Python and R are two of the most popular languages used for data analysis. They are often pitted against each other in pros and cons lists, where users feel forced to pick just one. Each has unique advantages, and it's now easier than ever to use them harmoniously. Python or R? Why not both?
How often do you hear the question "Python or R?"
Aspiring analytics professionals often feel the need to choose & learn a 'one size fits all' language for their scripting work. There are many cases, though, where a specific library in Python or R is more effective than similar libraries in the other language. This can lead to some painful tradeoffs when selecting a single language for your work. Great news: recent developments have made leveraging both languages in the same workflow easier than ever before.
In this talk, we’ll present methods for leveraging R from directly within Python environments (and vice versa). We will illustrate the use of these methods by using popular libraries to execute common analytics tasks across languages (web scraping, predictive modeling, time series forecasting, anomaly detection) without switching development environments.