Sex Ratio in Europe based on code from https://jschoeley.github.io/2018/07/03/bubble-grid_vs_choropleth.html Norway has a missing spot in future combine Nord Trøndelag and Sort Trønderlag to fill this out.

age15to64.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
library(RColorBrewer)

euro_pop <-
  get_eurostat('demo_r_pjanaggr3', stringsAsFactors = FALSE) %>%
  filter(year(time) %in% c(2016, 2017),
         str_length(geo) == 5, # NUTS-3
         age == 'Y15-64'
        )
         
 # 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(-Inf,-10,-5, -3,-2, -1,0,1, 2, 3, 5,10, Inf)#7
labels = c('10%+ more males ',
           '5-10% more males', '3-5% more males', '2% more males','1% more males','equal','1% more females ','2% more females ',
           '2-3% more females', '3-5% more females',
           '5-10% more females','10%+ more females')#6 one less

cols <-rev(brewer.pal(11,"RdBu"))

# then remove the first darkest blue that you don't need
#palette = 'RdBu'
cols <- cols[2:11]

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=c('#001748','#053061', '#2166AC', '#4393C3', '#92C5DE',  '#808080','#FDDBC7', '#F4A582', '#D6604D', '#B2182B', '#67001F','#4E0006'),
    name = "Sex Ratio 2017 age 15-64",
    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('EUage15-64.png')

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')#TODO I should have a filter in here for date 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)
bels = 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_brewer(name = 'Sex Ratio 2017',
  #                  type = 'div', palette = 'RdBu',direction=-1,#5,
  #                  breaks = labels, # to omit the NA level
  #                  guide = guide_legend(reverse = TRUE)) +
#improvement from https://gist.github.com/kiernann/d7173f18f81ce753c95290f8ca1186e0
  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)

Issue with Norway grey region is Nord Trøndelag and Sør Trøndelag
have become Trøndelag county
#NO061 and NO062 have become NO060
#get values out and put them in by hand
Nor2<-subset(euro_pop, grepl("NO06", geo))
Nor2
#looks like

unit	sex	age	geo	time	values
NR	F	Y15-64	NO061	2017-01-01	101957
NR	F	Y15-64	NO062	2017-01-01	41910

#use those figures to make your new row. Here these are M/F numbers
de<-data.frame("NR","F","Y15-64","NO060","2017-01-01",143867)
df<-data.frame("NR","M","Y15-64","NO060","2017-01-01",153519)

names(de)<-c("unit","sex","age","geo","time","values")
names(df)<-c("unit","sex","age","geo","time","values")

euro_pop <- rbind(euro_pop, de)
euro_pop <- rbind(euro_pop, df)