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Difference Between R and Python

To find out the difference between these two powerful programming languages one should what are these programming languages.

What is R?

R is a statistical language. Ross Ihaka and Robert Gentleman created the open-source language R in 1995 as an implementation of the statistical programming language. Along with statistical techniques, R also provides a wide variety of libraries for graphical techniques. The aim was to develop a language that concentrated on delivering a better and more user-friendly way to do data analysis, statistics and graphical models. 

What is Python?

Python was created by Guido Van Rossem in 1991 and emphasizes productivity and code readability.  It can be used in web development, software development, system scripting, etc. It can works on different platforms. Python was designed for better readability; hence it has some similarity with the English language. Python focuses on simple, less cluttered syntax and grammar. It’s a flexible language that is great to do something novel, and given its focus on readability and simplicity. It uses dynamic typing and late binding which bind the methods and variables at the runtime. 

When to use R?

R is mainly used when the data analysis task requires standalone computing or analysis on individual servers. It’s great for exploratory work, and it’s handy for almost any type of data analysis because of the huge number of packages and readily usable tests that often provide you with the necessary tools to get up and running quickly. R can even be part of a big data solution.

When to use Python?

You can use Python when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database. Being a fully fledged programming language, it’s a great tool to implement algorithms for production use.

Benefits and Limitations of R

  • Pro: A picture says more than a thousands words: Visualized data can often be understood more efficiently and effectively than the raw numbers alone. R and visualization are a perfect match. Some must-see visualization packages are ggplot2, ggvis, googleVis and rCharts.
  • Pro: R ecosystem: R has a rich ecosystem of cutting-edge packages and active community. Packages are available at CRAN, BioConductor and Github. You can search through all R packages at Rdocumentation
  • Pro: R lingua franca of data science: R is developed by statisticians for statisticians. They can communicate ideas and concepts through R code and packages, you don’t necessarily need a computer science background to get started.  Furthermore, it is increasingly adopted outside of academia.
  • Con: R is slow: R was developed to make the life of statisticians easier, not the life of your computer. Although R can be experienced as slow due to poorly written code, there are multiple packages to improve R’s performance: pqR, renjin and FastR, Riposte and many more.

Benefits and Limitations of Python:

  • Pro: IPython Notebook: The IPython Notebook makes it easier to work with Python and data. You can easily share notebooks with colleagues, without having them to install anything.  This drastically reduces the overhead of organizing code, output and notes files. This will allow you to spend more time doing real work.
  • Pro: A general purpose language: Python is a general purpose language that is easy and intuitive. This gives it a relatively flat learning curve, and it increases the speed at which you can write a program. In short,  you need less time to code and you have more time to play around with it!
  • Pro: A multi purpose language: Python brings people with different backgrounds together. As a common, easy to understand language that is known by programmers and that can easily be learnt by statisticians, you can build a single tool that integrates with every part of your workflow.
  • Con: Visualizations: Visualizations are an important criteria when choosing data analysis software. Although Python has some nice visualization libraries, such as Seaborn, Bokeh and Pygal, there are maybe too many options to choose from. Moreover, compared to R, visualizations are usually more convoluted, and the results are not always so pleasing to the eye.

R Vs Python

  1. R codes need more maintenance while python codes are more robust and easier to maintain.
  2. R is more of a statistical language and also used for graphical techniques while python is generally used for development and deployment.
  3. R is better for data visualization while python is good for deep learning.
  4. R is easy to start with as it has simple libraries and plots while python libraries can be a bit complex.
  5. R supports only procedural programming for some functions and object-oriented programming for others while python is a multi-paradigm language. That means python supports multiple paradigm like object-oriented, structured, functional, aspect-oriented programming.
  6. R is slower than Python.

Conclusion

Both R and Python languages have their benefits and limitations, it’s a tough fight between the two. Python seems to be a little more popular among data scientists. R is developed for statistical analysis and is very good at that. Whereas Python is a general-purpose language for application development. Both the languages provide a wide range of libraries and packages, cross-library support is also available in some cases. Hence it totally depends on the user’s requirements which one to choose. 

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