Get Started
GitHub Secrets
Before pushing changes to your new R package, you may want to set up one or more GitHub Secrets:
GITHUB_TOKEN[Optional]: Can grant access to private repos on GitHub Actions. You can generate your very own Personal Authentication Token using these instructions.
DOCKER_TOKEN[Optional]: Allows GitHub Actions to push to a DockerHub account.
use_workflow
dynamic action
User only have to run use_workflow once per R package to
create a workflow file that calls the rworkflows
action.
workflow <- rworkflows::use_workflow(run_bioccheck = FALSE,
run_rcmdcheck = TRUE,
run_pkgdown = TRUE,
run_docker = TRUE,
docker_user = "bschilder",
docker_org = "neurogenomicslab",
force_new = TRUE,
## Use default save_dir in practice
save_dir = tempdir())## Saving yaml ==> /tmp/Rtmpm7Mxwl/rworkflows.ymlstatic workflow
Alternatively, you may use
workflow_static <- rworkflows::use_workflow(name = "rworkflows_static",
run_bioccheck = FALSE,
run_rcmdcheck = TRUE,
run_pkgdown = TRUE,
run_docker = TRUE,
docker_user = "bschilder",
docker_org = "neurogenomicslab",
force_new = TRUE,
## Use default save_dir in practice
save_dir = tempdir())## Saving yaml ==> /tmp/Rtmpm7Mxwl/rworkflows_static.ymluse_badges
This function creates a banner containing badges, a hex sticker (if one is available), and author names.
## Finding hex sticker(s) for 1 package(s).## Adding license.## Adding version.## Adding code size## Adding commit.## Adding actions.## Adding codecov.## Adding authors.## When refs is provided, paths must have the same length (or be set to NULL). Setting paths=NULL.
Authors: Brian Schilder, Alan Murphy, Nathan Skene
use_dockerfile
Create a Dockerfile that installs your R package and all its Imports/Suggests. It is normally not necessary for users to run this function themselves unless they would like to use it to create a template Dockerfile and then customise it further (rather than creating one on the fly).
## Creating new Docker file ==> /tmp/Rtmpm7Mxwl/DockerfileTemplates
You can generate templates for each of the following R package elements. These templates are designed to dynamically autofill as as much information as possible (package name, date, authors) to minimise manual editing steps and ensure documentation is as up-to-date as possible.
README
README rmarkdown file. Allows you to run code inside to generate a README markdown file.
## Creating new README file ==> /tmp/Rtmpm7Mxwl/README.RmdVignette: Get started
Generate a vignette on how to get started with your R package. Requires additional editing to fill in examples.
## Use default save_dir in practice
vignette1 <- rworkflows::use_vignette_getstarted(package = "mypackage",
save_dir = tempdir())## Creating new vignette file ==> /tmp/Rtmpm7Mxwl/vignettes/mypackage.RmdVignette: docker
Generate a vignette on how to install your R package via with your R package, RStudio, and all dependencies already installed. This vignette does not require any manual editing after creation (unless you want to change things like port number, for example).
Note, this is only relevant when you have a container for your R
package stored in a public Docker registry (e.g. GitHub Container
Registry, DockerHub). You can automatically generate one by using the
rworkflows action with run_docker=TRUE:
If you’re using the GitHub Container Registry,
docker_org can simply be your GH organization name or user
name.
## Use default save_dir in practice
vignette2 <- rworkflows::use_vignette_docker(docker_org = "neurogenomics",
save_dir = tempdir())## Creating new vignette file ==> /tmp/Rtmpm7Mxwl/vignettes/docker.RmdAdding Python
If your R package depends on python (e.g. via
reticulate), the rworkflows action can also
automatically install python for you.
Using Python
You can instruct GitHub Actions to install a version of python, with whatever level of versioning specificiy you want (e.g. “3.10.11”, “3.10”, or even just “3”).
If you supply one python_version, it will be applied to
all runners. You also assign a different python_version to
each os, as we do here:
runners <- rworkflows::construct_runners(
python_version = list("ubuntu-latest"="3.10.11",
"macOS-latest"="3.9",
"windows-latest"="3.9.1"))
workflow <- rworkflows::use_workflow(runners = runners,
preview = TRUE,
force_new = TRUE,
## Use default save_dir in practice
save_dir = tempdir())## name: rworkflows
## 'on':
## push:
## branches:
## - master
## - main
## - devel
## - RELEASE_**
## pull_request:
## branches:
## - master
## - main
## - devel
## - RELEASE_**
## jobs:
## rworkflows:
## permissions: write-all
## runs-on: ${{ matrix.config.os }}
## name: ${{ matrix.config.os }} (${{ matrix.config.r }})
## container: ${{ matrix.config.cont }}
## strategy:
## fail-fast: no
## matrix:
## config:
## - os: ubuntu-latest
## bioc: devel
## r: auto
## cont: ghcr.io/bioconductor/bioconductor_docker:devel
## rspm: ~
## python-version: 3.10.11
## - os: macOS-latest
## bioc: release
## r: auto
## cont: ~
## rspm: ~
## python-version: '3.9'
## - os: windows-latest
## bioc: release
## r: auto
## cont: ~
## rspm: ~
## python-version: 3.9.1
## steps:
## - uses: neurogenomics/rworkflows@master
## with:
## run_bioccheck: no
## run_rcmdcheck: yes
## as_cran: yes
## run_vignettes: yes
## has_testthat: yes
## run_covr: yes
## run_pkgdown: yes
## has_runit: no
## has_latex: no
## GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
## run_docker: no
## DOCKER_TOKEN: ${{ secrets.DOCKER_TOKEN }}
## runner_os: ${{ runner.os }}
## cache_version: cache-v1
## docker_registry: ghcr.io## Saving yaml ==> /tmp/Rtmpm7Mxwl/rworkflows.ymlUsing conda
Optionally, you can tell it to create and activate a conda
environment to use. rworkflows::construct_conda_yml is a
helper function to create conda yaml files for you.
Here we construct our yaml file that specifies what we want in our conda env. Make sure you write the yaml to your project directory, so that it gets pushed to GitHub (and thus can be found after checking out your repo on GitHub Actions).
See here for some examples of
environment_file <- construct_conda_yml(name = "myenv",
channels = c("conda-forge",
"bioconda"),
dependencies = c("python>=3.7",
"scanpy",
"anndata"),
pip = c("scarches",
"magic-impute"),
preview = TRUE)## name: myenv
## channels:
## - conda-forge
## - bioconda
## dependencies:
## - python>=3.7
## - scanpy
## - anndata
## - pip
## - pip:
## - scarches
## - magic-impute## Saving yaml ==> /tmp/RtmpyfIbUA/Rbuild191016ab61f8/rworkflows/myenv_conda.ymlYou could also simply provide a URL to a publicly accessible conda yaml file stored elsewhere.
Now we can supply the conda environment_file path, and
Miniconda will automatically be installed to build the conda env on all
of your GitHub Actions runners.
workflow <- rworkflows::use_workflow(environment_file = environment_file,
preview = TRUE,
force_new = TRUE,
## Use default save_dir in practice
save_dir = tempdir())## environment_file supplied. Enabling miniforge.## python-version not set for runner os='ubuntu-latest'. Using default.## python-version not set for runner os='macOS-latest'. Using default.## python-version not set for runner os='windows-latest'. Using default.## name: rworkflows
## 'on':
## push:
## branches:
## - master
## - main
## - devel
## - RELEASE_**
## pull_request:
## branches:
## - master
## - main
## - devel
## - RELEASE_**
## jobs:
## rworkflows:
## permissions: write-all
## runs-on: ${{ matrix.config.os }}
## name: ${{ matrix.config.os }} (${{ matrix.config.r }})
## container: ${{ matrix.config.cont }}
## strategy:
## fail-fast: no
## matrix:
## config:
## - os: ubuntu-latest
## bioc: devel
## r: auto
## cont: ghcr.io/bioconductor/bioconductor_docker:devel
## rspm: ~
## - os: macOS-latest
## bioc: release
## r: auto
## cont: ~
## rspm: ~
## - os: windows-latest
## bioc: release
## r: auto
## cont: ~
## rspm: ~
## steps:
## - uses: neurogenomics/rworkflows@master
## with:
## run_bioccheck: no
## run_rcmdcheck: yes
## as_cran: yes
## run_vignettes: yes
## has_testthat: yes
## run_covr: yes
## run_pkgdown: yes
## has_runit: no
## has_latex: no
## GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
## run_docker: no
## DOCKER_TOKEN: ${{ secrets.DOCKER_TOKEN }}
## runner_os: ${{ runner.os }}
## cache_version: cache-v1
## docker_registry: ghcr.io
## miniforge_variant: ''
## environment_file: /tmp/RtmpyfIbUA/Rbuild191016ab61f8/rworkflows/myenv_conda.yml## Saving yaml ==> /tmp/Rtmpm7Mxwl/rworkflows.yml## [1] TRUESession Info
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## 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=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
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] data.table_1.16.2 rworkflows_1.0.3 rmarkdown_2.28
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 jsonlite_1.8.9 renv_1.0.11
## [4] BiocManager_1.30.25 dplyr_1.1.4 compiler_4.4.1
## [7] tidyselect_1.2.1 jquerylib_0.1.4 rvcheck_0.2.1
## [10] scales_1.3.0 yaml_2.3.10 fastmap_1.2.0
## [13] here_1.0.1 ggplot2_3.5.1 R6_2.5.1
## [16] generics_0.1.3 knitr_1.48 yulab.utils_0.1.7
## [19] tibble_3.2.1 desc_1.4.3 dlstats_0.1.7
## [22] maketools_1.3.1 rprojroot_2.0.4 munsell_0.5.1
## [25] bslib_0.8.0 pillar_1.9.0 RColorBrewer_1.1-3
## [28] rlang_1.1.4 utf8_1.2.4 cachem_1.1.0
## [31] badger_0.2.4 xfun_0.49 fs_1.6.5
## [34] sass_0.4.9 sys_3.4.3 cli_3.6.3
## [37] magrittr_2.0.3 digest_0.6.37 grid_4.4.1
## [40] lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.1
## [43] glue_1.8.0 buildtools_1.0.0 fansi_1.0.6
## [46] colorspace_2.1-1 tools_4.4.1 pkgconfig_2.0.3
## [49] htmltools_0.5.8.1