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.
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