Tidyverse code is not write-only; it is designed to be mostly read-only where the level of abstraction is at the level of the domain, which in this case is data frames and data pipelines akin to the same constructs in relational algebra/SQL or data processing (map/reduce).
Correct usage requires learning the right abstractions, but fortunately these abstractions are shared across language communities and frameworks.
If you are doing any data processing work and you do not know about foundational functional concepts ie map/reduce then I would argue the code you write is less readable, overly focused on the idiosyncracies of how to do something rather than how entities and data are related to each other - their logic and structure.
The main optimization is to be correct and expressive of one's intention and purpose. If you need performance use Julia.
I concur with your sentiments having cultivated data science teams from the ground up with diverse educational backgrounds.
Programming in base R is more akin to assembly language and has accreted a babel of inconsistencies that make it difficult to teach and learn. Learning base R isolates you into a Galapagos island of academics who are either ignorant of the needs of data workers or too elitist to engage with those not in their priesthood.
Learning Tidyverse is a considerably better transition for learning other languages, frameworks, and libraries.
Functional programming is closer to algebra than indexing into data structures with magic numbers. I've found more success teaching functional pipelines of data structures using the idioms in Tidyverse as a general framework for data work than base R. Abstraction has a cost but for learning it is the appropriate cost.
I sense that much of this `monopolistic` fear mongering is really about feeling out of date.
The Chinese government has prevented plenty of US companies from doing business in China including Google, but more importantly is the rejection of patents and intellectual property and espionage conducted by Chinese government, which has cost US companies billions and the US government hundreds of lives.
This is the best justification for R: Its statistical lineage and adoption by researchers. There's plenty of great thinking manifested in these libraries and its community. This can't be understated.
R is quite good at developing DSLs (quite Lispy), but Julia is even Lispy-er with a more flexible syntax and unicode identifiers. R, definitely, has a great statistical lineage, but that is also unfortunately what limits it from being as expressive - especially regarding new data types. No one in R creates their own domain-specific data types (that are performant as well) whereas this is the norm for the Julia ecosystem.
Speed is not the only computational constraint - the tremendous memory required for very large data sets is often a severe limitation especially within R (and I actually enjoy R).
This is Julia's native, in-language approach (no delegating to C/C++):
https://juliadb.org/
The irony is that if you use Patreon's search functionality to search for slurs and other hate speech you will find plenty more blatant and obvious instances on their own platform.
Reals are not equivalently as unrealizable or dubious as infinitesimals. Infintesimals can be constructively specified as nilpotent/nilsquare entities, numerical entities which when squared equal 0 but where those entities themselves aren't reducible to 0. All of this can be done in a constructive manner avoiding any use of infinity or classical logic that depends upon indirect proofs (ie excluded middle). John Bell's 'Primer of Infinitesimal Analysis' has good details.
The computational techniques from Automatic Differentiation use these types of entities to calculate derivatives exactly without approximating infinite (limiting) processes.
Calculus can be done constructively without infinite limiting processes purely algebraically using these nilpotents. And from a geometric interpretation there is nothing nonsensical about a tangent line to a curve.
Also you don't differentiate numbers (real or rational), you can only differentiate functions.
Also the idea of a actual infinity is a poetic mathematical one. It doesn't have to fit reality. The issue is whether it is useful and to what extent.
I'm unsure about what danger is being exposed here outside of what other agents have or are currently employing.
Can someone give some more perspective regarding the significance of this?
All of these guys have worked on static languages,
have a keener understanding of type theory than most,
and yet they seem to promote dynamic languages
at least when it comes to their pet languages.
As a former academic I find this kind of behavior antithetical to the principle's of science. The scientific publishing industry is archaic and a burden on the progress of science. These journals are gatekeepers for both consumers and producers of public research. I've worked on some research projects that took nearly 2 years to get published after the all of the data collection and analysis had been already completed. The peer-review process can always be over-ridden by the editors and it is the editors who get to choose who reviews the research. So instead of having all of science practitioners to bear on work, it's a select group that an editor deems worthy. Despite all of our technology, the politics of control remain the same.
Correct usage requires learning the right abstractions, but fortunately these abstractions are shared across language communities and frameworks.
If you are doing any data processing work and you do not know about foundational functional concepts ie map/reduce then I would argue the code you write is less readable, overly focused on the idiosyncracies of how to do something rather than how entities and data are related to each other - their logic and structure.
The main optimization is to be correct and expressive of one's intention and purpose. If you need performance use Julia.