For this week’s blog I will be taking a look at more of the data analysis side of the computer science world with a comparison of R vs Python. When looking to get into data science, business intelligence, or predictive analytics, we often hear of the two programming languages, but we don’t necessarily know which one to learn or use in different situations.
The R language is a statistical and visualization language that was developed in 1992. R has a rich library that makes it perfect for statistical analysis and analytical work. Python, on the other hand, is a software development language that is based on C. Python can be used to deploy and implement machine learning at a large-scale and can do similar tasks as R. The usability for the two languages is clear that Python is better for data manipulation and repeated tasks while R is better for ad-hoc analysis and general exploration of data sets. Python, being more of a general programming language, is the go to form Machine Learning while R is better at answering statistical problems.
R comes with many different abilities in terms of data visualization, which can be both static or interactive. R packages such as Plotly, Highcharter, and Dygraphs allow the user to interact with the data. Python has libraries such as SciKit-Learn, scipy, numpy, and matplotlib. Matplotlib is the standar Python library that is used to create 2D plots and graphs while numpy is used for scientific computing.
Although R has always been the favorite for data scientists and analysts recently Python has gained major popularity. Over the last few years, Python has risen in popularity by over 10 percent total while the use of R has fallen about 5 percent. Since R is more difficult to learn than Python, the general consensus is that the seasoned data scientist uses R, while the entry-level new generation of data analysts prefer Python extensively.
In the end, Python is a clear better choice for machine learning due to is flexibility, especially if the data analysis tasks need to be integrated with web applications. If you have the need for rapid prototyping and working with datasets to build machine learning models or require statistical analysis of a dataset, R can be used much easier.