even more jgb yield charts with r and lattice

Readme.md

More JGB Yield Charts with R and Lattice

See the post

Replicate in r by downloading the .rmd and using knitr

even more jgb yield with r and lattice.Rmd

## JGB Yields - Just a Couple More

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

```{r message = FALSE, warning = FALSE, error = FALSE, echo = FALSE }
opts_chunk$set(message = FALSE, warning = FALSE, error = FALSE, fig.width = 9, fig.height = 4, tidy.opts = list( width.cutoff = 50 ))

### 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)
)
```

### Variations on Favorite Plot - Time Series Line of JGB Yields by Maturity
```{r}
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
```{r}
#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 = .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](https://gist.github.com/timelyportfolio/5586468)

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</head>

<body>
<h2>JGB Yields - Just a Couple More</h2>

<p>See <a href="">the last post for all the details</a>.  I just could not help creating a couple more.</p>

<h3>Variations on 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 &lt;- p2 + layer(panel.abline(h = pretty(jgb.melt$value), 
    lty = 3))
p2
</code></pre>

<p><img 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<pre><code class="r">
jgb.xts.diff &lt;- jgb.xts[&quot;2012::&quot;, ] - matrix(rep(jgb.xts[&quot;2012::&quot;, 
    ][1, ], NROW(jgb.xts[&quot;2012::&quot;, ])), ncol = NCOL(jgb.xts), 
    byrow = TRUE)
jgb.diff.melt &lt;- xtsMelt(jgb.xts.diff)
jgb.diff.melt$date &lt;- as.Date(jgb.diff.melt$date)
jgb.diff.melt$value &lt;- as.numeric(jgb.diff.melt$value)
jgb.diff.melt$indexname &lt;- factor(jgb.diff.melt$indexname, 
    levels = colnames(jgb.xts))

p4 &lt;- xyplot(value ~ date | indexname, data = jgb.diff.melt, 
    type = &quot;h&quot;)

update(p2, ylim = c(min(jgb.diff.melt$value), max(jgb.melt$value) + 
    0.5)) + p4
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-2"/> </p>

<pre><code class="r">
update(p2, ylim = c(min(jgb.diff.melt$value), max(jgb.melt$value) + 
    0.5), par.settings = list(axis.line = list(col = &quot;gray70&quot;))) + 
    update(p4, panel = function(x, y, col, ...) {
        # do color scale from red(negative) to
        # blue(positive)
        cc.palette &lt;- colorRampPalette(c(brewer.pal(&quot;Reds&quot;, 
            n = 9)[7], &quot;white&quot;, brewer.pal(&quot;Blues&quot;, 
            n = 9)[7]))
        cc.levpalette &lt;- cc.palette(20)
        cc.levels &lt;- 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, 
            ...)
    })
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-2"/> </p>

<pre><code class="r">

p5 &lt;- 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
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-2"/> </p>

<pre><code class="r">
update(p2, ylim = c(0, max(jgb.melt$value) + 0.5), 
    panel = panel.xyplot) + p5 + update(p2, ylim = c(0, 
    max(jgb.melt$value)))
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-2"/> </p>

<h3>Variations on 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(400/(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;)

p3 &lt;- 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))

# make point rather than line
update(p3, type = &quot;p&quot;)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-3"/> </p>

<pre><code class="r">
# make point with just most current curve as line
update(p3, type = &quot;p&quot;) + xyplot(value ~ indexname, 
    data = jgb.melt[which(jgb.melt$date == max(jgb.melt$date)), 
        ], type = &quot;l&quot;, col = brewer.pal(&quot;Blues&quot;, n = 9)[7])
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-3"/> </p>

<h3>Replicate Me with <a href="https://gist.github.com/timelyportfolio/5586468">code at Gist</a></h3>

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