japan trade by region.r

japan trade by region.r

#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"))