The number of languages and tools for Statistical Analysis has been rapidly increasing in recent times. Every tool being developed however has it’s own pros and cons. For example, SAS, a very popular commercial tool is easy to use, but expensive; R, on the other hand, is free and powerful, but requires programming/tool expertise, due to lack of accountability and minimum support.
Continued efforts towards enabling usage of multiple languages in a single tool has allowed users proficient in a particular technology/language to also benefit from the advantages of other languages. To mention a few are the R packages to integrate C++ and SQL, Python package to integrate R, SAS package to integrate SQL.
Similar to PROC SQL, the programming interface that enables writing SQL scripts within SAS codes, PROC R is developed as an interface between R and SAS. It Enables Native R Programming in the Base SAS Environment. Using Proc_R, the SAS users can access the extensive statistical and visualization capabilities of R.
The Proc R SAS Macro was developed by Xin Wei, who published a paper in Jan 2012 in the “Journal of Statistical Software” explaining its implementation and usage.
Setup for Proc_R:
SAS Syntax to use Proc_R:
%include “C:\Proc_R.sas”;
%Proc_R (SAS2R =, R2SAS =);
******************************
***Your R Code Here***
******************************
;
%Quit;
Explanation:
Include the Proc R package in your SAS code. Specify the absolute or relative path of the Proc_R.sas source file.
The Proc_R macro contains two macro variables as input parameters called SAS2R (that defines the mechanism to convert SAS datasets to R dataframes) and R2SAS (that defines the mechanism to convert R dataframes into SAS datasets).
Write your R code snippet within the Macro.
The comfortable use of R in SAS has greatly simplified the analytics procedure, however the above Macro works only for windows and the updated Proc_R macro for the latest base SAS versions are also not available yet. However the mentioned drawbacks are negligible compared to the advantages the interface provides.
Reference:
For more coding examples and implementation of functionalities of Proc_R, you can refer to the paper written by Xin Wei :
https://www.jstatsoft.org/v46/c02/paper
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