In the tug of war between the two programming languages R and Python who gets the trophy?

In the war of "best" data science tools, python and R both have their Strengths and Weaknesses.Opting for one over the other will depend on the uses, the cost of learning, and other features.

Since the beginning of this world there have been many wars to determine who is better and it still continues to exist except that in this era it’s the war of intelligence, war of money, war of data, and war of being better. One such war is prevailing in the field of data science. What is data science? Well, according to Wikipedia, “Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.” In simple words, data science belongs to the field of study that helps us study the data. It consists of extracting, recording, storing ,and analysing the data to put it into proper use.

The objective of this field is to gain an understanding, Insight ,and knowledge from data whether it is structured or unstructured. Data science is practised by Data Analysts, Data Engineers, and Data Scientists. For understanding data science, one requires to have the knowledge of various programming languages like python, R, tensorflow, pytorch and many more. And the war that prevails in this field is between R and python.

  • So what is R? R is nothing but a programming language for data science. It is a free software environment for statistical computing and graphics which is supported by the R Foundation for Statistical Computing. The R language is widely used for developing statistical software and data analysis. According to Wikipedia,” R is an interpreted language; users typically access it through a command-line interpreter. ” One of the most important use of R is to standalone computing or analysis on individual servers when required by a data analysis task. It is also used for any type of data analysis because of its enormous number of packages and readily usable tests which provide one with the necessary tools.

  • So what is python? According to Wikipedia, "Python is an interpreted, high-level, general-purpose programming language." Having a characteristic of versatility, one can use Python for nearly all the steps that take place in data science processes. It can take numerous formats of data and one can easily import SQL tables into your code. It permits one to create datasets and one can find any type of dataset one needs on Google. It can also be used when data analysis tasks need to be integrated with web apps.

Strengths and Weakness of R:

1) Strength: Ecosystem

The ecosystem of R has evolved over the years and has only improved with time be it providing various packages or catering to the needs of the community. Over the years the ecosystem of R has enriched and has proven itself to be the better programming language than any other.

2) Weakness: Speed is compromised

The process of R is quite slower than any other programming language. Due to its slow speed many users switch to other programming languages . However, there are many ways to solve problems like pqr, renjin etc.

3) Strength: Visualisation overpowers reading

R provides many features, one of them being providing data in the form of Visualized data rather than raw numbers which makes it effective and efficient for users. Some visualization packages which are to be considered are ggplot2, ggvis, googlevis and rcharts.

4) Weakness: Downfall when it comes to learning

R’s learning curve is significant, for eg: when you come from a GUI for your statistical analysis.

Strengths and Weakness of Python:

1) Strength: Fast Speed

When it comes to python it is faster than R and users don’t switch to other programming languages . In today’s world Time is important for everyone and if one is not providing service at the right time then people will find substitutes .However python seems to have a hold on its speed.

2) Weakness: Visualisations

Python has a feature for visualisations like Seaborn, Bokeh and Pygal however, the quality of visualisations provided by python is not as good as that provided by R. Thus here python fails to cater to the needs of users.

3) Strength: Large community base

Python is one of the most famous programming languages throughout the world thus it caters to all data scientists , data analysts, and data engineers. This creates a huge community base. Python Package index is one of the features to explore the various horizons of Python Programming language.Thus any problems faced by anyone can be addressed easily and solutions can be found easily . The developers of python are constantly trying to improve the program to cater to the needs of people using it.

4) Weakness: Less Packages

When it comes to providing many options in case of packages to users, python fails. Currently R serves more packages to users than that served by Python. Although python is trying to make more and more features available to users It is lacking in providing more packages.

Thus when it comes to concluding that between R and Python who gets the trophy, It goes to both. Even though both have some flaws but it also has strengths which no other programming language has. Thus every programming language should be used for what its best for and hence ultimately users stay happy.

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