This question is motivated by `jupyter notebook` gives error: `"Could not open static file ''"` on macOS
After conda update jupyter, jupyter --version gives jupyter-notebook : 6.0.0
However on https://github.com/jupyter/notebook, clicking Branch: master -> tags I see a 6.0.1 tag.
How can I upgrade to 6.0.1?
> conda install jupyter=6.0.1
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
PackagesNotFoundError: The following packages are not available from current channels:
- jupyter=6.0.1
Current channels:
- https://repo.anaconda.com/pkgs/main/osx-64
- https://repo.anaconda.com/pkgs/main/noarch
- https://repo.anaconda.com/pkgs/r/osx-64
- https://repo.anaconda.com/pkgs/r/noarch
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and use the search bar at the top of the page.
I can't see any candidates on https://anaconda.org
Is this a dead-end?
First, note that the actual package you want to upgrade/install is notebook, not jupyter. The Anaconda channel hasn't released that version of notebook yet. Conda Forge has it, so you can get it with
conda install -c conda-forge notebook
However, just be aware that compatibility between Conda Forge and Anaconda package builds is not guaranteed. Best practice is to create a new env that prioritizes Conda Forge from the start:
conda create -n my_jupyter_env -c conda-forge jupyter
Generally it isn't a good idea to mess with base env, and if you want something other than a default Anaconda install, I recommend starting with Miniconda and leaving base alone (other than the occasional conda upgrade conda).
Related
I have installed Anaconda 3 for Mac M1 and I am trying to create a new environment for R. However, everytime I try to do this, I am faced with the below error:
Conda Prompt Error Message
I am faced with this error irrespective of whether I try to do this in anaconda navigator or conda prompt. I have already tried removing Anaconda completely and reinstalling it again but to no avail. Is there anything I can do here?
Update: As requested, here is an additional screenshot:
Mamba install r-eesentials
Mamba install r-essentials output
[Disclosure: I am a volunteer on the Conda Forge R team.]
Conda users who want to use R should prioritize Conda Forge. The Anaconda company has not actively maintained R packages since v3.6.
If installing Conda for the first time, I recommend Miniforge variants (specifically, Mambaforge), rather than the Anaconda distributions (which prioritize defaults/anaconda channel).
Also note that osx-arm64 support for R through Conda is currently sparse. We are actively working on migrating, but for simplicity of workflow, I still recommend Conda users to install a osx-64 version of Conda and use that platform when installing R. You can always create native osx-arm64 environments when you actually need them (e.g., native TensorFlow with Metal support).
Normally I create conda environments like...
conda env create -f environment.yml
conda activate env_name
Normally I work in Python, where a typical environment.yml simple file might looks like this...
name: env_name
dependencies:
- python=3.7
- pip=19.3
- pandas=0.24.2
- pip:
- scipy==1.2.1
What should the environment.yml file look like to install R packages? The packages are on CRAN
A general rule of thumb is that most R packages have corresponding packages in Anaconda Cloud with the prefix r- added. While the defaults channel covers commonly-used packages, the conda-forge channel has the most thorough coverage of CRAN and has helpful scripts for adding new ones. I would generally recommend prioritizing conda-forge when creating R environments.
For bioinformaticians, all Bioconductor packages are available through the bioconda channel, with a bioconductor- prefix and lowercase. For example, SingleCellExperiment is packaged as bioconductor-singlecellexperiment.
A good place to start is simply searching Anaconda Cloud (example search).
Example
Let's assume you want the tidyverse umbrella package and wish to use R v4.1. A YAML for this would be
name: my_r_env
channels:
- conda-forge
dependencies:
- r-base=4.1
- r-tidyverse
Additional Notes
Avoid using install.packages() from within any R sessions - it is prone to dynamic linking issues due to the R instance's unawareness of compiling inside the environment. This is not an issue for pure R packages, but in that case it should be simple to add the package to conda-forge (takes about 15 mins of work and a ~12-24hr turnaround, IME).
Avoid the RStudio packages from Conda - it is an abandoned project and the old versions are incompatible with newer R versions. This may change once RStudio switches from Qt to Electron. Still, there are better ways to load an environment into RStudio, without having to install the full IDE inside the environment.
I tried to install R Studio (version 1.1.456) using the anaconda navigator by simply clicking on the install button. It was taking more than an hour, so I just figured it should be stuck.
I then tried to install it through the anaconda prompt but now it has also been stuck for around 30 minutes here:
What can I do to get around this?
Thank you in advance!
For various reasons up-to-date RStudio versions are not availabe on any conda channel I know. #merv's answer is the easiest solution, if you are happy to work with an older version of rstudio. Here is another suggestion, where you install RStudio outside of conda, but configure it to use a particular R installation, which is maintained in your custom conda environment. Step by step, this is how you procede:
Install the latest RStudio from the official sources
Create your custom conda environment CUSTOMENV, including an installation of r-base
conda create -n CUSTOMENV -c conda-forge r-base'>=4.0.0' ... [further packages]
Activate the conda environment
conda activate CUSTOMENV
Start RStudio from console
rstudio &
Important Note: I strongly endorse #mfakaehler's answer since all RStudio builds on Conda have effectively been abandoned. Install RStudio natively and launch from activated environment.
Create a new env instead. E.g.,
conda create --name rstudio_env -c r rstudio
Best practice for Conda is to create new envs for each project rather than using a monolithic base env. Generally, I find that the less one installs in base the better their experience with Conda will be.
I'm getting a little bit crazy with this issue. I'm trying to install an R package using conda in my environment (python 2.7) in my home on a cluster (i.e. without root permissions). I firstly installed R in my env using:
conda install -c r r=3.4
Then:
conda install -c conda-forge python-igraph
(because igraph is required by my library of interest)
and finally:
conda install -c conda-forge r-diffusionmap
Unfortunately when I launch R the following message appears:
Error: package or namespace load failed for 'RevoUtilsMath': .onLoad
failed in loadNamespace() for 'RevoUtilsMath', details: call: NULL
error: Remove Microsoft R and then re-install. Be sure to select MKL
libraries as an install option.
During startup - Warning message:
package 'RevoUtils' was built under R version 3.4.3
What does it mean? How can I solve this?
Thank you in advance
I had this same issue after I installed some libraries (Rcpp included) in my root R, but not my conda environment (which screwed up conda). This would cause kernel death anytime a jupyter notebook running R was even opened.
The fix for me was:
Uninstall Anaconda3
Reinstall Anaconda3
Reinstall all the libraries I needed (mostly just Bioconductor in R)
A few other issues popped up, like package inconsistencies, but I dealt with those as described here.
All R packages on conda-forge (or Bioconda) are compiled against one single version or R for each new release branch (usually starting from patch 1, so 3.x.1, except for 3.4.3). This is due to ABI incompatibility problems.
Also note that defaults and conda-forge channels are (where) not binary compatible (although now they should be). And that since 2018 the default anaconda channel is distributing Microsoft R Open as default R, whether all packages from conda-forge should be preferably used with R from conda-forge.
You should be able to solve this issue by installing R using conda install -c conda-forge r-base.
the same error information for me when I open R for run code in ubuntu platform(18.4), and there is no other useful methods to solve it.My R version is 3.4.3.enter image description here
I am having a tough time installing R-packages that are not available in the Anaconda repositories. My attempts so far can be found here How to install R-packages not in the conda repositories?.
Currently, I am trying to build the R-package rafalib for conda by following the instructions from this article under the heading Building a conda R package.
The first part works fine.
conda skeleton cran rafalib
Out:
Tip: install CacheControl to cache the CRAN metadata
Fetching metadata from http://cran.r-project.org/
Writing recipe for rafalib
Done
The build command runs into errors
conda build r-rafalib
Out:
Removing old build environment
Removing old work directory
BUILD START: r-rafalib-1.0.0-r3.2.2_0
Using Anaconda Cloud api site https://api.anaconda.org
Fetching package metadata: ......
Solving package specifications: .
Error: Packages missing in current linux-64 channels:
- r 3.2.2*
- r-rcolorbrewer
I have r 3.2.2-64bit installed via conda and it runs without problems. I also already have r-colorbrewer installed via conda and I can use that package without issues in R. Why am I getting these errors when trying to build a conda package?
I am on Linux (Antergos, an Arch derivative) with kernel 4.4.5-1-ARCH.
UPDATE 2015/04/19
Thanks to this answer, I found out that I could include the dependencies by building them separately in the same directory as the package I want to install. That didn't work for me, but I also read that I can include a channel in the build command with -c, just as when installing. So now I do:
conda build -c r r-rafalib
This gets passed all the dependency problems, but after fetching, extracting and linking packages, it fails. Here is the end of the error message.
Removing old work directory
Source cache directory is: /home/joel/anaconda2/conda-bld/src_cache
Downloading source to cache: rafalib_1.0.0.tar.gz
Downloading http://cran.r-project.org/src/contrib/rafalib_1.0.0.tar.gz
rafalib_1.0.0. 100% |#######################| Time: 0:00:00 4.87 MB/s
Success
Extracting download
Package: r-rafalib-1.0.0-r3.2.2_0
source tree in: /home/joel/anaconda2/conda-bld/work/rafalib
+ mv DESCRIPTION DESCRIPTION.old
+ grep -v '^Priority: ' DESCRIPTION.old
+ /home/joel/anaconda2/envs/_build/bin/R CMD INSTALL --build .
sh: symbol lookup error: sh: undefined symbol: rl_signal_event_hook
Command failed: /bin/bash -x -e /home/joel/drafts/r-rafalib/build.sh
The error sh: symbol lookup error: sh: undefined symbol: rl_signal_event_hook is the same as I encounter when using install.packages() as reported here.
There is some related discussion in this thread. I have tried to get around this error by installing different versions of ncurses, including this patched version, and I have tried to link the readline libraries, as suggested here, but I keep running into the same error. I'm quite lost at this point and any help to solve this would be greatly appreciated.
Although I started out with a different problem, the final solution turned out to be the same as I posted elsewhere How to install R-packages not in the conda repositories?. I am adding it here for completeness.
In the end, I got around the rl_event_hookproblems by following the approach recommended here and symlinking anaconda's libreadline to the system one:
mv ~/anaconda3/lib/libreadline.s.6.2 ~/anaconda3/lib/libreadline.s.6.2.bak
ln -s /usr/lib/libreadline.so.6.3 ~/anaconda3/lib/libreadline.s.6.2
I am still having troubles installing some dependency heavy R-packages due to failure to load shared objects when using install.packages() from withing R. However, simpler packages work fine and I can get most of the dependency heavy packages from anacondas R-repositories.