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 450
CP02 Alcoholic beverages, tobacco and narcotics 450
CP03 Clothing and footwear 450
CP04 Housing, water, electricity, gas and other fuels 450
CP05 Furnishings, household equipment and routine household maintenance 450
CP06 Health 450
CP07 Transport 450
CP08 Communications 450
CP09 Recreation and culture 450
CP10 Education 447
CP11 Restaurants and hotels 450
CP12 Miscellaneous goods and services 450

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 8298
DEG2 Towns and suburbs 8271
DEG3 Rural areas 8110
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 4535
2010 5641
2015 5573
2020 4686

2015

France, Germany, Italy