If we are living in a simulation, you can be sure it is programmed in Lisp - that's why using it feels so magical, it's a glimpse behind the veil.

Lisp's advantages were negated decades ago. We have modern languages with modern type systems, modern tooling, and vastly more library support that make developing and maintaining complex applications way, way easier than Lisp. Try a large refactor that touches many aspects of a complex project (hundreds of thousands to millions of lines of code). It's a doddle in Java or TypeScript, especially with the refactoring tools in modern IDEs. The landscape has changed, massively, since Gat's 2000 paper comparing Lisp to Java. Strong static typing with modern ML-style type systems, alone, has been an unmitigated win in enabling the development of more complex applications with fewer bugs. Programmers are leaving a lot on the table by not adopting modern statically-typed languages, tools, and development environments.

R was heavily inspired by scheme, and I think that's a big part of why it's so popular in the scientific community (it's a great language for authoring DSLs). In fact, DSLs are so good in R that lots of midwit CS bros love to dunk on R the language, not realizing that what they're complaining about is in fact some library function. I like to tell people that R is "scheme on the streets, FORTRAN in the sheets". Just like Clojure deviated from I think R was very much developed as a Lisp designed to facilitate complex and flexible scientific applications (with an emphasis on statistical computing). I think you could develop a compelling analogy that Clojure:JVM::R:Numerics-oriented C/FORTRAN

Nice high level overview wuth some case studies briefly described.