How to install R packages that are not available in "R-essentials"?

Now I have found the documentation:

This is the documentation that explains how to generate R packages that are only available in the CRAN repository: https://www.continuum.io/content/conda-data-science

Go to the section "Building a conda R package".

(Hint: As long as the R package is available under anaconda.org use this resource. See here: https://www.continuum.io/blog/developer/jupyter-and-conda-r)

alistaire's answer is another possibility to add R packages:

If you install packages from inside of R via the regular install.packages (from CRAN mirrors), or devtools::install_github (from GitHub), they work fine. @alistaire

How to do this: Open your (independent) R installation, then run the following command:

install.packages("png", "/home/user/anaconda3/lib/R/library")

to add new package to the correct R library used by Jupyter, otherwise the package will be installed in /home/user/R/i686-pc-linux-gnu-library/3.2/png/libs mentioned in .libPaths() .

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To install other R Packages on Jupyter beyond R-essentials

install.packages('readr', repos='http://cran.us.r-project.org')

One issue is that the specific repository is the US.R-Project (as below). I tried others and it did not work.

N.B. Replace readr with any desired package name to install.

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Here's a conda-centric answer. It builds on Frank's answer and the continuum website: https://www.continuum.io/content/conda-data-science with a bit more detail.

Some packages not available in r-essentials are still available on conda channels, in that case, it's simple:

conda config --add channels r
conda install r-readxl

If you need to build a package and install using conda:

conda skeleton cran r-xgboost
conda build r-xgboost
conda install --use-local r-xgboost

that last line is absent in the continuum website because they assume it gets published to anaconda repository first. Without it, nothing will be put in the envs/ directory and the package won't be accessible to commandline R or Jupyter.

On a mac, I found it important to install the Clang compiler for package builds:

conda install clangxx_oxs-64

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I found an easy workaround. I suppose that you have an RStudio IDE for you R. It is weird to use RStudio for that, but I tried straight from R in my terminal and it didn't work. So, in RStudio console, just do the usual adding the path to your anaconda directory (in OSX,'/Users/yourusernamehere/anaconda/lib/R/library')

So, for example,

install.packages('package','/Users/yourusernamehere/anaconda/lib/R/library')

I feel ashamed to post such a non-fancy answer, but that is the only one that worked for me.

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Adding it here so other beginners already working with Jupyter notebooks with Python and interested in using it with R: additional packages available for Anaconda can be installed via terminal using the same command used to instal the essential packages.

Install r-essentials

conda install -c r r-essentials

Install microbenchmark (infrastructure to accurately measure and compare the execution time of R expressions)

conda install -c r r-microbenchmark

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To install a CRAN package from the command line:

R --slave -e "install.packages('missing-package', repos='http://cran.us.r-project.org')"

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I had a problem when trying to install package from github using install_github("user/package") in conda with r-essentials. Errors were multiple and not descriptive.

Was able to resolve a problem using these steps:

download and unzip the package locally

activate correct conda environment (if required)

run R from command line

library(devtools)

install('/path/to/unzipped-package')

Command failed due to missing dependancies, but now I know what's missing!

run install.packages('missing-package', repos='http://cran.us.r-project.org') for all dependancies

run install('/path/to/unzipped-package') again. Now it should work!

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Use Conda Forge

Five years out from the original question, I'd assert that a more contemporary solution would simply be: use Conda Forge. The Conda Forge channel not only provides broader coverage of CRAN, but also has a simple procedure and great turnaround time (typically under 24 hours) for adding a missing CRAN package to the channel.

Start from Conda Forge

I'd recommend using Conda Forge for the full stack, and use a dedicated environment for each R version you require.

conda create -n r41 -c conda-forge r-base=4.1 r-irkernel ...

where ... is whatever additional packages you require (like r-tidyverse). The r-irkernel package is optional, but included here because OP mentions using R in Jupyter.

If your environment with Jupyter (which should be in a separate environment) also has nb_conda_kernels installed, then this environment will automatically be discovered by Jupyter.

Install from Conda Forge

Generally, all R packages on CRAN have a r- prefix to the package name on Conda Forge. So, if your package of interest is pkgname, first try

conda install -n r41 -c conda-forge r-pkgname

If the package is not available, then proceed to either add it or request it.

Submit a CRAN package with Conda R Skeleton Helper

There is a helpful script collection, called conda_r_skeleton_helper for creating new Conda Forge recipes for CRAN packages. There are clear directions in the README.

In broad strokes, one will

clone the conda_r_skeleton_helper repository

edit the packages.txt file to include r-pkgname

run the script to generate the recipe

fork and clone the conda-forge/staged-recipes

copy the new recipe folder to the stage-recipes/recipes folder

commit changes, push to the fork, then submit a Pull Request back to Conda Forge

This takes maybe ~15 mins of work. Once submitted, most packages take under 24 hours to get accepted, feedstocked, and deployed to the Conda Forge channel. Once the feedstock is up and running, the Conda Forge infrastructure uses a bot to auto-detect version updates, generate new pull requests, and even auto-merge Pull Requests that successfully build. That is, maintainers have a very minimal workload, and if there are issues, a team is available to help out.

File a Package Request

For users uncomfortable with creating and maintaining a Conda Forge build, packages can be requested on Conda Forge's staged-recipes repository by filing a new Issue. There is a template for Package Request, that includes some information fields to be filled in.

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Install rpy2 with conda and add following line in your Jupyter notebook.

%load_ext rpy2.ipython

In next chunks, you can simply run any r code by specifying %R

Below is my favorite method to install and/or load r package

%R if (!require("pacman")) install.packages("pacman")
%R pacman::p_load(dplyr, data.table, package3, package4)

p_load argument will install + load the package if it's not in your lib else it will simply load it.

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Someone suggested a not so elegant way around it, but actually it doesn't matter as long as it works fine.

install.packages('package','/Users/yourusernamehere/anaconda/lib/R/library')

I spent almost an entire morning looking for an answer to this problem. I was able to install the libraries on RStudio but not on Jupyter Notebook (they have different versions of R) The above solution "almost" worked, it's just that I found the Jupyter Notebook was trying to install in a different directory, and it will report what directory. So I only changed that and it worked as a charm... thanks to Dninhos

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What worked for me is install.packages("package_name", type="binary"). None of the other answers have worked.

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