绘图技巧 | 绘制带饼图的地图可视化技巧分享

DataCharm

发布于 2021-04-16 12:02:11

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发布于 2021-04-16 12:02:11

文章被收录于专栏：数据学术商业新闻数据学术商业新闻数据学术商业新闻

这一期呢，小编回应读者的需求，绘制在地图上添加饼图的可视化作品，这类图表在GIS或生态方面涉及的比较多哈。本期内容主要如下：

R-scatterpie包主要函数介绍
R-scatterpie包实例演示

R-scatterpie包主要函数介绍

要想在地图上绘制饼图，使用常规的ggplot2较为繁琐，得益于丰富第三方包，我们可以使用scatterpie包轻松实现该需求。scatterpie包主要有：

geom_scatterpie()
geom_scatterpie_legend()

前者主要用于绘制饼图绘制，后者则对应绘制饼图大小的图例，具体可参考如下链接：https://cran.r-project.org/web/packages/scatterpie/vignettes/scatterpie.html。

接下来，小编将结合具体实际例子用于帮助小伙伴们理解scatterpie绘制函数，快速绘制带饼图的空间地图可视化作品。

R-scatterpie包实例演示

这一部分我们分别通过虚构数据绘制美国和中国的饼图地图作品，所使用的数据也会在文末给出免费获取方式。

「以美国地图为例」数据处理代码如下(较为简单且都有注释)：

#导入包
library(readxl)
library(albersusa)
library(biscale)
library(sf)
library(tidyverse)
library(hrbrthemes)
library(ggtext)
library(scatterpie)
library("ggsci")

#读入虚构数据
data_test <- read_excel("usa_lon_lat.xlsx")
#转换投影
data_test_pro <- data_test %>% sf::st_as_sf(coords = c("lon", "lat"),crs = 4326)%>% st_transform(crs = 2163)
#将结果经纬度信息单独列出并准换成DF类型
data_test_pro  <-  data_test_pro %>% dplyr::mutate(lon = sf::st_coordinates(.)[,1],
                                lat = sf::st_coordinates(.)[,2])%>% select(values01,values02,values03,total,lon,lat)%>%data.frame(stringsAsFactors = F) 

#可视化绘制
US_map <- ggplot()+
    geom_sf(data = us_sf,size=.125,aes(geometry = geometry)) +
    scatterpie::geom_scatterpie(data=data_select,
                                aes(x = lon,y = lat,r=total*40),
                                cols=c("values01","values02","values03"),
                                alpha=.9
                                ) +
    geom_scatterpie_legend(data_select$total*40, x=2068885.4, y=-2242789,n = 3,
                           labeller=function(x) x/40) +
    scale_fill_jco(name="Type") +

  labs(x="",y="",
    title = "Example of <span style='color:#D20F26'>scatterpie::geom_scatterpie function</span>",
    subtitle = "processed charts with <span style='color:#1A73E8'>usa map()</span>",
    caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +
    theme_ipsum(base_family = "Roboto Condensed") +
    theme(
    plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
                                  size = 20, margin = margin(t = 1, b = 12)),
    plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),
    plot.caption = element_markdown(face = 'bold',size = 12))

注意：geom_scatterpie_legend()中x,y的位置为转换后的数据哈。可视化结果如下：

Scatterpie Map Example of USA

「以中国地图为例」同样的，我们还是虚构数据构建关于中国的类似地图的绘制，详细绘制代码如下：

china_data <- read_excel("China_lon_lat.xlsx")
china_data_pro <- china_data %>% sf::st_as_sf(coords = c("lon", "lat"),crs = 4326)%>% st_transform(crs = 2343)%>% dplyr::mutate(lon = sf::st_coordinates(.)[,1],
                                lat = sf::st_coordinates(.)[,2])%>% select(data01,data02,data03,total,lon,lat) %>% 
               data.frame(stringsAsFactors = F) 
#读取中国地图文件信息
china_shp <- "中国省级地图GS（2019）1719号.geojson"
nine <- "九段线GS（2019）1719号.geojson"
china <- sf::read_sf(china_shp)
nine_line <- sf::read_sf(nine)
# 可视化绘制
china_map <- ggplot()+
     geom_sf(data = china,fill="gray90",size=.125,color="black") + 
     geom_sf(data = nine_line,size=.125) + 
     coord_sf(crs = 2343) +
    scatterpie::geom_scatterpie(data=china_data_select,
                                aes(x = lon,y = lat,r=total*500),
                                cols=c("data01","data02","data03"),
                                alpha=.9
                                ) +
    geom_scatterpie_legend(china_data_select$total*500,x = 25000,y=1500000,n = 3,
                           labeller=function(x) x/500) +
    scale_fill_nejm(name="Type") +

  labs(x="",y="",
    title = "Example of <span style='color:#D20F26'>scatterpie::geom_scatterpie function</span>",
    subtitle = "processed charts with <span style='color:#1A73E8'>China map</span>",
    caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +
    theme_ipsum(base_family = "Roboto Condensed") +
    theme(
    plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
                                  size = 20, margin = margin(t = 1, b = 12)),
    plot.subtitle = element_markdown(hjust = 0,vjust = 0,size=15),
    plot.caption = element_markdown(face = 'bold',size = 12))

可视化结果如下(小编使用了ggsci包的不同颜色主题哦)：