Deaths by week, sex and NUTS 3 region - demo_r_pjanaggr3

Data - Eurostat

Author

François Geerolf

Info

Last observation: Annual: 2025 (N = 26,760)

First observation: Annual: 1990 (N = 4,485)

Last data update: 23 jul 2026, 22:08. Last compile: 24 jul 2026, 01:12

Structure

Population Aging

Share of Population Aged 65+

Code

demo_r_pjanaggr3 %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "PT"),
         sex == "T",
         age %in% c("TOTAL", "Y_GE65")) %>%
  year_to_date %>%
  select(geo, Geo, date, age, values) %>%
  spread(age, values) %>%
  mutate(values = Y_GE65 / TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal() + scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Population aged 65 or over (% of total)") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1))

Total Population, Indexed (1990 = 100)

Code

demo_r_pjanaggr3 %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "PT"),
         sex == "T",
         age == "TOTAL") %>%
  year_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100 * values / first(values)) %>%
  ungroup() %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal() + scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Total population (1990 = 100)")

Age Structure, Latest Year

Code

latest_yr <- demo_r_pjanaggr3 %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "PT"),
         sex == "T",
         age == "TOTAL",
         !is.na(values)) %>%
  summarise(m = max(time)) %>%
  pull(m)

demo_r_pjanaggr3 %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "PT"),
         sex == "T",
         age %in% c("Y_LT15", "Y15-64", "Y_GE65"),
         time == latest_yr) %>%
  group_by(geo) %>%
  mutate(share = round(100 * values / sum(values), 1)) %>%
  ungroup() %>%
  select(Age, Geo, share) %>%
  spread(Geo, share) %>%
  print_table_conditional()

Age	France	Germany	Italy	Portugal	Spain
65 years or over	21.9	22.7	24.7	24.3	20.7
From 15 to 64 years	61.5	63.4	63.4	63.0	66.4
Less than 15 years	16.6	13.9	11.9	12.6	12.9