世界地图 - 使用GGPLOT2

Question

国家不同颜色的地图半我正在寻找的这个问题上GGPLOT2解决方案：

world map - map halves of countries to different colors

我从复制下面这个问题，这是基于该示例问题在这里（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") 

Answer 1

你有几个选项。绘制多边形非常简单，但不能有两个不同的fill比例。此解决方案使用点形注释，但可以更改为按颜色（或颜色和形状）缩放geom_point。我认为这是最好的，你将能够保存在一个单独的程序中手动覆盖两张地图。因为中心有点偏离（其中一些实际上只是其中的一个非常明显），您也可能（可能）想调整美国边界框。

我也去除了南极洲。如果你愿意的话，你可以重新加入，但是浪费了房地产海事组织。

library(rgdal) 
library(ggplot2) 
library(maptools) 
library(rgeos) 
library(RColorBrewer) 

world.map <- readOGR(dsn="/Users/bob/Desktop/TM_WORLD_BORDERS_SIMPL-0.3/", layer="TM_WORLD_BORDERS_SIMPL-0.3") 

# 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) 

whole <- world.map[grep("Antarctica", world.map$NAME, invert=TRUE),] 
half <- lSPDF[grep("Antarctica", lSPDF$NAME, invert=TRUE),] 

whole <- fortify(whole, region="ISO3") 
half <- fortify(half, region="ISO3") 

world.map$scaled_growth <- as.numeric(scale(world.map@data$growth, 
              center = min(world.map@data$growth), 
              scale = max(world.map@data$growth))) 

growth <- world.map@data[,c("ISO3", "scaled_growth")] 
colnames(growth) <- c("id", "scaled_growth") 
growth$scaled_growth <- factor(as.numeric(cut(growth$scaled_growth, 8))) # make it discrete 

half_centers <- data.frame(cbind(coordinates(gCentroid(lSPDF, byid = TRUE)), 
           id=world.map@data$ISO3, category=world.map@data$category)) 
half_centers$category <- factor(half_centers$category) 

gg <- ggplot() 
gg <- gg + geom_map(data=whole, map=whole, aes(x=long, y=lat, map_id=id), alpha=0, color="black", size=0.15) 
gg <- gg + geom_map(data=growth, map=whole, aes(fill=scaled_growth, map_id=id)) 
gg <- gg + geom_map(data=half, map=half, aes(x=long, y=lat, map_id=id), fill="white") 
gg <- gg + geom_point(data=half_centers, aes(x=x, y=y, shape=category), size=2) 
gg <- gg + scale_fill_brewer(palette="Pastel2") 
gg <- gg + scale_shape_discrete() 
gg <- gg + coord_equal() 
gg 

Answer 2

我认为你可以得到（有效）两种不同的填充尺度，与scale_fill_brewer和scale_fill_manual的小黑客。

这里是我的输出：

我使用的代码的第一位从你的问题贴在其他线程：

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

world.map <- readOGR(dsn="data", layer="TM_WORLD_BORDERS_SIMPL-0.3") 

# 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) 

现在我的贡献（借用用户名全/半！hrbrmstr）

# get two data.frames, one with whole countries and the other with the left half 
# this relies on code from SO user BenBarnes 
whole <- fortify(world.map, region="ISO3") 
half <- fortify(lSPDF, region="ISO3") 

# random growth/category data, similar to the random data originally 
# suggested by Xu Wang 
set.seed(123) 
df <- data.frame(id = unique(world.map@data$ISO3), 
       growth = 4*runif(n), 
       category = factor(sample(letters[1:5], n, replace=T))) 

# make growth a factor; 5 levels for convenience 
df$growth_fac <- cut(df$growth, 5) 

# append growth and category factor levels together 
growth_cat_levels <- c(levels(df$category), levels(df$growth_fac)) 

# adjust factors with new joint levels 
df$growth_fac <- 
    factor(df$growth_fac, levels=growth_cat_levels) 
df$category <- 
    factor(df$category, levels=growth_cat_levels) 

# create a palette with some sequential colors and some qualitative colors 
pal <- c(scale_fill_brewer(type='seq', palette=6)$palette(5), 
     scale_fill_brewer(type='qual', palette='Pastel2')$palette(5)) 


# merge data 
whole <- data.frame(merge(whole, df, by='id')) 
half <- data.frame(merge(half, df, by='id')) 

# plot 
ggplot() + 
    geom_polygon(data=whole, 
       aes(x=long, y=lat, group=group, fill=growth_fac), 
       color='black', size=0.15) + 
    geom_polygon(data=half, 
       aes(x=long, y=lat, group=group, fill=category), 
       color=NA) + 
    scale_shape_discrete() + 
    coord_equal() + 

    scale_fill_manual('Category, Growth', 
        values=pal, breaks=growth_cat_levels) + 
    guides(fill=guide_legend(ncol=2)) 

一些注意事项：

• 我仍然认为这是一本难以阅读的地图，但有趣的挑战
• 我将'category'的名称从数字更改为alpha，以避免与'增长'数据混淆。
• 我还保留了cut的“增长”数据标签，以帮助明确说明这是分级连续数据。
• 起初，我在图例左侧有增长颜色，但我换了一下;由于类别决定了左侧国家多边形的填充颜色，我认为类别应该出现在图例左侧
• 我尝试了几个不同的调色板选项。一种危险是定性量表的颜色与顺序量表的范围太相似了（就像我在编辑之前发表的帖子一样）。有一面灰度和一面颜色有助于避免这种情况