> Still, if you are new to (Julia) programming and statistics then most likely you should calibrate your tools first. Before you run some statistical analysis you may want to try it out on an example from a textbook written by an expert (not me though) and see if you get the same (or at least comparable) result on your own. Although this is a sound approach, I suspect you are more prone to visit some statistical blog or internet forum and go with the examples that are contained there. One such option is rseek.org, i.e. a search engine for the R programming language.

> ... Once I got both outputs that are similar enough I can be fairly sure I did right. Otherwise I should investigate where the differences come from and possibly make some necessary adjustments.

It doesn't say to compare it against R, it says that if you are new to programming and statistics you should check your work against known-good answers or against a second implementation before rushing ahead, and it gives R as an example of a place to find code that you can use to check your work.

This isn't advice about one language being better, it's the usual advice to solve the same problem in two different ways to make sure you got it right!