Total and active population by sex, age, employment status, residence one year prior to the census and NUTS 3 regions - cens_01rapop

Data - Eurostat

Author

wstatus

Code

cens_01rapop %>%
  left_join(wstatus, by = "wstatus") %>%
  group_by(wstatus, Wstatus) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

wstatus	Wstatus	Nobs
POP	Population	61909
ACT	Persons in the labour force	60011
EMP	Employed persons	60008
UNE	Unemployed persons	57545
INAC	Persons outside the labour force	41575
SAL	Employees	39807
INAC_OTH	Other persons outside the labour force	38582
SELF_S	Self-employed persons with employees (employers)	38105
EMP_OTH	Other employed persons	38098
INC	Retired persons and capital income recipients	37186
EDUC	Students	34811
CFAM	Contributing family workers	30374
NAP	Not applicable	14988
ACT_UNK	Persons in the labour force - unknown	4628
INAC_UNK	Persons outside the labour force - unknown	596

age

Code

cens_01rapop %>%
  left_join(age, by = "age") %>%
  group_by(age, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

sex

Code

cens_01rapop %>%
  left_join(sex, by = "sex") %>%
  group_by(sex, Sex) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

sex	Sex	Nobs
T	Total	186910
F	Females	186021
M	Males	185292

unit

Code

cens_01rapop %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

unit	Unit	Nobs
PER	Person	558223

geo

Code

cens_01rapop %>%
  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

cens_01rapop %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

time	Nobs
2001	558223

2001 Unemployed

Code

cens_01rapop %>%
  filter(nchar(geo) == 5,
         wstatus %in% c("ACT", "UNE"),
         age == "TOTAL",
         sex == "T") %>%
  select(geo, wstatus, values) %>%
  spread(wstatus, values) %>%
  left_join(geo, by = "geo") %>%
  right_join(europe_NUTS3, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = UNE/ACT)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       direction = -1,
                       labels = percent_format(accuracy = 1),
                       breaks = 0.01*seq(0, 90, 5),
                       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 = "Manuf Share")