What is R Programming Language? Who Should Learn R and Why?

1. What is this R?

In today's world where data analysis is gaining more popularity, R has become a frequently mentioned programming language. So what is this R?

R, developed in 1993 by Ross Ihaka and Robert Gentleman from the University of Auckland in New Zealand, gets its name from the initials of the two developers. R is generally an open source free software language used for statistical computation, graphics and data analysis. Being a free software language, R makes its users a part of the development process, and they gradually develop packages related to their specific interests and it grows as new users come.

Now, it continues to be developed by a team called the R Core Development Team. Today, it offers approximately 12000 packages to its users.

It has many similarities with the S programming language. Although most of its libraries are written with R, the libraries that require more intense computations are written in C, C++ and Fortran. It has versions for Windows, MacOS and Linux.

2. Who is this R for?

R offers a wide range of statistical (linear and non-linear modeling, classical statistical tests, time series analysis, classification, clustering and others) and drawing techniques. Therefore, it is very popular among data scientists and analysts.

Some of the popular areas it is used in are machine learning, big data, and bioinformatics.

3. Why R?

R and Python have quite similar usage areas. So why or in which situations can we prefer R?

• Python's usage area is more general compared to R. Python covers software development in addition to statistical calculations. But the aim of R is to perform statistical analysis and in this area, it offers more packages to its users compared to Python.

• While R is used more in the academic field, Python is popular in the development field.

• If you need to use the results of the analysis integrated with apps, Python is the right choice for you because it has a broader ecosystem and more mature tools for production deployment.

4. What are the downsides of R?

Many users complain about the slowdown of R when dealing with large data sets. This is due to R's operation as single-thread processing. In other words, only a single CPU is used. Another disadvantage is that all the objects you use in R are removed from your machine's RAM when the R program is closed. This is another reason for R's slow operation.

5. Conclusion

If you fit the features I explained above, R Project after selecting Turkey (or the nearest region to you if you are following us from abroad) and downloading, you can start by setting up your R Studio ...