What needs to be added is that before R the reproducibility problem in science was compounded by the fact that analyses were done with proprietary software limiting communication and replication of those analyses. This was and continues to be a major problem, particular in some fields, but at least now there is a common widely used language that can be used to overcome this. I wouldn't focus on idiosyncrasies but rather on the major problem it addresses. Any large system will grow over time and have some inconsistencies but after a while you learn the workarounds so they are less important than the big picture.
One Year with R
21–30 of 266 posts
Re: One Year with R
#22I love R more than any other language I have ever used. Perhaps more than any piece of software I've ever used. All of these points are valid, and yes, it's messy, and if you try to write the same type of code that you would in Python, it will frustrate you. And yet.. it somehow works. It makes data analysis and statistical modelling a pleasure. It somehow gives off a sense of lightness, and makes it easy to investig…
I very much agree with this. I use python for (different types of) data analysis too, and in python in particular it feels like the "boilerplate" to "science" ratio is rather high in the direction of "boilerplate". R manages to abstract this away very effectively, as the article highlights.
The beauty of R is that you can write one line of code and use some hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis in your totally different, completely whacky problem, and it's fast, and (by and large) works.
Of course, that's its biggest problem as well. Scientifically, it will quite happily give you a 150 mm howitzer to aim at your foot, assuming you know best.
Re: One Year with R
#23The common trope with R is that statisticians and love it and developers hate it.
The the main reason that statisticians love it is that the libraries useful to them are much better in R than elsewhere (though Python keeps encroaching in that turf, and "real developers" dislike Python a lot less than they do R).
The main reasons that developers hate it is that it is very unlike almost all other languages that they're used to. This is very valid since outside the narrow domain of statistics, there's probably nothing that R does better than other languages. So for a dev who occasionally dabbles with R by necessity, the otherness serves nothing but frustration.
Still, I wonder how much criticism there is against R as a programming language, that is not some variation on this works very differently from other languages. IMHO the sub-setting syntax, and countless x-apply variations are big warts. I'm not a big fan of Tidyverse, and even less of the schism between base- and Tidy-R. I read some seemingly fundamental criticism about R's deficient scoping rules, but I'm not nearly knowledgeable enough to judge their merits.
I guess it doesn't help that almost nobody learned R as their first computer (as opposed to statistics) language. Personally, I learned C, Matlab, Python/numpy, SQL, R in that order. R does seem to be quirkier than all the others, except maybe SQL. But I don't dislike working in R any more than working in any other language.
Re: One Year with R
#24The common trope with R is that statisticians and love it and developers hate it. The the main reason that statisticians love it is that the libraries useful to them are much better in R than elsewhere (though Python keeps encroaching in that turf, and "real developers" dislike Python a lot less than they do R). The main reasons that developers hate it is that it is very unlike almost all other languages that they're…
I had some Matlab experience about 3 decades ago. What's you take in Matlab vs R as programming languages?
Re: One Year with R
#25> R has two types of empty string: character(0) and "".
I understand it's frustrating trying to use a language you don't understand. And instead of reading the language manual you go on rambling.
"" is an empty string (almost) as you know it from other languages.
character(0) is an empty vector of type character (i.e. a vector with no elements). This vector doesn't even contain an empty string.
R is a vectorized language. You almost always deal with vectors. "" actually is a character(1), a character vector of length 1. Once you understand this, there is a chance for you to enjoy R.
Re: One Year with R
#26The common trope with R is that statisticians and love it and developers hate it. The the main reason that statisticians love it is that the libraries useful to them are much better in R than elsewhere (though Python keeps encroaching in that turf, and "real developers" dislike Python a lot less than they do R). The main reasons that developers hate it is that it is very unlike almost all other languages that they're…
I had some Matlab experience about 3 decades ago. What's you take in Matlab vs R as programming languages?
If signal processing and matrix algoritms are your thing, you should (and probably would) be using Matlab. Most statisticians don't really do much of that, what they mostly do is data management and try to mold their tables in some form accepted by an existing R package (or even Stata). As far as I remember, Matlab was pretty horrible for data munging, even worse than R for anything non-numerical. But my Matlab experience is also almost 2 decades old, so I don't know if it's any better today.
Re: One Year with R
#27The common trope with R is that statisticians and love it and developers hate it. The the main reason that statisticians love it is that the libraries useful to them are much better in R than elsewhere (though Python keeps encroaching in that turf, and "real developers" dislike Python a lot less than they do R). The main reasons that developers hate it is that it is very unlike almost all other languages that they're…
I had some Matlab experience about 3 decades ago. What's you take in Matlab vs R as programming languages?
Not the grandparent. Matlab is much simpler for matrices than R, approaching Python's numpy is ease of use (and very reminiscent of Fortran and Julia).
Re: One Year with R
#28I was expecting a rant, but the OP's article is actually very thoughtful. He definitely knows what he's talking about.
The thing about R, for me and many others, is that it's very much an everyday grind language. Especially with Rstudio, its natural domain is as one of "notebook" languages like python, julia, matlab, and mathematica but with a more clear focus towards the tasks of data-analysis. I just tell the BI-tool people that R is excel on 'roids.
R frustrates me a lot, however. But I think the frustration comes out of the fact that when I am using R and get stuck, I am always in the middle of doing something that I need to get done and I don't feel like diving into a long "vignette". Moreover, the documentation is usually too terse and generalized for me to just understand it immediately. Even though I've been using R for years (albeit in fits and starts rather than continuously), there are things about it that I've just never picked up-- I just DON'T KNOW (or care) what F S3 and S4 mean. Unlike the OP, who clearly knows more R than myself, I grit my teeth when I am looking at docs and see the "..." in the arg list.
I suspect that this is part of the heritage from R's beginnings. I once tried to read John Chamber's book but found the presentation complete ass-backwards and impractical for my immediate needs. The Tidyverse has been great, it's far more consistent and ggplot is a kick-ass tool to have in your box. The drawback is that it makes Base-R seem really alien and if you want to be good at R, you have to know more than just the Tidyverse, IMHO.
Re: One Year with R
#29I love R more than any other language I have ever used. Perhaps more than any piece of software I've ever used. All of these points are valid, and yes, it's messy, and if you try to write the same type of code that you would in Python, it will frustrate you. And yet.. it somehow works. It makes data analysis and statistical modelling a pleasure. It somehow gives off a sense of lightness, and makes it easy to investig…
The main issue I've had is speed. As soon as you have problems that can't be vectorized, models that take 30 hours to run in R take 30 minutes in python.
Re: One Year with R
#30Long-time R user here. Yes, many of these points are valid but I still think R is unbeaten when it comes to speed in (tabular) data exploration. In the article you mention that you missed using data.table - a significant portion of the problems you named would be solved or at least weakened by using data.table. Started working with it many years ago and never looked back. It's easy, powerful and efficient to use. I a…
.loc works smashingly. %>%?