I have a software system having 30+ Open Source packages, most of them using GNU Autotools suite.
Are there tools to automatically generate package-to-package dependency graph? I.e. I'd like to see something like gst-plugins-good -> gst-plugins-base -> gstreamer -> glib.
I don't think so, but you could probably whip something together with this knowledge:
Scan the file named either configure.ac or configure.in in the package's root directory.
Look for a string of the form PKG_CHECK_MODULES([...],[...]...)
The second argument of that macro consists of package requirements of the form package or package >= version separated by whitespace.
The requirement string might not be the same as the package tarball name; a tarball that contains package.pc or package.pc.in provides the package package.
This only works for dependencies that use pkg-config. Some don't and you'll need to keep track of those dependencies by hand.
Probably not, because this is a hard problem. If there were only one way to build a package, it might not be too bad, but in general this isn't the case. You have the --enable-foo and --with-foo options that you can pass into configure. Those are sometimes package dependent also, requiring more packages. Most Linux distros (I think but am not completely sure) maintain these sort of dependency lists for yum or zypper or apt or whatever the package manager is by hand, and only one layer deep, leaving it up to the package manager to traverse the graph. The packages for the distro are only built one way. It's not unusual for these lists to be broken, also.
Related
Probably a pretty basic question but a friend and I tried to run str(packge_name) and R threw us an error. Now that I'm looking at it, I'm wondering if an R package is like a .zip file in that it is a collection of objects, say pictures and songs, but not a picture or song itself.
If I tried to open a zip of pictures with an image viewer, it wouldn't know what to do until I unzipped it - just like I can't call str(forecast) but I can call str(ts) once I've loaded the forecast package into my library...
Can anyone set me straight?
R packages are generally distributed as compressed bundles of files. They can either be in "binary" form which are preprocessed at a repository to compile any C or Fortran source and create the proper headers, or they can be in source form where the various required files are available to be used in the installation process, but this requires that the users have the necessary compilers and tools installed at locations where the R build process using OS system resources can get at them.
If you read the documentation for a package at CRAN you see they are distributed in set of compressed formats that vary depending on the OS-targets:
Package source: Rcpp_0.11.3.tar.gz # the Linus/UNIX targets
Windows binaries: r-devel: Rcpp_0.11.3.zip, r-release: Rcpp_0.11.3.zip, r-oldrel: Rcpp_0.11.3.zip
OS X Snow Leopard binaries: r-release: Rcpp_0.11.3.tgz, r-oldrel: Rcpp_0.11.3.tgz
OS X Mavericks binaries: r-release: Rcpp_0.11.3.tgz
Old sources: Rcpp archive # not really a file but a web link
Once installed an R package will have a specified directory structure. The DESCRIPTION file is a text file with specific entries for components that determine whether the local installation meets the dependencies of the package. There are NAMESPACE, LICENSE, and INDEX files. There are directories named '/help', '/html', '/Meta', '/R', and possibly '/libs', '/demo', '/data', '/unitTests', and others.
This is the tree at the top of the ../library/Rcpp package directory:
$ ls
CITATION NAMESPACE THANKS examples libs
DESCRIPTION NEWS.Rd announce help prompt
INDEX R discovery html skeleton
Meta README doc include unitTests
So in the "life-cycle" of a package, there will be initially a series of required and optional files, which then get processed by the BUILD and CHECK mechanisms into an installed package, which than then get compressed for distribution, and later unpacked into a specified directory tree on the users machine. See these help pages:
?.libPaths # also describes .Library()
?package.skeleton
?install.packages
?INSTALL
And of course read Writing R Extensions, a document that ships with every installation of R.
Your question is:
What type of object is an R package?
Somehow, I’m still missing an answer to this exact question. So here goes:
As far as R is concerned, an R package is not an object. That is, it’s not an object in R’s type system. R is being a bit difficult, because it allows you to write
library(pkg_name)
Without requiring you to define pkg_name anywhere prior. In contrast, other objects which you are using in R have to be defined somewhere – either by you, or by some package that’s loaded either explicitly or implicitly.
This is unfortunate, and confuses people. Therefore, when you see library(pkg_name), think
library('pkg_name')
That is, imagine the package name in quotes. This does in fact work just as expected. The fact that the code also works without quotes is a peculiarity of the library function, known as non-standard evaluation. In this case, it’s mostly an unfortunate design decision (but there are reasons).
So, to repeat the answer: a package isn’t a type of R object1. For R, it’s simply a name which refers to a known location in the file system, similar to what you’ve assumed. BondedDust’s answer goes into detail to explain that structure, so I shan’t repeat it here.
1 For super technical details, see Joshua’s and Richard’s comments below.
From R's own documentation:
Packages provide a mechanism for loading optional code, data and
documentation as needed.…A package is a directory of files which
extend R, a source package (the master files of a package), or a
tarball containing the files of a source package, or an installed
package, the result of running R CMD INSTALL on a source package. On
some platforms (notably OS X and Windows) there are also binary
packages, a zip file or tarball containing the files of an installed
package which can be unpacked rather than installing from sources. A
package is not a library.
So yes, a package is not the functions within it; it is a mechanism to have R be able to use the functions or data which comprise the package. Thus, it needs to be loaded first.
I am reading Hadley's book Advanced-R (Chapter 6.3 - functions, p.79) and this quote will cover you I think:
Every operation is a function call
“To understand computations in R, two slogans are helpful:
Everything that exists is an object.
Everything that happens is a function call."
— John Chambers
According to that using library(name_of_library) is a function call that will load the package. Every little bit that has been loaded i.e. functions or data sets are objects which you can use by calling other functions. In that sense a package is not an object in any of R's environments until it is loaded. Then you can say that it is a collection of the objects it contains and which are loaded.
I've found several posts about best practice, reproducibility and workflow in R, for example:
How to increase longer term reproducibility of research (particularly using R and Sweave)
Complete substantive examples of reproducible research using R
One of the major preoccupations is ensuring portability of code, in the sense that moving it to a new machine (possibly running a different OS) is relatively straightforward and gives the same results.
Coming from a Python background, I'm used to the concept of a virtual environment. When coupled with a simple list of required packages, this goes some way to ensuring that the installed packages and libraries are available on any machine without too much fuss. Sure, it's no guarantee - different OSes have their own foibles and peculiarities - but it gets you 95% of the way there.
Does such a thing exist within R? Even if it's not as sophisticated. For example simply maintaining a plain text list of required packages and a script that will install any that are missing?
I'm about to start using R in earnest for the first time, probably in conjunction with Sweave, and would ideally like to start in the best way possible! Thanks for your thoughts.
I'm going to use the comment posted by #cboettig in order to resolve this question.
Packrat
Packrat is a dependency management system for R. Gives you three important advantages (all of them focused in your portability needs)
Isolated : Installing a new or updated package for one project won’t break your other projects, and vice versa. That’s because packrat gives each project its own private package library.
Portable: Easily transport your projects from one computer to another, even across different platforms. Packrat makes it easy to install the packages your project depends on.
Reproducible: Packrat records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go.
What's next?
Walkthrough guide: http://rstudio.github.io/packrat/walkthrough.html
Most common commands: http://rstudio.github.io/packrat/commands.html
Using Packrat with RStudio: http://rstudio.github.io/packrat/rstudio.html
Limitations and caveats: http://rstudio.github.io/packrat/limitations.html
Update: Packrat has been soft-deprecated and is now superseded by renv, so you might want to check this package instead.
The Anaconda package manager conda supports creating R environments.
conda create -n r-environment r-essentials r-base
conda activate r-environment
I have had a great experience using conda to maintain different Python installations, both user specific and several versions for the same user. I have tested R with conda and the jupyter-notebook and it works great. At least for my needs, which includes RNA-sequencing analyses using the DEseq2 and related packages, as well as data.table and dplyr. There are many bioconductor packages available in conda via bioconda and according to the comments on this SO question, it seems like install.packages() might work as well.
It looks like there is another option from RStudio devs, renv. It's available on CRAN and supersedes Packrat.
In short, you use renv::init() to initialize your project library, and use renv::snapshot() / renv::restore() to save and load the state of your library.
I prefer this option to conda r-enviroments because here everything is stored in the file renv.lock, which can be committed to a Git repo and distributed to the team.
To add to this:
Note:
1. Have Anaconda installed already
2. Assumed your working directory is "C:"
To create desired environment -> "r_environment_name"
C:\>conda create -n "r_environment_name" r-essentials r-base
To see available environments
C:\>conda info --envs
.
..
...
To activate environment
C:\>conda activate "r_environment_name"
(r_environment_name) C:\>
Launch Jupyter Notebook and let the party begins
(r_environment_name) C:\> jupyter notebook
For a similar "requirements.txt", perhaps this link will help -> Is there something like requirements.txt for R?
Check out roveR, the R container management solution. For details, see https://www.slideshare.net/DavidKunFF/ownr-technical-introduction, in particular slide 12.
To install roveR, execute the following command in R:
install.packages("rover", repos = c("https://lair.functionalfinances.com/repos/shared", "https://lair.functionalfinances.com/repos/cran"))
To make full use of the power of roveR (including installing specific versions of packages for reproducibility), you will need access to a laiR - for CRAN, you can use our laiR instance at https://lair.ownr.io, for uploading your own packages and sharing them with your organization you will need a laiR license. You can contact us on the email address in the presentation linked above.
I am building a debian package using dpkg.
The package has a dependency on libvirt which is not desired.
The rules file does not specify this dependency, but it is added by dpkg, I suppose due to some calls to libvirt-dev at build time.
However my package works fine without libvirt. As such, libvirt is a "Recommended" package but not "Required". How do I override this dependency and make sure it is not present in my final deb file ?
Hard to know without seeing your actual package, but I'd guess that you have a binary or shared library which is linked against libvirt. That would cause dh_shlibdeps to include libvirt in the ${shlibs:Depends} substvar.
If that's your problem, then the right fix depends on what's getting linked to libvirt. It should be straightforward to determine; just run ldd on each binary or shared library object in your package, and grep for "libvirt".
If the thing linked against libvirt is only incidental to the package, and isn't part of the main functionality, then using Recommends: would indeed be the right thing. To make dh_shlibdeps exclude that object from its dependency scanning, give it a -X option. Example target for debian/rules, assuming debhelper7-style packaging:
override_dh_shlibdeps:
dh_shlibdeps -Xname_of_your_object_to_exclude
If the thing(s) linked to libvirt actually are an important part of the package functionality, then the generated libvirt dependency is appropriate. If you still don't want it, you'll need to work out how to avoid linking against libvirt during your build.
Question: How do I move all of the most up-to-date R packages into one simple location that R (and everything else) will use from now and forever for my packages?
I have been playing around with R on Ubuntu 10.04 using variously RGedit, RCmdr, R shell, and RStudio. Meanwhile, I have installed packages, updated packages, and re-updated packages via apt, synaptic, install.packages(), etc... which apparently means these packages get placed everywhere, and (with the occasional sudo tossed in) with different permissions.
Currently I have different versions of different (and repeated) packages in:
/home/me/R/i486-pc-linux-gnu-library/2.10
/home/me/R/i486-pc-linux-gnu-library/2.14
/home/me/R/i486-pc-linux-gnu-library/
/usr/local/lib/R/site-library
/usr/lib/R/site-library
/usr/lib/R/library
First - I'm a single user, on a single machine - I don't want multiple library locations, I just want it to work.
Second - I am on an extremely slow connection, and can't keep just downloading packages repeatedly.
So - is there an easy way to merge all these library locations into one simple location? Can I just copy the folders over?
How do I set it in concrete that this is and always will be where anything R related looks for and updates packages?
This is maddening.
Thanks for your help.
Yes, it should almost work to just copy the folders over. But pre-2.14 packages WITHOUT a NAMESPACE file probably won't work in R 2.14 where all packages must have a namespace...
And you'd want to manually ensure you only copy the latest version of each package if you have multiple versions...
If you type .libPaths(), it will tell you where R looks for packages. The first in the list is where new packages are typically installed. I suspect that .libPaths() might return different things from RStudio vs. Rcmd etc.
After piecing together various bits of info here goes: A complete moron's guide to the R packages directory organization:
NB1 - this is my experience with Ubuntu - your mileage may vary
NB2 - I'm a single user on a single machine, and I like things simple.
Ubuntu puts anything installed via apt, or synaptic in:
/usr/lib/R/site-library
/usr/lib/R/library
directories. The default vanilla R install will try install packages here:
/usr/local/lib/R/site-library
Since these are system directories the user does not have write privileges to, depending on what method you are interacting with R you might be prompted with a friendly - "Hey buddy - we can't write there, you want us to put your packages in your home directory?" which seems innocent and reasonable enough... assuming you never change your GUI, or your working environment. If you do, the new GUI / environment might not be looking in the directory where the packages were placed, so won't find them. (Most interfaces have a way for you to point where your personal library of packages is, but who wants to muck about in config files?)
What seems to be the best practice for me (and feel free to correct me if I'm wrong) with a default install setup on Ubuntu, is to do any package management from a basic R shell as sudo: > sudo R and from there do your install.packages() voodoo. This seems to put packages in the usr/local/lib/R/site-library directory.
At the same time, update.packages() will update the files in /usr/lib/R/site-library and usr/lib/R/library directories, as well as usr/local/lib/R/site-library
(As for usr/lib/R/ division, it looks like /library/ has the core packages, while /site-library/ holds anything added, assuming they were installed by apt...)
Any packages previously installed and in the wrong place can be moved to the /usr/local/lib/R/site-library directory (assuming you are sudoing it) just by moving the directories (thanks #Tommy), but as usr/lib/R/ is controlled by apt - best not add or subtract anything from there...
Whew. Anyway - simple enough, and in simple language. Thanks everyone for the help.
Does R have a dependency management tool to facilitate project-specific dependencies? I'm looking for something akin to Java's maven, Ruby's bundler, Python's virtualenv, Node's npm, etc.
I'm aware of the "Depends" clause in the DESCRIPTION file, as well as the R_LIBS facility, but these don't seem to work in concert to provide a solution to some very common workflows.
I'd essentially like to be able to check out a project and run a single command to build and test the project. The command should install any required packages into a project-specific library without affecting the global R installation. E.g.:
my_project/.Rlibs/*
Unfortunately, Depends: within the DESCRIPTION: file is all you get for the following reasons:
R itself is reasonably cross-platform, but that means we need this to work across platforms and OSs
Encoding Depends: beyond R packages requires encoding the Depends in a portable manner across operating systems---good luck encoding even something simple such as 'a PNG graphics library' in a way that can be resolved unambiguously across systems
Windows does not have a package manager
AFAIK OS X does not have a package manager that mixes what Apple ships and what other Open Source projects provide
Even among Linux distributions, you do not get consistency: just take RStudio as an example which comes in two packages (which all provide their dependencies!) for RedHat/Fedora and Debian/Ubuntu
This is a hard problem.
The packrat package is precisely meant to achieve the following:
install any required packages into a project-specific library without affecting the global R installation
It allows installing different versions of the same packages in different project-local package libraries.
I am adding this answer even though this question is 5 years old, because this solution apparently didn't exist yet at the time the question was asked (as far as I can tell, packrat first appeared on CRAN in 2014).
Update (November 2019)
The new R package renv replaced packrat.
As a stop-gap, I've written a new rbundler package. It installs project dependencies into a project-specific subdirectory (e.g. <PROJECT>/.Rbundle), allowing the user to avoid using global libraries.
rbundler on Github
rbundler on CRAN
We've been using rbundler at Opower for a few months now and have seen a huge improvement in developer workflow, testability, and maintainability of internal packages. Combined with our internal package repository, we have been able to stabilize development of a dozen or so packages for use in production applications.
A common workflow:
Check out a project from github
cd into the project directory
Fire up R
From the R console:
library(rbundler)
bundle('.')
All dependencies will be installed into ./.Rbundle, and an .Renviron file will be created with the following contents:
R_LIBS_USER='.Rbundle'
Any R operations run from within this project directory will adhere to the project-speciic library and package dependencies. Note that, while this method uses the package DESCRIPTION to define dependencies, it needn't have an actual package structure. Thus, rbundler becomes a general tool for managing an R project, whether it be a simple script or a full-blown package.
You could use the following workflow:
1) create a script file, which contains everything you want to setup and store it in your projectd directory as e.g. projectInit.R
2) source this script from your .Rprofile (or any other file executed by R at startup) with a try statement
try(source("./projectInit.R"), silent=TRUE)
This will guarantee that even when no projectInit.R is found, R starts without error message
3) if you start R in your project directory, the projectInit.R file will be sourced if present in the directory and you are ready to go
This is from a Linux perspective, but should work in the same way under windows and Mac as well.