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

Data - Eurostat

Author

François Geerolf

Info

source dataset .html .RData
eurostat prc_hicp_ctrb 2025-02-01 2025-05-18

LAST_DOWNLOAD

Code 
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/nama_10r_2gdp.RData")$mtime)) %>%
  print_table_conditional()
LAST_DOWNLOAD
2025-05-18

LAST_COMPILE

LAST_COMPILE
2025-05-18

Last

Code 
nama_10r_2gdp %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2023 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 10707
MIO_NAC Million units of national currency 10707
MIO_PPS_EU27_2020 Million purchasing power standards (PPS, EU27 from 2020) 10707
EUR_HAB Euro per inhabitant 9865
EUR_HAB_EU27_2020 Euro per inhabitant in percentage of the EU27 (from 2020) average 9865
PPS_EU27_2020_HAB Purchasing power standard (PPS, EU27 from 2020), per inhabitant 9865
PPS_HAB_EU27_2020 Purchasing power standard (PPS, EU27 from 2020), per inhabitant in percentage of the EU27 (from 2020) average 9865

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
2023 3002
2022 3002
2021 3129
2020 3094
2019 3130
2018 3130
2017 3130
2016 3130
2015 3130
2014 3130
2013 3090
2012 3090
2011 3090
2010 3090
2009 3076
2008 3076
2007 2949
2006 2949
2005 2928
2004 2928
2003 2655
2002 2551
2001 2551
2000 2551

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")