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

Data - ILO

ref_area

Code
CPI_NCYR_COI_RT_A %>%
  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_A %>%
  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 (%) 38221

classif1

Code
CPI_NCYR_COI_RT_A %>%
  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 3681
COI_COICOP_CP01T12 COICOP2012: General - Individual consumption expenditure of households 4847
COI_COICOP_CP02 COICOP2012: 2. Alcoholic beverages, tobacco and narcotics 2721
COI_COICOP_CP03 COICOP2012: 3. Clothing and footwear 2768
COI_COICOP_CP04 COICOP2012: 4. Housing, water, electricity, gas and other fuels 2799
COI_COICOP_CP05 COICOP2012: 5. Furnishings, household equipment and routine household maintenance 2746
COI_COICOP_CP06 COICOP2012: 6. Health 2744
COI_COICOP_CP07 COICOP2012: 7. Transport 2770
COI_COICOP_CP08 COICOP2012: 8. Communication 2563
COI_COICOP_CP09 COICOP2012: 9. Recreation and culture 2731
COI_COICOP_CP10 COICOP2012: 10. Education 2558
COI_COICOP_CP11 COICOP2012: 11. Restaurants and hotels 2554
COI_COICOP_CP12 COICOP2012: 12. Miscellaneous goods and services 2739

source

Code
CPI_NCYR_COI_RT_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 .}

Inflation: DEU, FRA, ITA

Code
CPI_NCYR_COI_RT_A %>%
  filter(ref_area %in% c("FRA", "DEU", "ITA"),
         time == 2019) %>%
  left_join(classif1, by = "classif1") %>%
  select(ref_area, classif1, Classif1, obs_value) %>%
  spread(ref_area, obs_value) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}