Create a plot showing sex ratios at all ages for all countries from Human Mortality Database

HMD_all_countries_sex_ratio.R

################################################################################
#
# Sex ratios 11-10-2016
# Sex ratios VS age in all HMD countries
# Ilya Kashnitsky, [email protected]
#
################################################################################

# load required packages
require(dplyr) # version 0.5.0
require(readr) # version 1.0.0
require(tidyr) # version 0.6.0
require(ggplot2) # version 2.1.0
require(ggthemes) # version 3.2.0
library(extrafont) # version 0.17
library(HMDHFDplus) # version 1.1.8


# Download Exposures to risk
# read HMD data directly into R
# please note! the arguments "ik_user_hmd" and "ik_pass_hmd" are my login credidantials
# at the website of Human Mortality Database, which are stored locally at my computer. 
# In order to access the data, you need to create an account at
# http://www.mortality.org/
# and provide your own credidantials to the `readHMDweb()` function

country <- getHMDcountries()

exposures <- list()
for (i in 1: length(country)) {
        cnt <- country[i]
        exposures[[paste0(cnt,'_exp')]] <- readHMDweb(cnt,"Exposures_1x1",ik_user_hmd,ik_pass_hmd)
        # to see the progress
        print(paste(i,'out of',length(country)))
}


# plot Sex Ratio VS age as lines for all countries

sr_age <- list()

for (i in 1:length(exposures)) {
        di <- exposures[[i]]
        sr_agei <- di %>% select(Year,Age,Female,Male) %>% 
                filter(Year %in% 2012) %>%
                select(-Year) %>%
                transmute(country = gsub('_exp','',names(exposures)[i]),
                          age = Age, sr_age = Male / Female *100)
        sr_age[[i]] <- sr_agei
}
sr_age <- bind_rows(sr_age)

# remove optional populations
sr_age <- sr_age %>% filter(!country %in% c("FRACNP","DEUTE","DEUTW","GBRCENW","GBR_NP"))

# summarize all ages older than 90 (to jerky)
sr_age_90 <- sr_age %>% filter(age %in% 90:110) %>% 
        group_by(country) %>% summarise(sr_age = mean(sr_age, na.rm = T)) %>%
        ungroup() %>% transmute(country, age=90, sr_age)

sr_age_0090 <- bind_rows(sr_age %>% filter(!age %in% 90:110), sr_age_90)


# finaly - plot
gg_sr_age <- ggplot(sr_age_0090, aes(age, sr_age, color = country, group = country))+
        geom_hline(yintercept = 1, color = 'grey50', size = 1)+
        geom_line(size = 1)+
        scale_y_continuous(limits = c(0,120), expand = c(0,0), breaks = seq(0,120,20))+
        scale_x_continuous(limits = c(0,90), expand = c(0,0), breaks = seq(0,80,20))+
        xlab('Age')+
        ylab('Sex ratio, males per 100 females')+
        facet_wrap(~country,ncol=6)+
        theme_few(base_family = "DejaVu Sans Condensed", base_size = 15)+
        theme(legend.position='none')

ggsave('sr_lines.png', gg_sr_age, width = 8, height = 12, type="cairo-png") 

@timriffe Thanks a lot for HMDHFDplus