R vs Python: Which is the best programming language for data science?

Python is the most popular Data Science language, with R a close second. They’re both open-source and great at data analysis. Despite their competitive popularity, R and Python are, in some cases, significantly different, and one may be more suitable than the other for specific tasks.

This blog makes a case for learning both languages in Data Science. It also covers some of their significant differences, such as handling data and machine learning applications. Last but not least, we’ll tell you which one to learn and why.

R language for Data Science

The R programming language is gaining traction in the field of data science. In fact, according to the TIOBE Index 2021, R is one of the most popular languages (it stands at 18th position, at present.)

The first version of R came in 1993, created by Ross Ihaka and Robert Gentleman. It has earned a reputation for its competence in data science, visualisation projects, and statistics.

The R language was created exclusively for data analysis and to develop applications and software solutions to perform statistical analyses and data mining. It’s a complete ecosystem for data analysis, with a lot of plugins and libraries accessible.

Python for Data Science

Python is becoming the preferred choice for data scientists. It’s got an extensive library of packages and modules that can be used to handle big data analytics and perform machine learning tasks.

The first version of Python was released in 1990. Significant progress has been made in its usage as it became more powerful over time.

Programming languages like C, C++, Java are based on syntax that requires writing down instructions step by step. However, Python uses a dynamic type system that makes it easier for new users to pick up the three most important programming paradigms- procedural, object-oriented and functional programming.

R vs Python: How do they compare? 

R and Python are both open-source, cross-platform languages that have their advantages. They are used in different fields of data science to achieve similar results.

Let’s take a look at what makes these two popular programming languages differ from each other, shall we?

1) R is the best choice for statisticians, while Python is more suitable for developers

Both R and Python are good at statistics, but Python wins over R hands down when it comes to structuring the code. Although there are many statistical libraries available in R like plyr or reshape2 (that work specifically with data frames), they can be challenging for new users to understand.

Python has an advantage here since it was designed with programmers in mind. Developers prefer writing code that works logically instead of following step-by-step instructions to accomplish a particular task.

2) Python has better visualisation capabilities

Python could have outperformed R in this category if it weren’t for the ggplot2 package available only in R. However, alternatives like seaborn, bokeh and holoviews provide similar functionalities like ggplot2. 

They are all compatible with both Python 2.x and 3.x versions, so you won’t have any compatibility issues when dealing with different operating systems or installation procedures. No matter what data visualisation library you use, though, you’ll still need to learn how to build up layers using objects and functions in Python.

3) R has a more advanced set of machine learning algorithms, while Python is catching up

R is considered a better choice for statistical analysis and data mining projects. If you’re looking for a package that provides tools to build complex machine learning models, then Python will suit your needs perfectly. It seems like Google prefers using Python instead of R when working on its TensorFlow project. On the other hand, R has an edge in developing custom machine learning environments since it’s been around longer than Python. Some experts even claim that it’s easier for new programmers to understand the syntax used by R.

4) Both have pros and cons depending on what kind of data scientist you are

R and Python both have their strengths as well as weaknesses. It depends on the type of data scientist you are. If you’re a statistician, R will be the best option, but Python is more likely to suit your needs if you prefer developing new machine learning projects.

R vs Python: Which one to learn?

Python is regarded as quite simple to master due to its clear syntax. It has a low learning curve because of its readability and simplicity; thus, it is ideal for beginners. Furthermore, it’s a whole programming language with no significant drawbacks.

R, on the other hand, is simpler to learn for those without prior computer programming expertise. It allows users to get right into data analyses right away, but it may become more complicated as it employs more complex analytics and functionalities. Furthermore, R is popular among data scientists and scientists from a variety of disciplines who want to extract insights from data quickly using previous studies and other research efforts.

The aim of the data analyses is also an essential factor to consider when deciding which one to learn. R works well for people who are interested in statistical learning, data exploration, and experimental designs. At the same time, Python is mainly used for data analysis within web applications and is also the best choice for machine learning.

However, it’s not advisable to use one programming language for data science and abandon the other entirely since they have pros and cons that we couldn’t cover here due to space constraints. There’s no better or worse here – they’re just different from each other.

The most important thing is to learn both R and Python (or any other statistical tool), so you can make informed decisions depending on the specific task at hand. It will help you get the best results possible.

If you learn how to use these statistical programming languages correctly, it can be like adding wings to your data science project. Using R vs Python is only the first step, but if you want to create high-quality models and produce cutting-edge visualisations, you’ll need more than just this. You’ll also need professional help from experts who are trained in using these tools for data analysis.

For example, they can help you choose a suitable model depending on the unique characteristics of your dataset. It helps you take an informed decision on what statistical language will work best with your needs at that particular time.

When working with machine learning problems, even small mistakes can significantly impact overall accuracy, which is why it’s essential to work with a data science team instead of learning everything by yourself and working on projects that are advanced for you.

It isn’t just about statistics and programming languages – there are other tools available to help you get the best results possible.

Tools like Power BI provide a fantastic platform that makes it easy to connect with all your existing data sources, visualise them within minutes and take advantage of state-of-the-art machine learning algorithms without having to do much coding at all.


R and Python are the most popular programming languages for data science projects, but they’re entirely different from each other. Both have their set of features, pros and cons, so it’s not possible to declare one as being better than the other.

However, we can say that you should learn how to use them both if you want to become a professional data scientist. It will only make it easier for you to make informed decisions when planning or even troubleshooting projects, depending on your specific needs in time. It means improving efficiency, getting higher quality results, reducing development costs and saving time.

Truth be told, it doesn’t matter which statistical language you choose – R vs Python isn’t the issue here. What matters is ensuring that you have all the tools necessary to create fantastic data science projects.

It is why it’s essential to learn how to work with machine learning algorithms without doing much coding. It can save you time and make your life as a data scientist easier.

Tools like Power BI allow you to visualise even the most complex datasets within minutes by simply dragging and dropping visualisations on screen. Regardless of your experience level, it helps you create beautiful dashboards, analyse data points using state-of-the-art machine learning algorithms, uncover hidden insights that will help them take action at the right time, and more.

It allows companies from different industries to focus on using data to solve what used to be considered ‘unsolvable’ problems.

If you’re ready to learn, upskill and put it all to the test in a real-world, we can help! 

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