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nhp_tpma_rationale

A simple interactive table in a Quarto doc to explore New Hospital Programme (NHP) schemes' rationale for their choice of types of potentially-mitigatable activity (TPMAs) and prediction intervals. Shows only scenarios tagged with a run stage of 'final_report_ndg2', i.e. the primary scenario used in output reports.

Deployed to Posit Connect and re-rendered on schedule (login/permissions required).

Render locally

If you need to generate the report on your machine:

  1. Create a .Renviron file in the project root using .Renviron.example as a template. Ask a member of the Data Science team for the values required by each variable. You won't need to include CONNECT_* variables if you've already configured your publishing account in RStudio.
  2. Open index.qmd in RStudio and click the 'Render' button if in RStudio (otherwise quarto::quarto_render() or via the Quarto CLI).

During this process, you may be prompted to authorise with Azure through the browser. See the Data Science website for detail on authorisation.

Redeploy

If you make changes to the code in this repo, you can redeploy it to our Connect server by running deploy.R.

Refresh

The report runs on schedule, so any changes to the underlying data will be integrated on the next rendering. You may wish to manually refresh the app from Posit Connect if you want your changes to appear more quickly. To do this, open the app from the Posit Connect 'Content' page and click the 'refresh report' button (circular arrow) in the upper-right of the interface.

Data

The data processed by the report is collected from the model results files, hosted on Azure in the container given by the environmental variable AZ_STORAGE_CONTAINER_RESULTS. See the separate guidance for how to tag results files with run-stage metadata. Lookups for mitigators, scheme names and providers are read from the Azure container named by the AZ_STORAGE_CONTAINER_SUPPORT environmental variable.

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Quarto report: rationale for TPMA selections in the New Hospital Progamme (NHP) demand model

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