The SASSY System
SAS-like functions for R
What it does
SASSY is an ecosystem of R packages that aim to make R easier for everyone, especially people who have a background in SAS® software. This set of packages brings many useful concepts to 'R', including data libraries, data dictionaries, formats and format catalogs, a data step, and a traceable log. The system also includes a package that replicates several commonly-used SAS® procedures, like 'PROC FREQ', 'PROC MEANS', and 'PROC REG'.
Problem solved
The primary problem is that, by itself, R cannot reproduce the basic functionality of SAS in terms of logs, report outputs, formatting, and statistics. The SASSY ecosystem solves all these issues: - Create logs similar to SAS logs - Creates report output very similar to SAS PROC REPORT - Replicates SAS-style formatting and format catalogs - Statistical functions pre-validated and matching SAS to a high precision Therefore, if you want your R outputs to match your SAS outputs, SASSY is the best/easiest way to do it. -
Best suited for
Programmers coming from SAS who want to learn and use R for regulatory reporting.
Typical use
Creating TFLs in R. Supports several file formats: TXT, RTF, DOCX, PDF, and HTML.
Deployment
Downloadable
Implementation
A practical view of what it may take to use this product in your environment.
Implementation requirements
- R
- RStudio or VSCode
Technical considerations
Access to source data.
Resources
Creator-supplied materials for evaluating the product.
PharmaSUG-2025-AP-263
View PDF →PharmaSUG-2024-AP-295
View PDF →Compliance & validation
Product information below is creator-supplied unless ClinExchange explicitly states otherwise.
Validation & review information
- Validation available: Yes
- Security review available: No / not stated
Evidence
Validation document available
https://r-sassy.org/validation/Procs_Validation.pdf
Creator
David J Bosak
Senior Manager, Biostatistical Programming · AmgenDavid is a Senior Manager in the Statistical Programming Support group at Amgen. He focuses on application development, open source, and AI. Prior to his current position, he was a consultant to pharma and non-pharma companies for 28 years. He has extensive experience creating solutions in SAS, R, .NET, database, and Web Technology. In recent years he has become an active R package developer, and has 12 packages published on CRAN.