rm(list=ls())
load(file = "/Users/n.w./Documents/countypres_2000-2016.rdata")
library(tidyverse)
library(here)
library(janitor)
library(albersusa)
library(sf)
library(sp)
library(rgeos)
library(maptools)
library(ggthemes)
library(viridis)
library(scales)
library(glue)
library(jkmisc)

Package不是每个都用上的，准确说是大多数都没用的，习惯性全加上了
Data 是MIT election的，数据库为2000-2016年美国总统大选，以country为最小单位，具体link
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VOQCHQ
这里只选取2016年的做例子

data_2016_remove <- x %>% filter(year == 2016, party == 'NA') %>%
  mutate(totalvotes = totalvotes - candidatevotes)

data_2016 <- x %>% filter(year == 2016, party == "democrat") %>% 
  mutate(totalvotes = data_2016_remove$totalvotes) %>%
  mutate(percen = candidatevotes/totalvotes, 
  id = ifelse(str_length(as.character(FIPS)) < 5, glue("0{FIPS}"), as.character(FIPS)))

数据处理上因为不想要除*党和共和党以外政党的选票（对结果无意义），所以先把他们的选票移除出了totalvotes

选取*党为基数，删除共和党数据，因为*党的选票为x%时，共和党则为(1-x)%

选用FIPS为与地图的对应码，注意原data里面FIPS有的仅为4位数，地图数据里为5位数，首位要加0

# Mapping things
us <- counties_composite()
us_map <- fortify(us, region="fips") 

us_map_2016 <- data_2016 %>% right_join(us_map)

map <- ggplot() +
  geom_map(data = us_map, map = us_map,
           aes(x = long, y = lat, map_id = id),size = 0.05, fill = NA) +
  geom_map(data = us_map_2016, map = us_map,
           aes(x = long, y = lat, map_id = id, fill = percen), size = 0.05) + 
  scale_fill_gradient2(low = 'red', mid = 'white', high = 'blue', midpoint = 0.5) + 
  labs(title = "How people vote in 2016 president election",
       subtitle = ("Red for Republican,Trump.\nBlue for Democrat, Hillary."),
       caption = "Data: https://electionlab.mit.edu/ | Graphic: Norman WU") +
  coord_map() +
  theme_map(base_size = 16) +
  theme(legend.title = element_text(size = 10),
        plot.title = element_text(),
        legend.background = element_rect(fill = NA),
        plot.caption = element_text(size = 10),
        legend.position = c(0.9,0.1))


ggsave(here("final.png"), width = 10, height = 6)

最后储存图片

红色代表共和党蓝色代表*党，白色基本是投票50-50
可以看出面积来看川普是大胜的，但是人口多的城市基本又全输了，这张图配合人口密度，gdp，教育情况这些图来一起看应该会很有意思，但是那么细的数据不好搞，阿拉斯加那边不知道是怎么个情况数据出不来，但没所谓了