[Python Vs R] [Python and R]

After discussing the basics of data analysis last week (check it out-https://medium.com/p/9b35c010bd31/edit) I had a conversation with a fellow aspiring data scientist about the best language to get started with for any beginner. While he favored the programming language R, I insisted Python was the way to go, to get the whole job done hence the birth of today’s article on both programming languages.

I did a bit of an introduction to the Python programming language in the last article but for the purpose of this article, it will be important to talk a little bit more about it.

PYTHON Vs R

PYTHON

Python is a powerful programming language used for different applications. It is one of the if not the most essential language for data science. Over the years the development of tools in python has made it rather useful and easy for data analyzation. Because of its versatility, you can use Python for almost all the steps involved in data science processes.

An overview of the language will show that;

  • It is an Object-oriented language
  • It’s a general-purpose language
  • Lots of extensions and incredible community support
  • Simple and easy to understand and learn
  • Python offers packages like pandas, NumPy, and sci-kit-learn, which make Python an excellent choice for machine learning activities.

The three best and most important Python libraries for data science are NumPy, Pandas, and Matplotlib. NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Python is generally favored among data scientists because it is the only general-purpose programming language that comes with a solid ecosystem of scientific computing libraries. In addition, being an interpreted language with a very simple syntax, Python allows for rapid prototyping and reiterations.

R

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. It is mainly used for statistical analysis. R is quite important because of its versatility in the statistics field and the core of R is an interpreted computer language that allows branching and looping as well as modular programming using functions.

An overview of the language will show that;

  • It consists of packages for almost any statistical application one can think of
  • It is equipped with excellent visualization libraries like ggplot2 which is excellent for data visualization
  • It is capable of standalone analyses
  • It has effective in data handling and storage
  • R provides a suite of operators for calculations on arrays, lists, vectors, and matrices
  • R provides a large, coherent and integrated collection of tools for data analysis

PYTHON and R

Accepting both languages going forward because it is possible to combine the individual functions of both languages, we can utilize the statistical prowess of R along with the programming capabilities of Python. R and python for data science create a love story to help you develop, collaborate, manage and share your data projects.

Although R is mainly used for statistical analysis while Python provides a more general approach to data science it is possible to use both languages successfully.

  1. You can call R from python;

First: Install rpy2 with pip

Then import all the necessary libraries

2. Use Jupyter with the IR Kernel — Python and R and makes the interactivity of iPython available to other languages.

It is also possible to run Python scripts in R by using any one of the alternatives below:

1. rjython - This package implements an interface to Python via Jython. It is intended for other packages to be able to embed python code along with R.

2. rPython - rPython is again a Package allowing R to Call Python. It makes it possible to run Python code, make function calls, assign and retrieve variables, etc. from R.

3. SnakeCharmR - SnakeCharmR is a modern overhauled version of rPython. It is a fork from ‘rPython’ which uses ‘jsonlite’ and has a lot of improvements over rPython.

4. PythonInR - PythonInR makes accessing Python from within R very easy by providing functions to interact with Python from within R.

The Data Science community today has people who generally work with only a single language. However, there are still those who are using both Python and R. They are excellent tools to carry out the data Science tasks from start to end. Combining both languages makes us open to learning new tools and languages in solving problems, with ease.

Electrical Engineer with a budding career in Data & Business Analytics. Blogger and Freelance writer on the days I’m not figuring out large datasets…