It can sometimes be challenging to determine which programming language is (still) worth learning and which one could be a waste of your time. Both R and Python are two great programming languages that are worth learning. But, the question is: Is there any value in learning both of them? Should you learn R even if you know Python?
Yes, you should learn R even if you know Python. It can be beneficial, especially when working with statistical analysis. It’s never a bad idea to expand your programming toolbox if you want to become more versatile in the field of data analysis and machine learning.
The rest of this article will dive into the differences and similarities between R and Python, the benefits and downsides of learning R, when R is worth learning. Additionally, answers to some frequently asked questions are also discussed further in the article.
Important Sidenote: We interviewed numerous 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!
Table of Contents
R vs. Python
Before getting into why or why not R is worth learning on top of Python, it might be worth taking a closer look at the differences and similarities between the two programming languages. Knowing the differences and similarities could help you make a more informed decision about whether learning R would benefit you.
Differences
- Python is great for doing lots of data manipulation and repetitious labor, while R is great for visualization and examining datasets.
- R is mostly used for statistical analysis, whereas Python, on the other hand, has a more general approach to data science.
- Python’s main intent is to focus on production and deployment; R’s isn’t.
- As you might already know, Python users are often programmers and developers, whereas R’s users are mostly R&D professionals or scholars.
- R can be a bit more difficult to learn.
Similarities
- Both languages are modern and relevant programming languages.
- Neither R nor Python has available customer service, but they both have online communities where you can go if you run into any issues.
Benefits of Learning R
- Great for statistical analysis and machine learning.
- It’s free.
- One of the top languages in data science tools.
- Similar learning curve as Python.
- Expand your programming toolbox.
Intersection
R and Python intersect a lot in data science, so running into projects that use both Python and R isn’t uncommon. Being at least familiar with both languages will give you a big advantage in the programming world. Knowing only one of these can be limiting, and there might be some jobs that you won’t be able to do without the working knowledge of both these languages. Generating as much knowledge as you can only benefit you and your career.
Career
Expanding your knowledge will help you further along with your career. After acquiring both languages, you will have a lot of expert knowledge that can set you apart from other software developers and data science professionals, thereby making you more attractive to possible clients or employers. I will go into this more a bit further along in the article.
Downsides of Learning R
- Difficult to learn; I will go into this more deeply below.
- Less widely applicable than Python.
- R is less popular than Python, and its users are less loyal to it.
- R has ten times fewer packages/libraries than Python.
Complexity
According to many users, R is quite difficult to learn, which can be discouraging if you’re thinking of procuring it. Most people agree that learning how to use Python is considerably easier.
However, the level of difficulty also depends on your background. If you are familiar with Lisp and are a star in mathematics, learning R would be similar to learning Python. Once R has been studied and mastered, it is a very user-friendly tool if you’re working on projects that include many statistics and sometimes datasets analysis.
Popularity and Loyalty
R is a lot less popular than Python, and its users aren’t as loyal as Python’s users. In fact, a lot of R users switch over to Python. Python is a lot more user friendly, which is something to consider before going into learning R: Are you going to use it if Python is a more pleasant language to work with?
Less Widely Applicable
You would perhaps choose not to learn R (when you already know Python) because R is used less widely and is less applicable. Python has a lot more packages/libraries; in fact, it has ten times more packages than R.
Is R Worth Learning on Top of Python?
It is perhaps not necessary to become a specialist when it comes to mastering R and Python, but knowing your way around with both of these programs can be useful. Although it’s easy to stick to one and become an expert, here are a few reasons why it can be useful for you to learn both.
Job Opportunities
The more knowledge you have, the more you have to offer your client(s), so knowing Python and R will give you a wider variety of job opportunities. Different clients also prefer different programs to work with.
Even if you’re already working for a company and you’re not necessarily thinking of leaving, you can still become a more indispensable asset in that company, which is always a good thing. Remember that it doesn’t mean that you have to become an expert on both languages; knowing one well and the other one slightly less is still valuable.
Data Science Communication Skills
For many jobs, having a basic understanding of both R and Python is a requirement. If you have that, communicating data effectively and presenting it regardless of what language your audience prefers will be something that you can do.
Expansion of Your Abilities
As previously mentioned, if you learn more, you will know more, which will allow you to expand your knowledge and abilities. If you only commit yourself to one language, such as Python, you will most likely desire some of the features R has to offer and vice versa. R users often wish they knew more about object-oriented matters. In contrast, Python users can sometimes feel the need to access the wide range of statistical distributions obtained with R.
Colleagues
Which language do your colleagues use? Knowing and working with the same language is really beneficial for both you and your colleagues. If they use R, this might be reason enough to learn R yourself.
Frequently Asked Questions
- Why isn’t knowing one language sufficient? If you want to become a professional programmer and build a career, you must know the fundamentals of preferably more than one language. Of course, it’s completely up to you, but it will certainly only help you further along in your career path.
- Is R better than Python? It depends on what you want to use it for. As I mentioned previously in this article, learning R is most valuable if you’re going to use it for statistical purposes. Python, on the other hand, is a better program for machine learning.
- Can Python do everything R can do? Most things can be done with both R and Python, although syntax, performance, and implementation could be different for some algorithms.
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!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- 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
If learning R is valuable to you depends mainly on your situation. Will you have use for the features R offers on top of the knowledge you’ve already obtained by learning Python?
However, learning R on top of Python will only enhance your knowledge and experience within the programming field. Eventually, you will most likely have a preference for one of the two, which you might end up using more than the other. If you have the time to expand your knowledge and experience, it would be a good decision to learn R.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous 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.
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.
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