Income of households by NUTS 2 regions - nama_10r_2hhinc

Data - Eurostat

Author

unit

Code

nama_10r_2hhinc %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

unit	Unit	Nobs
MIO_EUR	Million euro	110568
MIO_NAC	Million units of national currency	110568
EUR_HAB	Euro per inhabitant	17197
PPS_EU27_2020_HAB	Purchasing power standard (PPS, EU27 from 2020), per inhabitant	17197
MIO_PPS_EU27_2020	Million purchasing power standards (PPS, EU27 from 2020)	16854

direct

Code

nama_10r_2hhinc %>%
  left_join(direct, by = "direct") %>%
  group_by(direct, Direct) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

direct	Direct	Nobs
BAL	Balance	104212
PAID	Paid	86820
RECV	Received	81352

na_item

Code

nama_10r_2hhinc %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

na_item	Na_item	Nobs
B5N	Balance of primary incomes/National income, net	41305
B6N	Disposable income, net	41305
D4	Property income	33452
D7	Other current transfers	33452
D61	Net social contributions	27844
D62	Social benefits other than social transfers in kind	27706
D1	Compensation of employees	18218
D5	Current taxes on income, wealth, etc.	18046
B2A3N	Operating surplus and mixed income, net	16818
P51C	Consumption of fixed capital	5806
B7N	Adjusted disposable income, net	4784
D63	Social transfers in kind	1838
P3	Final consumption expenditure	1810

geo

Code

nama_10r_2hhinc %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Table

Code

nama_10r_2hhinc %>%
  filter(time == "2015", 
         nchar(geo) == 4,
         direct == "BAL",
         na_item == "B6N") %>%
  select(geo, value_added = values) %>%
  full_join(nama_10r_3empers %>%
              filter(time == "2015",
                     nchar(geo) == 4,
                     wstatus == "EMP",
                     nace_r2 == "TOTAL") %>%
              select(geo, employment = values), by = "geo") %>%
  mutate(emp_person = round(1000*value_added / employment)) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, emp_person) %>%
  na.omit %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Maps

Code

nama_10r_2hhinc %>%
  filter(time == "2015", 
         nchar(geo) == 4,
         direct == "BAL",
         na_item == "B6N") %>%
  select(geo, value_added = values) %>%
  full_join(nama_10r_3empers %>%
              filter(time == "2015",
                     nchar(geo) == 4,
                     wstatus == "EMP",
                     nace_r2 == "TOTAL") %>%
              select(geo, employment = values), 
            by = "geo") %>%
  mutate(value = round(1000*value_added / employment)) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, value) %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33, value <= 80000) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = value/1000)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
                       breaks = c(seq(0, 80, 10), 100, 200),
                       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 = "Compensation / Person")