1-get-NATO-milex-data.R

setwd("~/Dropbox/projects/blog-posts/nato-spending")

library(WDI)
library(countrycode)
library(tidyverse)

Milit <- WDI(country="all", indicator=c("MS.MIL.XPND.GD.ZS", "MS.MIL.XPND.ZS","MS.MIL.TOTL.P1", "SP.POP.TOTL"), 
             start=1988, end=2015) %>%
  rename(milex = MS.MIL.XPND.GD.ZS, milgovexp = MS.MIL.XPND.ZS,
         milper = MS.MIL.TOTL.P1, tpop = SP.POP.TOTL) %>% tbl_df() %>%
  mutate(ccode = countrycode(iso2c, "iso2c", "cown"),
         ccode = ifelse(iso2c == "RS", 345, ccode),
         militarization = milper/tpop) %>%
  select(-iso2c) %>%
  arrange(ccode,year)

Milit %>%
  filter(ccode == 2 | ccode == 20 | ccode == 200 | (ccode >= 210 & ccode <= 220) |
           ccode == 230 | ccode == 235 | ccode == 255 | (ccode == 290 & year >= 1999) |
           (ccode == 310 & year >= 1999) | (ccode == 316 & year >= 1999) | 
           (ccode == 317 & year >= 2004) | ccode == 325 | (ccode == 339 & year >= 2009) |
           (ccode == 344 & year >= 2009) | (ccode == 349 & year >= 2004) | 
           ccode == 350 | (ccode == 355 & year >= 2004) | (ccode == 360 & year >= 2004) |
           (ccode == 366 & year >= 2004) | (ccode == 367 & year >= 2004) | 
           (ccode == 368 & year >= 2004) | (ccode >= 385 & ccode < 400) | 
           ccode == 640) -> NATO

theme_steve <- function() {
  theme_bw() +
    theme(panel.border = element_blank(), 
          plot.caption=element_text(hjust=1, size=9,
                                    margin=margin(t=10),
                                    face="italic"),
          plot.title=element_text(hjust=0, size=18,
                                  margin=margin(b=10),
                                  face="bold"),
          axis.title.y=element_text(size=12,hjust=1,
                                    face="italic"),
          axis.title.x=element_text(hjust=1, size=12, face="italic"))
  
}

NATO %>%
  filter(milex >= 2) -> meets2

Milit %>% filter(year == 2015) %>% summarize(med = median(milgovexp, na.rm=T)) -> medmilgovexp2015



ggplot(NATO, aes(year, milex)) + facet_wrap( ~ country, ncol=4) +
  geom_line() + geom_hline(yintercept = 2, linetype = "dashed") +
  scale_x_continuous(breaks=seq(1990, 2015, by=5)) + theme_steve() +
  xlab("") + ylab("") + ggtitle("NATO Military Expenditures as % of GDP, 1988-2015") +
  geom_ribbon(data=meets2, aes(x=year,ymax=milex),ymin=0, ymax=6, fill="red", alpha=0.3) +
  labs(caption="Military expenditure data come from SIPRI. Estonia and Poland approximate 2% though geom_ribbon in ggplot2 wants to connect the entire stretch. I'm working on it. Please don't yell at me. :-(")

ggsave(file="nato-milex-spending.png", width=13.33, height=9.77)

ggplot(NATO, aes(year, milgovexp)) + facet_wrap( ~ country, ncol=4) +
  geom_line() + geom_hline(yintercept = as.numeric(medmilgovexp2015[1,1]), linetype = "dashed") +
  scale_x_continuous(breaks=seq(1990, 2015, by=5)) + theme_steve() +
  xlab("") + ylab("") + ggtitle("NATO Military Expenditures as % of Central Government Expenditures, 1988-2015") +
#  geom_ribbon(data=meets2, aes(x=year,ymax=milex),ymin=0, ymax=6, fill="red", alpha=0.3) +
  labs(caption="Military expenditure data come from SIPRI. Dashed line communicates global median of military goverment expenditures for 2015.")

ggsave(file="nato-milex-gov-spending.png", width=13.33, height=9.77)