#26 La data visualisation pour la data science (et les data scientists)

#26 La data visualisation pour la data science (et les data scientists)

Le métier de data scientist comporte bien des facettes, mais repose avant tout sur une bonne appréhension des données. Qu’elles soient brutes ou déjà raffinées, simples ou complexes, ``small’’ ou ``big’’ (ou ``smart’’) , la compréhension de ces ``data’’ passe par une phase d’exploration visuelle. Construire les bons outils de visualisation est alors une étape importante pour deviner les premières caractéristiques de ces données, tester les premières relations, esquisser les premiers patterns, ... Ne pas tromper – ne pas tromper soi-même –, nécessite d’avoir de bons réflexes et de bonnes méthodes afin de voir – peut-être – « ce que l’on ne s’attendait pas à voir » pour reprendre la célèbre phrase de Tuckey. Représenter ou « donner à voir aux autres », est ensuite, un autre challenge, nécessitant d’autres outils, d’autres méthodes, et posant de nouvelles questions que nous aborderons sur la base d’exemples et de contre-exemples patiemment glanés lors de cours, conférences et dans la jungle des publications (web, journaux, TV, pubs…) qui nous assaille quotidiennement.

Christophe Bontemps, ingénieur de recherche (économétrie) à Toulouse School of Economics (INRA), nous propose un tour d'horizon pratique des méthodes et des graphiques qui permettent de visualiser efficacement différents types de données. Il faudra pour cela suivre quelques règles, puisées dans la littérature (statistique, graphisme, psychologie)… pour mieux s'en affranchir, parfois !

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Toulouse Data Science

January 16, 2018
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  1. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références Data Visualization for Data Scientists Christophe Bontemps Toulouse School of Economics, INRA @Xtophe_Bontemps
  2. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références MERCI À dream-team DE TDS
  3. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE “VISUAL PERCEPTION” OF A GRAPHIC What do you see ? 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 )
  4. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE “VISUAL PERCEPTION” OF A GRAPHIC And here, what do you see ? 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some other points (N = 500 )
  5. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références “VISUAL PERCEPTION” AS A STATISTICAL TEST 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some other points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 )
  6. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références “VISUAL PERCEPTION” AS A STATISTICAL TEST 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some other points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) “ The human eye acts is a broad feature detector and general statistical test”. Buja et al. (2009)
  7. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références “VISUAL PERCEPTION” AS A STATISTICAL TEST 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some other points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) “ The human eye acts is a broad feature detector and general statistical test”. Buja et al. (2009) Test : H0 : {There is "nothing" } = {No relation}
  8. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références “VISUAL PERCEPTION” AS A STATISTICAL TEST 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some other points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) 0 10 20 30 40 0 10 20 30 40 Some X variable Some Y variable Some points (N = 500 ) “ 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..) }
  9. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- WHAT IS DATA VISUALISATION ? -] For Tukey (1977) “The greatest value of a picture is when it forces us to notice what we never expected to see”
  10. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- WHAT IS DATA VISUALISATION ? -] 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”
  11. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- WHAT IS DATA VISUALISATION ? -] 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” Bertin (1970) (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.
  12. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- WHAT IS DATA VISUALISATION ? -] 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” Bertin (1970) (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”
  13. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GOALS OF DATA VISUALISATION Data visualisation serves at least two main purposes Data exploration Graphs as visual tests, comparisons → short time to built and to read
  14. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GOALS OF DATA VISUALISATION 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, comparisons, storytelling → long time to build, short time to read
  15. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GOALS OF DATA VISUALISATION 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, comparisons, storytelling → long time to build, short time to read The problem is that : “ Communicating implies simplification data exploration implies exhaustivity”
  16. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "DATA TO VIZ"
  17. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "DATA TO VIZ"
  18. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "DATA TO VIZ"
  19. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "DATA TO VIZ"
  20. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "DATA TO VIZ"
  21. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "VIZ TO DATA"
  22. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "VIZ TO DATA"
  23. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "VIZ TO DATA"
  24. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE BIG PICTURE : FROM "VIZ TO DATA"
  25. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- DECODING VISUAL INFORMATION -]
  26. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GRAPHICS IMMEDIATE TO UNDERSTAND FIGURE – Where do people run in Paris source : (N. Yau) http://flowingdata.com/2014/02/05/where-people-run/
  27. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GRAPHICS IMMEDIATE TO UNDERSTAND FIGURE – Climate forecast uncertainty (S. Planton)
  28. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GRAPHICS THAT NEED EXPLANATIONS : FIGURE – How people spend their days (NYT).
  29. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- MISTAKES ! -] Source : New Yorker
  30. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES FIGURE – (source Wikipedia)
  31. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES : TRUNCATED GRAPH (ZERO AXIS) FIGURE – (source Wikipedia)
  32. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES FIGURE – Are you looking at the right thing ?
  33. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES : LIMITED SCOPE FIGURE – Are you looking at the right thing ? from Flowing data
  34. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES FIGURE – Percentage of student starting and completing a “step” in MOOC Source : My 2017’ students (Jan & Mohamed), but also recent researchers’ presentations
  35. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES : PIE CHARTS
  36. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES : PIE CHARTS
  37. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES : PIE CHARTS Source https://twitter.com/freakonometrics/status/ 612742330160951296
  38. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES : STACKED BARS
  39. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL MISTAKES : LEGITIMATE COMPARISON IMPOSSIBLE ! FIGURE – from Dix and Ellis (1998) example
  40. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références VERY USUAL “Mistake”... FIGURE – Major Cause of Worker Disability (1975-2010) (J. Schwabish, 2014).
  41. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références LEGITIMATE QUESTIONS : What is the highest value observed ? When was it ?
  42. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références LEGITIMATE QUESTIONS : What is the highest value observed ? When was it ? In 2010, what is the major cause of disability ?
  43. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références LEGITIMATE QUESTIONS : What is the highest value observed ? When was it ? In 2010, what is the major cause of disability ? Is cancer (red curve) increasing over the period ?
  44. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références LEGITIMATE QUESTIONS : What is the highest value observed ? When was it ? In 2010, what is the major cause of disability ? Is cancer (red curve) increasing over the period ? In the recent years, which causes have increased (decreased) the most ?
  45. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références LEGITIMATE QUESTIONS : What is the highest value observed ? When was it ? In 2010, what is the major cause of disability ? Is cancer (red curve) increasing over the period ? In the recent years, which causes have increased (decreased) the most ? · · ·
  46. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références LEGITIMATE QUESTIONS : What is the highest value observed ? When was it ? In 2010, what is the major cause of disability ? Is cancer (red curve) increasing over the period ? In the recent years, which causes have increased (decreased) the most ? · · · You do not remember a damn thing of this graph !
  47. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références VERY USUAL “Mistake” : SMALL MULTIPLES FIGURE – Major Cause of Disability - 1975-2010 (J. Schwabish, 2014).
  48. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références VERY USUAL “Mistake” II...
  49. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références VERY USUAL “Mistake” II... What do you see here ?
  50. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références VERY USUAL “Mistake” II... What do you see here ?
  51. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références VERY USUAL “Mistake” II... What do you see here ? Easier to see the trend of each curve...
  52. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références VERY USUAL “Mistake” II... What do you see here ? Easier to see the trend of each curve... Idea shared by Gelman (2004) and Munzner (2014)
  53. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL “Mistakes” : RADAR PLOTS How to represent many characteristics on a single graph ? http://data.visualisation.free.fr/Blog/ Why-you-should-never-use-radar-plots.nb.html
  54. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références USUAL “Mistakes” : BEWARE OF RADAR PLOTS Representation (areas) change with axis order ! http://data.visualisation.free.fr/Blog/ Why-you-should-never-use-radar-plots.nb.html
  55. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- WHY CODING ? -] If decoding is hard, why coding ?
  56. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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
  57. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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
  58. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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)
  59. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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
  60. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- RULES OF THUMB -] “There are no “good” nor “bad” graphics (...), there are graphics answering legitimate questions and graphics that do not answer question at all ” Bertin (1970) It is easy to criticize ... but are there some rules ?
  61. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références COMPARISON IS DIFFICULT Yes ! there are some basic rules : Compare what is comparable → statistics
  62. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références COMPARISON IS DIFFICULT Yes ! there are some basic rules : Compare what is comparable → statistics Use comparable elements → data visualisation
  63. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références COMPARISON IS DIFFICULT Yes ! there are some basic rules : Compare what is comparable → statistics Use comparable elements → data visualisation And many people have already worked on that !
  64. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références COMPARISON IS DIFFICULT Yes ! there are some basic rules : Compare what is comparable → statistics Use comparable elements → data visualisation And many people have already worked on that ! Jacques Bertin
  65. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références COMPARISON IS DIFFICULT Yes ! there are some basic rules : Compare what is comparable → statistics Use comparable elements → data visualisation And many people have already worked on that ! Jacques Bertin Edward Tufte
  66. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références COMPARISON IS DIFFICULT Yes ! there are some basic rules : Compare what is comparable → statistics Use comparable elements → data visualisation And many people have already worked on that ! Jacques Bertin Edward Tufte Tuckey, Cleveland, .....
  67. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références FUNDAMENTAL QUESTIONS (CHEN ET AL. (2007)) 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. → should be consistent and complement each other.
  68. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références FUNDAMENTAL QUESTIONS (CHEN ET AL. (2007)) 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. → should be consistent and complement each other. Show or explore data ? Different purpose, different comparisons, different requirements !
  69. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références FUNDAMENTAL QUESTIONS (CHEN ET AL. (2007)) 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. → should be consistent and complement each other. Show or explore data ? Different purpose, different comparisons, 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, what is tested (compared).
  70. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références FUNDAMENTAL QUESTIONS (CHEN ET AL. (2007)) 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. → should be consistent and complement each other. Show or explore data ? Different purpose, different comparisons, 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, what is tested (compared). Unique solution ? There is not always a unique optimal choice → alternatives can be equally good or good in different ways, emphasizing different aspects of the same data.
  71. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] EDWARD R. TUFTE’S In his seminal book, Tufte (2001) propose some principles for displaying quantitative information. Data : Above all, show the data
  72. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] EDWARD R. TUFTE’S 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.
  73. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] EDWARD R. TUFTE’S 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
  74. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] EDWARD R. TUFTE’S 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
  75. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] EDWARD R. TUFTE’S 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)
  76. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] EDWARD R. TUFTE’S 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.
  77. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références PRACTICAL EXAMPLE : DATA-INK RATIO Let’s start with a classical graph (R default - Boxplot ) g1 g2 g3 g4 g5 98 100 102 104 106 108 110 112 Groupe Response FIGURE – Distribution of a continuous variable on 4 groups
  78. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références ERASE ALL NON DATA INK Groupe Response 1 2 3 4 5 98 100 102 104 106 108 110 112 FIGURE – Distribution of a continuous variable on 4 groups
  79. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références ERASE ALL REDUNDANT ! Groupe Response 1 2 3 4 5 98 100 102 104 106 108 110 112 FIGURE – Distribution of a continuous variable on 4 groups
  80. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GOING FURTHER... Groupe Response 1 2 3 4 5 98 100 102 104 106 108 110 112 FIGURE – Distribution of a continuous variable on 4 groups
  81. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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
  82. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références HAVE WE LOST SOMETHING ? g1 g2 g3 g4 g5 98 100 102 104 106 108 110 112 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()
  83. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références PRACTICAL EXAMPLE : 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)
  84. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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% ! ! !
  85. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références PRACTICAL EXAMPLE : CONTEXT In the next 2 minutes, you will design “the best way” to compare two numbers 75and 37. From S. Ortiz
  86. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  87. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  88. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  89. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  90. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  91. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  92. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  93. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  94. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  95. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  96. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  97. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  98. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  99. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  100. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  101. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  102. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  103. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  104. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  105. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  106. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  107. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  108. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  109. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  110. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  111. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  112. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  113. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  114. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS ! Solutions proposed by S. Ortiz
  115. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS (FOLLOW-UP) What are the problems you’ve encountered ?
  116. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS (FOLLOW-UP) What are the problems you’ve encountered ? Context ?
  117. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS (FOLLOW-UP) What are the problems you’ve encountered ? Context ? Audience ?
  118. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS (FOLLOW-UP) What are the problems you’ve encountered ? Context ? Audience ? What to compare ?
  119. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS (FOLLOW-UP) What are the problems you’ve encountered ? Context ? Audience ? What to compare ? Units ?
  120. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CONTEXT MATTERS (FOLLOW-UP) What are the problems you’ve encountered ? Context ? Audience ? What to compare ? Units ? Multiple solutions ?
  121. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] 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
  122. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références SHAPE IS NOT SUITABLE FOR PROPORTIONALITY Price of land in the East of France Bertin (1970)
  123. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références SIZE IS SUITABLE FOR PROPORTIONALITY Price of land in the East of France Bertin (1970)
  124. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références WHAT ELSE ? Is color suitable for proportionality ?
  125. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références A NOTE ON COLORS “Colors” are not suited for ordering nor for proportionality ! Try putting the following hues in order from low to high.
  126. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références A NOTE ON COLORS These colors are easy to order from low to high. Few (2008) provides meaningful solutions for choosing palettes of colors, for example for heatmaps. See also the ggplot theme theme_few()
  127. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] CLEVELAND-MCGILL
  128. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE CLEVELAND-MCGILL EFFECT
  129. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE CLEVELAND-MCGILL EFFECT
  130. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE CLEVELAND-MCGILL EFFECT From Cleveland and McGill (1984)
  131. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références WEBER’S LAW AND FRAMED BOXES
  132. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références WEBER’S LAW AND FRAMED BOXES
  133. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références WEBER’S LAW AND FRAMED BOXES From Cleveland and McGill (1984)
  134. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [RULES :] ACCESSIBILITY Normal → Color-blind →
  135. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- PERCEPTION -] // Source : http://flowingdata.com/2014/06/25/duck-vs-rabbit-plot/
  136. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références “PREATTENTIVE” VARIABLES How many "3" in that sequence ? (from Ware (2012))
  137. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références “PREATTENTIVE” VARIABLES How many "3" in that sequence ? (from Ware (2012))
  138. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références “PREATTENTIVE” VARIABLES How many "3" in that sequence ? (from Ware (2012))
  139. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références AND NOW... Find the red dot !
  140. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  141. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  142. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  143. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  144. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  145. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  146. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  147. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  148. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  149. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  150. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TEST : FIND THE RED DOT !
  151. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références HARDER : IS THERE A "STRANGER" ?
  152. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références HARDER : IS THERE A "STRANGER" ?
  153. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références HARDER : IS THERE A "STRANGER" ?
  154. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références HARDER : IS THERE A "STRANGER" ?
  155. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références HARDER : IS THERE A "STRANGER" ?
  156. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références HARDER : IS THERE A "STRANGER" ?
  157. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références HARDER : IS THERE A "STRANGER" ?
  158. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THAT WASN’T EASY Preattentive concept, Treisman (1985) 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 !
  159. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références TOO MUCH VARIATION DOESN’T HELP From Ware (2012)
  160. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références MOST PREATTENTIVE VISUAL VARIABLES From Ware (2012)
  161. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références [- BEFORE/AFTER -] Source : Emma Rathbone - The New Yorker
  162. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références BEFORE (SCHWABISH - JEP, 2014) FIGURE – An Unbalanced Chart - Original
  163. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références AFTER (SCHWABISH - JEP, 2014) FIGURE – An Unbalanced Chart - Revised
  164. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références BEFORE (SCHWABISH - JEP, 2014) FIGURE – A Clutterplot Example - Original
  165. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références AFTER (SCHWABISH - JEP, 2014) FIGURE – A Clutterplot Example - Revised
  166. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références BEFORE - NETWORK EXAMPLE Victor Hugo’s characters network (Les Miserables).
  167. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références ALTERNATIVE : ADJACENT MATRIX PLOT An adjacency matrix, where each cell ij represents an edge from vertex i to vertex j.
  168. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références ALTERNATIVE : SORTED ADJACENT MATRIX PLOT
  169. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références ALTERNATIVE : ARC DIAGRAM PLOT FromGaston Sanchez
  170. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références ALTERNATIVE : VERTICAL ARC DIAGRAM PLOT
  171. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références BEFORE - STUDENTS GRADES IN 4 COURSES Source Munzner (2014)
  172. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références BEFORE - STUDENTS GRADES IN 4 COURSES Source Munzner (2014)
  173. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références AFTER - STUDENTS GRADES IN 4 COURSES Source Munzner (2014)
  174. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références AFTER - STUDENTS GRADES IN 4 COURSES Source Munzner (2014)
  175. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références AFTER - STUDENTS GRADES IN 4 COURSES Source Munzner (2014)
  176. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références BEFORE : US AIRLINES MAP (C. HURTER) Source Christophe Hurter
  177. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références AFTER : BUNDLING THE NETWORK (C. HURTER) To simplify the understanding, researcher propose bundling techniques. 1 Source Christophe Hurter 1. To bundle : to tie or gather things together
  178. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références AFTER : BUNDLING IN NETWORKS (C. HURTER) Edges get closer and density gets sharper : Source Christophe Hurter
  179. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références BUNDLING IN NETWORKS (C. HURTER) Edges get even closer and density gets even sharper : Source Christophe Hurter
  180. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références BUNDLING IN NETWORKS (C. HURTER) At the end of the day...one can see through darkness ! Source Christophe Hurter
  181. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE STUDIES
  182. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Original data are percent of users in funnel data stages, or gates : Each stage represents some portion of the previous stage. Source : www.storytellingwithdata.com
  183. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Original data observed for 4 regions of the world
  184. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS ... and for 3 cohorts (Q1, Q2 & Q3)
  185. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Identify the interesting feature : What comparisons ?
  186. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Identify the interesting feature : What comparisons ?
  187. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS By cohort
  188. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Remove superfluous information (Tufte’s rule)
  189. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Remove superfluous information (Tufte’s rule)
  190. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Compare cohorts trends
  191. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Compare cohorts trends
  192. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Relabel the graphic
  193. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Relabel the graphic
  194. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Make comparison& visual choices (reference line)
  195. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Provide insights
  196. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Add color, context & analysis
  197. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : FUNNEL DATA ANALYSIS Add color, context & analysis
  198. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 1 : BEFORE/AFTER Source : www.storytellingwithdata.com
  199. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 2 : WHAT’S THE GOOD POINT SIZE ? FIGURE – Sample size = 500, point size = 3
  200. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 2 : WHAT’S THE GOOD POINT SIZE ? FIGURE – Sample size = 500, point size = 1
  201. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 2 : WHAT’S THE GOOD POINT SIZE ? FIGURE – Sample size = 500, point size = 6
  202. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 2 : WHAT’S THE GOOD POINT SIZE ? FIGURE – Sample size = 500, point size = 13
  203. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 2 : WHAT’S THE GOOD POINT SIZE ? FIGURE – With Transparency ! (Sample size = 500, point size = 13)
  204. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 2 : WHAT’S THE GOOD POINT SIZE ? FIGURE – With Transparency ! (Sample size = 500, point size = 13)
  205. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 2 : WHAT’S THE GOOD POINT SIZE ? FIGURE – Context matters ! (Sample size = 500, point size = 13)
  206. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC How learners behave during the course ?
  207. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC How learners behave during the course ? Learner’s "activity" over time /space
  208. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC How learners behave during the course ? Learner’s "activity" over time /space The MOOC’s stucture : FIGURE – The MOOC is made of 60 different steps (ressources)
  209. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC How learners behave during the course ? Learner’s "activity" over time /space The MOOC’s stucture : FIGURE – The MOOC is made of 60 different steps (ressources) Learners may pick whatever step, whenever they want.
  210. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Basic graphic # 1 : Barplot (stacked) 4.11 4.10 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 3.21 3.20 3.19 3.18 3.17 3.16 3.15 3.14 3.13 3.12 3.11 3.10 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 2.12 2.11 2.10 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 1.16 1.15 1.14 1.13 1.12 1.11 1.10 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 0 1000 2000 3000 Learners Step Completed Visited but not completed Comments Activity per step
  211. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Basic graphic # 2 : Heatmap FIGURE – Heatmap showing activity over time
  212. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Idea # 1 : Visualize steps visited over time (1 line = 1 learner) FIGURE – How to visualize “Trajectories” over time ?
  213. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Too many starting points and trajectories ! FIGURE – How to visualize “Trajectories” over time ?
  214. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Idea # 2 : Align starting points ! Change time for clicks sequence FIGURE – “Trajectories” as a sequence of “clicks”
  215. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Steps vs clicks sequence should reveal = patterns FIGURE – “Trajectories” as a sequence of “clicks”
  216. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Steps vs clicks sequence should reveal = patterns FIGURE – “trajectories” as a sequence of “clicks”
  217. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Should work with many lines (many learners) FIGURE – “trajectories” as a sequence of “clicks”
  218. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC Back to R..... FIGURE – First try : Each line is a learner’s path
  219. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC FIGURE – Trick one : Apply transparency and grey lines
  220. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC FIGURE – Trick two : Tiny lines and "jitter"
  221. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC FIGURE – Trick three : Colored lines, "jitter" and simple labels
  222. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC FIGURE – Trick four : Add context (analysis)
  223. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC FIGURE – Trick four : Add context (analysis)
  224. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références CASE 3 : SEARCHING FOR LEARNERS IN A MOOC FIGURE – Trick four : Add context (analysis)
  225. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références WHAT TO REMEMBER From the viewer“data visualisation” are implicitly or explicitly comparisons or even tests (in the statistical sense) Graphics should help questioning
  226. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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)
  227. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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
  228. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références WHAT TO REMEMBER : THERE ARE RULES Data visualisation is a visual language, so there are : Elements of language
  229. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références WHAT TO REMEMBER : THERE ARE RULES Data visualisation is a visual language, so there are : Elements of language Rules of use (spelling)
  230. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références WHAT TO REMEMBER : THERE ARE RULES Data visualisation is a visual language, so there are : Elements of language Rules of use (spelling) Grammar
  231. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE TRUTH DOES NOT LIVE IN 2D The “truth”....
  232. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références THE TRUTH DOES NOT LIVE IN 2D The “truth”....
  233. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GRAPHS I LIKE Visualizing thousands of runners (A. Ottenheimer)
  234. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GRAPHS I LIKE Atlas of Places (Muriz Djurdjevic & Thomas Paturet)
  235. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références GRAPHS I LIKE Experiments in Time-Distance map (Kohei Suguira)
  236. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références 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.
  237. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références REFERENCES II Bertin, J. (1983). Semiology of graphics, translation from sémilogie graphique (1967). 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. 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. Dix, A. and Ellis, G. (1998). Starting simple - adding value to static visualisation through simple interaction. In Eds. T. Catarci, M. F. Costabile, G. S. and Tarantino, L., editors, Proceedings of Advanced Visual Interfaces, pages 124–134. L’Aquila, Italy, ACM Press.
  238. Definitions [De]coding Howto ? Rules Perception Before/After Cases So what

    ? Références REFERENCES III Few, S. (2008). Practical rules for using color in charts. Visual Business Intelligence Newsletter, (11). Gelman, A. (2004). Exploratory data analysis for complex models. Journal of Computational and Graphical Statistics, 13(4). Munzner, T. (2014). Visualization Analysis and Design. AK Peters Visualization Series. A K Peters/CRC Press, 1 edition. 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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    ? Références STAY IN TOUCH ! My website Data.visualisation.free.fr @Xtophe_Bontemps