National consumer price index (CPI) by COICOP, percentage change from previous year (%) - CPI_NCYR_COI_RT_M

Data - ILO

Author

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ref_area

Code

CPI_NCYR_COI_RT_M %>%
  left_join(ref_area, by = "ref_area") %>%
  group_by(ref_area, Ref_area) %>%
  summarise(Nobs = n()) %>%
  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 .}

indicator

Code

CPI_NCYR_COI_RT_M %>%
  left_join(indicator, by = "indicator") %>%
  group_by(indicator, Indicator) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) print_table(.) else .}

indicator	Indicator	Nobs
CPI_NCYR_COI_RT	National consumer price index (CPI) by COICOP, percentage change from previous year (%)	535012

classif1

Code

CPI_NCYR_COI_RT_M %>%
  left_join(classif1, by = "classif1") %>%
  group_by(classif1, Classif1) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) print_table(.) else .}

classif1	Classif1	Nobs
COI_COICOP_CP01	COICOP2012: 1. Food and non-alcoholic beverages	49981
COI_COICOP_CP01T12	COICOP2012: General - Individual consumption expenditure of households	64910
COI_COICOP_CP02	COICOP2012: 2. Alcoholic beverages, tobacco and narcotics	38316
COI_COICOP_CP03	COICOP2012: 3. Clothing and footwear	39398
COI_COICOP_CP04	COICOP2012: 4. Housing, water, electricity, gas and other fuels	39488
COI_COICOP_CP05	COICOP2012: 5. Furnishings, household equipment and routine household maintenance	39097
COI_COICOP_CP06	COICOP2012: 6. Health	38695
COI_COICOP_CP07	COICOP2012: 7. Transport	39445
COI_COICOP_CP08	COICOP2012: 8. Communication	35368
COI_COICOP_CP09	COICOP2012: 9. Recreation and culture	38815
COI_COICOP_CP10	COICOP2012: 10. Education	35770
COI_COICOP_CP11	COICOP2012: 11. Restaurants and hotels	36764
COI_COICOP_CP12	COICOP2012: 12. Miscellaneous goods and services	38965

source

Code

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