Most efficient way to check the length of a variable mentioned in a formula.

I would use eval(), but I think that most formula-using functions do
it more like the following.

getRHSLength <-
function (formula, data = parent.frame())
{
    rhsExpr <- formula[[length(formula)]]
    rhsValue <- eval(rhsExpr, envir = data, enclos = environment(formula))
    length(rhsValue)
}

* use eval() instead of get() so you will find variables are in
ancestral environments
of envir (if envir is an environment), not just envir itself.
* just evaluate the stuff in the formula using the non-standard
evaluation frame,
call length() in the current frame.  Otherwise, if  envir inherits
directly from emptyenv() the 'length' function will not be found.
* use envir=data so it looks first in the data argument for variables
* the enclos argument is used if envir is not an environment and is used to
find variables that are not in envir.

Here are some examples:

  > X <- 1:10
  > getRHSLength(~X)

  [1] 10

  > getRHSLength(~X, data=data.frame(X=1:2))

  [1] 2

  > getRHSLength((function(){X <- 1:4; ~X})(), data=data.frame())

  [1] 4

  > getRHSLength((function(){X <- 1:4; ~X})(), data=data.frame(X=1:2))

  [1] 2

  > getRHSLength((function(){X <- 1:4; ~X})(), data=list2env(data.frame()))

  [1] 10

  > getRHSLength((function(){X <- 1:4; ~X})(), data=emptyenv())

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

I think you will see the same lookups if you try analogous things with lm().
Bill Dunlap
TIBCO Software
wdunlap tibco.com


On Fri, Oct 17, 2014 at 11:04 AM, Joris Meys <jorismeys at gmail.com> wrote:

Dear R gurus,

I need to know the length of a variable (let's call that X) that is
mentioned in a formula. So obviously I look for the environment from which
the formula is called and then I have two options:

- using eval(parse(text='length(X)'),
                    envir=environment(formula) )

- using length(get('X'),
            envir=environment(formula) )

a bit of benchmarking showed that the first option is about 20 times
slower, to that extent that if I repeat it 10,000 times I save more than
half a second. So speed is not really an issue here.

Personally I'd go for option 2 as that one is easier to read and does the
job nicely, but with these functions I'm always a bit afraid that I'm
overseeing important details or side effects here (possibly memory issues
when working with larger data).

Anybody an idea what the dangers are of these methods, and which one is the
most robust method?

Thank you
Joris

--
Joris Meys
Statistical consultant

Ghent University
Faculty of Bioscience Engineering
Department of Mathematical Modelling, Statistics and Bio-Informatics

tel : +32 9 264 59 87
Joris.Meys at Ugent.be
-------------------------------
Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php

        [[alternative HTML version deleted]]