daily_internet_users data set is created by this code, which is not evaluated here:
Simply downloading a regional statistics from the Eurostat data warehouse and placing the observation on the same map does not work. If you look into the data, you may realize that the geo codes of French, Lithuanian or Hungarian regions, to name a few, do not match in the years 2012 and 2018.
Let’s have a look at these countries in two years, using the helper function
get_country_code. The year 2012 is coded with the
NUTS2010 typology and the year 2018 with the
NUTS2016 typology. Then we use the
valideate_nuts_region function with the default
NUTS2016 (which is currently valid in the European Union) and the obsolete
test <- daily_internet_users %>% mutate ( country_code = get_country_code(geo = geo) ) %>% dplyr::filter ( time %in% c(2012, 2018), country_code %in% c("FR", "HU", "LT")) %>% mutate ( time = paste0("Y", time )) %>% pivot_wider (., names_from ="time", values_from = "values") %>% validate_nuts_regions() %>% # default year the current valid 2016 validate_nuts_regions(., nuts_year = 2010 )
The following NUTS regions codes are not valid in the 2010 definition. These sub-national divisions were defined in 2013 or 2016. Some of these regional boundaries did not change, but got new codes after altering the administrative divisions of France. Some of the seemingly missing 2012 data can be found under different codes.
And there are two regions that are not valid in 2016, because these typologies were changed. Vilnius and Budapest, two big cities, were detached from their larger containing regional units.
Especially in the case of Budapest and Central Hungary, the comparative data can be produced for different boundary definitions, because the boundary change was simple. (Budapest was removed from Central Hungary.) In Lithuania, the change was not more complex, but unfortunately it cut through a far less rarely used typology level,
NUTS3. While the change is simple, the replacement data is usually not published.