Difference Between SAS and R
SAS vs R is the comparison between two industry-leading analytics tools or language.SAS known as Statistical Analysis System is an enterprise provides the tools for business analytics.
Whereas, R is an open source programming language that is widely used for statistical and data science. SAS provides several out of the box features and supports for analytics and is limited to large enterprises for its licensing constraints. Whereas R language supported by the growing community and advanced packages are build and contributed to the community for R program implementation.SAS is efficient to process very high size datasets. Whereas, R is limited to the large size data processing as it processes the data.
Tools for Data Analytics
The most popular and used tools for data analytics are SAS vs R.
- SAS is largely initiated in big corporations because they have high customer service, that’s why they play a vital role in financial services and marketing companies.
- SAS code is executed within its own SAS system, R code executes within the R’s statistical environment.
- SAS has bit loops in data files record, in R loops are avoided.
- R is used in mid-sized firms; telecoms companies require unstructured data for the data analysis and hence they use machine learning algorithms to work with for which R language is more suitable.
- Ruses function like Decision trees, association rule, mining that is why they are used in the data mining process.
- Significant disadvantages of R are they work only on RAM, whereas SAS works for increased data size.
Some of the R application are:
- Largely used in the Finance process and market.
- They help in data importing, cleaning.
- Plays a vital role in data science as it gives a variety of statistics.
Where SAS can be applied and in which sectors?
- Finance, Government, Healthcare domains, etc.
- Predictive analytics
- Business intelligence
- Prescriptive analytics
Head To Head Comparison Between SAS and R(Infographics)
Below is the top 6 difference between SAS vs R
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.
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.
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||
|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.|
|Ranked in 31st place in Jan 2012.||Ranked in 24th place by TIOBE community.|
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.
This has a been a guide to the top difference between SAS vs R. Here we also discuss the key differences with infographics, and comparison table. You may also have a look at the following articles to learn more.