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Unmet Needs

This R package hosts the 'NHS Formula Health Inequality Impact Calculator' shiny app. The NHS Formula Health Inequality Impact Calculator shiny app allows interested users to visualise the national, regional and sub-regional health-related effects associated with changes in annual healthcare expenditure. For more information about the tool, please check the shiny app's 'About the tool' section.

This project was commissioned by Richard Cookson at the University of York, and developed by Wael Mohammed at Dark Peak Analytics.

Project Structure 
------------------------
    ├── data               <- internal data objects.
    │
    ├── data-raw           <- raw data files - generates the data objects.
    │
    ├── inst               <- files copied unmodified into the package.
    │   └── app            <- shiny app files.
    |
    ├── man                <- package-specific functions' documentation, Rd files.
    │
    ├── R                  <- package-specific functions' definitions, R files.
    │
    ├── renv               <- records R-level package/project status.
    │
    ├── .gitignore
    ├── .Rbuildignore
    ├── .Rhistory
    ├── .Rprofile
    ├── DESCRIPTION
    ├── LICENSE
    ├── NAMESPACE
    ├── README.md          <- Top-level README
    ├── renv-lock          <- records R-level package/project status.
    └── unmet-needs.Rproj

Basic Workflow
------------------------

  1. Update your local repo (`git pull origin main`)
  2. Create a working branch (`git checkout -b new_feature`)
  3. Make changes on your branch
  4. Commit your changes locally (`git add`, `git commit -m "description of your commit"`)
  5. Upload the changes (your branch) to GitHub (`git push origin new_feature`)
  6. Go on to GitHub, create a "Pull Request"
  7. Assign a reviewer who can check your code
  7. After successful review, the changes are merged into the main branch
  
----

Example Usage


# Load the UnmetNeeds package
library("UnmetNeeds")

# Run the shiny app
run_UnmetNeeds_app()

The web-based application starts with a landing page, during which required functions and data are loaded.

The first tab in the shiny application is the 'Inputs' tab. Users can study and/or change the assumptions and inputs employed in the calculations.

Switching to any of the two other tabs triggers the analysis behind the scene. Clicking on 'Mortality impact' loads the second tab.

Clicking on 'QALE impact' loads the third and final tab.

Please let us know if you have any comments or questions about the project structure, code or workflow. If you have questions about the theory and/or methods employed to generate the results, please get in touch with Richard Cookson (richard.cookson@york.ac.uk).

Wael Mohammed, Dark Peak Analytics.