Hi Alan,
It looks like what is slowing everything down significantly is the
approach you've taken to look up the ExperimentHub resources that you
control by name every time you need to access them. E.g:
Look up by name:
> system.time(tt_alzh <- ewceData::tt_alzh())
snapshotDate(): 2021-03-22
see ?ewceData and browseVignettes('ewceData') for documentation
loading from cache
user system elapsed
2.496 0.024 9.460
Direct access:
> system.time(tt_alzh <- eh[["EH5373"]])
see ?ewceData and browseVignettes('ewceData') for documentation
loading from cache
user system elapsed
1.195 0.012 2.060
ewceData::tt_alzh() is just one of the 18 utility functions defined in
ewceData that perform this lookup over and over again in the vignette
and man page. This lookup is expensive and not needed since the
ExperimentHub IDs that were assigned to your resources are fixed and
known in advance.
Note however that it's a good idea to not expose these IDs to the end
user (they might change at some point if you need to update these
resources on ExperimentHub) so it's actually recommended to lookup by
name in user-visible code.
Another easy improvement is that you drop dependency on
ExperimentHubData. This will reduce the nb of deps (direct and indirect)
from 130 to 94. There are likely other deps that you could try to get
rid of.
Hope this helps,
H.
On 3/22/21 2:38 AM, Murphy, Alan E wrote:
Hi all,
I am working on the development of [EWCE](https://github.com/NathanSkene/EWCE)
but have hit an issue with R CMD check's runtime. I have been informed this
test needs to be completed in 15 minutes but mine is currently running in ~24
minutes and I am looking for methods to speed this up. The main culprits for
the runtime issue are:
checking examples (5m 49.8s)
Running �testthat.R� [308s/469s] (7m 49.1s)
checking for unstated dependencies in vignettes (7m 49.4s)
checking re-building of vignette outputs (5m 12s)
With the exception of using smaller datasets which I will consider myself, is
there known ways of speeding these up? EWCE derives data from an Experimenthub
package [ewceData](https://github.com/neurogenomics/ewceData) for its examples,
tests and vignette. This is run repeatedly and I have noted this takes a
significant amount of time to load a dataset. Is there anyway of caching the
datasets for all the checks or more generally of speeding this up?
I have heard of the use of [long
tests](http://bioconductor.org/developers/how-to/long-tests/) which aren't run
daily by Bioconductor but are these still checked in R CMD Check? Is there any
other way to exclude my tests from the R CMD Check given they aren't a
necessity from Bioconductor?
Does checking for unstated dependencies in vignettes have a long runtime based
on the number of package dependencies? If I just export specific functions from
packages will this check time reduce?
Lastly, is there any way to get an exception of the 15 minute maximum? I may be
ill-informed but is the max time for packages on Bioconductor's daily check 40
minutes which my code in its current state would complete by.
Kind regards,
Alan.
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