# This code is based on the replication material of
# Mitchell et al. (2020): "What We Know (and Could Know) About 
# International Environmental Agreements" doi:10.1162/glep_a_00544
# made available under 
# https://iea.uoregon.edu/ieadb_article_online_appendix
# iea_replication/Stata13/iea.dat
# converted to csv using R library "haven"

# read data
ieas <- read.csv("iea.csv",as.is=TRUE)

# use only multilateral agreements (as in ieadb-graph.do)
ieas <- subset(ieas,inclusion == "MEA")

# create subject factor and translate subject names
ieas$subj_code <- factor(ieas$subj_code)
levels(ieas$subj_code)<-c("climate","energy","freshwater","habitat","human\nsphere","other","pollution","species")

# create a decennial factor and corresponding labels
bydecLabels <- c("pre\n1950", paste0((195:201)*10,"\n–\n",(195:201)*10+9))
bydec <- cut(ieas$tsig_yr, c(0,(195:202)*10), bydecLabels ,include.lowest=TRUE)

# create table of percent shares by subject and decennium
bySubjByDec <- table(ieas$subj_code,factor(bydec))
for (i in 1:ncol(bySubjByDec)) {
  bySubjByDec[,i] = 100 * bySubjByDec[,i] / sum(bySubjByDec[,i])
}

# plot
# allow placement in margings and widen margins (for legend and data attribution)
par(xpd=TRUE,mar=c(5, 4, 4, 7) + 0.1)

barplot(bySubjByDec,col=rainbow(8),
        main="Signed multilateral environmental agreements",
        xlab="decade",ylab="share, in %",
        las=1,lheight=0.5,
        cex.names=0.75,cex.axis=0.75, mgp=c(3,1.5,0))
legend(10,100, rev(rownames(bySubjByDec)), fill=rev(rainbow(8)), cex=0.75)
text(10,-25,"data:\nRonald B. Mitchell,\n2002–2020, IEADB",cex=0.75,adj=0)

par(xpd=FALSE, mar=c(5, 4, 4, 2) + 0.1)