Passing environments: why doesn't this work???

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This is a trivial example I set up to see if I could pass an environment and
use the variables in it
(this is for a function that will be called many times and might need to use
a lot of variables that
won't be changing, so it seemed more sensible to use an environment).

Here's the code:
#########################
#The outer function
run.internal.env <- function(x) {
  in.env <- new.env()
  assign('x', x, envir=in.env)
  print(eval(x^2, envir=in.env))
  print(ls(envir=in.env))
  return(use.internal.env(in.env))
  }

#The inner function
use.internal.env <- function(env) {
  print(ls(envir=env))
  return(eval(x^2, envir=env))
  }
##########################
Now if I type

run.internal.env(2), my output looks like

[1] 4        [This is the evaluation in the outer routine]
[1] "x"      [This is the "ls" from the outer routine]
[1] "x"      [This is the "ls" from the inner routine]
Error in eval(x^2, envir=env): object "x" not found   [??? It was listed, so
why can't it be evaluated???]

If there's anything called "x" in the global environment, it will evaluate
that x^2 instead, e.g.
x <- 1:3
run.internal.env(2)
[1] 4        [This is the evaluation in the outer routine]
[1] "x"      [This is the "ls" from the outer routine]
[1] "x"      [This is the "ls" from the inner routine]
[1] 1 4 9

Is there any way to force it to use the environment I actually want, and
keep all the values I assigned in there?
Yes, what you do. You just missed the difference between eval and evalq 
(been there, done that, got the gray hair...)
O__  ---- Peter Dalgaard             ?ster Farimagsgade 5, Entr.B
   c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
  (*) \(*) -- University of Copenhagen   Denmark      Ph:  (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)              FAX: (+45) 35327907
Note that you can do things like this too:
  env$x
  with(env, x^2)
Also its possible you are really getting into object oriented
programming without realizing it with the your environments
being the objects your functions being the methods.  In that
case check out the proto package which formalizes all this
using environments to implement the prototype model (also
called object-based model) of object oriented programming.
http://r-proto.googlecode.com
This is a trivial example I set up to see if I could pass an environment and
use the variables in it
(this is for a function that will be called many times and might need to use
a lot of variables that
won't be changing, so it seemed more sensible to use an environment).

Here's the code:
#########################
#The outer function
run.internal.env <- function(x) {
 in.env <- new.env()
 assign('x', x, envir=in.env)
 print(eval(x^2, envir=in.env))
 print(ls(envir=in.env))
 return(use.internal.env(in.env))
 }

#The inner function
use.internal.env <- function(env) {
 print(ls(envir=env))
 return(eval(x^2, envir=env))
 }
##########################
Now if I type

run.internal.env(2), my output looks like

[1] 4        [This is the evaluation in the outer routine]
[1] "x"      [This is the "ls" from the outer routine]
[1] "x"      [This is the "ls" from the inner routine]
Error in eval(x^2, envir=env): object "x" not found   [??? It was listed, so
why can't it be evaluated???]

If there's anything called "x" in the global environment, it will evaluate
that x^2 instead, e.g.
x <- 1:3
run.internal.env(2)
[1] 4        [This is the evaluation in the outer routine]
[1] "x"      [This is the "ls" from the outer routine]
[1] "x"      [This is the "ls" from the inner routine]
[1] 1 4 9

Is there any way to force it to use the environment I actually want, and
keep all the values I assigned in there?

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