GES 001 演習8

第8週

02/08(TH)　紛争と貧困の連鎖1

            紛争と貧困の連鎖2

第8週と第9週では、戦争、気候変動、COVID-19の複合的要因が世界的な食糧危機を引き起こし、格差と貧困を加速させている現状について、議論します。

02/13(TU)　Rでデータサイエンス8：難民、軍事支出　 [Main]・[授業]

Global Link-Refugees

Refugee population by country or territory of asylum：SM.POP.REFG [Link]

Refugee population by country or territory of origin：SM.POP.REFG.OR [Link]

Net ODA received (% of GNI)：DT.ODA.ODAT.GN.ZS [Link]

Net official development assistance and official aid received (current US$)：DT.ODA.ALLD.CD [Link]

Net ODA received (% of central government expense)：DT.ODA.ODAT.XP.ZS [Link]

Military expenditure (current USD)：MS.MIL.XPND.CD [Link]

Military expenditure (% of general government expenditure)：MS.MIL.XPND.ZS [Link]

Arms imports (SIPRI trend indicator values)：MS.MIL.MPRT.KD [Link]

Arms exports (SIPRI trend indicator values)：MS.MIL.XPRT.KD [Link]

内容

• Refugee population by country or territory of asylum：SM.POP.REFG [Link]

• Refugee population by country or territory of origin：SM.POP.REFG.OR [Link]

• Net ODA received (% of GNI)：DT.ODA.ODAT.GN.ZS [Link]

• Net official development assistance and official aid received (current US$)：DT.ODA.ALLD.CD [Link]

• Net ODA received (% of central government expense)：DT.ODA.ODAT.XP.ZS [Link]

• Military expenditure (current USD)：MS.MIL.XPND.CD [Link]

• Military expenditure (% of general government expenditure)：MS.MIL.XPND.ZS [Link]

• Arms imports (SIPRI trend indicator values)：MS.MIL.MPRT.KD [Link]

• Arms exports (SIPRI trend indicator values)：MS.MIL.XPRT.KD [Link]

df_peace <- WDI(
  indicator = c(refugee_asylum = "SM.POP.REFG",
                refugee_origin = "SM.POP.REFG.OR",
                oda_gni = "DT.ODA.ODAT.GN.ZS",
                oda_usd = "DT.ODA.ALLD.CD",
                oda_gov = "DT.ODA.ODAT.XP.ZS",
                military_usd = "MS.MIL.XPND.CD",
                military_gov = "MS.MIL.XPND.ZS",
                arms_imports = "MS.MIL.MPRT.KD",
                arms_exports = "MS.MIL.XPRT.KD"), extra = TRUE)

write_csv(df_peace, "data/peace.csv")

df_peace <- read_csv("data/peace.csv")

Rows: 16758 Columns: 21── Column specification ─────────────────────────────────────────────────
Delimiter: ","
chr   (7): country, iso2c, iso3c, region, capital, income, lending
dbl  (12): year, refugee_asylum, refugee_origin, oda_gni, oda_usd, od...
lgl   (1): status
date  (1): lastupdated
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df_peace_long <- df_peace |> 
  pivot_longer(7:15, names_to = "name", values_to = "value")

df_peace_long |> 
  group_by(year, name) |> drop_na(value) |>
  summarize(num = n()) |> arrange(desc(year))

`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

df_peace_long |> 
  group_by(year, name) |> drop_na(value) |>
  summarize(num = n()) |> 
  ggplot(aes(year, num, col = name)) + geom_line() +
  labs(title = "各指標の年毎のデータ数", y = "データ数", x = "年")

`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

World Development Indicators: [Link]

df_peace_long |> 
  filter(name %in% c("military_gov", "oda_gni", "oda_gov")) |>
  filter(iso2c %in% c("1W", "ZF")) |> drop_na(value) |>
  ggplot(aes(year, value, col = name, linetype = iso2c)) + geom_line()