Rainer M Krug <rai...@krugs.de> writes:
> Andreas Leha <andreas.l...@med.uni-goettingen.de> writes:
>
>> Hi Rainer,
>
> Hi Andreas,
>
>>
>> Rainer M Krug <rai...@krugs.de> writes:
>>> "Charles C. Berry" <ccbe...@ucsd.edu> writes:
>>>
>>>> On Wed, 17 Jun 2015, William Denton wrote:
>>>>
>>>>> On 17 June 2015, Xebar Saram wrote:
>>>>>
>>>>>> I do alot of modeling work that involves using huge datasets and run
>>>>>> process intensive R processes (such as complex mixed models, Gamms etc).
>>>>>> in
>>>>>> R studio all works well yet when i use the orgmode eval on R code blocks
>>>>>> it
>>>>>> works well for small simple process but 90% of the time when dealing with
>>>>>> complex models and bug data (up to 256GB) it will just freeze emacs/ess.
>>>>>> sometimes i can C-c or C-g it and other times i need to physically kill
>>>>>> emacs.
>>>>>
>>>>> I've been having the same problem for a while, but wasn't able to
>>>>> isolate it any more than large data sets, lack of memory, and heavy
>>>>> CPU usage. Sometimes everything hangs and I need to power cycle the
>>>>> computer. :(
>>>>>
>>>>
>>>> And you (both) have `ess-eval-visibly' set to nil, right?
>>>>
>>>> I do statistical genomics, which can be compute intensive. Sometimes
>>>> processes need to run for a while, and I get impatient having to wait.
>>>>
>>>> I wrote (and use) ox-ravel[1] to speed up my write-run-revise cycle in
>>>> org-mode.
>>>>
>>>> Basically, ravel will export Org mode to a format that knitr (and the
>>>> like) can run - turning src blocks into `code chunks'. That allows me
>>>> to set the cache=TRUE chunk option, etc. I run knitr on the exported
>>>> document to initialize objects for long running computations or to
>>>> produce a finished report.
>>>>
>>>> When I start a session, I run knitr in the R session, then all the
>>>> cached objects are loaded in and ready to use.
>>>>
>>>> If I write a src block I know will take a long time to export, I
>>>> export from org mode to update the knitr document and re-knit it to
>>>> refresh the cache.
>>>
>>> I have a similar workflow, only that I use a package like
>>> approach, i.e. I tangle function definitions in a folder ./R, data into
>>> ./data (which makes it possible to share org defined variables with R
>>> running outside org) and scripts, i.e. the things which do a analysis,
>>> import data, ... i.e. which might take long, into a folder ./scripts/. I
>>> then add the usual R package infrastructure files (DESCRIPTION,
>>> NAMESPACE, ...).
>>> Then I have one file tangled into ./scripts/init.R:
>>>
>>> #+begin_src R :tangle ./scripts/init.R
>>> library(devtools)
>>> load_all()
>>> #+end_src
>>>
>>>
>>> and one for the analysis:
>>>
>>> #+begin_src R :tangle ./scripts/myAnalysis.R
>>> ## Do some really time intensive and horribly complicated and important
>>> ## stuff here
>>> save(
>>> fileNames,
>>> bw,
>>> cols,
>>> labels,
>>> fit,
>>> dens,
>>> gof,
>>> gofPerProf,
>>> file = "./cache/results.myAnalysis.rds"
>>> )
>>> #+end_src
>>>
>>>
>>> Now after tangling, I have my code easily available in a new R session:
>>>
>>> 1) start R in the directory in which the DESCRIPTION file is,
>>> 2) run source("./scripts/init.R")
>>>
>>> and I have all my functions and data available.
>>>
>>> To run a analysis, I do
>>>
>>> 3) source("./scripts/myAnalysis.R")
>>>
>>> and the results are saved in a file fn
>>>
>>> To analyse the data further, I can then simply use
>>>
>>> #+begin_src R :tangle ./scripts/myAnalysis.R
>>> fitSing <- attach("./cache/results.myAnalysis.rds")
>>> #+end_src
>>>
>>>
>>> so they won't interfere with my environment in R.
>>>
>>> I can finally remove the attached environment by doing
>>>
>>> #+begin_src R :tangle ./scripts/myAnalysis.R
>>> detach(
>>> name = attr(fitSing, "name"),
>>> character.only = TRUE
>>> )
>>> #+end_src
>>>
>>> Through these caching and compartmentalizing, I can easily do some
>>> things outside org and some inside, and easily combine all the data.
>>>
>>> Further advantage: I can actually create the package and send it to
>>> somebody for testing and review and it should run out of the box, as in
>>> the DESCRIPTION file all dependencies are defined.
>>>
>>> I am using this approach at the moment for a paper and which will also
>>> result in a paper. By executing all the scripts, one will be able to do
>>> import the raw data, do the analysis and create all graphs used in the
>>> paper.
>>>
>>> Hope this gives you another idea how one can handle long running
>>> analysis in R in org,
>>>
>>> Cheers,
>>>
>>> Rainer
>>>
>>
>> That is a cool workflow. I especially like the fact that you end up
>> with an R package.
>
> Thanks. Yes - the idea of having a package at the end was one main
> reason why I am using this approach.
>
>
>>
>> So, I'll try my again. Is there there any chance to see working
>> example of this? I'd love to see that.
>
> Let's say I am working on it. I am working on a project which is using
> this workflow and when it is finished, the package will be available as
> an electronic appendix to the paper.
>
> But I will see if I can condense an example and blog it - I'll let you
> kow when it is done.
>
Thanks! Either way, I am really looking forward to this.
Regards,
Andreas
