Presented by Sasha Gutfraind, Chief Healthcare Data Scientist at Uptake

1. Cloud-Based Spatial Data
Analytics with R Shiny
Sasha Gutfraind, PhD
U of I + Uptake Inc.
Data Science Chicago 2017

2. Mission: what is the best way of getting
Value out of Data?
Data Insights or Products

3. The old way of doing things …
1. Obtain data and build a model/viz
Then:
2a. Write a Report
Or, rarely:
2b. Collaborate with software engineers
… to build UI, pipelines, databases
… to create an infrastructure for it

4. The new way:
2c: “Productivization toolkits”
Related solutions
Rook, Flask, Plotly/Dash, Tableau,
Spyre

5. Outline
1. Motivation: spatial disease data from Arequipa, Peru
2. The R+shiny+leaflet stack
3. Conclusions
- strengths (and weaknesses) of the solution

6. A case study of productivation:
Finding Triatomine bugs

7. Triatomine bugs - a cause of disease

8. Mission
1. Collect data in the field with smart phones
2. Calculate and represent probability over
a street map
3. Ensure security and tracking

9. The Shiny Package

10. R Shiny overview
● Shiny: an open-source R package by the people in RStudio
● Shinyapps.io: a cloud platform as a service
● Two main parts: server.R and ui.R
● input$foo = a variable containing input from the UI
● output$bar = a variable containing output data
● The app is event-driven by changes in these values

11. Demo app
https://shiny.rstudio.com/gallery/kmeans-example.html

12. Demo app - code
ui.R server.R

13. More on Shiny: Shiny deployment
1. Run on your desktop for testing
2. Run on your server ⟺ client devices
3. Run on shinyapps.io cloud ($$) ⟺ client devices

14. Leaflet library
● Javascript library for maps
● Well-integrated with shiny
● Draws on OpenStreetMaps
● Bindings in R and Python

15. Our product for spatial data

16. VectorPoint

17. Search and Inspection

18. Behind the scenes in VectorPoint
● AWS RDS/PostgreSQL database
● Shinyapps.io cloud platform
● Desktop-based
spatial predictive model

19. Successes
● Rapid development of an app (1 month to a prototype)
● Successful field tool used in Peru
● Easy maintenance

20. Implementation challenges with R/shiny
● R is not an easy language to learn
● Javascript is invisible until it breaks
● Code could become a messy mix of R & Javascript

21. ● Narrow focus: putting your R code online / on the cloud
● Client must be online
● Security is difficult
● Requires a UI
● R limits speed
Limitations of shiny

22. Acknowledgements
● VectorPoint research team
● Uptake Inc.
● U of I at Chicago
● National Institutes of Health

23. Conclusions
Shiny (and similar frameworks) make data scientists more valuable:
sasha.gutfraind@uptake.com
● Deliver much more than a model/report!
● Enables data collection, visualization, collaboration, prototyping
● Without the cost of traditional software engineering