R, the master troll of statistical languages (2012)

http://www.talyarkoni.org/blog/2012/06/08/r-the-master-troll-of-statistical-languages/

by misiti3780 • 13 years ago

66 69 13 years ago

tocomment2• 13 years ago

So why not just use Python these days?

Use both and you'll see. Python can do modeling, can do exploratory work and is great in production. R is brilliant at exploratory work and modeling. Having a core in functional programming helps too.

tocomment2• 13 years ago

Actually I just finished my first week working in R. And I could have done the same amount of work in Python in an hour :-(

It's like every line I wrote there was some catch or trick I had to know just to get it to work. Pretty frustrating.

rm9994• 13 years ago

At this point, mostly library support. Python is quickly getting closer to being a viable R replacement, but it's simply not there yet IMO. The two biggest holes in python for me are:

* The lack of built-in dataframes and libraries to work on them (like plyr). Pandas seems to be getting pretty good, but it's still not as mature as R's solutions.

* Visualization. ggplot for R is great, matplotlib for python not so good IMO. I've heard Bokeh and rplot are attempting to bring ggplot functionality to python. Again, not nearly as mature as R's solution.

I'd love to move to Python because R is not a fun language to develop software in. But at this point, R is by far the better tool for working with data (for my needs at least).

elyase• 13 years ago

Any specific example on what pandas is lacking in comparison to R packages?

hadley6• 13 years ago

I'd love to hear what makes R not-fun to develop in. I'm always on the look-out for common pain points.

misiti3780• 13 years ago

I agree, I work in python mostly but started doing all my exploratory + simple regressions in R just because of ggplot. I think eventually, matplotlib + ipython + numpy + scipy + pandas will be a great competitor, but they really need to improve matplotlib, it's no where near ggplot yet

minimaxir2• 13 years ago

I don't know how Python handles it, but R uses vector operations for almost every function, meaning that for large data sets (10000+), the efficiency of data manipulation in R is always O(1), and may otherwise be O(n) in Python.

I have a function in R that given a list of keywords, for each word (~100 keywords), it checks a data frame that stores a list of keywords (~5000 lists of keywords, ~10 keywords/list) to see which keywords matches the keywords in each row of the data frame.

Efficiency using straight for loops would be O(mno) and could take awhile. But in R, all those operations are vectors, and it takes less than a second.

teraflop• 13 years ago

"Vector operations" cannot magically turn an O(n) operation into an O(1) operation, no matter what language you're using.

pyoung• 13 years ago

I imagine your mileage may vary depending on the functions and operations that you are performing. I did a simple test of python vs R and python was the overwhelmingly faster. I believe both languages are primarily written in C, and as such, the speed of your program will depend on the 'efficiency' of the underlying code for the functions you are using.

guylhem• 13 years ago

This is the very same critic that was (and still is) made about perl.

>> I won’t bother to explain all of these; the point is that, as you can see, they all return the same result (namely, the first column of the ice.cream data frame, named ‘flavor’).

Having many ways to do one thing is good - whatever float your boat you know. Just for the given examples, I see how one may be better in a loop (the one where you use 1) while another one may be better when you type it on the command line to check stuff (the one with flavour)

>> The answer is that when you’re trying to learn a new programming language, you typically do it in large part by reading other people’s code–

No. You learn by doing. You write some stuff, test it, and if you don't get the result you expect go back to try and figure out what's wrong.

And even before doing that, you set aside some time to LEARN about the language.

But maybe, just maybe, the problem is not with the tool but with the person using it?

>> I have to confess that I’ve never set aside much time to really learn it very well; what basic competence I’ve developed has been acquired almost entirely by reading the inline help and consulting the Oracle of Bacon Google when I run into problems. I’m not very good at setting aside time for reading articles or books or working my way through other people’s code (probably the best way to learn), so the net result is that I don’t know R nearly as well as I should.

At least that's honest. Maybe you don't do well with a language (any language!) because huh, you didn't take time to study it and expect it to magically work??

I know I'm no good in java, but I also know why - I never took some time to actually learn it. I can do things with java, but not very complex things, and when I fight my way out of a problem I caused, I won't blame java but myself and my lack of knowledge of java.

pekk• 13 years ago

R has great capabilities. But it is not great for encouraging readability. For learners, it tends to feel confusing and lawless. It requires an investment. This is a significant fact about R. It is not equally true of all languages. It isn't ideal, either. We can do better. But for years there was nothing comparable in open source (perhaps even outside open source). That is also true of Perl.

Cowen• 13 years ago

> This is the very same critic that was (and still is) made about perl.

It's funny how similar the arguments are.

They can both be described as a language with some serious gotchas, but that's simple enough and has such a great package collection that it's easy to get a lot of things done very quickly.

That description could also fit Javascript.

RockyMcNuts• 13 years ago

Perl is a language that pursued extreme flexibility and programming-style agnosticism as a design goal...that made it less than ideal for large scale software engineering...but enabled a lot of experimentation with powerful, sometimes sweet and expressive and sometimes obfuscated idioms.

R is a bit of a train wreck. It makes hard things easy (if you know the right library or idiom), but often makes easy things hard.

Perl is the duct tape of languages, R is more like the A-10 Warthog, ugly but powerful for a specific job.

Locke16897• 13 years ago

No. You learn by doing. You write some stuff, test it, and if you don't get the result you expect go back to try and figure out what's wrong.

No. This is how you learn to do whatever simple things you want in a language. The way you learn how to do things correctly is mainly by reading code. If you have an expert to review your code that's better, but almost no one has that opportunity.

Edit:

I guess I should come right out and say what I'm thinking: not everyone's opinions about a programming language are equal. I suppose this is why people are constantly having discussions about the pluses and minuses of "dynamically typed" languages, while type theorists don't even recognize "dynamic typing" as a form of typing. Expert analysis has consistently shown problems with the R language design and implementation. It's not a good language. Its features are often misused or poorly used and it doesn't have a strong sense of what support it wants to give to its users.

http://channel9.msdn.com/Events/Lang-NEXT/Lang-NEXT-2012/Why...

http://www.cs.purdue.edu/homes/jv/pubs/ecoop12.pdf

jasonpbecker• 13 years ago

R works really remarkably well and clearly for folks who work in this domain day to day. It's easier to learn than a lot of other languages for people who are coming from subject knowledge and need to program to implement that knowledge.

It may be a terrible language by some objective external computer science criteria. But as a domain specific language, that's not really important. What is important is that the domain specific aspect works well for people with domain knowledge.

It's not the right tool for every job or every person, but it's incredibly expressive for people working with a specific set of problems.

bcoates• 13 years ago

How do type theorists distinguish Forth from PHP from Haskell without recognizing "dynamic typing" as a concept?

TrevorFancher2• 13 years ago

I've been interested in type theory for a while, but haven't found a good avenue for getting into it.

Could people here list any resources on type theory that comes to there mind? Books, blogs, people, etc. A book that explains the fundamentals would be great.

hadley6• 13 years ago

The second link is relevant but only speaks to the performance of the language, which is something fixable in the implementation. Personally, I think the R language is pretty nice, and well suited to its domain. It has first-class functions and first-class environments, and an object system inspired by common lisp and Dylan. While not homoiconic, it's pretty easy to compute on the language. Statistically, having support for missing values built in at a very low level is important.

But maybe you just don't like "dynamically typed" languages?

vsync• 13 years ago

>>> I won’t bother to explain all of these; the point is that, as you can see, they all return the same result

> Having many ways to do one thing is good - whatever float your boat you know.

I can't get the article itself to load, but it cracks me up that foo[range] and foo[range, ] do completely different things.

jimmar• 13 years ago

I think R's biggest strength of R is the package ecosystem. I would not trade the large community of active package developers for slightly friendlier syntax.

carterschonwald3• 13 years ago

Indeed, the ecosystem of actively deved numerical libs is the only upside to R over other tools. It's a pretty large cliff for any other tool chain to climb over.

--someone who's writing numeric/ data analysis tools in Haskell.

pilgrim6892• 13 years ago

Off-topic but: Can you comment on the significance of this cliff for a team considering moving from R to Haskell for data analysis? Is the availability for statistics packages really sparse in Haskell?

th0ma5• 13 years ago

The package management is weird. It makes sense, but it is weird they go into more detail with dependencies like "enhances" or "enhanced by" and other stuff. It is a more complex graph, and lends more to discovery, which is cool, but it through me for a loop for a second. You can still (after a bit of perusing the model) figure out what concrete dependencies are, but there are some packages that do some circular weirdness. There is also still somewhat of a binaries problem for the various flavors of Mac, Windows, Linux. You can get source for most things, but bubbling up of system dependencies you're on your own like other platforms, although Java & Maven seem to have a solution for distributing some binaries a little better possibly...

Gravityloss2• 13 years ago

The problem is that here the "generic" tools like "apply" are not really generic. Hence you can not use your intelligence and creativity to deduce a working program using a small toolset that you know fully.

Instead you must use a huge number of special tools that do only a few things. The code is hard to read, hard to write, slow, the compiled version is big. It is also error prone since you must use a large number of different paradigms. Some might have their arguments slightly differently. It's produced by googling for everything (or if there's a decent builtin help system, using that).

It seems strange that such concepts, like generic data types, operators and functions are not more widely spread. For example if you can calculate "max(a-b)" even if "a" is a matrix and "b" is a scalar, that's already quite nice. This can increase productivity a lot.

Since such design models are so fundamental, they are rarely found in "matlab-like" extensions for other programming languages: instead you must constantly do type conversions and transformations by hand and use awkward middle man functions to access data types. Most of your code is housekeeping and little actual progress.

See for example the problems with Julia where column and row vectors are totally different types: http://2pif.info/op/julia.html

pygy_2• 13 years ago

> See for example the problems with Julia where column and row vectors are totally different types: http://2pif.info/op/julia.html

That was one year ago. They are still different types, but they behave as you would expect wrt arithmetics.

    julia> x = [1,2,3]
    3-element Int64 Array:
     1
     2
     3

    julia> y = [1 2 3]
    1x3 Int64 Array:
     1  2  3

    julia> y'
    3x1 Int64 Array:
     1
     2
     3

    julia> x + y'
    3x1 Int64 Array:
     2
     4
     6

Their size and dimensions are still different, though, but it is mostly a concern for library writers.

    julia> ndims(x)
    1

    julia> ndims(y)
    2

    julia> ndims(y')
    2

    julia> size(x)
    (3,)

    julia> size(y)
    (1,3)

See https://groups.google.com/forum/?fromgroups=#!msg/julia-dev/... for the discussion that resulted from that post.

Edit: I should add that there's now a mailing list dedicated to statistics with Julia: https://groups.google.com/forum/#!forum/julia-stats

Gravityloss2• 13 years ago

Thanks, I actually went and tried myself as well and saw the same. So quite good though not yet fully "symmetric".

  >> z=size(a)
  (3,)
  
  >> z[2]
  BoundsError()

alok-g• 13 years ago

This is pretty much the only thing that I do not like about Julia. Matlab also uses this concept, and I have had my Matlab code broken several times because of this issue.

tel• 13 years ago

I was totally unable to learn R until I discovered plyr, which, goes a long way to making those operations more generic.

disgruntledphd2• 13 years ago

Plyr is wonderfully expressive, but it breaks down when you have hundreds of levels of a factor. tapply, on the other hand keeps trucking on up to a couple of thousand with very few problems. Hadley says that he may use data.table for plyr2 though, which would make my life a lot better.

jamesjporter• 13 years ago

The metaphor I use to explain R is that most programming languages are like different varieties of swiss army knife: general tools that can do a lot of different stuff with relative ease. R, on the other hand, is like a fillet knife: its totally obtuse for most things, but in spite of its oddities it outshines all other choices at one specific task (statistical data analysis / filleting a fish respectively). Depending on what sort of camping trip you're going on you might want to bring one or the other or both.

minimax• 13 years ago

R has a rich heritage of trolling unsuspecting programmers coming from other languages. In ancient versions of R from the early 2000s, the underscore operator was synonymous for '<-', aka assignment. This is why in R some use periods for separators (ice.cream) rather than underscores (ice_cream), though just to make things more lulzy some package authors have started to use Java style camel case (iceCream) too.

I saw an example of R code using underscore for assignment posted on twitter a couple of months ago. See if you can make heads or tails of this: http://www.stat.washington.edu/hoff/Code/hoff_raftery_handco...

pygy_2• 13 years ago

> This is why in R some use periods for separators (ice.cream) rather than underscores (ice_cream)

... and since the dot is legal in identifiers, they use $ as lookup operator.

    > ice.cream$flavor
    [1] "chocolate"    "vanilla"    "butterscotch advocado surprise"

rabz• 13 years ago

> In ancient versions of R from the early 2000s, the underscore operator was synonymous for '<-', aka assignment.

Huh, early Smalltalk mapped the left arrow to the underscore key; I'm not sure if Squeak and Pharo still do it, though.

camus• 13 years ago

let's stop using the word 'troll' at this point ok ? it's clearly an insult. doesnt make sense to call a language that way.

mwexler• 13 years ago

R is a great set of statistics wrapped in a crime of a programming language. You have to fight the latter to get to the former. Is the fight worth it? I and many others say yes... but it sucks that we even have to make that decision.

dnc• 13 years ago

When I think about R I find it difficult to stop making analogies between it and JavaScript. Both are dynamic, "script" languages with C-based syntax and with LISP-like nature that lurks underneath it. Both have functions as first class citizens. Both are inconsistent in different ways. Just to name a few that first come to my mind ... I think that these analogies reduced frustration that I had felt about R once when I "discovered" them and helped me to adopt and learn R.

rck• 13 years ago

That makes sense - they were both inspired by Scheme (R is in many ways just a Lisp that uses M-expressions), but evolved to deal with immediate concerns that, in retrospect, have created difficulties as the years have gone by. It makes R and JavaScript great and frustrating at the same time.

alexholehouse• 13 years ago

Language issues asside, R is being propelled by an awesome group of developers (such as Hadley Wickham, Dirk Eddelbuettel, John Myles White, Brian Ripley etc). These people are why R is as successful as it has become - both through continued work on various aspects and packages and direct interaction with users new and old. Frankly, there should be some kind of slightly awkward parade for them all.

cschmidt2• 13 years ago

I'd say that R is awesome, and that R is a fairly annoying language, all at the same time. (I'll go back to writing some R code now.)

gmac• 13 years ago

R has a million related built-in functions like sapply(), tapply(), lapply(), and vapply()

Exactly that. I've always found R a horribly confusing mess, compared to general programming languages (I'm proficient in Ruby, JS, Obj-C).

On the other hand, compared to some of the other commercial stats packages, it's beautiful and logical and reasonable. I regularly use Stata, where you're only allowed one data table in memory at once, and almost everything relies on side effects and Byzantine macros. E.g want to calculate a mean? First, 'summarise' the variable, then assign 'r(mean)' to a var name, then quote that the right way to be substituted into an expression where it's needed.

WayneDB• 13 years ago

This was actually a fantastic introduction to an important aspect of R which I had only briefly explored in the past.

R, the master troll of statistical languages (2012)

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