SAS or R-Yould Should Know!
SAS or R-Yould Should Know the background.
What is SAS?
Data analytics are performed using statistical analysis software, or SAS.
It enables you to apply high-quality methods and procedures that increase worker output and revenue for your company. SaaS is how SAS is pronounced.
Data is extracted and categorised in SAS, which makes it easier to spot and examine data patterns.
It is a software package that enables you to carry out advanced analysis, business intelligence, predictive analysis, and data management in order to function successfully in the challenging and evolving corporate environment.
Additionally, SAS is platform-independent, therefore it may be used with either Linux or Windows as an operating system.
What is meant by R?
R is a programming language that is frequently used for data analysis by data scientists and big businesses like Google, Airbnb, Facebook, etc.
For every type of data manipulation, statistical model, or visualisation that a data analyst would require, the R language provides a vast number of functions.
R provides built-in tools for data organisation, performing calculations on the provided data, and producing graphical displays of those data sets.
R is open-source software, whereas SAS is proprietary software that requires a cash commitment in order to utilise.
The simplest tool to learn is SAS. Therefore, even those with basic SQL experience may quickly pick it up; in contrast, R programmers must create laborious, lengthy scripts.
R is an open-source programme that is constantly updated, whereas SAS is updated somewhat less regularly.
While the R tool has weak graphical capabilities, SAS offers good graphical support.
While R has the largest online communities but no customer service assistance, SAS offers specialised customer support.
Why use SAS?
Obtain raw data files and information from an outside database.
Analyze data using linear programming, forecasting, modelling, descriptive statistics, and multivariate methods.
Aids in data entry, formatting, conversion, editing, and retrieval
You can modify and enhance your business procedures by using advanced analytics.
assists companies in understanding their historical data
Why use R?
For data analytics, R provides helpful programming features including conditionals, loops, input and output options, user-defined recursive functions, etc.
R has a robust and growing ecosystem, and there is a tonne of online documentation.
This utility can be used on a number of operating systems, including Windows, Unix, and MacOS.
a strong graphics capability backed by a sizable user network.
Background of SAS
At North Carolina University, Jim Goodnight and John Shall created SAS in 1970.
It was initially created for agricultural research.
Later, it grew to encompass a variety of tools, including BI, data management, and predictive analytics.
98 of the top 400 global corporations utilise the SAS data analytics tool today for data analysis.
Background of R
R’s history dates back to 1993 when Ross Ihaka and Robert Gentleman created the programming language.
R was first made available as an open-source programme in 1995 under the GPL2 licence.
R core group and CRAN were created in 1997.
Launch of the R website, r-project.org, in 1999
Release of R 1.0.0 in 2000
Release of R 2.0.0 in 2004
R 3.0.0 was launched in 2013 after the R Journal’s debut in 2009.
The New R logo was introduced in 2016
|Availability / Cost||SAS requires a monetary investment because it is commercial software.||R is free software that is available to everyone.|
|Ease of Learning||The simplest tool to learn is SAS. So even those with a basic understanding of SQL may pick it up quickly.||R programmers are required to develop laborious, extensive code.|
|Statistical Abilities||SAS is a robust software that provides all varieties of statistical procedures and analysis.||R is a free software programme that lets users submit their own packages and libraries. R frequently sees the debut of the newest technology.|
|File Sharing||Sharing files produced by SAS with someone who does not utilise SAS is not permitted.||It is significantly simpler to transfer files with another user because anyone can utilise r.|
|Updates||SAS is updated somewhat less regularly.||Since R is an open-source programme, updates are always being made.|
|Market Share||As a result of fierce competition from R and other data analytics tools, SAS’s market share is currently steadily decreasing.||With its rising popularity over the past five years, R has experienced exponential growth. Because of this, its market share is growing quickly.|
|Graphical Capabilities||SAS has remarkable graphic capabilities. Customization is not, however, possible.||R’s graphical support is also high.|
|Customer Support||SAS offers devoted client service.||The largest online communities belong to R, yet it offers no customer service.|
|Support for Deep learning||There is still more to be accomplished before deep learning in SAS reaches maturity.||R provides sophisticated integrations for deep learning.|
|Job Scenario||As far as corporate employment is concerned, SAS analytical tool continues to dominate the market. SAS is still in use by many large businesses.||Over the past few years, there have been reports of an increase in jobs on R.|
|Salary Range||In the United States, a SAS programmer makes an average yearly pay of $81,560.||Data scientists can expect to make between $127,937 and $147,189 per year on average as “R” programmers.|
|Best Features||VariablesMixinsNested rulesMaintainableFunctions||Data analysis Graphics and data Flexible statistical analysis incredibly interactive|
|Famous companies using||Airbnb, StacShare, Asana, Hubspot||Instacart, Adroll, Opbandit, Custora|