[R] object size vs. file size

Duncan Murdoch murdoch at stats.uwo.ca
Sun Mar 26 17:33:33 CEST 2006

On 3/25/2006 10:16 PM, Gabor Grothendieck wrote:
> You can place functions in lists or environments and pass the
> environment to the function and have it look there first. That way
> you can have different versions of a function with the same name.
> 1. Here is an example using lists:
> A <- list(f = sin)
> B <- list(f = cos)
> f <- function(x) x+2
> myfun <- function(x, L = NULL) with(L, f)(x)
> myfun(0) # 2
> myfun(0, A) # 0
> myfun(0, B) # 1
> All three of the above make a call to f but the first uses
> the f in the global environment, the second uses the f
> in A and the third uses the f in B.
> 2. Above we illustrated this using lists but it can also be done using
> environments. In the following we use the proto package to
> facilitiate this.  proto objects are built on top of environments.,
> For example, you could replace the first two lines in the
> prior example with:
> library(proto)
> A <- proto(f = sin)
> B <- proto(f = cos)
> Note that in #1 and #2 myfun did have to be programmed to handle
> this.   Another way to do this which does not require myfun to be
> preprogrammed is the following:
> library(proto)
> A <- proto(f = sin)
> B <- proto(f = cos)
> myfun <- function(x) f(x)
> myfun(0) # 2
> with(A$proto(myfun = myfun), myfun)(0) # 0
> with(B$proto(myfun = myfun), myfun)(0) # 1
> The first with statement defines a child object of A which contains
> a single method myfun, A$proto(myfun = myfun).   Then it calls the
> myfun in that new object.  Since the new object is a child of A,
> myfun will look for f in the new object and not finding it will search
> the parent A and find it there.   Similarly for B in the second with
> statement.
> Regarding removing environments, if if is a function you can do this:
> environment(f) <- NULL
> but you will likely need to restore the environment prior to using f.

That will get you a warning in 2.3.0 (and replace the NULL with 
baseenv()), and an error in 2.4.0.  In current and past versions, a NULL 
wasn't interpreted as "no environment", it was interpreted as the base 

If you want something that is like "no environment", you can use 
emptyenv() in 2.3.0, but this would rarely make sense for an R function: 
  even the most basic things involved in evaluation need to come from 
somewhere.  emptyenv() is mainly designed for situations where you want 
an entirely separate namespace, not related to R functions at all, but 
using the same syntax and rules for lookups.

Duncan Murdoch

> On 3/25/06, Steven Lacey <slacey at umich.edu> wrote:
>> 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
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