knitr Performance 3 with knitr and dprint.rnw

\documentclass{article}
%great guides at epslatex.pdf
%check miniplot for potential use

\usepackage{graphics}
\usepackage{caption}
%\usepackage{sidecap}
%\usepackage{textpos}
\usepackage[section]{placeins}


\title{Performance Report from knitr}
\author{Timely Portfolio}

\begin{document}

\maketitle

\SweaveOpts{concordance=TRUE}

<<eval=TRUE,echo=FALSE,results='hide',warning=FALSE>>=
#do requires and set up environment for reporting
require(ggplot2)
require(directlabels)
require(reshape2)
require(lattice)
require(latticeExtra)
require(xtable)
require(dprint)
require(quantmod)
require(PerformanceAnalytics)

#trying some new colors out
mycolors=c(brewer.pal(9,"Blues")[c(7,5)],brewer.pal(9,"Greens")[6])
#mycolors=c(brewer.pal(6,"Blues)[c(3,5)],"slategray4")

data(managers)
#get xts in df form so that we can melt with the reshape package
#will use just manager 1, sp500, and 10y treasury
managers <- managers[,c(1,8,9)]
#add 0 at beginning so cumulative returns start at 1
#also cumulative will match up datewise with returns
managers <- as.xts(rbind(rep(0,NCOL(managers)),coredata(managers)),
                   order.by=c(as.Date(format(index(managers)[1],"%Y-%m-01"))-1,index(managers)))
managers.df <- as.data.frame(cbind(index(managers),coredata(managers)),stringsAsFactors=FALSE)
#melt data which puts in a form that lattice and ggplot enjoy
managers.melt <- melt(managers.df,id.vars=1)
colnames(managers.melt) <- c("date","account","return")
managers.melt[,1] <- as.Date(managers.melt[,1])

#get cumulative returns starting at 1
managers.cumul <- as.xts(
  apply(managers+1,MARGIN=2,FUN=cumprod),
  #add end of first month to accommodate the 1 that we add
  order.by=index(managers))
managers.cumul.df <- as.data.frame(cbind(index(managers.cumul),
                                         coredata(managers.cumul)),
                                   stringsAsFactors=FALSE)
managers.cumul.melt <- melt(managers.cumul.df,id.vars=1)
colnames(managers.cumul.melt) <- c("date","account","return")
managers.cumul.melt[,1] <- as.Date(managers.cumul.melt[,1])
#this is tricky but necessary
#reorder accounts and indexes to preserve order with manager and then benchmarks
managers.cumul.melt$account <- factor(as.character(managers.cumul.melt$account),colnames(managers)[c(2,3,1)],ordered=TRUE)

#get rolling returns for 1y, 3y, 5y, since inception
trailing <- table.TrailingPeriods(managers[,c(2,3,1)], periods=c(12,36,60,NROW(managers)),FUNCS=c("Return.annualized"),funcs.names=c("return"))
trailing.df <- as.data.frame(cbind(c("1y","3y","5y",paste("Since Inception ",format(index(managers)[1],"%b %Y"),sep="")),
                                   c(rep("return",4)),  #will allow for multiple measures if we decide to include later
                                   coredata(trailing)),
                             stringsAsFactors=TRUE)
trailing.melt <- melt(trailing.df,id.vars=1:2)
colnames(trailing.melt) <- c("period","measure","account","value")
#this is tricky but necessary
#reorder the period so that they will be in correct chronological order
trailing.melt$period <- factor(as.character(trailing.melt$period),rev(c("1y","3y","5y",paste("Since Inception ",format(index(managers),"%b %Y"),sep=""))),ordered=TRUE)
#reorder accounts and indexes to preserve order with manager and then benchmarks
trailing.melt$account <- factor(as.character(trailing.melt$account),colnames(managers)[c(3,2,1)],ordered=TRUE)
@
%\newpage
\section{Overview}
This section should serve as a dashboard or executive summary for quick and easy access to the most informative risk and return measures.  Also, most marketing will have a text description of the strategy, process, objective, category, and potential investments of the product.

\newpage
\section{Returns}
Unfortunately, the Return section is generally the focus of the sales pitch and also is often the biggest concern for the prospect.  Although it easiest to sell on return in the short-term, long-term success requires much more focus on the graphs presented in the Overview and Risk sections.


\begin{figure}[!htb]
<<echo=FALSE,eval=TRUE,fig=TRUE,width=8,height=8,out.width='0.9\\linewidth',fig.keep='high'>>=
#while latticeExtra theEconomist.theme is beautiful
#I wanted to stretch my knowledge, so I will start from scratch

#example given to left justify strip
#http://maths.anu.edu.au/~johnm/r-book/xtras/boxcontrol.pdf
stripfun <- function(which.given, which.panel,factor.levels, ...){
  grid.rect(name = trellis.grobname("bg", type = "strip"), 
    gp = gpar(fill = "seashell3", col = "seashell3"))
  panel.text(x=0.10, y=0.5,
             lab = factor.levels[which.panel[which.given]],
             adj=0, font=3, cex=1.3)
  
  }

#heavily stripped and modified theEconomist.axis() from latticeExtra
timely.axis <- function (side = c("top", "bottom", "left", "right"), scales, 
                               components, ..., labels = c("default", "yes", "no"), ticks = c("default", 
                                                                                              "yes", "no"), line.col, noleft=TRUE) 
{
  side <- match.arg(side)
  if (side == "top") return()  

  labels <- match.arg(labels)
  ticks <- match.arg(ticks)

  if (side %in% c("left", "right")) {
    if (side == "right") {
      scales$draw=TRUE
      labels <- "no"
      ticks <- "no"
    }
    if (side == "left") {
        labels <- "yes"
        ticks <- "yes"
      }
    }

   axis.default(side, scales = scales, components = components, 
               ..., labels = labels, ticks = ticks, line.col = "black")
   if (side == "right" ) {#& panel.number()==1) {
        comp.list <- components[["right"]]
        if (!is.list(comp.list)) 
            comp.list <- components[["left"]]
        panel.refline(h = comp.list$ticks$at)   
    lims <- current.panel.limits()
    panel.abline(h = lims$y[1], col = "black")
  }
}


#set up ylimits to use for the two scales
ylimits<-c(pretty(c(min(managers.cumul.melt$return),
  max(managers.cumul.melt$return)),4),as.numeric(round(last(managers.cumul)[,order(last(managers.cumul))],2)))
ylabels<-c(ylimits[1:(length(ylimits)-3)],colnames(managers)[order(last(managers.cumul))])


returns <- list(
  bar = barchart(account~value|period,col=mycolors,data=trailing.melt,
         layout=c(1,4),
          box.ratio=100,
         origin=0,       
         reference=TRUE,
         par.settings=
           list(
                par.main.text = list(font = 1, cex=1.5, just = "left",x = grid::unit(5, "mm")),
                axis.line = list(col = NA)),
         scales=list(x=list(
           limits=c(0,max(trailing.melt$value)+0.025),  #snug labels right up to bars by setting to 0
           at=pretty(trailing.melt$value),
           labels=paste(round(100*as.numeric(pretty(trailing.melt$value)), 2), "%", sep="")
           )),
          xlab=NULL,
       axis = timely.axis,
         strip=stripfun,
         strip.left=FALSE,
        panel=function(...) {
             panel.barchart(...)  
             tmp <- list(...) 
             tmp <- data.frame(x=tmp$x, y=tmp$y) 
             # add text labels                        
             panel.text(x=tmp$x, y=tmp$y,                                 
                        label=sprintf("%1.2f%%", tmp$x * 100 ),                      
                        cex=1, col="black", pos=4) 
        },
         main="Annualized Returns"),
  
  cumulgrowth = 
    xyplot(return~date,groups=account,data=managers.cumul.melt,
#           col=mycolors,
           type="l",lwd=3,
           xlab=NULL,
           ylab=NULL,
        par.settings=
             list(
               par.main.text = list(font = 1, cex=1.5, just = "left",x = grid::unit(5, "mm")),
               axis.line = list(col = "transparent"),
               superpose.line=list(col=mycolors)),  #do this for direct.label
           
       scales=list(x=list(alternating=1,at=index(managers)[endpoints(managers,"years")],
        labels=format(index(managers)[endpoints(managers,"years")],"%Y")),
        y=list(alternating=3,at=ylimits,labels=ylabels)),
           
       axis=function (side = c("top", "bottom", "left", "right"), scales, 
                      components, ..., labels = c("default", "yes", "no"), ticks = c("default", 
                      "yes", "no"), line.col){
                         side <- match.arg(side)
                         labels <- match.arg(labels)
                         ticks <- match.arg(ticks)
                         axis.text <- trellis.par.get("axis.text")                         
                          if(side == "top") return()
                          if(side == "right") {
                            components[["right"]]<-components[["left"]]
                            components[["right"]]$ticks$at <- components[["right"]]$ticks$at[5:7]
                            components[["right"]]$labels$at <- components[["right"]]$labels$at[5:7]
                            components[["right"]]$labels$labels <- components[["right"]]$labels$labels[5:7]
                          }
                          if(side %in% c("bottom","right")){
                            axis.default(side, scales = scales, components = components, 
                                      ..., labels = labels, ticks = ticks, line.col = axis.text$col)
                            if (side == "right") {
                              comp.list <- components[["left"]]
                              panel.refline(h = comp.list$ticks$at[1:4])
                              lims <- current.panel.limits()
                              panel.abline(h = lims$y[1], col = axis.text$col)

                            comp.list.left<-components[["left"]]
                            comp.list.left$ticks$at <- components[["left"]]$ticks$at[1:4]
                            comp.list.left$labels$at <- components[["left"]]$labels$at[1:4]
                            comp.list.left$labels$labels <- components[["left"]]$labels$labels[1:4]
  						panel.axis(side="left",at=comp.list.left$ticks$at,outside=TRUE)

                            }                         
                          }
       },
           main=paste("Cumulative Growth Since Inception ",format(index(managers)[1],"%B %Y"),sep=""))
)


print(returns$cumulgrowth,position=c(0,0.6,1,1),more=TRUE)
print(returns$bar,position=c(0,0,1,0.6))
@
%\end{minipage}

\begin{center}
<<echo=FALSE,eval=TRUE,results='tex'>>=
percent <- function(x, digits = 2, format = "f", ...) 
{ 
  paste(formatC(100 * x, format = format, digits = digits, ...), "%", sep = "") 
} 

trailingtable <- apply(trailing,MARGIN=2,FUN=percent)
rownames(trailingtable) <- c("1y","3y","5y",paste("Since Inception ",format(index(managers)[1],"%b %Y")))
#commented out because I like the dprint better than xtable
#print(xtable(trailingtable), floating=FALSE)
@
\end{center}

\end{figure}

\begin{figure}[!htb]
\begin{center}
<<echo=FALSE,eval=TRUE,fig=TRUE,warning=FALSE,results='hide',out.width='0.5\\linewidth'>>=
win.graph(width=6,height=6)
cal_returns <- table.CalendarReturns(managers)[,13:15]
cal_returns.df <- as.data.frame(cbind(rownames(cal_returns),apply(cal_returns/100,MARGIN=2,percent)))
colnames(cal_returns.df)[1] <- "Date"
dprint(data=cal_returns.df,label="Date",pg.dim=c(6,6),fit=TRUE,margins=c(0,0,0,0),
       main="Returns By Year",row.hl=row.hl(which(cal_returns[,1]<0),col="indianred1"))
dev.off()
@
\end{center}
\end{figure}


\newpage
\section{Risk}

\end{document}