# Even More JGB Yield Charts with R lattice

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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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