Mean consumption expenditure by income quintile - hbs_exp_t133

Data - Eurostat

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 1114
PPS_HH Purchasing power standard (PPS) per household 1114

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 370
QUINTILE2 Second quintile 370
QUINTILE3 Third quintile 370
QUINTILE4 Fourth quintile 370
QUINTILE5 Fifth quintile 370
TOTAL Total 374
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 414
2020 324

2015

France, Germany, Italy

Code
hbs_exp_t133 %>%
  filter(time == "2015") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(quantile, values)
# # A tibble: 70 × 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 EE         5298      7423      9482     12568     18111 10575    NA
#  9 PPS_AE EL         9989     11592     13628     15513     21713 14483    NA
# 10 PPS_AE ES        11057     15165     18196     20475     27049 18388    NA
# # ℹ 60 more rows