NOW AVAILABLE: SAS Viya 2026.03 Release | SAS Viya March 2026 Release
NEW SAS® VIYA® FEATURES
Into The Viya Verse
Don't just read about it – see it in action. David Weik breaks down this month’s SAS Viya updates in a quick, visual walkthrough you won’t want to miss.
SAS Intelligent Decisioning
A variable assignment node lets you add multiple assignment statements directly into a decision instead of creating a rule set that contains only standalone assignment statements.
SAS Studio
Parameters enable you to request input from the user when code is run. Including parameters in your code increases your ability to reuse code and allows the code to be customized using the value that you enter for the prompt.
SAS Base
The R procedure enables you to run statements from the R programming language within SAS code. You can submit R statements from an external R script or enter R statements directly.
Use Case
SAS Studio: R Procedure
The R procedure (PROC R) creates a seamless bridge between SAS and R, allowing data scientists to harness R’s specialized statistical power without leaving the governed SAS environment. By enabling the execution of R scripts directly within a SAS session, it eliminates the friction of switching tools and ensures data transfer between SAS data sets and R data frames is fluid and efficient.
This integration provides a clear business advantage by centralizing workflows: you can use SAS for robust data management and security while leveraging R’s extensive library of experimental packages and ggplot2 visualizations. Ultimately, PROC R boosts productivity by combining the strengths of both languages in a single, unified pipeline – reducing the risk of data silos and manual export errors.
Tip of the Month
Brought to you by David Weik
Senior Software Development Engineer, SAS
Quick Tip: Now that we have PROC R available, we can combine SAS code, R and Python in one workflow to flex each language to its full potential.
The data exchange is very simple as the following sample demonstrates. In it, we will just move the data of the sashelp.cars data set into our R environment, then double the value in the MSRP column and move data back into to the SAS work library:
proc r;
submit;
df <- sd2df("cars", "sashelp")
df$MSRP <- df$MSRP * 2
df2sd(df, "cars", "work")
endsubmit;
run;
