SAS or R-Yould Should Know! | R-bloggers

SAS or R-Yould Should Know! | R-bloggers

The post SAS or R-Yould Should Know! appeared first on finnstats.

If you are interested to learn more about data science, you can find more articles here finnstats.

SAS or R-Yould Should Know the background.

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.

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.

Obtain raw data files and information from an outside database.

You can modify and enhance your business procedures by using advanced analytics.

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.

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.

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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

R 3.0.0 was launched in 2013 after the R Journal’s debut in 2009.

The New R logo was introduced in 2016

If you are interested to learn more about data science, you can find more articles here finnstats.

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