Autor: Codenga 02.02.2023

The R programming language may not be the most popular one. Still, in certain areas it plays an essential role.

R is a scripting language used most often in statistics and data science. So R is a pretty specialized language, as opposed to some of the more versatile ones like Java or C#.

Data science

Data is everywhere - literally and figuratively. It would be hard to find a field where data doesn't play a significant role.

Data is gathered to be analyzed and interpreted. This is exactly where R shines. The language has expansive libraries with various functions used for processing, analyzing and visualization of data. R is very scalable and flexible - it can get through sizable sets of data with no problem.

One more thing on top of all that: R’s syntax is pretty simple, which enables people with little to no programming experience to utilize it. Your typical data analyst would much rather not have to focus on programming quirks in addition to getting results from the data. That alone is a big advantage of R.

Statistics

Data science has rather close ties to statistics. More often than not data is gathered to get some interesting statistics.

R, again, has a lot to offer here. The built-in library provides you with many popular statistical functions, e.g. linear regression. It also has tools that make data visualization easier (creating graphs, summaries etc.). This combined with the aforementioned simplicity of the language, makes it a perfect tool for statistic experts.

Industries, where R plays the main role

Now that we know what R’s strengths are, let’s go over what lines of work use it the most. It’s not hard to guess that the areas are ones where data analysis is involved.

Finances

This mostly speaks for itself. In finances there's plenty of opportunities for graphs, trends, summaries, prognosis etc. - that is where R can show off all its potential.

Natural science

Another category where data collecting and analysis accompanies you every step of the way. Here you analyze outcomes of experiments, you create roundups - you do exactly what R was made for.

Business

Broadly defined business is another thing that leans heavily on data. Here R can prove its worth while gathering sales statistics, generating summaries or predicting future trends based on current data.

Medicine

Modern medicine involves more and more data as time goes by. Genetics is definitely one of the fields that’s able to function thanks to the powerful tools allowing us to run heavy calculations. It’s pretty obvious then, that R plays an important role in such fields.

Summary

As you can see, R has some real assets. It was created for data science and statistical calculations - and that’s exactly why it’s mainly utilized in areas where data is a vital part of the process.