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

Data - Eurostat

Author

François Geerolf

Info

source dataset Title .html .rData
eurostat prc_hicp_ctrb Contributions to euro area annual inflation (in percentage points) 2026-02-12 2026-02-12

LAST_DOWNLOAD

Code 
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/nama_10r_2gdp.RData")$mtime)) %>%
  print_table_conditional()
LAST_DOWNLOAD
2026-02-13

LAST_COMPILE

LAST_COMPILE
2026-02-14

Last

Code 
nama_10r_2gdp %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2024 3002

unit

Code 
nama_10r_2gdp %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
MIO_EUR Million euro 11453
MIO_NAC Million units of national currency 11453
MIO_PPS_EU27_2020 Million purchasing power standards (PPS, EU27 from 2020) 11453
EUR_HAB Euro per inhabitant 10393
EUR_HAB_EU27_2020 Euro per inhabitant in percentage of the EU27 (from 2020) average 10393
PPS_EU27_2020_HAB Purchasing power standard (PPS, EU27 from 2020), per inhabitant 10393
PPS_HAB_EU27_2020 Purchasing power standard (PPS, EU27 from 2020), per inhabitant in percentage of the EU27 (from 2020) average 10393

geo

Code 
nama_10r_2gdp %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

time

Code 
nama_10r_2gdp %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()
time Nobs
2024 3002
2023 3017
2022 3064
2021 3129
2020 3129
2019 3129
2018 3129
2017 3129
2016 3129
2015 3129
2014 3129
2013 3125
2012 3125
2011 3077
2010 3077
2009 3077
2008 3077
2007 2950
2006 2950
2005 2929
2004 2929
2003 2929
2002 2825
2001 2873
2000 2873

France

Table

Code 
nama_10r_2gdp %>%
  filter(grepl("FR", geo),
         unit %in% c("EUR_HAB", "PPS_EU27_2020_HAB"),
         time == "2020") %>%
  left_join(geo, by = "geo") %>%
  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")) %>%
  left_join(geo, by = "geo") %>%
  spread(unit, values) %>%
  mutate(values = PPS_EU27_2020_HAB/EUR_HAB) %>%
  year_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("PPS") +
  theme(legend.title = element_blank(),
        legend.position = c(0.75, 0.85)) +
  scale_x_date(breaks = seq(1960, 2026, 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") %>%
  left_join(geo, by = "geo") %>%
  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")) %>%
  left_join(geo, by = "geo") %>%
  spread(unit, values) %>%
  mutate(values = PPS_EU27_2020_HAB/EUR_HAB) %>%
  year_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("PPS") +
  theme(legend.title = element_blank(),
        legend.position = c(0.75, 0.15)) +
  scale_x_date(breaks = seq(1960, 2026, 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") %>%
  left_join(geo, by = "geo") %>%
  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") %>%
  left_join(geo, by = "geo") %>%
  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")