Is R Enough for Data Science?


Data science is an integral part of many people’s jobs. Currently, Python and R are the two most popular programming tools for data science work. Both tools are free and open-source; however, the question is, is R enough for data science?

R is not enough for data science. R is simply a programming language that forms one bit of the data analytics domain. In addition to R, it would be ideal to use Python and its libraries. Out of these two flexible data analytic languages, it is hard to pick one over the other.

The rest of this article will discuss other topics related to this question, including what is R programming, who uses R programming, and its benefits.

Important Sidenote: We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

What Is R Programming?

R programming is an open-source programming language commonly used in data analytics. It is a command-line driven program and the language commonly used in performing statistical operations. R rates among the most popular analytic tools in the world.

For many years, R, developed in 1992, was the most preferred language for data scientists. The language works by breaking down a programming task into procedures, steps, or routines.

Since it is a procedural language, R is ideal when building data models. This language makes it easy to understand complex operations. R programming language is commonly used among data miners and statisticians for data analysis and developing statistical software. However, some scientists avoid the R programming language due to a lack of crucial features like web frameworks and unit testing.

R offers a high level of flexibility, making it easy to use complex functions. You can access all types of statistical models and tests and readily use them.

Why Is R Programming Language Popular?

R programming is one of the most popular languages used by data analysts, researchers, statisticians, and marketers to retrieve, analyze, visualize, clean, and present data. In recent years, the language has grown in popularity due to its easy-to-use interface and expressive syntax.

R programming language is open-source and free. The language is licensed under the GNU general public license, and it is free to download. You can use most R programs under the same license without worrying about violating the law. In 2016, R ranked 5th in IEEE’s list of most common programming languages.

This rank was an improvement from the 6th position in 2016. R is popular in data science and other fields like machine learning. Distributions of R are available for all popular platforms like Linux, Mac, and Windows. You can write R codes in one platform and transfer them to another without complications.

In today’s computing world, interoperability across different platforms is an important feature. The world’s largest data techs are using R to make major decisions backed by concrete data analysis.

When solving data analysis needs, most researchers learn R as their first language. R is simple enough to learn as long as you have data and a clear intent to conclude based on the data analysis. However, R uses a different syntax, and programmers from a PHP, Python, or Java background users may find it confusing at first.

R enables data scientists to collect data in real-time, perform predictive and statistical analysis, develop visualizations, and provide results to stakeholders. R also comes in handy in machine learning and statistical computing.

Here is a video to help you understand the basics of R:

Data Science Projects That Commonly Use R

In the past, R was mainly confined to academia; currently, R has users across private and public sectors. This software environment/programming language has made its way into financial institutions, social networking platforms, and media outlets. Some of the big names that use R include Google, Bank of America, New York Times, Facebook, and Twitter.

The Bank of America uses R for financial modeling, while Google relies on R for real-time textual analysis. At Facebook, data analysts use R for new data exploration through custom visualizations. The New York Times relies on R for data journalism and data visualization from different sources.

R has been widely used in research and academics, mainly for exploratory data analysis. Enterprise usage of R has expanded in recent years. Engineers, statisticians, and scientists with limited computer programming skills find R language ideal. The language is popular in finance, academia, media, pharmaceuticals, and marketing fields.

Data Collection

With R programming, you can bring in statistics from CSV, text files, and Excel into R. You can also turn files that are built in SPSS and Minitab into R. Compared to Python, R is less versatile at obtaining data from the web. 

However, it can comfortably manage data from typical data sources. The modern packages of R for data gathering have overcome this issue. With a modern package, you can use R for simple web scraping.

Data Exploration

You will have many options while exploring data with R since the language was made to do numerical and statistical analysis of huge data groups. You can apply a variety of numerical tests to your data and build probability distributions. 

A rudimentary R functionality includes the fundamentals of optimization, analytics, statistical processing, signal processing, indiscriminate generation of numbers, and machine learning.

Data Visualization

The main purpose of R is to conduct statistical analysis in addition to portraying the results. Therefore, it is a powerful language appropriate for scientific visualization. It has numerous packages which enable it to enhance the graphical exhibition of results. 

R’s base graphics element enables you to create basic plots and charts from data mediums. You can then store these documents into formats like PDFs or formats like jpg. 

Choosing Between Python and R

A data scientist or a data analyst has the liberty to choose a programming language that suits their unique needs. Certain questions can help you choose the right programming language: 

  • Which language do your colleagues or your organization use? You should go for a language that will allow you and your colleagues to share codes and maintain a simple software stack.
  • What is the scope of your data project? Before you pick up the right programming language, you should have an agenda for your project. R might turn out to be the best choice if you intend to solve a statistical problem through a data set and prepare a dashboard or a report showing the results. R would be the best choice because of its powerful communication libraries and visualization.

You should consider your level of experience in data science while choosing a programming language. R might not be the ideal option if you are new to data science and are not familiar with mathematical and statistical concepts. However, if you are familiar with algorithms and machine learning fundamentals, you can pick any language you desire. 

The amount of time you intend to invest in a project will also influence the programming language you choose. If you have a high-priority project and do not understand any programming languages, R might be an easier option for you. You can get started on R even with minimal or no experience in programming. You can use existing R libraries to write statistical models with few codes.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.

Conclusion

Some users claim that Python is more approachable than R and is also broadly applicable. However, R advocates counter this statement by pointing out that R has certain field-specific benefits. Neither R nor Python can trump the other language. The language you choose to use will depend on your preference and the particulars of your data project.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. Fundamentals of Optimization. (n.d.). University of Washington. https://sites.math.washington.edu/~rtr/fundamentals.pdf
  2. How to choose the best programming language for your data science project. (2020, July 1). freeCodeCamp.org. https://www.freecodecamp.org/news/how-to-choose-the-best-programming-language-for-your-data-science-project/
  3. R (programming language). (2003, November 23). Wikipedia, the free encyclopedia. Retrieved December 3, 2020, from https://en.wikipedia.org/wiki/R_(programming_language)
  4. R moves up to 5th place in IEEE language rankings. (2016, July 29). Revolutions. https://blog.revolutionanalytics.com/2016/07/r-moves-up-to-5th-place-in-ieee-language-rankings.html
  5. R moves up to 5th place in IEEE language rankings. (2016, July 29). Revolutions. https://blog.revolutionanalytics.com/2016/07/r-moves-up-to-5th-place-in-ieee-language-rankings.html
  6. R vs. Python: What’s the best language for data science? (2019, December 17). RStudio Blog | RStudio Blog. https://blog.rstudio.com/2019/12/17/r-vs-python-what-s-the-best-for-language-for-data-science/
  7. Reasons why using R for data science projects is your best bet. (n.d.). Hackr.io. https://hackr.io/blog/r-for-data-science
  8. SPSS system data file format family (.sav). (2017, June 4). Library of Congress. https://www.loc.gov/preservation/digital/formats/fdd/fdd000469.shtml
  9. What data scientists really do, according to 35 data scientists. (2018, August 15). Harvard Business Review. https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists?ab=at_articlepage_relatedarticles_horizontal_slot2
  10. What do data scientists do? (2019, March 13). University of Wisconsin Data Science Degree. https://datasciencedegree.wisconsin.edu/data-science/what-do-data-scientists-do/
  11. What is data science? (2020, July 17). I School Online – UC Berkeley School of Information. https://ischoolonline.berkeley.edu/data-science/what-is-data-science/
  12. Why you should learn R — Learn data science with Dataquest. (2020, August 4). Dataquest. https://www.dataquest.io/blog/three-mighty-good-reasons-to-learn-r-for-data-science/

Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.

Daisy

Daisy is the founder of DataScienceNerd.com. Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

Recent Posts