> There's a lot more to scientific computing than wrangling tabular data.

Also a point that gets ignored way too often. My original post differentiated between time spent writing models and time spent data wrangling.

I would never even attempt to write a symplectic integrator in base R (OK maybe Rcpp would be fine but that's not really "R"). Julia, by design, is better at that. But the R ecosystem is so good that I can use the best practical implementation of a symplectic integrator to solve common modeling problems via RStan.

Yes, Stan is a standalone framework that can be accessed from Julia as well. But the following workflow can be done in R much easier:

  1) Read in badly formatted CSV data
  2) Wrangle the data into a useable form
  3) Do some basic exploratory analysis (including plots)
  4) Write several models in brms/raw Stan (via rstan)
  5) Simulate from the priors and reset them to more sensible values
  6) Run the model over the data to generate the posterior
  7) Plot/run posterior predictive checks, counterfactual analysis, outlier analysis (PSIS or WAIC), etc.

Again, the above represents my common use case. I fully appreciate that people use Julia to do awesome stuff like "the exploration of chaos and nonlinear dynamics." [0]. I understand that the modern R ecosystem isn't really built for this.

[0] https://juliadynamics.github.io/DynamicalSystems.jl/latest/