Regional gross domestic product (PPS per inhabitant) by NUTS 2 regions - tgs00005

Data - Eurostat

~/data/eurostat/

Info

LAST_DOWNLOAD

tibble(LAST_DOWNLOAD = as.Date(file.info("~/Dropbox/website/data/eurostat/tgs00005.RData")$mtime)) %>%
  print_table_conditional()

LAST_DOWNLOAD
2024-02-14

LAST_COMPILE

LAST_COMPILE
2024-02-15

Last

tgs00005 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()

time	Nobs
2021	273

unit

tgs00005 %>%
  group_by(unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

unit	Nobs
PPS_EU27_2020_HAB	3413

time

tgs00005 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

time	Nobs
2010	279
2011	279
2012	283
2013	283
2014	290
2015	290
2016	290
2017	290
2018	290
2019	283
2020	283
2021	273

geo

tgs00005 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

Map - Cross Section

2014

tgs00005 %>% 
  filter(time == 2014, 
         nchar(geo) == 4) %>% 
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/1000)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
                       breaks = seq(0, 2000, 10),
                       values = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "PPS GDP / person")

2015

tgs00005 %>% 
  filter(time == 2015, 
         nchar(geo) == 4) %>% 
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/1000)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
                       breaks = seq(0, 2000, 10),
                       values = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "PPS GDP / person")

2018

tgs00005 %>% 
  filter(time == 2018, 
         nchar(geo) == 4) %>% 
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/1000)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
                       breaks = seq(0, 2000, 10),
                       values = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "PPS GDP / person")

2019

tgs00005 %>% 
  filter(time == 2019, 
         nchar(geo) == 4) %>% 
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/1000)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
                       breaks = seq(0, 2000, 10),
                       values = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "PPS GDP / person")

Change 2010-2018

tgs00005 %>% 
  filter(time %in% c("2010", "2018"), 
         nchar(geo) == 4) %>%
  select(-unit) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = `2018`/`2010`-1)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = 0.01*seq(-100, 100, 10)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "% Growth 2008-18 \n PPS GDP / person")

Change 2015-2019

tgs00005 %>% 
  filter(time %in% c("2015", "2019"), 
         nchar(geo) == 4) %>%
  select(-unit) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = `2019`/`2015`-1)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = 0.01*seq(-100, 100, 10)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "% Growth 2015-19 \n PPS GDP / person")

Tables: 2008, 2013, 2018, Growth

tgs00005 %>%
  filter(time %in% c("2010", "2013", "2018")) %>%
  select(-unit) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  mutate(`2008-18` = round(100*(`2018`/`2010`-1), 1)) %>%
  mutate(`2013-18` = round(100*(`2018`/`2013`-1), 1)) %>%
  arrange(-`2008-18`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Tables: # Of observations

tgs00005 %>%
  select(-unit) %>%
  left_join(geo, by = "geo") %>%
  mutate(time = as.numeric(time)) %>%
  group_by(geo, Geo) %>%
  summarise(MinYear = min(time),
            MaxYear = max(time),
            Nobs = n()) %>%
  arrange(Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}