Gross domestic product (GDP) at current market prices by NUTS 2 regions

Data - Eurostat

Author

François Geerolf

Info

Last observation: Annual: 2024 (N = 3,002)

First observation: Annual: 2000 (N = 2,873)

Last data update: 11 aoû 2026, 22:06. Last compile: 12 aoû 2026, 01:54

Structure

France

Table

Code

nama_10r_2gdp |>
  filter(grepl("FR", geo),
         unit %in% c("EUR_HAB", "PPS_EU27_2020_HAB"),
         time == "2020") |>
  
  spread(unit, values) |>
  mutate(pps = PPS_EU27_2020_HAB/EUR_HAB) |>
  select(-time) |>
  arrange(-pps) |>
  print_table_conditional()

Ile de France

Code

nama_10r_2gdp |>
  filter(unit %in% c("EUR_HAB", "PPS_EU27_2020_HAB"),
         geo %in% c("FRC2", "FR10", "FRG0", "FRD1")) |>
  
  spread(unit, values) |>
  mutate(values = PPS_EU27_2020_HAB/EUR_HAB) |>
  year_to_date() |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_flags + 
  theme_minimal() + xlab("") + ylab("PPS") +
  theme(legend.title = element_blank(),
        legend.position = c(0.75, 0.85)) +
  scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

Germany

Table

Code

nama_10r_2gdp |>
  filter(grepl("DE", geo),
         unit %in% c("EUR_HAB", "PPS_EU27_2020_HAB"),
         time == "2020") |>
  
  spread(unit, values) |>
  mutate(pps = PPS_EU27_2020_HAB/EUR_HAB) |>
  select(-time) |>
  arrange(-pps) |>
  print_table_conditional()

Régions

Code

nama_10r_2gdp |>
  filter(unit %in% c("EUR_HAB", "PPS_EU27_2020_HAB"),
         geo %in% c("DEB3", "DEE0", "DE40", "DE13")) |>
  
  spread(unit, values) |>
  mutate(values = PPS_EU27_2020_HAB/EUR_HAB) |>
  year_to_date() |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_flags + 
  theme_minimal() + xlab("") + ylab("PPS") +
  theme(legend.title = element_blank(),
        legend.position = c(0.75, 0.15)) +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

Maps

2019

Code

nama_10r_2gdp |>
  filter(time == "2019",
         unit == "EUR_HAB") |>
  
  select(geo, Geo, values) |>
  right_join(europe_NUTS2, by = "geo") |>
  filter(long >= -15, lat >= 33) |>
  ggplot(aes(x = long, y = lat, group = group, fill = values)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = "€"),
                       breaks = seq(10000, 120000, 10000),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "GDP Per inhabitant")

2018

Code

nama_10r_2gdp |>
  filter(time == "2018",
         unit == "EUR_HAB") |>
  
  select(geo, Geo, values) |>
  right_join(europe_NUTS2, by = "geo") |>
  filter(long >= -15, lat >= 33) |>
  ggplot(aes(x = long, y = lat, group = group, fill = values)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = "€"),
                       breaks = seq(10000, 120000, 10000),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "GDP Per inhabitant")