Core SAS pattern

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.

Try the SAS to R converter

Example

Translate a left join and grouped summary

SAS

proc sql;
  create table summary as
  select a.trt01p, count(*) as n, mean(b.age) as mean_age
  from adsl as a
  left join demo as b on a.usubjid = b.usubjid
  group by a.trt01p;
quit;

library(dplyr)

summary <- adsl %>%
  left_join(demo, by = 'usubjid') %>%
  group_by(trt01p) %>%
  summarise(
    n = n(),
    mean_age = mean(age, na.rm = TRUE),
    .groups = 'drop'
  )

Always validate join cardinality, duplicate keys, row counts, and summary values against the SAS output and your standards.

What to validate after conversion

Make join type and key columns explicit with dplyr joins.
Check one-to-one and one-to-many assumptions before merging.
Use grouped summaries with explicit missing-value handling.

Frequently asked questions

Does PROC SQL always become dplyr?

Many joins, filters, and grouped summaries map naturally to dplyr. The appropriate R approach can differ for database-backed data, very large files, or SAS-specific implicit type conversion.

How do I safely validate a converted join?

Confirm both input keys, declare the expected relationship, compare before-and-after record counts, and investigate duplicate keys before accepting a result.

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.

Clinical workflows

SAS to R for Clinical Trial Programming

SAS2R.ai helps clinical programming teams begin translating deterministic SAS logic into readable R while preserving the review, QC, traceability, and validation work required by their processes.