[R] object size vs. file size

[Steven Lacey]

Duncan, 

Thanks! This is progress! One solution might be to remove all environments
from the objects that I want to save in the "sa" object, thereby avoiding
the problem of saving environments altogther. But, can I remove the
environment from a function? Does that even make sense given how R operates
under the hood? Even if I could, would the functions still work? 

Here is my more general problem. As I learn more about R and the demands
made on my code change, I sometimes change a function referenced by a given
name rather than explicitly defining a new version of that function. This
creates a problem when I want to review how the model stored in the "sa"
object was originally created. If only the function name is stored in the
"sa" object, I won't necessarily know what version was actually called at
the time the model was constructed because I did not rename it. To deal with
this I decided to store the function itself. 

Sounds like this may not be a great idea, or at least comes with serious
trade-offs, particularly as some functions are generic like the mean. Is
there a better way to save a function than to save the function itself or
just its name? For instance, do args() and body() return an associated
environment? I assume I could recreate the original function from these
objects, correct? If so, is there some easy way to do it?

Alternatively, are there any version control tools built into R? That is, is
there a way R can keep track of the version for me (as opposed to explicitly
declaring different verions foo<-..., foo.v1<-..., foo.v2<-...)? I am not
sure exactly what I am asking for here. The more I write the more this seems
unreasonable. A new function requires a new name, right? I just find myself
writing lots of new versions and keeping track of their names, which one
does what, and changing the names in other functions that call them a little
overwhelming. Maybe the way to deal with this is to write different versions
of same package. That way the versions will effect the naming of and the
call to load the package, but not the calls to individual functions. This
way functions can have the same name, but do different things depending on
the package version, not the function name. However, I have never created a
package and would prefer not to do so in the short-term (my dissertation is
due in August), unless it is fairly straightforward. 

The more I think about it a package is more accurately what I want. I want
to be able to recreate the analysis of my data long after it has been
completed. If I had packages, then I would just need to know what version of
the package was used, load it, and re-run the analysis. I wouldn't need to
store the critical functions in the object. Where might I find good
introduction to writing packages?

In the short-term would the solution above (using body and args) work?

Thanks again, 
Steve


-----Original Message-----
From: Duncan Murdoch [mailto:murdoch at stats.uwo.ca] 
Sent: Saturday, March 25, 2006 5:31 PM
To: Steven Lacey
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] object size vs. file size


On 3/25/2006 7:32 AM, Steven Lacey wrote:
> Hi,
>  
> There is rather large discrepancy in the size of the object as it 
> lives in R and the size of the object when it is written to the disk. 
> The object in question is an S4 of a homemade class "sa". I first call 
> a function that writes a list of these objects to a file called 
> "data.RData". The size of this file is 14,411 KB. I would assume on 
> average then, that each list component--there are 32 sa objects in 
> data.RData--would be approximately 450 KB (14,111/32). However, when I 
> load the data into R and call object.size on just one s4 object (call 
> it tmp) it returns 77496 bytes (77 KB)! What is even stranger is that 
> if I save this S4 object alone by calling save(tmp, file="tmp.RData"), 
> tmp.RData is 13.3 MB! I understand from the help on object.size that 
> the object size is only approximate and excludes the space recquired 
> to store its name in the symbol table. But, this difference in object 
> size and file size is huge! This phenomenon occurs no matter which S4 
> object I save from data.RData.
>  
> Why is the object so big when it is in a file? What else is getting 
> stored with it? I have examined the object in R to find additional 
> information stored with it, but have not found anything that would 
> account for the size of the object in the file system. For example,
>> environment(tmp)
> NULL

I'm not 100% sure where the problem is, but I think it probably does 
involve environments.  Your tmp object contains a number of functions. 
I think when some function is saved, its environment is being saved too, 
and the environment contains much more than you thought.

R doesn't normally save a new copy of a package or namespace environment 
when it saves a function, nor does it save a complete copy of .GlobalEnv 
with every function defined there, but it does save the environment in 
some other circumstances.  For example, look at this code:

 > f <- function() {
+       notused <- 1:1000000
+       value <- function() 1
+       return(value)
+  }
 >
 >  g <- f()
 >  g
function() 1
<environment: 01B10D1C>
 >  save(g, file='g.RData')
 > object.size(g)
[1] 200

The g object is 200 bytes or so, but when it is saved, the defining 
environment containing that huge "notused" variable is saved with it, so 
  g.RData ends up being about 4 Megabytes in size.

I don't know any function that will help to diagnose where this happens. 
  Here's one that doesn't quite work:

findenvironments <- function(x) {
     e <- environment(x)
     if (is.null(e)) result <- NULL
     else result <- list(e)
     x <- unclass(x)
     if (is.list(x)) {
        for (i in seq(along=x)) {
          contained <- findenvironments(x[[i]])
          if (length(contained)) result <- c(result, contained)
        }
     }
     if (length(result)) browser()
     result
}

This won't recurse into the slots of an S4 object, so it doesn't really 
help you, and I'm not sure how to do that.  But maybe someone else can 
fix it.

Duncan Murdoch