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A newcomer’s (angry) guide to R

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Re: A newcomer’s (angry) guide to R

#51

Post by curiousgal »

natalyarostova wrote: post #10
bunderbunder wrote: post #7

Earlier quoted context omitted.

> At any rate, at least it's not Pandas and matplotlib... There's a certain yin and yang to the space, isn't there? You get to choose between a hacky language that has pretty good tooling built on top of it, or a pretty good language with hacky tooling built on top of it. I think that Python is probably winning because being a decent language gives you a decent escape hatch, whereas no amount of great libraries can s…

Pandas has a lot of defensive copying. I love Pandas, but I think it demands a lot from the user. When I started using it I was new to traditional coding (i.e. knowing anything about datastructures), and have come from R. Over time as I've learned a real amount about legitimate data structures, I've become far far better with Pandas.

What data structures do you recommend learning about?

Re: A newcomer’s (angry) guide to R

#52

Post by edtechdev »

dxbydt wrote: post #34

I don’t hate R as much as I hate universities insisting on mandatory use of R for all comp-stat courses. This to my mind verges on civil rights infringements. Bear in mind we aren’t talking about a private institution such as a company, where when you sign on as a dev for a paycheck, you do so voluntarily, knowing that the company uses X language and you won’t have a choice in the matter. In a public university, stud…

It's better than forcing students to pay for proprietary statistical analysis tools like SPSS, etc.

Re: A newcomer’s (angry) guide to R

#53

Post by curiousgal »

JVerstry wrote: post #16

I learned R coming from Java, Node, PHP and Python and I love it !!! It is awful as an application development programming language, but it was never designed for that purpose. It was designed for STATISTICS. Try to achieve advanced statistics with your traditional software engineer's preferred language and see which language you hate then. The only tricky R concepts to learn for newbies are: recycling, formulas and…

Any recommended ressources for Learning those concepts?

Re: A newcomer’s (angry) guide to R

#54

Post by curiousgal »

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?

Re: A newcomer’s (angry) guide to R

#55

Post by Wolog »

cwyers wrote: post #5
zaphar wrote: post #4

Earlier quoted context omitted.

He's not testing for equality. He's assigning to a variable.

He's saying to use = instead of <- because it won't let you assign to a variable there. But that's because it assumes you mistyped the equality operator. The only reason you need safety there is because it's easy to forget that == is the equality operator, not =. It's not easy to confuse == and <-.

The danger is that you can intend to type

  if(b
and instead type

  if(b
The latter always evaluates to true and assigns the value you're trying to compare with to your variable. This can be extremely difficult to catch and detect, especially for people who aren't software developers. They aren't writing unit tests.

Re: A newcomer’s (angry) guide to R

#56

Post by curiousgal »

edtechdev wrote: post #52
dxbydt wrote: post #34

I don’t hate R as much as I hate universities insisting on mandatory use of R for all comp-stat courses. This to my mind verges on civil rights infringements. Bear in mind we aren’t talking about a private institution such as a company, where when you sign on as a dev for a paycheck, you do so voluntarily, knowing that the company uses X language and you won’t have a choice in the matter. In a public university, stud…

It's better than forcing students to pay for proprietary statistical analysis tools like SPSS, etc.

There are running jokes amongst stats students about those who resort to using SPSS and EViews. It's mostly because of the GUI as most of the students use SAS (proprietary and expensive) as well as R.

Re: A newcomer’s (angry) guide to R

#57

Post by yodsanklai »

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 until they don't, and then you need to understand all the weirdness of R.

I don't know what's the typical experience of a non-programmer with R, but as a programmer, I had some headache trying to understand R semantics (apparently I'm not the only one [1]).

[1] http://r.cs.purdue.edu/pub/ecoop12.pdf

Re: A newcomer’s (angry) guide to R

#58

Post by roenxi »

riskneutral wrote: post #45
bokstavkjeks wrote: post #32

Earlier quoted context omitted.

It's also worth noting that R becomes much more pleasurable with the Tidyverse libraries. The pipe alone makes everything more readable. I'm also coming from more of an office setting where everything is in Excel. I've used R to reorganize and tidy up Excel files a lot. Ggplot2 (part of the Tidyverse) is also fantastic for plotting, the grammar of graphics makes it really easy to make nice and slightly complex graphs…

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 using a language with actual data structures (and maybe one that is a bit easier to parallelism than R). The vast bulk of the work done in R is not going to be time sensitive but is going to be very vulnerable to small bugs corrupting data values.

Tidyverse, and really anything that Hadley Wickham is involved in, should be the starting point for everyone who learns R in 2018.

> languages like R and MATLAB that were designed for data frames and matrices

Personal bugbear; the vast majority of data I've used in R has been 2-dimensional, often read directly out of a relational database. It makes a lot of sense why the data structures are as they are (language designed a long time ago in a RAM-lite environment), but it is just so unpleasant to work with them. R would be vastly improved by /single/ standard "2d data" class with some specific methods for "all the data is numeric so you can matrix multiply" and "attach metadata to a 2d structure".

There are 3 different data structures used in practice amongst the R libraries (matrix, list-of-lists, data.frame). Figuring out what a given function returns and how to access element [i,j] is just an exercise in frustration. I'm not saying a programmer can't do what I want, but I am saying that R promotes a complicated hop-step-jump approach to working with 2d data that isn't helpful to anyone - especially non-computer engineers.

Re: A newcomer’s (angry) guide to R

#59

Post by thousandautumns »

dxbydt wrote: post #34

I don’t hate R as much as I hate universities insisting on mandatory use of R for all comp-stat courses. This to my mind verges on civil rights infringements. Bear in mind we aren’t talking about a private institution such as a company, where when you sign on as a dev for a paycheck, you do so voluntarily, knowing that the company uses X language and you won’t have a choice in the matter. In a public university, stud…

Because then students start using 3 or 4 different languages, can't figure something out, and come bitching to the TA to tell them what they did wrong, and now the TA needs to know 5 different ways to implement the same thing.

Ironically, you complain about wanting to learn statistical concepts, and not a language, but by focusing on a single language, they can minimize the amount of time spent on agonizing over lines of code trying to get to the same answer, and instead focus on the actual statistical problem.

Its also important to note that there are lots of potential pitfalls for people who don't know about them. For example, scikit-learn is the most popular python package out there for most data analysis work. But I would bet my ass that at least 50+% of its users don't realize that the logistic regression implementation implements L2 regularization by default, and in fact there is no (non-hackish) way of implementing non-penalized logistic regression. So you will be getting completely different answers than someone who would be using R, SAS, SPSS, etc. And the only way for someone to know this is for someone to understand the implementation of the function in all possible languages.

Re: A newcomer’s (angry) guide to R

#60

Post by jakubp »

wdkrnls wrote: post #50

I don't understand why HN hates R. HN loves lisp, and R as a language shares a much greater affinity with lisp languages than python or Go do. The language was born out of the original authors reading SICP (as statisticians). Sure, many of the users of R molded it to look like what they were used to (S), but that just highlights the powerful metaprogramming capabilities of the language.

I won't speak for HN, but here's a tongue in cheek summary of my experience with R.

I write a script. It doesn't work. I don't know why. I look at the error message, and then google for 30 minutes to understand what it really means - which parts of the code broke, why, how to fix them. Because none of the 3 things (which, why, how) is easy to get to.

OK, I fix it, having learned something new (like that there are infinite special cases with almost any functions).

I commit it to repo, go for coffee. In the afternoon, a colleague asks how to run that code. Well, it was a simple script, half a page, what's the problem?

I take a look, and on their machine it doesn't run. We don't know why. An hour later we discover she has some R profile file with a setting that changes behavior of some standard library... and she also has different encoding set as default, and so on, and so forth... whatever. I don't know why runtime environment encoding changes behavior of code that only deals with numbers, but hey! It's interesting at least. We fix it, we are happy.

A few days later I run the script again. It works. The result doesn't look right though. It's mostly zeroes. Hmm.

I run it a few more times, playing around with input, trying to figure out what's up.

OK, after a few minutes I realize there's lots of red color that flashes on running the script on my screen - just so fast I barely see it.

It turns out half the code isn't really running, the script just ignores it though (errors do NOT stop the code from running), and keeps going. It produces partial output happily announcing it finished.

That is the most serious mindfuck. Everything is OK, says the prompt, here's your 1 megabyte result of the calculation, oh, just don't look at the numbers, because I havent' really run any of the code... I couldn't find one of the functions.

I sit there wondering. Which is worse: the fact that every time I try launching the script something else is happening, or the fact that the runtime environment by default will return garbage with NO warning at the end (which is the only thing you see on screen) but with a million warnings in between (which you won't see unless you have really good reflexes...).

Which is worse?

I decided at some point, that I want a language to fail, and to always give me the same result. An error, an exception, this should kill the program and shout as loud as possible "Won't give you anything". Also I want code that ran yesterday to run today, and to run on my colleague's machine, and on a newer version of R. This was never our experience.

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