Today, I’ll explain the key differences between SAS and R so you can decide which one is appropriate for your statistics needs. Let’s begin by defining both of these technologies: –
The acronym SAS refers to the Statistical Analysis System. SAS is now one of the most used corporate software programs. That is why it is quickly establishing itself as the market leader in commercial analytics.
SAS has a lot of features and a wonderful user interface. These characteristics assist users in quickly learning this software.
Aside from that, the clients receive technical assistance anytime they need it. SAS is pricey and lacks the most up-to-date and effective statistical capabilities.
R is an open-source programming language. The researchers came up with it. That is why R is used by the vast majority of professors and researchers.
R programming is unique in that it is an open-source programming language. R’s open-source nature means that all new techniques are released immediately.
This language provides a lot of documentation as well as a community to aid people who are having problems with R.
SAS vs R Trending Chart: Which is more popular, SAS or R?
SAS and R are two popular programming languages. As a result, both graphs continue to fluctuate. When we look at the graph as a whole, it’s evident that R is in high demand. As a result, there are a variety of work alternatives available to you.
The pay packages for each skills are listed below. R is the clear winner here as well. R is in high demand because to its many beneficial features.
SAS is also willing to provide you a competitive remuneration. We can conclude that both can provide you the best employment opportunities and pay packages.
Should I learn SAS in 2021, given its falling popularity?
As previously said, SAS offers various advantages, one of which is its ability to deploy end-to-end infrastructure (like Data warehouse, reporting, and analytics, Visual Analytics, Data quality). It is minimized by R’s support and integration on platforms like Tableau and HANA.
That is why large corporations continue to use this software. It is also simple to comprehend and learn. As a result, if you are a newbie, it is always a good idea to learn it.
The main distinction between SAS and R (Tabular form)
Differences between SAS and R
Price / Availability
SAS is commercial software, not open-source, as I previously stated. It is one of the most costly statistical software packages. SAS is beyond of reach for the vast majority of professionals.
However, this software is used by the vast majority of large businesses. Because of this, it has a bigger market share than R. In other words, SAS is for the organization rather than the person.
R, on the other hand, is free software. As a result, anyone can download and use it without paying any fees. Between SAS and R, cost is a major consideration.
Learning is simple.
SAS is simple to learn for everyone, whether they are professionals or novices. SAS is based on PROC SQL, which is simple to grasp for those who are already familiar with SQL. Apart from that, SAS has a fantastic user interface as well as several tutorials.
These lessons are really helpful in learning how to use SAS. When you buy the software, it also comes with a comprehensive user manual.
R, on the other hand, is a difficult programming language to master. The R programming language is difficult to learn for a beginner.
Mastering the R programming language takes a lot of time and effort. R is a programming language for low-level tasks. As a result, it will need to write more code in it.
The application is making progress.
Because R is an open-source programming language, it always gets the most recent features before SAS. SAS, on the other hand, includes the most recent features in its new upgrades.
SAS development is currently moving at a quicker pace. R, on the other hand, was previously used by a few professors. The finest aspect about upgrading SAS is that all of its new features are examined in the same environment.
When we obtain the latest R update, however, there are not many well-analyzed features. R, in fact, has an open-source community where programmers and scholars collaborate to improve the software.