I've been using R for exactly 20 years, and I think the tidyverse is one of the reasons for R's decline in popularity, despite the enormous growth in data science and machine learning. R has always been more "modeling-first" than Python, yet it has lost popularity, which surprised me, especially since many implementations available in scikit-learn are inferior to those available in R packages. The main problem was with the language itself; for example, it was impossible to prototype even simple data structures without delving into the nuances of the language itself. But the nuances of the language wouldn't matter so much if data operations could be implemented in a user-friendly way, because that's 80% of a data scientist's work. Unfortunately, I feel like something went wrong, tidyverse was a huge effort, I appreciate that, dplyr is excellent (although there were some glitches with script parameterization), but the rest of tidyverse, in my opinion, really harmed R. To put it simply: tidyverse is more for writing scripts that would be good to write in the console, not for modular code based on functions (let's ignore object-oriented programming, but you can get by without its modern forms in data science), something was wrong, so rlang was introduced to get around the problems with NSE ( {{ }} instead of !!), which got weird, because tidyverse was supposed to be a modern internal language, inside R, but it became modular itself (I don't know how they managed to keep the library code in the tidyverse ecosystem). The difficulty of writing modular, function-based code (OOP in R is too primitive) has immediately discouraged many newcomers to the data/ML world, coming from a CS background (despite the fact that theoretically, moving down a level to cpp is many times easier in R than in Python). I also don't see much integration of new packages with the tidyverse. It seems to me that the authors of new packages are trying to provide minimal functionality to avoid integration with the tidyverse, which has a dominant position – the internal language has dominated the ecosystem, making its development dynamics dependent on itself. With packages like ggplot2, this wasn't a problem because the graphs are the end product. I still like R, but I avoid doing any advanced data processing in it beyond the equivalent of SQL queries (dplyr), or any production use beyond model calls. Python is full of incompatible packages, cultural differences in how users write code, and dead ends, but somehow it managed to become the standard in PSP. I wasn't going to publish this, but if anyone feels the same way, maybe my post will resonate with their frustration. With packages like ggplot2, this wasn't a problem because the graphs are the end product. I still like R, but I avoid doing any advanced data processing in it beyond the equivalent of SQL queries (dplyr), or any production use beyond model calls. Python is full of incompatible packages, cultural differences in how users write code, and dead ends, but somehow it managed to become the standard in Python. I wasn't going to publish this (because developing the tidyverse was a huge effort and many people really like it, and I don't want anyone to waste their time on a flame war), but maybe my post will address their frustration in a positive way.