Mean consumption expenditure by income quintile - hbs_exp_t133

Data - Eurostat

Author

unit

Code

hbs_exp_t133 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

unit	Unit	Nobs
PPS_AE	Purchasing power standard (PPS) per adult equivalent	1138
PPS_HH	Purchasing power standard (PPS) per household	1138

quantile

Code

hbs_exp_t133 %>%
  left_join(quantile, by = "quantile") %>%
  group_by(quantile, Quantile) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

quantile	Quantile	Nobs
QUINTILE1	First quintile	378
QUINTILE2	Second quintile	378
QUINTILE3	Third quintile	378
QUINTILE4	Fourth quintile	378
QUINTILE5	Fifth quintile	378
TOTAL	Total	382
UNK	Unknown	4

geo

Code

hbs_exp_t133 %>%
  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_t133 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

time	Nobs
1988	120
1994	204
1999	204
2005	468
2010	494
2015	426
2020	360

2015

France, Germany, Italy

Code

hbs_exp_t133 %>%
  filter(time == "2015") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(quantile, values)

# # A tibble: 72 × 9
#    unit   geo   QUINTILE1 QUINTILE2 QUINTILE3 QUINTILE4 QUINTILE5 TOTAL   UNK
#    <chr>  <chr>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl> <dbl> <dbl>
#  1 PPS_AE AT        17096     20274     23350     23639     28874 22645    NA
#  2 PPS_AE BE        16192     21014     23043     25153     27496 22578    NA
#  3 PPS_AE BG         4751      6247      7441      8955     11717  7821    NA
#  4 PPS_AE CY        12334     16034     19786     22953     29926 20199    NA
#  5 PPS_AE CZ         7074      8864      9560     10517     12374  9677    NA
#  6 PPS_AE DE        12404     17427     20766     23675     30692 20992    NA
#  7 PPS_AE DK        14537     17770     20912     21838     26885 20384    NA
#  8 PPS_AE EA20      12410     16222     18866     21241     27462 19239    NA
#  9 PPS_AE EE         5298      7423      9482     12568     18111 10575    NA
# 10 PPS_AE EL         9989     11592     13628     15513     21713 14483    NA
# # ℹ 62 more rows