Old versions of the vim-r-plugin offered an option for choosing whether all vim/gvim instances used their own R processes (and screen sessions) or a single shared process (using the vimrplugin_single_r variable). See section 5.2 of the documentation of version 101217.
The current version of the plugin use separate R processes by default and despite my best efforts I am at a loss on how to change it back to a single process. Does anybody know how?
Edit
The best solution I have found is to use version 0.9.9.1, which was the last version including this option.
Related
My problem is that I can't use R-studio at my work place as the IT does not support it . I want to use R and R-studio that installed on my personnel laptop on my company laptop ( using a modern browser which is behind firewall ) . Some of the options I am thinking of two two things
should I need to build a docker for R and R-studio (I see base images are already available) , I am mostly interested in basic R , Dplyr (haven ,xporter, and Reticulate ) packages .
Should I have to use a binder . I am not technical person and my programming skills are very limited can any one suggest me way .
What exactly are the difference between using Docker option vs Binder ?
I know I can use R-Studio online and get my work done but with the new paid account I am running out of project hours and very slow sometimes . Thanks in advance
Here are some examples beyond the modern RStudio MyBinder example:
https://github.com/fomightez/pythonista_skewedf
https://github.com/fomightez/r_phylogenetics_worshop
https://github.com/fomightez/chapter7/tree/master/binder
The modern RStudio MyBinder example has been set as a template on GitHub so you can use
The first one is for a special use of a package not on conda. And I started that one from square one.
The other two were converted from content by others to aid in making them Binder-ready.
You essentially list everything you need from conda in the environment.yml along with the appropriate channels. If you need special stuff not on conda, you need the other configuration files included there.
Getting everything working can take some iterations on adding things, letting the image get built, and testing your libraries are available. Although you seem to think your situation is not overly complex.
The binder launch badges you see are just images where you modify the URL to point the MyBinder federation site at your repository. Look at the URL and you should see the pattern where you put studio at the end of the URL pointing at your repo. The form at MyBinder.org site can help with this; however, most often it is easier to just adapt a working launch badge's code copied from elsewhere. The form isn't set up at this time for making the URLs for launching to RStudio.
Download anything useful your create in a running session. The sessions timeout after 10 minutes, although RStudio usually keeps them active.
Lack of Persistence and limited memory, storage, & power can be drawbacks. The inherent reproducibility and portability are advantages.
MyBinder.org doesn't work with private repos. If you have code you don't want to share, you can upload it to the temporary session, using the repo for specifying the environment. You could host a private binderhub that does allow the use of private git repositories; however, that is probably overkill for your use case and exceed your ability level at this time.
GitHub isn't the only place to host repositories that can be pointed at the MyBinder system. If you go to the MyBinder.org page and click where it says 'GitHub' on the left side of the top line of the form, you can see a list of the sources at which you can host a repository and point the system to build an image and launch a container with that specified image.
Building the image from a source repository takes some minutes the first time. Once the image is built though on the service, launch is typically less than 30 seconds. Each time you make a change on the source repo, a build is necessary. Some changes don't cause the new build to be as long as the initial one as some optimizing is done to only build what is necessary after a change. Keep in mind there are several members of the federation around the workd and if traffic on the internet gets sent to where the built image isn't yet available, it will be built from scratch again first.
The Holepunch project is out there to offer some help for users working in the R ecosystem; however, with the R-Conda system that is now integrated into MyBinder it is pretty much as easy to do it the way I described. Last I knew, the Holepunch route makes a Dockerfile that isn't as easy to troubleshoot as using the current the R-Conda system route. Dockerfiles are essentially a last ditch configuration file that MyBinder can handle. The reason being the other configuration files are much easier and don't require knowing Dockerfile syntax. MyBinder aims to offer the ability to take advantage of Docker offering containers with a specified environment without users needing to know anything about Docker.
There is a Binder Help category for posting to get help at the Jupyter Discourse Forum. Some other examples of posts already there may help you troubleshoot.
Notice of a common pitfall
Most of the the configuration files for making a repository Binder-ready are simply text and can be edited right in the GitHub browser interface, without need to git or even cloning the repo locally.
Last I knew, there are two exceptions to this. The postBuild and start configuration files have settings that allow them to be run as scripts and these get altered in a way they no longer work if you edit them via the GitHub browser interface. (This was my experience when last I tried. Your mileage may vary or things may have changed now.) To edit those, you have to have git available on a system you have and pull one from some other source. Then edit that on your machine that has git working & add it your repo and push it back up from your local computer.
(If this is a problem, you can post in the Jupyter Discourse Forum Binder help category and you and I could coordinate where I fork and edit those files in your repo to your specifications and then make a pull request to update your source of the fork with those changes.)
If you are using Jupyter notebooks extensively then it may make sense to use Binder
But if you simply want to use R and Rstudio, then all you need is docker. A good resource is
https://github.com/rocker-org/rocker
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.
In RStudio there's a Tools menu which allows you to select an installed version/architecture of R under Global Options.
That's great, but my issue with that is that, as the name implies, it is a Global option, so once you select a different architecture (or version number) you then have to restart RStudio and it applies to all of your RStudio instances and projects.
This is a problem for me because:
I have some scripts within a given project that strictly require 32-bit R due to the fact that they're interfacing with 32-bit databases, such as Hortonworks' Hadoop
I have other scripts within the same project which strictly require 64-bit R, due to (a) availability of certain packages and (b) memory limits being prohibitively small in 32-bit R on my OS
which we can call "Issue #1" and it's also a problem because I have certain projects which require a specific architecture, though all the scripts within the project use the same architecture (which should theoretically be an easier to solve problem that we can call "Issue #2").
If we can solve Issue #1 then Issue #2 is solved as well. If we can solve Issue #2 I'll still be better off, even if Issue #1 is unsolved.
I'm basically asking if anyone has a hack, work-around, or better workflow to address this need for frequently switching architectures and/or needing to run different architectures in different R/RStudio sessions simultaneously for different projects on a regular basis.
I know that this functionality would probably represent a feature request for RStudio and if this question is not appropriate for StackOverflow for that reason then let me know and I'll delete it. I just figured that a lot of other people probably have this issue, so maybe someone has found a work-around/hack?
There's no simple way to do this, but there are some workarounds. One you might consider is launching the correct bit-flavor of R from the current bit-flavor of R via system2 invoking Rscript.exe, e.g. (untested code):
source32 <- function(file) {
system2("C:\\Program Files\\R\\R-3.1.0\\bin\\i386\\Rscript.exe", normalizePath(file))
}
...
# Run a 64 bit script
source("my64.R")
# Run a 32 bit script
source32("my32.R")
Of course that doesn't really give you a 32 bit interactive session so much as the ability to run code as 32 bit.
One other tip: If you hold down CTRL while launching RStudio, you can pick the R flavor and bitness to launch on startup. This will save you some time if you're switching a lot.
This is sort of related to a previous post of mine. I have the need to use the bigmemory library on my 32bit Windows PC to do some ugly matrix calculations. Unfortunately, it appears that the maintainers have temporarily ceased production of Windows binaries. I have Ubuntu on my home PC. I would really like to take the .tar.gz file and build it into a Windows binary that I can actually run at work. I realize there are more efficient ways, like installing RTools on the Windows device. However, our IT keeps our admin rights on lockdown, so I can never edit my PATH enviro variable. Could anyone provide some general guidance for doing this? Are there any tools I need to install on my Ubuntu PC above and beyond R?
I found similar questions, but nothing that thoroughly answered my questions.
Unless the package source is incompatible with current versions of R, you could use the R project's win-builder site to build a Windows binary. Quoting from the linked site, win-builder is a service:
intended for useRs who do not have Windows available for checking and building Windows binary packages.
As a convenience, Hadley Wickham's devtools package includes a utility function, build_win(), that you can use for this purpose. From ?build_win:
Works by building source package, and then uploading to http://win-builder.r-project.org/>. Once building is complete you'll receive a link to the built package in the email address listed in the maintainer field. It usually takes around 30 minutes.
Windows has four sets of environment variables (system, user, volatile and process sets). The first three sets are stored in the registry but the process set is not so even if they have locked down the registry its typically still possible to set the process environment variables (including the PATH) in a local process, i.e. on a temporary basis, so you might double check your assumptions that you can't modify anything. Its more likely that you can't modify the system variables and registry but can still modify the set in your local process. To check this from the Windows cmd line enter this:
set mytest=123
set mytest
and if the second line shows that mytest has the value 123 then you likely have all the permissions you need.
Furthermore anything you need to set is all handled automatically for you by R.bat in the batchfiles distribution so you don't have to set anything yourself.
Just ensure that Rtools and R are installed into the standard locations (you can tell them to skip the setting of any registry keys during the installation process), ensure R.bat is on your path or in current directory and run:
R.bat CMD INSTALL mypackage.tar.gz
without setting environment variables, registry keys or path.
If that does not work try Rpathset.bat also from the batchfiles which is not automatic like R.bat but on the other hand is extremely flexible since you must modify the SET statments in it to whatever you want.
There is a PDF document that comes with the batchfiles which gives more info.