Code
%>%
lfsa_epgan6 left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
unit | Unit | Nobs |
---|---|---|
THS_PER | Thousand persons | 835851 |
Data - Eurostat
%>%
lfsa_epgan6 left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
unit | Unit | Nobs |
---|---|---|
THS_PER | Thousand persons | 835851 |
%>%
lfsa_epgan6 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 | 283649 |
M | Males | 278976 |
F | Females | 273226 |
%>%
lfsa_epgan6 left_join(age, by = "age") %>%
group_by(age, Age) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
age | Age | Nobs |
---|---|---|
Y_GE15 | 15 years or over | 36600 |
Y15-74 | From 15 to 74 years | 36593 |
Y15-64 | From 15 to 64 years | 36540 |
Y15-59 | From 15 to 59 years | 36496 |
Y20-64 | From 20 to 64 years | 36470 |
Y_GE25 | 25 years or over | 36454 |
Y25-74 | From 25 to 74 years | 36447 |
Y25-64 | From 25 to 64 years | 36371 |
Y25-59 | From 25 to 59 years | 36312 |
Y25-49 | From 25 to 49 years | 36029 |
Y15-39 | From 15 to 39 years | 36006 |
Y40-64 | From 40 to 64 years | 35896 |
Y40-59 | From 40 to 59 years | 35790 |
Y_GE50 | 50 years or over | 35409 |
Y50-74 | From 50 to 74 years | 35394 |
Y15-29 | From 15 to 29 years | 35356 |
Y50-64 | From 50 to 64 years | 35206 |
Y50-59 | From 50 to 59 years | 34954 |
Y55-74 | From 55 to 74 years | 34817 |
Y15-24 | From 15 to 24 years | 34687 |
Y55-64 | From 55 to 64 years | 34520 |
Y15-19 | From 15 to 19 years | 32804 |
Y_GE65 | 65 years or over | 31789 |
Y_GE75 | 75 years or over | 18911 |
%>%
lfsa_epgan6 left_join(nace_r1, by = "nace_r1") %>%
group_by(nace_r1, Nace_r1) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
nace_r1 | Nace_r1 | Nobs |
---|---|---|
TOTAL | Total - all NACE activities | 106431 |
G-Q | Services | 100309 |
C-K | Industry and services (except public administration and community services; activities of households and extra-territorial organizations) | 99804 |
G-K | Services (except public administration and community services; activities of households and extra-territorial organizations) | 99040 |
M-Q | Education; health; other service activities; activities of households; extra-territorial organizations | 98613 |
C-F | Industry | 97090 |
A_B | Agriculture; fishing | 95811 |
F | Construction | 91651 |
NRP | No response | 47102 |
%>%
lfsa_epgan6 left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
%>%
lfsa_epgan6 filter(geo %in% c("EU15", "EU28", "EU27_2020"),
== "Y15-64",
age == "TOTAL",
nace_r1 == "T") %>%
sex %>%
year_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(4)[1:3]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 2), "-01-01")),
labels = date_format("%y")) +
theme(legend.position = c(0.3, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Employment Rates") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))