• CLDR website: http://cldr.unicode.org/

library(eurostat)
library(dplyr)
library(ggplot2)
dat <- get_eurostat("ilc_peps01", time_format = "raw", stringsAsFactors = FALSE) %>% 
  dplyr::filter(time == 2014, 
                age == "TOTAL",
                sex == "T", 
                unit == "PC") 

## If you want only label the geo variable
# dat$geo_lab <- label_eurostat(dat$geo, countrycode = "cldr.short.ru", dic = "geo")
dat <- label_eurostat(dat, countrycode = "cldr.short.fi", dic = "geo", code = "geo")


# Download geospatial data from GISCO
geodata <- get_eurostat_geospatial(resolution = "60", nuts_level = "0", year = 2013)
geodata <- rename(geodata, geo_code = geo)

# merge with attribute data with geodata
map_data <- inner_join(geodata, dat)

labdat <- bind_cols(map_data %>% sf::st_set_geometry(NULL) %>% select(geo,values), 
                   as_data_frame(sf::st_coordinates(sf::st_centroid(map_data))))

ggplot(data=map_data) + 
  geom_sf(aes(fill=values),color="dim grey", size=.1) + 
  labs(title="People at risk of poverty or social exclusion in 2014",
       fill = "population share (%)",
       caption="(C) EuroGeographics for the administrative boundaries 
                Map produced in R with a help from Eurostat-package <github.com/ropengov/eurostat/>") +
  theme_light() +
  geom_label(data = labdat, aes(x = X, y = Y, label = geo, fill = values), color = "white", alpha = .6, show.legend = FALSE) +
  coord_sf(xlim=c(-12,44), ylim=c(35,70))