Rick J. Scavetta and Boyan Angelov's guide to using Python and R together for modern data science instead of picking sides.
Python and R for the Modern Data Scientist takes a different approach to the classic “Python vs R” debate. Instead of arguing which language is better, Scavetta and Angelov show you how to use both. The book walks through each language’s strengths, teaches you the other language if you already know one, and then shows you how to combine them in real projects.
The book is split into four parts. Part one covers the history of both languages and how they ended up competing for data science dominance. Part two teaches R to Python developers and Python to R users, covering everything from setup to data manipulation. Part three puts both languages in context, comparing which is better for different data formats and workflows. Part four brings it all together with tools like reticulate and rpy2 that let you run both languages in the same project, plus a real case study.
This is for data scientists, analysts, and developers who already know one language and want to pick up the other. It’s also great for anyone tired of the language war who just wants practical advice on when to use which tool. Published by O’Reilly Media in 2021, it remains relevant because the core principles of bilingual data science haven’t changed.
I picked up “Python and R for the Modern Data Scientist” by Rick J. Scavetta and Boyan Angelov a while back. It’s an O’Reilly book from 2021, and it caught my eye because it doesn’t pick sides in the Python vs R debate. Instead, it argues you should use both.
The preface of “Python and R for the Modern Data Scientist” sets up the whole book in a few pages. And it does something rare for a tech book. It actually defines what it means by its own title.
Chapter 1 is titled “In the Beginning” and it’s written by Rick Scavetta. He opens with a tongue-in-cheek Dickens reference, saying it’s just the best of times for data science. But to understand where we are, we need to look at where Python and R came from. Their origin stories explain why they feel so different today.
Chapter 2 is where the book gets hands-on. Rick Scavetta takes the wheel and walks Python developers through R. Not from scratch, but with the assumption you already know how to code. The chapter is big, so I split it into two posts. This is the first half.
In Part 1 we covered R basics: setting up your environment, installing packages, working with tibbles, and understanding R’s type system. Now we get to the good stuff. Lists, factors, finding things in your data, and the iteration patterns that make R feel so different from Python.
Chapter 2 showed Pythonistas how to pick up R. Chapter 3 flips the script. Now it’s the R user’s turn to step into Python territory. Rick Scavetta writes this one, and he does a good job easing R folks into a world that feels messier at first glance.
Chapter 4 is where the book stops teaching you the languages and starts telling you when to use which one. This is Part III, “The Modern Context,” and Boyan Angelov takes the lead here. The question is simple: given a specific data format, which language gives you a better experience?
Chapter 5 is where Boyan Angelov gets practical about the question everyone dances around: which language should you actually use for which job?
Chapter 6 is where the book finally delivers on its promise. All that talk about using both languages together? This is where it actually happens. Rick Scavetta walks through the nuts and bolts of making Python and R talk to each other in the same project.
The whole book has been building to this. Six chapters of philosophy, syntax comparisons, and interoperability tricks. Now Chapter 7 drops a real project on the table. Build it with both languages. Together. Start to finish.
The appendix of “Python and R for the Modern Data Scientist” is basically a bilingual dictionary. It runs about 40 tables long and covers everything from package management to indexing. You could spend a whole afternoon reading through it.
So we made it through the whole book. And honestly? It was worth the ride.
The biggest thing Scavetta and Angelov got right is the framing. They didn’t write a “Python is better” or “R is better” book. They wrote a “both are useful, here’s when to use which” book. And that’s the mature take.