R VS STATA: WHICH ONE IS BEST DATA SCIENCE SOFTWARE?

Do you want to know what the difference is between R and Stata? How do they differ from one another? Programming students are likely to be familiar with R and STATA. It may be confusing for those new to programming and unfamiliar with these two programming languages.

Knowing the differences between STATA and R can help you pick the right software. Check it out! The following blog will compare R vs Stata. It is important that you know about R vs Stata as a student of statistics, for example, which one is better for data science.

What is R?

Robert Gentleman and Ross Ihaka created the R programming language in 1993. R is a powerful statistical and graphical programming language. These include machine learning algorithms, linear regression, time series, and statistical inference. It is recommended to use C, C++, and Fortran when performing heavy computations. In addition to R libraries, you can also use libraries written in C, C++, and Fortran.

What is Stata?

Data can be managed, analyzed, and visualized with Stata. Data patterns are analyzed by economists, biomedical researchers, and political scientists. It has graphical and command-line user interfaces, making it easier to use.

R vs Stata

Online Support

There are no licensing fees associated with R, which means that it is freely available to everyone. As a result, there may not be any legal support for the R programming language. For help with R programming, you can use documentation, journals, manuals, and community support.

Cost

Everyone can use R because it is free. The program can be downloaded from the Internet. It is available immediately following the download.

Easy to learn

Statistics students may find it difficult to learn R. The language is a scripting and programming language. Nevertheless, they can learn R. New programming languages can be difficult for someone who has never worked with them before.

R vs Stata: Strengths and weakness

R

Strengths

• There are many functions available.

• We implement a new statistical method quickly.

• It is very easy to automate and integrate (e.g., with Git, LaTeX, ODBC, Oracle R Enterprise, Apache Hadoop, MicroStrategy, etc.).

Weaknesses

• R is not an easy language to learn

• Typically, little-used packages have less stability and quality than the core distribution

STATA

Strengths

• Most established statistical methods are available in STATA.

• GUIs make it easier to use

• Easy to automate

• There is no compatibility issue with older versions

• There is a stable community support system and extensive literature available

Weaknesses

• It takes a while for updates to appear

• There isn't an easy way to integrate it with other programs

Conclusion (R Vs Stata)

An in-depth comparison of R and Stata is provided in the following article. Users have the ability to perform different tasks with R compared to Stata. As a result of reading this article, you will be able to gain extensive knowledge about these programming languages and determine which will best suit your needs.

Having coding knowledge, you should choose R over Stata, according to our experience. You should pick Stata over R if you have no coding knowledge.

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