Building data products is no easy feat. At TCB Analytics, we always start with the question(s) we want to answer and then immediately identify relevant data sets. At what point though, does it make sense to build something? We discuss various examples of client work in which we used Shiny to rapidly protoype, pull data from APIs, leverage Google Vision's image recognition service and more.

1. When and Why to Use Shiny for Commercial Applications
EARL Boston 2017 -11/03/2017
Tanya Cashorali
@TanyaCash21

2. 2004 201220072005 2006 20142013 2015
SPEAKER PROFILE
TANYA CASHORALI
@TANYACASH21
2

3. FIRST THINGS FIRST:
What is a data product?

4. VALUE IS IN THE DATA ITSELF… ?

5. MYTHICAL DATA SCIENCE MACHINE
RAW
DATA INSIGHTS
$$$

6. SUPPORT DECISION MAKING
 Get used to asking yourself: “So what?”
 Provides actionable insights – DO SOMETHING
 Ability to ask “What-If?” questions

7. RAPID PROTOTYPING
 Demonstrate value before full-scale implementation
 Data scientists are able to do what they’re good at

8. WHY NOT JUST USE TABLEAU?
Source: http://nandeshwar.info/data-science-2/tableau-vs-r/
Depends on…
• Use Case
• Audience
• In-house skills
• Great for aggregations and summary
dashboards with drill-downs and filters

9. REPRODUCIBILITY
Source: http://www.jonzelner.net/statistics/make/docker/reproducibility/2016/05/31/reproducibility-pt-1/
Knitr rocker Gitlab-CI
“It’s not reproducible if
it only runs on your
laptop.”

10. COLLABORATION
RStudio ConnectVersion Control

11. CUSTOMIZATION + FLEXIBILITY
 Certain charts in Tableau require complicated workarounds
(gantt, spider, chord diagrams, network viz, etc.)
 Capable of machine learning, advanced statistical functions

12. BUILD PROCESS

13. VISUALIZE MODEL PREDICTIONS
Whiteboard / Brainstorm / Wireframe
Quickly build prototype dashboard

14. ITERATE WITH CLIENT
Sketches over e-mail to reality in just several days

15. RSTUDIO MARKETING AND SALES ANALYTICS

16. GOOGLE VISION API
Powerful image analysis that enables:
• Logo detection
• Landmark detection
• Optical Character Recognition
• Face and emotion detection
• Classification of objects from trains to animals

17. GOOGLE VISION API
Scraped 5,000+ images
from the New England
Patriots Facebook page
Classified all pictures to
understand brand /
sponsor presence on
Patriots page

18. EXAMPLE RESULTS
Dunkin’ Donuts
High Confidence
Dunkin’ Donuts
Low Confidence
Snickers
Snapchat Lord of the Rings
Cancer

19. FANTASY OPTIMIZERS
http://apps.fantasyfootballanalytics.net/
https://tcb-analytics.shinyapps.io/dfsnba/
Additional Reading on scaling: https://www.slideshare.net/appsilon/scaling-shiny-apps-earl-2017-san-francisco
2013 - Started with Shiny Server – ran into
scaling issues
2015 - Moved to OpenCPU/Azure – ran into
scaling issues in OpenCPU (greater
capacity servers may have resolved, but
$$$). Azure also required Java.
2017 - Moved back to Shiny (shinyapps.io
w/ professional plan) – so far no scaling
issues

20. WHEN TO USE SHINY SERVER PRO
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https://support.rstudio.com/hc/en-us/articles/218294997-What-is-the-difference-between-shinyapps-io-and-Shiny-Server-Pro-
Outside firewall? Data in cloud? Users must register? Shared Resources?
If your organization is not OK with any of the above… you may want to consider Shiny Server Pro

21. PRODUCTION PITFALLS
 Too good, too fast
 Inability to keep up with demands
and scale
 Best to implement unit tests,
documentation, deployment best
practices in advance or at least
be prepared to do so quickly
http://schd.ws/hosted_files/user2017/7d/UseR2017_ShinyPrototypesOutOfControl.pdf
Building the plane while flying

22. SPECIAL THANKS TO…
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Grace MeyerIsaac Petersen Josh Walker Bill Carney Paul Doetsch Roger Oberg