Increasingly, R and Python are occupying an ever larger part of the data scientist's toolbox [1, 2]. For Python developers, using R means having access to an extensive set of tools for statistical analysis, data manipulation, machine learning, and graphing.

This talk is aimed at Python developers who are looking for a quick and comprehensive guide to the R language. The talk will begin with a brief introduction to the basics of R, followed by a deep dive into its most essential features (e.g. data frames, lists, and data manipulation tools). Along the way, we will also explore how to write efficient R code (e.g. using apply() vs. for loops), and discuss many of R's quirks (e.g. = vs. <- vs. <<-).