R：如何根据数据值为voronoi细分着色？

我想

从SpatialPointDataFrame在R中创建Voronoi细分确定

获取空间多边形数据帧OK

在我的原始SpatialPointDataFrame中通过值对其着色如何

至于： 我创建并更新了Voronoi tesselation，如下所示： 并在此处更新：

我知道我可以根据库（“dismo”）给它上色：

然而，使用上面的Voronoi函数，在我的voronoipolygons中只有一个变量：“dummy”。但是，我想用变量“z”给多边形上色，这个变量在我的.voro多边形中没有更多的内容

voronoipolygons = function(layer) {
  require(deldir)
  crds = layer@coords
  z = deldir(crds[,1], crds[,2])
  w = tile.list(z)
  polys = vector(mode='list', length=length(w))
  require(sp)
  for (i in seq(along=polys)) {
    pcrds = cbind(w[[i]]$x, w[[i]]$y)
    pcrds = rbind(pcrds, pcrds[1,])
    polys[[i]] = Polygons(list(Polygon(pcrds)), ID=as.character(i))
  }
  SP = SpatialPolygons(polys)
  voronoi = SpatialPolygonsDataFrame(SP, data=data.frame(dummy = seq(length(SP)), 
                                                         row.names=sapply(slot(SP, 'polygons'), 
                                                                                                   function(x) slot(x, 'ID'))))
}

我的问题是：如何通过

“z”

变量为我的

.voro

多边形着色，或/和如何直接将其包含在上面的

voronoipolygons（）

函数中？我不能只是将

“z”

变量添加到

中。voro@data

，因为值的顺序已更改。我的R技能还没有那么强。。多谢各位

虚拟数据：

x <- c(32.5, 32.1, 33.5, 32.2, 33.0)
y <- c(-2.2, -3.3, -2.3, -2.9, -3.0)
z <- c(1, 2, 5, 8, 4)

# make df
df<-as.data.frame(cbind(x,y,z))
coordinates(df)<- ~ x + y  #make SPDF 

df.voro <- voronoipolygons(df)   # calculated VORONOI

require('dismo')
spplot(df.voro, "dummy")   # colorize Polygons

# add z variable to newly created data
df.voro@data$z<-df$z    ## !!! can't use this, because this change order of values in df !!!
spplot(df.voro, "z")

---

x我知道了！！如何修改Voronoi函数

我需要首先从data.frame中读取my.variable:my.variable=
layer@data[，1]
然后将其作为：
y.data=my.variable
添加到我的SP对象中

voronoipolygons2 = function(layer) {
  require(deldir)
  crds = layer@coords
  z = deldir(crds[,1], crds[,2])
  w = tile.list(z)
  my.variable = layer@data[,1]   ## HERE
  polys = vector(mode='list', length=length(w))
  require(sp)
  for (i in seq(along=polys)) {
    pcrds = cbind(w[[i]]$x, w[[i]]$y)
    pcrds = rbind(pcrds, pcrds[1,])
    polys[[i]] = Polygons(list(Polygon(pcrds)), ID=as.character(i))
  }
  SP = SpatialPolygons(polys)
  voronoi = SpatialPolygonsDataFrame(SP, data=data.frame(dummy = seq(length(SP)), 
                                                         my.data = my.variable, # HERE add new column to my voronoi data 
                                                         row.names=sapply(slot(SP, 'polygons'), 
                                                                          function(x) slot(x, 'ID'))))
}

通过修改的voronoi函数创建voronoi细分多边形：

df.voro2 <- voronoipolygons2(df)  

以及它们与voronoi1数据的不同之处

> df.voro@data
  dummy
1     1
2     2
3     3
4     4
5     5

在一张图纸上显示两个SPP批次

require(gridExtra)
grid.arrange(spplot(df.voro, "dummy", xlab = "x", ylab = "y", main = "original" ),
             spplot(df.voro2, "my.data", xlab = "x", ylab = "y", main = "z value applied !;-)"))

贸易协定（AAA；）


require(gridExtra)
grid.arrange(spplot(df.voro, "dummy", xlab = "x", ylab = "y", main = "original" ),
             spplot(df.voro2, "my.data", xlab = "x", ylab = "y", main = "z value applied !;-)"))