splash image for getTBinR 5.4

splash.5.4.getTBinR

## Get required packages - managed using pacman
if (!require(pacman)) install.packages("pacman"); library(pacman)
p_load("getTBinR")
p_load("ggplot2")
p_load("viridis")
p_load("dplyr")
p_load("forcats")
p_load("ggridges")
p_load_gh("thomasp85/patchwork")

## Pull TB data and summarise TB incidence rates by region using the median
tb_sum <- summarise_tb_burden(metric = "e_inc_100k",
                              stat = "median",
                              compare_all_regions = TRUE,
                              samples = 1000)

## Plot the median and IQR for each region
sum <- tb_sum %>% 
  rename(Region = area) %>% 
  ggplot(aes(x = year, y = e_inc_100k, col = Region, fill = Region)) +
  geom_ribbon(alpha = 0.2, aes(ymin = e_inc_100k_lo, ymax = e_inc_100k_hi)) +
  scale_color_viridis(discrete = TRUE) +
  scale_fill_viridis(discrete = TRUE) +
  geom_line(alpha = 0.6, size = 1.2) +
  geom_point(size = 1.3) +
  theme_minimal() +
  facet_wrap(~Region, scales = "free_y") +
  theme(legend.position = "none") +
  labs(y = search_data_dict("e_inc_100k")$definition,
       x = "Year",
       title = "Regional Summary of Tuberculosis Incidence Rates - 2000 to 2016",
       subtitle = "Median country level incidence rates (with 95% interquartile ranges) are shown")

## Get the full TB burden dataset (including MDR TB)
tb <- get_tb_burden()

## Plot the distribution of country level TB incidence rates using ggridges
dist <- tb %>% 
  rename(Region = g_whoregion) %>% 
  mutate(year = year %>% 
           factor(ordered = TRUE) %>% 
           fct_rev) %>% 
  ggplot(aes(x = e_inc_100k, y = year, col = Region, fill = Region)) +
  geom_density_ridges(alpha = 0.6) +
  scale_color_viridis(discrete = TRUE) +
  scale_fill_viridis(discrete = TRUE) +
  theme_minimal() +
  facet_wrap(~Region, scales = "free_x") +
  theme(legend.position = "none") +
  labs(x = search_data_dict("e_inc_100k")$definition,
       y = "Year",
       title = "Distribution of Country Level Tuberculosis Incidence Rates by Region - 2000 to 2016",
       caption = "By @seabbs | Made with getTBinR | Source: World Health Organisation")

## Map global TB incidence rates for 2016 using getTBinR
map <- map_tb_burden() +
  labs(caption = "",
       title = "Map of Tuberculosis Incidence Rates - 2016")

## Extract the top 10 high incidence countries in 2016.
high_inc_countries <- tb %>% 
  filter(year == 2016) %>% 
  arrange(desc(e_inc_100k)) %>% 
  slice(1:10) %>% 
  pull(country)

## Plot an overview of TB incidence rates in 2016.
high_inc_overview <- plot_tb_burden_overview(countries = high_inc_countries) +
  labs(caption = "",
       title = "10 Countries with the Highest Tuberculosis Incidence Rates - 2016") 

## Compose storyboard
storyboard <- (map + high_inc_overview) /
                 (sum | dist) +
                 plot_layout(heights = c(1, 2))

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