Unemployment by sex and age – monthly data
Data - Eurostat
Info
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-12-22 |
Last
Code
une_rt_m %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2024M11 | 162 |
s_adj
Code
une_rt_m %>%
left_join(s_adj, by = "s_adj") %>%
group_by(s_adj, S_adj) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}| s_adj | S_adj | Nobs |
|---|---|---|
| SA | Seasonally adjusted data, not calendar adjusted data | 244358 |
| NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 239062 |
| TC | Trend cycle data | 223130 |
geo
Code
une_rt_m %>%
left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", 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 .}unit
Code
une_rt_m %>%
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 | 355994 |
| PC_ACT | Percentage of population in the labour force | 350556 |
freq
Code
une_rt_m %>%
left_join(freq, by = "freq") %>%
group_by(freq, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}| freq | Freq | Nobs |
|---|---|---|
| M | Monthly | 706550 |
sex
Code
une_rt_m %>%
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 | 237338 |
| F | Females | 234606 |
| M | Males | 234606 |
age
Code
une_rt_m %>%
left_join(age, by = "age") %>%
group_by(age, Age) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}| age | Age | Nobs |
|---|---|---|
| TOTAL | Total | 237720 |
| Y25-74 | From 25 to 74 years | 234552 |
| Y_LT25 | Less than 25 years | 234278 |
time
Code
une_rt_m %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Young
France, Germany, Spain, Italy, Eurozone
All
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
age == "Y_LT25",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_5flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
France, Germany, Netherlands, Sweden
All
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "NL", "SE", "EA20"),
age == "Y_LT25",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_5flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
All age, All sex
France, Germany, Spain, Italy, Netherlands, Portugal, EA
All
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "NL", "PT", "EA20"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_7flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
2002-
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "NL", "PT", "EA20"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
filter(date >= as.Date("2002-01-01")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))+
scale_color_identity() + add_7flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1)) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = values, label = percent(values)))
2002-
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "NL", "PT", "EA20"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
filter(date >= as.Date("2002-01-01")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))+
scale_color_identity() + add_6flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1)) +
geom_label_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = values, label = percent(values, acc = .1), color = color))
2007-
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "NL", "PT", "EA20"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
filter(date >= as.Date("2007-01-01")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))+
scale_color_identity() + add_6flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Taux de chômage (% de la population active)") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1),
limits = c(0, 0.19)) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = values, label = percent(values, acc = .1), color = color))
France, Germany, Spain, Italy, Eurozone
All
All age
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_5flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
Youth
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
age == "Y_LT25",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_5flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Younth Unemployment, % of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 5),
labels = scales::percent_format(accuracy = 1))
2010-
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
filter(date >= as.Date("2010-01-01")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_5flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
Youth
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
age == "Y_LT25",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
filter(date >= as.Date("2010-01-01")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_5flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Younth Unemployment, % of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 5),
labels = scales::percent_format(accuracy = 1))
2014-
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
filter(date >= as.Date("2014-01-01")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_5flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
France, Germany, Spain, Italy, Netherlands, Portugal
All
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "NL", "PT"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_6flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
1998-
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "NL", "PT"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100) %>%
filter(date >= as.Date("1998-01-01")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_6flags +
scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 4), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
2014-
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "IT", "ES", "NL", "PT"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100) %>%
filter(date >= as.Date("2014-01-01")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_6flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 2),
labels = scales::percent_format(accuracy = 1))
France, Germany, Portugal
Code
une_rt_m %>%
filter(geo %in% c("FR", "DE", "PT"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_3flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
Germany, Euro Area
Code
une_rt_m %>%
filter(geo %in% c("EA20", "EU15", "DE"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_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, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.25),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
Euro Area, France
All
Code
une_rt_m %>%
filter(geo %in% c("EA20", "FR"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_2flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = "none") +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
1998-
Code
une_rt_m %>%
filter(geo %in% c("EA20", "FR"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
filter(date >= as.Date("1998-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_2flags +
scale_x_date(breaks = as.Date(paste0(seq(1998, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = "none") +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
2010-
Code
une_rt_m %>%
filter(geo %in% c("EA20", "FR"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
filter(date >= as.Date("2010-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_2flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = "none") +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, .5),
labels = scales::percent_format(accuracy = .1)) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = values, label = percent(values), color = color))
Euro Area, Bulgaria
All
Code
une_rt_m %>%
filter(geo %in% c("EA20", "BG"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_2flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = "none") +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
2010-
Code
une_rt_m %>%
filter(geo %in% c("EA20", "BG"),
age == "TOTAL",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
filter(date >= as.Date("2010-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(values = values/100,
Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + scale_color_identity() + add_2flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = "none") +
xlab("") + ylab("Unemployment, Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
By age
Euro Area
Code
une_rt_m %>%
filter(geo %in% c("EA20"),
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(age, by = "age") %>%
ggplot + geom_line() + theme_minimal() +
aes(x = date, y = values/100, color = Age) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
France
Code
une_rt_m %>%
filter(geo == "FR",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(age, by = "age") %>%
ggplot + geom_line() + theme_minimal() +
aes(x = date, y = values/100, color = Age) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
Germany
Code
une_rt_m %>%
filter(geo == "DE",
sex == "T",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(age, by = "age") %>%
ggplot + geom_line() + theme_minimal() +
aes(x = date, y = values/100, color = Age) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
By sex
Euro Area
Code
une_rt_m %>%
filter(geo %in% c("EA20"),
age == "TOTAL",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(sex, by = "sex") %>%
ggplot + geom_line() + theme_minimal() +
aes(x = date, y = values/100, color = Sex) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
France
Code
une_rt_m %>%
filter(geo == "FR",
age == "TOTAL",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(sex, by = "sex") %>%
ggplot + geom_line() + theme_minimal() +
aes(x = date, y = values/100, color = Sex) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.6, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
Germany
Code
une_rt_m %>%
filter(geo == "DE",
age == "TOTAL",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(sex, by = "sex") %>%
ggplot + geom_line() + theme_minimal() +
aes(x = date, y = values/100, color = Sex) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
Italy
Code
une_rt_m %>%
filter(geo == "IT",
age == "TOTAL",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(sex, by = "sex") %>%
ggplot + geom_line() + theme_minimal() +
aes(x = date, y = values/100, color = Sex) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.6, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
Greece
Code
une_rt_m %>%
filter(geo == "EL",
age == "TOTAL",
unit == "PC_ACT",
s_adj == "SA") %>%
month_to_date %>%
left_join(sex, by = "sex") %>%
ggplot + geom_line() + theme_minimal() +
aes(x = date, y = values/100, color = Sex) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Percentage of active population") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))