rif resistant TB

rif_res_tb

## Get packages
if (!require(pacman)) install.packages("pacman"); library(pacman)
p_load_gh("seabbs/getTBinR")
p_load_gh("thomasp85/patchwork")
p_load("tidyverse")
p_load("ggridges")
p_load("viridis")


## Get data
tb <- get_tb_burden()
dict <- get_data_dict()

## Search for resistant variable
search_data_dict(def = "resistant")


## Get estimated incidence of rif res
tb_res <- tb %>% 
  mutate(rr_inc_rate = e_inc_rr_num / e_pop_num * 100000,
         rr_inc_rate_lo = e_inc_rr_num_lo / e_pop_num * 100000,
         rr_inc_rate_hi = e_inc_rr_num_lo / e_pop_num * 100000
         )

## Map of rifampicin resistance in previously treated cases
map_rr_inc_rates <- map_tb_burden(tb_res,
                             metric = "rr_inc_rate",
                             metric_label = "Incidence rate (per 100,000 population) of rifampicin resistant TB",
                             year = 2016, viridis_palette = "cividis") +
  labs(title = "Map of Rifampicin Resistant TB Incidence Rates",
       subtitle = "In 2016",
       caption = "")


## 10 countries with the highest incidence rates of rif res
high_rif_res_countries <- tb_res %>% 
  filter(year == 2016) %>% 
  arrange(desc(rr_inc_rate)) %>% 
  slice(1:20) %>% 
  pull(country)

## Get overview for these countries
high_rr_countries_plot <- plot_tb_burden_overview(tb_res,
                        metric = "rr_inc_rate",
                        metric_label = "Incidence rate (per 100,000 population) of rifampicin resistant TB",
                        viridis_palette = "cividis",
                        viridis_direction = -1,
                        countries = high_rif_res_countries
                        ) +
  theme(legend.position = "none") +
  labs(title = "Countries with the Highest Incidence Rate of Rifampicin Resistant TB",
       subtitle = "Top 20 countries, in 2016",
       caption = "")

## Distribution of new rif res cases by region in 2016
dist_rif_res_new <- tb %>% 
  filter(year == 2016) %>% 
  ggplot(aes(y = g_whoregion, x = e_rr_pct_new, fill = g_whoregion)) +
  geom_density_ridges() +
  scale_fill_viridis(discrete = TRUE, option = "cividis", direction = -1, end = 0.9) +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(x = search_data_dict("e_rr_pct_new")$definition,
       y =  "Region",
       caption = "",
       title = "Estimated percentage of new TB cases with rifampicin resistant TB",
       subtitle = "By region, in 2016")


## Distribution of previously treated rif res cases by region in 2016
dist_rif_res_prev <- tb %>% 
  filter(year == 2016) %>% 
  ggplot(aes(y = g_whoregion, x = e_rr_pct_ret, fill = g_whoregion)) +
  geom_density_ridges() +
  scale_fill_viridis(discrete = TRUE, option = "cividis", direction = -1, end = 0.9) +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(x = search_data_dict("e_rr_pct_ret")$definition,
       y =  "Region",
       title = "Estimated percentage of previously treated TB cases with rifampicin resistant TB",
       subtitle = "By region, in 2016",
       caption = "@seabbs Source: World Health Organisation")


storyboard <- (map_rr_inc_rates / high_rr_countries_plot)  | (dist_rif_res_new / dist_rif_res_prev )


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

storyboard