如何使用ggplot2编辑图例的位置

Question

我在使用多个图层分隔ggplot2图形中的图例时遇到了麻烦。我的情节是根据在那里的纺织公司的数量来填补不同的城市，我还用geom_point绘制工厂本地化。我的猜测是以某种方式使用aes.override()，但我还是无法做到这一点。我读过的解决方案没有处理aes() geom_point()中详述的图表的不同变量。

如果你想测试下面的代码，你可以下载巴西市政府here的shapefile，使用readOGR和fortify，然后选择填写您喜欢的城市fill并在巴西境内设置任意随机点，geom_point()创建一个不同的变量，例如下面的lat_plant和long_plant。下面的region列详细介绍了巴西地区 - 在这种情况下，“1”详细介绍了巴西北部地区。

代码

#setting the ggplot    
library(ggplot2)

gg2 < -ggplot(data = out[out$region =="1",], 
              aes(x = long, y = lat, group = group, fill = as.factor(companies))) + 
       geom_polygon() + 
       ggtitle("title") + 
       scale_fill_discrete(name = "Number of Textile Companies") +
       theme(plot.title = element_text(size = 30, face = "bold")) +
       theme(legend.text = element_text(size = 12),
             legend.title = element_text(colour = "blue", size = 16, face = "bold")) 

#graph output

gg2 + 
geom_point(data = out[out$region =="1",], aes(x = long_plant, y = lat_plant), color = "red") 

我得到的是传说：

我想把它分开，详细说明作为本地化的点和颜色作为该地区纺织公司数量的填充。

Answer 1

我为你留下另一个选择。 hmgeiger将纺织公司的数量视为因素。但是，我宁愿将变量视为连续变量。由于没有可重现的数据，我自己创建了一个样本数据。在这里，我创建了巴西经度和纬度的随机样本，并确保一些数据点留在巴西。 whatever2包含留在巴西的数据点。我也在这里做了一些技巧。我添加了一个名为Factory location的新列。这是用于向最终图形中的数据点添加颜色的虚拟变量。 hmgeiger为您创建了包含字符的Dummy.var。我宁愿在此列中留下""，因为您可能不希望在图例中看到任何文字。

对于你的传奇问题，正如Antonio提到的和hmgeiger所做的那样，你需要在aes() geom_point()中添加颜色。这解决了它。我为你做了一件事。如果您不知道每个市政当局有多少工厂，您需要计算工厂数量。我使用poly.count()包中的GISTools完成了这项工作，并创建了另一个包含每个市政工厂数量的数据框。

当我绘制地图时，我有三层。一个用于多边形，另一个用于填充多边形的颜色。它们是使用geom_cartogram()包中的ggalt完成的。关键是你需要有map_id的公共密钥列。第一个id中的geom_cartogram()和第二个ind中的geom_cartogram()是相同的信息。在geom_point()中，您需要color中的aes()。图例中有一个连续的工厂数量栏和一个工厂位置点。旁边没有文字。所以这让传说变得整洁，我想。

library(raster)
library(tidyverse)
library(GISTools)
library(RColorBrewer)
library(ggalt)
library(ggthemes)

# Get polygon data for Brazil
brazil <- getData("GADM", country = "brazil", level = 1)

mymap <- fortify(brazil)

# Create dummy data staying in the polygons
# For more information: https://stackoverflow.com/questions/47696382/removing-data-outside-country-map-boundary-in-r/47699405#47699405
set.seed(123)
mydata <- data.frame(long = runif(200, min = quantile(mymap$long)[1], max = quantile(mymap$long)[4]),
                     lat = runif(200, min = quantile(mymap$lat)[1], max = quantile(mymap$lat)[4]),
                     factory = paste("factory ", 1:200, sep = ""),
                     stringsAsFactors = FALSE)

spdf <- SpatialPointsDataFrame(coords = mydata[, c("long", "lat")], data = mydata,
                               proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))


whatever <- spdf[!is.na(over(spdf, as(brazil, "SpatialPolygons"))), ]

whatever2 <- as.data.frame(whatever) %>%
             mutate(`Factory location` = "")


# Now I check how many data points (factories) exist in each polygon
# and create a data frame
factory.num <- poly.counts(pts = whatever, polys = brazil)
factory.num <- stack(factory.num)


ggplot() +
geom_cartogram(data = mymap, aes(x = long, y = lat, map_id = id),
               map = mymap) +
geom_cartogram(data = factory.num, aes(fill = values, map_id = ind),
               map = mymap) +
geom_point(data = whatever2, aes(x = long, y = lat, color = `Factory location`)) +
scale_fill_gradientn(name = "Number of factories", colours = brewer.pal(5, "Greens")) +
coord_map() +
theme_map()

this

Answer 2

仅供参考，您发布的用于下载形状文件的链接非常慢，至少要下载到美国计算机上。

此链接的下载效果更好，还展示了如何读取形状数据：https://dioferrari.wordpress.com/2014/11/27/plotting-maps-using-r-example-with-brazilian-municipal-level-data/

我做了一个例子，使用区域而不是市政数据来保持简单。

我在此处使用的数据可供下载：https://drive.google.com/file/d/0B64xLcn8DZfwakNMbHFLQWo4YzA/view?usp=sharing

#Load libraries.

library(rgeos)
library(rgdal)
library(ggplot2)

#Read in and format map data.

regions_OGR <- readOGR(dsn="/Users/hmgeiger/Downloads/regioes_2010",
layer = "regioes_2010")
map_regions <- spTransform(regions_OGR,CRS("+proj=longlat +datum=WGS84"))
map_regions_fortified <- fortify(map_regions)

#We make there be 0, 1, or 3 textile companies.
#map_regions_fortified is in  order by ID (region).
#So, we add a column with the number of textile companies 
#repeated the right number of times for how many of each region there is.

num_rows_per_region <- data.frame(table(map_regions_fortified$id))

map_regions_fortified <- data.frame(map_regions_fortified,
Num.factories = factor(rep(c(1,0,1,3,1),times=num_rows_per_region$Freq)))

#First, plot without any location dots.

ggplot()+geom_polygon(data=map_regions_fortified,
aes(x = long,y = lat, group=group, fill=Num.factories),colour="black")

现在，让我们添加工厂位置。

#Set latitude and longitude based on the number of factories per region.

factory_locations <- data.frame(long = c(-65,-55,-51,-44,-42,-38),
lat = c(-5,-15,-27,-7,-12,-8))

#Add a dummy variable, which then allows the colour of the dots 
#to be a part of the legend.

factory_locations <- data.frame(factory_locations,
Dummy.var = rep("One dot = one factory location",times=nrow(factory_locations)))

#Replot adding factory location dots.
#We will use black dots here since will be easier to see.

ggplot()+geom_polygon(data=map_regions_fortified,
aes(x = long,y = lat, group=group, fill=Num.factories),colour="black")       
+ geom_point(data = factory_locations,aes(x = long,y = lat,colour = Dummy.var)) 
+ scale_colour_manual(values="black") + labs(colour="")

#Bonus: Let's change the color vector to something more color-blind friendly.

mycol <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", 
"#0072B2", "#D55E00", "#CC79A7","#490092")

ggplot()+geom_polygon(data=map_regions_fortified,
aes(x = long,y = lat, group=group, fill=Num.factories),colour="black")       
+ geom_point(data = factory_locations,aes(x = long,y = lat,colour = Dummy.var)) 
+ scale_colour_manual(values="black") + labs(colour="") 
+ scale_fill_manual(values=mycol)