將geom_map與transition_time一起使用會得到錯誤：insert_points(polygon$x,polygon$y,splits,n)中的錯誤

我試圖展示紐約州 COVID 病例的增長情況

這段代碼得到了我想要的情節，但沒有影片或時間方面。

完整錯誤：

insert_points(polygon$x,polygon$y,splits,n) 中的錯誤：
與請求的型別不兼容：[type=NULL; 目標=雙]。

county_map = map_data("county", region = "New York")

county_map$region = county_map$subregion

covidCounties = read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv", header = T)
covidCounties = covidCounties %>%
  mutate(date = as.Date(date)) %>%
  filter(state == "New York") %>%
  arrange(date)%>%
  group_by(county) %>%
  mutate(county = tolower(county)) %>%
  mutate(newCases = diff(c(0, cases))) %>%
  mutate(newDeaths = diff(c(0, deaths))) %>%
  ungroup() %>%
  select(date, state, county, cases, newCases, deaths)

covidCountyMap = covidCounties %>%
  ggplot(aes(
    map_id = county,
    fill = newCases,
    group = county
  )) 
  geom_map(
    map = county_map,
    color = "black"
  ) 
  expand_limits(x = county_map$long, y = county_map$lat) 
  scale_fill_gradientn(colors = c("green", "yellow", "red"), breaks = c(0, 100, 500)) 
  labs(
    title = "New cases over time in New York State",
    subtitle = "{frame_time}"
  )

covidCountyMap

covidCountyMap 
  transition_time(date)

uj5u.com熱心網友回復：

您需要告訴 {gganimate} 哪些多邊形要相互轉換。它無法為您猜測。換句話說，您需要為每個過渡狀態添加一個組識別符號（意思是按日期顯示每個縣）。

我只過濾到一種狀態，因為整個資料的 reprex 一直崩潰。我已將您的計數轉換為對數刻度，以便更好地表示資料范圍。（有一些負值，因此警告）

library(tidyverse)
library(gganimate)

county_map = map_data("county", region = "New York")

county_map$region = county_map$subregion

## I'd advise to create a separate data frame for your raw data, and not overwrite it
covidCounties_raw = read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv", header = T)

covidCounties <- covidCounties_raw %>%
  mutate(date = as.Date(date)) %>%
  filter(state == "New York") %>%
  arrange(date) %>%
  group_by(county) %>%
  mutate(county = tolower(county)) %>%
  mutate(newCases = diff(c(0, cases))) %>%
  mutate(newDeaths = diff(c(0, deaths))) %>%
  ungroup() %>%
  select(date, state, county, cases, newCases, deaths) %>%
  ## this is the main trick
  group_by(date, county) %>%
  mutate(id = cur_group_id()) %>%
  ungroup() %>%
  ## I'm filtering for only one county because the reprex took too long with the entire data
  filter(county == "nassau")

covidCountyMap <- covidCounties %>%
  ggplot(aes(
    map_id = county,
    fill = newCases,
## use the group identifier for your grouping
    group = id
  ))  
  geom_map(
    map = county_map,
    color = "black"
  )  
  expand_limits(x = county_map$long, y = county_map$lat)  
  scale_fill_gradientn(colors = c("green", "yellow", "red"),
## log transformed scale
                       trans = "log")  
  labs(
    title = "New cases over time in New York State",
    subtitle = "{frame_time}"
  )

anim <- covidCountyMap  
  transition_time(date)

## have slightly reduced the frame rate to make it slightly faster
animate(anim, fps = 5, nframes = 50)
#> Warning: Transformation introduced infinite values in discrete y-axis

^{由reprex 包(v2.0.1)于 2021 年 11 月 30 日創建}

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