What Is the Difference Between SAS vs. R? (With Benefits)

By Indeed Editorial Team

Published June 9, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data analysis is an essential part of the operations of different IT companies and departments. SAS and R are two popular tools used in data science. As an IT professional, understanding the differences between these tools can help you select the one that best fits the company's needs. In this article, we explore the differences between SAS vs. R, review the benefits of each tool, and highlight some necessary skills for data analysts.

SAS vs. R

When considering SAS vs. R, IT professionals typically refer to them as counterparts. Some key differences between both tools include:

Definitions

SAS is an acronym for statistical analysis software. IT professionals use this software system for advanced data and statistical analysis. This type of program reads and stores data, analyzes it, and creates reports based on the results. These reports may be in tables, graphs, HTML, or PDF. Companies may use SAS to gather information from large amounts of raw data, manage data, and perform predictive and advanced analytics. Organizations also use this tool to make strategic decisions, known as business intelligence.

Data scientists use R as an alternative to SAS when performing data analysis. This tool is an open-source platform, meaning its code is public, and anybody can use it. In addition, it organizes data, uses formulas to analyze the data, and creates reports of the results. As a result, R is a popular tool among startup companies. The statistical methods include linear regression, statistical inference, machine learning algorithms, and time regression.

Related: How to Learn Data Science (A Complete Guide)

Uses

Most R users are companies in the marketing, finance, and business industries. Some functions you can perform with this tool include importing and cleaning data, providing statistics for data science, and accessing programming elements, such as loops and conditionals used for data analysis. Companies in different industries use SAS, such as healthcare, finance, and government institutions. Some functions you can perform with this tool include performing prescriptive and predictive analysis, accessing and analyzing raw data, analyzing historical data, and managing data entry, formatting, and recovery.

Cost

Cost is an essential factor when choosing between these analysis tools. Using SAS requires a licence, which means it's necessary to purchase it before using it. As this program is expensive, it's advisable to use it if you work for a large organization. In contrast, R is free and open-source, which means that the program is available to those who want to download and use it. You can consider using R if you work for a medium or small company.

Learning ability

Comparatively, SAS is easier to learn than R, which means learning R may take longer than learning SAS. SAS has numerous tutorials, instruction manuals, and learning resources, making it easy to learn how to use the software even if you have no programming language knowledge. In addition, SAS uses PROC SQL, which makes it easier for you to learn if you understand structured query language (SQL). You may also consider offering SAS certification programs to help you understand this tool correctly.

In comparison, understanding how to use R requires you to have a good understanding of computer programming. This program is a low-level programming language, meaning that it requires you to write complex and extensive lines of code. In addition, minor errors in your code may result in significant issues.

Graphics

Data visualization is a fundamental part of data analytics and science. R's a better tool than SAS for visualizing data because it produces better graphics through its interactive interface. In addition, the former offers several graphic creation packages such as Lattice, ggplot, and RGIS. It also has various advanced options for users to customize their graphics. In contrast, SAS limited data visualization features with little opportunity for you to customize your graphics.

Data management

In terms of data management, SAS has better features to manage large amounts of data than R. The former is more secure and processes data faster and smoother than the latter. R is less efficient because it uses random access memory (RAM) to analyze data. As a result, the data processing speed depends on the device's RAM size, and it may be tedious to analyze a large amount of data. In addition, although R offers different packages called dplyr and plyr to speed up analysis, SAS still has better data management capabilities.

Customer support

SAS has a dedicated technical and customer support service to offer assistance to its users. As a result, when customers have issues with installation, understanding features, and troubleshooting issues, they can easily and quickly get assistance. In addition, it also provides information about new features, software updates, and releases. In contrast, as R is an open-source program, so it doesn't provide customer support. Instead, users typically get support and technical assistance from the online community. Although R has a large online community, it may be time-consuming to get accurate information.

Application updates

With the rapid advancements in technology, programs like SAS and R get new features and updates frequently. With R, users get the latest updates and features faster because it's open-source. In contrast, using SAS may require you to wait for the SAS Institute to release new updates before you can access new features and updates. Usually, when users create and share new techniques through R, they may not have passed through the same level of troubleshooting and testing as SAS. As a result, you're more likely to find bugs and errors in new R features and updates than in SAS.

File sharing

If you use SAS, you can only share reports and files you generated with the program with other individuals or companies that use SAS. If you attempt to send these files outside the organization or to those who don't use the program, they may not be able to open them. In contrast, you can easily share files and reports with different people if you use R. This feature helps foster collaboration.

Benefits of R

Here are some of the benefits of using R in data analytics:

  • Data visualization: It offers excellent data visualization.

  • Multiple packages: The program has many statistical and algorithms packages available.

  • Broad access: It allows you to access a variety of databases and data types.

  • Data pulling: It allows you to pull data from different websites.

  • Data handling and storage: R is an excellent tool for data handling and storage.

  • Integration with other programming languages: R also allows you to integrate the program with other programming languages.

  • Social media analysis: The program allows you to analyze data from social media.

Benefits for SAS

Here are some of the benefits of using SAS:

  • Reads nearly any data format: You can analyze data in various formats because the SAS program can read and assess data in almost any format.

  • Updating and changing data: With SAS, it's easier to update and change data.

  • Easy to debug: As the program is easy to understand, you can easily find and correct any errors.

  • Tested algorithms: Developers perform thorough tests and analyses on the algorithm before releasing it.

  • Data security: The program is entirely secure, and outsiders cannot extract information without a licence.

Necessary skills for data analysts

Here are some essential skills to help you build a successful career as a data analyst:

Proficiency in programming languages

As an IT professional, it's essential to have code-related skills such as proficiency in programming languages. Various coding tasks may require particular programming languages. As a result, the more languages you know, the higher your career prospects. Some examples of programming languages include C, C++, HTML, PHP, Python, and JavaScript.

Research

As a data scientist, your duties may require you to conduct research. It may also require you to develop solutions for technical issues or evaluate new processes. As a result, it's essential to have adept research skills to perform your duties. This involves gathering relevant information from various sources.

Project management

Project management entails supervising and monitoring all the project elements, including the team members and their responsibilities. Data analysts may manage a project with other analysts and IT professionals. As a result, it's essential to have the necessary skills to allocate resources efficiently, keep the project on track, and ensure you meet the vital objectives.

Related: What Is Project Management? Definition, Steps, and Skills

Data visualization

A primary responsibility of data scientists after gathering and analyzing data is presenting the results to others. You may also use visual tools such as charts and graphs to present relevant information. As a result, it's essential for you to know how to create easy-to-understand presentations.

Communication

Communication is an essential soft skill for you to possess as a data analyst. You may use these skills to discuss with other IT professionals and stakeholders during your work. Communication skills include written and active listening skills. You may apply your written communication skills when providing reports of your analysis, and your active listening skills can help you when gathering data for your analysis.

Related: 10 Skills Business Analysts Need For Workplace Success

Attention to detail

As a data analyst, it's crucial to have a precise focus. The technical aspects of data analysis involve reading and assessing intricate technical and coding structures. As a result, it's essential to pay attention to relevant details to ensure the code functions properly.

Please note that none of the companies, institutions, or organizations mentioned in this article are affiliated with Indeed.

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