These are the slides used for a 2 hours course on data visualisation. The course was addressed to biologists, hence most examples come from scientific publications in this area (but not only)

1. Introduction to Data
Visualization
Philippe Julien
CRG – 19/05/2015

2. • This course contains lot of visualisations from
scientific articles, newspapers or made up.
• Not all of them are good, try to be critic on
each of them

3. Why?

4. Data visualization is not really
a recent field

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7. A good data visualization…
• Conveys one or several message(s)
• Does it as accurately as possible
• And as efficiently as possible
• Is not necessarily unique!

8. A good data visualization…
• Conveys one or several message(s)

9. What is your message?

16. Is your figure understandable?
Hint: ask your colleagues. =)

17. • Is the visualization readable?
• Are axis properly labeled?
• Are legends provided?
• Do you show error bars?
• Is the scale identifiable?
• Are the observed differences significant?
• Does the reader have all the information to understand the visualization?
• Do the visualization’s annotations help to convey the message?

21. Why is it important to get external
review…
• Get feedback from persons with different profiles
• Don’t tell them what you meant to show

22. Is a visualization necessary?

25. Visualizing uni- and multi-dimensional
data

26. Visual dimensions
• Length
• X position
• Y position
• Z position
• Category
• Color
• Icons/glyphs
• Element size

27. One dimension data
Q1 Q3
IQR
Median
Q3 + 1.5 × IQRQ1− 1.5 ×IQR
−0.6745σ 0.6745σ 2.698σ−2.698σ
50%24.65% 24.65%
68.27% 15.73%15.73%
−4σ −3σ −2σ −1σ 0σ 1σ 3σ2σ 4σ
−4σ −3σ −2σ −1σ 0σ 1σ 3σ2σ 4σ
−4σ −3σ −2σ −1σ 0σ 1σ 3σ2σ 4σ

28. Two dimensions data
0
25
50
75
Fri Sat Sun Thu
day
count
3.5
4.0
4.5
5.0
5.5
−0.50 −0.25 0.00 0.25 0.50
x
y

29. Three dimensions data

30. More dimensions….

31. Circularize to gain space

32. Get creative

34. A good data visualization…
• Conveys one or several message(s)
• Does it as accurately as possible

35. Length vs Surfaces

36. Length vs Surfaces

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40. From: https://infoactive.co/data-design/ch17.html

41. Use the right visualization

46. Avoid 3D

53. Show your data

54. Anscombe’s quartet (1973)

55. Weissgerber et al, PLOS Biol., 2015

56. Weissgerber et al, PLOS Biol., 2015

57. Weissgerber et al, PLOS Biol., 2015

60. Show your data…
… from different manners

61. From: https://infoactive.co/data-design/ch18.html

62. From: https://infoactive.co/data-design/ch18.html

63. Mind the axes

66. From: https://infoactive.co/data-design/ch18.html

67. Compared to what?

68. From: https://infoactive.co/data-design/ch18.html

70. A good data visualization…
• Conveys one or several message(s)
• Does it as accurately as possible
• And as efficiently as possible

71. Data to ink ratio
• (term coined by E. Tufte in 1983)
• All elements (printed elements = ink) should be
informative
• Avoid “chartjunk”, elements which does not add
information or distract the reader
• Avoid information duplication

77. Colours

78. Be colorblind friendly
http://www.vischeck.com/vischeck/vischeckImage.php

79. Color consistency

80. Premade palettes
Palettes in:
- R packages
- Illustrator (Adobe products in general)
- Tableau
- Websites
- ….

82. From: http://www.tigercolor.com/color-lab/color-theory/color-harmonies.htm

83. Softwares

84. • R
• Excel
• Tableau
• Matlab
• Python
• Google Doc
• …