I can't access many Bioconductor packages in R 3.1.1 and I am quite disappointed with that. How can I downgrade from R 3.1.1 to R 3.0.2 or to some other version?
Note that this solution is not good enough for me as I don't have any issues with Bioconductor installation.
As point by #Deleet, this is FOR WINDOWS ONLY.
For the rest of the platforms, see: https://support.rstudio.com/hc/en-us/articles/200486138-Changing-R-versions-for-RStudio-desktop
Go to: Tools > Global Options > press the "Change" button (marked in Yellow) >
Select the version you want to use:
OK > OK > Apply > Restart R
Although your problem is not clear enough, I think it could be similar to my current problem ... I am not able to install or use Bioconductor 3.0 packages inside Rkward 0.6.2 with R 3.1.1, but with previous versions of R there were no problems.
I do not know the reasons for this problem, but when I tried to install Bioconductor inside a console (xterm for example, I am using Debian 8.0) there were no problems at all.
I used
source("http://bioconductor.org/biocLite.R")
biocLite()
and it worked fine.
I hope it is useful for you.
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 did a lot of scripting on R 3.3.2 , and then I figured out that I have to use version 3.3.3
Problem is package AnomalyDetection doesn't exist on 3.3.3 (don't confuse with anomalyDetection (lower case 'a')).
Can I somehow use that package from older version? Isn't there some thing in R that makes older stuff work on newer version ?
If you have both versions installed and use emacs with ESS, is pretty straightforward to open any of them by typing
M-x R-3.3.2
or
M-x R-3.3.3
So you could just go back to the older version for your specific purposes.
The alternative is, Do you really need to have R-3.3.3 for any specific reason? if no, just install back R-3.3.2 and use it.
I've been trying to install the library using brute force - trying different combinations of the things people have posted in mailing lists (I'm too lazy to list them out one by one, but I think I tried the most of them. I can list that too if it helps anyone.). The results have varied from a harmless message of a missing dll to RGui not being able to start before I remove the library manually. Nevertheless, I haven't succeeded...
Do you know how to install it properly, so that it works? I'm running 64bit Windows 7 and I'm not keen of compiling packages from source. Thanks!
Install Rgraphviz 2.2.1 from Bioconductor
According to the latest README:
Rgraphviz now comes bundles with Graphviz. This should greatly simplify
installation on all platforms, compared with earlier versions.
Bioconductor 2.11 contains a lot of libraries that you might not want or need, but it does seem to be the easiest path to achieving what you want. These are the instructions on the Rgraphviz homepage:
source("http://bioconductor.org/biocLite.R")
biocLite("Rgraphviz")
These instructions work for R x64 2.15.2 on Windows 7
library("Rgraphviz")
Loading required package: graph
Loading required package: grid
set.seed(123)
V <- letters[1:10]
M <- 1:4
g1 <- randomGraph(V, M, 0.2)
plot(g1)
The README inside the source package for RGraphviz contains very some pretty clear instructions.
I think it's a bit user-unfriendly to those who only want to install the binary package, to hint that they might also want to download and unpack a tar.gz file containing the complete source, in order to find some technical info .... which turns out to be absolutely crucial.
One word of warning. If you follow the installation instructions for Rgrahpviz precisely, things won't work. Installing the package graphviz edits the environment PATH, but incorrectly. I didn't notice and I bet lots of others missed it too.
Rgraphviz wants to look for the binary in ...;C:\Graphviz2.20\bin\;
BUT the graphviz install adds a path only as far as ;C:\Graphviz2.20\;.
You'll have to edit it. Older instructions suggest a manual edit anyway, but newer ones leave it to graphviz.
I have same problems to install Rgraphviz (for using it in Bayesian Network packages). I used to get brute force solutions, but, now I trying an other one described here in this page
maybe you had also tried. If you succeed in installing Rgraphviz, I will be grateful if you learn me how to do.
I am using R-Studio on Ubuntu 12.10 x64 and installed Rgraphviz from the BioC software repository. Hope this helps. Eg: http://www.biotricks.net/2012/03/rstudio-and-bioconductor.html
I don't know if you have managed to solve your problem 100% but my installation problem came with the R version. Rgraphiz is developed by Bioconductor and it seems to be outdated. However, I had to use it since I moved to a new company and they are using it in one of their shiny apps for whatever reason.
With that being said, I have a dirty solve that I came up with after a couple of days of struggling with it.
First I include a line in the code which seems to be needed since without that line the shiny app simply doesn't do anything after opening:
if(!requireNamespace('BiocManager', quietly = TRUE))
install.packages('BiocManager')
It's weird that it won't work otherwise.The above line is found on Bioconductor's site: https://www.bioconductor.org/packages/devel/bioc/html/Rgraphviz.html
The second thing you need to do only once is to run the biocLite command below in the R console to install the Rgraphviz package
source("http://bioconductor.org/biocLite.R")
biocLite("Rgraphviz")
The above command I found on another thread: R: RGraphviz installation
Hopefully this update will help you or anyone else.
Since with the version of R 3.6.1 the script http://bioconductor.org/biocLite.R returns this message " Error: With R version 3.5 or greater, install Bioconductor packages using BiocManager; see https://bioconductor.org/install " I solved the problem with the following steps:
Get the list of directories used by R to install libraries and
choose the one with write permissions using: .libPaths()
Installing the "BiocManager" library using: install.packages("BiocManager")
Installation of the library "bioconductor" by forcing the directory
with write permissions using: BiocManager::install("Rgraphviz", lib = "C:/Users/tizbet/Documents/R/win-library/3.6")
I worked on the following R installation:
platform x86_64-w64-mingw32, arch x86_64, os mingw32, system x86_64, mingw32, version.string R version 3.6.1 (2019-07-05)
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.