Japan Trade by Geographic Region

To further the analysis presented in Japanese Trade and the Yen, I thought I would take the more granular data provided by the Japanese Ministry of Finance on trade by geographic region.  Of course, I will use R to read, analyze, and plot the .csv data.  Below you will see there are only two remaining regions where Japan has a trade surplus (North and South America), and those surpluses are declining very rapidly.

From TimelyPortfolio

R code from GIST:

	#further explore Japanese trade data by region
	#website is http://www.customs.go.jp/toukei/suii/html/time_e.htm
	#format is http://www.customs.go.jp/toukei/suii/html/data/d42ma001.csv
	#and the filename increments from 001 to 008 for the geographic areas
	require(quantmod)
	require(reshape)
	require(lattice)
	require(latticeExtra)
	numgeog <- 8
	urls <- paste("http://www.customs.go.jp/toukei/suii/html/data/d42ma00",
	1:numgeog,".csv",sep="")
	geogdata <- vector("list",numgeog)
	for (i in 1:numgeog) {
	#read the csv file with geographic area trade data
	geogdata[[i]] <- read.csv(urls[i],stringsAsFactors=FALSE,skip=3)
	#get date in usable form yyyy-mm-dd
	origdate <- geogdata[[i]][,1]
	xtsdate <- as.Date(paste(substr(origdate,0,4),substr(origdate,6,8),"01",sep="-"))
	#get one xts with summay geographic area data
	ifelse(i==1,geogdata.xts<-merge(as.xts(geogdata[[i]][,2:3],order.by=xtsdate),as.xts(geogdata[[i]][,2]-geogdata[[i]][,3],order.by=xtsdate)),
	geogdata.xts<-merge(geogdata.xts,merge(as.xts(geogdata[[i]][,2:3],order.by=xtsdate),as.xts(geogdata[[i]][,2]-geogdata[[i]][,3],order.by=xtsdate))))
	}
	#set up labels for geographic areas
	geogarea <- c("Asia","Oceania","NorthAmerica","SouthAmerica","WestEurope","EastEurope","MiddleEast","Africa")
	#name columns by appending geographic area with "Exports" and "Imports"
	colnames(geogdata.xts)[seq(from=1,to=numgeog*3,by=3)]<-paste(geogarea,"Exports",sep="")
	colnames(geogdata.xts)[seq(from=2,to=numgeog*3,by=3)]<-paste(geogarea,"Imports",sep="")
	colnames(geogdata.xts)[seq(from=3,to=numgeog*3,by=3)]<-paste(geogarea,"SurpDef",sep="")
	#melt data for lattice plotting
	geogdata.melt <- melt(as.data.frame(cbind(index(geogdata.xts),coredata(geogdata.xts)),
	stringsAsFactors=FALSE),
	id.vars=1)
	#get melted data into form -- date, geographic area, direction of trade, and amount
	geogdata.melt <- as.data.frame(cbind(format(index(geogdata.xts),"%Y-%m-%d"),
	substr(geogdata.melt[,2],1,nchar(as.vector(geogdata.melt[,2]))-7),
	substr(geogdata.melt[,2],nchar(as.vector(geogdata.melt[,2]))-6,nchar(as.vector(geogdata.melt[,2]))),
	geogdata.melt[,3]),stringsAsFactors=FALSE)
	#convert to Date object
	geogdata.melt[,1] <- as.Date(geogdata.melt[,1])
	#get strings as numeric since I do not know any better way to preserve numeric
	geogdata.melt[,4] <- as.numeric(geogdata.melt[,4])
	#data comes with 0s filling remainder of months left in the year
	geogdata.melt <- geogdata.melt[which(geogdata.melt[,4]!=0),]
	colnames(geogdata.melt)<-c("Date","Area","Direction","Amount")
	#add alpha to colors
	addalpha <- function(cols,alpha=180) {
	rgbcomp <- col2rgb(cols)
	rgbcomp[4] <- alpha
	return(rgb(rgbcomp[1],rgbcomp[2],rgbcomp[3],rgbcomp[4],maxColorValue=255))
	}
	xyplot(Amount~Date|factor(Area),groups=Direction,data=geogdata.melt,
	type="l",lwd=0.5,
	layout=c(8,1),
	col=apply(as.matrix(c("steelblue4","steelblue2","gray70")),MARGIN=1,addalpha),
	par.settings = theEconomist.theme(box = "transparent"),
	scales=list(x=list(alternating=c(1,0),draw=TRUE,
	at=as.Date(index(geogdata.xts)[c(1,NROW(geogdata.xts))]),
	labels=format(as.Date(index(geogdata.xts)[c(1,NROW(geogdata.xts))]),"%Y"))),
	auto.key = list(space = "right",col=apply(as.matrix(c("steelblue4","steelblue2","gray70")),MARGIN=1,addalpha,alpha=210),
	points=FALSE))+
	layer(panel.abline(h=0, col="black"))

view raw japan trade by region.r hosted with ❤ by GitHub