AnVILPublish 1.6.0
This package produces AnVIL workspaces from R packages. An example uses the new Gen3 package as a basis for the Bioconductor-Package-Gen3 workspace (permission to access this workspace is required, but there are no restrictions on granting permission).
If necessary, install the AnVILPublish library
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.
gcloud SDKIt is necessary to have the gcloud SDK available to copy notebook files to the workspace. Test availability with
AnVIL::gcloud_exists()and verify that the account and project are appropriate (consistent with AnVIL credentials) for use with AnVIL
AnVIL::gcloud_account()
AnVIL::gcloud_project()Note that these be used to set, as well as interrogate, the acount and project.
notedown softwareConversion of .Rmd vignettes to .ipynb notebooks uses notedown python software. It must be available from within R, e.g.,
system2("notedown", "--version")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.
The one-stop route is to create a workspace from the package source
(e.g., github checkout) directory use 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 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.
Some R resources, e.g., bookdown sites, are not in packages. These can be processed to workspaces with minor modifications.
Add a standard DESCRIPTION file (e.g.,
use_this::use_description()) to the directory containing the
.Rmd files.
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.
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
)These steps are performed automatically by as_workspace(), but may
be useful when developing a new workspace or revising existing
workspaces.
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 notedown.
It is likely that some of the limitations of vignette rendering can be reduced.
.Rmd files need to be converted to jupyter notebooks. Currently
there is not an ‘ideal’ solution, with details listed in the
‘Additional notes…’ section. Consequently, there are ‘best
practices’ that lead to results that are more likely to be
satisfactory, as outlined here.
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.
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: 7.1.1The 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.
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.
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.
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>.
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.
The current state of affairs with respect to notebook conversion is imperfect. Conversion is currently a two-step process: Rmarkdown to markdown, and markdown to ipynb.
The conversion from Rmarkdown to markdown is currently accomplished with
knitr::opts_chunk$set(eval=FALSE)
rmarkdown::render(..., md_document())to create a markdown
document from the .Rmd source.
This correctly processes the markdown content, including yaml metadata, but renders all code chunks identically.
Using other knitr options may allow, e.g., conditional inclusion of code chunks.
Use notedown to convert from markdown to jupyter notebook, adding
metadata to indicate that the notebook has an R kernel.
Here are some notes on alternative solutions.
jupytext (version 1.5.1) but has difficulty with some
markdown. For instance, reference-style links [foo][1] are only
rendered correctly when the reference is in the same code chunk as
the link. It is under active development and may mature into a
possible alternative.
pandoc (version 2.10.1) provides a one-step conversion from .Rmd
to .ipynb, but code chunks are rendered as pre-formatted text
rather than evaluable cell.
notedown (version 1.5.1) also provides one-step conversion,
but does not exclude yaml from vignettes. The project has not had
commits for several years, and has several open issues.
sessionInfo()## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] bookdown_0.26 digest_0.6.29 R6_2.5.1
## [4] jsonlite_1.8.0 magrittr_2.0.3 evaluate_0.15
## [7] stringi_1.7.6 rlang_1.0.2 cli_3.3.0
## [10] jquerylib_0.1.4 bslib_0.3.1 rmarkdown_2.14
## [13] tools_4.2.0 stringr_1.4.0 xfun_0.30
## [16] yaml_2.3.5 fastmap_1.1.0 compiler_4.2.0
## [19] BiocManager_1.30.17 htmltools_0.5.2 knitr_1.38
## [22] sass_0.4.1