SAS vs R- Shikshaglobe

Content Creator: Satish kumar

What is SAS?

SAS represents Statistical Analysis Software which is utilized for Data Analytics. It assists you with utilizing subjective strategies and cycles which permits you to improve worker efficiency and business benefits. SAS is articulated as SaaS.In SAS, information is extricated and sorted which assists you with recognizing and dissect information designs. It is a product suite which permits you to perform progressed examination, Business Intelligence, Predictive Analysis, information the executives to work really in the cutthroat and changing business conditions. Additionally, SAS is stage free which implies you can run SAS on any working framework either Linux or Windows.

What is mean by R?

R is a programming language is broadly utilized by information researchers and large companies like Google, Airbnb, Facebook and so on for information investigation.R language offers a great many capabilities for each datum control, factual model, or outline which is required by the information investigator. R offers inbuilt systems for arranging information, running computations on the given data and making graphical portrayals of that informational collections.

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Why use SAS?

Access crude information documents and information in an outside data set

Dissect information utilizing statics, enlightening, multivariate methods, guaging, displaying, and straight programming

Assists you with overseeing information passage, designing, change, altering and recovery

Progressed examination highlight permits you to make changes and upgrades in strategic approaches.

Assists organizations with being familiar with their verifiable information

Why use R?

R offers a helpful programming develops for information examination like conditionals, circles, info and result offices, client characterized recursive capabilities, and so on.R has a rich and growing biological system and a lot of documentation accessible over the web.You can run this device on different stages including Windows, Unix, and MacOS.Great illustrations abilities Supported by a broad client organization

SAS vs. R: A Comprehensive Comparison

SAS (Statistical Analysis System) and R are two prominent programming languages and software suites used in the field of data analytics, statistical analysis, and data science. They each have unique strengths and are widely employed by professionals in various industries. In this comprehensive comparison, we'll explore the key aspects of SAS and R to help you make an informed choice for your data analysis needs.

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Table of Contents

  1. Introduction to SAS and R
  2. Syntax and Learning Curve
  3. Data Manipulation and Analysis
  4. Visualization
  5. Statistical Analysis and Modeling
  6. Community and Support
  7. Integration with Other Tools
  8. Cost and Licensing
  9. Performance and Speed
  10. Scalability
  11. Popularity and Job Market

Let's delve into each section to understand the differences and similarities between SAS and R.

1. Introduction to SAS and R

  • SAS: SAS is a software suite primarily used for advanced analytics, data management, and business intelligence. It has been an industry standard for decades and is trusted for its reliability and robustness.
  • R: R is an open-source programming language and environment designed for statistical computing and data analysis. It is known for its flexibility and extensive libraries.

2. Syntax and Learning Curve

  • SAS: SAS has a proprietary language with a relatively steep learning curve, especially for beginners. It uses a data step and a procedure step for data manipulation and analysis.
  • R: R's syntax is considered more user-friendly and is often preferred by data scientists and statisticians. Its straightforward syntax makes it accessible to those with programming backgrounds.

3. Data Manipulation and Analysis

  • SAS: SAS offers powerful data manipulation and analysis capabilities. It has procedures like PROC SQL and PROC MEANS for data summarization.
  • R: R excels in data manipulation and analysis with packages like dplyr and tidyr, which provide efficient ways to manipulate and reshape data.

4. Visualization

  • SAS: SAS provides visualization capabilities through procedures like PROC SGPLOT and PROC GPLOT. While it offers decent visualization options, it may not be as customizable as R.
  • R: R is renowned for its data visualization capabilities. Packages like ggplot2 and lattice allow for highly customized and publication-quality plots.

5. Statistical Analysis and Modeling

  • SAS: SAS is known for its comprehensive statistical procedures. It offers a wide range of built-in statistical models and techniques for regression, ANOVA, and more.
  • R: R boasts an extensive ecosystem of packages for statistical analysis and modeling. It offers flexibility and customization in building statistical models.

6. Community and Support

  • SAS: SAS has a dedicated user base and offers commercial support. It is widely used in regulated industries like healthcare and finance.
  • R: R benefits from a large and active open-source community, providing extensive online resources, forums, and packages. Commercial support is also available.

7. Integration with Other Tools

  • SAS: SAS has strong integration with databases and enterprise systems, making it suitable for large organizations with complex data infrastructures.
  • R: R can integrate with a variety of databases and tools, but it may require more effort for extensive enterprise-level integration.

8. Cost and Licensing

  • SAS: SAS is commercial software, and licensing costs can be substantial, especially for enterprise-level usage.
  • R: R is open-source and free to use, which can be a significant cost advantage for individuals and organizations.

9. Performance and Speed

  • SAS: SAS is known for its robust performance and efficiency, particularly when dealing with large datasets.
  • R: R's performance can vary depending on coding practices and the use of optimized packages.

10. Scalability

  • SAS: SAS is designed for scalability and can handle large-scale data processing efficiently.
  • R: R may face scalability issues with extremely large datasets and complex operations.

11. Popularity and Job Market

  • SAS: SAS has a significant presence in industries like healthcare, finance, and government, resulting in a dedicated job market.
  • R: R's popularity is growing rapidly, especially in the data science and academic communities, leading to increased job opportunities.

 History of SAS

SAS was created by Jim Goodnight and John Shall in 1970 at N.C. College

At first, it was created for Agricultural Research.

Afterward, it extended to a range of devices to incorporate Predictive Analytics, Data Management, BI, among others.

Today 98 of the world's top organizations in fortune 400 purposes SAS information logical apparatus for Data examination.

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History of R

1993-R is a programming language created by Ross Ihaka and Robert Gentleman

1995: R initially dispersed as an open-source instrument under GPL2 permit

1997: R center gathering and CRAN established

1999: The R site,, sent off

2000: R 1.0.0 delivered

2004: R 2.0.0 delivered

2009: First version of the R Journal

2013: R 3.0.0 delivered

2016: New R logo took on

Element of R

R assists you with interfacing with numerous data sets and information types

An enormous number of calculations and bundles for measurements adaptable

Offers compelling information taking care of and storage space

Gather and examine online entertainment information

Train machines to make forecasts

Scratch information from sites

A complete and incorporated assortment of moderate instruments for information examination

Communicate with different dialects and prearranging abilities

Adaptable, extensible, and complete for efficiency

Optimal stage for information perception

Highlights of SAS

Tasks Research and Project Management

Report arrangement with standard illustrations

Information refreshing and adjustment

Strong Data dealing with language

Peruse and compose practically any information design

Best information-purifying capabilities

Permits you to Interact with various host frameworks

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The Final Verdict: R versus SAS

In the wake of contrasting a few principal contrasts between both these devices, we can say that both have their own arrangement of clients. There are many organizations, who favor SAS in light of information security issues, which show notwithstanding a drop in a new year, there is as yet a gigantic interest for SAS confirmed experts. Then again, R is an ideal device for those experts who maintain that should do profound savvy Data investigation occupations. The quantities of new businesses are expanding everywhere. Hence, the interest for R-guaranteed designers is likewise expanding. Right now, both have equivalent potential for development on the lookout, and both are similarly well known apparatuses.

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Must Know!

R Aggregate Function 

R Select(), Filter(), Arrange(), Pipeline 

R Scatterplots 

boxplot() in R 

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