Even More JGB Yield Charts with R lattice

May 15, 2013
By

(This article was first published on Timely Portfolio, and kindly contributed to R-bloggers)

See the last post for all the details. I just could not help creating a couple more.

Variations on 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 <- p2 + layer(panel.abline(h = pretty(jgb.melt$value),
lty = 3))
p2
From TimelyPortfolio


jgb.xts.diff <- jgb.xts["2012::", ] - matrix(rep(jgb.xts["2012::",
][1, ], NROW(jgb.xts["2012::", ])), ncol = NCOL(jgb.xts),
byrow = TRUE)
jgb.diff.melt <- xtsMelt(jgb.xts.diff)
jgb.diff.melt$date <- as.Date(jgb.diff.melt$date)
jgb.diff.melt$value <- as.numeric(jgb.diff.melt$value)
jgb.diff.melt$indexname <- factor(jgb.diff.melt$indexname,
levels = colnames(jgb.xts))

p4 <- xyplot(value ~ date | indexname, data = jgb.diff.melt,
type = "h")

update(p2, ylim = c(min(jgb.diff.melt$value), max(jgb.melt$value) +
0.5)) + p4
From TimelyPortfolio

update(p2, ylim = c(min(jgb.diff.melt$value), max(jgb.melt$value) +
0.5), par.settings = list(axis.line = list(col = "gray70"))) +
update(p4, panel = function(x, y, col, ...) {
# do color scale from red(negative) to
# blue(positive)
cc.palette <- colorRampPalette(c(brewer.pal("Reds",
n = 9)[7], "white", brewer.pal("Blues",
n = 9)[7]))
cc.levpalette <- cc.palette(20)
cc.levels <- level.colors(y, at = do.breaks(c(-0.3,
0.3), 20), col.regions = cc.levpalette)
panel.xyplot(x = x, y = y, col = cc.levels,
...)
})
From TimelyPortfolio


p5 <- horizonplot(value ~ date | indexname, data = jgb.diff.melt,
layout = c(1, length(unique(jgb.diff.melt$indexname))),
scales = list(x = list(tck = c(1, 0))), xlab = NULL,
ylab = NULL)

p5

From TimelyPortfolio


update(p2, ylim = c(0, max(jgb.melt$value) + 0.5),
panel = panel.xyplot) + p5 + update(p2, ylim = c(0,
max(jgb.melt$value)))
From TimelyPortfolio

Variations on 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(400/(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")

p3 <- 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))

# make point rather than line
update(p3, type = "p")
From TimelyPortfolio

# make point with just most current curve as line
update(p3, type = "p") + xyplot(value ~ indexname,
data = jgb.melt[which(jgb.melt$date == max(jgb.melt$date)),
], type = "l", col = brewer.pal("Blues", n = 9)[7])
From TimelyPortfolio

Replicate Me with code at Gist

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