Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/lfsa_urgaed.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2024-11-21 |
Data - Eurostat
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/lfsa_urgaed.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2024-11-21 |
LAST_COMPILE |
---|
2024-11-22 |
%>%
lfsa_urgaed group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2023 | 19344 |
%>%
lfsa_urgaed left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
unit | Unit | Nobs |
---|---|---|
PC | Percentage | 418975 |
%>%
lfsa_urgaed left_join(sex, by = "sex") %>%
group_by(sex, Sex) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
sex | Sex | Nobs |
---|---|---|
T | Total | 140070 |
F | Females | 139474 |
M | Males | 139431 |
%>%
lfsa_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 | 15844 |
Y25-29 | From 25 to 29 years | 15675 |
Y20-24 | From 20 to 24 years | 15657 |
Y30-34 | From 30 to 34 years | 15629 |
Y15-74 | From 15 to 74 years | 14471 |
Y15-64 | From 15 to 64 years | 14465 |
Y15-59 | From 15 to 59 years | 14464 |
Y15-39 | From 15 to 39 years | 14414 |
Y15-19 | From 15 to 19 years | 14337 |
Y20-64 | From 20 to 64 years | 14335 |
Y25-74 | From 25 to 74 years | 14329 |
Y25-64 | From 25 to 64 years | 14323 |
Y25-59 | From 25 to 59 years | 14321 |
Y25-54 | From 25 to 54 years | 14314 |
Y25-49 | From 25 to 49 years | 14288 |
Y40-64 | From 40 to 64 years | 14246 |
Y25-39 | From 25 to 39 years | 14243 |
Y40-59 | From 40 to 59 years | 14242 |
Y50-74 | From 50 to 74 years | 14226 |
Y50-64 | From 50 to 64 years | 14194 |
Y50-59 | From 50 to 59 years | 14174 |
Y55-64 | From 55 to 64 years | 14132 |
Y45-49 | From 45 to 49 years | 14130 |
Y40-44 | From 40 to 44 years | 14113 |
Y50-54 | From 50 to 54 years | 14111 |
Y35-39 | From 35 to 39 years | 14097 |
Y55-59 | From 55 to 59 years | 14085 |
Y60-64 | From 60 to 64 years | 14064 |
Y65-69 | From 65 to 69 years | 14052 |
load_data("eurostat/isced11_fr.RData")
%>%
lfsa_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 | 95178 |
ED0-2 | Inférieur à l'enseignement primaire, enseignement primaire et premier cycle de l'enseignement secondaire (niveaux 0-2) | 85083 |
ED3_4 | Deuxième cycle de l'enseignement secondaire et enseignement post-secondaire non-supérieur (niveaux 3 et 4) | 85081 |
ED5-8 | Enseignement supérieur (niveaux 5-8) | 83728 |
NRP | Sans réponse | 44219 |
ED3_4VOC | Deuxième cycle de l'enseignement secondaire et enseignement post-secondaire non-supérieur (niveaux 3 et 4) - professionnel | 12903 |
ED3_4GEN | Deuxième cycle de l'enseignement secondaire et enseignement post-secondaire non-supérieur (niveaux 3 et 4) - général | 12711 |
NAP | Non applicable | 72 |
%>%
lfsa_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 .} {
%>%
lfsa_urgaed group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()
%>%
lfsa_urgaed filter(isced11 == "ED0-2",
== "Y15-74",
age %in% c("EA19", "DE", "ES", "FR", "IT"),
geo == "T") %>%
sex year_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))
%>%
lfsa_urgaed filter(isced11 == "ED3_4",
== "Y15-74",
age %in% c("EA19", "DE", "ES", "FR", "IT"),
geo == "T") %>%
sex year_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))
%>%
lfsa_urgaed filter(isced11 == "ED5-8",
== "Y15-74",
age %in% c("EA19", "DE", "ES", "FR", "IT"),
geo == "T") %>%
sex year_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))
%>%
lfsa_urgaed filter(isced11 == "TOTAL",
== "Y15-74",
age %in% c("ES", "DE", "FR", "IT", "EA19"),
geo == "T") %>%
sex year_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))
%>%
lfsa_urgaed filter(geo %in% c("EU15", "EU28", "EU27_2020"),
== "Y15-64",
age == "TOTAL",
isced11 == "T",
sex == "PC") %>%
unit %>%
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))
%>%
lfsa_urgaed filter(geo %in% c("EU15", "EU28", "EU27_2020"),
== "Y15-64",
age == "TOTAL",
isced11 == "F",
sex == "PC") %>%
unit %>%
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 (Female)") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
%>%
lfsa_urgaed filter(geo %in% c("EU15", "EU28", "EU27_2020"),
== "Y15-64",
age == "TOTAL",
isced11 == "M",
sex == "PC") %>%
unit %>%
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(0.22, 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))