Code
%>%
lfsq_ergan left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
unit | Unit | Nobs |
---|---|---|
PC | Percentage | 1846759 |
Data - Eurostat
%>%
lfsq_ergan left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
unit | Unit | Nobs |
---|---|---|
PC | Percentage | 1846759 |
%>%
lfsq_ergan left_join(sex, by = "sex") %>%
group_by(sex, Sex) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
sex | Sex | Nobs |
---|---|---|
T | Total | 628938 |
F | Females | 608988 |
M | Males | 608833 |
%>%
lfsq_ergan left_join(age, by = "age") %>%
group_by(age, Age) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
%>%
lfsq_ergan left_join(citizen, by = "citizen") %>%
group_by(citizen, Citizen) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
citizen | Citizen | Nobs |
---|---|---|
TOTAL | Total | 359740 |
NAT | Reporting country | 333598 |
FOR | Foreign country | 324256 |
NEU27_2020_FOR | Non-EU27 countries (from 2020) nor reporting country | 317344 |
EU27_2020_FOR | EU27 countries (from 2020) except reporting country | 301058 |
NRP | No response | 109873 |
STLS | Stateless | 100890 |
%>%
lfsq_ergan left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
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 .} {
%>%
lfsq_ergan group_by(time) %>%
summarise(Nobs = n()) %>%
print_table_conditional
%>%
lfsq_ergan filter(geo %in% c("FR", "DE", "PT", "EA19"),
== "M",
sex == "TOTAL",
citizen == "Y25-54") %>%
age %>%
quarter_to_date left_join(geo, by = "geo") %>%
+ geom_line() + theme_minimal() +
ggplot aes(x = date, y = values/100, color = Geo, linetype = Geo) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
labels = percent_format(a = 1)) +
theme(legend.position = c(0.15, 0.55),
legend.title = element_blank()) +
xlab("") + ylab("Employment Rate, 25-54, Women (%)")
%>%
lfsq_ergan filter(geo %in% c("FR", "DE", "PT", "EA19"),
== "T",
sex == "TOTAL",
citizen == "Y25-54") %>%
age %>%
quarter_to_date left_join(geo, by = "geo") %>%
+ geom_line() + theme_minimal() +
ggplot aes(x = date, y = values/100, color = Geo, linetype = Geo) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
labels = percent_format(a = 1)) +
theme(legend.position = c(0.15, 0.55),
legend.title = element_blank()) +
xlab("") + ylab("Employment Rate, 25-54, Total (%)")
%>%
lfsq_ergan filter(geo %in% c("FR", "DE", "PT", "EA19"),
== "F",
sex == "TOTAL",
citizen == "Y25-54") %>%
age %>%
quarter_to_date left_join(geo, by = "geo") %>%
+ geom_line() + theme_minimal() +
ggplot aes(x = date, y = values/100, color = Geo, linetype = Geo) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
labels = percent_format(a = 1)) +
theme(legend.position = c(0.15, 0.55),
legend.title = element_blank()) +
xlab("") + ylab("Employment Rate, 25-54, Female (%)")
%>%
lfsq_ergan filter(geo %in% c("FR", "IT", "EL", "EA19"),
== "M",
sex == "TOTAL",
citizen == "Y25-54") %>%
age %>%
quarter_to_date left_join(geo, by = "geo") %>%
+ geom_line() + theme_minimal() +
ggplot aes(x = date, y = values/100, color = Geo, linetype = Geo) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
labels = percent_format(a = 1)) +
theme(legend.position = c(0.15, 0.55),
legend.title = element_blank()) +
xlab("") + ylab("Employment Rate, 25-54, Men (%)")
%>%
lfsq_ergan filter(geo %in% c("FR", "IT", "EL", "EA19"),
== "T",
sex == "TOTAL",
citizen == "Y25-54") %>%
age %>%
quarter_to_date left_join(geo, by = "geo") %>%
+ geom_line() + theme_minimal() +
ggplot aes(x = date, y = values/100, color = Geo, linetype = Geo) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
labels = percent_format(a = 1)) +
theme(legend.position = c(0.15, 0.55),
legend.title = element_blank()) +
xlab("") + ylab("Employment Rate, 25-54, Total (%)")
%>%
lfsq_ergan filter(geo %in% c("FR", "IT", "EL", "EA19"),
== "F",
sex == "TOTAL",
citizen == "Y25-54") %>%
age %>%
quarter_to_date left_join(geo, by = "geo") %>%
+ geom_line() + theme_minimal() +
ggplot aes(x = date, y = values/100, color = Geo, linetype = Geo) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
labels = percent_format(a = 1)) +
theme(legend.position = c(0.15, 0.55),
legend.title = element_blank()) +
xlab("") + ylab("Employment Rate, 25-54, Women (%)")