Mean consumption expenditure per household by COICOP consumption purpose

Data - Eurostat

Author

coicop

Code

hbs_exp_t121 %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

unit

Code

hbs_exp_t121 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

unit	Unit	Nobs
PPS_HH	Purchasing power standard (PPS) per household	30321

geo

Code

hbs_exp_t121 %>%
  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_exp_t121 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

time	Nobs
1988	1307
1994	2326
1999	2505
2005	6109
2010	7586
2015	5928
2020	4560

Example

Code

hbs_exp_t121 %>%
  filter(time == "2015",
         coicop %in% c("CP0722", "CP072"))

# # A tibble: 68 × 6
#    freq  coicop unit   geo   time  values
#    <chr> <chr>  <chr>  <chr> <chr>  <dbl>
#  1 A     CP072  PPS_HH AT    2015    2613
#  2 A     CP072  PPS_HH BE    2015    2368
#  3 A     CP072  PPS_HH BG    2015     646
#  4 A     CP072  PPS_HH CY    2015    2811
#  5 A     CP072  PPS_HH CZ    2015    1060
#  6 A     CP072  PPS_HH DE    2015    2128
#  7 A     CP072  PPS_HH DK    2015    1838
#  8 A     CP072  PPS_HH EE    2015     856
#  9 A     CP072  PPS_HH EL    2015    1441
# 10 A     CP072  PPS_HH ES    2015    2147
# # ℹ 58 more rows