Package installation

If necessary, install the AnVILPublish library

if (!"AnVILPublish" %in% rownames(installed.packages()))
    BiocManager::install("AnVILPublish")

Best practices

There are only a small number of functions in the package; it is likely best practice to invoke these using AnVILPublish::...() rather than attaching the package to the search path.

The gcloud SDK

It is necessary to have the gcloud SDK available to copy notebook files to the workspace. Test availability with

AnVILGCP::gcloud_exists()

and verify that the account and project are appropriate (consistent with AnVIL credentials) for use with AnVIL

AnVILGCP::gcloud_account()
AnVILGCP::gcloud_project()

Note that these be used to set, as well as interrogate, the account and project.

Quarto software

Conversion of Rmarkdown (.Rmd) or Quarto (.Qmd) vignettes to Jupyter (.ipynb) notebooks uses Quarto software. It must be available from within R, e.g.,

system2("quarto", "--version")

The user must determine if they want their vignettes converted or rendered into Jupyter notebooks. The difference is that render automatically executes R code blocks and embeds images, while convert will not.

Use of Python notedown for conversion is no longer supported.

From package source

The one-stop route is to create a workspace from the local package source (e.g., GitHub checkout) directory using as_workspace().

AnVILPublish::as_workspace(
    "path/to/package",
    "bioconductor-rpci-anvil",     # i.e., billing account
    create = TRUE                  # use update = TRUE for an existing workspace
)

Use create = TRUE to create a new workspace. Use update = TRUE to update (and potentially overwrite) an existing workspace. One of create and update must be TRUE. The command illustrated above does not specify the name = argument, so creates or updates a workspace "Bioconductor-Package-<pkgname>, where <pkgname> is the name of the package read from the DESCRIPTION file; provide an explicit name to create or update an arbitrary workspace. The option use_readme = TRUE appends a README.md file to the formatted content of the DESCRIPTION file.

AnVILPublish::as_workspace() invokes as_notebook() so this step does not need to be performed ‘by hand’.

See the command add_access(), below, to make the workspace available to a wider audience.

From collections of Rmd files

Some R resources, e.g., bookdown sites, are not in packages. These can be processed to workspaces with minor modifications.

1. Add a standard DESCRIPTION file (e.g., use_this::use_description()) to the directory containing the .Rmd files.

2. Use the Package: field to provide a one-word identifier (e.g., Package: Bioc2020CNV) for your material. Add a key-value pair Type: Workshop or similar. The Pacakge: and Type: fields will be used to create the workspace name as, in the example here, Bioconductor-Workshop-Bioc2020CNV.

3. Add a ‘yaml’ chunk to the top of each .Rmd file, if not already present, including the title and (optionally) name information, e.g.,

   ---
   title: "01. Introduction to the workshop"
   author:
   - name: Iman Author
   - name: Imanother Author
   ---

Publish the resources with

AnVILPublish::as_workspace(
    "path/to/directory",      # directory containing DESCRIPTION file
    "bioconductor-rpci-anvil",
    create = TRUE
)

Updating workspace notebooks from vignettes

Transforming vignettes to notebooks may require several iterations, and is available as a separate operation. Use update = FALSE to create local copies for preview.

AnVILPublish::as_notebook(
    "paths/to/files.Rmd",
    "bioconductor-rpci-anvil",     # i.e., billing account
    "Bioconductor-Package-Foo",    # Workspace name
    update = FALSE                 # make notebooks, but do not update workspace
)

The vignette transformation process has several limitations. Only .Rmd vignettes are supported. Currently, the vignette is transformed first to a markdown document using the rmarkdown command render(..., md_document()). The markdown document is then translated to Python notebook using quarto.

It is likely that some of the limitations of vignette rendering can be reduced.

Adding user access credentials to share the notebook

The "Bioconductor_User" group can be added to the entities that can see the workspace. AnVIL users wishing to view the workspace should be added to the Bioconductor_User group, rather than to the workspace directly. To add the user group, use

AnVILPublish::add_access(
    "bioconductor-rpci-anvil",
    "Bioconductor-Package-Foo"
)

Orientation

.Rmd files need to be converted to jupyter notebooks. These ‘best practices’ lead to results that are more likely to be satisfactory, as outlined here.

Best practices

1. For packages, make sure the DESCRIPTION file is complete. Use the Authors@R notation for fully specifying authors. Add a Date: field indicating date of last modification. Follow other Bioconductor best practices, e.g., using and incrementing appropriate version numbers.

2. For collections of vignettes not in a package (e.g., a bookdown folder), add a DESCRIPTION file at the top level. An example is

   Package: BCC2020
   Type: Workshop
   Title: R / Bioconductor in the AnVIL Cloud
   Version: 1.0.0
   Authors@R: 
       c(person(
           given = "Martin",
           family = "Morgan",
           role = c("aut", "cre"),
           email = "[email protected]",
           comment = c(ORCID = "0000-0002-5874-8148")
       ),
       person("Nitesh", "Turaga", role = "ctb"),
       person("Lori", "Shepherd", role = "ctb"))
   Description:
       This book contains material for a 2 1/2 hour course offered at the
       Bioinformatics Community Conference 2020. Bioconductor provides
       more than 1900 R packages for the analysis and comprehension of
       high-throughput genomic data. Most users install and run
       Bioconductor on a personal computer or perhaps use an academic
       cluster. Cloud-based solutions are increasing appealing, removing
       the headaches of local installation while providing access to (a)
       better, scalable computing resources; and (b) large-scale
       'consortium' and other reference data sets. This session
       introduces the AnVIL cloud computing environment. We cover use of
       the cloud as a replacement to desktop-style computing; integrating
       workflows for 'upstream' processing of large data resources with
       interactive 'downstream' analysis and comprehension, using Human
       Cell Atlas single-cell datasets as an example; and querying
       cloud-based consortium data for integration with a users own data
       sets.
   License: CC-BY
   Date: 2020-07-17
   Encoding: UTF-8
   LazyData: true
   Roxygen: list(markdown = TRUE)
   RoxygenNote: 7.1.1

   The Type and Package fields are used to construct the second and third elements of the workspace name (in this case, Bioconductor-Workshop-BCC2020). Title, Version, Authors@R, Description, License, and Date fields are used to construct the DASHBOARD page.

3. Start each vignette with ‘yaml’ containing essential metadata about the document – title and author(s). Include other information if desired, e.g., abstract, (static) date of last modification.

4. Use a file naming system AND a yaml title field that sorts files into the order in which the document content is to be presented, e.g., using file names 01-Setup.Rmd, 02-... and titles (in the yaml) title: "01 Setup", … Naming both files and titles in this way provides some chance that the Rmd files are presented, or can be made to be presented, sensibly across the Bioconductor package landing page and Workspace / NOTEBOOK interface.

5. All code chunks, regardless of annotations such as eval = FALSE or echo = FALSE are converted to visible, evaluated cells in jupyter notebooks. Replace code chunks that you do not wish the user to evaluate with HTML tags <pre></pre>.

6. Although both Rmarkdown and python notebooks support code chunks in multiple languages, there is no support for this in the conversion process – all cells are presented as R code.

Additional notes on .Rmd conversion

Current best practice is to use quarto for conversion of .Rmd to ipynb. Quarto is available on the Bioconductor docker image, or easily installed on Linux, macOS, or Windows.

Support for conversion using the Python notedown module is no longer supported.