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Disadvantages of Python Python performs poorly in statistical analysis compared to R due to a lack of statistical packages. Sometimes developers may face runtime errors due to the dynamically typed nature. The flexible data type in Python consumes a lot of memory, causing tasks requiring heavy memory to suffer.

R is a free software environment for statistical computing and graphics.

R is considered one of the more difficult programming languages to learn due to how different its syntax is from other languages like Python and its extensive set of commands. It takes most learners without prior coding experience roughly four to six weeks to learn R. Of course, this depends on several factors.

Does R Have Any Drawbacks? It's a complicated language. R has a steep learning curve. ... It's not as secure. R doesn't have basic security measures. ... It's slow. R is slower than other programming languages like Python or MATLAB. It takes up a lot of memory. ... It doesn't have consistent documentation/package quality.

The truth is, R is far from dead. While it's true that Python has gained significant traction in recent years, R remains a powerful language that offers unique benefits for data scientists. One of the critical advantages of R is its focus on statistics and data visualization.

3 Disadvantages of using R R also requires a lot of coding and manual work that other software can do more easily and intuitively. R also has some limitations in terms of performance, scalability, and security.

The main disadvantage of R is, it does not have support for dynamic or 3D graphics. The reason behind this is its origin. It shares its origin with a much older programming language "S."

R is a programming language and a software environment for statistical computing and graphics. Microsoft R Open is a version of R that was created by the Microsoft Corporation. Both R and Microsoft R Open are free and open-source tools for data science and analytics.