Plot Summaries of TB Burden - By Region, Globally and for Custom Groups

Plot summaries of TB burden metrics by region, globally, and for custom groupings. For variables with uncertainty represented by confidence intervals bootstrapping can be used (assuming a normal distribution) to include this in any estimated summary measures. Currently four statistics are supported; the mean (with 95% confidence intervals) and the median (with 95% interquartile range), rates and proportions.

plot_tb_burden_summary(df = NULL, dict = NULL, metric = "e_inc_num",
  metric_label = NULL, conf = c("_lo", "_hi"), years = NULL,
  samples = 1000, countries = NULL, compare_to_region = FALSE,
  compare_to_world = TRUE, custom_compare = NULL,
  compare_all_regions = TRUE, stat = "rate", denom = "e_pop_num",
  rate_scale = 1e+05, truncate_at_zero = TRUE, annual_change = FALSE,
  smooth = FALSE, facet = NULL, legend = "top", trans = "identity",
  scales = "fixed", interactive = FALSE, viridis_palette = "viridis",
  viridis_direction = -1, viridis_end = 0.9, download_data = TRUE,
  save = TRUE, verbose = TRUE, ...)

Arguments

df	Dataframe of TB burden data, as sourced by get_tb_burden. If not specified then will source the WHO TB burden data, either locally if available or directly from the WHO (if download_data = TRUE).
dict	A tibble of the data dictionary. See get_data_dict for details. If not supplied the function will attempt to load a saved version of the dictionary. If this fails and download_data = TRUE then the dictionary will be downloaded.
metric	Character string specifying the metric to plot
metric_label	Character string defaulting to NULL. If supplied this metric will be looked up using the WHO data dictionary to provide a label. A use case would be when calculating incidence rates using e_inc_100k to get the WHO TB incidence rate label.
conf	Character vector specifying the name variations to use to specify the upper and lower confidence intervals. Defaults to c("_lo", "_hi"), if set to NULL then no confidence intervals are shown. When annual_change = TRUE the confidence intervals represent the annual percentage change in the metrics confidence intervals.
years	Numeric vector of years. Defaults to NULL which includes all years in the data.
samples	Numeric, the number of samples to use to generate confidence intervals (only used when conf are present)
countries	A character string specifying the countries to target.
compare_to_region	Logical, defaults to FALSE. If TRUE all countries that share a region with those listed in countries will be plotted. Note that this will override settings for facet, unless it is set to "country".
compare_to_world	Logical, defaults to TRUE. Should a comparison be made to the metric of interests global value.
custom_compare	Logical, defaults to NULL. A named list of custom countries.
compare_all_regions	Logical, defaults to TRUE. Should all regions be compared.
stat	Character string, defaults to "rate". The statistic to use to summarise the metric, currently "mean", "median", "rate" and "prop" are supported. Note "mean" and "median" do not recompute the supplied country levels values but can be used to summarise the distribution of region or global metrics. "prop" and"rate" compute the overall incidence rate for a given grouping (i.e the sum of the metric divided by the sum of the denominator).
denom	Character string defaulting to e_pop_num (country level population). If stat is set to rate or prop then this is the parameter to use as the denominator.
rate_scale	Numeric defaults to 100,000. The scaling to use for rates. If stat is to set to prop then this defaults to 1.
truncate_at_zero	Logical, defaults to TRUE. Should lower bounds be truncated at zero?
annual_change	Logical, defaults to FALSE. If TRUE then the percentage annual change is computed for the specified metric.
smooth	Logical, defaults to FALSE. Should the data be smoothed (using ggplot2::geom_smooth) prior to plotting. If set to TRUE then the confidence intervals shown are derived from the smooth and do not represent the underlying uncertainty in the data.
facet	Character string, the name of the variable to facet by.
legend	Character string, defaults to "top". Position of the legend see ?ggplot2::theme for defaults but known options are: "none", "top", "right" and "bottom".
trans	A character string specifying the transform to use on the specified metric. Defaults to no transform ("identity"). Other options include log scaling ("log") and log base 10 scaling ("log10"). For a complete list of options see ggplot2::continous_scale.
scales	Character string, see ?ggplot2::facet_wrap for details. Defaults to "fixed", alternatives are "free_y", "free_x", or "free".
interactive	Logical, defaults to FALSE. If TRUE then an interactive plot is returned.
viridis_palette	Character string indicating the viridis colour palette to use. Defaults to "viridis". Options include "cividis", "magma", "inferno", "plasma", and "viridis". For additional details see viridis_pal for additional details.
viridis_direction	Numeric, indicating the direction for the colour palette (1 or -1), defaults to -1. See scale_color_viridis for additional details.
viridis_end	Numeric between 0 and 1, defaults to 0.9. The end point of the viridis scale to use. #' See scale_color_viridis for additional details.
download_data	Logical, defaults to TRUE. If not found locally should the data be downloaded from the specified URL?
save	Logical, should the data be saved for reuse during the current R session. Defaults to TRUE. If TRUE then the data is saved to the temporary directory specified by tempdir.
verbose	Logical, defaults to TRUE. Should additional status and progress messages be displayed.
...	Additional arguments to pass to get_tb_burden.

Value

A plot of TB Incidence Rates by Country

See also

search_data_dict plot_tb_burden summarise_tb_burden

summarise_tb_burden get_tb_burden search_data_dict

Examples

## Get an overview of incidence rates regionally and globally compared to the UK
plot_tb_burden_summary(metric = "e_inc_num",
                       metric_label = "e_inc_100k",
                       stat = "rate",
                       countries = "United Kingdom",
                       compare_to_world = TRUE,
                       compare_all_regions = TRUE,
                       verbose = FALSE,
                       scales = "free_y",
                       facet = "Area")
if (FALSE) {


## Get summary data for the UK, Europe and the world
## Bootstrapping CI's 
plot_tb_burden_summary(metric = "e_inc_num",
                       samples = 100,
                       stat = "mean",
                       countries = "United Kingdom",
                       compare_to_world = TRUE,
                       compare_to_region = TRUE,
                       verbose = FALSE,
                       facet = "Area",
                       scales = "free_y")

}