I have R installed on one linux computer where there are number of packages installed. Now I am setting up R on another linux computer. Installing R is easy from their repository but I will have to install many packages using
install.packages("pkgname")
which will involve repeat downloading as well. Is there any way I can copy all the installed packages from first computer to the second one? Thanks for your help.
I would recommend against this approach. Some of those packages will have been installed from source which includes compile-time checks based on what is installed on "computer one" and that will not necessarily be true on the other computer.
You have two basic choices
Use binary package (ie r-cran-pkgname for various packages). These will work but a) not all of CRAN exists that way and b) they may lag the current release.
Install from source. Just run saveRDS(installed.packages(), file="/tmp/pkgs.rds") on the first computer and pkgs <- readRDS("/tmp/pkgs.rds"); install.packages(rownames(pkgs)) on the second after transfering the file.
Related
We have a ubuntu linux server in our office which is a air-gapped environment. There is no internet access to external network.
However I would like to install few R packages like ggplot2, Database Connector, dplyr, Tidyverse etc. I have more than 10-15 packages to download
While I cannot write the usual command install.packages("DatabaseConnector"), I have to download the zipped folders from CRAN as shown here.
I am new to R. So, can you help me with my questions given below?
a) Why is there are no files for linux systems? I only see windows binaries and macOS binaries. Which one should I download?
b) Should I download binaries or package source? which one is easy to install?
c) When I download packages like above as zipped file from CRAN like shown here, will the dependencies be automatically downloaded as well? Or should I look at error messages and keep downloading them one by one?
d) Since I work in a Air-gapped environment, what would be the best way to do this process efficiently.
Under linux packages are always installed from source. There are no official binary packages for linux. However, your distro might offer some of them in the official repositories. Ubuntu does. However these tend to be quite old versions and usually limited to a handfull of the most important packages. So, for linux you have to download the source packages. The zip files are for windows and will not work.
You will also need to download all of the dependencies of the packages. For something like tidyverse this will be a huge number. Tracking those by hand is a lot of work. Easiest is probably to use a package like miniCRAN outside of your airgapped system to build a selective copy of CRAN. You can specify the packages you want and the package will download all dependencies. You can then copy the downloaded directories to your server, point install.packages in the right direction and install as usually using install.packages. For details see https://andrie.github.io/miniCRAN/articles/miniCRAN-introduction.html.
You might also run into the problem that your system does not have all of the depencies needed to build all of the packages. Under ubuntu you need for example to install libxml2-dev to be able to install the xml package. For that you need to use the package manager of ubuntu. How to do that on an airgapped system is another issue
When I install the packages in R, sometimes it is used by devtools::install_github(). other times it is used by install.packages().
Could I ask what is the essential difference between them?
R's official repository for packages is located on CRAN (Comprehensive R Archive Network). The process of publishing a package there is very strict and is reachable via install.packages(). For the most part, binary packages (opposed to source code, which is not "properly translated" yet) are available and no additional tools need to be present for proper installation (see next paragraph).
GitHub is one of many webservices that offers repositories for code, incl. R code. Author can upload her or his package and if everything is in its place, the user can install a package from source via devtools::install_github(). This means you need to have a proper toolchain installed (also a distributoin of LaTeX). In Windows, this means Rtools. Linux based OS are likely to be shipped with most of the necessary tools.
I have found an old R package with a .zip extension on my PC.
I would like to run it, but I do not have the tar.gz that was used to
create it and I use linux. What are my options?
Few, essentially.
A .zip package for R is almost surely a binary built for Windows so you need to find a suitable Windows computer -- or emulator -- to use it.
So, this can be done this way:
install wine (wine is not an emulator),
install R for Windows, which you download manually from CRAN
install the zip package using the usual commands (install.packages("filename.zip",source=NULL)). You will probably get error messages for the dependencies, but incrementally installing those, it should work.
My work setup relies heavily on rcom library, which is so far unavailable for R 3.x (due to licensing problems).
I'm happy to keep using the R v. 2.15.3, but the install.packages doesn't fetch new packages anymore.
Is there any way of making old R download packages on demand with something like install.packages("MASS")?
OK, this is the fallback solution:
We can always download (windows) old packages from this location on the CRAN site and unpack the ZIPs into C:\Program Files\R\R-2.15.3\library or equivalent. But it would be cool to use some form of simple R command for doing it.
It is also possible to try using different mirrors, because mirrors are not equivalent with respect to volume of archives.
At least this is how I do it on (virtual) Windows XP 32 bit.
I am developing a framework for reproducible computing with R. One problem that I am struggling with is that some R code might run perfectly in version X.Y-Z of a package, but then why you try to reproduce it 3 years later, the packages have updated, some functions are changed, and the code doesn't run anymore. This problem affects also for example Sweave documents that use packages.
The only way to confidently reproduce the results is by installing the R version and version of the packages that were used by the original author. If this was a single case, one could pull stuff from the CRAN archives and install appropriate versions. But for my framework this is impractical, and I need to have the package versions preinstalled.
Assume for now that I restrict myself to a single version of R, e.g. 2.14. What would be a practical way to install many versions of R packages, so that I can load them on the fly? I suppose I can do something like creating separate library directories for every version of every package and then using custom lib.loc arguments while loading them. This is going to be messy though. Any tips or previous attempts to do something similar?
My framework runs on Ubuntu server.
You could install packages with versions (e.g. rename to foo_1.0 directory instead of foo) and softlink the versions you want to re-create a given R + packages snapshot into one library. Obviously, the packages could actually live in a separate tree, so you could have library.projectX/foo -> library.all/foo/1.0.
The operating system gives you even more handles for complete separation, and the Debian / Ubuntu stack as a ton of those available. Two I have played with are
chroot environments: We use this to complete separate build environments from host machines. For example, all Debian uploads I produced are built in a i386 pbuilder chroot hosted on my amd64 Ubuntu server. Chroot is a very powerful Unix system call. Chroots, and particularly the pbuilder system built on top of it (for Debian package building) are meant to operate headless.
Virtual machines: This gives you full generality. My not-so-powerful box easily handles three virtual machines: Debian i386, Ubuntu i386 as well as Windoze XP. For this, I currently use KVM along with libvirt; this is Linux specific. I have also used VirtualBox and VMware in the past.
I would try to modify the DESCRIPTION file, and change the field "Package" there by adding the version number.
For example, you download the package source a from CRAN page (http://cran.r-project.org/web/packages/pls/). Unpack the compressed file (pls_2.3-0.zip) to a directory ("pls/"). The following steps are to change the package name in DESCRIPTION ("pls/DESCRIPTION") and installation with R command 'R CMD INSTALL pls/', where 'pls/' is a path to the package source with modified DESCRIPTION file.
Playing with R library paths seems a dangerous thing to me.