I was loading AIMS package and got a warning:
package ‘AIMS’ was built under R version 3.2.0
I thought R-3.1.2 ("Pumpkin Helmet") was the latest release so I checked on CRAN and couldn't see anything about a R-3.2.0.
Before calling it a typo, I googled the "problem" with different keywords and ended on this page from r-project site mentioning two R versions running, a R-release (R-3.1.2) and a "R-devel, to be R-3.2.0".
Googling "r_devel" confirmed what I was guessing, that this is the current development version.
I'm a bit surprised that packages can be built under the development version and I have a twofold question:
- How can a package already be built under a "to be released" R version and is it "safe"?
- Given that the development version is not released yet, how could I use the package with the R version it was built under? (Having a previous R version doesn't prevent me from using the package but I'm not sure I feel comfortable about the warning...)
Or maybe it was really just a typo?...
Related
I am running R 3.6.1 on a Mac Mini running Sierra and a MacBook Pro running El Capitan. I normally get all the R packages that I need from CRAN or github and use them without issues, but I am trying to install and use an R package (NicheMapR) that requires a fortran compiler and this is giving me issues. Even after installing gfortran, the R package still does not work (the fortran code seems to be compiled but the package installation fails). The package developer suggested that installing R via homebrew might solve the problem. On the contrary, my hunch is that it would lead to a world of pain, to quote Walter from the Big Lebowski. My questions are:
What is the advantage of a homebrew version of R for MacOSX over the "regular" version installed from CRAN?
Can the two versions coexist?
Is the homebrew version going to affect the regular one?
Finally: is homebrew going to help or will it simply open a whole
new can of worms?
Many thanks in advance.
Yes, installing from homebrew is a recipe for pain. It's specifically recommended against by the official CRAN binary maintainer see his remarks from March 2016 on r-sig-mac.
Regarding your questions, this can be summarized as:
What is the advantage of a homebrew version of R for MacOSX over the "regular" version installed from CRAN?
Positives: Select your own BLAS and easily work with geospatial tools.
Downsides: Always needing to compile each R package.
Can the two versions coexist?
Yes. The homebrew version installs into a different directory. But, watch out for library collision (see next question). However, you will have to deal with symbolic linking regarding what version of R is accessible from the console and you will also need to look into using RSwitch to switch between R versions.
Is the homebrew version going to affect the regular one?
Yes, if the library paths overlap. There will be problems regarding package installation and loading. Make sure to setup different library paths. To do so, please look at the .libPaths() documentation.
Finally: is homebrew going to help or will it simply open a whole new can of worms?
Yes and no. Unless you know what you're doing, opt for the CRAN version of R and its assorted goodies.
I need to install a package that gives the following error so I am looking for a solution to that. The first approach is looking at ways to chose the right R version.
Should I spend some more time on this or there are other approaches to overcome this issue?
For this package you need R (≥ 3.5.0), so R 3.4.4 is not sufficient for standard installation.
Check on the cran website of the package for further information.
For upgrading R on ubuntu check here (old link!)
Edit: wibeasley mentioned a new link in the commentary for updating R 3.5.0 on Ubuntu.
Check the new link
I want to install a package that is listed in https://cran.r-project.org/web/packages/available_packages_by_name.html as available in CRAN, but when I check in R the install packages menu or the available.packages() command, I can't see the package there.
Do I need to do something different to install those packages? Why aren't those packages available?
The packages I'm interested on are WikipediR ( https://cran.r-project.org/web/packages/WikipediR/index.html ), WikidataR and WikipediaR.
If it matters, I'm using R 2.15.0 in Windows XP.
See the documentation for ?available.packages...
By default, the return value includes only packages whose version and
OS requirements are met by the running version of R, and only gives
information on the latest versions of packages.
In other words... your R 2.15 is likely too old for the package you are looking to download.
You can try to download the package source manually add the package to the package library usually found somewhere like "win-library/2.15/" but like Cory mentioned it is likely that the older version of R does not support the package build.
The advice given so far is a bit incomplete although I do agree you need to update your R version if you want to use these packages. Looks like they don't need compilation so you might have been able to either install from a local copy or drop R code in, but critically they depend on httr which requires R 3.0.0 or above. They were released only relatively recently, so there will be no Windows binaries from back in 2012. (Your copy of R is from 30-Mar-2012.) Look in the DESCRIPTION file which is presented in a nice web format at the CRAN/package listing:
https://cran.r-project.org/web/packages/WikidataR/index.html
Imports: httr, jsonlite, WikipediR
Suggests: testthat, knitr, pageviews
# only one version of these two
https://cran.r-project.org/src/contrib/Archive/WikidataR/WikidataR_1.0.0.tar.gz
https://cran.r-project.org/src/contrib/Archive/WikipediaR/WikipediaR_1.0.tar.gz
# pick one of these
https://cran.r-project.org/src/contrib/Archive/WikipediR/
I'm having some extremely frustrating difficulty installing the bigrf R package.
I have tried the following:
install.packages('bigrf',repo='https://github.com/aloysius-lim/bigrf.git',type='source')
^^with dev mode on
install_github("aloysius-lim","bigrf")
install.packages("bigrf",source=true)
These have all been tried on R build 2.14 (which bigrf was built on), 2.15, 3.0.2 and 3.1.1
I am getting a not available for build x' every time.
Is bigrf defunct? I have seen a couple of posts here on SO which have used various builds of R requesting help with bigrf. the solutions IO have tried came from these threads :/
To install the current version of bigrf on CRAN:
install.packages("bigrf", dependencies=TRUE)
To install the development version on GitHub:
library(devtools)
install_github("bigrf", repo='aloysius-lim/bigrf')
Package ‘bigrf’ was removed from the CRAN repository.
Formerly available versions can be obtained from the archive.
Archived on 2015-11-21 as check problems were not corrected despite reminders from CRAN people that's what the note i get from the https://cran.r-project.org/web/packages/bigrf/index.html
I want to upgrade the package ggplot2:
library(ggplot2)
packageDescription("ggplot2")["Version"]
> 0.8.3
But the current version is 0.8.7.
I tried update.packages(), which seemed to work OK. But it still returned older version 0.8.3.
So I downloaded and installed the package source from Cran, which says 0.8.7 in the download page.
I then install it via the GUI menu in R. It returns
** building package indices ...
* DONE (ggplot2)
I then run:
packageDescription("ggplot2")["Version"]
> 0.8.3
And still I have the older version!
I don't know why this is not working, what's more I had already come across this problem before and solved it (I can't remember exactly what) but now it has gone back to the older version! What's the easiest way to keep packages like this updated automatically and not have them refer back to older packages?
What version of R are you using? CRAN binaries are only kept up-to-date for the latest R release (i.e. 2.10.1). If you have an older version of R and have the development tools installed, you can use install.packages("ggplot2",type="source").
Did you do unloadNamespace('ggplot2')? and the library(ggplot2) and then check the version? Because, once you load a package, it stays in memory of R, even though you might have already installed a newer version of the package, R does not see, until you do the above.