Using geom_map with transition_time gets Error: Error in insert_points(polygon$x, polygon$y, splits, n)
Using geom_map with transition_time gets Error: Error in insert_points(polygon$x, polygon$y, splits, n)
我正在尝试展示纽约州 COVID 病例的增长情况
这段代码得到了我想要的情节，但没有动画或时间方面。
完整错误：
Error in insert_points(polygon$x, polygon$y, splits, n):
Not compatible with requested type: [type=NULL; target=double].
library(ggplot2)
library(gganimate)
library(transformer)
library(tidyverse)
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)
您需要告诉 {gganimate} 哪些多边形可以相互过渡。它无法为您猜测。换句话说，您需要为每个过渡状态（按日期表示每个县）添加一个组标识符。
我只过滤到一个状态，因为整个数据的 reprex 一直在崩溃。为了更好地表示数据范围，我已将您的计数转换为对数刻度。 （有一些负值，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 package (v2.0.1)
于 2021-11-30 创建
我正在尝试展示纽约州 COVID 病例的增长情况
这段代码得到了我想要的情节，但没有动画或时间方面。
完整错误：
Error in insert_points(polygon$x, polygon$y, splits, n):
Not compatible with requested type: [type=NULL; target=double].
library(ggplot2)
library(gganimate)
library(transformer)
library(tidyverse)
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)
您需要告诉 {gganimate} 哪些多边形可以相互过渡。它无法为您猜测。换句话说，您需要为每个过渡状态（按日期表示每个县）添加一个组标识符。
我只过滤到一个状态，因为整个数据的 reprex 一直在崩溃。为了更好地表示数据范围，我已将您的计数转换为对数刻度。 （有一些负值，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 package (v2.0.1)
于 2021-11-30 创建