I've been a Python lover for decades. I dislike a lot of characteristics of the R language, but the ggplot2 package is superior to anything in Python visualization space. It is really excellent.
I wish more projects would use that way of thinking but it seems that in the jupyter/julia/python world there's too many choices and they all attempt a "kitchen-sink" approach for visualization.
It’s interesting that you say this. I’ve been using R daily for almost 3 years now, and I was originally taught the Tidyverse.
However, I also tutored in biostats and have collaborated with many different faculty and students.
On one hand the Tidyverse created an opening for R learners especially, but it has lead to some controversy as well.
Trust me when I say there are plenty of R users who have never heard of or used the Tidyverse.
It leads to some difficulty in teaching/collaborating because do I use grepl or str_detect? lapply or map? Do they know the magrittr syntax (%>%)?
Then there’s non-standard evaluation, which often forces some arbitrary meta-programming on novice programmers.
R is foremost the stats language, and tidyverse has attempted to make it more general purpose, for better or worse depending on who you ask.
True story: writing tidyverse functions is so hard that I once worked for a company with business critical code running in incredibly long R-scripts with almost no functions, and 100 line pipes.
While that may be an extreme example, base-R is much, much easier to get started writing functions for (like I've been using R for a decade now, and the "idiomatic" way to use NSE with ggplot and dplyr has changed multiple times over that time).
Meanwhile, base-R is fugly, but its solid and backwards-compatible to a fault.
However, once they get that right, then tidyverse is close to perfection
I think the newish “interpolation” syntax with double braces {{ }} is getting there.
Damnit Hadley, why must you do this to me?
(Really I'm bitter because I know that I'll end up maintaining a bunch of important code in version N-2 of Hadley's NSE adventures at some point in the future).
https://www.tidyverse.org/blog/2020/02/glue-strings-and-tidy...
There's a huge shadow universe of scientists running particular R packages for these analyses, which never gets seen from HN/programmers.
While Python is definitely (sadly, IMO) a better language for building ML systems (because more people know it, and it's harder to write impenetrable code), R is definitely a better DSL for statistical analysis, modelling and graphics.
It's a shame that R takes its lineage from a language developed in the 70's (S), as that's a cause of many of the inconsistencies in the core language.
I suspect, however, that it would not be anywhere near the top 10 of the TIOBE index like it is now and that it's userbase would consist of mostly of statistics practitioners rather than huge swathes of people that need to perform any basic data analysis.
Also for a lot of people R is going to be the first and often only language they're going to learn because they have no use for programming outside of statistical analyses and it's damn practical for that single use. Right now generations of students are being taught in R, right now bioconductor is booming and being added to everyday. That stupid arrow assignment operator has plenty of good years ahead of it.