Looking at the expenditure and funding gap for TB

spending_gap

# Install and load the package --------------------------------------------
#Use install.packages(c("getTBinR", "dplyr", "getTBinR")) to get required packages
library(getTBinR) #TB data + visualisations
library(dplyr) # For data manipulation
library(ggplot2) # For additional visualisations etc.
#For storyboards - install with commented out code
# install.packages("devtools")
# devtools::install_github("thomasp85/patchwork")
library(patchwork)
# Look at available datasets\ ---------------------------------------------

available_datasets



# Download data of interest -----------------------------------------------

tb <- get_tb_burden(additional_datasets = "Expenditure and utilisation")

dict <- get_data_dict()



# Look up variables -------------------------------------------------------

search_data_dict(dataset = "Expenditure and utilisation")


## expenditure (exp_tot) and funding (rcvd_tot_sources) are likely to be of some interest

tb <- tb %>% 
  mutate(spending_gap = (rcvd_tot_sources - exp_tot) / (1e6))


label <- "Difference between funding and expenditure (US Dollars (million))"


# Map budget gap ----------------------------------------------------------

map <- map_tb_burden(df = tb, metric = "spending_gap", 
              metric_label = label,
              year = 2017) +
  labs(title = "Tuberculosis (TB) spending gap",
       subtitle = paste0(label, " - data from 2017"),
       caption = "") +
  theme(plot.title = element_text(size=22),
        plot.subtitle = element_text(size=20))



# Get countries with largest spending gap ---------------------------------

largest_gap_countries <- tb %>% 
  filter(year == 2017) %>% 
  arrange(desc(spending_gap)) %>% 
  slice(1:10) %>% 
  pull(country)

# Plot countries with largest spending gap --------------------------------

plot_top <- plot_tb_burden_overview(df = tb, 
                        countries = largest_gap_countries,
                        metric = "spending_gap", 
                        metric_label = label,
                        year = 2017) +
  theme(legend.position = "none") +
  labs(title = "Countries with the larget TB spending gap",
       subtitle = "Data from 2017",
       caption = "")


# Plot incidence rates in these countries ----------------------------------


plot_inc <- plot_incidence_rates <- plot_tb_burden_summary(df = tb, 
                       countries = largest_gap_countries,
                       stat = "rate",
                       compare_to_region = FALSE,
                       compare_all_regions = FALSE,
                       facet = "Area",
                       scales = "free_y") +
  theme(legend.position = "none") +
  labs(title = "TB incidence rates",
       subtitle = "For countries with the largest spending gap",
       caption = "@seabbs | Using #getTBinR | Data sourced from: World Health Organization")


# Make storyboard ---------------------------------------------------------

storyboard <- (map) /
  (plot_top + plot_inc)

## Save storyboard
ggsave("storyboard.png",
       storyboard, width = 20, height = 15, dpi = 330)