Unemployment by sex and age (thousands) - UNE_TUNE_SEX_AGE_NB_A

Data - ILO

Author

classif1

Code

UNE_TUNE_SEX_AGE_NB_A %>%
  left_join(classif1, by = "classif1") %>%
  group_by(classif1, Classif1) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

source

Code

UNE_TUNE_SEX_AGE_NB_A %>%
  left_join(source, by = "source") %>%
  group_by(source, Source) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

sex

Code

UNE_TUNE_SEX_AGE_NB_A %>%
  left_join(sex, by = "sex") %>%
  group_by(sex, Sex) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

indicator

Code

UNE_TUNE_SEX_AGE_NB_A %>%
  left_join(indicator, by = "indicator") %>%
  group_by(indicator, Indicator) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Ex 1: Nombre de chômeurs

Code

UNE_TUNE_SEX_AGE_NB_A %>%
  filter(classif1 == "AGE_YTHADULT_YGE15",
         sex == "SEX_T",
         time == 2018) %>%
  mutate(value = round(obs_value)) %>%
  arrange(-time) %>%
  select(iso2c = ref_area, value) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}