---
title: "Publishing R / Bioconductor Packages To AnVIL Workspaces"
author:
- name: Martin Morgan
  affiliation: Roswell Park Comprehensive Cancer Center
  email: Martin.Morgan@RoswellPark.org
package: AnVILPublish
output:
  BiocStyle::html_document
abstract: |
  This vignette introduces the AnVILPublish package for transforming R /
  Bioconductor packages to AnVIL workspaces. Data from the package
  DESCRIPTION file and vignette YAML chunks are used to create the 'DASHBOARD'
  workspace landing page. Vignettes are processed to Python notebooks and added
  to the workspace bucke for access via the 'NOTEBOOKS' tab.
vignette: |
  %\VignetteIndexEntry{Publishing R / Bioconductor packages to AnVIL Workspaces}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Introduction

This package produces AnVIL workspaces from R packages. An example
uses the new [Gen3][4] package as a basis for the
[Bioconductor-Package-Gen3][3] workspace (permission to access this
workspace is required, but there are no restrictions on granting
permission).

[4]: https://github.com/Bioconductor/Gen3
[3]: https://anvil.terra.bio/#workspaces/bioconductor-rpci-anvil/Bioconductor-Package-Gen3

## Package installation

If necessary, install the AnVILPublish library

```{r}
if (!"AnVILPublish" %in% rownames(installed.packages()))
    BiocManager::install("AnVILPublish")
```

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][1] available to copy notebook
files to the workspace. Test availability with

```{r, eval = FALSE}
AnVIL::gcloud_exists()
```

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

```{r, eval = FALSE}
AnVIL::gcloud_account()
AnVIL::gcloud_project()
```

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

[1]: https://cloud.google.com/sdk

## `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.,

```{r, eval = FALSE}
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.

[Quarto]: https://quarto.org

# 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 create)
  workspace, as returned by `avbucket()`.
  
Tables are processed first with `whisker.render()` for variable
substitution, and then `readr::read_csv()` and `avtable_import()`.Q

In vignettes, the title: and author: 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 package source
(e.g., github checkout) directory using `as_workspace()`.

```{r, eval = FALSE}
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 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.

[bookdown]: https://bookdown.org/

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

1. Use the `Package:` field to provide a one-word identifier (e.g.,
   `Package: Bioc2020_CNV`) 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-Bioc2020_CNV`.

1. 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, eval = FALSE}
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, eval = FALSE}
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, eval = FALSE}
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.

1. 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.

1. 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.

1. 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.

1. 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>`.

1. 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][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.

# Session info {.unnumbered}

```{r sessionInfo}
sessionInfo()
```