I'm trying to write some SPARQL queries in R using the rrdf package. However, I get this error every time I try to load the library.
Error: package 'rrdflibs' 1.1.2 was found, but == 1.1.0 is required by 'rrdf'
Not sure why they didn't write it as >= 1.1.0. Is what they did a good programming practice?
Go to http://cran.r-project.org/src/contrib/Archive/rrdflibs/ to retrieve an older version. This is a source archive, so you will have to be able to build from source (typically easy on Linux, pretty easy on MacOS, and hard on Windows; you can use the http://win-builder.r-project.org/ service to build a Windows binary if necessary).
Actually, based on a quick look at the package, I think you should be able to install in this case (even on Windows without Rtools) via
download.file("http://cran.r-project.org/src/contrib/Archive/rrdflibs/rrdflibs_1.1.0.tar.gz",
dest="rrfdlibs_1.1.0.tar.gz")
install.packages("rrfdlibs_1.1.0.tar.gz",repos=NULL,type="source")
because the package doesn't actually contain anything that needs to be compiled.
Don't know about programming practice, you'd have to ask the authors if they had some particular reason to do it that way. (See maintainer("rrdf").) Maybe they knew the versions would not be backward/forward compatible?
Related
I am using renv to collaborate on an R projects with my colleagues. This seems to work qute nicely as soon as everyone uses the same version of R. Is this really a mandotory prerequisite or is there a possibility to still collaborate using different versions of R?
I could not find any answers on https://rstudio.github.io/renv/index.html...
Any ideas?
You can use different versions of R; however, be aware of issues that can arise (assuming you want everyone to be using the same packages as defined in the renv.lock lockfile):
The packages as declared in the lockfile might not be compatible, or available, for different versions of R;
Packages might behave differently depending on the version of R being used;
The version of R recorded in the lockfile may change depending on the person who is generating the lockfile (via renv::snapshot()).
Ultimately, it may work fine, but if your collaborators run into issues you might want to ask them to ensure they install and use the same version of R as defined in the lockfile.
I have written an R package, hosted on Github, that I would like users to always run the most up-to-date version. What is the best practice for checking within R, preferably at package loading, if there is a newer version? And if there is a newer version to download and install it.
I know I can get the current installed version using packageVersion('MyPackage').
I guess one way to get the repository version number would be to download the DESCRIPTION file, and use some regex to search for the version number. But is there a better way?
Having established that an update is available, is there then a safe way to install it from within the same package at loading. For example having something like
if (github_ver > installed_ver) {
install_github('MyRepository/MyPackage')
}
embedded in the .onLoad() hook? Although this way looks risky to me.
The development of RStudio and the packages devtools and roxygen2 has made R package creation pretty easy. I use GitHub for version control and devtools allows others to easily install directly from my account.
As my package gradually changes with each version, I'm wondering if I should be maintaining .zip files (or other format) of my past stable builds, in case anyone would ever want to use a previous version.
It's easy to download a .zip of an R package directly from GitHub, but I'm wondering if I should add this to the same GitHub directory (e.g. https://github.com/myaccount/mypackage/previous_versions/mypackage_0.1.zip) without messing up somebody's installation via install_github("myaccount/mypackage").
So, the main Qs are:
Should I keep an old package version at all?
Should I keep old package versions in a sub-folder of my GitHub R package directory?
Should I save .zip files downloaded from GitHub as my old version, or produce a Source or Binary file during the package build itself (i.e. in RStudio)?
Is this a superfluous activity if one isn't yet willing to publish to CRAN?!
When you think your package is at a good solid place, you should tag a release. This archives the branch at that point in time and stores the zip file with the source code, and the tar.gz file.
I tend to mark my CRAN packages as a release each time I release it to CRAN (for example, see https://github.com/nutterb/pixiedust/releases) and with some intemediary tags that I consider noteworthy.
Another good strategy for managing changes in between tagged releases is to maintain a development branch below your main branch. That way your development changes won't pollute or break anything being used by those pulling from your main branch. It makes you free to experiment in the dev branch while always having a clean, working copy to push to and restore from.
1. Should I keep an old package version at all?
It's subjective, but I'd definitely say "yes" unless there's a space constraint, which is probably unlikely.
This serves 2 purposes. One is for your own convenience, such as if you want to make sure that you always have a quick way to test the results of older versions versus a newer version.
The other is that people often need older versions of packages, such as if someone wants to use your package but they're using an older version of R on a server where the policies prevent an update to R. Perhaps a newer version of your package includes a new dependency which only works with a package that depends on a certain version of R or higher.
Of course, packages can always be installed without the compressed or binary files, but it's a nice convenience.
2. Should I keep old package versions in a sub-folder of my GitHub R package directory?
I would put it in a trunk or special subfolder that won't be automatically downloaded when someone tries to install_github or clone your master branch. Having a separate branch is a good idea.
3. Should I save .zip files downloaded from GitHub as my old version, or produce a Source or Binary file during the package build itself (i.e. in RStudio)?
As the package author you're in a position to know if these differ significantly and which if either is better, but by default I'd recommend the RStudio build because I assume (if you're like me) that you're less likely to include unnecessary files this way.
4. Is this a superfluous activity if one isn't yet willing to publish to CRAN?!
No, not necessarily. If people rely on your package then it really doesn't matter if it's on CRAN or not. In fact, not being on CRAN may be a reason to be more proactive like this to ensure that your users will always have access to the needed version of your package.
We have just installed R version 2.13.0 as an RSERVE and it seems to be working well. We want to try and take advantage of pre-compiling loaded packages using the bytecode compiler. We think we got it configured correctly, but wanted to verify.
Is there a command we can issue that shows which packages are loaded in bytecode compiled form?
By default none are, so you would have to tell us more about what you did in order for us to have a chance to assess whether you were in fact successful.
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.