I am pretty dang stumped on this one. I am attempting to install the R package diceR (https://github.com/AlineTalhouk/diceR) on an HPC system where software must be compiled locally. The way things were setup -- before I came in -- was to try using conda for everything we could. So we have system install of conda, with user environments saved locally (I've heard this is poopoo'd - but hey... I'm just working with what I was given).
In comes diceR. Most R installs are easy easy, create a new R-environment, go into R and do a package.install("package"). I have had the strangest time with this package - circular or dead end dependencies that I can't explain.
I was hoping someone might have experience with something similar and have ideas on next steps. I know this is a niche package, and I'm not necessarily asking to get help on this exact problem - more along the lines of what to do when something like this happens. Below is the "dependency" chain issue I've been having (from CRAN Depends: R ≥ 3.5)
$ conda activate r-env
$ R --version
R version 4.1.1 (2021-08-10) -- "Kick Things"...
$ R
>install.packages("diceR")
lots of compilation happens, but enevitabley fails...
ERROR: dependency ‘NMF’ is not available for package ‘diceR’
removing ‘/projects/gehi0941/software/anaconda/envs/R_diceR/lib/R/library/diceR’
So I try installing NMF...
ERROR: dependency ‘Biobase’ is not available for package ‘NMF’
removing ‘/projects/gehi0941/software/anaconda/envs/R_diceR/lib/R/library/NMF’
Then Biobase...
Warning message:
package ‘Biobase’ is not available for this version of R
Then I'm done, I've followed this chain all the way down, right? Talking language/manager agnostic now: what do folks to at this step?
Related
I'm really stuck and need some help.
After installing R 4.0.2, I'm getting this error message on R visualizations in Power BI Desktop which were previously working
"Error: package or namespace load failed for 'ggplot2':
package 'ggplot2' was installed before R 4.0.0: please re-install it.
Execution halted"
I already tried to:
reinstall R 4.0.2
reinstall RStudio
reinstall ggplot2
reinstall all packages
reinstall Power BI Desktop. My version is August-2020
Still it does not work.
Considerations:
My visualizations were working well before I installed R 4.0.2
My visualizations are working well on other PCs
When I run the code on RStudio I don't get any error
Any thoughts?
You probably need to go here: https://powerbi.microsoft.com/en-us/support/, but some guesses:
When you did the reinstall, you didn't clean up the old one, so you now have a mix of old and new versions of things. If you can run R code, run
system.file(package="ggplot2")
That's where it is finding the bad version of ggplot2. Get rid of it, and reinstall. If that's not enough, run
.libPaths()
in the broken copy of R. Completely eradicate every directory mentioned there, and reinstall R and all of your contributed packages. (I don't use Power BI, so this might also require you to wipe out some part of it: do so, and reinstall it.)
After all of this, run
packageDescription("ggplot2")
In the last line (or close to it) of what gets printed, you should see something like this:
Built: R 4.0.2; ; 2020-07-16 22:49:23 UTC; unix
If you see that it was built before 4.0.2, then either you missed deleting something, or you (or Microsoft) reinstalled some old version. Then it's probably time to contact that URL above.
I am trying to install package mvtnorm, but it says that package ‘mvtnorm’ is not available (for R version 3.4.4)....
I believe this is happening cause of R version..... but When I am trying to upgrade R...it is not allowing me to do so.
I tried to install package "installr" for upgrading R, but it showed the same error - "installr’ is not available (for R version 3.4.4).
There is update tab under "HELP" on the console panel of the studio...it is not responding....
I am using Linux machine for the first time...so i am really confused on how to go about.
I hope to get this thing solved with some help from an expert who can guide me on how to proceed.
I am asking this question because I recently solved a problem installing R Shiny (see below). However, the answers I find don't expand in detail about why this problem occurs and I really want to understand to improve my knowledge of R and why these things happen.
So my attempt to install Shiny in RStudio failed and I believe these are the important error messages:
Error in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]) :
namespace 'httpuv' 1.3.3 is being loaded, but >= 1.4.3 is required
ERROR: lazy loading failed for package 'shiny'
* removing 'C:/Program Files/R/R-3.2.2/library/shiny'
* restoring previous 'C:/Program Files/R/R-3.2.2/library/shiny'
Warning in install.packages :
running command '"C:/PROGRA~1/R/R-32~1.2/bin/x64/R" CMD INSTALL -l "C:\Program Files\R\R-3.2.2\library" C:\Users\Sam\AppData\Local\Temp\RtmpuI3XHe/downloaded_packages/shiny_1.1.0.tar.gz' had status 1
Warning in install.packages :
installation of package ‘shiny’ had non-zero exit status
I went through the usual processes first to find a solution.
Checked my RStudio was up to date
Checked for and installed package updates
Among the further solutions I tried were:
Downloading the package directly from the R Studio interface
Using devtools to install Shiny from Github
Using install.packages("shiny", dependencies=TRUE).
None of these solutions worked and I believe it is to do with the dependency httpuv, but I'm not sure why.
So in the end the code I was able to use is: install.packages('shiny', type = "binary") and this allowed me to begin using Shiny.
While it wasn't too difficult for me to find a work around for this problem, I would really appreciate someone taking some time out of their day to explain why my version of RStudio in Windows (Version 1.1.456) doesn't support the source package of Shiny and why installing the binary package works. I hate fixing an issue but having zero understanding of it.
R version 3.2.2
Thank you.
Binary versus Source Code Options and Issue
You are experiencing the push-pull friction between the old and new code and R environment dependencies. The reality here is you have to choose between stability and progress. It is difficult to have both.
Consider:
You are running R version 3.2.2 in binary form, that R version was released on August 15th 2015. Yet, you are trying to install the latest source code version of Shiny.
There is a 3-year delta between the package source code version and R version you are using. The package code and R environment source code have evolved.
A lot of changes have occurred between then and now in the compile and source code environment.
Think of it this way.
The binary package image is a snapshot of the compile and source code environment assumptions at the time of compilation. If you download the current source code image you are using a snapshot of the current code environment (Now) which includes literally thousands of small incremental changes to the assumptions and dependencies in the source code files and compile environment. if you look in the shiny package description you note:
Shiny depends on httpuv which depends on Rcpp
httpuv is built on top of the libuv and http-parser C libraries.
To get httpuv to compile from source you'll need to match the R Environment, the dependent libraries and source dependencies manually. No small feat.
Case in point to compile the above packages from source code (github) you'll likely be using Devtools which if you download in binary form was compiled under R version 3.2.5.
The changes, assumptions and inter-dependencies make for a complex backport compile situation. Hence, the advantage of binary package snapshots.
Options:
In this situation, you generally have two options. You can either:
Download a source version that is old that matches your R version and environment.
The downside of this approach is you cannot access current package features.
The upside is that the package and R environment match historical context.
Upgrade to a current R environment
The downside of this approach is you have to roll with the R releases.
The upside is that you get to gain access to the latest package features made available in the current source code.
Solution Options:
How to do the above? In the first case, you might use devtools::install_git and pull the source for a particular branch or version of a package and compile it. In the second, you can upgrade your environment, and then pull and upgrade your packages.
If you want to work off a particular R environment version you have to use the binary versions. Why? These will match the R environment version coding environment assumptions.
This is a classic problem highlighting the push-pull between current code and old code. You have to choose between stability and progress. It is difficult to have both.
I hope the above helps explain the situation.
My end goal is to be able to use the geoR and sp packages in R on an AWS EC2 Linux instance. The first hurdle is getting an older version of R since 3.3 doesn't support TCL/TK (at least that I've found).
After unpacking R-3.2.5.tar.gz and following the instructions in the INSTALL file (along with these configure options), a search in the config.log shows the following for TCL and TK:
configure:32908: checking for tclConfig.sh
configure:32950: checking for tclConfig.sh in library (sub)directories
configure:33143: checking for tcl.h
conftest.c:228:17: fatal error: tcl.h: No such file or directory
#include <tcl.h>
config.status:1893: creating src/library/tcltk/DESCRIPTION
config.status:1893: creating src/library/tcltk/Makefile
config.status:1893: creating src/library/tcltk/src/Makefile
ac_cv_env_TCLTK_CPPFLAGS_set=
ac_cv_env_TCLTK_CPPFLAGS_value=
ac_cv_env_TCLTK_LIBS_set=
ac_cv_env_TCLTK_LIBS_value=
r_cv_header_tcl_h=no
TCLTK_CPPFLAGS=''
TCLTK_LIBS=''
TCL_CONFIG=''
use_tcltk='no'
R installs successfully, but running capabilities("tcltk") confirms that something went wrong, as it returns FALSE.
Following the hints here, I searched for tclConfig.sh and tkConfig.sh, and realized I hadn't installed the appropriate dependencies, namely tcl-devel, and tk-devel.
Running yum list available shows a package for tcl-devel.x86_64 but nothing for tk. I downloaded source files for TCL and TK from here, but couldn't find source files for any devel packages.
So now I'm at a loss on how to proceed. Has anyone been through this process that can shed some light on it?
UPDATE
After reading the comment below, I'm afraid I didn't do my due diligence in researching TCL/TK and R 3.3.
My very early efforts to troubleshoot the whole problem consisted of the following workflow- First, without even considering an older version of R, I ran install.packages("geoR") in 3.3.2. This resulted in installation of package 'geoR' had non-zero exit status, along with:
Error: package 'tcltk' does not have a namespace
ERROR: lazy loading failed for package 'geoR'
So, naturally, I ran install.packages("tcltk") which resulted in package 'tcltk' is not available (for R version 3.3.2)
As I mentioned before, this was very early on. I took the message literally and didn't think to look further into TCL/TK for 3.3, but instead started looking to get an earlier version of R, since I had been successfully running geoR and sp on another system that had R 3.2.5.
My Fedora system (Fedora 20, all up to date) has just had R updated to version 3.1.0. Since then, I've had issues installing multiple packages. glmnet failed previously, and now I'm having trouble with treemap. More specifically, I get an error during treemap installation that httpuv has zero exit status.
I never had issues with the previous version of R. Any reason this version should have such problems??
There could be many causes to do with your OS, version, permissions, other installed packages/software, etc, etc. Without seeing the full error message it's hard to know.
One possibility specific to httpuv is root privileges. I've noticed a few threads on various forums when searching for installation errors with this package and Linux, many of them mentioning root v. non-root issues. In another case, libuv needed to be upgraded.
I encounter package installation problems daily and I have some more general work-arounds as well. Hopefully one of these will solve your problem.
Install the package from source
download.file(url="http://cran.r-project.org/src/contrib/httpuv_1.3.0.tar.gz", destfile = "httpuv.tar.gz")
install.packages("httpuv.tar.gz", type = "source", repos = NULL)
Install using devtools via GitHub if the package supports it
Install RTools and re-try your package installation
Install an older version of the package
If those above do not work, then I dig deeper by referring to advice given to me by a VP of IT in my company. These comments were made in reference to frequent package installation problems I encountered when switching from Windows to Solaris:
There are two types of install/make problems. Missing .h files
and/or missing .so/.a libs. The reason for these are multiple:
1.- the package that delivers these is not installed. This means that those files cannot be found anywhere in the /usr tree. Solution is
install right package, make sure the files are there
2.- the includes are not found by the install configurator. This means some environment variable or install option is not properly set (this
is our case for RODBC). Figuring out which variable to set is
challenging without looking at the package documentation [fortunately, documentation is not hard to find!]
3.- the libs are not in the LD_LIBRARY_PATH, easy to fix.
4.- There is a deeper compile/link error, meaning the package is not compatible with the rest of the sw, or has not been properly ported.