Scoping and functions in R 2.11.1 : What's going wrong?

Question

This question comes from a range of other questions that all deal with essentially the same problem. For some strange reason, using a function within another function sometimes fails in the sense that variables defined within the local environment of the first function are not found back in the second function.

The classical pattern in pseudo-code :

ff <- function(x){
    y <- some_value
    some_function(y)
}
ff(x)

Error in eval(expr, envir, enclos) : object 'y' not found

First I thought it had something to do with S4 methods and the scoping in there, but it also happens with other functions. I've had some interaction with the R development team, but all they did was direct me to the bug report site (which is not the most inviting one, I have to say). I never got any feedback.

As the problem keeps arising, I wonder if there is a logic explanation for it. Is it a common mistake made in all these cases, and if so, which one? Or is it really a bug?

Some of those questions :

• Using functions and environments
• R (statistical) scoping error using transformBy(), part of the doBy package.
• How to use acast (reshape2) within a function in R?
• Why can't I pass a dataset to a function?
• Values not being copied to the next local environment

PS : I know the R-devel list, in case you wondered...

Answer 1

R has both lexical and dynamic scope. Lexical scope works automatically, but dynamic scope must be implemented manually, and requires careful book-keeping. Only functions used interactively for data analysis need dynamic scope, so most authors (like me!) don't learn how to do it correctly.

See also: the standard non-standard evaluation rules.

Answer 2

There are undoubtedly bugs in R, but a lot of the issues that people have been having are quite often errors in the implementation of some_function, not R itself. R has scoping rules ( see http://cran.r-project.org/doc/manuals/R-intro.html#Scope) which when combined with lazy evaluation of function arguments and the ability to eval arguments in other scopes are extremely powerful but which also often lead to subtle errors.

Answer 3

As Dirk mentioned in his answer, there isn't actually a problem with the code that you posted. In the links you posted in the question, there seems to be a common theme: some_function contains code that messes about with environments in some way. This messing is either explicit, using new.env and with or implicitly, using a data argument, that probably has a line like

y <- eval(substitute(y), data)

The moral of the story is twofold. Firstly, try to avoid explicitly manipulating environments, unless you are really sure that you know what you are doing. And secondly, if a function has a data argument then put all the variables that you need the function to use inside that data frame.