In the world of data science, the two most widely used programming languages are SAS and R. Although both are powerful tools that can be used to analyze data and create predictive models, the two programs bring different advantages and disadvantages to the table. In this blog post, we’ll be taking a deep dive into the debate of SAS vs. R, exploring their individual capabilities, usage scenarios, strengths and weaknesses. We’ll also discuss the different benefits both languages can bring to data science and business analytics projects, and provide a few tips to help you decide which program is right for you. Whether you’re an experienced data scientist or a beginner just getting started in the field, this post will provide you with the knowledge you need to make an informed decision.
Python vs R vs SAS | R, Python And SAS Comparison | What I Should Learn In 2021? | Simplilearn
What is R?
Data scientists use the programming language R as an alternative to SAS for data analysis. It is a free, open-source platform, which means that anyone can use its code. R organizes data, analyzes it using formulas, and generates visual reports with the information it discovers. The statistical methods it uses include:
R is used in research, academia, and business, especially by new businesses.
What is SAS?
IT professionals use a software system known as SAS, or statistical analysis software, for advanced statistical and data analysis. The software reads and stores data, analyzes it, and generates reports based on its conclusions. These reports are available in rich text, graph, table, PDF, and HTML formats. Companies use SAS to:
SAS is primarily used by large companies and organizations.
SAS vs. R
SAS and R are viewed as counterparts by the IT industry. Although they carry out comparable tasks, they differ greatly in terms of their features, functionality, and use. The following are some of the key distinctions between SAS and R:
R is widely used in the business, marketing, and finance sectors. Companies use the programming language to:
Many industries use SAS, including finance, healthcare and government. Companies use the software to:
The benefits of using R in data analytics include:
The benefits of using SAS include:
When companies choose between these data analysis tools, price is a key consideration. Companies must purchase the licensed commercial software SAS in order to use it. As a result of the program’s high cost, large organizations are more likely to fund it. However, it is one of the statistical software tools that big businesses use the most frequently.
R, in contrast, is free and open-source, so anyone who wants to download and use it can. SAS is less frequently used by individuals and small to medium-sized businesses than R.
SAS is much easier to learn than R. With the help of its numerous instruction manuals, tutorials, and resources, even people without any programming language experience can learn how to use SAS. Because it uses PROC SQL, SAS is especially simple to learn for professionals who are familiar with Structured Query Language (SQL). In order to aid in user training, several organizations also provide SAS certification programs.
Professionals typically need to first comprehend computer programming in order to use R. Because it is a low-level programming language, users must write lengthy and intricate lines of code. Therefore, even small mistakes in that code can have a big impact. Learning R can therefore take a longer time than SAS.
An essential component of data science and analytics is data visualization. Through its interactive interface for visualizing data, R generates better graphics than SAS. This is due to the fact that R provides a number of packages for creating graphics, including ggplot, Lattice, and RGIS, as well as advanced options that let users customize their graphics. Although SAS has data visualization features as well, they are less customizable and have fewer options than those in Rs.
R is less capable than SAS of handling large amounts of data. Compared to R, it processes data more quickly, smoothly, and securely. R uses random access memory (RAM) to compute all of its data, which makes it less efficient. Analysis of even small amounts of data can take a long time because the speed at which R processes data depends on the RAM size of the computer. To expedite data manipulation, R does provide packages called plyr and dplyr, but SAS still has superior data management capabilities.
To assist its users, SAS offers a committed customer and technical support service. Customers can quickly and easily get assistance if they need it with installation, troubleshooting, or understanding features. SAS also offers details on software updates, additions, and releases.
R is an open-source program, so it doesn’t offer customer support. Users must seek assistance from the online community if they have queries or technical problems. Despite the size of the R community, it can take some time to find accurate answers.
Programs like R and SAS receive frequent updates and new features as a result of the constant advancement of technology. R is open-source, so users can use it faster and get the newest features. In order to access new features with SAS, businesses must wait for software updates to be released.
R users, however, do not receive the same level of testing and troubleshooting as SAS updates do when they create and distribute new techniques. New R features are more likely than SAS to have errors that users will discover.
SAS-using businesses can only exchange the reports and files that the software generates with other SAS users. They cannot open a file they send to a person outside the company who does not have SAS. With R, experts can easily share files with anyone, facilitating efficient collaboration.
Is SAS better than R?
Since the amount of data is growing at a rapid rate every day and SAS is better at handling it, SAS is in a better position in terms of handling and managing it. Additionally, R only functions with RAM, so it is not practical to increase the RAM as and when the data volume increases. This is where R uses packages of plyr and dplyr.
Is SAS and R the same?
SAS’s open-source counterpart, R, has been used by academics and researchers. The fact that it is open-source ensures that the most recent methods are readily available. There is a ton of documentation available online, and it is a very affordable alternative.
Which is better SAS R or Python?
Before beginning to learn the Python data mining ecosystem, if you are familiar with R, you should learn the fundamentals of the Python programming language. Therefore, despite popular belief, Python and R are both simple to learn and SAS is incredibly effective at sequential data access, and SQL-based database access is well integrated.