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 495
CP02 Alcoholic beverages, tobacco and narcotics 495
CP03 Clothing and footwear 495
CP04 Housing, water, electricity, gas and other fuels 495
CP05 Furnishings, household equipment and routine household maintenance 495
CP06 Health 495
CP07 Transport 495
CP08 Communications 495
CP09 Recreation and culture 495
CP10 Education 492
CP11 Restaurants and hotels 495
CP12 Miscellaneous goods and services 495

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 9398
DEG2 Towns and suburbs 9363
DEG3 Rural areas 9210
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 5759
2020 4731

2015

France, Germany, Italy