r-statistics.co by Selva Prabhakaran

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R vs SAS: Cost, Speed, Career & What Fortune 500s Are Using

SAS has been the enterprise standard for statistics and analytics for 40+ years. R is the open-source challenger that Fortune 500 companies, the FDA, and major pharma firms are increasingly adopting. Here's how they compare on cost, capability, career prospects, and compliance.

SAS and R serve overlapping markets but come from very different philosophies. SAS is a commercial product with world-class support and regulatory track records. R is a community-driven open-source language with unmatched statistical depth. Understanding the trade-offs is critical if you're choosing between them — or planning a migration.

Licensing and Cost

The cost difference is dramatic and is the single biggest factor driving enterprise R adoption.

Factor R SAS
Base license Free (GPL-2) $8,000-50,000+/year per user
Enterprise deployment Free $150,000-1,000,000+/year
Cloud (SAS Viya) N/A Subscription (6-7 figures for enterprise)
Additional modules Free (CRAN packages) $5,000-25,000 each
Support Community + Posit (paid option) Included with license
Total cost of ownership (50 users) Near zero (software) $500K-2M+/year

SAS advantage: The license includes support, training, documentation, and guaranteed stability. For regulated industries, this "one throat to choke" model has real value.

R advantage: The money saved on licensing can be invested in people, training, and infrastructure. Organizations with 50+ analysts can save millions annually by switching to R.

Statistical and Analytical Capabilities

Capability R SAS
Classical statistics Comprehensive (21,000+ packages) Comprehensive (PROC-based)
Machine learning tidymodels, caret, xgboost SAS Viya, Enterprise Miner
Deep learning torch, keras SAS DLPy (limited)
Bayesian statistics brms, rstanarm, JAGS PROC MCMC (limited)
Time series forecast, fable, tseries PROC ARIMA, PROC ESM
Text analytics quanteda, tidytext SAS Text Miner
Visualization ggplot2 (best-in-class) PROC SGPLOT (adequate)
Spatial analysis sf, terra, leaflet SAS/GIS (limited)
Bioinformatics Bioconductor (2,200+ pkgs) SAS Genetics (limited)

R advantage: New statistical methods appear on CRAN within months of publication. In SAS, you wait for the next release cycle — if the method is added at all. R's package ecosystem is orders of magnitude larger.

SAS advantage: SAS procedures are battle-tested and produce consistent, well-documented output. PROC SQL, PROC MEANS, PROC REG have been refined over decades.

Performance and Scalability

Aspect R SAS
In-memory processing Default (data.table is very fast) Default
Larger-than-memory data arrow, duckdb, sparklyr SAS can handle larger datasets natively
Parallel processing future, furrr, foreach Built-in (threaded procedures)
Database integration DBI, dbplyr Built-in (PROC SQL, libname)
Distributed computing sparklyr, Rmpi SAS Grid Manager, SAS Viya

SAS advantage: SAS was designed for large enterprise datasets from day one. It handles datasets that don't fit in memory without special configuration. SAS Grid Manager provides enterprise-grade distributed computing.

R advantage: With data.table, arrow, and duckdb, R can process billions of rows efficiently. The sparklyr package connects R to Spark clusters for true big data workloads.

FDA Compliance and Regulated Industries

This is the most important section for pharma and healthcare readers.

SAS's regulatory history: SAS has been used in FDA submissions for 30+ years. Pharma companies trust it because regulators are familiar with SAS output, and SAS Institute provides validation documentation.

R's regulatory status in 2026: R is fully accepted by the FDA. Key milestones:

  • The FDA uses R internally for statistical review
  • The R Consortium's R Submissions Working Group has successfully completed pilot FDA submissions using R
  • The pharmaverse — a collection of R packages for clinical trials — provides validated, GxP-compliant tools
  • Major pharma companies (Roche, Pfizer, Novartis, J&J) have adopted R for regulatory submissions
# Example: The pharmaverse provides validated clinical trial tools # admiral: creates ADaM datasets # teal: builds interactive Shiny apps for clinical data review # rtables: creates regulatory-compliant tables library(admiral) library(rtables) # These packages follow ICH E9 guidelines and produce # submission-ready output accepted by the FDA


  






Key R validation resources:



    


  • riskmetric package: quantifies R package risk for validation
    • 


  • R Validation Hub (pharmar.org): industry-wide effort to establish R validation frameworks
    • 


  • valtools: tools for creating validated R packages in GxP environments
    • 





Verdict: As of 2026, R is a fully viable choice for FDA submissions. The pharma industry's migration from SAS to R is well underway.



Job Market and Career Impact







Metric
R
SAS






LinkedIn job postings (US, 2026)
~15,000/month
~8,000/month




Salary trend
Rising
Flat to declining




Median salary (data analyst)
$85,000
$90,000




Median salary (data scientist)
$125,000
$115,000




New graduates learning
Growing rapidly
Declining




Industry direction
Adoption increasing
Legacy maintenance







SAS career reality: SAS skills still command decent salaries, especially in banking, insurance, and government. But new SAS projects are rare. Most SAS jobs involve maintaining existing systems.



R career reality: R skills are in demand for data science, biostatistics, research, and analytics roles. R knowledge often comes paired with modern data science skills (Git, cloud, ML), making R users more versatile.



The generational shift: University statistics departments increasingly teach R instead of SAS. The pipeline of new SAS users is shrinking, while the R community grows.



Enterprise Adoption



Fortune 500 companies using R:



    


  • Google: Uses R for analytics and causal inference research
    • 


  • Facebook/Meta: R for experimentation analysis
    • 


  • Microsoft: Invested in R (acquired Revolution Analytics), Azure ML supports R
    • 


  • Roche, Pfizer, Novartis: Migrating clinical trials infrastructure to R
    • 


  • The New York Times, BBC, Financial Times: R for data journalism and visualization
    • 





SAS's enterprise strongholds:



    


  • Banking and financial services (risk modeling, fraud detection)
    • 


  • Government agencies (Census Bureau, defense)
    • 


  • Insurance (actuarial, claims analysis)
    • 


  • Legacy pharma (gradually migrating)
    • 





Migration Strategy: SAS to R



If your organization is considering the switch, here's a practical approach:



Phase 1 (Months 1-3): Pilot project



    


  • Pick one non-critical analysis workflow
    • 


  • Replicate SAS output in R
    • 


  • Use haven::read_sas() to import SAS datasets
    • 


  • Map SAS PROCs to R equivalents
    • 





Phase 2 (Months 3-6): Training and tooling



    


  • Train team on tidyverse, ggplot2, R Markdown
    • 


  • Set up RStudio Server or Posit Workbench for team access
    • 


  • Establish coding standards and package validation processes
    • 





Phase 3 (Months 6-12): Gradual migration



    


  • Move new projects to R
    • 


  • Keep SAS running for legacy workflows
    • 


  • Build R validation documentation for regulatory needs
    • 









SAS Procedure
R Equivalent






PROC MEANS
summary(), dplyr::summarise()




PROC FREQ
table(), janitor::tabyl()




PROC REG
lm()




PROC LOGISTIC
glm(family = binomial)




PROC MIXED
lme4::lmer()




PROC SORT
dplyr::arrange()




PROC SQL
dplyr verbs or sqldf




PROC SGPLOT
ggplot2




DATA step
dplyr::mutate(), dplyr::filter()




PROC EXPORT
readr::write_csv(), haven::write_sas()







FAQ



Q: Is SAS dying? A: SAS isn't dying, but it's declining. SAS Institute still generates $3+ billion in annual revenue, and existing installations will run for years. However, new adoptions are rare, and the trend is clearly toward open-source tools.



Q: Will the FDA reject my submission if I use R instead of SAS? A: No. The FDA has publicly stated it accepts submissions using any validated software. The R Submissions Working Group has completed successful pilot submissions. Multiple pharma companies now submit using R.



Q: Can I run SAS code in R? A: Not directly, but the haven package reads SAS datasets (.sas7bdat), and the translation from SAS PROCs to R functions is straightforward. The sasr package can interface with a SAS installation if you need to run both during migration.



What's Next




        


        
      


      

        

        
© 2016- Selva Prabhakaran.
          This work is licensed under the Creative Commons License.