R vs Python

If you wish to start your career in Data Science, then one of the most obvious decisions you have to make is on which language to choose. Two of the most popular languages for Data Science are R and Python. Both have their pros and cons, and it can be difficult to decide on which one is more suitable for you. Keeping that in mind, this is what we are going to cover in this R vs Python blog.

R vs Python
06th Nov, 2019
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In this article, we will help you decide between these languages by pitting them against each other and evaluating them under multiple criteria.

We will be comparing these languages on the following parameters:

  1. History
  2. Syntax
  3. Performance
  4. Where are they used?
  5. Tooling
  6. Trends
  7. Salary
  8. Conclusion

R vs Python: History

Before diving deep into the nitty-gritty details of these languages, let us take a closer look at their history.

 Here’s a video from Intellipaat on R vs Python:

R Programming

R is a programming language (which was developed in Bell Labs) built specifically for Statisticians and Data Scientists. It is an implementation of the S programming language and is also inspired by the Scheme programming language.

Learn more about data structure in R programming tutorial.

R was conceived in 1992, released in the year 1995, and was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. Its current stable release is version 3.6.1. R is currently being maintained by the R-core group and R Foundation.

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Python

Python is a scripting language that was developed by Guido van Rossum and was released in the year 1991. Python’s name is based on the famous British comedy group ‘Monte Python’. Python is a general-purpose, high-level, object-oriented scripting language and is used in different fields. It is currently being maintained by the Python Software Foundation (PSF) and its current stable release version is 3.7.4.

R vs Python: Syntax

Even though both these languages are quite popular with Statisticians and Data Scientists, they could not be more different than in their syntax and language semantics.

R Programming

R’s syntax is going to be more familiar to you if you are comfortable with the C programming language or with a language that was inspired by C, such as C++, Java, C#, etc. It follows the basic syntax rules of the C programming language, such as curly braces for code blocks.

In R, semicolons are optional and you don’t need them to end a statement, but if you wish to put multiple commands on a single line then a semicolon is needed.

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Watch this end-to-end R Programming Tutorial:

Python

Python is one of the easiest languages to get familiar with because of its easy-to-read syntax that favours readability over preciseness. Python does not use curly braces or semicolons; instead, it uses indentation to group related lines of code in a code block.

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Also, since there are no semicolons used to end statements, each line can contain only one programming statement, such as print, if, etc.

R vs Python: Performance

Now, let us compare these languages on the basis of one of the most important criteria, speed.

R Programming

Generally speaking, R is comparatively slower than Python. This is mainly because R was not designed keeping speed in mind but rather was created by Statisticians for data analysis and crunching through numbers with very high precision.

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Python

Python is comparatively faster than R. This difference in speed holds up quite well if we are comparing these languages without the packages as these languages are quite versatile and can be interfaced with packages written in much faster languages like C or C++ and thus can reduce this disparity in speed.

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R vs Python: Where are they used?

In this section, we’ll see in which domains these popular programming languages are being used.

R Programming

As R is developed by statisticians for statistical data analysis, it is mainly used by scientists and researchers to process large sets of data. It is mainly used for research and academic purposes like statistical forecasting, data visualization, and so on.

However, R is now quickly expanding into the enterprise market as well, mainly because of its popularity and the availability of a large number of packages for statistical computation.

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Python

As Python is a general-purpose programming language, we can use it to build different kinds of applications. We can use Python to build web applications using popular frameworks like Django or Flask.

Check out this Python community by Intellipaat

We can build GUI-based desktop applications using the Tkinter library. We can also build statistical models using Python with libraries such as Theano, Keras, TensorFlow, etc. These libraries also make it quite easy for developers to build Deep Learning models using convolutional neural networks.

 

R vs Python: Tooling

Now, let us compare these languages based on the tools available to ease the development of products.

R Programming

R has got many IDEs and packages to boost developer productivity and prevent developers from having to write everything from scratch. Packages such as dplyr, plyr, and data.table allows developers to easily manipulate data. Stringr is a package to manipulate strings.

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Zoo is a package used with regular and irregular time series. Also, there is caret package used for Machine Learning. RStudio is the de-facto IDE used with R.

Python

Python also has a large number of packages and IDEs for developers. Python has considerably more packages than R, because Python is a general-purpose programming language and R is mainly used for scientific computations. Python can be used with several IDEs like Spyder, PyCharm, etc. For Data Science, developers usually use Jupyter Notebooks, mainly because it allows them to save both the code and its output for later inspection. Python has packages like Scikit-Learn, OpenCV, Theano, Keras, TensorFlow, NumPy, etc. for Machine Learning and Data Science.

Watch this end-to-end Python Programming Tutorial:

Let us compare the trends between these two languages.

R vs Python

As per the graph above, R and Python are both quite popular, and R seems to be more popular. Although, Python is catching up the race.

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But recently, it’s been noted that R’s popularity is decreasing. Because of the ease in learning and the tooling available, around 26 percent of people left R to use Python, while only 18 percent left Python to use R.

Here’s a video on Python Django Tutorial from Intellipaat:

R vs Python: Salary

Now, let us see how much salary these languages can help you achieve.

Learn more about R in this insightful article on R programming

R Programming

The average salary of a Data Scientist with skills in R in India is around ₹7.3 LPA.

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Python

The average salary of a Data Scientist with skills in Python in India is around ₹8.14 LPA.

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One thing that should be noted here is that these salaries are also highly influenced by job location, experience, and proficiency in these languages.

R vs Python: Conclusion

As we can see from our R vs Python comparison, like many other things in life, there is no clear winner here. In case you are on the crossroads and wish to choose between these languages, think about what you wish to build, as this is one of the things that can highly influence you to choose one language over the other. For example, if you wish to do research in a particular field and have a lot of time, then you can use R as it is heavily used in the field of research and academia; although it has a steep learning curve, it is better for research and analysis. On the other hand, if you wish to build an application that relies on some Data Science or Machine Learning technology, like Deep Learning, and you wish to develop this application quickly, then choosing Python is better as it allows you get started and build applications for the market faster. Now, if you have time, why not learn both? These are both great languages well worth your time.

Hopefully, this article has helped you demystify the differences between R and Python languages and has whetted your appetite for learning more about these technologies. We wish you the best for your future endeavours with these languages.

 

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