Should You Learn R or Python First? Let’s Find Out

For anyone looking to become a data scientist or data analyst, R and Python are the two main programming languages to master. Ideally, you should learn both of them at once, but since picking one programming language is hard enough, most people have to choose. So, which should you go with first?

To decide if you should learn R or Python first, you need to look at your specific situation. If you are looking to learn a language that is best suited for data science jobs alone, R is the language you should go with. Python, on the other hand, is more versatile and goes beyond data science.

The rest of the article will take a closer look at both languages to help you decide where to pitch your tent first.

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!

What Is R?

R is a programming language developed in 1992 for statistical and mathematical use cases. It was mainly developed as a user-friendly way to do data analysis, graphical models, and statistics. At first, the language was mostly used in the academics and research world. However, since the corporate world discovered its use, it has grown into one of the most popular statistical languages in the world today.

One of the biggest advantages of R is CRAN. This is a huge open-source repository of packages. Any member of the public can contribute to it. With R and its repository of packages, it is easy to find a library for any analysis you’d want to perform, especially for analytical work. 

One of the main features of using R is that it is powerful for creating visualization reports and communicating any findings from any bit of analysis.

What Is Python?

Python is a programming language based on C. This is predominantly a software development language. Its depth and intuitiveness mean that it is one of the most popular programming languages in the world today.

One reason for its popularity is that it is easier to learn when compared to many other languages, and you don’t need complete fluency in the language before you start using it for projects.

It is a great scripting language that can also do some statistics and works great when it comes to combining your pipeline or workflow components together. The language was released in 1989, with an emphasis on efficiency and code readability. It’s an object-oriented programming language just like Scala, C++, and Java.

This means that the data and codes are grouped into objects that can work with and modify one another.

This high-level programming language is useful in a wide range of applications, which is why it is popular in a variety of industries ranging from data analysis to web application development.

Which Should You Go for First?

The debate of whether to go with R or Python first for people looking to get into data science has been going on for a while, with each side having some valid supportive points. To make the right decision, however, you need to look closer to home, beyond the generic argument points.

Choosing to start with Python or R first is, in many cases, a personal decision. What are your individual circumstances? What industries would you like to break into in the future? Here some key factors that should guide your decision.

Your Personal Preference

Which of these languages come naturally to you? Go for the one that you can grasp the fastest. If you have a good knowledge of statistics or mathematics, you will find it easier to learn R and become comfortable working with it. If you have a computer science or software engineering background, however, you’ll find Python easier to learn.

Starting with the language you are most comfortable with also makes it easier to learn the other in the future as the concepts will be easier to grasp.

The Type of Projects You’ll Work On

If you are thinking about learning one of these programming languages to make the transition into a certain industry, you should allow the type of projects you’ll be working on inside the industry to guide you in terms of what to go with first.

If you are just going into data science, R is the language you should start with. If you want to be more than just a data scientist, however, but also want to take up opportunities in data science, you should go with Python first.

Even inside the field of data science, you still have to pay attention to the type of data you’ll be working on. If you’re working with data that’s already been gathered and cleaned, where you’ll have to mostly focus on analysis, you should go with R. If you have to scrape jumbled data from external sources on your own, on the other hand, you’ll need Python.

Collaboration Potential

Another factor that can influence what type of programming language you should go with is the language your prospective or current teammates are using. If you’re already working in an environment where Python is used for everything, for example, it makes perfect sense to start with it first and add R later.

This way, you can ensure better collaboration and make the learning process a lot easier for yourself.

Job Prospects

If there are no limitations keeping you from learning either Python or R, you can base your decision simply on which one holds more job prospects for you. Presently on Indeed.com, there are over 50,000 Python-related job listings. Revising the search for R-related vacancies showed less than 10,000 results.

This is not surprising because the expansion of the Python ecosystem means that tools for most areas of computing can be easily designed in Python.

Also, since Python is a language that is also used for web application development, many companies are hiring Python developers even when they need data analysts. This is because it ensures they have staff that can switch to Python development projects when necessary while being capable enough to function as a part of a data analysis team.

Popularity

Both R and Python are popular languages. A survey from ‘The Redmonk Programming ranking’ involving participants from Stack Overflow and Github clearly shows how both R and Python are highly rated.

However, the TIOBE Index, which ranks programming languages based on their popularity, has shown that currently (September 2020), Python is only behind C and Java as the most popular programming language. R is the 9th most popular language today, but it’s important to note that it was ranked 19th back in September 2019.

So, although both R and Python are popular, the latter is the language to go for if you want to have the third-highest in-demand skill today.

Usability

While R is great at exploring datasets and visualization, Python is the better language if you are going to do a lot of data manipulation and repetitive work.

If you are only going to run automated analysis regularly, and focus on producing visual presentations of the data (charts and maps), then R is the language you should work with. It is very user-friendly if you are going to be working on a lot of statistics-heavy projects and occasionally analyzing datasets.

Learning Difficulty

For a person with no statistics and analytics skills, it’s hard to learn R programming language. The steep learning curve can discourage many from getting started with it.

Python, on the other hand, is considered easier to pick up. However, if as we’ve mentioned above, you’ve got a mathematics background or even better, have knowledge of Lisp, the learning curve for Python and R will be similar. Python will, however, regain the upper hand again if you already know languages like JavaScript, Ruby, Java, or C#.

R vs. Python: Pros and Cons

To further help you in your decision making, here is a summary of the pros and cons of both programming languages.

Pros of R

Listed below are the primary advantages of R programming language:

• It is an excellent language for statistical analysis.

• It is based on a command line. However, programmers using it can work within environments such as R commander (which has debugging support, data editor, and graphics window) and RStudio. Python’s answer to this is IDEs, such as Visual Studio and Eclipse.

• R language is rated highly as the best data visualization tool. It comes with a few packages that simplify the visualization process. Data is generally better understood when it is visual, but python makes it a bit harder with the fewer visualization libraries to choose from.

• R works across many platforms and can run on multiple operating systems ranging from Windows to Linux to Mac OS X. Additionally, importing data from tools such as Microsoft Access, Excel, and Oracle is fairly straightforward.

• R packages generally support many new statistical developments. This is because it is highly flexible and versatile. This makes it easy to run specialized statistical processes in finance, but also in genetics and psychometrics.

• There are over 2000 free libraries to use when working on statistical areas such as high-performance computing, cluster analysis, finance, and more.

• As a scripting language, R can handle large and complex data sets. You can also rely on it when running resource-intensive simulations over a cluster of high-performance computers.

• R programming makes it easier to create a visual presentation of results for use in white papers. The results remain fully traceable and can be reproduced when there’s a need for a different result structure.

• There is a large R language support community made up of data scientists from across the globe. The community provides packages in different domains, including machine learning, web technologies, and pharmacy.

Cons of R

Listed below are the primary disadvantages of R programming language:

• As we’ve mentioned above, it is not a novice-friendly language. If you are looking to get into data science or programming in general for the first time, there are other languages with a friendlier learning curve.

• If the code is written poorly, deriving proper solutions in R can be slow. Programmers often have to include libraries to ensure the achievement of proper output.

Pros of Python

Listed below are the primary advantages of python programming language:

• As you’ve seen above, python is a general programming language that arms you with more than just the skills to become a data scientist. Python programming language is broadly used in automation testing, web development, and more.

• Many programmers agree that Python aligns better with programmer logic when compared to R. This makes it easier to translate to other languages. Since R language’s roots can be traced to statistics, its design is very different. If you are interested in learning other object-oriented languages in the future, starting with Python is a good idea.

• Data cleanup is an important part of data analysis. As a full-service language, Python makes this relatively easy. You can easily add new functions and layers to disintegrate the data. It is also easy to include web access and local storage, depending on the project’s needs.

• Python is constantly changing with time. A new code is regularly introduced to replace the existing one. This is why some experts refer to python as an animate language. The open-source nature has made the programming language more robust with each passing year. R, meanwhile, has remained largely the same since it was first rolled out. though it’s also open-source.

• Python is faster than R. This is because, while R was developed for use by statisticians, Python was developed for the computer.

• Python’s syntax is clearer to understand. This has made it very popular. Data scientists that have mastered programming with Python know the exact number of steps to take to achieve the desired output at all times.

• Since Python is open source, it is appealing to corporations that are looking for a low-cost structure for data analysis.

• Due to its high-performance nature, Python is more useful in business-critical scenarios.

• Python is an excellent language for machine learning, deep learning, and the building of tools and services.

Cons of Python

Listed below are the primary disadvantages of python programming language:

• Working with Python means accounting for the time required for rigorous tests so as to capture all errors that may show up in runtime.

• Python may be faster than R, but it is slower when compared to similar languages because it is an interpreted language.

• Even with the strides made in the use of this language, Python programming is still weak on the mobile computing front. There aren’t many apps created with Python as the major language.

Why You Need Both in the Long Run?

You don’t necessarily need to be an expert in Python and R, but having both skills will most likely be more beneficial to you in the future. It’s easy to settle on one side of the divide after you have chosen which of these languages to get started with. However, it’s not always a good idea. There are a few reasons why you should consider becoming proficient in both R and Python over time.

You’ll Be Able to Do More

Many people who ended up getting committed to either R or Python find themselves wishing they could do some of the things another programmer on the other side could accomplish. For example, R users often end up yearning for object-oriented capabilities only possible with Python.

On the other hand, Python users sometimes wish they had access to the wide range of statistical distributions mostly available within R.

You’ll Have Stronger Data Science Communication Skills

The online communities for these programming languages (python.org and R-bloggers) give the impression that both languages are completely different and can’t work together, but this isn’t the reality.

When you get into the world of data science, you’ll find out that Python and R users mix up a great deal. Regardless of the industry, you end up in, there’s a high chance that you’ll run into projects that require proficiency in both languages. At the very least, you’ll need a basic understanding of both languages.

By having some understanding of both languages, you’ll be able to present and communicate data effectively regardless of what language your audience is most comfortable with.

You’ll Have More Job Opportunities

Having adequate knowledge of both R and Python will give you extra advantage when looking for jobs. Even inside one company, one data analysis may be comfortable working with R, while the other will prefer Python.

As an entrant into this field, you should try to gather as many skills as you can. You don’t have to be a master of both languages even. Sometimes, being very good at one and having some knowledge of the other is enough.

Conclusion

Python sounds like the obvious choice to go with first if you are going into data science with an eye on other programming fields. The learning curve will be more forgiving for a newbie when compared to R. However, if you are only focused on data science and also have a strong mathematics or statistics background, going with R is the better decision.

If you want to be a complete data scientist, however, you will have to work hard towards proficiency in both languages with time regardless of which one you choose to start with. A programmer with both Python and R skills is always more valuable to brands than one that has just one of these skills.

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.

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