Re: [Rd] Large discrepancies in the same object being saved to .RData
DM == Duncan Murdoch murdoch.dun...@gmail.com
on Sun, 11 Jul 2010 17:25:45 -0400 writes:
DM On 11/07/2010 1:30 PM, Prof Brian Ripley wrote:
[]
On 7/10/2010 10:10 PM, bill.venab...@csiro.au wrote:
Well, I have answered one of my questions below. The hidden
environment is attached to the 'terms' component of v1.
Well, not really hidden. A terms component is a formula
(see ?terms.object), and a formula has an environment
just as a closure does. In neither case does the print()
method tell you about it -- but ?formula does.
DM I've just changed the default print method for formulas to display the
DM environment if it is not globalenv(), which is the rule used for
DM closures as well. So now in R-devel:
as.formula(y ~ x)
DM y ~ x
DM as before, but
as.formula(y ~ x, env=new.env())
DM y ~ x
DM environment: 01f83400
I see that our print.formula() actually has not truely fulfilled
our own rule about print methods:
?print has
Description:
‘print’ prints its argument and returns it _invisibly_
..
Further, I completely agree that it's good to mention the
environment, however, it can be a nuisance when it's part of a
larger print(.) method, so I'd like allowing to suppress that
and hence I've committed the current
print.formula - function(x, showEnv = !identical(e, .GlobalEnv), ...)
{
e - environment(.x - x) ## return(.) original x
attr(x, .Environment) - NULL
print.default(unclass(x), ...)
if (showEnv) print(e)
invisible(.x)
}
--
Martin Maechler, ETH Zurich
__
R-devel@r-project.org mailing list
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Re: [Rd] Large discrepancies in the same object being saved to .RData
I only wish to add a request for further documentation of hidden environments, their consequences, and how to turn them off. Perhaps a page in the Extending R guide, and a suggestion for book authors. I was bitten by this with the coxph frailty functions. They are called during the model frame creation and create a matrix object with various small attached functions as attributes. In creating the 'x' columns they have to deal with factors and can create a huge transient temporary matrix while doing so; something that will never be needed again. A user was exceeding disk quotas when he saved a model fit. As someone with years of experience with functional languages (which S once was), I wasn't used to the idea that one would have to take explicit --- and mysterious --- steps to make local variables go away. This discussion has revealed that the hidden rules causing local variables to be kept are more complex than I thought. Perhaps a don't save environments option to save could be added to help mere mortals get rid of all this stuff in the attic (with its secret staircase)? [[Soapbox on]] Environments have proven useful for many things, and certainly aren't going away. But to quote the bard Oh what tangled webs we weave, when first we practice to decieve. Terry Therneau __ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Large discrepancies in the same object being saved to .RData
On Sun, 11 Jul 2010, Tony Plate wrote:
Another way of seeing the environments referenced in an object is using
str(), e.g.:
f1 - function() {
+ junk - rnorm(1000)
+ x - 1:3
+ y - rnorm(3)
+ lm(y ~ x)
+ }
v1 - f1()
object.size(f1)
1636 bytes
grep(Environment, capture.output(str(v1)), value=TRUE)
[1] .. ..- attr(*, \.Environment\)=environment: 0x01f11a30
[2] .. .. ..- attr(*, \.Environment\)=environment: 0x01f11a30
'Some of the environments in a few cases': remember environments have
environments (and so on), and that namespaces and packages are also
environments. So we need to know about the environment of
environment(v1$terms), which also gets saved (either as a reference or
as an environment, depending on what it is).
And this approach does not work for many of the commonest cases:
f - function() {
+ x - pi
+ g - function() print(x)
+ return(g)
+ }
g - f()
str(g)
function ()
- attr(*, source)= chr function() print(x)
ls(environment(g))
[1] g x
In fact I think it works only for formulae.
-- Tony Plate
On 7/10/2010 10:10 PM, bill.venab...@csiro.au wrote:
Well, I have answered one of my questions below. The hidden
environment is attached to the 'terms' component of v1.
Well, not really hidden. A terms component is a formula (see
?terms.object), and a formula has an environment just as a closure
does. In neither case does the print() method tell you about it --
but ?formula does.
To see this
lapply(v1, environment)
$coefficients
NULL
$residuals
NULL
$effects
NULL
$rank
NULL
$fitted.values
NULL
$assign
NULL
$qr
NULL
$df.residual
NULL
$xlevels
NULL
$call
NULL
$terms
environment: 0x021b9e18
$model
NULL
rm(junk, envir = with(v1, environment(terms)))
usedVcells()
[1] 96532
This is still a bit of a trap for young (and old!) players...
I think the main point in my mind is why is it that object.size()
excludes enclosing environments in its reckonings?
Bill Venables.
-Original Message-
From: Venables, Bill (CMIS, Cleveland)
Sent: Sunday, 11 July 2010 11:40 AM
To: 'Duncan Murdoch'; 'Paul Johnson'
Cc: 'r-devel@r-project.org'; Taylor, Julian (CMIS, Waite Campus)
Subject: RE: [Rd] Large discrepancies in the same object being saved to
.RData
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(1000)
+ 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(1000)
+ 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-boun...@r-project.org [mailto:r-devel-boun...@r-project.org]
On Behalf Of Duncan Murdoch
Sent: Sunday, 11 July 2010 10:36 AM
To: Paul Johnson
Cc: r-devel@r-project.org
Subject: Re: [Rd] Large discrepancies in the same object being saved to
.RData
On 10/07/2010 2:33 PM, Paul Johnson wrote:
On Wed, Jul 7, 2010 at 7:12 AM, Duncan Murdochmurdoch.dun...@gmail.com
wrote:
On 06/07/2010 9:04 PM, julian.tay...@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
Re: [Rd] Large discrepancies in the same object being saved to .RData
On 11/07/2010 1:30 PM, Prof Brian Ripley wrote:
On Sun, 11 Jul 2010, Tony Plate wrote:
Another way of seeing the environments referenced in an object is using
str(), e.g.:
f1 - function() {
+ junk - rnorm(1000)
+ x - 1:3
+ y - rnorm(3)
+ lm(y ~ x)
+ }
v1 - f1()
object.size(f1)
1636 bytes
grep(Environment, capture.output(str(v1)), value=TRUE)
[1] .. ..- attr(*, \.Environment\)=environment: 0x01f11a30
[2] .. .. ..- attr(*, \.Environment\)=environment: 0x01f11a30
'Some of the environments in a few cases': remember environments have
environments (and so on), and that namespaces and packages are also
environments. So we need to know about the environment of
environment(v1$terms), which also gets saved (either as a reference or
as an environment, depending on what it is).
And this approach does not work for many of the commonest cases:
f - function() {
+ x - pi
+ g - function() print(x)
+ return(g)
+ }
g - f()
str(g)
function ()
- attr(*, source)= chr function() print(x)
ls(environment(g))
[1] g x
In fact I think it works only for formulae.
-- Tony Plate
On 7/10/2010 10:10 PM, bill.venab...@csiro.au wrote:
Well, I have answered one of my questions below. The hidden
environment is attached to the 'terms' component of v1.
Well, not really hidden. A terms component is a formula (see
?terms.object), and a formula has an environment just as a closure
does. In neither case does the print() method tell you about it --
but ?formula does.
I've just changed the default print method for formulas to display the
environment if it is not globalenv(), which is the rule used for
closures as well. So now in R-devel:
as.formula(y ~ x)
y ~ x
as before, but
as.formula(y ~ x, env=new.env())
y ~ x
environment: 01f83400
Duncan Murdoch
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Large discrepancies in the same object being saved to .RData
On Wed, Jul 7, 2010 at 7:12 AM, Duncan Murdoch murdoch.dun...@gmail.com wrote: On 06/07/2010 9:04 PM, julian.tay...@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 __ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Large discrepancies in the same object being saved to .RData
On 10/07/2010 2:33 PM, Paul Johnson wrote: On Wed, Jul 7, 2010 at 7:12 AM, Duncan Murdoch murdoch.dun...@gmail.com wrote: On 06/07/2010 9:04 PM, julian.tay...@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 __ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Large discrepancies in the same object being saved to .RData
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(1000)
+ 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(1000)
+ 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-boun...@r-project.org [mailto:r-devel-boun...@r-project.org] On
Behalf Of Duncan Murdoch
Sent: Sunday, 11 July 2010 10:36 AM
To: Paul Johnson
Cc: r-devel@r-project.org
Subject: Re: [Rd] Large discrepancies in the same object being saved to .RData
On 10/07/2010 2:33 PM, Paul Johnson wrote:
On Wed, Jul 7, 2010 at 7:12 AM, Duncan Murdoch murdoch.dun...@gmail.com
wrote:
On 06/07/2010 9:04 PM, julian.tay...@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
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Large discrepancies in the same object being saved to .RData
Well, I have answered one of my questions below. The hidden
environment is attached to the 'terms' component of v1.
To see this
lapply(v1, environment)
$coefficients
NULL
$residuals
NULL
$effects
NULL
$rank
NULL
$fitted.values
NULL
$assign
NULL
$qr
NULL
$df.residual
NULL
$xlevels
NULL
$call
NULL
$terms
environment: 0x021b9e18
$model
NULL
rm(junk, envir = with(v1, environment(terms)))
usedVcells()
[1] 96532
This is still a bit of a trap for young (and old!) players...
I think the main point in my mind is why is it that object.size()
excludes enclosing environments in its reckonings?
Bill Venables.
-Original Message-
From: Venables, Bill (CMIS, Cleveland)
Sent: Sunday, 11 July 2010 11:40 AM
To: 'Duncan Murdoch'; 'Paul Johnson'
Cc: 'r-devel@r-project.org'; Taylor, Julian (CMIS, Waite Campus)
Subject: RE: [Rd] Large discrepancies in the same object being saved to .RData
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(1000)
+ 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(1000)
+ 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-boun...@r-project.org [mailto:r-devel-boun...@r-project.org] On
Behalf Of Duncan Murdoch
Sent: Sunday, 11 July 2010 10:36 AM
To: Paul Johnson
Cc: r-devel@r-project.org
Subject: Re: [Rd] Large discrepancies in the same object being saved to .RData
On 10/07/2010 2:33 PM, Paul Johnson wrote:
On Wed, Jul 7, 2010 at 7:12 AM, Duncan Murdoch murdoch.dun...@gmail.com
wrote:
On 06/07/2010 9:04 PM, julian.tay...@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
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Large discrepancies in the same object being saved to .RData
On 10/07/2010 10:10 PM, bill.venab...@csiro.au wrote:
Well, I have answered one of my questions below. The hidden
environment is attached to the 'terms' component of v1.
To see this
lapply(v1, environment)
$coefficients
NULL
$residuals
NULL
$effects
NULL
$rank
NULL
$fitted.values
NULL
$assign
NULL
$qr
NULL
$df.residual
NULL
$xlevels
NULL
$call
NULL
$terms
environment: 0x021b9e18
$model
NULL
rm(junk, envir = with(v1, environment(terms)))
usedVcells()
[1] 96532
This is still a bit of a trap for young (and old!) players...
I think the main point in my mind is why is it that object.size()
excludes enclosing environments in its reckonings?
I think the idea is that the environment is not part of the object, it
is just referenced by the object. In fact, there are at least two
references to the environment in your second example:
environment(v1$terms)
and
attr(v1$terms, .Environment)
both refer to it. So you can't just add the size of an environment every
time you come across it, you would need to keep track of whether it had
already been counted or not. So as ?object.size says,
Associated space (e.g. the environment of a function and what the
pointer in a ‘EXTPTRSXP’ points to) is not included in the
calculation.
If you really want to know how much space an object will take when saved,
probably the only reliable way is to save the object and look at how much space
the file takes.
Duncan Murdoch
Bill Venables.
-Original Message-
From: Venables, Bill (CMIS, Cleveland)
Sent: Sunday, 11 July 2010 11:40 AM
To: 'Duncan Murdoch'; 'Paul Johnson'
Cc: 'r-devel@r-project.org'; Taylor, Julian (CMIS, Waite Campus)
Subject: RE: [Rd] Large discrepancies in the same object being saved to .RData
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(1000)
+ 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(1000)
+ 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-boun...@r-project.org [mailto:r-devel-boun...@r-project.org] On
Behalf Of Duncan Murdoch
Sent: Sunday, 11 July 2010 10:36 AM
To: Paul Johnson
Cc: r-devel@r-project.org
Subject: Re: [Rd] Large discrepancies in the same object being saved to .RData
On 10/07/2010 2:33 PM, Paul Johnson wrote:
On Wed, Jul 7, 2010 at 7:12 AM, Duncan Murdoch murdoch.dun...@gmail.com wrote:
On 06/07/2010 9:04 PM, julian.tay...@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
[Rd] Large discrepancies in the same object being saved to .RData
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?
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(10), 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 35 9.4 35 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 35 9.4
Vcells 842169 6.51300721 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 27.44175939 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 27.66933688 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 2email: julian.tay...@csiro.au
Glen Osmond, SA, 5064
---
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Re: [Rd] Large discrepancies in the same object being saved to .RData
On 06/07/2010 9:04 PM, julian.tay...@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.
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 us!
e.
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(10), 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 35 9.4 35 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 35 9.4
Vcells 842169 6.51300721 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 27.44175939 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 27.66933688 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 =
