if your examples repeatedly calculate the same thing, and this is also typical
of how users use your package, it might make sense to 'memoise' key functions
in your package https://cran.r-project.org/package=memoise
Martin
On 3/22/21, 7:41 AM, "Bioc-devel on behalf of Kern, Lori"
<bioc-devel-boun...@r-project.org on behalf of lori.sheph...@roswellpark.org>
wrote:
If your data is using ExperimentHub, it should already be caching the
downloaded data. Once it is downloaded once, it should be using the cached
download for subsequent calls to the hub. We will investigate to ensure that
the caching mechanism is functioning properly on all of our Bioconductor
builders.
Lori Shepherd
Bioconductor Core Team
Roswell Park Comprehensive Cancer Center
Department of Biostatistics & Bioinformatics
Elm & Carlton Streets
Buffalo, New York 14263
________________________________
From: Bioc-devel <bioc-devel-boun...@r-project.org> on behalf of Murphy,
Alan E <a.mur...@imperial.ac.uk>
Sent: Monday, March 22, 2021 5:38 AM
To: bioc-devel@r-project.org <bioc-devel@r-project.org>
Subject: [Bioc-devel] Methods to speed up R CMD Check
Hi all,
I am working on the development of
[EWCE](https://secure-web.cisco.com/1uG0LGgCjdg85VowwaeRHk2fMjXFkOtQWsgL8p2MQD2j2PZFh_tqvJWaCHJfArA8O4B2WLG1JOwn31NISgSrPW3syUdiPlWNi7cHAMCWKZUQ8d9RrlR-d81LDXXx0xtfCI5ZjjTyFS2xxM2tDea27Y51bWk4Y7jpSnC8Bx768AHBeaJAg3YAK_HTxR6hMzFW99X6Pg8bETgPYi92ccneqdgAJcDBIdfwZnd9OMaM4JS0kY9kYT3F58ho2jM_k0n6EqMzhuXl3HEM7uneL7twMxTTxSZ-vFC1U1eFSkAr0sp38AyD3g6gTbf-vUbghaGV-JBKoybZto3ZDmHhs8OE6cQ/https%3A%2F%2Fgithub.com%2FNathanSkene%2FEWCE)
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://secure-web.cisco.com/1r4B8NJkUGCpdQsdBW8RWLwGvwEA9TlvXY7VUYgAKS-TBmT7s-6a3zMLfS6rXRVUUxG4x8SCYzXUXZKYMtZ_ysyEzk56tVxfvju-9mo6l11KLQ7CzEpFMikVqdyT25f0G3SQK5u9b0_5JK2gNhR4l0j_5_b_B-uPxzyFF0jtLCZFHKW2-pD7e2P4RVOfbgRALwBXM-hQvhcoaxxrR8tWz3JLjKxWqNIhTrsJdATsAnUO0EnQ5U8JNXClmS9LvWwyTf-0ZqokYXTkjdfYDUAm6KiAGNJo4oX99GUBQZllyiIDprF07KeqjsMNMg4dbmMh0t6jl-UEiUaV3j1xRG8UyyA/https%3A%2F%2Fgithub.com%2Fneurogenomics%2FewceData)
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://secure-web.cisco.com/1yfwFXFFfUKBuFTwUeuS8XGYbh53YduG9ZGKMVmVU9Yrgxg4DbKA0_prEIOCNcgc8uANWYzUw115x_8njawa33mjqM5ZBEvTPTJhmXRzttl1eaRVu3Pa0FTA-d-wPRK3Xxa4miiXob79k_exN0isifYlHPTK7WRxh9_LbFye17PwVVOGsfxjEFKi8WF27D6LWJynf8k-L7iEqB2MSDkf_1zWmfA2qJByna147_Jkaa-nLx9FFl4VhsosBoNDE_qnC939XrCLLCT7RgV0jPukrVdahccxXfT6bgtGBR8ZKfj25BoCeE1_hTJXFgGP0CGmegMYqqmsbd3pGTbo63vTW-A/http%3A%2F%2Fbioconductor.org%2Fdevelopers%2Fhow-to%2Flong-tests%2F)
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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