Unemployment rate - annual data - tipsun20
Data - Eurostat
Info
Data on employment
source | dataset | .html | .RData |
---|---|---|---|
bls | jt | 2024-11-12 | NA |
bls | la | 2024-11-12 | NA |
bls | ln | 2024-11-12 | NA |
eurostat | nama_10_a10_e | 2024-11-22 | 2024-11-21 |
eurostat | nama_10_a64_e | 2024-11-16 | 2024-11-22 |
eurostat | namq_10_a10_e | 2024-11-22 | 2024-11-22 |
eurostat | une_rt_m | 2024-11-21 | 2024-11-21 |
oecd | ALFS_EMP | 2024-04-16 | 2024-11-22 |
oecd | EPL_T | 2024-11-12 | 2023-12-10 |
oecd | LFS_SEXAGE_I_R | 2024-09-15 | 2024-04-15 |
oecd | STLABOUR | 2024-11-17 | 2024-11-17 |
DOWNLOAD_TIME
Code
tibble(DOWNLOAD_TIME = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/tipsun20.RData")$mtime)) %>%
print_table_conditional()
DOWNLOAD_TIME |
---|
2024-10-09 |
Last
Code
%>%
tipsun20 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2023 | 87 |
geo
Code
%>%
tipsun20 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="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
age
Code
%>%
tipsun20 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 | 445 |
Y15-74 | From 15 to 74 years | 445 |
Y25-74 | From 25 to 74 years | 445 |
France, Germany, Italy, Spain, Portugal
Code
%>%
tipsun20 filter(geo %in% c("FR", "DE", "PT", "ES", "IT"),
== "Y25-74") %>%
age %>%
year_to_date left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100) %>%
+ geom_line(aes(x = date, y = values, color = color)) + theme_minimal() +
ggplot scale_color_identity() + add_5flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-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))
France, Germany, Portugal
Code
%>%
tipsun20 filter(geo %in% c("FR", "DE", "PT"),
== "Y25-74") %>%
age %>%
year_to_date left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100) %>%
+ geom_line(aes(x = date, y = values, color = color)) + theme_minimal() +
ggplot scale_color_identity() + add_3flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-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))
Poland, Hungary, Slovenia
Code
%>%
tipsun20 filter(geo %in% c("PL", "HU", "SI"),
== "Y25-74") %>%
age %>%
year_to_date left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(values = values/100) %>%
+ geom_line(aes(x = date, y = values, color = color)) + theme_minimal() +
ggplot scale_color_identity() + add_3flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-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))