Structure of consumption expenditure by degree of urbanisation and COICOP consumption purpose - hbs_str_t226

Data - Eurostat

Author

François Geerolf

coicop

All

Code 
hbs_str_t226 %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

2-digit

Code 
hbs_str_t226 %>%
  filter(nchar(coicop) == 4) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()
coicop Coicop Nobs
CP01 Food and non-alcoholic beverages 492
CP02 Alcoholic beverages, tobacco and narcotics 492
CP03 Clothing and footwear 492
CP04 Housing, water, electricity, gas and other fuels 492
CP05 Furnishings, household equipment and routine household maintenance 492
CP06 Health 492
CP07 Transport 492
CP08 Communications 492
CP09 Recreation and culture 492
CP10 Education 489
CP11 Restaurants and hotels 492
CP12 Miscellaneous goods and services 492

3-digit

Code 
hbs_str_t226 %>%
  filter(nchar(coicop) == 5) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

4-digit

Code 
hbs_str_t226 %>%
  filter(nchar(coicop) == 6) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

quantile

Code 
hbs_str_t226 %>%
  left_join(deg_urb, by = "deg_urb") %>%
  group_by(deg_urb, Deg_urb) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()
deg_urb Deg_urb Nobs
DEG1 Cities 9339
DEG2 Towns and suburbs 9304
DEG3 Rural areas 9151
UNK Unknown 708

geo

Code 
hbs_str_t226 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

Code 
hbs_str_t226 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()
time Nobs
1988 1449
1994 1578
1999 1925
2005 5403
2010 7834
2015 5582
2020 4731

2015

France, Germany, Italy