+ +
+

R environments#

+
+

How to set-up renv for reproducible research#

+

Create DESCRIPTION file e.g.

+
Title: quarto_huang_2019
+Depends: 
+    R (>= 3.7)
+Imports:
+    knitr (==1.47),
+    rmarkdown (==2.27),
+    remotes (==2.5.0),
+    tiff (==0.1-12)
+
+
+

Then start new empty environment with renv::init(bare=TRUE).

+

When initialising, you should be prompted to only install from the DESCRIPTION - select yes to this. Otherwise, run the command yourself: renv::settings$snapshot.type("explicit").

+

You can then install the packages from DESCRIPTION by running renv::install(), and then create the lock file by running renv::snapshot.

+

If you make any changes to the packages and versions, simply modified the DESCRIPTION file and then run renv::install() followed by renv::snapshot.

+

If you run into issues where it cannot find a specific package/version, this may be due to the formatting of the version number. For example, for the package tiff:

+
    +

  • tiff - installs latest version (0.1.12)

    • +

  • tiff (==0.1.11) - cannot find package

    • +

  • tiff (==0.1-11) - installs older version (0.1.11)

    • +
+

The error was due to how those versions are formatted on CRAN, as you can see on the tiff archive.

+
+
+

Basic renv commands#

+

To start new project environment, creating .RProfile: renv::init()
+To save state of project library to lockfile renv.lock: renv::snapshot()
+To return to environment in lockfile: renv::restore()

+
+
+

Binder#

+

Created using instructions from here and here.

+
    +

  1. Create runtime.txt file with R version

    2. +

  2. Create install.R file with package installations

    3. +

  3. Navigate to https://mybinder.org/, paste in GitHub repository, set to “URL to open (optional)” and type in “rstudio”, then launch

    4. +
+
+
+

Rough notes#

+

R - +\begin{itemize} +\item Renv +\item Posit Public Package Manager - can use Snapshot (earliest is Oct 2017, and 5 most recent versions of R), for Linux can install binary packages (which is much quicker, as usually R installs from source rather than binary unlike for Windows and Mac which makes it really slow) - \href{https://packagemanager.posit.co/client/#/repos/cran/setup}{source 1}, \href{https://docs.posit.co/faq/p3m-faq/#frequently-asked-questions}{source 2} +\item Groundhog - can go back to R 3.2 and April 2015 (and apparently can patch to go earlier) - \href{https://www.brodrigues.co/blog/2023-01-12-repro_r/}{source 1} +\item miniCRAN - \href{https://learn.microsoft.com/en-us/sql/machine-learning/package-management/create-a-local-package-repository-using-minicran?view=sql-server-ver16}{source 1} +\item Docker - requires license for non-academic (e.g. NHS) use - but Podman can drop in as replacement. To do development inside a container isn’t natively supported by RStudio but can use RStudioServer via Rocker. By default, it runs in ephemeral mode - any code created or saved is lost when close - but you can use volume argument to mount local folders - \href{https://towardsdatascience.com/running-rstudio-inside-a-container-e9db5e809ff8}{source 1} +\end{itemize}

+
+
+ + +