R pexpect interface doesn't handle errors well

[kcrisman]

For instance:

sage: r.length([1,2,3,4])
[1] 4
sage: r.vector('c(1,2,3,4,3)')
[1] 4
sage: r.length([1,2,3,4])
[1] 4
sage: r.vector('c(1,2,3,4,3)')
[1] 4
sage: r.vector('c(1,2,3,4,3)')
[1] 2
sage: r.vector('c(1,2,3,4,3)')
[1] 1 2 3 4 3
sage: r.vector('c(1,2,3,4,3)')
[1] 1 2 3 4 3
sage: r.vector('c(1,2,3,4,3)')
Error: object 'sage49' not found

Somehow the R interface is keeping stuff from previous calls and returning it, and then at some point choking. Incidentally, in the above session, after trying many other R commands this way and always getting similar errors, all of a sudden

[1] 1 2 3 4 3

showed up - as the answer to something else! Where it had been hiding, I can only guess.

[schilly]

This interface to R (which is entirely different from the rpy2 python module) is rather hard to do and still needs work. I think the problem is that "vector" in R does something differently than you imagine. I.e. an error happens which isn't shown and the pexpect interface is confused.

sage: x = r.c(1,2,3)
sage: r.as_vector(x)
[1] 1 2 3
sage: r.is_vector(r.as_vector(x))
[1] TRUE

works for me.

Here what really happens in R:

> length(c(1,2,3,4))
[1] 4
> vector(c(1,2,3,4,3))
Error in vector(c(1, 2, 3, 4, 3)) : 
  vector: cannot make a vector of mode '1'.
> as.vector(c(1,2,3,4,3))
[1] 1 2 3 4 3

[kcrisman]

Okay, now I understand this example. But clearly the error should be shown, correct? The current behavior is bizarre, and in general this sort of thing happens a lot when trying to use it.

Should we not use the R interface and use rpy2 instead? But it looks rather more difficult to use, upon a quick perusal of the documentation.

[kcrisman]

And here's just something randomly annoying:

sage: r([1,1/2,1/2])
[1] 1.0 0.5 0.5
sage: r([0,sqrt(3)/2,sqrt(3)/2])
Error: object 'sage10' not found

Even though R knows what sqrt(3) is natively:

> sqrt(3)/2
[1] 0.8660254

I'm not saying this is really related to the summary of the ticket, but it's not unrelated, either.

[schilly]

yes of course, wrong error handling + hickup is bad. these examples might be useful to track down the bug.

​http://rpy.sourceforge.net/rpy2/doc/html/rpy_classic.html might be helpful when using rpy, though...

[kcrisman]

Changing summary to be more accurate - hopefully fixing this will fix the issues reported.

[kcrisman]

Okay, the problem is that instead of handling errors, we are ignoring them:

        # don't abort on errors, just raise them! 
        # necessary for non-interactive execution
        self.eval('options(error = expression(NULL))') 

But see ​here - it turns out that this is R's way of just totally ignoring them, not just 'raising' them. This should be fixed.