使用流图来说明肯尼亚各县的不同水源，并用肯尼亚国旗的阴影作为颜色

library(tidyverse)
library(janitor)
#devtools::install_github("Shelmith-Kariuki/rKenyaCensus")
library(rKenyaCensus)
# remotes::install_github("davidsjoberg/ggstream")
library(ggstream)
library(paletteer)
library(patchwork)
library(showtext)

kenya_pal <- rev(c("#006300","#257D25", "#35B035",
"#97FC97", "#FFFFFF","#B50000", "#C20000", 
"#9C0000", "#F04848", "#FFFFFF", "#D6D6D6",
"#999999","#4D4D4D","#000000"))
map_bck_clr <- "#BAE7F7"
map_neutral <- "grey40"

water <- rKenyaCensus::V4_T2.15
water <- clean_names(water)

water_clean <- water %>%
  ungroup() %>%
  filter(county != "xxx", admin_area == "County") %>%
  select(-not_stated) %>%
  pivot_longer(cols = pond:publictap_standpipe,
names_to = "source", values_to = "value") %>%
  filter(!is.na(value)) %>%
  mutate(
    source = case_when(
      source == "borehole_tube_well" ~ "Borehole\ntubewell",
      source == "bottledwater" ~ "Bottled\n water",
      source == "dam_lake" ~ "Dam lake",
      source == "pipedintodwelling" ~ "Piped into\ndwelling",
      source == "pipedtoyard_plot" ~ "Piped to\nyard/plot",
      source == "pond" ~"Pond",
      source == "publictap_standpipe" ~"public \ntap/standpipe",
      source == "rain_harvestedwater" ~ "Rain harvested\n / water",
      TRUE ~ str_replace_all(str_to_title(source), "_", "\n")
    )
  )

water_prep <- water_clean %>%
  arrange(county) %>%
  mutate(x_num = as.numeric(as_factor(county)),
         x_labels = county) 

county_breaks <- water_prep$x_num
county_labels <- water_prep$x_labels

plt <-  water_prep %>%
  ggplot() +
  geom_stream(aes(x_num,value,fill=source,color = source))  +
  scale_x_continuous(breaks = county_breaks,
                     labels = county_labels)+
  scale_fill_manual(values = kenya_pal) +
  scale_color_manual(values = kenya_pal) +
  guides(fill = FALSE,color = FALSE) +
  theme_void() +
  theme(axis.text.x = element_text(family ="Times",face = "plain",
                                   angle = 90, hjust = 1,size =10),
        plot.margin = margin(20,0,30,0))
plt

map = rKenyaCensus::KenyaCounties_SHP %>%
  sf::st_as_sf() %>%
  clean_names()
water_map <- water_clean %>%
  group_by(source) %>%
  slice_max(value,n = 10)
source_list <- water_map %>%
  select(source) %>%
  arrange() %>%
  pull() %>% unique()
color_pal <- c()
color_pal[source_list] <- kenya_pal
map_source1 <- map %>%
  right_join(filter(water_map, source %in% source_list[1:7]))
map_source2 <- map %>%
  right_join(filter(water_map, source %in% source_list[8:14]))

map1_plt <-  ggplot() +
  geom_sf(data = map, fill = map_bck_clr, size = 0.2, 
alpha = 0.3, color = "black") +
  geom_sf(data = map_source1, aes(fill = source), 
size = 0.3, color = map_neutral) +
  scale_fill_manual(values = color_pal) +
  facet_wrap(~source, nrow = 1, strip.position="top") +
  guides(fill = FALSE) +
  theme_void() +
  theme(panel.spacing.x = unit(2, "lines"),
  strip.text.x = element_text(family = "robot",
size = 18, margin = margin(0,0,5,0)))

map2_plt <-  ggplot() +
  geom_sf(data = map, fill = map_bck_clr, size = 0.2, 
alpha = 0.3,color = "black") +geom_sf(data = map_source2, 
aes(fill = source), size = 0.3, color = map_neutral) +
  scale_fill_manual(values = color_pal) +
  facet_wrap(~source, nrow = 1, strip.position="bottom") +
  guides(fill = FALSE) +
  theme_void() +
  theme(panel.spacing.x = unit(2, "lines"),
 strip.text.x = element_text(family = "robot",
size = 18, margin = margin(5,0,5,0)))

final <- map1_plt / plt / map2_plt +
  plot_layout(nrow = 3, heights = c(1,3,1)) +
  plot_annotation(
    caption = "Visualization: Christophe Nicault | Data: rKenyaCensus",
    theme = theme(plot.caption = element_text(family = "heebo", size = 16,
    color = "black", margin = margin(20,0,0,0)))
  )

final