(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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