Even More JGB Yield Charts with R lattice

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

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

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, 
            ...)
    })

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

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

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")

# 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])

Replicate Me with code at Gist

Japan - JGB Yields–More Lattice Charts

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