[R] Dynamic Dictionary Data Type?

[Gabor Grothendieck]

On 6/2/05, Gabor Grothendieck <ggrothendieck at gmail.com> wrote:
> On 6/2/05, Kjetil Brinchmann Halvorsen <kjetil at acelerate.com> wrote:
> > Prof Brian Ripley wrote:
> >
> > > On Thu, 2 Jun 2005, hadley wickham wrote:
> > >
> > >>> An environment is a hash table, and copying occurs only on
> > >>> modification,
> > >>> when any language would have to copy in this context.
> > >>
> > >
> > > Caveat: the default is new.env(hash=FALSE), so an environment is a
> > > hash table in one sense but not necessarily so in the strict sense.
> > >
> > >> Yes, I'm aware that copying only occurs on modification.  However, it
> > >> was my understanding that
> > >>
> > >> a <- list(a =1)
> > >> a$b <- 4
> > >>
> > >> would create a new copy of a,
> > >
> > >
> > > Thomas was talking about environments, not lists (and $ works
> > > differently for them).
> > >
> > >> whereas in Java say
> > >>
> > >> HashMap a = new HashMap();
> > >> a.put("a", 1);
> > >> a.put("b", 2);
> > >>
> > >> wouldn't create a copy of a. (please bear in mind my java syntax is
> > >> very rusty!)
> > >>
> > >> Caching data implies updating at each step, thus possibly creating n
> > >> copies of the list.  Is that wrong?
> > >
> > >
> > > It depends what you mean by `copy'.  If you expand a hash table you at
> > > some point need to re-arrange the hashing of the entries.  That's what
> > > will happen with an R environment or an R list too.  The possible
> > > difference is that you might expect the table to have some room for
> > > expansion, and in your list example you did not give any.
> > >
> > > R's operations on lists make more copies than are strictly necessary,
> > > but it is not clear that this is more costly than the housekeeping
> > > that would otherwise be necessary.  In a$b <- 4, the wrapper VECSXP is
> > > recreated and the pointers copied across, but the list elements are
> > > not copied.  For
> > > a$a <- 4 it is probable that no copying is done (although if a2 <- a
> > > had been done previously, the pending recursive copy would then be done).
> > > (It is also possible that I have overlooked something in the rather
> > > complex code used to do these subassignments.)
> > >
> > The original poster asked for caching of results. here is an example
> > using new.env():
> >
> > memo <- function(fun) {
> >   mem <- new.env()
> >   function(x) {
> >       if (exists(as.character(x), envir=mem)) get(as.character(x),
> > envir=mem, inherits=FALSE) else {
> >       val <- fun(x)
> >       assign(as.character(x), value=val, envir=mem)
> >       val }
> >   }
> > }
> >
> >  > fib <- memo(function(n) if(n<=1) 1 else fib(n-1)+fib(n-2))
> >  > system.time( fib(300) )
> > [1] 0.01 0.00 0.02   NA   NA
> >
> > ls(get("mem", env=environment(fib)))
> >   *output supressed*
> >
> > To compare:
> > system.time( {fib2 <- function(n)if(n<=1)1 else
> > fib2(n-1)+fib2(n-2);fib2(30)})
> > [1]  8.07  0.08 12.75    NA    NA
> >
> >
> > (there is (at least) one problem with this solution: if the global
> > workspace contains a
> >  variable `6`, it gives error. Why?)
> 
> I think you need to add the argument inherits = FALSE to the call
> to exists.
> 

or alternately use new.env(parent = NULL) in which case you don't
need the inherits = FALSE on the get either.