SAS and R are two things that are often used for data processing. These two devices have a significant influence on the development of a business.
This is because companies need the results of data analysis to make new business decisions. If data processing is right and on target, a business can develop properly following market tastes and ongoing trends.
However, which is better than SAS vs R? To find out the complete answer, see the following explanation!
SAS stands for Statistical Analysis Software, a data analysis tool widely used by experts to improve the performance of a business.
SAS can be used by various lines, from Business Intelligence (BI) to data scientists. It can produce statistical data analysis to predict future business trends.
- Facilitate statistical calculations of an agency.
- Does not require high computer specifications.
- Shorten the time in doing the calculations.
- Providing solutions for business interests.
- Results are more accurate and reliable.
- Suitable to help market research from a business.
- Limited to the concept of social statistics.
- Not yet integrated with the database program.
Meanwhile, R is a program that is no less interesting than SAS. R is a programming language often used by data scientists in various industries, from Facebook, Google, Airbnb, and so on, because of its ease of operation.
R also enables an interactive and exciting display of data analysis results, making it suitable for presentation to other business colleagues who do not understand data.
R is a programming language that offers sophisticated data analysis capabilities. In fact, R is free.
Moreover, this language has many users, so many communities continue developing it. Technology giants such as Facebook, Google, and Microsoft use the R language.
Apart from that, there are other big companies such as Bing, Merck, TechCrunch, and Mozilla.
R is not a language for beginners, because the command–line display of this language is a little confusing, as a solution, you can use an integrated development environment like RStudio.
The data in R is also stored in physical memory. This can be one of the drawbacks. You might run out of memory if you work with a lot of data.
R already integrates with Hadoop, a framework for extensive data. Execution, aka reading R code, also takes a long time. Long. If you really want to speed things up, you have to be prepared to optimize your code.
When comparing SAS vs R, the main thing that an organization or business considers is cost. When compared, SAS has a price that is more expensive than R because of the myriad of benefits and various functions.
However, this is certainly not friendly for companies still on a small scale.
On the other hand, R is a more friendly programming language for various business channels, including newly established ones.
This is because R is open-source software. So, anyone can download and use it for data analysis purposes that drive business progress.
2. Ease of Use
In terms of ease of use, SAS vs R also has differences. SAS is a programming language that doesn’t require many requirements. T
he important thing is that you can operate SQL. In addition, R is a software that is quite difficult when compared to SAS because it requires more diverse code to run. This is undoubtedly worth the price it offers.
3. Platform Development
For platform development, SAS vs R has its own advantages and disadvantages. The result of R is relatively fast, with various advanced features that can be obtained.
Meanwhile, SAS is also developing management following the rapid development of R.
4. Statistic Capacity
SAS programs and SAS Stat provide a wide range of techniques and statistical evaluations, however, since R is open-source, the latest cutting-edge techniques are often first released in R.
With nearly 15,000 programs in the CRAN repository, R offers access to modern techniques such as GLMET and AdaBoost RF, which are not available in SAS.
R is also widely used in experimental projects, and in Kaggle competitions, the winners often use R to build their models.
It’s important to note that SAS is a paid application with support, and any new invention or statistical technique must be thoroughly tested and approved.
This makes SAS a reliable choice for mission-critical tasks, but it also means it may fall behind R in terms of the latest developments.
However, since anyone can upload a program in R, users must be cautious when using it.
5. File Sharing
The last thing you also need to consider is the convenience of various files. On SAS, you can’t share files with other users who don’t use this programming language and tend to be limited.
Meanwhile, if you use R, you are free to share files with colleagues and other companies. With this program, it will be easier for you to collaborate.
6. Customer Care and Supports
R has the largest online community but needs customer service support. If you have a problem, you are alone in solving the problem. We will get a lot of help if we have customer service support.
On the other hand, SAS has dedicated customer service in addition to the community. Therefore, if you encounter difficulties in some challenges or other technical arrangements, you can ask for help.
7. Data Visualization
When it comes to statistical data analytics, the ability to create graphical and data visualizations is a crucial aspect for Data Scientists and Data Analysts. SAS does offer some data visualization features, however, they are quite limited and do not offer much in terms of customization.
R, on the other hand, offers numerous packages for easy data visualization, such as RGIS, ggplot, and Lattice, and also provides advanced customization options, making it the preferred choice.
If you are considering a career shift towards data analytics and data science, learning SAS and R is a great starting point.
Springboard offers online learning courses in data science and data analytics, with a mentoring-led, project-driven approach, along with job guarantee that will help you make the transition smoothly.