Warning: what follows is a somewhat technical discussion of my love-hate relationship with the R statistical language, in which I somehow manage to waste 2,400 words talking about a single line of code. Reader discretion is advised.

I’ve been using R to do most of my statistical analysis for about 7 or 8 years now–ever since I was a newbie grad student and one of the senior grad students in my lab introduced me to it. Despite having spent hundreds (thousands?) of hours in R, 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.

That said, if I’ve learned one thing about R, it’s that R is all about flexibility: almost any task can be accomplished in a dozen different ways. I don’t mean that in the trivial sense that pretty much any substantive programming problem can be solved in any number of ways in just about any language; I mean that for even very simple and well-defined tasks involving just one or two lines of code there are often many different approaches.

To illustrate, consider the simple task of selecting a column from a data frame (data frames in R are basically just fancy tables). Suppose you have a dataset that looks like this:

In most languages, there would be one standard way of pulling columns out of this table. Just one unambiguous way: if you don’t know it, you won’t be able to work with data at all, so odds are you’re going to learn it pretty quickly. R doesn’t work that way. In R there are many ways to do almost everything, including selecting a column from a data frame (one of the most basic operations imaginable!). Here are four of them:

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’).

This type of flexibility enables incredibly powerful, terse code once you know R reasonably well; unfortunately, it also makes for an extremely steep learning curve. You might wonder why that would be–after all, at its core, R still lets you do things the way most other languages do them. In the above example, you don’t have to use anything other than the simple index-based approach (i.e., data[,1]), which is the way most other languages that have some kind of data table or matrix object (e.g., MATLAB, Python/NumPy, etc.) would prefer you to do it. So why should the extra flexibility present any problems?

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–and nothing is more frustrating to a newbie when learning a language than trying to figure out why sometimes people select columns in a data frame by index and other times they select them by name, or why sometimes people refer to named properties with a dollar sign and other times they wrap them in a vector or double square brackets. There are good reasons to have all of these different idioms, but you wouldn’t know that if you’re new to R and your expectation, quite reasonably, is that if two expressions look very different, they should do very different things. The flexibility that experienced R users love is very confusing to a newcomer. Most other languages don’t have that problem, because there’s only one way to do everything (or at least, far fewer ways than in R).

Thankfully, I’m long past the point where R syntax is perpetually confusing. I’m now well into the phase where it’s only frequently confusing, and I even have high hopes of one day making it to the point where it barely confuses me at all. But I was reminded of the steepness of that initial learning curve the other day while helping my wife use R to do some regression analyses for her thesis. Rather than explaining what she was doing, suffice it to say that she needed to write a function that, among other things, takes a data frame as input and retains only the numeric columns for subsequent analysis. Data frames in R are actually lists under the hood, so they can have mixed types (i.e., you can have string columns and numeric columns and factors all in the same data frame; R lists basically work like hashes or dictionaries in other loosely-typed languages like Python or Ruby). So you can run into problems if you haphazardly try to perform numerical computations on non-numerical columns (e.g., good luck computing the mean of ‘cat’, ‘dog’, and ‘giraffe’), and hence, pre-emptive selection of only the valid numeric columns is required.

Now, in most languages (including R), you can solve this problem very easily using a loop. In fact, in many languages, you would have to use an explicit for-loop; there wouldn’t be any other way to do it. In R, you might do it like this*:

numeric_cols = rep(FALSE, ncol(ice.cream))
for (i in 1:ncol(ice.cream)) numeric_cols[i] = is.numeric(ice.cream[,i])

We allocate memory for the result, then loop over each column and check whether or not it’s numeric, saving the result. Once we’ve done that, we can select only the numeric columns from our data frame with data[,numeric_cols].

This is a perfectly sensible way to solve the problem, and as you can see, it’s not particularly onerous to write out. But of course, no self-respecting R user would write an explicit loop that way, because R provides you with any number of other tools to do the job more efficiently. So instead of saying “just loop over the columns and check if is.numeric() is true for each one,” when my wife asked me how to solve her problem, I cleverly said “use apply(), of course!”

apply() is an incredibly useful built-in function that implicitly loops over one or more margins of a matrix; in theory, you should be able to do the same work as the above two lines of code with just the following one line:

apply(ice.cream, 2, is.numeric)

Here the first argument is the data we’re passing in, the third argument is the function we want to apply to the data (is.numeric()), and the second argument is the margin over which we want to apply that function (1 = rows, 2 = columns, etc.). And just like that, we’ve cut the length of our code in half!

Unfortunately, when my wife tried to use apply(), her script broke. It didn’t break in any obvious way, mind you (i.e., with a crash and an error message); instead, the apply() call returned a perfectly good vector. It’s just that all of the values in that vector were FALSE. Meaning, R had decided that none of the columns in my wife’s data frame were numeric–which was most certainly incorrect. And because the code wasn’t throwing an error, and the apply() call was embedded within a longer function, it wasn’t obvious to my wife–as an R newbie and a novice programmer–what had gone wrong. From her perspective, the regression analyses she was trying to run with lm() were breaking with strange messages. So she spent a couple of hours trying to debug her code before asking me for help.

Anyway, I took a look at the help documentation, and the source of the problem turned out to be the following: apply() only operates over matrices or vectors, and not on data frames. So when you pass a data frame to apply() as the input, it’s implicitly converted to a matrix. Unfortunately, because matrices can only contain values of one data type, any data frame that has at least one string column will end up being converted to a string (or, in R’s nomenclature, character) matrix. And so now when we apply the is.numeric() function to each column of the matrix, the answer is always going to be FALSE, because all of the columns have been converted to character vectors. So apply() is actually doing exactly what it’s supposed to; it’s just that it doesn’t deign to tell you that it’s implicitly casting your data frame to a matrix before doing anything else. The upshot is that unless you carefully read the apply() documentation and have a basic understanding of data types (which, if you’ve just started dabbling in R, you may well not), you’re hosed.

At this point I could have–and probably should have–thrown in the towel and just suggested to my wife that she use an explicit loop. But that would have dealt a mortal blow to my pride as an experienced-if-not-yet-guru-level R user. So of course I did what any self-respecting programmer does: I went and googled it. And the first thing I came across was the all.is.numeric() function in the Hmisc package which has the following description:

Tests, without issuing warnings, whether all elements of a character vector are legal numeric values.

Perfect! So now the solution to my wife’s problem became this:

library(Hmisc)
apply(ice.cream, 2, all.is.numeric)

…which had the desirable property of actually working. But it still wasn’t very satisfactory, because it requires loading a pretty large library (Hmisc) with a bunch of dependencies just to do something very simple that should really be doable in the base R distribution. So I googled some more. And came across a relevant Stack Exchange answer, which had the following simple solution to my wife’s exact problem:

sapply(ice.cream, is.numeric)

You’ll notice that this is virtually identical to the apply() approach that crashed. That’s no coincidence; it turns out that sapply() is just a variant of apply() that works on lists. And since data frames are actually lists, there’s no problem passing in a data frame and iterating over its columns. So just like that, we have an elegant one-line solution to the original problem that doesn’t invoke any loops or third-party packages.

Now, having used apply() a million times, I probably should have known about sapply(). And actually, it turns out I did know about sapply–in 2009. A Spotlight search reveals that I used it in some code I wrote for my dissertation analyses. But that was 2009, back when I was smart. In 2012, I’m the kind of person who uses apply() a dozen times a day, and is vaguely aware that R has a million related built-in functions like sapply(), tapply(), lapply(), and vapply(), yet still has absolutely no idea what all of those actually do. In other words, in 2012, I’m the kind of experienced R user that you might generously call “not very good at R”, and, less generously, “dumb”.

On the plus side, the end product is undeniably cool, right? There are very few languages in which you could achieve so much functionality so compactly right out of the box. And this isn’t an isolated case; base R includes a zillion high-level functions to do similarly complex things with data in a fraction of the code you’d need to write in most other languages. Once you throw in the thousands of high-quality user-contributed packages, there’s nothing else like it in the world of statistical computing.

Anyway, this inordinately long story does have a point to it, I promise, so let me sum up:

• If I had just ignored the desire to be efficient and clever, and had told my wife to solve the problem the way she’d solve it in most other languages–with a simple for-loop–it would have taken her a couple of minutes to figure out, and she’d probably never have run into any problems.
• If I’d known R slightly better, I would have told my wife to use sapply(). This would have taken her 10 seconds and she’d definitely never have run into any problems.
• BUT: because I knew enough R to be clever but not enough R to avoid being stupid, I created an entirely avoidable problem that consumed a couple of hours of my wife’s time. Of course, now she knows about both apply() and sapply(), so you could argue that in the long run, I’ve probably still saved her time. (I’d say she also learned something about her husband’s stubborn insistence on pretending he knows what he’s doing, but she’s already the world-leading expert on that topic.)

Anyway, this anecdote is basically a microcosm of my entire experience with R. I suspect many other people will relate. Basically what it boils down to is that R gives you a certain amount of rope to work with. If you don’t know what you’re doing at all, you will most likely end up accidentally hanging yourself with that rope. If, on the other hand, you’re a veritable R guru, you will most likely use that rope to tie some really fancy knots, scale tall buildings, fashion yourself a space tuxedo, and, eventually, colonize brave new statistical worlds. For everyone in between novice and guru (e.g., me), using R on a regular basis is a continual exercise in alternately thinking “this is fucking awesome” and banging your head against the wall in frustration at the sheer stupidity (either your own, or that of the people who designed this awful language). But the good news is that the longer you use R, the more of the former and the fewer of the latter experiences you have. And at the end of the day, it’s totally worth it: the language is powerful enough to make you forget all of the weird syntax, strange naming conventions, choking on large datasets, and issues with data type conversions.

Oh, except when your wife is yelling at gently reprimanding you for wasting several hours of her time on a problem she could have solved herself in 5 minutes if you hadn’t insisted that she do it the idiomatic R way. Then you remember exactly why R is the master troll of statistical languages.

* R users will probably notice that I use the = operator for assignment instead of the <- operator even though the latter is the officially prescribed way to do it in R (i.e., a <- 2 is favored over a = 2). That’s because these two idioms are interchangeable in all but one (rare) use case, and personally I prefer to avoid extra keystrokes whenever possible. But the fact that you can do even basic assignment in two completely different ways in R drives home the point about how pathologically flexible–and, to a new user, confusing–the language is.