R help - May 2013 - Size of a refClass instance

I'm using refClass for a complex multi-directional tree structure with
possibly 100,000s of nodes.  The refClass design is very impressive and I'd
love to use it, but I've found that the size of refClass instances are very
large and creation time is slow.  For example, below is a RefClass and normal S4
class.  The RefClass requires about 4KB per instance vs 500B for the S4 class --
based on adding the Ncells and Vcells of used memory reported by gc().  And
instantiation is more than twice as slow for a RefClass.  (R 2.14.2)

Anyone have thoughts on this and whether there's any hope for improving
resources on either front?

I wonder what others are doing.  I've been thinking about lightweight
alternative implementations, but nothing particularly elegant has come to mind,
yet!

Thanks!


simple <- 
  setRefClass('simple', 
              fields = list(a = "character", b="numeric")
)
gc()
system.time(simple.list <- lapply(1:100000, function(i) {
simple$new(a='foo',b=i) }))
gc()

setClass('simple2',
representation(a="character",b="numeric"))
setMethod("initialize", "simple2",
          function(.Object, a, b) {
            .Object at a <- a
            .Object at b <- b
            .Object
          })

gc()
system.time(simple2.list <- lapply(1:100000, function(i) {
new('simple2',a='foo',b=i) }))
gc()

On 05/01/2013 11:20 AM, David Kulp wrote:
> I'm using refClass for a complex multi-directional tree structure with
> possibly 100,000s of nodes.  The refClass design is very impressive and
I'd
> love to use it, but I've found that the size of refClass instances are
very
> large and creation time is slow.  For example, below is a RefClass and
normal
> S4 class.  The RefClass requires about 4KB per instance vs 500B for the S4
> class -- based on adding the Ncells and Vcells of used memory reported by
> gc().  And instantiation is more than twice as slow for a RefClass.  (R
> 2.14.2)
>
> Anyone have thoughts on this and whether there's any hope for improving
> resources on either front?
Hi David -- not necessarily helpful but creating a few large objects is always 
better than creating many small in R, so perhaps re-conceptualize your data 
structure? As a rough analogy, instead of constructing a graph as a large number
of 'Node' instances each pointing to one another, a graph could be
represented
as a data.frame containing columns of 'from' and 'to' indexes
(neighbour-edge
list, a few large objects) or as an adjacency matrix. One would also implement 
creation and update of the few large objects in an R-friendly (vectorized) way.

Perhaps there are existing packages that already model the data you're 
interested in? If your multi-directional tree can be represented as a graph, 
then perhaps

   http://bioconductor.org/packages/release/bioc/html/graph.html

including facilities in the Boost graph library (RBGL, on the Bioconductor web 
site, too) or the igraph package can be put to use.

Martin
>
> I wonder what others are doing.  I've been thinking about lightweight
> alternative implementations, but nothing particularly elegant has come to
> mind, yet!
>
> Thanks!
>
>
> simple <- setRefClass('simple', fields = list(a =
"character", b="numeric")
> ) gc() system.time(simple.list <- lapply(1:100000, function(i) {
> simple$new(a='foo',b=i) })) gc()
>
> setClass('simple2',
representation(a="character",b="numeric"))
> setMethod("initialize", "simple2", function(.Object, a,
b) { .Object at a <- a
> .Object at b <- b .Object })
>
> gc() system.time(simple2.list <- lapply(1:100000, function(i) {
> new('simple2',a='foo',b=i) })) gc()
>
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