Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/lfsq_urgaed.RData")$mtime)) %>%
print_table_conditional()| LAST_DOWNLOAD |
|---|
| 2026-02-04 |
Data - Eurostat
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/lfsq_urgaed.RData")$mtime)) %>%
print_table_conditional()| LAST_DOWNLOAD |
|---|
| 2026-02-04 |
| LAST_COMPILE |
|---|
| 2026-02-05 |
lfsq_urgaed %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2025Q3 | 19262 |
lfsq_urgaed %>%
left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| unit | Unit | Nobs |
|---|---|---|
| PC | Percentage | 1508692 |
lfsq_urgaed %>%
left_join(sex, by = "sex") %>%
group_by(sex, Sex) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| sex | Sex | Nobs |
|---|---|---|
| T | Total | 504484 |
| M | Males | 502262 |
| F | Females | 501946 |
lfsq_urgaed %>%
left_join(age, by = "age") %>%
group_by(age, Age) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| age | Age | Nobs |
|---|---|---|
| Y15-24 | From 15 to 24 years | 57485 |
| Y25-29 | From 25 to 29 years | 56930 |
| Y30-34 | From 30 to 34 years | 56905 |
| Y20-24 | From 20 to 24 years | 56882 |
| Y15-74 | From 15 to 74 years | 52072 |
| Y15-64 | From 15 to 64 years | 52014 |
| Y15-59 | From 15 to 59 years | 51997 |
| Y15-39 | From 15 to 39 years | 51779 |
| Y25-74 | From 25 to 74 years | 51571 |
| Y20-64 | From 20 to 64 years | 51569 |
| Y25-64 | From 25 to 64 years | 51508 |
| Y25-59 | From 25 to 59 years | 51486 |
| Y25-54 | From 25 to 54 years | 51449 |
| Y25-49 | From 25 to 49 years | 51372 |
| Y50-74 | From 50 to 74 years | 51271 |
| Y40-64 | From 40 to 64 years | 51248 |
| Y40-59 | From 40 to 59 years | 51204 |
| Y25-39 | From 25 to 39 years | 51189 |
| Y50-64 | From 50 to 64 years | 51110 |
| Y50-59 | From 50 to 59 years | 51015 |
| Y55-64 | From 55 to 64 years | 50925 |
| Y50-54 | From 50 to 54 years | 50838 |
| Y40-44 | From 40 to 44 years | 50808 |
| Y45-49 | From 45 to 49 years | 50793 |
| Y35-39 | From 35 to 39 years | 50773 |
| Y55-59 | From 55 to 59 years | 50710 |
| Y65-69 | From 65 to 69 years | 50673 |
| Y60-64 | From 60 to 64 years | 50639 |
| Y15-19 | From 15 to 19 years | 50477 |
load_data("eurostat/isced11_fr.RData")
lfsq_urgaed %>%
left_join(isced11, by = "isced11") %>%
group_by(isced11, Isced11) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| isced11 | Isced11 | Nobs |
|---|---|---|
| TOTAL | Ensemble des niveaux de la CITE 2011 | 315375 |
| ED0-2 | Inférieur à l'enseignement primaire, enseignement primaire et premier cycle de l'enseignement secondaire (niveaux 0-2) | 313896 |
| ED3_4 | Deuxième cycle de l'enseignement secondaire et enseignement post-secondaire non-supérieur (niveaux 3 et 4) | 313883 |
| ED5-8 | Enseignement supérieur (niveaux 5-8) | 307561 |
| NRP | Sans réponse | 110140 |
| ED35_45 | Deuxième cycle du secondaire et post-secondaire non-supérieur - professionnel (niveaux 35 et 45) | 74301 |
| ED34_44 | Deuxième cycle du secondaire et post-secondaire non-supérieur - général (niveaux 34 et 44) | 73392 |
| NAP | Non applicable | 144 |
lfsq_urgaed %>%
left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
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_urgaed %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()lfsq_urgaed %>%
filter(isced11 == "ED0-2",
age == "Y15-74",
geo %in% c("EA19", "DE", "ES", "FR", "IT"),
sex == "T") %>%
quarter_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "EA19", color2, color)) %>%
mutate(color = ifelse(geo == "ES", color2, color)) %>%
mutate(values = values / 100) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Taux d'emploi (1er cycle de l'enseignement secondaire)") +
scale_color_identity() + add_5flags +
scale_x_date(breaks = seq(1960, 2026, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 2),
labels = percent_format(accuracy = 1))
lfsq_urgaed %>%
filter(isced11 == "ED3_4",
age == "Y15-74",
geo %in% c("EA19", "DE", "ES", "FR", "IT"),
sex == "T") %>%
quarter_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "EA19", color2, color)) %>%
mutate(color = ifelse(geo == "ES", color2, color)) %>%
mutate(values = values / 100) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Taux d'emploi") +
scale_color_identity() + add_5flags +
scale_x_date(breaks = seq(1960, 2026, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 2),
labels = percent_format(accuracy = 1))
lfsq_urgaed %>%
filter(isced11 == "ED5-8",
age == "Y15-74",
geo %in% c("EA19", "DE", "ES", "FR", "IT"),
sex == "T") %>%
quarter_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "EA19", color2, color)) %>%
mutate(color = ifelse(geo == "ES", color2, color)) %>%
mutate(values = values / 100) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Taux d'emploi") +
scale_color_identity() + add_5flags +
scale_x_date(breaks = seq(1960, 2026, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 2),
labels = percent_format(accuracy = 1))
lfsq_urgaed %>%
filter(isced11 == "TOTAL",
age == "Y15-74",
geo %in% c("ES", "DE", "FR", "IT", "EA19"),
sex == "T") %>%
quarter_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "EA19", color2, color)) %>%
mutate(color = ifelse(geo == "ES", color2, color)) %>%
mutate(values = values / 100) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Taux d'emploi") +
scale_color_identity() + add_5flags +
scale_x_date(breaks = seq(1960, 2026, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 2),
labels = percent_format(accuracy = 1))
lfsq_urgaed %>%
filter(geo %in% c("EU15", "EU28", "EU27_2020"),
age == "Y15-64",
isced11 == "TOTAL",
sex == "T",
unit == "PC") %>%
quarter_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))
lfsq_urgaed %>%
filter(geo %in% c("EU15", "EU28", "EU27_2020"),
age == "Y15-64",
isced11 == "TOTAL",
sex == "F",
unit == "PC") %>%
quarter_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 (Female)") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
lfsq_urgaed %>%
filter(geo %in% c("EU15", "EU28", "EU27_2020"),
age == "Y15-64",
isced11 == "TOTAL",
sex == "M",
unit == "PC") %>%
quarter_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(0.22, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Employment Rates (Male)") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))