ITT:
people who don't understand R complain about R.
R, the master troll of statistical languages (2012)
81–90 of 154 posts
Re: R, the master troll of statistical languages (2012)
#82At my previous job we used to play "Guess what R does" over lunch. Someone would write a few R statements and we'd have to guess the output. Extremely difficult! >> a=c(1,2,3,4) >> b=c(1,2) >> a+b Any guesses?
2, 4, 4, 6. The second vector is recycled along the first one:
1 + 1, 2 + 2, 3 + 1, 4 + 2.
Re: R, the master troll of statistical languages (2012)
#83Earlier quoted context omitted.
I've translated plenty of numerical code from (pure-ish) python to c and c++, and usually get about a 100x speedup, sometimes as high as 800x, implementing the same algorithms.
At the risk of being overly pragmatic, note that I said "fast enough" and not "as fast as possible." My comment was more on the perception that python is unworkably slow in many situations, where I can count the number of times on my hands that I've NEEDED to C-ify some hot paths. If you're writing a plasma fluid simulation to run on a HHPC cluster, yes, you probably damn well want some straight C/C++. Outside of sim…
> Outside of similarly exceedingly high throughput situations, CPUs are normally more than fast enough, especially if the application in any way brushes up against people and thus falls into "human time" scales, in which case you'd typically be hard pressed to make things slow enough for someone to notice.
This has simply not been my experience. (In a previous job I had reasonably optimized numerical python code sitting on the back end of an api and it was incredibly easy to go over our time budget).
Re: R, the master troll of statistical languages (2012)
#84At my previous job we used to play "Guess what R does" over lunch. Someone would write a few R statements and we'd have to guess the output. Extremely difficult! >> a=c(1,2,3,4) >> b=c(1,2) >> a+b Any guesses?
+ is a vector operation, and does it elementwise. R recycles vectors. You get warnings if there are elements left over. This is something you should know within the first 5 minutes of learning the language, hopefully?
Re: R, the master troll of statistical languages (2012)
#85Earlier quoted context omitted.
At the risk of being overly pragmatic, note that I said "fast enough" and not "as fast as possible." My comment was more on the perception that python is unworkably slow in many situations, where I can count the number of times on my hands that I've NEEDED to C-ify some hot paths. If you're writing a plasma fluid simulation to run on a HHPC cluster, yes, you probably damn well want some straight C/C++. Outside of sim…
> Outside of similarly exceedingly high throughput situations, CPUs are normally more than fast enough, especially if the application in any way brushes up against people and thus falls into "human time" scales, in which case you'd typically be hard pressed to make things slow enough for someone to notice. This has simply not been my experience. (In a previous job I had reasonably optimized numerical python code sitt…
For what it's worth, I believe you; I'd be curious what the workload was / what the time window was, if you're able to say?
I could certainly see myself as having been spoiled with respect to beefy hardware and feasible workload/SLA ratios, but it's lead me to a prior where I take the age old advice against premature optimization pretty aggressively. (Starting projects in python, naive brute force implementations for a first pass, readability over a better O(N), etc)
Re: R, the master troll of statistical languages (2012)
#86I'm a Post-Doc in a small social sciences department in a major university, and am probably the department's ranking R-geek. I did my dissertation, and much of my current work, doing modeling, analysis, and even machine learning in R. In many ways, I owe much of my success to the power that R has allowed me to wield. Multicore lapplys and ggplot2 are my life these days. But even with this, R drives me absolutely batt…
I am in an almost identical situation, just still in the PhD stage.
I moved from Matlab (originally a mechanical engineer) and the biggest shock was documentation and just the internal help stuff in general. The help files on a regular basis require you to understand how something works to understand the thing explaining how it works.
I am often just shocked at little quirks I find trying to do things in R, not that it is worse than SATA of SAS but the goals was to be better. I am all for FOSS, and R provides many extensive capabilities not available in Matlab, but in terms of being user friendly Matlab is so superior it is honestly sad. Oh and '<-' just drives me nuts...I will never understand the choice of two characters where one is entirely sufficient.
Re: R, the master troll of statistical languages (2012)
#87My own personal rant, I think the specific feeling I get is the conceptual idea of R has long since outpaced the reality of R. People like to fetishize data, and R sure lets you do that. The data science landscape however is growing such that R is really just a one-trick pony, however, that one trick is for better or worse being the gold standard of statistics and modeling, somehow. But everything else wants to sugar…
I'd divide my work into three categories
- Exploratory plots just for me.
- Plots that I'm going to show to my boss or coworkers.
- Things we're going to distribute to the whole world.
Plots in category #1 are often quick and dirty--I just want to see if an idea worked and don't really care about communicating that idea cleanly.
I could show these plots internally, but it often helps to clean them up a bit first. This avoids us getting bogged down in whether we should be comparing the red/blue lines here or the circle/diamond points there.
This is where ggplot shines--I can go from #1 to #2 with minimal effort. The final version usually still needs some tweaking, but only a small fraction of the plots ever get this far and some of this customization really needs a human in the loop (e.g., in Illustrator or something).
Similarly, while you can use Shiny as final product, it's actually great for letting moderate-sized groups play "what-if" with the data. It's certainly easier than sending them a huge powerpoint deck with "choose your own adventure" style instructions.
Re: R, the master troll of statistical languages (2012)
#88Earlier quoted context omitted.
R does actually ship with the ability to byte-compile functions these days, and as that functionality matures it may become the default behavior. It's still better to actually learn the language; it's far easier to optimize something like: apply(X, 1, function(x){ # do stuff to the row of X }) than: for (i in 1:nrow(X)){ # do stuff to X[i,], and store it somewhere }
As far as I know byte-compiling won’t actually alleviate the repeated name lookup (or does it?). Unless the R byte compiler is fiendishly clever, every single name lookup in the loop body will still incur what essentially amounts to a `get(name, environment(), inherits = TRUE)` call.
Probably not, but I'll admit to not having dug into it too deeply. In my initial experiments, I found only modest speed gains when byte compiling. Then again, I'm already using C functions wherever possible.
Re: R, the master troll of statistical languages (2012)
#89Yes, I'm well aware of R's many faults, I have my own long list of R caveats I hand to new hires, but not bothering to learn the damn language is no reason to complain about it. First, RTFM.
look, I am all for RTFM but my experience with R has been a different case in my experience. Can I make it do powerful things? yes. can I take code that wont run, copy it to a new file and have it run? yes.
1) There are just quirks in implementation that are nonintuitive. I constantly find myself doing things I would do in other languages, to do simple tasks, which fail for no good reason, albeit an obvious one once I find the right documentation.
2) The manual you describe is flat out unhelpful in many cases. The suggestions that constantly come about "check stack / google" are suggestive of just how poor that documentation is
Re: R, the master troll of statistical languages (2012)
#90I'm a Post-Doc in a small social sciences department in a major university, and am probably the department's ranking R-geek. I did my dissertation, and much of my current work, doing modeling, analysis, and even machine learning in R. In many ways, I owe much of my success to the power that R has allowed me to wield. Multicore lapplys and ggplot2 are my life these days. But even with this, R drives me absolutely batt…
I just wonder if there's rationality behind not changing this (by either switching to a different language, fixing it or, hardcore mode, create a new one). I know from a very, very different field that you often have to deal with decade(s) old technology because your employee/professor/etc is just used to it and, 15 years ago, it simply was the best option. I guess it's an equation that puts time spent learning its q…
I started in SPSS. This is so much better. And although it drives me nuts, I still spend the other half of the time working in R feeling like a goddamned wizard. So even if it takes an hour and a half some days to figure out how to turn a list of unique factors into a list for lapplying (or something else stupid), it's not bad enough.
Also, remember that I'm the R geek around. Whether it's the best tool for the job or not, in my field, R is the lingua franca for stats. I could swear off R and move to Python Stats, but I'd still be supporting R among colleagues and friends. It's hard enough to convince folks who grew up in SAS to move to R, let alone to learn Python.
Finally, I'll have a hard time convincing editors that some weird-ass python implementation of GAMs or LMER is kosher when they're barely OK with the idea of GAMs. Reviewer two is, shall we say, technologically conservative.