Code
%>%
lfst_r_lfur2gan 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 | 852024 |
Data - Eurostat
%>%
lfst_r_lfur2gan 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 | 852024 |
%>%
lfst_r_lfur2gan left_join(citizen, by = "citizen") %>%
group_by(citizen, Citizen) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
citizen | Citizen | Nobs |
---|---|---|
TOTAL | Total | 168405 |
NAT | Reporting country | 151061 |
FOR | Foreign country | 148703 |
NEU27_2020_FOR | Non-EU27 countries (from 2020) nor reporting country | 146296 |
EU27_2020_FOR | EU27 countries (from 2020) except reporting country | 142540 |
NRP | No response | 52895 |
STLS | Stateless | 42124 |
%>%
lfst_r_lfur2gan 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 | 289250 |
M | Males | 281926 |
F | Females | 280848 |
%>%
lfst_r_lfur2gan 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 | 175003 |
Y15-64 | From 15 to 64 years | 174425 |
Y20-64 | From 20 to 64 years | 174020 |
Y25-54 | From 25 to 54 years | 171833 |
Y55-64 | From 55 to 64 years | 156743 |
%>%
lfst_r_lfur2gan left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
%>%
lfst_r_lfur2gan group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
%>%
lfst_r_lfur2gan filter(unit == "PC",
== "FOR",
citizen == "T",
sex == "Y15-74",
age nchar(geo) == 4,
== "2018") %>%
time 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, 5),
values = c(0, 0.1, 0.3, 0.5, 0.7, 0.8, 1)) +
theme_void() + theme(legend.position = c(0.15, 0.85)) +
labs(fill = "Unemployment (%) \nForeign country")
%>%
lfst_r_lfur2gan filter(unit == "PC",
== "FOR",
citizen == "T",
sex == "Y15-64",
age nchar(geo) == 4,
== "2018") %>%
time 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, 5),
values = c(0, 0.1, 0.3, 0.5, 0.7, 0.8, 1)) +
theme_void() + theme(legend.position = c(0.15, 0.85)) +
labs(fill = "Unemployment (%) \nForeign country")
%>%
lfst_r_lfur2gan filter(unit == "PC",
== "NAT",
citizen == "T",
sex == "Y15-74",
age nchar(geo) == 4,
== "2018") %>%
time 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, 0.3, 0.5, 0.7, 0.8, 1)) +
theme_void() + theme(legend.position = c(0.15, 0.85)) +
labs(fill = "Unemployment (%) \nNational country")