世界地图 - 将国家的一半地图映射到不同的颜色 [英] world map - map halves of countries to different colors

问题描述

我正在用这个例子来讨论：
，关于多个填充量表的问题单一的图表（你不能），一个 ggplot2 解决方案似乎不太可能没有打分（这可能是一个很好的方法，如上面的评论建议的）。 >

---

编辑re：将一半的重心映射到某些东西：东部的重心（左 ）一半可以通过

 坐标（lSPDF）
  

可以通过以类似的方式创建一个 rSPDF 对象来获得西方（右）半部分： / p>

 ＃创建覆盖每个国家的bbox的一半的正方形多边形
 rpolys<  -  lapply（seq_along pl），function（x）{
 rbox<  -  bbox（pl [[x]]）
 rbox [1，1]<  -  theCents [x，1] 
多边形expand.grid（rbox [1，]，rbox [2，]）[c（1,3,4,2,1），]）
}）
 
＃Dete每个国家与相应的正多边形的交点
 rPolys<  -  lapply（seq_along（rpolys），function（x）{
 curRPol < -  SpatialPolygons（list（Polygons（rpolys [x ]，
 proj4string = CRS（proj4string（world.map）））
 curPl<  -  SpatialPolygons（pl [x]，proj4string = CRS（proj4string（world.map） ））
 theInt<  -  gIntersection（curRPol，curPl，id = wmRN [x]）
 theInt 
}）
 
＃创建西方的SpatialPolygonDataFrame右边的交集
 rSPDF<  -  SpatialPolygonsDataFrame（SpatialPolygons（unlist（lapply（rPolys，
 slot，polygons）），proj4string = CRS（proj4string（world.map））），
 world.map@data）
  

然后，信息可以根据 lSPDF 或 rSPDF ：

  points（coordinates（rSPDF），col = factor（rSPDF @ data $ REGION））
＃或
文本（coordinates（lSPDF），labels = lSPDF @ data $ FIPS，cex = 
  

I am using the example here for discussion: ggplot map with l

library(rgdal)
library(ggplot2)
library(maptools)

# Data from http://thematicmapping.org/downloads/world_borders.php.
# Direct link: http://thematicmapping.org/downloads/TM_WORLD_BORDERS_SIMPL-0.3.zip
# Unpack and put the files in a dir 'data'

gpclibPermit()
world.map <- readOGR(dsn="data", layer="TM_WORLD_BORDERS_SIMPL-0.3")
world.ggmap <- fortify(world.map, region = "NAME")

n <- length(unique(world.ggmap$id))
df <- data.frame(id = unique(world.ggmap$id),
                 growth = 4*runif(n),
                 category = factor(sample(1:5, n, replace=T)))

## noise
df[c(sample(1:100,40)),c("growth", "category")] <- NA


ggplot(df, aes(map_id = id)) +
     geom_map(aes(fill = growth, color = category), map =world.ggmap) +
     expand_limits(x = world.ggmap$long, y = world.ggmap$lat) +
     scale_fill_gradient(low = "red", high = "blue", guide = "colorbar")

Gives the following results:

I would like to map one variable to the left "half" of a country and a different variable to the right "half" of the country. I put "half" in quotes because it's not clearly defined (or at least I'm not clearly defining it). The answer by Ian Fellows might help (which gives an easy way to get the centroid). I'm hoping for something so that I can do aes(left_half_color = growth, right_half_color = category) in the example. I'm also interested in top half and bottom half if that is different.

If possible, I would also like to map the individual centroids of the halves to something.

解决方案

This is a solution without ggplot that relies on the plot function instead. It also requires the rgeos package in addition to the code in the OP:

EDIT Now with 10% less visual pain

EDIT 2 Now with centroids for east and west halves

library(rgeos)
library(RColorBrewer)

# Get centroids of countries
theCents <- coordinates(world.map)

# extract the polygons objects
pl <- slot(world.map, "polygons")

# Create square polygons that cover the east (left) half of each country's bbox
lpolys <- lapply(seq_along(pl), function(x) {
  lbox <- bbox(pl[[x]])
  lbox[1, 2] <- theCents[x, 1]
  Polygon(expand.grid(lbox[1,], lbox[2,])[c(1,3,4,2,1),])
})

# Slightly different data handling
wmRN <- row.names(world.map)

n <- nrow(world.map@data)
world.map@data[, c("growth", "category")] <- list(growth = 4*runif(n),
                 category = factor(sample(1:5, n, replace=TRUE)))

# Determine the intersection of each country with the respective "left polygon"
lPolys <- lapply(seq_along(lpolys), function(x) {
  curLPol <- SpatialPolygons(list(Polygons(lpolys[x], wmRN[x])),
    proj4string=CRS(proj4string(world.map)))
  curPl <- SpatialPolygons(pl[x], proj4string=CRS(proj4string(world.map)))
  theInt <- gIntersection(curLPol, curPl, id = wmRN[x])
  theInt
})

# Create a SpatialPolygonDataFrame of the intersections
lSPDF <- SpatialPolygonsDataFrame(SpatialPolygons(unlist(lapply(lPolys,
  slot, "polygons")), proj4string = CRS(proj4string(world.map))),
  world.map@data)

##########
## EDIT ##
##########
# Create a slightly less harsh color set
s_growth <- scale(world.map@data$growth,
  center = min(world.map@data$growth), scale = max(world.map@data$growth))
growthRGB <- colorRamp(c("red", "blue"))(s_growth)
growthCols <- apply(growthRGB, 1, function(x) rgb(x[1], x[2], x[3],
  maxColorValue = 255))
catCols <- brewer.pal(nlevels(lSPDF@data$category), "Pastel2")

# and plot
plot(world.map, col = growthCols, bg = "grey90")

plot(lSPDF, col = catCols[lSPDF@data$category], add = TRUE)

Perhaps someone can come up with a good solution using ggplot2. However, based on this answer to a question about multiple fill scales for a single graph ("You can't"), a ggplot2 solution seems unlikely without faceting (which might be a good approach, as suggested in the comments above).

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EDIT re: mapping centroids of the halves to something: The centroids for the east ("left") halves can be obtained by

coordinates(lSPDF)

Those for the west ("right") halves can be obtained by creating an rSPDF object in a similar way:

# Create square polygons that cover west (right) half of each country's bbox
rpolys <- lapply(seq_along(pl), function(x) {
  rbox <- bbox(pl[[x]])
  rbox[1, 1] <- theCents[x, 1]
  Polygon(expand.grid(rbox[1,], rbox[2,])[c(1,3,4,2,1),])
})

# Determine the intersection of each country with the respective "right polygon"
rPolys <- lapply(seq_along(rpolys), function(x) {
  curRPol <- SpatialPolygons(list(Polygons(rpolys[x], wmRN[x])),
    proj4string=CRS(proj4string(world.map)))
  curPl <- SpatialPolygons(pl[x], proj4string=CRS(proj4string(world.map)))
  theInt <- gIntersection(curRPol, curPl, id = wmRN[x])
  theInt
})

# Create a SpatialPolygonDataFrame of the western (right) intersections
rSPDF <- SpatialPolygonsDataFrame(SpatialPolygons(unlist(lapply(rPolys,
  slot, "polygons")), proj4string = CRS(proj4string(world.map))),
  world.map@data)

Then information could be plotted on the map according to the centroids of lSPDF or rSPDF:

points(coordinates(rSPDF), col = factor(rSPDF@data$REGION))
# or
text(coordinates(lSPDF), labels = lSPDF@data$FIPS, cex = .7)

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