A newcomer’s (angry) guide to R • Hacker News Board

Re: A newcomer’s (angry) guide to R

#91

Post by maxander » Thu, Jun 14, 2018, 1:31 AM UTC

extr wrote: post #19

HN is predisposed to hate R because everyone here is coming from a "real" programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installi…

No, you are wrong. R is terrible, and especially so for non-professional programmers, and it is an absolute disaster for the applications where it routinely gets used, namely statistics for scientific applications. The reason is its strong tendency to fail silently (and, with RStudio, to frequently keep going even when it does fail.) As a result, people get garbage results without realizing, and if they're unlucky, these results are similar enough to real results that they get put somewhere important. Source: I'm a CS grad working with biologists; I've corrected errors in the R code of PhD'd statisticians, in "serious" contexts.

Scientific applications require things to fail hard and often, to aggressively fail whenever anything is potentially behaving incorrectly. R does the exact opposite of that in several different, pernicious ways. IMHO, Python is more dangerous than a scientific computing language should be, but at least it will stop when it hits an error. R has undoubtedly cost humanity millions of dollars in wasted research costs and caused untold confusion, from otherwise perfectly-performed studies reporting corrupted statistical results. The world would be a noticeably better place without it.

I simply cannot articulate my opinion about R without sounding grossly hyperbolic. I'm sad that HN, a place which is typically enlightened in the ways of the programming arts, is so confused what this article is on about. If we tolerate such blatantly hostile design in something as important as the language of scientific statistics, where do we expect to get?

Re: A newcomer’s (angry) guide to R

#92

Post by extr » Thu, Jun 14, 2018, 1:34 AM UTC

yodsanklai wrote: post #57

extr wrote: post #19

HN is predisposed to hate R because everyone here is coming from a "real" programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installi…

> Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed To me, the opposite is true. People with no CS background would benefit the most from a simple design. > in R, data types are pretty fungible, everything is a vector, coercing things generally "just works". Things just work u…

I have a lot of experience teaching non-programmers R. Most of them come from an Excel/SQL background. I have found the amount of weirdness that presents a real problem is very low. And when it does get weird, I usually advise we just brush it under the rug and use a different method to accomplish the same thing. This probably sounds horrifying, but it's really not. Most of the code people write in R is not like other programming languages, it's rarely bound for anything other than the end user's laptop, if its even saved in the first place.

Re: A newcomer’s (angry) guide to R

#93

Post by buchmayne » Thu, Jun 14, 2018, 1:38 AM UTC

curiousgal wrote: post #54

andrewla wrote: post #15

As a long-time R user, I agree with all of these complaints. The language itself is ugly and actively tries to get in your way. I'll add that concepts like data frames are not really intrinsic, and you get needless complexities like "length", "nrow", "dim", each of which does the wrong thing in 90% of the scenarios of interest. The confusion of lvalues is another strange quirk -- a If you discipline yourself to learn…

>split/lapply/aggregate/ave I always feel bad when I resort to a for loop in R. Do you have any recommended ressources about mastering those specific manipulations?

https://github.com/jennybc/row-oriented-workflows

getting comfortable with purrr has been enormously helpful in consolidating my code and avoiding unnecessary loops

Re: A newcomer’s (angry) guide to R

#94

Post by educationdata » Thu, Jun 14, 2018, 1:42 AM UTC

extr wrote: post #19

HN is predisposed to hate R because everyone here is coming from a "real" programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installi…

One common thing across most the "real" programming languages makes them unfit for data work: 0-based indexing.

It is just ridiculous to call the first row in a data set as 0th row, and the last row as (n-1)th row. It does not make any sense for data analytic work.

Re: A newcomer’s (angry) guide to R

#95

Post by fermienrico » Thu, Jun 14, 2018, 1:42 AM UTC

thousandautumns wrote: post #63

This is a stupendous example of someone going overboard on their criticisms in order to grandstand. R may not be the most "beautiful" language in a general perspective, but it certainly is more beautiful than Python when it comes to actual data analysis. There is nothing in R that is as ugly as even the best implemented pandas, numpy, and matplotlib code. All of the options in Python, which is generally pointed to as…

R is a poor name, whether you can google it or not. The name can get lost in the minefield of text on the internet. Just because you never had any issues with google R does not make it any better. I have had many issues googling R and it always makes me second guess if this thread is about R language at all. On SO, I have to check if R is tagged.

R is a terrible name and it is not up for a debate. Whenever you name a product, company or in this case a language as a letter "R", you're literally asking for trouble.

Just to be fair, C is also a horrible name. On the other end of the spectrum - Julia and Rust are excellent names for a programming language because they're unique in the context of programming.

Re: A newcomer’s (angry) guide to R

#96

Post by rpier001 » Thu, Jun 14, 2018, 1:51 AM UTC

maxander wrote: post #91

extr wrote: post #19

HN is predisposed to hate R because everyone here is coming from a "real" programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installi…

No, you are wrong. R is terrible , and especially so for non-professional programmers, and it is an absolute disaster for the applications where it routinely gets used, namely statistics for scientific applications. The reason is its strong tendency to fail silently (and, with RStudio, to frequently keep going even when it does fail.) As a result, people get garbage results without realizing , and if they're unlucky,…

Have you ever worked with other major statistical packages? Have you ever caught people doing data munging in Excel? R fails far less silently than the credible alternatives. Source: I've been around the academic block and seen many types of horrors.

It's unfortunate that you've gotten to a _terrible_ feeling about R without realizing that many of the 'silent' failures are easily configured away (some examples, https://github.com/hadley/strict). That R isn't noisy about things that CS majors might think it should be by default is, BTW, entirely appropriate. Many of what one might call 'silent' failure modes in R are for the express purposes of making exploratory data analysis easier... and that was one of the original purposes for R.

Re: A newcomer’s (angry) guide to R

#97

Post by bograt » Thu, Jun 14, 2018, 1:53 AM UTC

educationdata wrote: post #94

extr wrote: post #19

HN is predisposed to hate R because everyone here is coming from a "real" programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installi…

One common thing across most the "real" programming languages makes them unfit for data work: 0-based indexing. It is just ridiculous to call the first row in a data set as 0th row, and the last row as (n-1)th row. It does not make any sense for data analytic work.

I'm not sure I understand this, and I'm genuinely interested in why it would be.

I find zero indexing logical: zero is the first natural number and is thus a fine candidate for being the first ordinal.

In my experience most mathematical series lose nothing in terms of elegance or readability by being indexed from zero instead of using more traditional indexing from one.

Re: A newcomer’s (angry) guide to R

#98

Post by extr » Thu, Jun 14, 2018, 2:10 AM UTC

maxander wrote: post #91

extr wrote: post #19

HN is predisposed to hate R because everyone here is coming from a "real" programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installi…

No, you are wrong. R is terrible , and especially so for non-professional programmers, and it is an absolute disaster for the applications where it routinely gets used, namely statistics for scientific applications. The reason is its strong tendency to fail silently (and, with RStudio, to frequently keep going even when it does fail.) As a result, people get garbage results without realizing , and if they're unlucky,…

That's too bad, I wish R could be better and easier for these people, but I don't think it warrants your hyperbole. I can point to many of my own anecdotes where R has saved millions of dollars by empowering analysts to conduct data exploration and modeling that would have been vastly more complex undertakings using any other tool. They seem to handle the silent failures just fine (usually by double checking their results before presenting them). Poor rigor and coding practices in academia are practically a meme at this point. You really want to lay all of that at the feet of R? Suggest a code review step for their publishing process or use a different tool. R is certainly not perfect but the idea that "The world would be a noticeably better place without it" is silly.

Re: A newcomer’s (angry) guide to R

#99

Post by rpier001 » Thu, Jun 14, 2018, 2:11 AM UTC

roenxi wrote: post #58

riskneutral wrote: post #45

Earlier quoted context omitted.

Tidy features (like pipes) are detrimental to performance. The best things R has going for it are data.table, ggplot, stringr, RMarkdown, RStudio, and the massive, unmatched breadth and depth of special-purpose statistics libraries. Combined, this is a formidable and highly performant toolset for data analytics workflows, and I can say with some certainty that even though “base Python” might look prettier than “base…

> Tidy features (like pipes) are detrimental to performance. Detrimental to the runtime performance; if you happen to be reading and processing tabular data from a csv (which is all I've ever used R for, I must admit), then you get real performance gains as a programmer. For one thing, it allows a functional style where it is much harder to introduce bugs. If someone is trying to write performant code they should be…

I think what you're saying is mostly on point. I wanted to share a couple possible balms for your bugbears.

For attach metadata to an anything, why not use attributes()/attr() or the tidy equivs? Isn't that what it is for?

It might not make you feel much better, but data.frame is just a special list, c.f. is.list(data.frame()). So, if you don't want to use the connivence layers for data.frame you can just pretend it is a list and reduce the ways of accessing data structures by one.

You can paper over the distinction between data.frames and matrices if it comes up for you often enough. E.g.

`%matrix_mult%` d1 %matrix_mult% d2

... but I'll grant that isn't the language default.

Re: A newcomer’s (angry) guide to R

#100

Post by rpier001 » Thu, Jun 14, 2018, 2:19 AM UTC

haberman wrote: post #67

thousandautumns wrote: post #63

This is a stupendous example of someone going overboard on their criticisms in order to grandstand. R may not be the most "beautiful" language in a general perspective, but it certainly is more beautiful than Python when it comes to actual data analysis. There is nothing in R that is as ugly as even the best implemented pandas, numpy, and matplotlib code. All of the options in Python, which is generally pointed to as…

One time I was struggling with some odd R behavior of the sort described by the author. I asked my local R expert. He told me how to fix my program, but I protested that none of it made any sense, even when explained. He didn't disagree, he just laughed and said "don't worry about it." That works great for him, he can "not worry about it" and things work because he knows all the quirks. If I just "don't worry about i…

> It seems likely that R could have been designed to have the same strengths without having so many weird and arbitrary quirks.

Not really... at least not practically. R is a successor to S which was written in 1976 (fun talk on the history: http://blog.revolutionanalytics.com/2016/07/rick-becker-s-ta...).

Many of the arbitrary quirks started out for the sake of backward compatibility with S. All that being said, yeah, you could probably design a 'modern R' without the weird and arbitrary quirks.