[streamofdigits]

R is frequently compared with python and julia which are general purpose programming languages but it is not really a proper comparison. Once you approach R as a domain specific language / system then its various quirks and pecularities are more palatable and explainable: they are in a sense the price to pay for tapping a large domain of statistical analysis expertise that is not available elsewhere.

[jstx1]

This is mental gymnastics. People have some job to do and are looking for an appropriate tool for it; sometimes that’s R and other times it isn’t. Who cares if you call it a DSL or a general purpose language. If I want to do something and the language makes it difficult, telling myself “oh but it’s a DSL” doesn’t get me any closer to solving my problem.

[tmoertel]

> If I want to do something and the language makes it difficult, telling myself “oh but it’s a DSL” doesn’t get me any closer to solving my problem.

Unless the thing that makes the language difficult is your expecations. In that case, offering you an alternative mental model that helps you make better decisions when using the language does get you closer to solving your problem.

[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.

[stewbrew]

It's important to keep this in mind though because R (or rather S) is primarily supposed to be used interactively. A prof of mine used to call the R REPL and then go on from there. He called an editor from the REPL, wrote source files from the REPL etc. Once you see someone working with R like that, you start seeing R as what it is.

The beautiful it is to be used interactively, it really takes a lot of practice to write reliable code that doesn't abort with some error now and then.

[jstx1]

I think the point about interactivity is pretty well understood. Another comment in the thread pointed out how the majority of people who write R do it in RStudio and RStudio's defaults push an interactive workflow on the users (the nature of the work you do has a similar effect). So even for someone very new to the language it's pretty obvious.