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01 三维散点图/3D散点图

三维散点图就是在由3个变量确定的三维空间中研究变量之间的关系，由于同时考虑了3个变量，常常可以发现在两维图形中发现不了的信息。

library(plot3D)
library(scales)
library(RColorBrewer)
library(fields) 

df<-read.csv("ThreeD_Scatter_Data.csv",header=T)

pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))
with(df, scatter3D(x = mph, y = Gas_Mileage, z = Power, #bgvar = mag,
                   pch = 21, cex = 1.5,col="black",bg="#F57446",
                   xlab = "0-60 mph (sec)",
                   ylab = "Gas Mileage (mpg)",
                   zlab = "Power (kW)", 
                   zlim=c(40,180),
                   ticktype = "detailed",bty = "f",box = TRUE,
                   #panel.first = panelfirst,
                   theta = 60, phi = 20, d=3,
                   colkey = FALSE)#list(length = 0.5, width = 0.5, cex.clab = 0.75))
)

效果如下：

02

index <- ceiling(((prc <- 0.7 * df$Weight/ diff(range(df$Weight))) - min(prc) + 0.3)*100)
for (i in seq(1,length(index)) ){
  prc[i]=colormap[index[i]]
}
pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))
with(df, scatter3D(x = mph, y = Gas_Mileage, z = Power, #bgvar = mag,
                   pch = 21, cex = 1.5,col="black",bg=prc,
                   xlab = "0-60 mph (sec)",
                   ylab = "Gas Mileage (mpg)",
                   zlab = "Power (kW)", 
                   zlim=c(40,180),
                   ticktype = "detailed",bty = "f",box = TRUE,
                   #panel.first = panelfirst,
                   theta = 60, phi = 20, d=3,
                   colkey = FALSE)#list(length = 0.5, width = 0.5, cex.clab = 0.75))
)
colkey (col=colormap,clim=range(df$Weight),clab = "Weight", add=TRUE, length=0.5,side = 4)

效果如下：

index <- ceiling(((prc <- 0.7 * df$Weight/ diff(range(df$Weight))) - min(prc) + 0.3)*100)
for (i in seq(1,length(index)) ){
  prc[i]=colormap[index[i]]
}
pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))
with(df, scatter3D(x = mph, y = Gas_Mileage, z = Power, #bgvar = mag,
                   pch = 21, cex = rescale(df$Weight, c(.5, 5)),col="black",bg=prc,
                   xlab = "0-60 mph (sec)",
                   ylab = "Gas Mileage (mpg)",
                   zlab = "Power (kW)", 
                   zlim=c(40,180),
                   ticktype = "detailed",bty = "f",box = TRUE,
                   theta = 60, phi = 20, d=3,
                   colkey = FALSE)
)
#colkey (col=colormap,clim=range(df$Weight),clab = "Weight", add=TRUE, length=0.5,side = 4)

breaks<-round(seq(500,2000,length.out=4),3)

legend_index <- ceiling(((legend_prc <- 0.7 *breaks/ diff(range(breaks))) - min(legend_prc) + 0.3)*100)
for (i in seq(1,length(legend_index)) ){
  legend_prc[i]=colormap[legend_index[i]]
}
legend("right",title =  "Weight",legend=breaks,pch=21,
       pt.cex=rescale(breaks, c(.5, 5)),y.intersp=1.6,
       pt.bg = legend_prc,bg="white",bty="n")

pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))
with(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length,pch = 21, 
                     cex = rescale(iris$quan, c(.5, 4)),col="black",bg=colormap[iris$quan],
                     xlab = "Sepal.Length",
                     ylab = "Sepal.Width",
                     zlab = "Petal.Length", 
                     ticktype = "detailed",bty = "f",box = TRUE,
                     theta = 30, phi = 15, d=2,
                     colkey = FALSE)
)
breaks =1:6
legend("right",title =  "Weight",legend=breaks,pch=21,
       pt.cex=rescale(breaks, c(.5, 4)),y.intersp=1.6,
       pt.bg = colormap[1:6],bg="white",bty="n")

效果如下：

参考资料

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