Sex Ratio in Europe based on code from https://jschoeley.github.io/2018/07/03/bubble-grid_vs_choropleth.html

sexratio.R

library(eurostat)      # eurostat data
library(rnaturalearth) # worldwide map data
library(tidyverse)     # tidy data transformation
library(lubridate)     # date and time support
library(sf)            # simple features GIS

euro_pop <-
  get_eurostat('demo_r_pjanaggr3', stringsAsFactors = FALSE) %>%
  filter(
    str_length(geo) == 5, # NUTS-3
    age == 'TOTAL')#I should have a filter in here for data as well

# download geospatial data for NUTS-3 regions
euro_nuts3_sf <-
  get_eurostat_geospatial(output_class = 'sf',
                          resolution = '60', nuts_level = 3) %>%
  st_transform(crs = 3035)

# download geospatial data for European and Asian countries
eura <-
  ne_countries(continent = c('europe', 'asia'), returnclass = 'sf') %>%
  st_transform(crs = 3035)

# calculate difference in absolute population numbers of male and female
euro_sex_diff <-
  euro_pop %>%
  filter(sex %in% c('M', 'F')) %>%
  spread(sex, values) %>%
  mutate(sex_diff = ((`F` - `M`)/(`F` + `M`))*100) %>%
  drop_na()


#find most skewed area 
euro_sex_diff[which.max(euro_sex_diff$sex_diff),]

# choropleth-map
breaks = c(-16, -5,-2, -1,0,1, 2, 3, 5,8, Inf)
labels = c('5%+ more males ',
           '2 to 5% more males', '2% more males', '1% more males','equal','1% more females ',
           '2 to 3% more females', '3 to 5% more females',
           '5-8% extra females','8%+ extra females')#one less than breaks as this is the space between the breaks
plot_choropleth <-
  euro_nuts3_sf %>%
  left_join(y = euro_sex_diff, by = c('id' = 'geo')) %>%
  ggplot() +
  geom_sf(data = eura, color = 'white', fill = 'grey95') +
  geom_sf(aes(fill = cut(sex_diff, breaks, labels)),
          color = 'white', lwd = 0.1) +
  coord_sf(xlim = c(2.5e6, 7e6), ylim = c(1.35e6, 5.55e6), datum = NA) +
  scale_fill_manual(
    values = rev(RColorBrewer::brewer.pal(n = 11, name = "RdBu")[1:10]),
    name = "Sex Ratio 2017",
    breaks = labels, # to omit the NA level
    guide = guide_legend(reverse = TRUE)
  ) +
  theme_void() +
  theme(legend.position = c(0.83, 0.7)) +
  labs(caption = 'Data: Eurostat')

ggsave('EUGender2.png', width=10, height=10)

There is an issue in Norway is two regions merged. I figured out how to show this data
https://gist.github.com/cavedave/eeb7b7110d029fec0f158305f24ece57#gistcomment-3047007
I also figured out how to get the data for North Macedonia and Serbia though it is not in Eurostat.