Changing world, Changing JGB term structure

Writing the article "How much does "Beta" change depending on time?", I learned how to create an animation by using R language. Then, I would like to continue do that in this article.

In this article, I visualize time series of JGB term structure Ministry of Finance Japan publishes because I'm japanese!

You can download these data from here. You can get daily yield curve data, but I visualized these as monthly data for simplicity.

You can easily understand how JGB term structure behave and Japanese yield gradually go down.

The source code to create it is below.

(To run, you need to install xts, animation package, and ImageMagick)

library(xts)
#Source of JGB curve
source.jgb <- NULL
source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_1974-1979.csv"
source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_1980-1989.csv"
source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_1990-1999.csv"
source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_2000-2009.csv"
source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_2010.csv"
source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/jgbcm.csv"
#From Japanese era To ChristianEra
ToChristianEra <- function(x)
{
  era  <- substr(x, 1, 1)
  year <- as.numeric(substr(x, 2, nchar(x)))
  if(era == "H"){
    year <- year + 1988
  }else if(era == "S"){
    year <- year + 1925
  }
  as.character(year)
}
#Down load yield curve and convert to xts object
GetJGBYield <- function(source.url)
{
  jgb <- read.csv(source.url, stringsAsFactors = FALSE)
  #Extract date only
  jgb.day  <- strsplit(jgb[, 1], "\\.")
  #stop warning
  warn.old <- getOption("warn")
  options(warn = -1)
  #From Japanese era To ChristianEra
  jgb.day <- lapply(jgb.day, function(x)c(ToChristianEra(x[1]), x[2:length(x)]))
  #From date string to date object
  jgb[,  1] <- as.Date(sapply(jgb.day, function(x)Reduce(function(y,z)paste(y,z, sep="-"),x)))
  #Convert data from string to numeric
  jgb[, -1] <- apply(jgb[, -1], 2, as.numeric)
  options(warn = warn.old)
  as.xts(read.zoo(jgb))
}
#Down load JBG yield
jgb.list <- lapply(source.jgb, GetJGBYield)
#convert one xts object
jgb.xts <- Reduce(rbind, jgb.list)
#Interpolate(nearest value)
coredata(jgb.xts) <- na.locf(t(na.locf(t(coredata(jgb.xts)))))
#to monthly
jgb.xts <- jgb.xts[endpoints(jgb.xts, on="months",k = 1)]
 
#Label for x-axis
label.term <- paste(gsub("X", "", colnames(jgb.xts)), "Y", sep="")
#The range of y 
y.max <- c(min(jgb.xts), max(jgb.xts))
#plot one image
Snap <- function(val){
  term.structure <- coredata(val)
  index.date     <- index(val)
  par(xaxt="n")
  plot(t(term.structure),type="l",lwd=3, col = 2, xlab = "Term", ylab = "Rate", ylim = y.max)
  par(xaxt="s")
  axis(1, 1:length(label.term), label.term)
  text(0.5, y.max[2], as.character(index.date), pos = 4)
}
#save as animation
library(animation)
saveGIF({
  for(i in 1:nrow(jgb.xts)){Snap(jgb.xts[i])}
},interval = 0.005)

Created by Pretty R at inside-R.org