Overcrowding rate by age, sex and poverty status - total population - EU-SILC survey - ilc_lvho05a

Data - Eurostat

Author

incgrp

Code

ilc_lvho05a %>%
  left_join(incgrp, by = "incgrp") %>%
  group_by(incgrp, Incgrp) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

incgrp	Incgrp	Nobs
A_MD60	Above 60% of median equivalised income	38151
B_MD60	Below 60% of median equivalised income	38151
TOTAL	Total	38151

age

Code

ilc_lvho05a %>%
  left_join(age, by = "age") %>%
  group_by(age, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

sex

Code

ilc_lvho05a %>%
  left_join(sex, by = "sex") %>%
  group_by(sex, Sex) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

sex	Sex	Nobs
F	Females	38151
M	Males	38151
T	Total	38151

unit

Code

ilc_lvho05a %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

unit	Unit	Nobs
PC	Percentage	114453

geo

Code

ilc_lvho05a %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

time

Code

ilc_lvho05a %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Overcrowding rate by age, sex and poverty status

Javascript

Code

ilc_lvho05a %>%
  filter(sex == "T",
         time == "2019",
         age == "TOTAL") %>%
  left_join(geo, by = "geo") %>%
  select(incgrp, Geo, values) %>%
  spread(incgrp, values) %>%
  arrange(-TOTAL) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}