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 | 836507 |
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 | 836507 |
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 | 283821 |
| M | Males | 279258 |
| F | Females | 273428 |
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 | 36615 |
| Y15-74 | From 15 to 74 years | 36608 |
| Y15-64 | From 15 to 64 years | 36552 |
| Y15-59 | From 15 to 59 years | 36509 |
| Y20-64 | From 20 to 64 years | 36482 |
| Y_GE25 | 25 years or over | 36471 |
| Y25-74 | From 25 to 74 years | 36464 |
| Y25-64 | From 25 to 64 years | 36385 |
| Y25-59 | From 25 to 59 years | 36327 |
| Y25-49 | From 25 to 49 years | 36055 |
| Y15-39 | From 15 to 39 years | 36030 |
| Y40-64 | From 40 to 64 years | 35915 |
| Y40-59 | From 40 to 59 years | 35811 |
| Y_GE50 | 50 years or over | 35439 |
| Y50-74 | From 50 to 74 years | 35424 |
| Y15-29 | From 15 to 29 years | 35385 |
| Y50-64 | From 50 to 64 years | 35233 |
| Y50-59 | From 50 to 59 years | 34986 |
| Y55-74 | From 55 to 74 years | 34854 |
| Y15-24 | From 15 to 24 years | 34722 |
| Y55-64 | From 55 to 64 years | 34555 |
| Y15-19 | From 15 to 19 years | 32853 |
| Y_GE65 | 65 years or over | 31860 |
| Y_GE75 | 75 years or over | 18972 |
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 | 106458 |
| G-Q | Services | 100348 |
| C-K | Industry and services (except public administration and community services; activities of households and extra-territorial organizations) | 99837 |
| G-K | Services (except public administration and community services; activities of households and extra-territorial organizations) | 99087 |
| M-Q | Education; health; other service activities; activities of households; extra-territorial organizations | 98695 |
| C-F | Industry | 97169 |
| A_B | Agriculture; fishing | 95969 |
| F | Construction | 91784 |
| NRP | No response | 47160 |
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"),
age == "Y15-64",
nace_r1 == "TOTAL",
sex == "T") %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
ggplot + geom_line() + theme_minimal() +
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))