1. Abhijit Dasgupta ARAASTAT @webbedfeet
Graphics and data visualizations to enhance storytelling: Revealing rather than obfuscating
Abhijit Dasgupta ARAASTAT
@webbedfeet

2. Context “Academic” consulting (with NIH) Healthcare sensor startup
Business intelligence consulting
Member of the DC data community and a meet up organizer

3. “Insights” Exploration Contextual understanding Reporting Data
Information
Evaluation / Action

4. “Insights” Processes are complex
Evaluating actions of multiple factors on an outcome
Subgroups, interactions

5. Visualizations
Takes advantage of our human capability to understand patterns quickly and, often, intuitively
Can be a gateway to interpreting what the data is saying

6. Visualizations
Can enable information compression as well as information dissemination
We can look at different aspects of the data in a single visualization
GO WILD!!
Lines, shapes, colors, sizes

7. Mental gymnastics should not be required

8. no Mental gymnastics
The consumer of a visualization should not require too much effort to understand it
Context should be clear (labels, axes, and so on)
The interpretation should be “obvious”
The “punch line” should be clear

9. no Mental gymnastics Sometimes a visualization is not the right tool
A table or textual display might be simpler
Sometimes hard to resist the temptation to create graphics

10. The task at hand

11. Exploring data Really value flexible ways to visualize data
Small multiples and trellis graphics
Linked graphs
Interactivity
Rendering speed
Ability to hone in

12. Presenting information
Visualizations need to
be clear & uncluttered
make the point
provide easy access to underlying data “talking points” and metadata
allow the consumer to explore a bit and think
be geared towards what the consumer wants

13. Toolsets

14. Tools for visualization
Ability to create static graphs has been around
Grammar of Graphics & ggplot
Conceptually easier (for me) to create layered, thematically consistent, graphics

15. Tools for Visualization
Javascript-based graphics
D3.js
Leaflet
Various others

16. Tools for Visualization
Javascript-based graphics
Potential for interaction
Linking graphs
Fast
Additional information through popouts

17. Tools for Visualization
Javascript-based graphics
Good wrappers in R and Python
Don’t actually need to know Javascript per se R
Python
plotly, rbokeh, highcharts, r2d3, htmlwidgets
plotly, bokeh, altair,

18. Keeping the consumer in mind

19. Client/consumer focus
What’s important to the consumer?
Often not what’s important to the analyst
We may have a perfectly clear graphic….
which is not what the consumer wants
and not what (s)he needs

20. Client/consumer focus
“Instead of a version of data science that is narrowly focused on researching new statistical models or building better data visualizations, a design-thinking approach recognizes data scientists as creative problem solvers.”

21. Examples

22. 26th August, 2015

23. Meta-analysis of 129 studies published between 1955 and 2016
Retrospective or prospective designs
Developing and developed countries
Adults and children
Objective is to understand patterns in SLE-related mortality over time (in 5 year intervals)
Looking at 5 year intervals to estimate mortality

26. Temptation is of course to add more information to the graph

28. Data compression vs mental gymnastics
This is the big balancing act
You can add more “information”, but you make it harder to read
In retrospect, we might have kept a simpler graphic

30. Caveat emptor
I don’t play a data visualization expert on TV, or in real life for that matter
I’m sharing thoughts I’ve had through many interactions with clients and colleagues, and my own narrow experiences
There are GREAT resources out there. Use them
Would love to hear and see better ideas

31. No snores? Perfect!!
@webbedfeet
webbedfeet.netlify.com
#datadc