Employment rates by sex, age, educational attainment level, citizenship and NUTS 2 regions - lfst_r_lfe2emprtn

Data - Eurostat

Author

François Geerolf

citizen

Code 
lfst_r_lfe2emprtn %>%
  left_join(citizen, by = "citizen") %>%
  group_by(citizen, Citizen) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
citizen Citizen Nobs
TOTAL Total 592922
NAT Reporting country 535401
FOR Foreign country 493005
NEU27_2020_FOR Non-EU27 countries (from 2020) nor reporting country 471755
EU27_2020_FOR EU27 countries (from 2020) except reporting country 449218
NRP No response 144194
STLS Stateless 92341

isced11

Code 
lfst_r_lfe2emprtn %>%
  left_join(isced11, by = "isced11") %>%
  group_by(isced11, Isced11) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
isced11 Isced11 Nobs
TOTAL All ISCED 2011 levels 677021
ED3_4 Upper secondary and post-secondary non-tertiary education (levels 3 and 4) 637295
ED0-2 Less than primary, primary and lower secondary education (levels 0-2) 621080
ED5-8 Tertiary education (levels 5-8) 618930
NRP No response 224450
UNK Unknown 60

unit

Code 
lfst_r_lfe2emprtn %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
PC Percentage 2778836

sex

Code 
lfst_r_lfe2emprtn %>%
  left_join(sex, by = "sex") %>%
  group_by(sex, Sex) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
sex Sex Nobs
T Total 953991
F Females 912432
M Males 912413

age

Code 
lfst_r_lfe2emprtn %>%
  left_join(age, by = "age") %>%
  group_by(age, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
age Age Nobs
Y15-64 From 15 to 64 years 736839
Y20-64 From 20 to 64 years 721146
Y25-54 From 25 to 54 years 706995
Y55-64 From 55 to 64 years 613856

geo

Code 
lfst_r_lfe2emprtn %>%
  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="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

Code 
lfst_r_lfe2emprtn %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()
time Nobs
2023 104732
2022 104172
2021 104314
2020 106799
2019 123415
2018 123983
2017 124314
2016 123897
2015 124051
2014 124070
2013 123260
2012 119978
2011 119646
2010 120590
2009 117464
2008 118201
2007 117346
2006 113871
2005 106285
2004 99191
2003 96159
2002 95288
2001 92733
2000 87583
1999 87494

Education: levels 0-2

Code 
lfst_r_lfe2emprtn %>%
  filter(unit == "PC",
         sex == "T",
         isced11 == "ED0-2",
         citizen == "TOTAL",
         nchar(geo) == 4,
         time == "2018") %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -13.5, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/100)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = 0.01*seq(20, 100, 10),
                       values = c(0, 0.1, 0.3, 0.4, 0.5, 0.6, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Employment \nPrimary Education (%)")

Upper secondary and post-secondary non-tertiary education (levels 3 and 4)

Education: levels 3-4

Code 
lfst_r_lfe2emprtn %>%
  filter(unit == "PC",
         sex == "T",
         isced11 == "ED3_4",
         citizen == "TOTAL",
         nchar(geo) == 4,
         time == "2018") %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -13.5, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/100)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = 0.01*seq(20, 100, 10),
                       values = c(0, 0.1, 0.3, 0.4, 0.5, 0.6, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Employment \nSecondary Education (%)")

Education: levels 5-8

Code 
lfst_r_lfe2emprtn %>%
  filter(unit == "PC",
         sex == "T",
         isced11 == "ED5-8",
         citizen == "TOTAL",
         nchar(geo) == 4,
         time == "2018") %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -13.5, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/100)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = 0.01*seq(20, 100, 10),
                       values = c(0, 0.1, 0.3, 0.4, 0.5, 0.6, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Employment \nTertiary Education (%)")