Looking at normalised spending gap for global Tuberculosis funding

spending_gap_normalised

# Install and load the package --------------------------------------------
if(!require(pacman))install.packages("pacman")

pacman::p_load('dplyr', 'ggplot2', 'getTBinR')
pacman::p_load_gh('thomasp85/patchwork') 
pacman::p_load_gh('bbc/bbplot')

# 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 = 100 * (rcvd_tot_sources - exp_tot) / rcvd_tot_sources) %>% 
  mutate(spending_gap = replace(spending_gap, rcvd_tot_sources < 10e6 | e_inc_100k < 10, 
                                NA))


label <- "% difference between funding and expenditure"


# 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 = "") +
  bbc_style() +
  theme(panel.background = element_blank(),
        axis.text = element_blank())



# 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) +
  bbc_style() + 
  theme(legend.position = "none") +
  labs(subtitle = "Countries with the larget % spending \n gap",
       caption = "")


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


plot_inc <- 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") +
  bbc_style() +
  theme(legend.position = "none", plot.caption = element_text(hjust = 1)) +
  labs(
       subtitle = "TB incidence rates - for countries with the largest % spending gap",
       caption = "@seabbs | Using #getTBinR | Data sourced from: World Health Organization | Countries with funding below 10 million dollars or with TB incidence rates below 10 per 100,000 people have been removed.")


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

storyboard <- (map + plot_top + plot_layout(widths = c(5, 2))) /
  (plot_inc)

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