基于geojson和ggplot2的氯普烈地图

Question

我试图用Human Poverty Index和ggplot2在R中为尼泊尔各地区绘制choropleth地图。

我与从这里开始区一起阅读了尼泊尔的从这里开始数据。

我看到了一些例子，这里，这里。

我就是这样做的：

# Read geojson data for nepal with districts
library(tidyverse)
library(geojsonio)
#> 
#> Attaching package: 'geojsonio'
#> The following object is masked from 'package:base':
#> 
#>     pretty
spdf <- geojson_read("nepal-districts.geojson",  what = "sp")
##https://github.com/mesaugat/geoJSON-Nepal/blob/master/nepal-districts.geojson




#tidy data for ggplot2
library(broom)
spdf_fortified <- tidy(spdf)
#> Regions defined for each Polygons

# plot
ggplot() +
    geom_polygon(data = spdf_fortified, aes( x = long, y = lat, group = group)) +
    theme_void() +
    coord_map()

​

​

names(spdf_fortified)
#> [1] "long"  "lat"   "order" "hole"  "piece" "group" "id"



#Now read the data to map to districts
data=read.csv("data.csv")
#data from here
#https://github.com/opennepal/odp-poverty/blob/master/Human%20Poverty%20Index%20Value%20by%20Districts%20(2011)/data.csv

#filter and select data to reflect Value of HPI in various districts
data <- data %>% filter(Sub.Group=="HPI") %>% select(District,Value)


head(data)
#>       District Value
#> 1       Achham 46.68
#> 2 Arghakhanchi 27.37
#> 3        Banke 32.10
#> 4      Baglung 27.33
#> 5      Baitadi 39.58
#> 6      Bajhang 45.32

# Value represents HPI value for each district.

#Now how to merge and fill Value for various districts
#
#
#
#

由reprex封装创建于2018-06-14 (v0.2.0)。

如果我可以将spdf_fortified和data合并到merged_df中，我想我可以用下面的代码得到叶绿体地图：

ggplot(data = merged_df, aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = Value), color = 'gray', size = 0.1)

在合并两个数据方面有帮助吗？

Answer 1

不是为了颠覆整个系统，但我最近一直在使用sf，并且发现使用它比sp容易得多。ggplot也有很好的支持，所以您可以用geom_sf绘图，通过将变量映射到fill而变成一个合唱团。

library(sf)
library(tidyverse)

nepal_shp <- read_sf('https://raw.githubusercontent.com/mesaugat/geoJSON-Nepal/master/nepal-districts.geojson')
nepal_data <- read_csv('https://raw.githubusercontent.com/opennepal/odp-poverty/master/Human%20Poverty%20Index%20Value%20by%20Districts%20(2011)/data.csv')

# calculate points at which to plot labels
centroids <- nepal_shp %>% 
    st_centroid() %>% 
    bind_cols(as_data_frame(st_coordinates(.)))    # unpack points to lat/lon columns

nepal_data %>% 
    filter(`Sub Group` == "HPI") %>% 
    mutate(District = toupper(District)) %>% 
    left_join(nepal_shp, ., by = c('DISTRICT' = 'District')) %>% 
    ggplot() + 
    geom_sf(aes(fill = Value)) + 
    geom_text(aes(X, Y, label = DISTRICT), data = centroids, size = 1, color = 'white')

​

​

在这两个数据框架中，有三个地区的名称是不同的，必须进行清理，但这是一个很好的起点，不需要做大量的工作。

ggrepel::geom_text_repel是避免重叠标签的一种可能性。