Test Case Title	TC7.4 - From R Studio process to MicroService for IOT application, data analytics, machine learning
Goal	Create a MicroService starting from R Studio processes, for data analytics, anomaly detection and machine learning.
Prerequisites	A simple skill for using R Studio to execute Data Analytics and machine learning algorithms Using a PC or Mobile with a web browser. Conquer a minimal skill on producing R programme if you need to change the program or develop a new one. The following functionalities are available only for specific Snap4city users with specific privileges.
Expected successful result	Create a MicroService for IOT app with data analytics, detecting anomalies, creating correlation, predictions, estimate the descriptive statistics, model identification, produce the graphics for data trends, comparing trends, etc.
Steps

 

Please note that to correctly perform this Test Case you need and access to the R Studio Virtual Machine as described below. To have access to a Virtual Machine to perform R Studio please contact snap4city@disit.org.

 

From R Studio process to MicroService

1. In this test case we see how to create a MicroService starting from R Studio processes, for data analytics, anomaly detection and machine learning.  
2. The installation of the R-package "plumber" is a necessary requirement.
   
   Plumber package converts the R code into a web API (https://www.rplumber.io/). Each R script contains a relative function: a single specific function is related to a single data analytic application.

Fig: ‘Anomaly detection.R’ script and relative function.

3. The R script function presented above is called from the specific R script ‘Stat4cityFunction.R’: 

• #' @get /anomaly is the requested annotation by plumber package to create the web API related to the R script. In particular the annotation presented creates a get web API type that can be called by the following link: https://rstudio.snap4city.org/api/anomaly?sensorTypeList=carpark&anomalyDate=2018-05-25
  
  Note that, there is a specific annotation related to each R script (only the functions related to the anomaly detection R script are reported in the example). After the annotation there is the function that is executed with the caller of the rest API.  

Fig: ‘Stat4cityFunction.R’ script.

 

4. The R script ‘RunRestApi.R’ has to be executed to activate the call reception and pull-up the service.

Fig: ‘RunRestApi.R’ script to pull-up the service.

 

5. The data analytic results are saved in JSON format: the relative instruction is at the end of the R script related to the data analytic application.

Fig: R instruction to save results in JSON format.

6. In the last instruction, the R object to be returned via the API must be returned. Plumber will automatically transform it into a JSON.
7. Once the API has been created, it is possible to create the microservice using a service provided by Snap4City inside the ProcessLoader Tool under the Uploaded Resources.
   
   
   
    
8. Click on button “upload new resource”

 

9. And remember the link of the rest api  http://rstudio.snap4city.org/api/anomaly?sensorTypeList=carpark&anomalyDate=2018-05-25

 

10. It is possible to fill the form and click Confirm. A zip file containing the desired microservice is created.

 

11. The zip contain a node that can be installed on NodeRed
    
    
    
     
12. With desired configuration:
    
    
    
     
13. And it can be used in simple flow like:
    
    
    
     
14. To get the JSON result
    
    
15. That can be used in a more advanced flow