(sticking bioc-devel back in the recipient list so others can learn / improve / 
disagree with this suggestion.)

my suggestion was to memorize the function in your package, not in the example. 
Examples are not run independently, but collated into a single file (EWCR-Ex.R 
in the EWCR.Rcheck directory, after running R CMD check) and sourced. And the 
suggestion was not to solve the problem of examples running slowly, but 
avoiding repeatedly calculating the same value. For instance, from Hervé’s 
email ewceData::tt_alzh could be memorized in the package. The first call would 
take several seconds, but subsequent calls would be instantaneous. But as Hervé 
says that function should be cleaned up anyway so that 'tricks' like 
memorization might not be necessary.


From: "Murphy, Alan E" <a.mur...@imperial.ac.uk>
Date: Monday, March 22, 2021 at 12:37 PM
To: Martin Morgan <mtmorgan.b...@gmail.com>
Subject: Re: [Bioc-devel] Methods to speed up R CMD Check

Hey Martin,

Thanks for the suggestion but how would I go about using this, let's say, for 
the examples? If I redefine the memoise function in each example (as it won't 
otherwise exist) would this not take the same amount of time?

Kind regards,
Alan.

From: Martin Morgan <mtmorgan.b...@gmail.com>
Sent: 22 March 2021 13:34
To: Kern, Lori <lori.sheph...@roswellpark.org>; Murphy, Alan E 
<a.mur...@imperial.ac.uk>; 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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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://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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