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Use R language with InterSystems IRIS


R Gateway via RServe for InterSystems IRIS Data Platform. Author Shiao-Bin Soong: email, github profile.

ML Toolkit Webinar in German

Machine Learning ist gegenwärtig einer der wichtigsten Trends in der Anwendungsentwicklung. In unserer dreiteiligen Webinar-Serie „Machine Learning Toolkit für InterSystems IRIS“ erläutern unsere Experten Aleksandar Kovacevic und Thomas Nitzsche, wie sich Machine Learning Ansätze einfach und effizient mit InterSystems IRIS umsetzen lassen. Zum Auftakt der Webinar-Reihe widmen wir uns der Frage, was passiert, wenn InterSystems IRIS im Rahmen eines Machine Learning Projekts auf Python trifft. Neben den technischen Aspekten – dem „wie“ – stellen wir Ihnen auch einige relevante Praxisbeispiele vor, um die Frage nach dem „warum Machine Learning“ zu beantworten. FREITAG, 26 JULI 2019 von 10:30 Uhr bis 11:00 Uhr. JETZT REGISTRIEREN.

ML Toolkit user group

ML Toolkit user group is a private GitHub repository set up as part of InterSystems corporate GitHub organization. It is addressed to the external users that are installing, learning or are already using ML Toolkit components. To join ML Toolkit user group, please send a short e-mail at the following address: and indicate in your e-mail the following details (needed for the group members to get to know and identify you during discussions):

  • GitHub username
  • Full Name (your first name followed by your last name in Latin script)
  • Organization (you are working for, or you study at, or your home office)
  • Position (your actual position in your organization, or “Student”, or “Independent”)
  • Country (you are based in)


  1. Install and start Rserve:
  • Install a recent version of R.
  • Install Rserve package from a R terminal: install.packages("Rserve",,"")
  • Launch Rserve from a R terminal:
  1. Load and Compile IRIS R package (i.e. do $system.OBJ.ImportDir("C:\InterSystems\Repos\R\","*.cls","c",,1)).

  2. The following ObjectScript code illustrates the simple integration with Rserve:

	R.RConnection c = ##class(R.RConnection).%New() // Create a R client
	Set x = ##class(R.REXPDouble).%New(3.0) // A single double value
	Do c.assign("x", x) // Assign the value to R variable x
	Do c.eval("y<-sqrt(x)") // Evaluate R script
	Set y = c.get("y") // Get the value of R variable y

It is advised to wrap all ObjectScript code in a try catch block. More test cases and usages can be found in class R.Test.

  1. A demo case is in R.Demo package.
  • Import data from csv file pima-diabetes.csv to class R.Demo.Pima.
  • Run Do ##class(R.Demo.Pima).LogReg(). This will use pima dataset to train and save a logistic regression model.
  • Run Do ##class(R.Demo.Pima).ScoreDataset(). This will score the pima dataset using the previously saved model and save the results to the table.
  1. Check test production for InterOperability examples.