS Relationships of all of Victor Hugo’s characters of "Les Miserables". http://bl.ocks.org/mbostock/4062045_
  200. 200. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ NETWORKS : ADJACENT MATRIX PLOT An adjacency matrix, where each cell ij represents an edge from vertex i to vertex j. Here, vertices represent characters in a book, while edges represent co-occurrence in a chapter.
  201. 201. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ NETWORKS : ADJACENT MATRIX PLOT Here again, sorting is very useful !
  202. 202. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER : THERE ARE RULES Data visualisation is a visual language, so there are : Elements of language
  203. 203. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER : THERE ARE RULES Data visualisation is a visual language, so there are : Elements of language Rules of use (spelling)
  204. 204. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER : THERE ARE RULES Data visualisation is a visual language, so there are : Elements of language Rules of use (spelling) Grammar
  205. 205. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER : A GOOD TECHNIQUE DOES NOT PRECLUDE GOOD COMMON SENSE ! let’s... KISS
  206. 206. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER : A GOOD TECHNIQUE DOES NOT PRECLUDE GOOD COMMON SENSE ! let’s... KISS Keep It Simple Stupid !
  207. 207. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER : A GOOD TECHNIQUE DOES NOT PRECLUDE GOOD COMMON SENSE ! let’s... KISS Keep It Simple Stupid ! Keep It Statistical Stupid !
  208. 208. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ WHAT TO REMEMBER : A GOOD TECHNIQUE DOES NOT PRECLUDE GOOD COMMON SENSE ! let’s... KISS Keep It Simple Stupid ! Keep It Statistical Stupid ! Keep It Statistical and Simple !
  209. 209. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ REFERENCES I Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1) :17–21. Bertin, J. (1970). La graphique. Communications, 15(1) :169–185. Bertin, J. (1981). Théorie matricielle de la graphique. Communication et langages, 48(1) :62–74. Bertin, J. (1983). Semiology of graphics, translation from sémilogie graphique (1967). Bertin, J. (2005). Sémiologie graphique : Les diagrammes, les réseaux, les cartes. Les Réimpressions des Éditions de l’École des Hautes Études en Sciences Sociales. Éditions de l’École des Hautes Études en Sciences Sociales. Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E.-K., Swayne, D. F., and Wickham, H. (2009). Statistical inference for exploratory data analysis and model diagnostics. Philosophical Transactions of the Royal Society of London A : Mathematical, Physical and Engineering Sciences, 367(1906) :4361–4383. Cairo, A. (2012). The Functional Art : An introduction to information graphics and visualization. Voices That Matter. Pearson Education.
  210. 210. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ REFERENCES II Chen, C.-h., Härdle, W. K., and Unwin, A. (2007). Handbook of data visualization. Springer Science & Business Media. Cleveland, W. S. (1994). The Elements of Graphing Data. Hobart Press, Summit : NJ, 2 edition. Cleveland, W. S. and McGill, R. (1984). Graphical perception : Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387) :531–554. Few, S. (2008). Practical rules for using color in charts. Visual Business Intelligence Newsletter, (11). Friendly, M. and Kwan, E. (2012). Comment. Journal of Computational and Graphical Statistics. Gelman, A. (2004). Exploratory data analysis for complex models. Journal of Computational and Graphical Statistics, 13(4). Gelman, A. (2011). Why tables are really much better than graphs. Journal of Computational and Graphical Statistics, 20(1) :3–7. Gelman, A., Pasarica, C., and Dodhia, R. (2002). Let’s practice what we preach : turning tables into graphs. The American Statistician, 56(2) :121–130.
  211. 211. Definitions Typologies Good vs bad Tables Principles Before After Visual perception An example What to remember Référ REFERENCES III Gelman, A. and Unwin, A. (2011). Visualization, graphics, and statistics. Statistical Computing and graphics, 22(1) :9–12. Gordon, I. and Finch, S. (2015). Statistician heal thyself : Have we lost the plot ? Journal of Computational and Graphical Statistics, 24(4) :1210–1229. Healey, C. (2007). Perception in visualization. Huff, D. (1993). How to Lie with Statistics. W. W. Norton & Company. Munzner, T. (2014). Visualization Analysis and Design. AK Peters Visualization Series. A K Peters/CRC Press, 1 edition. Theus, M. and Urbanek, S. (2009). Interactive graphics for data analysis : principles and examples. Series in computer science and data analysis. CRC Press. Treisman, A. (1985). Preattentive processing in vision. Computer Vision, Graphics, and Image Processing, 31(2) :156–177. Tufte, E. R. (2001). The Visual Display of Quantitative Information. Graphics Press, 2 edition. Tukey, J. W. (1977). Exploratory data analysis. Reading, Mass. Ware, C. (2012). Information visualization : perception for design. Elsevier.

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