Harmonised unemployment (1 000) - monthly data - ei_lmhu_m

Data - Eurostat

Author

s_adj

Code

ei_lmhu_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	122553
NSA	Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data)	120150

indic

Code

ei_lmhu_m %>%
  left_join(indic, by = "indic") %>%
  group_by(indic, Indic) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

indic	Indic	Nobs
LM-UN-T-TOT	Unemployment according to ILO definition - Total	27505
LM-UN-F-TOT	Unemployment according to ILO definition - Females	27289
LM-UN-M-TOT	Unemployment according to ILO definition - Males	27289
LM-UN-T-GT25	Unemployment according to ILO definition - Over 25 years - Total	26980
LM-UN-T-LE25	Unemployment according to ILO definition - Under 25 years - Total	26940
LM-UN-F-GT25	Unemployment according to ILO definition - Over 25 years - Females	26764
LM-UN-M-GT25	Unemployment according to ILO definition - Over 25 years - Males	26764
LM-UN-F-LE25	Unemployment according to ILO definition - Under 25 years - Females	26586
LM-UN-M-LE25	Unemployment according to ILO definition - Under 25 years - Males	26586

geo

Code

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

unit

Code

ei_lmhu_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	242703