I recently began receiving warnings that prior installations of R packages cannot be removed when I try to re-install packages:
install.packages("gtools")
#> Warning: cannot remove prior installation of package ‘gtools’
#> Warning: restored ‘gtools’
I found solutions to this issue encouraging me to delete the packages manually from my library folder, which I could find with .libPaths(). However, (a) this seems like a way of addressing symptoms rather than the underlying issue (which remains unclear) and (b) there are two paths for seemingly different versions of R and I'm not sure which to delete from anyway:
.libPaths()
#> [1] "C:/Users/foo/Documents/R/win-library/4.1"
#> [2] "C:/Program Files/R/R-4.1.2/library"
How can I fix the problem so I don't have to manually delete package folders every time I want to re-install a package? If there is no alternative, do I need to delete the subdirectories for the package from one of those folders or both? FWIW, I'm working in RStudio.
The problem is that you have installed packages using different permissions. On Windows, you need elevated permissions to write to Program Files. At some point you (or an admin) probably used "Run as admin" to install gtools there, and now using regular permissions you can't delete that.
You should be able to delete the Users/foo copy, if you are running as user foo, but even that one may have had permissions changed. But I'd guess the issue is that gtools is in the Program files location.
The error message from R doesn't tell you which location it is trying to delete from, which is unfortunate. In fact, allowing installations of different versions in those two locations is a bad design feature in R that just leads to confusion, because you don't necessarily always use the same version each time you load packages. (The rule for which one you use is the first acceptable one found in the .libPaths list, but since you can change .libPaths, and since packages can load other packages, it's hard to predict which one you'll have loaded at any given time.)
To fix this, you can delete both copies (if you have two) and start over, but that's risky because other packages might be depending on gtools. If you are the only user on your computer, you could instead delete the entire "C:/Users/foo/Documents/R/win-library/4.1" library, and then do all your installs using "Run as admin", but that's also easy to mess up.
(On a Mac, that's effectively what happens, because most single user systems put the user in the "admin" group, so they can always install packages to the system location. It causes a lot less confusion, but some "purists" think the Windows way is better.)
So I don't have any good advice for you, but maybe this explains the situation, and you can work out for yourself the best way forward.
Related
I added the package Knet with Pkg.add("Knet") and noticed that several packages were installed including CUDA. However, after the installation finished when I try:
using CUDA
it says that this package is not found but that it is available from a registry. It seems that this package is a requirement for Knet and it is installed but then one cannot access it right away. Do you know what is happening behind scenes? Thanks.
The underlying mechanism is a bit complex, and is described in detail here.
But the general logic is as follows: you can use (with using or import) the packages that you have explicitly installed. However, such packages might depend on other packages. Julia will automatically decide what other packages are needed to be installed, but they will be not visible in your project unless you explicitly install them.
In fact, typically, on one computer you will have hundreds of packages installed in one place (to avoid having to download and precompile them each time), but each individual project will have access only to packages that you explicitly specify you want to use in this project. The information what packages should be visible in an individual project is typically contained in the Project.toml file as is described here.
You can find more information how to manage projects in Julia here.
I'm trying to use a linux server with R installed. Apparently the R system library has old versions of non-base packages installed like dplyr and testthat.
Because i don't have permission to edit the system library, i'm unable to update the packages.
My plan is to only use a user library, so I can controll the package versions myself. However i'm unable to remove the "/usr/lib64/R/library" folder from .libPaths(). I tried changing the environment variables R_LIBS_SITE and R_LIBS with the .Renviron and .Rprofile files to a different folder, but the /usr/lib64/R/library folder will always be present. Removing it with the command .libPaths(.libPaths()[1:2]) doesn't work either.
Is there a way to remove the system library from .libPaths(), so I'm not depending on the update policy of the server admin?
You can't remove the system library, because that's where the base packages live. They can't be installed anywhere else, and R won't work without them.
Best would be for you to get your sysadmin to update the system library. Those obsolete packages probably contain bugs.
If you can't do that, then run update.packages(instlib = "local") to install all the latest versions in the library named "local". (Substitute your own local lib name, of course.) This requires all your users to specify .libPaths("local") when they start, and some will likely forget, so it's not as good.
It might be easiest for you to just install a full copy of R in your own account. Then you'll have control of things, and anyone using your copy will get your library.
(There's a new release (3.5.3) coming in ten days; you might wait for that, or install one of the betas or RCs, which should be available now, then update again when the final release arrives.)
For me, it works to use
.libPaths(.libPaths()[2:1])
This will still search the system library, but only after it searches my personal library, so if I have a newer version, it uses that. Note: I used .libPaths()[2:1] not .libPaths()[1:2]
The default R library, .Library, is normally not writeable under Windows.
You need to run R as Administrator. For new packages you can set and use a personal library, but this doesn't work when updating packages in the base setup (e.g. by update.packages()).
If you forget (or don't know you need) to run as Administrator, you get duplicate versions of the same packages, messing the installation.
I think one solution could be copying all packages to a personal library and disabling the default one. I know how to add a new library path to R, i.e. .libPaths ("my/path"), but how to remove the default library from .libPaths ()?
Update for non-Windows users
Some clarifications might help mostly non-Windows R users to understand the mentioned problem.
In Windows "Log on as Administrator" (or better as a user belonging to administrators' group) and "Run as Administrator" are quite different things.
In the former case you just give your credentials at logon, much like in Linux; in the latter you are already logged as a "superuser", but in order to carry out a potentially dangerous action, you have to ask an ad hoc permission to Windows (proving that it's you and not a malware acting).
That's said, programs (and developers), before accessing known Windows' protected objects (i.e. C:\Program Files folder), ask permission to the user to avoid being blocked by the OS.
Even when they don't ask (because they assume the knowledgeable user should give this permission in advance), failure to access is normally reported like "Permission denied to access to folder etc.".
As for R version 3.0.2, update.packages() involves one of the situations, where an elevated permission request should be triggered, because this might involve writing to protected program folders. Unfortunately R doesn't ask and cannot update the directory with old packages.
What about the second safe net: user notifications? While install.packages() gives messages like:
stop ... "'lib' element %s is not a writable directory" ...
and you get the idea of a permission problem, with others functions, such as update.packages(), you get:
warning ... "package '%s' in library '%s' will not be updated"
whose causes can be everything.
Can this scenario be even worse? Yes. Besides not asking for permission to write to "Program Folders", besides not issuing a notification of the permission error, update.packages(), when unable to update packages in protected folders, actually installs them to the personal user folder, but without notifying this. This is similar to what install.packages() does, except that the latter notifies and asks permission to do this.
So you end up with two versions of the same packages in different folders! Your calculations will be therefore dependent on library priorities.
Can this scenario be even worse? Yes. You are clever (or Google) enough to understand that you need to "Run as Administrator", when you want to update packages. You restart R as Administrator and hope this will fix everything. Not at all. R sees the updated packages in the personal library and does not act. So you remain with two versions of the same packages.
To solve this you have to detect duplicate packages and remove them manually, then restart R as administrator and update again (or write a script to do this).
Clearly the solution would be R conforming to Windows apps expected behaviour, or at least do nothing when prevented to act (instead of taking non-notified decisions).
In the meantime I think that totally disabling the default library (located in a protected area) would be a temporary workaround.
A final note. Packages and package updating are crucial for using R, so my humble opinion is that the topic should deserve specific careful attention even for less GNU-blessed systems like Windows.
One solution is to change R_LIBS environment variable. You can see for example this question.
But If you don't have admin rights, you can specify location when you load the package:
library(my_package, lib.loc="my/path")
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.
In order to be able to compare two versions of a package, I need to able to choose which version of the package that I load. R's package system is set to by default to overwrite existing packages, so that you always have the latest version. How do I override this behaviour?
My thoughts so far are:
I could get the package sources, edit the descriptions to give different names and build, in effect, two different packages. I'd rather be able to work directly with the binaries though, as it is much less hassle.
I don't necessarily need to have both versions of the packages loaded at the same time (just installed somewhere at the same time). I could perhaps mess about with Sys.getenv('R_HOME') to change the place where R installs the packages, and then .libpaths() to change the place where R looks for them. This seems hacky though, so does anyone have any better ideas?
You could selectively alter the library path. For complete transparency, keep both out of your usual path and then do
library(foo, lib.loc="~/dev/foo/v1") ## loads v1
and
library(foo, lib.loc="~/dev/foo/v2") ## loads v2
The same works for install.packages(), of course. All these commands have a number of arguments, so the hooks you aim for may already be present. So don't look at changing R_HOME, rather look at help(install.packages) (assuming you install from source).
But AFAIK you cannot load the same package twice under the same name.
Many years have passed since the accepted answer which is of course still valid. It might however be worthwhile to mention a few new options that arised in the meanwhile:
Managing multiple versions of packages
For managing multiple versions of packages on a project (directory) level, the packrat tool can be useful: https://rstudio.github.io/packrat/. In short
Packrat enhances your project directory by storing your package dependencies inside it, rather than relying on your personal R library that is shared across all of your other R sessions.
This basically means that each of your projects can have its own "private library", isolated from the user and system libraries. If you are using RStudio, packrat is very neatly integrated and easy to use.
Installing custom package versions
In terms of installing a custom version of a package, there are many ways, perhaps the most convenient may be using the devtools package, example:
devtools::install_version("ggplot2", version = "0.9.1")
Alternatively, as suggested by Richie, there is now a more lightweight package called remotes that is a result of the decomposition of devtools into smaller packages, with very similar usage:
remotes::install_version("ggplot2", version = "0.9.1")
More info on the topic can be found:
https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages
I worked with R for a longtime now and it's only today that I thought about this. The idea came from the fact that I started dabbling with Python and the first step I had to make was to manage what they (pythonistas) call "Virtual environments". They even have dedicated tools for this seemingly important task. I informed myself more about this aspect and why they take it so seriously. I finally realized that this is a neat and important way to manage different projects with conflicting dependencies. I wanted to know why R doesn't have this feature and found that actually the concept of "environments" exists in R but not introduced to newbies like in Python. So you need to check the documentation about this and it will solve your issue.
Sorry for rambling but I thought it would help.