What's your data visualization vegetables? What's your candy? This session will look at data visualization theory and practice of hot data visualization topics such as: how can you choose which chart to choose and when?
How can you best structure your dashboard?
What about pie charts? What is the fuss about, and when are they best used?
Color blindness - how can you cater for the 1 out of 12 color blind males (and not forgetting the 1 out of 100 color blind females?)
To 3D or not to 3D? Why is it missing in Power View? And any other data visualization topics you care to mention! Come along for dataviz fun, and to learn the "why" along with practical advice.
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Guidelines for data visualisation: eye vegetables and eye candy
1. Eye Vegetables and Eye
Candy: How to Visualize Your
Data
Jen Stirrup
Principal, Data Relish Ltd
Level: Intermediate
2. What is Data Visualisation?
• Data Visualisation tells stories
contained within the data
– focused on analysing large datasets
– which allows the data consumer to draw their own
conclusions.
• Very often, the data is not static in
nature, but fluid and dynamic.
3. The Business Intelligence
Chocolate Cake problem
• Everybody wants their own chocolate cake…
• They want chocolate cake now….
• Their way….
• But who cleans up the mess?
• Who is going to pay for it?
• How can we stop the cakes from mixing?
17. Benefits of Data Visualisation
• "A good sketch is better than a long speech..." -- a
quote often attributed to Napoleon Bonaparte
• Data has increased in quality, timeliness,
granularity, and volume
17
18. Benefits of Data Visualisation
• Visualization as a key enabler of self-service
business intelligence
• Bridging the human – machine learning gap
18
23. Advice!
• Never represent something in 3 Dimensions if it
can be represented in two
• NEVER use pie charts, 3-D pie charts, stacked
bar charts, or 3-D bar charts.
23
24. Advice!
• Remove as much chart junk as possible–
unnecessary gridlines, shading, borders, etc.
• Give your audience a sense of the noise present
in your data–draw error bars or confidence bands
if you are plotting estimates.
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25. Guidelines
• Forecasted data - include actuals as well
• Major and minor tick marks
• Standardisation
• Design has 'affordances'
• Fit for purpose
26. Tables
•Tables work best when the data presentation:
• Is used to look up individual values
• Is used to compare individual values
• Requires precise values
• Values involve multiple units of measure.
30. The Science!
30
In experiments,
Cleveland and McGill
examined how
accurately our visual
system can process
visual elements or
“perceptual units”
representing
underlying data
34. The Results!
• Judgements about position relative to a baseline
are dramatically more accurate than judgements
about angles, area, or length (with no baseline).
• Cleveland and McGill suggests that we replace
pie charts with bar charts or dot plots and that
we substitute stacked bar charts for grouped bar
charts.
34
37. Perceptual Patterns
Attribute Example Assumption
Spatial Position 2D Grouping
2D Position
Sloping to the right = Greater
Form Length
Width
Orientation
Size
Longer = Greater
Higher = Greater
Colour Hue
Intensity
Brighter = Greater
Darker = Greater
38. Perceptual Patterns
Attribute Example Graph Type
Spatial Position 2D Grouping
2D Position
Line Graph
Form Length
Width
Orientation
Size
Bar Chart
Colour Hue
Intensity
Scatter Chart
40. • Bullet 1 for the slide
• Sub-bullet
• Sub bullet
• Bullet 2 for the slide
• Just to see how the copy looks if it goes deep enough
to reach the bottom.
You never know how much copy will be on a slide
• Bullet 3 for the slides
• This is a critical point that needs to be communicated
41. Mobilising Visual Integration
• Affordance
• Highlighting – bright colours
• Increasing Intensity = Increasing Values
• Eye Tracking Studies
• Eye Path going from cluster to legend, and back
again (Ratwani, 2008)