Unemployment rates by sex, age, country of birth and NUTS 2 regions (1999-2019) - lfst_r_lfur2gac

Data - Eurostat

Author

François Geerolf

unit

Code 
lfst_r_lfur2gac %>%
  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 846610

c_birth

Code 
lfst_r_lfur2gac %>%
  group_by(c_birth) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
c_birth Nobs
TOTAL 175200
NAT 163563
FOR 162040
NEU27_2020_FOR 146568
EU27_2020_FOR 143253
NRP 55986

sex

Code 
lfst_r_lfur2gac %>%
  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 284967
F Females 280895
M Males 280748

age

Code 
lfst_r_lfur2gac %>%
  left_join(age, by = "age") %>%
  group_by(age, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
age Age Nobs
Y15-74 From 15 to 74 years 172185
Y15-64 From 15 to 64 years 171712
Y20-64 From 20 to 64 years 171119
Y25-54 From 25 to 54 years 169167
Y55-64 From 55 to 64 years 162427

geo

Code 
lfst_r_lfur2gac %>%
  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 
lfst_r_lfur2gac %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

20-64

Total

Code 
lfst_r_lfur2gac %>%
  filter(c_birth == "TOTAL",
         sex == "T",
         age == "Y20-64",
         nchar(geo) == 4,
         time == "2019") %>%
  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(0, 100, 5),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Chômage")

Foreign

Code 
lfst_r_lfur2gac %>%
  filter(c_birth == "FOR",
         sex == "T",
         age == "Y20-64",
         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(0, 100, 5),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Chômage (né étranger)")

25-54

Total

Code 
lfst_r_lfur2gac %>%
  filter(c_birth == "TOTAL",
         sex == "T",
         age == "Y25-54",
         nchar(geo) == 4,
         time == "2019") %>%
  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(0, 100, 5),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Chômage")