Last observation: Q2 2026 (N = 253)
First observation: Q1 1948 (N = 60)
Last data update: 01 août 2026, 21:45
Last compile: 14 sept. 2026, 22:13
classif1
Code
UNE_DEAP_SEX_AGE_RT_Q |>
left_join(classif1, by = "classif1") |>
group_by(classif1, Classif1) |>
summarise(Nobs = n()) |>
arrange(-Nobs) |>
print_table_conditional()| classif1 | Classif1 | Nobs |
|---|---|---|
| AGE_YTHADULT_YGE15 | Age (Youth, adults): 15+ | 29998 |
| AGE_YTHADULT_YGE25 | Age (Youth, adults): 25+ | 26686 |
| AGE_YTHADULT_Y15-24 | Age (Youth, adults): 15-24 | 26681 |
| AGE_YTHADULT_Y15-64 | Age (Youth, adults): 15-64 | 25981 |
| AGE_AGGREGATE_YGE15 | NA | 25978 |
| AGE_AGGREGATE_Y15-24 | Age (Aggregate bands): 15-24 | 25971 |
| AGE_AGGREGATE_Y25-54 | Age (Aggregate bands): 25-54 | 25927 |
| AGE_10YRBANDS_YGE15 | NA | 25858 |
| AGE_10YRBANDS_Y15-24 | Age (10-year bands): 15-24 | 25851 |
| AGE_AGGREGATE_Y55-64 | Age (Aggregate bands): 55-64 | 25836 |
| AGE_10YRBANDS_Y25-34 | Age (10-year bands): 25-34 | 25793 |
| AGE_10YRBANDS_Y35-44 | Age (10-year bands): 35-44 | 25763 |
| AGE_10YRBANDS_Y45-54 | Age (10-year bands): 45-54 | 25727 |
| AGE_10YRBANDS_Y55-64 | Age (10-year bands): 55-64 | 25723 |
| AGE_5YRBANDS_YGE15 | NA | 25243 |
| AGE_5YRBANDS_Y20-24 | Age (5-year bands): 20-24 | 25232 |
| AGE_5YRBANDS_Y15-19 | Age (5-year bands): 15-19 | 25225 |
| AGE_5YRBANDS_Y25-29 | Age (5-year bands): 25-29 | 24893 |
| AGE_5YRBANDS_Y30-34 | Age (5-year bands): 30-34 | 24889 |
| AGE_5YRBANDS_Y35-39 | Age (5-year bands): 35-39 | 24877 |
| AGE_5YRBANDS_Y40-44 | Age (5-year bands): 40-44 | 24861 |
| AGE_5YRBANDS_Y45-49 | Age (5-year bands): 45-49 | 24854 |
| AGE_5YRBANDS_Y50-54 | Age (5-year bands): 50-54 | 24809 |
| AGE_5YRBANDS_Y55-59 | Age (5-year bands): 55-59 | 24734 |
| AGE_5YRBANDS_Y60-64 | Age (5-year bands): 60-64 | 24150 |
| AGE_AGGREGATE_YGE65 | Age (Aggregate bands): 65+ | 22612 |
| AGE_10YRBANDS_YGE65 | Age (10-year bands): 65+ | 22255 |
| AGE_5YRBANDS_YGE65 | Age (5-year bands): 65+ | 22068 |
source
Code
UNE_DEAP_SEX_AGE_RT_Q |>
left_join(source, by = "source") |>
group_by(source, Source) |>
summarise(Nobs = n()) |>
arrange(-Nobs) |>
print_table_conditional()sex
Code
UNE_DEAP_SEX_AGE_RT_Q |>
left_join(sex, by = "sex") |>
group_by(sex, Sex) |>
summarise(Nobs = n()) |>
arrange(-Nobs) |>
print_table_conditional()| sex | Sex | Nobs |
|---|---|---|
| SEX_T | Sex: Total | 236937 |
| SEX_M | Sex: Male | 236128 |
| SEX_F | Sex: Female | 235324 |
| SEX_O | Sex: Other | 86 |
Nombre de chômeurs
Code
UNE_DEAP_SEX_AGE_RT_Q |>
filter(sex == "SEX_T",
classif1 == "AGE_AGGREGATE_YGE15") |>
left_join(ref_area, by = "ref_area") |>
mutate(obs_value = round(obs_value, 1)) |>
group_by(ref_area, Ref_area) |>
arrange(time) |>
summarise(Nobs = n(),
`Year 1` = first(time),
`Inflation 1` = first(obs_value),
`Year 2` = last(time),
`Inflation 2` = last(obs_value)) |>
arrange(-Nobs) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Ref_area)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Canada, Japan, United States
Code
UNE_DEAP_SEX_AGE_RT_Q |>
filter(ref_area %in% c("USA", "JPN", "CAN"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_YGE15") |>
left_join(ref_area, by = "ref_area") |>
quarter_to_date() |>
mutate(obs_value = obs_value/100) |>
add_flag_color("Ref_area") |>
ggplot() + geom_line(aes(x = date, y = obs_value, color = color)) +
scale_color_identity() + add_flags + theme_minimal() +
xlab("") + ylab("Unemployment rate") +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.85)) +
scale_x_date(breaks = seq(1900, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
France, Germany, Spain
Code
UNE_DEAP_SEX_AGE_RT_Q |>
filter(ref_area %in% c("FRA", "DEU", "ESP"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_YGE15") |>
left_join(ref_area, by = "ref_area") |>
quarter_to_date() |>
mutate(obs_value = obs_value/100) |>
add_flag_color("Ref_area") |>
ggplot() + geom_line(aes(x = date, y = obs_value, color = color)) +
scale_color_identity() + add_flags + theme_minimal() +
xlab("") + ylab("Unemployment rate") +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.85)) +
scale_x_date(breaks = seq(1900, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Hong Kong, Italy, Korea
Code
UNE_DEAP_SEX_AGE_RT_Q |>
filter(ref_area %in% c("HKG", "ITA", "KOR"),
sex == "SEX_T",
classif1 == "AGE_AGGREGATE_YGE15") |>
left_join(ref_area, by = "ref_area") |>
quarter_to_date() |>
mutate(obs_value = obs_value/100) |>
add_flag_color("Ref_area") |>
ggplot() + geom_line(aes(x = date, y = obs_value, color = color)) +
scale_color_identity() + add_flags + theme_minimal() +
xlab("") + ylab("Unemployment rate") +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.85)) +
scale_x_date(breaks = seq(1900, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")