Can anybody give me some direction on editing source code of an R package? From what I've seen, changing the package from within R does not seem to be possible. In editing outside of R, I'm stuck at unpacking the tar.gz. While I can now modify the function to my heart's content, the folder looks nothing like the working snow library. I presume I will need to turn the contents into a tar.gz once again and install it in the standard way?
My colleagues and I have been attempting to get makeSOCKcluster() to work with remote IPs for the past three days. Hangs indefinitely. After digging into the snow package I've found the issue to be in the way newSOCKnode() calls socketConnection(). If I run makeSOCKcluster("IP", manual=T) and then put the output into powershell, it results in the connection being made but the program not completing. However, I can run makeSOCKcluster("IP", manual=T) in one R instance and then run system("output", wait=F, input="") in another instance which results in the program completing. I believe I can simply modify snow to do this automatically.
Related
I want to follow the advice I've read and heard to have both a main library in R_HOME/library and a user library. I'm using W10 on a desktop machine (not important, except that it gives me a name by which to refer to it), and I can't make R use the user library.
I have succeeded in doing that on a W10 laptop: C:/R/R-4.0.2/library contains some 30 recommended packages, and C:/Users/[username]/Documents/R/win-library/4.0 con contains a much larger number of packages in my user library.
As I recall, and as I wrote down when I did an upgrade on a server, all you have to do to create a site-library is to create a directory called C:/R/R-4.0.2/site-library, and R will use that the next time it starts.
To create a user library, create the directory C:/Users/[username]/Documents/R/win-library/4.0.
That seemed to work on my laptop, for I have seemingly a working R library and a user library there.
That seemed to work on the server, too: I have a library and a site-library.
In both cases, .libPaths() shows the same libraries that I see with Dired on the disk.
I tried to do the same thing on the desktop machine, and i can't make it work.
I created a directory C:/Users/[username]/Documents/R/win-library/4.0, restarted R, and ran .libPaths(); the only directory that was listed was C:/R/R-4.0.2/library.
Because I thought the Documents in that path seemed odd, I tried it again using C:/Users/[username]/R/win-library/4.0, still with no success.
https://cran.r-project.org/doc/manuals/r-release/R-admin.html#Managing-libraries seems pertinent, but I'm not sure how to interpret the output of Sys.getenv("R_LIBL_USER). I get "\\[toplevel]\[nextlevel]\Home$\[username]/R/win-library/4.0", which I presume is a long-winded way to get to /Home$/[username]/R/win-library/4.0 (aka C:/Users/[username]/R/win-library/4.0.
Suggestions? I've tried a number of other suggestions from SO, all to no avail.
I have two different versions of R installed, one which is up to date and which I use for all my regular R coding (needs to be up to date so that I can use various updated and new packages) and one which I use to access OLAP cubes (needs to be the R Client from Microsoft, because this is the only one which supports the olapR package, and which currently uses R version 3.4.3).
Since, in theory, I only have to access the OLAP cube once a month, I "outsourced" this task to a different RStudio project, in which I download and save the required data for all other projects. Hence, all other projects never require the olapR package to be installed and can and will be run in the up to date R version.
Now, ideally I would like to link my R version to my projects, so that I do not have to change my global R version and restart RStudio every time I access the OLAP cube or work on this data retrieval project (and then switch it back). However, I could not find any options in RStudio to achieve this result.
There are a few threads out there describing the same problem, but with no satisfactory answer in my opinion:
https://support.rstudio.com/hc/en-us/community/posts/200657296-Link-Project-and-R-Version
Rstudio project using different version of R
I also tried looking for a different package than olapR but with similar functionality, but could not find anything except X4R, which seems outdated and does not work for me (https://github.com/overcoil/X4R). Sadly, I am also unable to directly access the databases which the OLAP cube uses for its results, so I cannot go "around" it.
I am happy for any help or suggestions you can offer, whether it is a general workaround to link a project to a specific R version or the (less helpful for the community) solution of accessing the OLAP cube in a different way.
Thanks in advance!
Using the answer from MrGumble I created a .bat file that will execute my .R file using the desired R installation. Even though it is not the answer I thought I would get, I think it is an even better solution to the problem.
For all facing a similar issue, here is the .bat file (never created one before, so also had to google how to do it and I guess some might be in the same position):
#echo off
title Getting data for further processing in R
echo Retrieving OLAP data
echo.
"C:\Program Files\Microsoft\R Client\R_SERVER\bin\Rscript.exe" "C:\Users\me\Documents\Projects\!Data\script.R"
echo.
echo Saved data
echo.
pause
Thanks again to MrGumble for his help.
Skip RStudio.
RStudio is really just an editor (albeit powerful and useful) editor, which starts an R console for you (and the surrounding PATH variables, library locations, etc.).
If your monthly task only requires you to run the R-script (or a bit of interactive work), you can simply execute your preferred version of R from the command line and have it run your R script. E.g.
C:\Users\me>"C:\Program Files (x64)\Microsoft R\bin\Rscript" myscript.R
You might have to define some PATH variables so that the older R doesn't look for packages in the newer R's libraries, but that depends entirely on your current setup.
I use R, Rstudio and Rcpp and I spent over a week debugging some code, that was just giving errors and warnings in unexpected places, in some cases with direct sample code from online or package documentation.
I often restart the R session or Rstudio if there are obvious problems and they usually go away.
But this morning it was really bad to the point were basic R commands would fail and restarting R did nothing. I closed all the Rstudio sessions and restarted the machine for good measure, (which was unnecessary).
When it came back and I re-loaded the sessions everything seems to be working.
Even the some rcpp code I was working on for weeks with outside packages will now compile and run where it gave gibberish errors before.
I have known for a while that R needs to be restarted once in a while, but I know it when basic functions don't run, how can I know earlier.
I am looking for a good general resource or function that can tell me I need to restart because something is not running right. I would be nice if I can also know what to restart.
Whether the R session, the GUI such as Rstudio, all sessions and GUIs or a full machine restart.
For as long as I have been dabbling with or actually using R (ie more than two decades), it has always been recommended to start a clean and fresh session.
Which is why I prefer to work on command-line for tests. When you invoke R, or Rscript, or, in my case, r (from littler) you know you get a fresh session free of possible side-effects. By keeping these tests to the command-line, my main sessions (often multiple instances inside Emacs via ESS, possibly multiple RStudio sessions too) are less affected.
Even RStudio defaults to 'install and restart' when you rebuild a package.
(I will note that a certain development package implies you could cleanly unload a package. That has been debated at length, and I think by now even its authors qualify that claim. I don't really know or case as I don't use it, having had established workflows before it appeared.)
And to add: You almost never need to restart the computer. But a fresh clean process is a something to use often. Your computer can create millions of those for you.
One of my RStudio projects stalls even before I've run any code or loaded any packages or data. I can edit scripts but it won't save them and it won't run code in the console. I am still able to use R from the terminal. After some time (in the range of an hour or so), multiple dialog boxes will pop up with the message Unable to establish connection with R session. I've seen a similar thing before when loading big datasets or running something computationally intensive but never before I've even run any code or loaded any data.
My other Projects don't seem to have the same problem.
I've also filed an issue here on the RStudio github with some screenshots and log files in case that's helpful.
The solution was to update the data.table package. See the issue I filed or this thread on RStudio community
I'm writing an R package using devtools and roxygen2. Note that this is my first time making a package, so maybe I am missing something important.
I want to add a dependency, the package, DESeq2. I have tried to do this in a number of ways, but believe the right way is to add the following to my DESCRIPTIONS file.
Depends:
DESeq2
If I add this, when I build and reload I get a fatal error. When running in RStudio it just gives a "Fatal Error" dialog and restarts the application. It appears to build, but when it does, library(myPackage), it crashes immediately.
If I remove the Depends section from the DESCRIPTIONS file it builds and loads fine. What's more, if I include a different package, for example, ggplot2, then it builds and loads fine as well.
What about a package would cause it to fail to load as a dependency and completely crash? Is there another way to require it or is there any way to dig deeper into the cause?
Thanks very much in advance.