Using plotting and preparation functions

This vignette shows how to use the plotting-preparation and plotting functions in vimcheck.

Note that all data used here are placeholders.

library(vimcheck)

Compare demography

Users can check demographic alignment of burden data using check_demography_alignment(), then prepare it for plotting using prep_plot_demography(), and plot it using plot_compare_demography().

burden <- eg_burden_template

burden <- check_demography_alignment(burden, eg_wpp)
burden <- prep_plot_demography(burden)

plot_compare_demography(burden, 1)

Examine age patterns

Users can check age patterns in burden data using prep_plot_age() and plotting using plot_age_patterns().

Note that values are placeholders and you should expect to see real age-wise burden patterns look very different.

burden <- eg_burden_template
burden <- prep_plot_age(burden)

# manually set values as template default is NA, prevents ggplot warnings
burden$value_millions <- 1.0

plot_age_patterns(burden, 1)

Global burden by decade

Users can check the global burden in each decade for each scenario using prep_plot_burden_decades() and plot_global_burden_decades().

burden <- eg_burden_template
year_max <- 2100
burden <- prep_plot_burden_decades(burden, year_max)

# manually set values as template default is NA, prevents ggplot warnings
burden$value_millions <- 1.0

plot_global_burden_decades(burden, 1)

Global burden timeseries

Users can check a timeseries of global burdens by scenario and age group. In contrast with the plotting scheme above, prep_plot_global_burden() converts the burden data to long-format and transforms the data tibble into a nested-tibble. This gives a tibble with as many rows as burden outcomes: cases, deaths, DALYs and YLLs, and a tibble giving the annual values by age for each burden outcome.

The function plot_global_burden() is intended to be applied row-wise, taking the burden outcome name (e.g. “cases”) and the burden outcome data to plot a timeseries with values by age.

burden <- eg_burden_template
burden <- prep_plot_global_burden(burden)

# NOTE: expected use case is to loop over nested column DFs
# set values to a dummy placeholder
burden$burden_data[[1]]$value_millions <- 1

plot_global_burden(
  burden$burden_data[[1]],
  burden$burden_outcome[[1]],
  1
)

Coverage sets

Users can check trends in coverage sets using prep_plot_coverage_set() to prepare coverage sets data, and plot_coverage_set() to prepare a plot facetted by country and scenario.

# load some example data
coverage <- eg_coverage
coverage <- prep_plot_coverage_set(coverage)

plot_coverage_set(coverage, 1)

Fully-vaccinated persons

Users can check trends in fully-vaccinated persons (FVPs) over time using prep_plot_fvp() to prepare FVPs sets data, and plot_fvps() to prepare a plot facetted by country and scenario.

# load some example data
fvps <- eg_fvps
fvps <- prep_plot_fvp(fvps, 2030, 2040) # example data has year limits 2030-40

plot_fvp(fvps, 1)