I have a Rdata file containing various objects:
New.Rdata
|_ Object 1 (e.g. data.frame)
|_ Object 2 (e.g. matrix)
|_...
|_ Object n
Of course I can load the data frame with load('New.Rdata'), however, is there a smart way to load only one specific object out of this file and discard the others?
.RData files don't have an index (the contents are serialized as one big pairlist). You could hack a way to go through the pairlist and assign only entries you like, but it's not easy since you can't do it at the R level.
However, you can simply convert the .RData file into a lazy-load database which serializes each entry separately and creates an index. The nice thing is that the loading will be on-demand:
# convert .RData -> .rdb/.rdx
e = local({load("New.RData"); environment()})
tools:::makeLazyLoadDB(e, "New")
Loading the DB then only loads the index but not the contents. The contents are loaded as they are used:
lazyLoad("New")
ls()
x # if you had x in the New.RData it will be fetched now from New.rdb
Just like with load() you can specify an environment to load into so you don't need to pollute the global workspace etc.
You can use attach rather than load which will attach the data object to the search path, then you can copy the one object you are interested in and detach the .Rdata object.
This still loads everything, but is simpler to work with than loading everything into the global workspace (possibly overwriting things you don't want overwritten) then getting rid of everything you don't want.
Simon Urbanek's answer is very, very nice. A drawback is that it doesn't seem to work if an object to be saved is too large:
tools:::makeLazyLoadDB(
local({
x <- 1:1e+09
cat("size:", object.size(x) ,"\n")
environment()
}), "lazytest")
size: 4e+09
Error: serialization is too large to store in a raw vector
I'm guessing that this is due to a limitation of the current implementation of R (I have 2.15.2) rather than running out of physical memory and swap. The saves package might be an alternative for some uses, however.
