I was reading the 12 factor App and was struck by how much data science practitioners could use these principles too, for example when making a Shiny Dashboard App
Also I hope we can have more plug and play data science for mobile data or data generated by mobile apps (which is increasing)
An example is this app here https://gallery.shinyapps.io/CampaignPlanner_v3/ which can possible modified to add integration with Google Web Analytics API (etc).
This approach can make R more enterprise ready for production environments where it currently lags behind Python in terms of both appeal as well as trained people.
The Twelve Factors
I. Codebase
One codebase tracked in revision control, many deploys
II. Dependencies
Explicitly declare and isolate dependencies
III. Config
Store config in the environment
IV. Backing services
Treat backing services as attached resources
V. Build, release, run
Strictly separate build and run stages
VI. Processes
Execute the app as one or more stateless processes
VII. Port binding
Export services via port binding
VIII. Concurrency
Scale out via the process model
IX. Disposability
Maximize robustness with fast startup and graceful shutdown
X. Dev/prod parity
Keep development, staging, and production as similar as possible
XI. Logs
Treat logs as event streams
XII. Admin processes
Run admin/management tasks as one-off processes
DECISION STATS 