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	107612
MIO_NAC	Million units of national currency	107612
MIO_PPS_EU27_2020	Million purchasing power standards (PPS, EU27 from 2020)	17390
EUR_HAB	Euro per inhabitant	16955
PPS_EU27_2020_HAB	Purchasing power standard (PPS, EU27 from 2020), per inhabitant	16955

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	105598
PAID	Paid	81456
RECV	Received	79470

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	42134
B6N	Disposable income, net	42124
D4	Property income	34536
D7	Other current transfers	34536
D61	Net social contributions	25332
D62	Social benefits other than social transfers in kind	25072
D1	Compensation of employees	17628
B2A3N	Operating surplus and mixed income, net	17548
D5	Current taxes on income, wealth, etc.	17268
B7N	Adjusted disposable income, net	3792
P51C	Consumption of fixed capital	2844
D63	Social transfers in kind	1970
P3	Final consumption expenditure	1740

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")