Unemployment by sex, age and metropolitan regions - met_lfu3pers

Data - Eurostat

Author

François Geerolf

Info

Last observation: Annual: 2023 (N = 5,117)

First observation: Annual: 1999 (N = 2,113)

Last data update: 11 aoû 2026, 20:26. Last compile: 12 aoû 2026, 01:22

Structure

Capital Metropolitan Regions

Paris, Berlin, Roma, Madrid

Code

met_lfu3pers |>
  filter(metroreg %in% c("FR001MC", "DE001MC", "IT001MC", "ES001MC"),
         age == "Y15-74",
         sex == "T") |>
  year_to_date() |>
  ggplot() + geom_line(aes(x = date, y = values, color = Metroreg)) +
  theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Unemployed persons (thousands)") +
  theme(legend.position = c(0.2, 0.85), legend.title = element_blank())

National Totals

France, Germany, Italy, Spain, Portugal

Code

met_lfu3pers |>
  filter(metroreg %in% c("FR", "DE", "IT", "ES", "PT"),
         age == "Y15-74",
         sex == "T") |>
  year_to_date() |>
  mutate(Country = recode(metroreg,
                           FR = "France", DE = "Germany", IT = "Italy",
                           ES = "Spain", PT = "Portugal")) |>
  left_join(colors, by = c("Country" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = Country)) +
  theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Unemployed persons (thousands)") +
  theme(legend.position = c(0.15, 0.8), legend.title = element_blank())

Latest Year, Capital Metros

Code

latest_y_metro <- met_lfu3pers |>
  filter(metroreg %in% c("FR001MC", "DE001MC", "IT001MC", "ES001MC"),
         age == "Y15-74",
         sex == "T",
         !is.na(values)) |>
  summarise(m = max(time)) |>
  pull(m)

met_lfu3pers |>
  filter(metroreg %in% c("FR001MC", "DE001MC", "IT001MC", "ES001MC"),
         age == "Y15-74",
         sex %in% c("F", "M", "T"),
         time == latest_y_metro) |>
  select(sex, Sex, Metroreg, values) |>
  spread(Metroreg, values) |>
  print_table_conditional()

sex	Sex	Berlin	Madrid	Paris	Roma
F	Females	60.2	204.8	216.2	68.6
M	Males	73.2	163.1	254.8	56.7
T	Total	133.4	367.9	471.0	125.3