3 ways to do this, one that doesn't work, and two that work ;-)
1. Simple way that doesn't work:
## Just a place holder. Will be initialized at run-time the first
## time it's needed.
.some_internal_global_variable <- NULL
toto <- function()
{
if (is.null(.some_global_variable))
.some_internal_global_variable <<- 55L
}
However, if you put this in your package, you'll get the following
error the first time toto() is called:
cannot change value of locked binding for
'.some_internal_global_variable'
2. Simple way that works: initialize the global variable in the
.onLoad() hook. This works because the .onLoad() hook is executed
right before the package namespace gets locked.
## Just a place holder. Will be initialized at load-time.
.some_internal_global_variable <- NULL
.onLoad <- function(libname, pkgname)
{
.some_internal_global_variable <<- 55L
}
However, I don't really like using this approach when initialization
of the global variable requires access to the internet. It means that
in case of connectivity issue your users won't be able to load the
package and troubleshooting can become really tricky when you can't
even load a package. So in that case I prefer the solution below.
3. Define the internal global variable in an environment:
.my_internal_global_variables <- new.env(parent=emptyenv())
.get_eh <- function() get("eh", envir=.my_internal_global_variables)
.set_eh <- function(value) assign("eh", value,
envir=.my_internal_global_variables)
toto <- function()
{
eh <- try(.get_eh(), silent=TRUE)
if (inherits(eh, "try-error")) {
eh <- ExperimentHub()
.set_eh(eh)
}
eh
}
Hope this helps,
H.
On 3/23/21 10:05 AM, Murphy, Alan E wrote:
Hey Hervé,
I get the idea now thanks for clarifying. Where do I place the call to
ExperimentHub in the package?:
eh <- ExperimentHub() # the only call to ExperimentHub() in
# the entire R session
The package contains calls to the datasets in internal functions,
examples, tests and the vignette so eh it would need to be available to
all. Sorry I don't have much experience using experiment datasets.
Kind regards,
Alan.
------------------------------------------------------------------------
*From:* Hervé Pagès <hpages.on.git...@gmail.com>
*Sent:* 23 March 2021 16:46
*To:* Murphy, Alan E <a.mur...@imperial.ac.uk>; Martin Morgan
<mtmorgan.b...@gmail.com>; Kern, Lori <lori.sheph...@roswellpark.org>;
bioc-devel@r-project.org <bioc-devel@r-project.org>
*Subject:* Re: [Bioc-devel] Methods to speed up R CMD Check
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On 3/23/21 4:11 AM, Murphy, Alan E wrote:
Hi,
Thank you very much Martin and Hervé for your suggestions. I have reverted my zzz.R on load function to that advised by ExperimentHub and had used the ID look up (system.time(tt_alzh <- eh[["EH5373"]])) on internal functions and unit tests. However, the check is still taking ~18 minutes so I need to do a bit more work. Even with
my new on load function, calling datasets by name still takes
substantially longer, see below for the example Hervé gave on my new code:
a<-function(){
eh <- query(ExperimentHub(), "ewceData")
The above line is not needed. Creating an ExperimentHub instance can be
quite expensive and with the current approach 'R CMD check' will do it
dozens of times. My suggestion was to create an ExperimentHub instance
once for all the first time you need it, and reuse it in all your data
access functions:
eh <- ExperimentHub() # the only call to ExperimentHub() in
# the entire R session
Also there's no need to query(). Just use the EHb ID directly on the
ExperimentHub instance to load your data:
eh[["EH5373"]]
This should reduce 'R CMD check' by a few more minutes.
H.
--
Hervé Pagès
Bioconductor Core Team
hpages.on.git...@gmail.com
--
Hervé Pagès
Bioconductor Core Team
hpages.on.git...@gmail.com
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