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r lattice plots of JGB yields

Blog post:

http://timelyportfolio.blogspot.com/2013/05/japan-jgb-yieldsmore-lattice-charts.html

Replicate:

Download the .rmd and use knitr to create the html.

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<body>
<h2>JGB Yields - More Charts with Lattice</h2>

<p>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 <a href="https://ramnathv.github.io/rCharts">rCharts</a> give me the power to see.  I thought some of my recent lattice charts might help or interest some folks.</p>

<h3>Get and Transform the Data</h3>

<pre><code class="r"># get Japan yield data from the Ministry of
# Finance Japan data goes back to 1974

require(xts)
# require(clickme)
require(latticeExtra)

url &lt;- &quot;https://www.mof.go.jp/english/jgbs/reference/interest_rate/&quot;
filenames &lt;- paste(&quot;jgbcme&quot;, c(&quot;&quot;, &quot;_2010&quot;, &quot;_2000-2009&quot;, 
    &quot;_1990-1999&quot;, &quot;_1980-1989&quot;, &quot;_1974-1979&quot;), &quot;.csv&quot;, 
    sep = &quot;&quot;)

# load all data and combine into one jgb
# data.frame
jgb &lt;- read.csv(paste(url, filenames[1], sep = &quot;&quot;), 
    stringsAsFactors = FALSE)
for (i in 2:length(filenames)) {
    jgb &lt;- rbind(jgb, read.csv(paste(url, &quot;/historical/&quot;, 
        filenames[i], sep = &quot;&quot;), stringsAsFactors = FALSE))
}

# now clean up the jgb data.frame to make a jgb
# xts
jgb.xts &lt;- as.xts(data.matrix(jgb[, 2:NCOL(jgb)]), 
    order.by = as.Date(jgb[, 1]))
colnames(jgb.xts) &lt;- paste0(gsub(&quot;X&quot;, &quot;JGB&quot;, colnames(jgb.xts)), 
    &quot;Y&quot;)

# get Yen from the Fed
# getSymbols(&#39;DEXJPUS&#39;,src=&#39;FRED&#39;)

xtsMelt &lt;- 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 &lt;- data.frame(cbind(format(index(data), 
        &quot;%Y-%m-%d&quot;), coredata(data)))
    colnames(data.df)[1] = &quot;date&quot;
    data.melt &lt;- melt(data.df, id.vars = 1, stringsAsFactors = FALSE)
    colnames(data.melt) &lt;- c(&quot;date&quot;, &quot;indexname&quot;, &quot;value&quot;)
    # remove periods from indexnames to prevent
    # javascript confusion these . usually come from
    # spaces in the colnames when melted
    data.melt[, &quot;indexname&quot;] &lt;- apply(matrix(data.melt[, 
        &quot;indexname&quot;]), 2, gsub, pattern = &quot;[.]&quot;, replacement = &quot;&quot;)
    return(data.melt)
    # return(df2json(na.omit(data.melt)))

}

jgb.melt &lt;- xtsMelt(jgb.xts[&quot;2012::&quot;, ])
jgb.melt$date &lt;- as.Date(jgb.melt$date)
jgb.melt$value &lt;- as.numeric(jgb.melt$value)
jgb.melt$indexname &lt;- factor(jgb.melt$indexname, levels = colnames(jgb.xts))
</code></pre>

<h3>Favorite Plot - Time Series Line of JGB Yields by Maturity</h3>

<pre><code class="r">p2 &lt;- xyplot(value ~ date | indexname, data = jgb.melt, 
    type = &quot;l&quot;, 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 = &quot;Yield&quot;, main = &quot;JGB Yields by Maturity Since Jan 2012&quot;)
p2 + layer(panel.abline(h = pretty(jgb.melt$value), 
    lty = 3))
</code></pre>

<h3>Good Chart but Not a Favorite</h3>

<p>As you can tell, I did not spend a lot of time formatting this one.</p>

<pre><code class="r">p1 &lt;- xyplot(value ~ date | indexname, data = jgb.melt, 
    type = &quot;l&quot;)
p1
</code></pre>

<h3>Another Favorite - Yield Curve Evolution with Opacity Color Scale</h3>

<pre><code class="r"># add alpha to colors
addalpha &lt;- function(alpha = 180, cols) {
    rgbcomp &lt;- col2rgb(cols)
    rgbcomp[4] &lt;- alpha
    return(rgb(rgbcomp[1], rgbcomp[2], rgbcomp[3], 
        rgbcomp[4], maxColorValue = 255))
}

p3 &lt;- xyplot(value ~ indexname, group = date, data = jgb.melt, 
    type = &quot;l&quot;, lwd = 2, col = sapply(255/(as.numeric(Sys.Date() - 
        jgb.melt$date) + 1), FUN = addalpha, cols = brewer.pal(&quot;Blues&quot;, 
        n = 9)[7]), main = &quot;JGB Yield Curve Evolution Since Jan 2012&quot;)

update(asTheEconomist(p3), scales = list(x = list(cex = 0.7))) + 
    layer(panel.text(x = length(levels(jgb.melt$indexname)), 
        y = 0.15, label = &quot;source: Japanese Ministry of Finance&quot;, 
        col = &quot;gray70&quot;, font = 3, cex = 0.8, adj = 1))
</code></pre>

<h3>Replicate Me</h3>

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