Base R is also perfectly logical and coherent. If anything, it's so coherent for statistics work that it makes non-statistics work harder than it should be. For a 30-year old language that started life as a statistics package, that's pretty good.

R is no harder to read than C IMO, even badly-written R without spaces, indentation, proper line breaks, etc.

One where it was not necessary to specially create a ‘tidy’ version of everything to escape the horrific mess that exists by default

It would help if you said explicitly what language you think is better in this respect. Having Data Frames in Python at all requires importing Pandas afaik, so criticizing R for requiring an extra data-manipulation package wouldn't make sense from a Python perspective.

The point is not that you have to import a package, but whether by default package layouts and interop make sense. Notably, python scientific libraries build on a set of common foundations and apis (numpy, pandas etc). Additions to the ecosystem aim to build on familiar apis irrespective of implementation details (e.g dask). Python’s heavy use of namespaces makes organising a working environment simple, while it’s focus on consistent style and naming conventions means that code from different sources will look familiar. R meanwhile uses a global namespace (with at least two ways of loading libraries into it) so requiring more than a few extra libraries risks name collisions. Thus the need to create another ‘tidy’ library to fix a fundamental limitation of the language.

> R meanwhile uses a global namespace

This is just plain wrong.

OK, I see. The debate is gradually coming to the pitchforks and torches phase. Tons of adjectives and still zero meaningful input. I insist on explaining what the "horrific mess" part is about.

Your language was perfect the day it was created was it? R and it’s ecosystem stand head and shoulders above the rest.