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Plot DHIS2-Based Indicator Coverage with Different Denominators and Survey Coverage

Source: R/plot_absolute_differences.R
plot_absolute_differences.Rd

plot_absolute_differences generates a bar plot comparing facility-based coverage percentages for a selected health indicator with different denominator sources (e.g., DHIS2, ANC1-derived, Penta1-derived, UN projection) and overlays a horizontal line representing national survey coverage for the specified year.

Usage

plot_absolute_differences(
  .data,
  indicator = c("penta3", "measles1", "bcg"),
  survey_coverage = 88,
  coverage_year = 2021
)

Arguments

.data

A data frame containing columns for country, year, and the coverage values for various denominators. Each indicator should have columns ending with suffixes like _dhis2, _anc1, _penta1, and _un.

indicator

A character string specifying the health indicator to plot. Options include, but are not limited to: mcv1, penta3, bcg.

survey_coverage

A numeric value representing the national survey coverage percentage to be displayed as a reference line on the plot. Default is 88.

coverage_year

A single integer specifying the year of interest for the plot. Only data for this year will be plotted.

Value

A ggplot2 object showing the coverage percentages for the selected indicator from various denominator sources, with a reference line for survey coverage.

Details

This function allows for visualization of the discrepancy between facility-based coverage data sourced from different denominators (e.g., DHIS2 projections, ANC1-derived, Penta1-derived, UN projections) and the national survey coverage. It helps in comparing how coverage estimates vary depending on the denominator, aiding in the assessment of data completeness and consistency.

Examples

if (FALSE) { # \dontrun{
  # Plot for the "penta3" indicator in the year 2021 with a survey coverage of 90
  plot.cd_indicator_coverage(data, indicator = "penta3", survey_coverage = 90, year = 2021)

  # Plot for the "bcg" indicator with default survey coverage and year 2020
  plot.cd_indicator_coverage(data, indicator = "bcg", year = 2020)
} # }