连续数据的千层面图

Question

此代码适用于分类数据

library(longCatEDA)
library(colorspace)
set.seed(642531)
y <- matrix(sample(1:24, 500, replace=TRUE), 100, 5)
set.seed(963854)
times <- matrix(runif(600, 1, 3), 100, 6)

# times must be cumulative
times <- t(apply(times, 1, cumsum))
lc <- longCat(y, times=times)

par(mfrow=c(1,1), bg='white', mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)


cols <- longCatPlot(lc,  colScheme='heat', legendBuffer=0, groupBuffer=0,main='Individually test Varying Times of Observation')
legend(15.5, 100, legend=lc$Labels, lty=1, col=cols, lwd=2)

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但如图所示，如果我有很多类别(超过10个)，图例应该是一个连续的比例，而不是24个类别。在他们推荐在这种情况下使用longContPlot的longCatEDA库中，我尝试如下所示：

longContPlot(lc, times, jog=TRUE, main='Individually varying times', ylab='', xlab='Days')

但是它不起作用..

Answer 1

不确定如何在longCatEDA中执行此操作，但在plotrix中使用color.legend函数非常简单：

library(colorspace)
library(plotrix)
set.seed(642531)
y <- matrix(sample(1:24, 500, replace=TRUE), 100, 5)
set.seed(963854)
times <- matrix(runif(600, 1, 3), 100, 6)

# times must be cumulative
times <- t(apply(times, 1, cumsum))
lc <- longCat(y, times=times)

par(mfrow=c(1,1), bg='white', mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)


cols <- longCatPlot(lc,  colScheme='heat', legendBuffer=0, groupBuffer=0,main='Individually test Varying Times of Observation')
plotrix::color.legend(xl=par('usr')[1]+1.05*(par('usr')[2]-par('usr')[1]),
                      xr=par('usr')[1]+1.15*(par('usr')[2]-par('usr')[1]),
                      yb=par('usr')[3]+0.20*(par('usr')[4]-par('usr')[3]),
                      yt=par('usr')[3]+0.80*(par('usr')[4]-par('usr')[3]),
                      rect.col = cols,gradient="y",
                      legend = lc$Labels[seq(1,length(lc$Labels),3)])

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