Generate_RespiCast_Forecasts #55
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| name: Generate_RespiCast_Forecasts | |
| # This example workflow is meant to depict steps needed to run R or Python code | |
| # from a tools repository saving the output data to another data repository | |
| # A good starting point for a better understandig of how GitHub actions work | |
| # is this: https://docs.github.com/en/actions/quickstart | |
| # Firts step is to define di events that can trigger this workflow | |
| on: | |
| # You can trigger the workflow at a scheduled time | |
| schedule: | |
| # run it it at pre-defined time | |
| - cron: "00 17 * * 1" | |
| # Or you can manually trigger the workflow | |
| workflow_dispatch: | |
| # then you need to define the flow of your jon step by step | |
| jobs: | |
| # here we need a single job, running on a standard Ubuntu virtual machine | |
| main_job: | |
| runs-on: ubuntu-latest | |
| steps: | |
| # Checkout the GitHub tools repo where R | |
| # scripts used to run your model resides | |
| # In this example tools repo is https://github.com/EU-ECDC/ECDC-Mathematical-Models | |
| # -------------------------------------------------------------------------------- | |
| - name: checkout ECDC Mathematical Models repo | |
| uses: actions/checkout@v3 | |
| with: | |
| token: ${{ secrets.GITHUB_TOKEN }} | |
| repository: 'EU-ECDC/ECDC-Mathematical-Models' | |
| ref: 'main' # the git branch to work on | |
| path: './models' # Relative path under $GITHUB_WORKSPACE to place the repository | |
| # Set up an R environment, with needed dependencies | |
| # e.g. some GitHub hosted package or "jsonlite" package | |
| # ---------------------------------------------------------------- | |
| - uses: r-lib/actions/setup-r@v2 | |
| with: | |
| r-version: "4.3.3" | |
| #install-r: false | |
| use-public-rspm: true | |
| - name: Install system dependencies | |
| run: sudo apt-get update && sudo apt-get install -y libmagick++-dev && sudo apt-get install -y libcurl4-openssl-dev | |
| - name: Installing R dependencies | |
| run: | | |
| install.packages("hubVis", repos = c("https://hubverse-org.r-universe.dev", "https://cloud.r-project.org")) | |
| install.packages("devtools") | |
| message("######################## installed devtools") | |
| Sys.setenv(GITHUB_PAT = Sys.getenv("GITHUB_TOKEN")) | |
| message("######################## set github token") | |
| devtools::install_github('epiforecasts/[email protected]') | |
| #install.packages("scoringutils") | |
| install.packages("summarytools") | |
| env: | |
| GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} | |
| shell: Rscript {0} | |
| # Now you can run your R script from "R-code" folder under tools repo | |
| # The --hub_path parameter is passed so that you can use it inside your script | |
| # to write the output into the data-repo | |
| # -------------------------------------------------------------------- | |
| - name: do the R stuff | |
| id: do_R_stuff | |
| run: | | |
| # call R script from tools | |
| # Rscript ./models/Forecasting-hubs_models/00_main.R --hub_path "./repo" | |
| cd ./models | |
| Rscript ./Forecasting-hubs_models/00_main.R | |
| # Finally, when you are done with your model processign, | |
| # commit changes to the data-repo | |
| - name: Commit data repo changes | |
| uses: EndBug/add-and-commit@v7 | |
| with: | |
| cwd: './models' | |
| message: "Update data storage" | |
| default_author: github_actions | |
| push: true |