Core SAS pattern

Convert SAS Macros to R Functions

Move SAS macro variables and repeatable macro logic toward explicit R functions and parameters, giving teams a clearer path to testable, reviewable migration code.

Try the SAS to R converter

Example

Replace a simple macro loop with a function

SAS

%macro make_domain(domain);
  data &domain;
    set raw.&domain;
    source = '&domain';
  run;
%mend;
%make_domain(dm);
%make_domain(ae);

library(dplyr)

make_domain <- function(domain, raw) {
  raw[[domain]] %>%
    mutate(source = domain)
}

domains <- c('dm', 'ae')
output <- setNames(lapply(domains, make_domain, raw = raw), domains)

What to validate after conversion

Replace hidden text substitution with explicit function arguments.
Use lists and iteration for repeatable domain-level work.
Convert large macro libraries in small, testable stages.

Always validate macro-variable substitution, input datasets, generated outputs, and iteration order against the SAS implementation and your standards.

Frequently asked questions

What replaces a SAS macro variable in R?

An R variable or a function argument is usually clearer than text substitution. Passing values explicitly makes the conversion easier to test and review.

Can a large SAS macro library be converted at once?

A staged migration is safer. Convert one deterministic macro or domain flow at a time, add focused tests, and compare outputs before moving to the next dependency.

Core SAS patterns

Convert SAS DATA Steps to R

Use SAS2R.ai to start translating DATA step transformations into readable R, then review row order, missing values, and derived-variable behavior against your source data.