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 615962
NAT Reporting country 554290
FOR Foreign country 510966
NEU27_2020_FOR Non-EU27 countries (from 2020) nor reporting country 489207
EU27_2020_FOR EU27 countries (from 2020) except reporting country 465358
NRP No response 147521
STLS Stateless 95865

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 702267
ED3_4 Upper secondary and post-secondary non-tertiary education (levels 3 and 4) 661443
ED0-2 Less than primary, primary and lower secondary education (levels 0-2) 644245
ED5-8 Tertiary education (levels 5-8) 642485
NRP No response 228669
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 2879169

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 988275
F Females 945548
M Males 945346

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 762773
Y20-64 From 20 to 64 years 746963
Y25-54 From 25 to 54 years 732428
Y55-64 From 55 to 64 years 637005

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
2024 103810
2023 103601
2022 102406
2021 103015
2020 107237
2019 123864
2018 123983
2017 124314
2016 123897
2015 124051
2014 124070
2013 123188
2012 119882
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 (%)")