SAS2R.aiMigration Guides

Core SAS pattern

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.

Try the SAS to R converter

Example

Filter records and derive a visit flag

SAS

data analysis;
  set raw.vs;
  if visitnum > 0;
  if aval >= 140 then high_bpfl = 'Y';
  else high_bpfl = 'N';
run;

R

library(dplyr)

analysis <- raw_vs %>%
  filter(visitnum > 0) %>%
  mutate(high_bpfl = if_else(aval >= 140, 'Y', 'N'))

What to validate after conversion

  • Map row-wise conditions to explicit dplyr transformations.
  • Make sort order and grouping assumptions visible in R code.
  • Review SAS special missing values before replacing them with R NA.

Always validate record counts, ordering assumptions, data types, and missing-value behavior against the SAS output and your standards.

Frequently asked questions

How do SAS IF statements map to R?

Simple conditions often map to filter() for row selection and if_else() or case_when() for derived variables. Review type conversion and missing-value behavior rather than relying on a literal line-by-line translation.

What should I review after converting a DATA step?

Compare record counts, key uniqueness, variable types, missingness, and a sample of subject-level records. Check any logic that depends on input order, RETAIN, or BY-group processing separately.

Related SAS to R resources

Core SAS patterns

Convert SAS PROC SQL to R

Translate common PROC SQL joins, filters, and summaries into clear R data pipelines, then validate key uniqueness, join cardinality, and record counts before using the result.

Core SAS patterns

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.