LFS by sex and age - composition

Data - OECD

Author

SEX

Code

LFS_SEXAGE_I_C %>%
  left_join(LFS_SEXAGE_I_C_var$SEX, by = "SEX") %>%
  group_by(SEX, Sex) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SEX	Sex	Nobs
MW	All persons	104552
MEN	Men	104280
WOMEN	Women	103509

AGE

Code

LFS_SEXAGE_I_C %>%
  left_join(LFS_SEXAGE_I_C_var$AGE, by = "AGE") %>%
  group_by(AGE, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SERIES

Code

LFS_SEXAGE_I_C %>%
  left_join(LFS_SEXAGE_I_C_var$SERIES, by = "SERIES") %>%
  group_by(SERIES, Series) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SERIES	Series	Nobs
PER_P	Share of population	80553
PER_E	Share of employment	79701
PER_L	Share of labour force	79698
PER_U	Share of unemployment	72389

FREQ

Code

LFS_SEXAGE_I_C %>%
  left_join(LFS_SEXAGE_I_C_var$FREQUENCY, by = "FREQUENCY") %>%
  group_by(FREQUENCY, Frequency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

FREQUENCY	Frequency	Nobs
A	Annual	312341

COUNTRY

Code

LFS_SEXAGE_I_C %>%
  left_join(LFS_SEXAGE_I_C_var$COUNTRY, by = "COUNTRY") %>%
  group_by(COUNTRY, Country) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         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 .}

obsTime

Code

LFS_SEXAGE_I_C %>%
  group_by(obsTime) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(obsTime)) %>%
  print_table_conditional()