Question

我想在charts.PerformanceSummary包中提供PerformanceAnalytics基本功能的“ggplot版本”，因为我认为ggplot通常更漂亮，理论上更强大编辑图像。我已经相当接近，但有一些问题，我想要一些帮助。即：

减少了图例所占用的空间量，当它上面有10行以上时会变得骇人/丑......（只是线条颜色和名称就够了）
增加Daily_Returns构面的大小以匹配chart.PerformanceSummary PerformanceAnalytics
有一个选项可指定要在Daily_Returns构面中的每日回复系列中显示哪个资产，而不是始终使用第一列，而不是charts.PerformanceSummary

如果有更好的方法可以使用gridExtra而不是方面来做到这一点......我并不反对那些让我看起来会更好看的人......

这里的问题是美学，而且我觉得潜在易操作，因为PerformanceAnalytics已经有一个很好的工作示例，我只是想让它更漂亮/更专业...

除了这个奖励积分之外，我希望能够在每个资产的图表一侧或下方某处显示与其相关的一些表现统计数据......不太确定哪里最好是显示或显示此信息。

此外，如果他们对此提出建议，我建议部分清理我的代码并不是不利的。

这是我可重复的例子......

首先生成返回数据：

require(xts)
X.stock.rtns <- xts(rnorm(1000,0.00001,0.0003), Sys.Date()-(1000:1))
Y.stock.rtns <- xts(rnorm(1000,0.00003,0.0004), Sys.Date()-(1000:1))
Z.stock.rtns <- xts(rnorm(1000,0.00005,0.0005), Sys.Date()-(1000:1))
rtn.obj <- merge(X.stock.rtns , Y.stock.rtns, Z.stock.rtns)
colnames(rtn.obj) <- c("x.stock.rtns","y.stock.rtns","z.stock.rtns")

我想从以下结果中复制图像：

require(PerformanceAnalytics)
charts.PerformanceSummary(rtn.obj, geometric=TRUE)

aim

这是我迄今为止的尝试......

gg.charts.PerformanceSummary <- function(rtn.obj, geometric=TRUE, main="",plot=TRUE){

    # load libraries
suppressPackageStartupMessages(require(ggplot2))
suppressPackageStartupMessages(require(scales))
suppressPackageStartupMessages(require(reshape))
suppressPackageStartupMessages(require(PerformanceAnalytics))
    # create function to clean returns if having NAs in data
    clean.rtn.xts <- function(univ.rtn.xts.obj,na.replace=0){
    univ.rtn.xts.obj[is.na(univ.rtn.xts.obj)]<- na.replace
    univ.rtn.xts.obj
}
    # Create cumulative return function
cum.rtn <- function(clean.xts.obj, g=TRUE){
    x <- clean.xts.obj
    if(g==TRUE){y <- cumprod(x+1)-1} else {y <- cumsum(x)}
    y
}
    # Create function to calculate drawdowns
dd.xts <- function(clean.xts.obj, g=TRUE){
    x <- clean.xts.obj
    if(g==TRUE){y <- Drawdowns(x)} else {y <- Drawdowns(x,geometric=FALSE)}
    y
}
    # create a function to create a dataframe to be usable in ggplot to replicate charts.PerformanceSummary
cps.df <- function(xts.obj,geometric){
    x <- clean.rtn.xts(xts.obj)
    series.name <- colnames(xts.obj)[1]
    tmp <- cum.rtn(x,geometric)
    tmp$rtn <- x
    tmp$dd <- dd.xts(x,geometric)
    colnames(tmp) <- c("Cumulative_Return","Daily_Return","Drawdown")
    tmp.df <- as.data.frame(coredata(tmp))
    tmp.df$Date <- as.POSIXct(index(tmp))
    tmp.df.long <- melt(tmp.df,id.var="Date")
    tmp.df.long$asset <- rep(series.name,nrow(tmp.df.long))
    tmp.df.long
}
# A conditional statement altering the plot according to the number of assets
if(ncol(rtn.obj)==1){
            # using the cps.df function
    df <- cps.df(rtn.obj,geometric)
            # adding in a title string if need be
    if(main==""){
        title.string <- paste0(df$asset[1]," Performance")
    } else {
        title.string <- main
    }
            # generating the ggplot output with all the added extras....
    gg.xts <- ggplot(df, aes_string(x="Date",y="value",group="variable"))+
                facet_grid(variable ~ ., scales="free", space="free")+
                geom_line(data=subset(df,variable=="Cumulative_Return"))+
                geom_bar(data=subset(df,variable=="Daily_Return"),stat="identity")+
                geom_line(data=subset(df,variable=="Drawdown"))+
                ylab("")+
                geom_abline(intercept=0,slope=0,alpha=0.3)+
                ggtitle(title.string)+
                theme(axis.text.x = element_text(angle = 45, hjust = 1))+
                scale_x_datetime(breaks = date_breaks("6 months"), labels = date_format("%d/%m/%Y"))

} else {
            # a few extra bits to deal with the added rtn columns
    no.of.assets <- ncol(rtn.obj)
    asset.names <- colnames(rtn.obj)
    df <- do.call(rbind,lapply(1:no.of.assets, function(x){cps.df(rtn.obj[,x],geometric)}))
    df$asset <- ordered(df$asset, levels=asset.names)
    if(main==""){
        title.string <- paste0(df$asset[1]," Performance")
    } else {
        title.string <- main
    }
    if(no.of.assets>5){legend.rows <- 5} else {legend.rows <- no.of.assets}
    gg.xts <- ggplot(df, aes_string(x="Date", y="value",group="asset"))+
      facet_grid(variable~.,scales="free",space="free")+
      geom_line(data=subset(df,variable=="Cumulative_Return"),aes(colour=factor(asset)))+
      geom_bar(data=subset(df,variable=="Daily_Return"),stat="identity",aes(fill=factor(asset),colour=factor(asset)),position="dodge")+
      geom_line(data=subset(df,variable=="Drawdown"),aes(colour=factor(asset)))+
      ylab("")+
      geom_abline(intercept=0,slope=0,alpha=0.3)+
      ggtitle(title.string)+
      theme(legend.title=element_blank(), legend.position=c(0,1), legend.justification=c(0,1),
            axis.text.x = element_text(angle = 45, hjust = 1))+
      guides(col=guide_legend(nrow=legend.rows))+
      scale_x_datetime(breaks = date_breaks("6 months"), labels = date_format("%d/%m/%Y"))

}

assign("gg.xts", gg.xts,envir=.GlobalEnv)
if(plot==TRUE){
    plot(gg.xts)
} else {}

}
# seeing the ggplot equivalent....
gg.charts.PerformanceSummary(rtn.obj, geometric=TRUE)

result

Answer 1

我一直在寻找。你非常接近。站在你的肩膀上，我能够解决一些问题。

编辑（2015年5月9日）：现在可以通过三重冒号运算符Drawdown()调用函数PerformanceAnalytics:::Drawdown()。编辑以下代码以反映此更改。 修改（2018年4月22日）： show_guide已弃用并由show.legend取代。

require(xts)

X.stock.rtns <- xts(rnorm(1000,0.00001,0.0003), Sys.Date()-(1000:1))
Y.stock.rtns <- xts(rnorm(1000,0.00003,0.0004), Sys.Date()-(1000:1))
Z.stock.rtns <- xts(rnorm(1000,0.00005,0.0005), Sys.Date()-(1000:1))
rtn.obj <- merge(X.stock.rtns , Y.stock.rtns, Z.stock.rtns)
colnames(rtn.obj) <- c("x","y","z")

# advanced charts.PerforanceSummary based on ggplot
gg.charts.PerformanceSummary <- function(rtn.obj, geometric = TRUE, main = "", plot = TRUE)
{

    # load libraries
    suppressPackageStartupMessages(require(ggplot2))
    suppressPackageStartupMessages(require(scales))
    suppressPackageStartupMessages(require(reshape))
    suppressPackageStartupMessages(require(PerformanceAnalytics))

    # create function to clean returns if having NAs in data
    clean.rtn.xts <- function(univ.rtn.xts.obj,na.replace=0){
        univ.rtn.xts.obj[is.na(univ.rtn.xts.obj)]<- na.replace
        univ.rtn.xts.obj  
    }

    # Create cumulative return function
    cum.rtn <- function(clean.xts.obj, g = TRUE)
    {
        x <- clean.xts.obj
        if(g == TRUE){y <- cumprod(x+1)-1} else {y <- cumsum(x)}
        y
    }

    # Create function to calculate drawdowns
    dd.xts <- function(clean.xts.obj, g = TRUE)
    {
        x <- clean.xts.obj
        if(g == TRUE){y <- PerformanceAnalytics:::Drawdowns(x)} else {y <- PerformanceAnalytics:::Drawdowns(x,geometric = FALSE)}
        y
    }

    # create a function to create a dataframe to be usable in ggplot to replicate charts.PerformanceSummary
    cps.df <- function(xts.obj,geometric)
    {
        x <- clean.rtn.xts(xts.obj)
        series.name <- colnames(xts.obj)[1]
        tmp <- cum.rtn(x,geometric)
        tmp$rtn <- x
        tmp$dd <- dd.xts(x,geometric)
        colnames(tmp) <- c("Index","Return","Drawdown") # names with space
        tmp.df <- as.data.frame(coredata(tmp))
        tmp.df$Date <- as.POSIXct(index(tmp))
        tmp.df.long <- melt(tmp.df,id.var="Date")
        tmp.df.long$asset <- rep(series.name,nrow(tmp.df.long))
        tmp.df.long
    }

    # A conditional statement altering the plot according to the number of assets
    if(ncol(rtn.obj)==1)
    {
        # using the cps.df function
        df <- cps.df(rtn.obj,geometric)
        # adding in a title string if need be
        if(main == ""){
            title.string <- paste("Asset Performance")
        } else {
            title.string <- main
        }

        gg.xts <- ggplot(df, aes_string( x = "Date", y = "value", group = "variable" )) +
            facet_grid(variable ~ ., scales = "free_y", space = "fixed") +
            geom_line(data = subset(df, variable == "Index")) +
            geom_bar(data = subset(df, variable == "Return"), stat = "identity") +
            geom_line(data = subset(df, variable == "Drawdown")) +
            geom_hline(yintercept = 0, size = 0.5, colour = "black") +
            ggtitle(title.string) +
            theme(axis.text.x = element_text(angle = 0, hjust = 1)) +
            scale_x_datetime(breaks = date_breaks("6 months"), labels = date_format("%m/%Y")) +
            ylab("") +
            xlab("")

    } 
    else 
    {
        # a few extra bits to deal with the added rtn columns
        no.of.assets <- ncol(rtn.obj)
        asset.names <- colnames(rtn.obj)
        df <- do.call(rbind,lapply(1:no.of.assets, function(x){cps.df(rtn.obj[,x],geometric)}))
        df$asset <- ordered(df$asset, levels=asset.names)
        if(main == ""){
            title.string <- paste("Asset",asset.names[1],asset.names[2],asset.names[3],"Performance")
        } else {
            title.string <- main
        }

        if(no.of.assets>5){legend.rows <- 5} else {legend.rows <- no.of.assets}

        gg.xts <- ggplot(df, aes_string(x = "Date", y = "value" )) +

            # panel layout
            facet_grid(variable~., scales = "free_y", space = "fixed", shrink = TRUE, drop = TRUE, margin = 
                           , labeller = label_value) + # label_value is default

            # display points for Index and Drawdown, but not for Return
            geom_point(data = subset(df, variable == c("Index","Drawdown"))
                       , aes(colour = factor(asset), shape = factor(asset)), size = 1.2, show.legend = TRUE) + 

            # manually select shape of geom_point
            scale_shape_manual(values = c(1,2,3)) + 

            # line colours for the Index
            geom_line(data = subset(df, variable == "Index"), aes(colour = factor(asset)), show.legend = FALSE) +

            # bar colours for the Return
            geom_bar(data = subset(df,variable == "Return"), stat = "identity"
                     , aes(fill = factor(asset), colour = factor(asset)), position = "dodge", show.legend = FALSE) +

            # line colours for the Drawdown
            geom_line(data = subset(df, variable == "Drawdown"), aes(colour = factor(asset)), show.legend = FALSE) +

            # horizontal line to indicate zero values
            geom_hline(yintercept = 0, size = 0.5, colour = "black") +

            # horizontal ticks
            scale_x_datetime(breaks = date_breaks("6 months"), labels = date_format("%m/%Y")) +

            # main y-axis title
            ylab("") +

            # main x-axis title
            xlab("") +

            # main chart title
            ggtitle(title.string)

        # legend 

        gglegend <- guide_legend(override.aes = list(size = 3))

        gg.xts <- gg.xts + guides(colour = gglegend, size = "none") +

            # gglegend <- guide_legend(override.aes = list(size = 3), direction = "horizontal") # direction overwritten by legend.box?
            # gg.xts <- gg.xts + guides(colour = gglegend, size = "none", shape = gglegend) + # Warning: "Duplicated override.aes is ignored"

            theme( legend.title = element_blank()
                   , legend.position = c(0,1)
                   , legend.justification = c(0,1)
                   , legend.background = element_rect(colour = 'grey')
                   , legend.key = element_rect(fill = "white", colour = "white")
                   , axis.text.x = element_text(angle = 0, hjust = 1)
                   , strip.background = element_rect(fill = "white")
                   , panel.background = element_rect(fill = "white", colour = "white")
                   , panel.grid.major = element_line(colour = "grey", size = 0.5) 
                   , panel.grid.minor = element_line(colour = NA, size = 0.0)
            )

    }

    assign("gg.xts", gg.xts,envir=.GlobalEnv)
    if(plot == TRUE){
        plot(gg.xts)
    } else {}

}

# display chart
gg.charts.PerformanceSummary(rtn.obj, geometric = TRUE)

控制面板的大小在facet_grid：facet_grid（变量〜。，scales =“free_y”，space =“fixed”）内。这些选项的作用在手册中有解释，引用：

比例：比例是在所有方面共享（默认为“固定”），还是在行（“free_x”），列（“free_y”）或两行之间有所不同列（“免费”）

空间：如果“固定”，默认情况下，所有面板都具有相同的尺寸。如果“free_y”，它们的高度将与y标度的长度成比例;如果“free_x”，它们的宽度将与x标度的长度成比例;或者如果“自由”，高度和宽度都会有所不同。除非适当的比例也有所不同，否则此设置无效。

更新：标签

可以使用以下功能获得自定义标签：

# create a function to store fancy axis labels 

    my_labeller <- function(var, value){ # from the R Cookbook
        value <- as.character(value)
        if (var=="variable") 
        {
              value[value=="Index"] <- "Cumulative Returns"
              value[value=="Return"] <- "Daily Returns"
              value[value=="Drawdown"] <- "Drawdown"
        }
        return(value)
    }

并将labeller选项设置为“labeller = my_labeller”

更新：背景

可以在theme（）函数内控制背景，网格线，颜色等的外观：上面的代码已更新以反映这些更改。

Answer 2

有关图例的大小，请参阅？theme。图例的大多数方面都可以通过那里进行调整......你想要调整的是legend.key.size我想，以及legend.background去除每个图例周围的框...

刻面中每个面板的尺寸有点复杂。我有一个hack，允许你在调用facet_grid时指定每个面板的相对大小，但它需要从源等安装...更好的解决方案是将你的绘图转换为gtable对象并修改它...假设你的情节被称为p：

require(gtable)
require(grid)

pTable <- ggplot_gtable(ggplot_build(p))
pTable$heights[[4]] <- unit(2, 'null')

grid.newpage()
grid.draw(pTable)

这将使顶部面板的高度增加到每个其他面板的两倍......它是pTable $高度[[4]]而不是pTable $高度[[1]]的原因是切面面板不是剧情中的顶级格栅。

我将避免比这更具体，因为你最好通过自己探索gtable的属性（因为我没有时间）

最好的

托马斯

ggplot版本的chart.PerformanceSummary

2 个答案: