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	112038
MIO_NAC	Million units of national currency	112038
EUR_HAB	Euro per inhabitant	17407
PPS_EU27_2020_HAB	Purchasing power standard (PPS, EU27 from 2020), per inhabitant	17407
MIO_PPS_EU27_2020	Million purchasing power standards (PPS, EU27 from 2020)	17064

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	105472
PAID	Paid	88080
RECV	Received	82402

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	41830
B6N	Disposable income, net	41830
D4	Property income	33872
D7	Other current transfers	33872
D61	Net social contributions	28264
D62	Social benefits other than social transfers in kind	28126
D1	Compensation of employees	18428
D5	Current taxes on income, wealth, etc.	18256
B2A3N	Operating surplus and mixed income, net	17028
P51C	Consumption of fixed capital	6016
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")