Question

在R中，我有一个数据集，它有一个独立变量和9个因变量，我想看到散点图，直方图和相关值，如chart.Correlation()，但我不想看到相关性在因变量之间...因为它是不必要的。

即在下面的模拟示例中，我只关心/想要看到最上面的行和最左边的列，所有的直方图，所有的红线和重要的星等等，但我不关心/不希望所有其他散点图和相关值。这有可能/有一种巧妙的方式在一个可视化中看到所有这些......即。自变量vs所有因变量......？

模拟示例：

d <- xts(matrix(rnorm(10000),ncol=10), Sys.Date()-1000:1)
library(PerformanceAnalytics)
chart.Correlation(d)

旁注...我对chart.Correlation生成的某些相关值的字体大小感到有些恼火...任何设置最小和最大字体大小的方法都会使字体变粗尺寸不会变得难以理解......

另外，请随意使用您认为可能有用的任何其他包（例如ggplot2等）来帮助找到问题的解决方案。

提前致谢

编辑：

所以这是我到目前为止使用ggplot和plyr得出的...我仍然缺少自变量的直方图...哦和multiplot来自这里：http://wiki.stdout.org/rcookbook/Graphs/Multiple%20graphs%20on%20one%20page%20(ggplot2)/

现在已将其作为答案包括在内......但任何其他建议/改进都会很受欢迎....

require(plyr)
require(ggplot2)

indep.dep.cor <- function(xts.obj, title=""){

        # First column always assumed to be independent
        df <- data.frame(coredata(xts.obj))
        assign('df',df,envir=.GlobalEnv)

        df.l <- melt(df, id.vars=colnames(df)[1], measure.vars=colnames(df)[2:ncol(df)])
        assign('df.l',df.l, envir=.GlobalEnv)

        cor.vals <- ddply(df.l, c("variable"), summarise, round(cor(df[,1],value),3))
        stars <- ddply(df.l, c("variable"), summarise, symnum(cor.test(df[,1],value)$p.value, corr = FALSE, na = FALSE, cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", "**", "*", ".", " ")))
        cor.vals$stars <- stars[,2]
        assign('cor.vals',cor.vals,envir=.GlobalEnv)

        bin.w <- min((ddply(df.l,c("variable"),summarise,diff(range(value))/30))[,2])

        m1 <- ggplot(df.l,aes_string(x="value"))+
            facet_grid(.~variable)+
            stat_density(aes(y=..density..),fill=NA, colour="red", size=1.2)+
            geom_histogram(aes(y=..density..),fill="white", colour="black", binwidth=bin.w)+
            opts(title=title)

        m2 <- ggplot(df.l,aes_string(x=colnames(df.l)[1], y="value"))+
            facet_grid(.~variable)+geom_point(aes(alpha=0.2))+
            opts(legend.position="none")+
            geom_text(data=cor.vals,aes(label=paste(cor.vals[,2],cor.vals[,3]),size=abs(cor.vals[,2])*2,colour=cor.vals[,2]),x=Inf,y=Inf,vjust=1,hjust=1,show_guide=FALSE)+
                scale_colour_gradient(low = "red", high="blue")+
                geom_smooth(method="loess")

        multiplot(m1,m2,cols=1)
}

indep.dep.cor(d)

Answer 1

所以这是我到目前为止使用ggplot和plyr得出的...我仍然缺少自变量的直方图...哦和multiplot来自这里：http://wiki.stdout.org/rcookbook/Graphs/Multiple%20graphs%20on%20one%20page%20(ggplot2)/

require(plyr)
require(ggplot2)

indep.dep.cor <- function(xts.obj, title=""){

        # First column always assumed to be independent
        df <- data.frame(coredata(xts.obj))
        assign('df',df,envir=.GlobalEnv)

        df.l <- melt(df, id.vars=colnames(df)[1], measure.vars=colnames(df)[2:ncol(df)])
        assign('df.l',df.l, envir=.GlobalEnv)

        cor.vals <- ddply(df.l, c("variable"), summarise, round(cor(df[,1],value),3))
        stars <- ddply(df.l, c("variable"), summarise, symnum(cor.test(df[,1],value)$p.value, corr = FALSE, na = FALSE, cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", "**", "*", ".", " ")))
        cor.vals$stars <- stars[,2]
        assign('cor.vals',cor.vals,envir=.GlobalEnv)

        bin.w <- min((ddply(df.l,c("variable"),summarise,diff(range(value))/30))[,2])

        m1 <- ggplot(df.l,aes_string(x="value"))+
            facet_grid(.~variable)+
            stat_density(aes(y=..density..),fill=NA, colour="red", size=1.2)+
            geom_histogram(aes(y=..density..),fill="white", colour="black", binwidth=bin.w)+
            opts(title=title)

        m2 <- ggplot(df.l,aes_string(x=colnames(df.l)[1], y="value"))+
            facet_grid(.~variable)+geom_point(aes(alpha=0.2))+
            opts(legend.position="none")+
            geom_text(data=cor.vals,aes(label=paste(cor.vals[,2],cor.vals[,3]),size=abs(cor.vals[,2])*2,colour=cor.vals[,2]),x=Inf,y=Inf,vjust=1,hjust=1,show_guide=FALSE)+
                scale_colour_gradient(low = "red", high="blue")+
                geom_smooth(method="loess")

        multiplot(m1,m2,cols=1)
}

indep.dep.cor(d)

chart.Correlation independent vs dependent variables visualization

1 个答案: