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Christian Mladenov @ Intuitics
The document discusses building web apps on top of R to enable self-service analytics. It describes how statistical programming languages like R can be used to create customizable web applications through solutions like RStudio Shiny and Intuitics. These solutions allow non-programmers to run R code and access analytics through a web interface without needing to write code. The document provides an example of turning an existing R forecasting script into an interactive web app using Intuitics to allow parameterizing inputs and running multiple scenarios.
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Christian Mladenov @ Intuitics
- 1. Building a webapp on top of R Christian Mladenov, CEO
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- 3. This happens quiteoften
- 4. This happens quiteoften Decision maker
- 5. This happens quiteoften Ad-hoc request Decision maker
- 6. This happens quiteoften Ad-hoc request Develops analytics code Decision maker Data scientist
- 7. This happens quiteoften Runs code Ad-hoc request Develops analytics code Decision maker Data scientist
- 8. This happens quiteoften Generates Runs code report Ad-hoc request Develops analytics code Decision maker Data scientist
- 9. This happens quiteoften Reads report Generates report Runs code Ad-hoc request Develops analytics code Decision maker Data scientist
- 10. Reads report Generates report Runs code Requests changes This happens quite often Ad-hoc request Develops analytics code Decision maker Data scientist
- 11. Adjusts parameters Readsreport Generates report Runs code Requests changes This happens quite often Ad-hoc request Develops analytics code Decision maker Data scientist
- 12. Adjusts parameters Readsreport Generates report Runs code Requests changes This happens quite often Ad-hoc request Develops analytics code Decision maker Data scientist
- 13. Adjusts parameters Readsreport Generates report Runs code Requests changes This happens quite often Ad-hoc request Develops analytics code Decision maker Data scientist
- 14. Problem in largeorganizations
- 15. Problem in largeorganizations Manual loop wastes time
- 16. Problem in largeorganizations Manual loop wastes time Slower and less capable decision making
- 17. Problem in largeorganizations Manual loop wastes time Slower and less capable decision making Inefficient data science
- 18. Problem in largeorganizations Manual loop wastes time Slower and less capable decision making Inefficient data science €20M+ annual loss in a 2000-people organization
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- 20. Solution Self-service analyticsweb apps
- 21. Solution Self-service analyticsweb apps Decision makers get better insights, faster
- 22. Solution Self-service analyticsweb apps Decision makers get better insights, faster Data scientists can focus on new knowledge
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- 29. R solutions RStudioShiny
- 30. R solutions RStudioShiny Custom web app (Python, Ruby, etc)
- 31. R solutions RStudioShiny Custom web app (Python, Ruby, etc) Remote R: OpenCPU, rServe, rApache, httpuv
- 32. R solutions RStudioShiny Custom web app (Python, Ruby, etc) Remote R: OpenCPU, rServe, rApache, httpuv Integrated R: rJava, rpy2, RinRuby
- 33. R solutions RStudioShiny Custom web app (Python, Ruby, etc) Remote R: OpenCPU, rServe, rApache, httpuv Integrated R: rJava, rpy2, RinRuby Native (local or remote R)
- 34. R solutions RStudioShiny Custom web app (Python, Ruby, etc) Remote R: OpenCPU, rServe, rApache, httpuv Integrated R: rJava, rpy2, RinRuby Native (local or remote R) RGtk2, tcltk, Excel+VBA
- 35. R solutions RStudioShiny Custom web app (Python, Ruby, etc) Remote R: OpenCPU, rServe, rApache, httpuv Integrated R: rJava, rpy2, RinRuby Native (local or remote R) RGtk2, tcltk, Excel+VBA Intuitics
- 36. Comparison RStudio ShinyIntuitics
- 37. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally
- 38. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop
- 39. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise
- 40. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API
- 41. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment
- 42. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment Code needs to be imported
- 43. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment Code needs to be imported Highly customizable
- 44. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment Code needs to be imported Highly customizable Active community
- 45. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment Code needs to be imported Highly customizable Active community RMarkdown integration
- 46. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment Code needs to be imported Highly customizable Not only R (import/export) Active community RMarkdown integration
- 47. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment Code needs to be imported Highly customizable Not only R (import/export) Active community Long-running computations RMarkdown integration
- 48. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment Code needs to be imported Highly customizable Not only R (import/export) Active community Long-running computations RMarkdown integration Save and share app snapshots
- 49. Comparison RStudio ShinyIntuitics R framework + server. Server and interface built with code locally Web platform. Stateless R functions, interface built with drag & drop Cloud + on-premise Cloud + on-premise. REST API Same development environment Code needs to be imported Highly customizable Not only R (import/export) Active community Long-running computations Save and share app snapshots Export, dashboards, recurring reports RMarkdown integration
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- 53. Background FMCG company Forecasting script in R with parameters:
- 54. Background FMCG company Forecasting script in R with parameters: Country & product (build a model based on newest data)
- 55. Background FMCG company Forecasting script in R with parameters: Country & product (build a model based on newest data) Next 3 months: price, marketing budget, # of pages in catalog, competition strength
- 56. Background FMCG company Forecasting script in R with parameters: Country & product (build a model based on newest data) Next 3 months: price, marketing budget, # of pages in catalog, competition strength Monthly forecast cycle: many what-if scenario runs
- 57. Background FMCG company Forecasting script in R with parameters: Country & product (build a model based on newest data) Next 3 months: price, marketing budget, # of pages in catalog, competition strength Monthly forecast cycle: many what-if scenario runs Aim: turn the script into an interactive app
- 58. Steps 1. SplitR script into functions to allow interactivity 2. Create interface that links functions 3. Share a snapshot with a colleague
- 59. Thank you! ! ! Christian Mladenov christian@intuitics.com ! www.intuitics.com @intuitics facebook.com/intuitics