CaretakerRatio.R

pacman::p_load(
  openxlsx,
  tidyverse,
  sf,
  rmapshaper)



URL <- "https://www.soumu.go.jp/main_content/000892857.xlsx"
RATIO <- read.xlsx(URL, colNames = FALSE) |> 
  slice(-(1:9)) |> 
  filter(X4 != "男")　|> 
  mutate(value = ifelse(X4 == "計",
                        as.numeric(X22) +
                          as.numeric(X23) +
                          as.numeric(X24) +
                          as.numeric(X25) + 
                          as.numeric(X26), 
                        as.numeric(X16) +
                          as.numeric(X17) +
                          as.numeric(X18))) |> 
  mutate(value2 = lag(value, 1)) |>
  filter(X4 == "女") |>
  mutate(ratio = value2 / value * 100) |> 
  mutate(JCODE = substring(X1, 1, 5)) |> 
  select(JCODE, ratio)


"%!in%" <- Negate("%in%")
NorthernTerr <- c("01695", "01696", "01697",
                  "01698", "01699", "01700")

JP_geo <- "https://github.com/igproj-fusion/R-gis/raw/main/ESRI_japan_84.geojson"
JP <- read_sf(JP_geo) |> 
  filter(JCODE %!in% NorthernTerr) |>
  filter(SIKUCHOSON != "所属不明地") |> 
  left_join(RATIO, by = c("JCODE" = "JCODE"))

JP_outline <- JP |> ms_dissolve()


KANTO <- c("茨城県", "栃木県", "群馬県", "埼玉県",
           "千葉県", "東京都", "神奈川県")
Kanto <- JP |> 
  filter(KEN %in% KANTO)

g <- ggplot()
g <- g + geom_sf(data = JP, aes(fill = ratio))
g <- g + scale_fill_gradient(low = "#ffffcc", high = "#781100")
g <- g + geom_sf(data = JP_outline, 
                 color = "white", fill = "white")
g <- g + geom_sf(data = Kanto, aes(fill = ratio))
g <- g + xlim(c(138.39709, 141))
g <- g + ylim(c(34.5, 37.15506))
g <- g + labs(fill = "介護者比")
g <- g + theme_void()
print(g)