When developing your own R package locally on your machine with devtools, is there is an editable install option? I.e. if you make a local package in python you can pip install with -e which means that if you then change the scripts of the package then it automatically updates your package rather than working with a frozen version since (I believe) it creates pointers to scripts rather than compiled copies. The alternative of removing and reinstalling the package each time is 20 seconds of my life I could do without after each change. I know I could source(rscript) but then I would lose the fact it is a package, for example package::function would no longer work.
Related
I am facing a particularly vexing problem with R package development. My own package, called ggstatsplot (https://github.com/IndrajeetPatil/ggstatsplot), depends on userfriendlyscience, which depends on another package called MBESS, which itself ultimately depends on another package called gsl. There is no problem at all for installation of ggstatsplot on a Windows machine (as assessed by AppVeyor continuous integration platform: https://ci.appveyor.com/project/IndrajeetPatil/ggstatsplot).
But whenever the package is to be installed on Unix machines, it throws the error that ggstatsplot can't be downloaded because userfriendlyscience and MBESS can't be downloaded because gsl can't be downloaded. The same thing is also revealed on Travis continuous integration platform with virtual Unix machines, where the package build fails (https://travis-ci.org/IndrajeetPatil/ggstatsplot).
Now one way to solve this problem for the user on the Unix machine is to configure GSL (as described here:
installing R gsl package on Mac), but I can't possibly expect every user of ggstatsplot to go through the arduous process of configuring GSL. I want them to just run install.packages("ggstatsplot") and be done with it.
So I would really appreciate if anyone can offer me any helpful advice as to how I can make my package user's life simpler by removing this problem at its source. Is there something I should include in the package itself that will take care of this on behalf of the user?
This may not have a satisfying solution via changes to your R package (I'm not sure either way). If the gsl package authors (which include a former R Core member) didn't configure it to avoid a pre-req installation of a linux package, there's probably a good reason not to.
But it may be some consolation that most R+Linux users understand that some R packages first require installing the underlying Linux libraries (eg, through apt or dnf/yum).
Primary Issue: making it easy for the users to install
Try to be super clear on the GitHub readme and the CRAN INSTALL file. The gsl package has decent CRAN directions. This leads to the following bash code:
sudo apt-get install libgsl0-dev
The best example of clear (linux pre-req package) documentation I've seen is from the curl and sf packages. sf's CRAN page lists only the human names of the 3 libraries, but the GitHub page provides the exact bash commands for three major distribution branches. The curl package does this very well too (eg, CRAN and GitHub). For example, it provides the following explanation and bash code:
Installation from source on Linux requires libcurl. On Debian or Ubuntu use libcurl4-openssl-dev:
sudo apt-get install -y libcurl-dev
Ideally your documentation would describe how do install the gsl linux package on multiple distributions.
Disclaimer: I've never developed a package that directly requires a Linux package, but I use them a lot. In case more examples would help, this doc includes a script I use to install stuff on new Ubuntu machines. Some commands were stated explicitly in the package documentation; some had little or no documentation, and required research.
edit 2018-04-07:
I encountered my new favorite example: the sys package uses a config file to produce the following message in the R console. While installing 100+ packages on a new computer, it was nice to see this direct message, and not have to track down the R package and the documentation about its dependencies.
On Debian/Ubuntu this package requires AppArmor.
Please run: sudo apt-get install libapparmor-dev
Another good one is pdftools, that also uses a config file (and is also developed by Jeroen Ooms).
Secondary Issue: installing on Travis
The userfriendly travis config file apparently installs a lot of binaries directly (including gsl), unlike the current ggstatsplot version.
Alternatively, I'm more familiar with telling travis to install the linux package, as demonstrated by curl's config file. As a bonus, this probably more closely replicates what typical users do on their own machines.
addons:
apt:
packages:
- libcurl4-openssl-dev
Follow up 2018-03-13 Indrajeet and I tweaked the travis file so it's working. Two sections were changed in the yaml file:
The libgsl0-dev entry was added under the packages section (similar to the libcurl4-openssl-dev entry above).
Packages were listed in the r_binary_packages section so they install as binaries. The build was timing out after 50 minutes, and now it's under 10 min. In this particular package, the r_binary_packages section was nested in the Linux part of the Travis matrix so it wouldn't interfere with his two OS X jobs on Travis.
Whenever I want to run a complex application in R that needs many packages and libraries, I have to install one by one all the dependencies and then re run again to see the next dependency. I am having the same problem with running a shinydashboard app. I wonder if there is a way the first time I am about to execute an R script, at the same time to install on the fly all the dependencies this script needs. I am running an R server on windows. Thanks!
Perhaps you want to use pacman
library(pacman)
p_load(dplyr, reshape2) # install if needed, then load
I currently have testthat installed on my machine. I want to install the package and all its dependencies (recursively) into a separate library. The problem is that when I try to do this using install.packages("testthat", lib = "newdir"), its dependencies, such as xml2 are not installed along with it. How can I install a package and all its dependencies into a new library?
I would do the following:
use install2.r from littler with its -l argument for the target library (and I do things like this all the time for reverse dependency checks
maybe use a properly set/reset .libPaths() so that the current installation you are doing does not "see" the existing installations; worst case you make a copy of install2.r and set/reset .libPaths() there; you may need to experiment with Rscript vs r to launch it as r gets some values "baked in" during its compilation
Taken together it is basically what we do when we keep a separate R-devel on a box as well.
Edit: You can of course script this with install2.r -- it is just a wrapper to install.packages(). But it happens to be setting the relevant arguments.
I am trying to implement a reducer for Hadoop Streaming using R. However, I need to figure out a way to access certain libraries that are not built in R, dplyr..etc. Based on my research seems like there are two approaches:
(1) In the reducer code, install the required libraries to a temporary folder and they will be disposed when the session is done, like this:
.libPaths(c(.libPaths(), temp <- tempdir()))
install.packages("dplyr", lib=temp, repos='http://cran.us.r-project.org')
library(dplyr)
...
However, this approach will have a dramatic overhead depending on how many libraries you are trying to install. So most of the time will be wasted on installing libraries(sophisticated libraries like dplyr has tons of dependencies which will take minutes to install on a vanilla R session).
So sounds like I need to install it before hand, which leads us to approach2.
(2) My cluster is fairly big. And I have to use some tool like Ansible to make it work. So I prefer to have one Linux shell command to install the library. I have seen R CMD INSTALL... before, however, it feels like will only install packages from source file instead of doing install.packages() in R console, figure out the mirror, pull the source file, install it in one command.
Can anyone show me how to use one command line in shell to non-interactively install a R package?
(sorry for this much background knowledge, if anyone thinks I am not even following the right phylosophy, feel free to leave in the comment how this whole cluster R package should be managed.)
tl;dr
Rscript -e 'install.packages("drat", repos="https://cloud.r-project.org")'
You mentioned you are trying to install dplyr into custom lib location on your disk. Be aware that dplyr package does not support that. You can read more in dplyr#4641.
Moreover if you are installing private package published in internal CRAN-like repository (created by drat or tools::write_PACKAGES), you can easily combine repos argument and resolve dependencies from CRAN automatically.
Rscript -e 'install.packages("priv.pkg", repos=c("cran.priv","https://cloud.r-project.org"))'
This is very handy feature of R repositories, although for production use I would recommend to cache packages from CRAN locally, and use those, so you will never be surprised by a breaking changes in your dependencies. For quality information about handling R in production I suggest to look into talk by Wit Jakuczun at WhyR2019 How to make R great for machine learning in (not only) Enterprise: slides, video.
You may find littler useful. It is a command-line front-end / variant of R (which uses the R-embedding interface).
I use the install.r script all the time to install package from the shell. There is a second variant with more command-line argument parsing but it has an added dependency.
I am developing an R package, but every time I make some modifications
I have to use R CMD INSTALL to install it and see if the new version
is working.
I would like to know if there is some easier way to develop a package in R.
Specifically I would like to be able to develop the package without having
to install it every time I want to test it.
If you are familiar with Python and setuptools, I would like to achieve the
same effect you get using
python setup.py develop.
Install devtools, then all you need to do is:
require(devtools)
load_all("/wherever/your/package/is")
It reloads all the changed code in .R files, recompiles, links, loads C code and so on.
devtools will also compile your documentation, and run checks.
Nothing else comes close for package development.