Python and R are the two most commonly used languages in data science. Today, most of the novices get confused, whether they should use R or Python to kick-start their careers in the field of data science. I am gonna tell you the long and the short of both of these topics.
R is a programming language made by statisticians and data miners. Its mainly used for statistical analysis and graphics supported by the R foundation for statistical computing. R’s functionality is developed by statisticians mind, thereby giving it a field-specific advantage
Python is a fully-fledged, Object-oriented & high-level programming language made by programmers and developers. Its mainly used for general-purpose programming. It is widely used in GUI based applications such as games, graphic designs, Web applications, and many more. Python is often praised for being a general-purpose language with an easy-to-understand syntax.
When it comes to speed, python is faster than R only till 1000 iterations but, after the 1000 iterations, R starts using the lapply function which increases its speed, in that case, R becomes faster than python. both do have their advantages. Let’s move on and learn more.
In this topic, I am going to give you a brief about the variable declaration, Data handling capacity with the scatterplot visualization, and the ClusPlot graphics.
Starting with the Variable Declaration. Let’s take the case of String here. R uses arrow signs to initialize the variable. These arrows can be used from right to left or left to right indicating who to assign the variables whereas python uses an assignment operator to initialize the variables. R developers thought that it would be better to tell the direction of the assignment rather than just using an assignment operator, which could confuse any new programmer about which variable is being assigned.
Next is the Data Handling capability. R data science ecosystem has many smaller packages like GGally, which is a package that helps ggplot2, and also, it is the most-used R plotting package. whereas in Python, matplotlib is the primary plotting package, and seaborn is a widely used layer over the matplotlib. R has Many packages supporting different methods of doing things Whereas there is usually one way to do something in python.
Moving on to the next point that is Graphics. Here we will take the case of ClusPlots. As we already discussed that R was built for statistical analysis, it has many specific libraries for plotting. This is the reason R comes up with beautiful charts and graphs whereas Python’s main agenda was not a statistical analysis, so in the early stages of Python, packages for data analysis were an issue, but it has improved a lot. R comes up with beautiful graphical representations as compared to python. So here we can say that R is handy when it comes to Data Handling.
Our next point of attention is Deep Learning, which is today’s trend. As you all know, almost the majority of the companies are working on Artificial Intelligence, And Deep Learning is the main part of Artificial intelligence So, When it comes to Deep Learning, Python is more versatile than R as it provides more features to deep learning whereas R is new to Deep Learning.R has newly added APIs like Keras and KerasR which are written in Python.
So now somewhere in your mind, this question might be floating why Keras? Keras in Python has the capabilities to run over Python strong APIs like TensorFlow or Theano or Microsoft’s CNTK. So we can say that Python has a greater advantage here. Till now, we have seen that both are useful in their areas.
Beginners may find this hurdle in the starting. In the past years of research, the percentage of people switching from R to Python is more as compared to Python to R.Lets say, if 30% people are switching from Python to R then, 60% are switching from R to Python, which is twice as compared to the before scenario.
Before 2016, R was more in use. But here we can see that from 2016, Python is in trend. So, its more popular than R. And because of its popularity, it has overall good support for general-purpose programming. Well if we talk about community support, Then Python and R support aspects are almost similar to Python’s support is found at the Mailing list, user-contributed code, documentation, and StackOverflow. It has more adoption from developers and programmers’ end. Whereas R language support is also found at the Mailing list, user-contributed documentation, and active StackOverflow members. R has more adoption from researchers, data scientists, and statisticians end.
Now if we talk about Job trends, let’s check the Google Job Trends graph right here, this is the Job postings for R and Python in the past 12 months WORLDWIDE where python is asked more as compared to R. How is it possible? Because of its popularity and its need in the current industry. Python is more versatile and an all-rounder programming language that can be used for the majority of the purposes such as web and application development, game development, artificial intelligence, data science, statistical analysis, etc, whereas R language is used among statisticians and data miners for developing statistical software and data analysis. This clearly depicts that, there are more jobs for python than R.
This is the frequently asked question by the majority of the learners. I would suggest using both if you have the choice. They complete each other gracefully and will make your life better if you leverage their strengths and avoid their weaknesses. Everything has its own pros as well as cons, so as in the case of R and Python.
Learn how they inter-operate together.Start with one and then add the other to your workflow. It only adds another skill-set into your resume, which comes as a bonus to your career.
Thank you so much for reading this article. we would love to hear from you on which one according to you is better and why? Please reply to us in the comment section below.