Tips for Effective Data Visualization

Transcription

1 Tips for Effective Data Visualization Angela Zoss Eric Monson Data and Visualization Services STA 199L Spring 2018 Slides:

2 What is data visualization? Anything that converts data sources into a visual representation charts, graphs, maps, even just tables

3 Why do we visualize? x y x y x y x y Almost identical summary statistics: x & y mean x & y variance x-y correlation x-y linear regression

4 We visualize to see patterns Anscombe s Quartet

5 Designing effective visualizations

6 Keep it simple Skilled Crafts Executive/Admin Professional (non-faculty) Faculty Service Clerical Tech/Paraprof Professional (non-faculty) Tech/Paraprof Service Executive/Admin Duke Job Categories Clerical Skilled Crafts Faculty Duke Job Categories

7 Use color to draw attention Current Duke Employment by Generation Current Duke Employment by Generation Veteran (pre 46) Baby Boom ( 46-64) Gen X ( 65-79) Millennial ( 80-95) 0 Veteran (pre 46) Baby Boom ( 46-64) Gen X ( 65-79) Millennial ( 80-95)

8 Tell a story Duke Hires by Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

9

10 Common missteps

11 Default ordering hides patterns

12 Sorting reveals patterns

13 Default ordering hides patterns

14 Cluster ordering reveals patterns

15 Trust rank Index rank Borough Amount approved ( ) Number of grants 1 3 Tower Hamlets 9,692, Hackney 7,809, Southwark 7,266, Camden 6,140, Islington 5,424, Lambeth 5,257, Newham 5,217, Hammersmith and Fulham 4,085, Merton 3,656, Croydon 3,629, Lewisham 3,537, Westminster 3,357, Ealing 3,057, Bromley 3,038, Kensington and Chelsea 2,979, Brent 2,898, Greenwich 2,837, Barnet 2,796, Wandsworth 2,592, Waltham Forest 2,505, Sutton 2,468, Hounslow 2,383, Haringey 2,360, Redbridge 2,285, Rechmond upon Thames 2,249, Hullingdon 2,181, Enfield 2,145, Barking and Dagenham 1,943, Havering 1,934, Bexley 1,631, Harrow 1,516, Kingston upon Thames 1,353, City of London 402, Several Additional Inner Bouroughs 18,704, Several Additional Outer Boroughs 6,392, Other 28,566, London-wide 86,583, Total 252,883, Tables easily hide patterns Total grants spend by London Borough September 1995 to March blog/2012/02/grables-and-taphs

16 Help people see patterns in tables blog/2012/02/grables-and-taphs

17 Help viewers interpret tables Limit and standardize decimal places Emphasize important values with color, bold text and annotations Sort rows by values Turn table into another chart or a handout

18 Leave out non-story details

19 Leave out non-story details

20 All the data doesn t tell a story

21 All the data doesn t tell a story

22 All the data doesn t tell a story

23 Original

24 Reworked as single plot

25 Reworked as small multiples

26 Original

27 Reworked 0.1 Color Coordinate Distance (Δu v ) 0.01 (a) (b) (d) (e) (c) (f) LED distance from 0 cm Laser distance from 0 cm 1 mm 4 mm 16 mm 32 mm

28 Reworked 0.1 Color Coordinate Distance (Δu v ) 0.01 (a) (b) (d) (e) (c) (f) LED distance from 0 cm Laser distance from 0 cm 1 mm 4 mm 16 mm 32 mm

29

30

31 ColorBrewer for good colormaps

32 Do data have a natural center?

33 Text to clarify

34 Keep text horizontal UCNI Johns Hopkins UC San Francisco UCLA Mass General U of Pittsburgh Mayo Clinic Northwestern Barrow Neuro. Institute Ohio State Cleveland Clinic Kettering Neuro. Institute Barnes-Jewish

35 Annotate figures directly

36 Annotate figures directly

37 Use descriptive titles Active titles summarize trends in the figure and reinforce your message. Accuracy versus Color and Shape 100% 80% 60% 40% 20% Accuracy Improved by Color, not Shape 100% 80% 60% 40% 20% 0% Control Color Shape 0% Control Color Shape

38 Figure rework

39 Jon Schwabish:

40 Jon Schwabish:

41 Jon Schwabish:

42 Jon Schwabish:

43 Jon Schwabish:

44 Jon Schwabish:

45 Other chart makeover examples The Why Axis chart remakes Storytelling With Data visual makeovers: akeover

46 Excel rework activity