[ineedasername]

>makes it more difficult

Yes, sure, as long as you recognize that as a very subjective determination.

From the statistician's non-programmer POV the syntax of R or some other language are similarly opaque. Learning one vs. another will present similar investments in time. From their perspective, R does not make things more difficult, and the fact that it's more of the lingua franca within the field has it's own benefits.

The people I see complain about R are usually people that learned a different general purpose language first and find that when work requires data analysis they much prefer the GPL for working through the non-analytical portions if their work. (Especially with python where pandas and numpy have made less specialized tasks much easier)

[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.