WIP impact diagnostics fns#17
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…ots central impact estimates, coverage and fvps. Associated documentation also added from attempts at roxygen!
| #' @export | ||
| plot_impact <- function( | ||
| data, | ||
| country = PINE, |
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I think this will fail for certain diseases. Although we use PINE as a default for a quick glace at estimates instead of running every country, there will not be some diseases in PINE countries, for example YF.
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Could we add something like, if some or any of PINE present, plot those? Likely at least one PINE country will be present in all disease datasets.
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I think a failure for a disease-country mismatch is okay, we provide PINE as a sensible default but there's no expectation that country is always present - the function will error informatively, and the user can change either data or country.
| ggplot2::geom_line(aes(colour = .data$short_name), linewidth = 0.5) + | ||
| ggplot2::geom_point(aes(colour = .data$short_name), size = 0.5) + | ||
| theme_vimc() + | ||
| facet_wrap(ggplot2::vars("country"), scales = "free_y") + |
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I think the facet_wrap should be facet_wrap(ggplot2::vars(country), scales = "free_y") to correctly facet the plots into countries
| labs( | ||
| x = x_lab, | ||
| y = glue::glue("{burden_type} averted"), | ||
| title = title |
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Add two labs to make the legend title more intuitive:
fill = "Vaccination activity", colour = "Vaccination activity"
| labs( | ||
| x = "Year", | ||
| y = ylab, | ||
| title = title |
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Same comment re tidying up legend title:
fill = "Vaccination acitivty", colour = "Vaccination activity"
| ggplot( | ||
| data, | ||
| aes( | ||
| x = .data$year, |
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Years are being plotted with 2 dp. not sure what the easiest way to deal with it is, force as.integer?
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This PR is WIP replacing #15.
PR desc copied over:
This PR adds additional functions from [https://github.com/vimc/montagu-reports/tree/main/src/rapid-model-run-impact] a key report for the VIMC Science & Policy team.
Functions added generate central estimate impact plots and coverage and fvp plots per year, country and view.
First attempt at ensuring functions are package-ready/ utilising roxygen etc. so any comment welcome.
TODO's in script fn_plotting_impact.R with specific questions. I understand testing may need to be stronger/ the next step.