Large discrepancies in the same object being saved to .RData

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Hi developers,

After some investigation I have found there can be large discrepancies in the same object being saved as an external "xx.RData" file. The immediate repercussion of this is the possible increased size of your .RData workspace for no apparent reason.

The function and its three scenarios below highlight these discrepancies. Note that the object being returned is exactly the same in each circumstance. The first scenario simply loops over a set of lm() models from a simulated set of data. The second adds a reasonably large matrix calculation within the loop. The third highlights exactly where the discrepancy lies. It appears that when the object is saved to an "xx.RData" it is still burdened, in some capacity, with the objects created in the function. Only deleting these objects at the end of the function ensures the realistic size of the returned object. Performing gc() after each of these short simulations shows that the "Vcells" that are accumulated in the function environment appear to remain after the function returns. These cached remains are then transferred to the .RData upon saving of the object(s). This is occurring quite broadly across the Windows 7 (R 2.10.1) and 64 Bit Ubuntu Linux (R 2.9.0) systems that I use.

A similar problem was partially pointed out four years ago

http://tolstoy.newcastle.edu.au/R/help/06/03/24060.html

and has been made more obvious in the scenarios given below.

Admittedly I have had many problems with workspace .RData sizes over the years and it has taken me some time to realise what is actually occurring. Can someone enlighten myself and my colleagues as to why the objects created and evaluated in a function call stack are saved, in some capacity, with the returned object?

I haven't worked through your example, but in general the way that local 
objects get captured is when part of the return value includes an 
environment.  Examples of things that include an environment are locally 
created functions and formulas.  It's probably the latter that you're 
seeing.  When R computes the result of "y ~ ." or a similar formula, it 
attaches a pointer to the environment in which the calculation took 
place, so that later when the formula is used, it can look up y there.  
For example, in your line

lm(y ~ ., data = dat)

from your code, the formula "y ~ ." needs to be computed before R knows 
that you've explicitly listed a dataframe holding the data, and before 
it knows whether the variable y is in that dataframe or is just a local 
variable in the current function.

Since these are just pointers to the environment, this doesn't take up 
much space in memory, but when you save the object to disk, a copy of 
the whole environment will be made, and that can end up wasting up a lot 
of space if the environment contains a lot of things that aren't needed 
by the formula.

Duncan Murdoch
Cheers,

Julian

####################### small simulation from a clean directory

lmfunc <- function(loop = 20, add = FALSE, gr = FALSE){

  lmlist <- rmlist <- list()

  set.seed(100)

  dat <- data.frame(matrix(rnorm(100*100), ncol = 100))

  rm <- matrix(rnorm(100000), ncol = 1000)

  names(dat)[1] <- "y"

  i <- 1

  for(i in 1:loop) {

    lmlist[[i]] <- lm(y ~ ., data = dat)

    if(add)

        rmlist[[i]] <- rm

  }

  fm <- lmlist[[loop]]

  if(gr) {

    print(what <- ls(envir = sys.frame(which = 1)))

    remove(list = setdiff(what, "fm"))

  }

  fm

}

# baseline gc()

gc()

          used (Mb) gc trigger (Mb) max used (Mb)

Ncells 153325  4.1     350000  9.4   350000  9.4

Vcells  99228  0.8     786432  6.0   386446  3.0

###### 1. simple lm() simulation

lmtest1 <- lmfunc()

gc()

          used (Mb) gc trigger (Mb) max used (Mb)

Ncells 184470  5.0     407500 10.9   350000  9.4

Vcells 842169  6.5    1300721 10.0  1162577  8.9

save(lmtest1, file = "lm1.RData")

system("ls -s lm1.RData")

4312 lm1.RData

## A moderate increase in Vcells; .RData object around 4.5 Mb

###### 2. add matrix calculation to loop

lmtest2 <- lmfunc(add = TRUE)

gc()

           used (Mb) gc trigger (Mb) max used (Mb)

Ncells  209316  5.6     407500 10.9   405340 10.9

Vcells 3584244 27.4    4175939 31.9  3900869 29.8

save(lmtest2, file = "lm2.RData")

system("ls -s lm2.RData")

19324 lm2.RData

## A enormous increase in Vcells; .RData object is now 19Mb+

###### 3. delete all objects in function call stack

lmtest3 <- lmfunc(add = TRUE, gr = TRUE)

gc()

           used (Mb) gc trigger (Mb) max used (Mb)

Ncells  210766  5.7     467875 12.5   467875 12.5

Vcells 3615863 27.6    6933688 52.9  6898609 52.7

save(lmtest3, file = "lm3.RData")

system("ls -s lm3.RData")

320 lm3.RData

## A minimal increase in Vcells; .RData object is now 320Kb

sapply(ls(pattern = "lmtest*"), function(x) object.size(get(x, envir = .GlobalEnv)))

lmtest1 lmtest2 lmtest3

 358428  358428  358428

## all objects are deemed the same size by object.size()

######################### End sim

--
---
Dr. Julian Taylor                     phone: +61 8 8303 8792
Postdoctoral Fellow                     fax: +61 8 8303 8763
CMIS, CSIRO                          mobile: +61 4 1638 8180
Private Mail Bag 2                    email: julian.taylor at csiro.au
Glen Osmond, SA, 5064
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On 06/07/2010 9:04 PM, Julian.Taylor at csiro.au wrote:
Hi developers,

After some investigation I have found there can be large discrepancies in
the same object being saved as an external "xx.RData" file. The immediate
repercussion of this is the possible increased size of your .RData workspace
for no apparent reason.

I haven't worked through your example, but in general the way that local
objects get captured is when part of the return value includes an
environment.
Hi, can I ask a follow up question?

Is there a tool to browse *.Rdata files without loading them into R?

In HDF5 (a data storage format we use sometimes), there is a CLI
program "h5dump" that will spit out line-by-line all the contents of a
storage entity.  It will literally track through all the metadata, all
the vectors of scores, etc.  I've found that handy to "see what's
really  in there" in cases like the one that OP asked about.
Sometimes, we find that there are things that are "in there" by
mistake, as Duncan describes, and then we can try to figure why they
are in there.

pj
Paul E. Johnson
Professor, Political Science
1541 Lilac Lane, Room 504
University of Kansas
On Wed, Jul 7, 2010 at 7:12 AM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote:

On 06/07/2010 9:04 PM, Julian.Taylor at csiro.au wrote:

Hi developers,

After some investigation I have found there can be large discrepancies in
the same object being saved as an external "xx.RData" file. The immediate
repercussion of this is the possible increased size of your .RData workspace
for no apparent reason.

I haven't worked through your example, but in general the way that local
objects get captured is when part of the return value includes an
environment.

Hi, can I ask a follow up question?

Is there a tool to browse *.Rdata files without loading them into R?

I don't know of one.  You can load the whole file into an empty 
environment, but then you lose information about "where did it come from"?

Duncan Murdoch
In HDF5 (a data storage format we use sometimes), there is a CLI
program "h5dump" that will spit out line-by-line all the contents of a
storage entity.  It will literally track through all the metadata, all
the vectors of scores, etc.  I've found that handy to "see what's
really  in there" in cases like the one that OP asked about.
Sometimes, we find that there are things that are "in there" by
mistake, as Duncan describes, and then we can try to figure why they
are in there.

pj

I'm still a bit puzzled by the original question.  I don't think it
has much to do with .RData files and their sizes.  For me the puzzle
comes much earlier.  Here is an example of what I mean using a little
session
usedVcells <- function() gc()["Vcells", "used"]
usedVcells()        ### the base load
[1] 96345

### Now look at what happens when a function returns a formula as the
### value, with a big item floating around in the function closure:
f0 <- function() {
+ junk <- rnorm(10000000)
+ y ~ x
+ }
v0 <- f0()
usedVcells()   ### much bigger than base, why?
[1] 10096355
v0             ### no obvious envirnoment
y ~ x
object.size(v0)  ### so far, no clue given where
### the extra Vcells are located.
372 bytes

### Does v0 have an enclosing environment?
environment(v0)             ### yep.
<environment: 0x021cc538>
ls(envir = environment(v0)) ### as expected, there's the junk
[1] "junk"
rm(junk, envir = environment(v0))  ### this does the trick.
usedVcells()
[1] 96355

### Now consider a second example where the object
### is not a formula, but contains one.
f1 <- function() {
+ junk <- rnorm(10000000)
+ x <- 1:3
+ y <- rnorm(3)
+ lm(y ~ x)
+ }
v1 <- f1()
usedVcells()  ### as might have been expected.
[1] 10096455

### in this case, though, there is no 
### (obvious) enclosing environment
environment(v1)  
NULL
object.size(v1)  ### so where are the junk Vcells located?
7744 bytes
ls(envir = environment(v1))  ### clearly wil not work
Error in ls(envir = environment(v1)) : invalid 'envir' argument
rm(v1)     ### removing the object does clear out the junk.
usedVcells()
[1] 96366

And in this second case, as noted by Julian Taylor, if you save() the
object the .RData file is also huge.  There is an environment attached
to the object somewhere, but it appears to be occluded and entirely
inaccessible.  (I have poked around the object components trying to
find the thing but without success.)

Have I missed something?

Bill Venables.

-----Original Message-----
From: r-devel-bounces at r-project.org [mailto:r-devel-bounces at r-project.org] On Behalf Of Duncan Murdoch
Sent: Sunday, 11 July 2010 10:36 AM
To: Paul Johnson
Cc: r-devel at r-project.org
Subject: Re: [Rd] Large discrepancies in the same object being saved to .RData
On Wed, Jul 7, 2010 at 7:12 AM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote:

On 06/07/2010 9:04 PM, Julian.Taylor at csiro.au wrote:

Hi developers,

After some investigation I have found there can be large discrepancies in
the same object being saved as an external "xx.RData" file. The immediate
repercussion of this is the possible increased size of your .RData workspace
for no apparent reason.

I haven't worked through your example, but in general the way that local
objects get captured is when part of the return value includes an
environment.

Hi, can I ask a follow up question?

Is there a tool to browse *.Rdata files without loading them into R?

I don't know of one.  You can load the whole file into an empty 
environment, but then you lose information about "where did it come from"?

Duncan Murdoch
In HDF5 (a data storage format we use sometimes), there is a CLI
program "h5dump" that will spit out line-by-line all the contents of a
storage entity.  It will literally track through all the metadata, all
the vectors of scores, etc.  I've found that handy to "see what's
really  in there" in cases like the one that OP asked about.
Sometimes, we find that there are things that are "in there" by
mistake, as Duncan describes, and then we can try to figure why they
are in there.

pj

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