6 Reasons to Learn R for Data Science

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Learning R programming language for data science is essential and could spell the difference between succeeding and failing at a career in this field.

The big question, however, is why R? 

Out of hundreds of programming languages out there, why is R crucial to learn for data science? 

Just like how you need to choose a programming language for AI, if you want to work in this field, you’ll also have to decide on the kind of coding language to learn if you’re going to build a career in data science – and one of the best ones to become proficient in is R. 

In this post, we’ll take a look at the reasons why learning R is an excellent skill to have if you want to work in the field of data science.

Let’s jump right in. 

1. R is designed for statistical analysis.

Originally, R was designed by statisticians to do statistical analysis, making it the choice of programming language for most statisticians even today. 

With R’s syntax, you can easily build complex statistical models using only a few lines of code, and find simple measures of central tendency. 

Because R is a powerful scripting language that can handle complex and large data sets, you can use it on high-performance computer clusters, and resource-intensive simulations. 

Additionally, since statistical analysis is used in different fields like genetics, biology, businesses, and more, learning the R programming language gives you the advantage of being able to work in different areas where data science is applied.  

2. R is open-source.

With R being a widely-used programming language in many data science applications, you might think that it will more or less cost you an arm and a leg. 

However, R is free and open-source software that is maintained by a large community of active users. 

It’s like a plug and play software that you need to install once and use it in every way you like. 

Plus, you can add your innovations and modify the different functions of R to create your own packages. 

Before you can do that, though, you’ll need to gain in-depth understanding of the language, and one of the best ways to achieve that is to enroll in an R for data science certification training.  

You can take a look at the course outline and check if it covers the topics and skills you would like to learn. 

R language also has no license restrictions since it’s issued under the General Public License (GNU), which gives you the freedom to use and modify it depending on your preferences.

Many top tech firms use R for data science, which means your chances of working for big companies in this industry and growing in your career will likely increase if you are proficient in this programming language. 

For example, Facebook uses R for behavioral analysis based on user post data, and Twitter uses R for semantic clustering and data visualization. 

Companies as big as these are bound to employ teams of data scientists who are proficient in R, which can make you an excellent candidate in the running if you can speak the language.  

Aside from tech firms, the popularity of R extends to a spectrum of fields where data science is applied – from research labs, financial and academic institutions, consulting and analysis firms, and more. 

This shows that the demand for data science using R is high, making it an excellent language to learn if you want to establish a career that covers a variety of different industries and fields.  

4. R offers stunning visualizations.

Working in data science means you’ll be creating visualizations to communicate the insights you get from data, and learning R can certainly help you with that. 

R offers packages like plotly, ggplot, and ggplot2 that you can use to create beautiful and high-quality graphical plots and visualizations.

Here’s what a sample ggplot2 looks like. 

Image Source: Tidyverse

Due to R’s built-in visualization functionality and capabilities, creating your graphs and plots will be more straightforward and less time-consuming.

Plus, this helps you do away with using acquiring, learning, and using other visualization tools for your data science projects.  

5. R offers a vast community and support.

One of the challenges you might face with learning programming languages is the lack of support to assist you in case you encounter issues. 

However, you’re less likely to find yourself in that situation with R because the programming language offers a robust and growing community of statisticians and data scientists.  

This means you can have easy access to questions, answers, and support from the R community, whether you’re still learning the language, or if you’re already working on projects in R.

For instance, you can go on online message boards and forums like Stack Overflow to ask questions and get answers about programming related topics, including R. 

Just go on the website, and search within a tag. 

There are also R developers who actively participate on discussion boards about the packages and tools they designed, and this is crucial because R has MANY community packages. 

With R’s broad community and support, getting answers to your burning questions about the programming language will be more pain-free and making R more accessible if you’re starting to learn it. 

6. R is a stepping stone for a career in data science.

According to The Burtch Works Study, the median entry-level salary, if you’re a data scientist, is $95,000 and can range up to $250,000 if you have experience in the field. 

And if you’re proficient in R and work in data science, you can earn an average of more than $117,000 annually. 

Although the salary for data scientists can vary depending on the company, with R programming extensively used in data science, it makes sense for you to learn the language if you want to venture into this lucrative field. 

Not only will proficiency in R as a data scientist give you the chance to get a high-paying job, but you’ll also get a competitive edge since the demands for this kind of position is continuing to grow. 

For example, the prices of statistical packages for enterprises are high, so employers are now looking for data scientists who are skilled in using R to save on the costs for purchasing proprietary statistics packages.  

If you have the R programming language in your arsenal of data science skills, you can get a job as a Data Analyst, Financial Analyst, and more.  

That being said, learning R can only work to your favor in terms of attracting your potential employers, having the skill set to work and excel in data science, plus the chance to earn a lucrative salary. 


Although learning R isn’t all peaches and cream since having a good background in statistics, data analysis, and mathematics will make the learning process easier for you.

However, it’s still possible to learn R programming even if you’re a beginner. 

Once you have a good grasp of R and its use in data science, you can start building your other skills, competencies, and, ultimately, a career as a data scientist.

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