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You live in the age of data. As capabilities for collecting and storing information grow exponentially, organizations need to make sense of it all. Making data-driven decisions requires statistical analysis to turn raw numbers into valuable insights. Two leading platforms for such analytical tasks are SAS and R. But with distinct capabilities and limitations across areas like cost, ease-of-use and flexibility, how do you determine the right tool for your needs?
To help with this decision, we will compare the key qualities of SAS and R. We’ll provide recommendations based on use cases and priorities. Let’s explore the story behind these two analytical juggernauts and how they stack up. Gain the knowledge to pick the best software for empowering your data science initiatives.
The Origins of SAS and R
To understand the philosophical differences between SAS and R, it helps to review their origins.
SAS arose from a frustration with available analysis software in the 1970s. North Carolina State University graduate students James Goodnight and Anthony Barr struggled with the shortcomings of then-popular packages like SPSS. They believed they could develop something better. Their goal was to create enterprise-grade software accessible to non-technical domain experts across various industries.
Goodnight and Barr‘s vision led them to build SAS on core principles of:
- Ease-of-use: Enabling user-friendly point-and-click access to advanced statistics
- Scalability: Supporting large, complex business datasets and infrastructure
- Completeness: Covering the full analytical process from data to discovery to reporting
- Support: Providing education, training, and customer service
SAS grew rapidly as government agencies, banks, insurers, and manufacturers adopted it. After being hired in 1976, current CEO Jim Goodnight led commercialization of the toolkit they had developed. This software basis became SAS/STAT for statistical analysis and SAS/GRAPH for visualization. The SAS language was also created for handling data tasks like cleaning and joining tables.
Conversely, R was born out of necessity in an academic research environment. Statisticians Ross Ihaka and Robert Gentleman at the University of Auckland created R in 1995 because available software could not easily do all they needed for data analysis. Ihaka and Gentleman designed R to provide:
- Flexibility: The ability to extend functionality with custom scripts and modules
- Interactivity: Rapidly execute code chunks to iterate analysis
- Visualization: Graphical data analysis integrated with statistics
- Reproducibility: Create sharable documents containing analysis and results
As an open source tool, R quickly spread between colleagues at research institutions. R‘s package system allowed users to distribute statistical methods, data manipulation abilities, and visualization techniques. By 1997, Robert Gentleman and Ross Ihaka has established a user community via mailing list to coordinate R‘s evolution.
Today, both SAS and R retain foundational traits from their respective origins even decades later. This drives ongoing differences in product direction and philosophy.
SAS vs R Usage Trends
Gaining analysts skilled in SAS or R remains a key hiring priority as organizations become more data-driven. Indeed.com job posting trends over the past five years shows rising demand for both skillsets:
However, R has seen more rapid growth as data science and machine learning teams expand. While SAS remains deeply used across traditional industries like banking, insurance, and manufacturing, R garners significant mindshare in tech. Surveys by industry analysts indicate over 65 percent of data professionals prefer R and Python over SAS today.
| Pros | SAS | R |
|---|---|---|
| User Base | Used at 98% of Fortune 500 companies | Utilized at leading tech firms like Google, Facebook, Airbnb |
| Salary Range | $67K – $148K | $88K – $163K |
| Job Openings (2025) | ~15,000 | ~14,000 |
| Growth (2018-2023) | 34% | 55% |
SAS continues holding significance across business intelligence and advanced analytics use cases as well. A recent Gartner Magic Quadrant report placed it as a “Leader” based on technical capabilities and market penetration. However, the same report found over 20% of SAS customers using R alongside or migrating existing workflows.
This highlights the rise of R within corporate analytics teams rather than just academia and research. The 2022 Kaggle ML/DS survey of over 23,000 practitioners reinforced R’s popularity. 87% of respondents reported using R compared to 59% for SAS. As data science converges across functions, expect R capabilities to grow in importance.
Comparing Pros and Cons
We’ve covered the historical context and direction of SAS vs R ecosystems. Now let’s directly compare pros and cons across key decision factors:
Cost
| SAS | R |
|---|---|
| Pros – Support services included for issues – Professional training/certification – Cloud offerings may reduce need for own hardware |
Pros – No software licensing fees – Open source flexibility |
| Cons – Expensive licensing model – Cost for support and new versions |
Cons – Infrastructure, personnel costs can still be high – No included formal support |
The need to purchase licenses makes SAS a significant investment depending on scope. But formal support channels can facilitate learning and troubleshooting. R offers free access but more dependence on documentation, online communities for assistance.
Functionality
| SAS | R |
|---|---|
| Pros – Broad native statistics for analysis – Advanced abilities like forecasting, econometrics – High performance distributed execution |
Pros – Extensive specialized packages – Easy to add new analytical methods – Embed directly in other apps |
| Cons – SAS programming required for full access – Mostly closed-source development |
Cons – Less consistent capability quality – Standard APIs still maturing |
Each platform provides abundant analytical functionality – but leveraging it differs. SAS requires learning its proprietary language but natively delivers advanced statistics beyond basics like regression. R‘s open ecosystem provides cutting-edge techniques and experiments but can lack cohesion.
Ease of Use
| SAS | R |
|---|---|
| Pros – Menu-driven workflows – GUI for visual data exploration – Generates production-ready reports |
Pros – Tight workbench for coding – Faster iterative analysis – Customizable visualizations |
| Cons – Programming for full access – Exporting models can be difficult |
Cons – Steep learning curve – Less automation – Sparse for reporting |
SAS‘s point-and-click interface enables faster ramp up for non-programmers. But integrating advanced methods or exporting models involves SAS coding. R offers great control for developers but less guidance for beginners. Visualizations and reporting need more manual work.
Administration
| SAS | R |
|---|---|
| Pros – Unified data governance – Scales across enterprise – Hybrid cloud deployments – Dedicated account management |
Pros – Integration with SQL, Hadoop, Spark – Containerization capabilities – Multi-cloud deployments |
| Cons – Migration challenges off SAS – Primarily Windows servers |
Cons – Open source tooling gaps – Needs DevOps processes – Limited enterprise platform |
Large organizations often standardize SAS for its security, availability, and support services. But costs and vendor lock-in issues can emerge over time. R offers abundant integration and cloud hosting options but requires more planning for governance, maintenance.
Community
| SAS | R |
|---|---|
| Pros – Mature training programs – Stable long-term vendor |
Pros – Open collaboration culture – Frequent conferences and meetups |
| Cons – Primarily corporate users – Limited informal support |
Cons – Fragmented versioning – Rapid package changes |
SAS cultivates an extensive professional network with conferences and certifications. R relies more on ad hoc user groups and events. The pace of growth brings less standardization but energy.
By reviewing these pros and cons, we see each platform has clear strengths based on user priorities around elements like costs, skill levels, and use cases. Understanding your specific analytics environment helps determine which solution works best.
Recommendations Based on Use Cases
We‘ve compared the origins, trends, and tradeoffs between platforms. Now we‘ll cover recommendations for specific analytics use cases across business domains.
Business Intelligence
For traditional business intelligence needs like dashboarding and reporting, SAS remains hard to beat. Task-driven users can leverage SAS Visual Analytics to access data, analyze it, and create sharable reports. SAS VA also connects to common data sources like Oracle, Salesforce, and SAP. The unified BI environment covers everything from ETL to calculations to pixel-perfect visualizations.
R lacks the guided domain tools around reporting seen in SAS VA. But it provides abundant flexibility with Shiny apps for custom views. And RStudio‘s LearnR service offers structured tutorials even for non-coders. For simple analysis and socialization, R works well. But SAS better addresses complex enterprise BI priorities via governance, promotion of standards, and project management features.
Finance
From flagging credit card fraud to pricing insurance premiums, financial organizations run on advanced analytics. Modeling techniques used go well beyond basic statistics to machine learning, simulation methods, and AI for predictive accuracy and automation.
SAS owns strong financial sector penetration thanks to these high-end analytical abilities paired with model monitoring, deployment, and decisioning tools. Surfacing predictions directly into business applications with SAS Model Manager streamlines operationalization. Monitoring dashboards track model drift. R support for real-time fraud analysis, customer propensity scoring continues maturing with sparklyr and other packages. But for mission-critical financial decisions, SAS enterprise strengths help drive adoption.
Marketing
Marketing teams applying analytics have exploded with the rise of digital experiences and vast data availability on customer actions. Predictive models can guide decisions for acquisition campaign targeting, personalized experiences to nurture loyalty or conversion rate optimization.
Here R possesses some advantages over SAS with greater agility to bake statistical models directly into apps, crawl web data for analysis, or modify techniques. inferior open source algorithm development outpaces proprietary approaches. Packages like h2o bring autoML capabilities with automatic modeling. And data products built fully in R allow models to continuously retrain on new data. Marketing‘s dynamic environment rewards R‘s flexibility – especially marketing tech teams leaning on open source tooling and languages.
Clinical Trials
Drug and health sciences organizations rely on analytics to assess new treatment efficacy and safety compared to existing interventions. SAS is long used in clinical trials for reliable, compliant data integration across hospitals and labs. Robust SAS analytics identify patient cohorts for recruitment and subgroups benefiting most post-trial. Metadata layers enable unified health data governance. State validation kits certify SAS environments meet regulations.
However R also sees heavy clinical research usage – its Document-Reproduce-Share paradigm aids transparency. R reproducibility packages like Rmarkdown document analysis logic and package versions used alongside outputs. Sharing code and data findings is simplified for review. R flexibility allows specific medical research needs to be addressed. Overall SAS‘s enterprise scalability and R‘s reproducibility each answer important clinical trial demands.
Research Science
Research roles were the initial beachhead and remain an R stronghold. Experimenting with emerging methods like neural networks for image analysis happens here first. R‘s blend of interactivity compared to Python, visualization capabilities, huge corpus of packages drive adoption. Sharing code on GitHub or RStudio Connect allows collaboration before peer reviewed publication.
SAS does progress in areas like machine learning model building, interpretation and tuning parameters. SAS Model Studio provides a workflow interface that allows coding in multiple languages like Python and R too. But research freedom still favors the R ecosystem for bottom-up innovation versus commercial offerings like SAS.
The above analysis shows good reasons for either platform to be favored depending on users within an organization. Sometimes both used in concert makes perfect sense as well.
Conclusion: Finding the Right Mix
Deciding between investing in SAS vs R skills and infrastructure depends greatly on aligning usage to current and desired analytical maturity. With its enterprise orientation, SAS answers scalability, governance, and regulatory demands better. SAS allows groups not desiring or ready to program to still apply statistics. But R provides greater access to the latest techniques and community problem-solving.
For most organizations, the ideal is no longer an “either/or” choice but rather a “yes, and” synergistic approach. SAS offers openings to run R models directly within its interfaces now. Numerous integration pathways tie open source tools like Python and R into SAS model building, monitoring and even environments like SAS Viya.
This shared direction shows the value each brings. SAS remains the gold standard for many across industries based on factors like security, support, and visualization capabilities. But R has won over analysts desiring flexibility plus developer velocity. Using both together allows organizations to access the best of cutting-edge open source and battle-tested enterprise analytics.