[asdff]

From a statisticians POV the R syntax is great. Here is the t test:

t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95, …)

A statistician opens the vignette and already knows what all of these variables represent mathematically, and can begin producing analysis immediately.

[ineedasername]

Yes, precisely. Very much not the pythonic way but that only matters if your prior background before R was python. If your background was SPSS then many of these would be drop downs or check boxes, and (IMO) it's superior to the SPSS scripting language as well.

Heck, my background before using R was python and SPSS and I still prefer R for precisely the example you gave: fine-grained control built in as above, specifying how to handle missing values etc.

I end up using python for large scale data prep.