# AnVILPublish This package produces AnVIL workspaces from R packages. Use this package to create or update AnVIL workspaces from resources such as R / Bioconductor packages. The metadata about the package (e.g., select information from the package `DESCRIPTION` file and from vignette `YAML` headings) are used to populate the ‘DASHBOARD’ page on AnVIL. Vignettes are translated to python notebooks ready for evaluation in AnVIL. ## Package installation If necessary, install the AnVILPublish library ``` r if (!"AnVILPublish" %in% rownames(installed.packages())) BiocManager::install("AnVILPublish") ``` # Requirements **Note**. The package currently works for Google Cloud Platform workspaces and does *NOT* support AnVIL workspaces that use the Azure platform. ## 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](https://cloud.google.com/sdk) available to copy notebook files to the workspace. Test availability with ``` r AnVILGCP::gcloud_exists() ``` and verify that the account and project are appropriate (consistent with AnVIL credentials) for use with AnVIL ``` r 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](https://quarto.org) software. It must be available from within *R*, e.g., ``` r 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. # Creating or updating workspaces **CAUTION** updating an existing workspace will replace existing content in a way that cannot be undone – you will lose content! Workspace creation or update uses information from the DESCRIPTION file, CSV files in inst/tables, and from the YAML metadata at the top of vignettes. It is therefore worth-while to make sure this information is accurate. In the DESCRIPTION file, the Title, Version, Date, <Authors@R> (preferred) or Author / Maintainer fields, Description, and License fields are used. Tables in inst/tables must be CSV files. Individual entries in the CSV file may contain ‘whisker’ expressions for variable substitution, as follows: - `{{ bucket }}`: the bucket location of the (possibly newly created) workspace, as returned by `avstorage()`. Tables are processed first with `whisker.render()` for variable substitution, and then `readr::read_csv()` and `avtable_import()`. In vignettes, the `title:`, `author:`, and `name:` fields are used. The abstract is a good candidate for future inclusion. ## 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()`. ``` r 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](https://bookdown.org/) 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 ``` r AnVILPublish::as_workspace( "path/to/directory", # directory containing DESCRIPTION file "bioconductor-rpci-anvil", create = TRUE ) ``` # Updating notebooks or workspace permissions These steps are performed automatically by `as_workspace()`, but may be useful when developing a new workspace or revising existing workspaces. ## 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. ``` r 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 ``` r AnVILPublish::add_access( "bioconductor-rpci-anvil", "Bioconductor-Package-Foo" ) ``` # Vignette and .Rmd best practices ## 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 = "Martin.Morgan@RoswellPark.org", 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](https://quarto.org) 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. # Session info ``` r sessionInfo() #> R version 4.4.1 Patched (2024-08-13 r87005) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 24.04.1 LTS #> #> Matrix products: default #> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0 #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 #> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 #> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C #> [9] LC_ADDRESS=C LC_TELEPHONE=C #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C #> #> time zone: America/New_York #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> loaded via a namespace (and not attached): #> [1] compiler_4.4.1 fastmap_1.2.0 cli_3.6.3 htmltools_0.5.8.1 #> [5] tools_4.4.1 rstudioapi_0.16.0 yaml_2.3.10 codetools_0.2-20 #> [9] rmarkdown_2.28 knitr_1.48 xfun_0.47 digest_0.6.37 #> [13] rlang_1.1.4 evaluate_1.0.0 ```