SAS Vs R

SAS vs R is one of the biggest dilemmas for learners trying to pick a statistical tool. Both of them have their own benefits and they are used extensively by data analysts and data scientists around the world. It is incredibly important to pick the right tool because if you realise halfway through the process that you have picked the wrong one, then migrating to a new tool will be an even bigger challenge. If you have been confused between SAS and R, then you should start by understanding the two tools and their benefits. The right choice depends on your budget and your specific business requirements. In this article, we’ll be comparing SAS vs R in order to understand their differences.

SAS vs R: What is the Difference?

Before we go into the comparison, let’s discuss these two separately.

What is SAS?

Statistical Analysis System or SAS is a business analytics tool with business intelligence and data management capabilities. SAS makes it possible to extract insights from raw data. It is a commercially licensed product mainly used by big companies and organisations. It has a user-friendly interface which makes it easy for even new users to start using the tool. Though, you would need basic SQL knowledge in order to use SAS applications. Organisations around the world opt for SAS in order to drive insights from their business data and understand the underlying patterns. One of the biggest advantages of using SAS is its dedicated support and stable releases.

What is R?

R is an open-source programming language which is considered SAS’s counterpart. A machine-friendly language considerably similar to C++, it is powerful and flexible with advanced graphical capabilities that are comparable to SAS. The only drawback of the R programming language is that it has a higher learning curve and it can seem rather complicated and overwhelming to new users. Since it is open-source, the latest features are directly released to the public and it is also free to download by anyone. R includes a wide catalogue of graphical and statistical methods including linear regression, machine learning algorithm, statistical inference, and time regression. Most of the R libraries are written in R as well, but for some heavy computational tasks, Fortran, C++, and C are preferred. Some of the applications of R include data cleaning, importing, and mining, and statistical inference

SAS vs R: Understanding the difference:

1-SAS vs R: Learning curve:

SAS’s simple interface makes it easy for individuals to use the tool with just basic knowledge of SQL. There are also many resources, tutorials, and even instruction manuals available for new users. Since SAS is a paid tool used by big organizations around the world, there are many certifications available for SAS training, though they come at a hefty price, just like the tool itself. 

R is a machine-friendly programming language with straightforward processes and extended codes. In order to leverage its power, you would essentially have to learn a whole new language which can take a really long time.

2-SAS vs R: Pricing:

Being a licensed tool, SAS is one of the most expensive statistical software available in the market. As a paid tool, it comes with its own benefits like dedicated support and thorough technical documentation, but it also means that SAS isn’t affordable to small organizations and individuals. 

R, on the other hand, is an open-source software which is available for free to everyone. That means anyone can download it and start using it without paying anything. For most individuals, price is a big deciding factor when comparing SAS vs R. 

3-SAS vs R: File sharing:

You can only share SAS generated files with users who already have SAS installed on their system. Otherwise, even if you share the files, users won’t be able to open the files which can make it rather difficult to share and collaborate outside of the organization. 

On the other hand, since R is an open-source programming language available to everyone, you can share its files easily with anyone and collaborate. 

4-SAS vs R: Data management :

The biggest drawback of R is that it works only on RAM. So, even the smallest of the procedures take considerably longer time to run, depending on the local machine’s RAM configuration. On the other hand, SAS is much faster, safer, and better at handling large amounts of data because it has no such limitations.

5-Data visualisation:

When it comes to statistical data analytics, graphical and data visualisation capabilities are very important factors. SAS does provide some data visualisation features, but they are rather limited with hardly any customisation options.

R offers many packages for easy data visualization including RGIS, ggplot, and Lattice. It also offers advanced customization options, which makes it the clear winner.

If you have been planning to pivot your career towards data analytics and data science, then learning SAS and R is the perfect place to start. 

Springboard offers dedicated online learning courses in data science and data analytics that comes with a mentoring-led, project-driven approach along with a job guarantee to help you make this transition in the smoothest way possible.

Key Differences Between SAS and R

Both SAS vs R are popular choices in the market; let us discuss some of the major Difference Between SAS vs R.

1.Easy to learn

SAS is not difficult to learn they have complete instructions manual. As it’s a commercially licensed product there won’t be many levels of difficulty when it comes to coding where a user has to learn and build the code. whereas R needs a Programming language to learn. They need to be implemented correctly or else leads to complex codes. The overall curve leads to average to high.

2.Customer Service

SAS have good customer service; technical challenges are easily sorted has the largest online community but no customer support which makes much difficult for the user to tackle technical issues. SAS is beneficial to end to end infrastructure with good quality.

3.Language dependent

R is Object- Oriented and functional language, it is a highly extensive language. The source code for the R software is written in C and FORTRAN. It is platform independent and supports all Operating System. SAS is based on SQL Language& is a procedural Language.

4.Packages

R has built-in library function and packages, so it is the best option for plot visualization. SAS provides components during installation in SAS system (ETS, database). In SAS inputs are given in excel or from several data sources and the statistical analysis of the result is given in form of tables, graphs, HTML.

5.GUI

R has key advantages over statistical package is that sophisticated graphical abilities. R’s base graphics system allows us to have a fine control over essential plot and graph.

6.Data Security

SAS – Security is highly maintained in SAS where huge MNCs rely on them to protect their data as there is a lot of predictive analytics being done. When it comes to security, there is always a gap between the open source and the commercial product. Whereas Securities were not built well into R.

SAS vs R Comparison Table

Below is the 6 topmost comparison between SAS Vs R

The basis of comparison	SAS	R
Availability/Cost	It is expensive, cost a lot of memory. It is not a free tool requires licensed software. It is a click and runs the software.	R is Completely free and can be downloaded by anyone. They are low cost.
Graphical system	They offer good GUI. an array of statistical function with technical support.	They have highly advanced Graphical capabilities
Data Handling	They handle large datasets (Terabytes of data)	R has the largest drawback in handling Big dataset. R works on Ram, which makes difficult to run the small task.
Ease of use	SAS is a commercial software. This tool has user-friendly GUI. It comes with documentation and tutorial base which can help learners to learn easily.	Learning R is quite steep as we need to learn code at the root level.
Data science capabilities	SAS are efficient are sequential data access. The drag and drop interface make it easy to create a statistical model.	Statistical modes are written in few lines of code. R is mainly used when the task requires a standalone server.
Ranking	Ranked in 31st place in Jan 2012.	Ranked in 24th place by TIOBE community.

Conclusion

To remain competitive in the field of data analytics, high-level coding and programming are necessary for expertise. One limitation of R is its functionality is based on consumer and user’s involvement. The scalability issue associated with it is due to speed less of RAM. Statistical analyses in SAS is done by direct Program and use of SAS Analyst. They are leading in the present market as advanced predictive analytics. If we are data mining specialization or need in advanced graphical plots, then R is the best option to go for.