This blog is littered with posts about Japan. In one sentence, I think Japan presents opportunity and is a very interesting real-time test of much of my macro thinking. Proper visualization is absolutely essential for me to understand all of the dynamics. The R packages lattice and the new rCharts give me the power to see. I thought some of my recent lattice charts might help or interest some folks.
Get and Transform the Data
# get Japan yield data from the Ministry of
# Finance Japan data goes back to 1974
require(xts)
# require(clickme)
require(latticeExtra)
url <- "http://www.mof.go.jp/english/jgbs/reference/interest_rate/"
filenames <- paste("jgbcme", c("", "_2010", "_2000-2009",
"_1990-1999", "_1980-1989", "_1974-1979"), ".csv",
sep = "")
# load all data and combine into one jgb
# data.frame
jgb <- read.csv(paste(url, filenames[1], sep = ""),
stringsAsFactors = FALSE)
for (i in 2:length(filenames)) {
jgb <- rbind(jgb, read.csv(paste(url, "/historical/",
filenames[i], sep = ""), stringsAsFactors = FALSE))
}
# now clean up the jgb data.frame to make a jgb
# xts
jgb.xts <- as.xts(data.matrix(jgb[, 2:NCOL(jgb)]),
order.by = as.Date(jgb[, 1]))
colnames(jgb.xts) <- paste0(gsub("X", "JGB", colnames(jgb.xts)),
"Y")
# get Yen from the Fed
# getSymbols('DEXJPUS',src='FRED')
xtsMelt <- function(data) {
require(reshape2)
# translate xts to time series to json with date
# and data for this behavior will be more generic
# than the original data will not be transformed,
# so template.rmd will be changed to reflect
# convert to data frame
data.df <- data.frame(cbind(format(index(data),
"%Y-%m-%d"), coredata(data)))
colnames(data.df)[1] = "date"
data.melt <- melt(data.df, id.vars = 1, stringsAsFactors = FALSE)
colnames(data.melt) <- c("date", "indexname", "value")
# remove periods from indexnames to prevent
# javascript confusion these . usually come from
# spaces in the colnames when melted
data.melt[, "indexname"] <- apply(matrix(data.melt[,
"indexname"]), 2, gsub, pattern = "[.]", replacement = "")
return(data.melt)
# return(df2json(na.omit(data.melt)))
}
jgb.melt <- xtsMelt(jgb.xts["2012::", ])
jgb.melt$date <- as.Date(jgb.melt$date)
jgb.melt$value <- as.numeric(jgb.melt$value)
jgb.melt$indexname <- factor(jgb.melt$indexname, levels = colnames(jgb.xts))
Favorite Plot - Time Series Line of JGB Yields by Maturity
p2 <- xyplot(value ~ date | indexname, data = jgb.melt,
type = "l", layout = c(length(unique(jgb.melt$indexname)),
1), panel = function(x, y, ...) {
panel.abline(h = c(min(y), max(y)))
panel.xyplot(x = x, y = y, ...)
panel.text(x = x[length(x)/2], y = max(y),
labels = levels(jgb.melt$indexname)[panel.number()],
cex = 0.7, pos = 3)
}, scales = list(x = list(tck = c(1, 0), alternating = 1),
y = list(tck = c(1, 0), lwd = c(0, 1))), strip = FALSE,
par.settings = list(axis.line = list(col = 0)),
xlab = NULL, ylab = "Yield", main = "JGB Yields by Maturity Since Jan 2012")
p2 + layer(panel.abline(h = pretty(jgb.melt$value),
lty = 3))
Good Chart but Not a Favorite
As you can tell, I did not spend a lot of time formatting this one.
p1 <- xyplot(value ~ date | indexname, data = jgb.melt,
type = "l")
p1
Another Favorite - Yield Curve Evolution with Opacity Color Scale
# add alpha to colors
addalpha <- function(alpha = 180, cols) {
rgbcomp <- col2rgb(cols)
rgbcomp[4] <- alpha
return(rgb(rgbcomp[1], rgbcomp[2], rgbcomp[3],
rgbcomp[4], maxColorValue = 255))
}
p3 <- xyplot(value ~ indexname, group = date, data = jgb.melt,
type = "l", lwd = 2, col = sapply(255/(as.numeric(Sys.Date() -
jgb.melt$date) + 1), FUN = addalpha, cols = brewer.pal("Blues",
n = 9)[7]), main = "JGB Yield Curve Evolution Since Jan 2012")
update(asTheEconomist(p3), scales = list(x = list(cex = 0.7))) +
layer(panel.text(x = length(levels(jgb.melt$indexname)),
y = 0.15, label = "source: Japanese Ministry of Finance",
col = "gray70", font = 3, cex = 0.8, adj = 1))
Replicate Me
code at Gist
Hi, I'm doing the same thing for peruvian sovereign and corporate yield curves, however the timeframe I'm using is larger than the one you used in the example. In the last graph I get very pale colors, how can I make this pale colors stronger?
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