來自 https://mp.weixin.qq.com/s/9IrY9kajZG2SJcoewUVH8w

• 1 簡介

數(shù)據(jù)介紹:
該數(shù)據(jù)是從丁香園·丁香醫(yī)生通過爬蟲獲取的全國2019-ncov病毒的感染病例编饺。
時間的分辨率：1小時
空間分辨率：城市和省份
起止時間：從2020/1/25/17時到疫情結(jié)束

• 2 需要的包

devtools::install_github("microly/alimap")

library(alimap) # to get China map at the prefecture city level
library(sf)
library(ggplot2)
library(dplyr)
library(tibble)
library(tidyr)
library(magrittr)
library(purrr)
library(readr)
library(stringr)
library(gganimate)
library(lubridate)
library(Cairo)
library(magick)

• 3 地圖數(shù)據(jù)

如果有本地數(shù)據(jù)，可以自行讀取响驴。
因為很多市級地名存在變化透且，
而且爬取的比較亂，部分沒有“市”這個字豁鲤，
所以使用前2個漢字進行聯(lián)結(jié)表秽誊。以地圖數(shù)據(jù)集中的城市名為準。

Chinamap_cities_sf <- map_prefecture_city() %>% 
  mutate(c2 = str_sub(name, 1, 2))

• 4 時間序列

每12小時更新1次琳骡，從早上9點到晚上9點锅论。

# set start day
startTime <- ymd_h("2020/1/25 21")
nowTime <- Sys.time() %>% with_tz(tz = "Asia/Shanghai") # only support Shanghai timezone
endTime <- if(hour(nowTime) > 21) {
  date(nowTime) + dhours(21)
} else if (hour(nowTime) > 9){
  date(nowTime) + dhours(9)
} else {
  date(nowTime) - ddays(1) + dhours(21)
}

timeLength <- interval(startTime, endTime) %>% 
  time_length("hour") %>% `/`(12)
# time sequence
mytime <- startTime + dhours(12*(0:timeLength)) %>% .[-6] # 404 at the time
mymonth <- month(mytime)
myday <- day(mytime)
myhour <- hour(mytime) %>% as.character() %>% 
  str_pad(width = 2, side = "left", pad = "0") # make character string same length

myAPI <- paste(date(mytime), myhour, sep = "T")

• 5 疫情數(shù)據(jù)

通過API接口讀取疫情歷史數(shù)據(jù)，API接口由網(wǎng)友提供日熬，爬取自丁香園棍厌。

# define a function to read epidemic data of a day
read_epidemic <- function(oneAPI) {
  url_API <- paste0("http://69.171.70.18:5000/download/city_level_", oneAPI, ".csv")
  epidemic_df <- read_csv(file = url_API)
  colnames(epidemic_df) <- c("x1","unnamed", "city", "confirmed_c", "suspected_c",
                             "cured_c", "dead_c", "province", "short_p", "confirmed_p",
                             "suspected_p", "cured_p", "dead_p", "comment")
  epidemic_df %<>% select(city, confirmed_c)
  return(epidemic_df)
}
  
  
epidemic_nest <- tibble(time = mytime,
                        myAPI = myAPI) %>% 
  mutate(., data = map(.$myAPI, ~read_epidemic(.x))) %>% 
  select(-myAPI) %>% unnest()

• 5.1 分箱

因為很多市級地名存在變化，
而且爬取的比較亂竖席，部分沒有“市”這個字耘纱，
所以使用前2個漢字進行聯(lián)結(jié)表。以地圖數(shù)據(jù)集中的城市名為準毕荐。

mybreaks <- c(0, 1, 10, 50, 100, 500, 1000, 5000, 100000)
mylabels <- c("0", "1-9", "10-49", "50-99", "100-499",
              "500-999", "1000-4999", ">=5000")

epidemic_df <- epidemic_nest %>% 
  mutate(conf2 = cut(confirmed_c, breaks = mybreaks,
                     labels = mylabels, include.lowest = TRUE,
                     right = FALSE, ordered_result = TRUE)) %>% 
  mutate(c2 = str_sub(city, 1, 2))

• 6 聯(lián)結(jié)表及循環(huán)繪圖

# create temporary document
dir.create(dir1 <- file.path(tempdir(), "testdir"))

for (i in 1:length(mytime)) {
  # join epidemic data with map data
  epidemic_time <- epidemic_df %>% filter(time == mytime[i])
  epidemic_city <- Chinamap_cities_sf %>% left_join(epidemic_time, by = "c2")
  # treatment NA
  conf2 <- epidemic_city$conf2 %>% replace_na(0)
  epidemic_city %<>% select(-c2, -city, -conf2)
  epidemic_city$conf2 <- conf2

  # plot
  gg_epidemic <-  ggplot(epidemic_city) + 
    geom_sf(aes(fill = conf2)) + 
    coord_sf() + 
    scale_fill_brewer(palette = "YlOrRd", direction = 1) + 
    guides(fill = guide_legend(title = "確診人數(shù)", reverse = T)) + 
    labs(title = "2019-ncov疫情數(shù)據(jù)可視化",
        subtitle = mytime[i],
        caption = "數(shù)據(jù)來源：丁香園·丁香醫(yī)生") + 
    theme(
      # 標題
      plot.title = element_text(face = "bold", hjust = 0.5,
                                color = "black"),
      plot.subtitle = element_text(face = "bold", hjust = 0.5, size = 20,
                                color = "red"),
      plot.caption = element_text(face = "bold", hjust = 1,
                                color = "blue"),
      # 圖例
      legend.title = element_text(face = "bold",
                                  color = "black"),
      legend.text = element_text(face = "bold",
                                 color = "black"),
      legend.background = element_rect(colour = "black"),
      legend.key = element_rect(fill = NA), # 圖例箱體無背景
      legend.position = c(0.85, 0.2),
      axis.ticks = element_blank(),
      axis.text = element_blank(),
      # 繪圖面板
      panel.background = element_blank(),
      panel.border = element_rect(color = "black", linetype = "solid", size = 1, fill = NA)
      )
  
  # save picture
  ggsave(filename = paste0(date(mytime[i]), "_", hour(mytime[i]), ".png"),
         plot = gg_epidemic, path = dir1,
         width = 20, height = 20, units = "cm")
}

• 7 動畫

path_pre <- "./"
animate_epidemic <- 
  image_animate(image = image_read(path = paste0(dir1, "/",
                                            date(mytime), "_", hour(mytime), ".png")))
anim_save(filename = "疫情地圖可視化動態(tài)圖.gif",
          animation = animate_epidemic, path = path_pre)

unlink(dir1)

2019-nCoV疫情地圖動態(tài)可視化