knitr::opts_chunk$set(echo = FALSE, warnings = FALSE, eval = TRUE)
## Load the package library(getTBinR) ## Load additional packages library(ggplot2) ## Get the data tb <- get_tb_burden(verbose = FALSE) ## Get the data dictionary dict <- get_data_dict(verbose = FALSE) ##Assign parameters country <- params$country interactive <- params$interactive
inc_sum <- summarise_metric(tb, "e_inc_100k", country)
In r inc_sum$year
r country
had an estimated Tuberculosis incidence rate of r inc_sum$metric
per 100,000 people making it number r inc_sum$world_rank
in the world and number r inc_sum$region_rank
regionally. In the last 10 years this has changed by r inc_sum$avg_change
on average each year.
plot_tb_burden_summary(countries = country, metric_label = "e_inc_100k", compare_to_world = TRUE, compare_to_region = TRUE, compare_all_regions = FALSE, annual_change = FALSE, facet = "Area", scales = "free_y", legend = "none", interactive = interactive, verbose = FALSE)
plot_tb_burden_overview(countries = country, compare_to_region = TRUE, interactive = interactive, verbose = FALSE)
cdr_sum <- summarise_metric(tb, "c_cdr", country)
r country
had an estimated case detection rate of r cdr_sum$metric
% in r cdr_sum$year
making it number r cdr_sum$world_rank
in the world (with number 1 having the highest CDR) and number r cdr_sum$region_rank
regionally. In the last 10 years this has changed by r cdr_sum$avg_change
on average each year.
plot_tb_burden_overview(metric = "c_cdr", countries = country, compare_to_region = TRUE, interactive = interactive, verbose = FALSE)
mort_exc_hiv_sum <- summarise_metric(tb, "e_mort_exc_tbhiv_100k", country)
In r mort_exc_hiv_sum$year
r country
had an estimated Tuberculosis mortality rate (excluding HIV) of r mort_exc_hiv_sum$metric
per 100,000 people making it number r mort_exc_hiv_sum$world_rank
in the world and number r mort_exc_hiv_sum$region_rank
regionally. In the last 10 years this has changed by r mort_exc_hiv_sum$avg_change
on average each year.
plot_tb_burden_summary(metric = "e_mort_exc_tbhiv_num", denom = "e_inc_num", rate_scale = 100, countries = country, compare_to_region = TRUE, compare_all_regions = FALSE, interactive = interactive, verbose = FALSE, facet = "Area", scales = "free_y", legend = "none") + labs(y = "Proportion (%) of TB cases that died (excluding HIV)")
plot_tb_burden_overview(metric = "e_mort_exc_tbhiv_100k", countries = country, compare_to_region = TRUE, interactive = interactive, verbose = FALSE)
mort_inc_hiv_sum <- summarise_metric(tb, "e_mort_tbhiv_100k", country)
In r mort_inc_hiv_sum$year
r country
had an estimated Tuberculosis mortality rate (related to HIV) of r mort_inc_hiv_sum$metric
per 100,000 people making it number r mort_inc_hiv_sum$world_rank
in the world and number r mort_inc_hiv_sum$region_rank
regionally. In the last 10 years this has changed by r mort_inc_hiv_sum$avg_change
on average each year.
plot_tb_burden_summary(metric = "e_mort_tbhiv_num", denom = "e_inc_num", rate_scale = 100, countries = country, compare_to_region = TRUE, compare_all_regions = FALSE, interactive = interactive, verbose = FALSE, facet = "Area", scales = "free_y", legend = "none") + labs(y = "Proportion (%) of TB cases that died (related to HIV)")
plot_tb_burden_overview(metric = "e_mort_tbhiv_100k", countries = country, compare_to_region = TRUE, interactive = interactive, verbose = FALSE)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.