Virtual environment in R?
Question:
I’ve found several posts about best practice, reproducibility and workflow in R, for example:
- How to increase longer term reproducibility of research (particularly using R and Sweave)
- Complete substantive examples of reproducible research using R
One of the major preoccupations is ensuring portability of code, in the sense that moving it to a new machine (possibly running a different OS) is relatively straightforward and gives the same results.
Coming from a Python background, I’m used to the concept of a virtual environment. When coupled with a simple list of required packages, this goes some way to ensuring that the installed packages and libraries are available on any machine without too much fuss. Sure, it’s no guarantee – different OSes have their own foibles and peculiarities – but it gets you 95% of the way there.
Does such a thing exist within R? Even if it’s not as sophisticated. For example simply maintaining a plain text list of required packages and a script that will install any that are missing?
I’m about to start using R in earnest for the first time, probably in conjunction with Sweave, and would ideally like to start in the best way possible! Thanks for your thoughts.
Answers:
I’m going to use the comment posted by @cboettig in order to resolve this question.
Packrat
Packrat is a dependency management system for R. Gives you three important advantages (all of them focused in your portability needs)
-
Isolated : Installing a new or updated package for one project won’t break your other projects, and vice versa. That’s because packrat gives each project its own private package library.
-
Portable: Easily transport your projects from one computer to another, even across different platforms. Packrat makes it easy to install the packages your project depends on.
-
Reproducible: Packrat records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go.
What’s next?
-
Walkthrough guide: http://rstudio.github.io/packrat/walkthrough.html
-
Most common commands: http://rstudio.github.io/packrat/commands.html
-
Using Packrat with RStudio: http://rstudio.github.io/packrat/rstudio.html
-
Limitations and caveats: http://rstudio.github.io/packrat/limitations.html
Update: Packrat has been soft-deprecated and is now superseded by renv, so you might want to check this package instead.
The Anaconda package manager conda supports creating R environments.
conda create -n r-environment r-essentials r-base
conda activate r-environment
I have had a great experience using conda to maintain different Python installations, both user specific and several versions for the same user. I have tested R with conda and the jupyter-notebook and it works great. At least for my needs, which includes RNA-sequencing analyses using the DEseq2 and related packages, as well as data.table and dplyr. There are many bioconductor packages available in conda via bioconda and according to the comments on this SO question, it seems like install.packages() might work as well.
Check out roveR, the R container management solution. For details, see https://www.slideshare.net/DavidKunFF/ownr-technical-introduction, in particular slide 12.
To install roveR, execute the following command in R:
install.packages("rover", repos = c("https://lair.functionalfinances.com/repos/shared", "https://lair.functionalfinances.com/repos/cran"))
To make full use of the power of roveR (including installing specific versions of packages for reproducibility), you will need access to a laiR – for CRAN, you can use our laiR instance at https://lair.ownr.io, for uploading your own packages and sharing them with your organization you will need a laiR license. You can contact us on the email address in the presentation linked above.
To add to this:
Note:
1. Have Anaconda installed already
2. Assumed your working directory is “C:”
To create desired environment -> “r_environment_name”
C:>conda create -n "r_environment_name" r-essentials r-base
To see available environments
C:>conda info --envs
.
..
…
To activate environment
C:>conda activate "r_environment_name"
(r_environment_name) C:>
Launch Jupyter Notebook and let the party begins
(r_environment_name) C:> jupyter notebook
For a similar “requirements.txt”, perhaps this link will help -> Is there something like requirements.txt for R?
It looks like there is another option from RStudio devs, renv. It’s available on CRAN and supersedes Packrat.
In short, you use renv::init() to initialize your project library, and use renv::snapshot() / renv::restore() to save and load the state of your library.
I prefer this option to conda r-enviroments because here everything is stored in the file renv.lock, which can be committed to a Git repo and distributed to the team.
For .ipynb files a R kernel is enough, you do NOT need to install a virtual environment with R, just:
- install R kernel: https://irkernel.github.io/installation/#linux-panel
- run
jupyter notebook in terminal
- select the .ipynb in browser and switch in tab "Kernel" / Change kernel / R
I’ve found several posts about best practice, reproducibility and workflow in R, for example:
- How to increase longer term reproducibility of research (particularly using R and Sweave)
- Complete substantive examples of reproducible research using R
One of the major preoccupations is ensuring portability of code, in the sense that moving it to a new machine (possibly running a different OS) is relatively straightforward and gives the same results.
Coming from a Python background, I’m used to the concept of a virtual environment. When coupled with a simple list of required packages, this goes some way to ensuring that the installed packages and libraries are available on any machine without too much fuss. Sure, it’s no guarantee – different OSes have their own foibles and peculiarities – but it gets you 95% of the way there.
Does such a thing exist within R? Even if it’s not as sophisticated. For example simply maintaining a plain text list of required packages and a script that will install any that are missing?
I’m about to start using R in earnest for the first time, probably in conjunction with Sweave, and would ideally like to start in the best way possible! Thanks for your thoughts.
I’m going to use the comment posted by @cboettig in order to resolve this question.
Packrat
Packrat is a dependency management system for R. Gives you three important advantages (all of them focused in your portability needs)
-
Isolated : Installing a new or updated package for one project won’t break your other projects, and vice versa. That’s because packrat gives each project its own private package library.
-
Portable: Easily transport your projects from one computer to another, even across different platforms. Packrat makes it easy to install the packages your project depends on.
-
Reproducible: Packrat records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go.
What’s next?
-
Walkthrough guide: http://rstudio.github.io/packrat/walkthrough.html
-
Most common commands: http://rstudio.github.io/packrat/commands.html
-
Using Packrat with RStudio: http://rstudio.github.io/packrat/rstudio.html
-
Limitations and caveats: http://rstudio.github.io/packrat/limitations.html
Update: Packrat has been soft-deprecated and is now superseded by renv, so you might want to check this package instead.
The Anaconda package manager conda supports creating R environments.
conda create -n r-environment r-essentials r-base
conda activate r-environment
I have had a great experience using conda to maintain different Python installations, both user specific and several versions for the same user. I have tested R with conda and the jupyter-notebook and it works great. At least for my needs, which includes RNA-sequencing analyses using the DEseq2 and related packages, as well as data.table and dplyr. There are many bioconductor packages available in conda via bioconda and according to the comments on this SO question, it seems like install.packages() might work as well.
Check out roveR, the R container management solution. For details, see https://www.slideshare.net/DavidKunFF/ownr-technical-introduction, in particular slide 12.
To install roveR, execute the following command in R:
install.packages("rover", repos = c("https://lair.functionalfinances.com/repos/shared", "https://lair.functionalfinances.com/repos/cran"))
To make full use of the power of roveR (including installing specific versions of packages for reproducibility), you will need access to a laiR – for CRAN, you can use our laiR instance at https://lair.ownr.io, for uploading your own packages and sharing them with your organization you will need a laiR license. You can contact us on the email address in the presentation linked above.
To add to this:
Note:
1. Have Anaconda installed already
2. Assumed your working directory is “C:”
To create desired environment -> “r_environment_name”
C:>conda create -n "r_environment_name" r-essentials r-base
To see available environments
C:>conda info --envs
.
..
…
To activate environment
C:>conda activate "r_environment_name"
(r_environment_name) C:>
Launch Jupyter Notebook and let the party begins
(r_environment_name) C:> jupyter notebook
For a similar “requirements.txt”, perhaps this link will help -> Is there something like requirements.txt for R?
It looks like there is another option from RStudio devs, renv. It’s available on CRAN and supersedes Packrat.
In short, you use renv::init() to initialize your project library, and use renv::snapshot() / renv::restore() to save and load the state of your library.
I prefer this option to conda r-enviroments because here everything is stored in the file renv.lock, which can be committed to a Git repo and distributed to the team.
For .ipynb files a R kernel is enough, you do NOT need to install a virtual environment with R, just:
- install R kernel: https://irkernel.github.io/installation/#linux-panel
- run
jupyter notebookin terminal - select the .ipynb in browser and switch in tab "Kernel" / Change kernel / R