Design Flaws in R #3 — Zero Subscripts

Unlike the two design flaws I posted about before (here, here, and also here), where one could at least see a reason for the design decision, even if it was unwise, this design flaw is just  incomprehensible.  For no reason at all that I can see, R allows one to use zero as a subscript without triggering an error.  (Remember that in R, indexes for vectors and matrices start at one, not zero.)

This is of course a terrible decision, because it makes debugging harder, and makes it more likely that bugs will exist that have never been noticed.

R Design Flaws #1 and #2: A Solution to Both?

I’ve previously posted about two design flaws in R. The first post was about how R produces reversed sequences from a:b when a>b, with bad consequences in “for” statements (and elsewhere). The second post was about how R by default drops dimensions in expressions like M[i:j,] when i:j is a sequence only one long (ie, when i equals j).

In both posts, I suggested ways of extending R to try to solve these problems. I now think there is a better way, however, which solves both problems with one simple extension to R. This extension would also make R programs run faster and use less memory.

Design Flaws in R #2 — Dropped Dimensions

In a comment on my first post on design flaws in the R language, Longhai remarked that he has encountered problems as a result of R’s default behaviour of dropping a dimension of a matrix when you select only one row/column from that dimension. This was indeed the design flaw that I was going to get to next! I think it also points to what is perhaps a deeper design flaw.

Design Flaws in R #1 — Reversing Sequences

The R language for statistical computing has become the standard for academic statistical research, for the very good reason that it’s better than the alternatives. It’s far from perfect however. I could come up with a long “wish list” of desired features it lacks, but that’s not what I’ll do in this series of posts. Instead, I’ll concentrate on real design flaws — aspects of the language that make it all too easy to write unreliable programs. Many of these are inherited from R’s predecessors, S and S-Plus, and may even originate in the very earliest version of S, which was seen more as a calculator than as a programming language.

By far the most commonly-encountered design flaw in R is in the “:” operator for producing sequences, whose most common use is in “for” statements. Here’s an example: