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 483
CP02 Alcoholic beverages, tobacco and narcotics 483
CP03 Clothing and footwear 483
CP04 Housing, water, electricity, gas and other fuels 483
CP05 Furnishings, household equipment and routine household maintenance 483
CP06 Health 483
CP07 Transport 483
CP08 Communications 483
CP09 Recreation and culture 483
CP10 Education 480
CP11 Restaurants and hotels 483
CP12 Miscellaneous goods and services 483

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 9214
DEG2 Towns and suburbs 9179
DEG3 Rural areas 9026
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 4356

2015

France, Germany, Italy