如何为每个方面构建一个具有各自比例的热图，而不是 r 中所有的一个通用比例？

如何解决如何为每个方面构建一个具有各自比例的热图，而不是 r 中所有的一个通用比例？

我正在尝试创建一个 heatmap，它应该根据 % vaccinated 每个月（对于每一行）分配颜色

例如一月份所有州之间的颜色比较，然后

例如 3 月份所有州之间的颜色比较 .. .

然后四月...君等

问题：基本上我希望每个月都有自己的高低比例，我正试图用 facet 做到这一点，但它正在分配所有方面/月的一个常见的低-高量表。

library(tidyverse)
library(lubridate)
library(scales)

file_url1 <- url("https://raw.githubusercontent.com/johnsNow09/covid19-df_stack-code/main/df_vaccination.csv")

df_vaccination <- read.csv(url(file_url1))

df_vaccination <- df_vaccination %>%
  mutate(Updated.On = as.Date(Updated.On))

代码：我试过了

df_vaccination %>% 
  filter(State != "India") %>% 

  # summarise each month,state's vaccination
  mutate(month_abbr = month(Updated.On,label = TRUE,abbr = TRUE),State = fct_reorder(State,Population,max)) %>% 
  group_by(month_abbr,State) %>% 
  summarise(monthly_ind_vaccinated = sum(Total.Individuals.Vaccinated_Dailycalc,na.rm = TRUE),Population = first(Population),.groups = "drop") %>% 
  
  # get % Vaccination to State population for each month
  group_by(State) %>% 
  mutate(prc_vaccinated_per_pop = monthly_ind_vaccinated / Population) %>% 
  na.omit() %>%
  ungroup() %>% 

  filter(State %in% c("Delhi","Maharashtra")) %>%
  
  # group_by(month_abbr) %>%
  
  ggplot(aes(x = State,y = month_abbr,fill = prc_vaccinated_per_pop)) +
  geom_tile() +
  
  scale_fill_gradient2(low = "white",high = "darkblue",labels = percent) +
    
  facet_wrap(~as.factor(month_abbr),scales = "free_y",nrow = 6) +
  
  theme(axis.text.x = element_text(angle = 90,vjust = -.02),strip.text = element_blank()) +
  labs(title = "States with highest % Vaccination each month ?",caption = "created by ViSa",fill = "% Vaccinated each month",x = "",y = "")

输出：

我认为由于颜色值基于 fill，因此不会让不同的比例应用于不同的方面。

有没有类似于 (scales = free_fill) 而不是 (scales = free_y) 的东西？

数据输出：

# A tibble: 12 x 5
# Groups:   month_abbr [6]
   month_abbr State     monthly_ind_vaccina~ Population prc_vaccinated_per_~
   <ord>      <fct>                    <int>      <dbl>                <dbl>
 1 Jan        Delhi                    43948   18710922              0.00235
 2 Jan        Maharash~               228424  123144223              0.00185
 3 Feb        Delhi                   322859   18710922              0.0173 
 4 Feb        Maharash~               794370  123144223              0.00645
 5 Mar        Delhi                   666628   18710922              0.0356 
 6 Mar        Maharash~              4590035  123144223              0.0373 
 7 Apr        Delhi                  1547324   18710922              0.0827 
 8 Apr        Maharash~              7942882  123144223              0.0645 
 9 May        Delhi                  1613335   18710922              0.0862 
10 May        Maharash~              4455440  123144223              0.0362 
11 Jun        Delhi                   250366   18710922              0.0134 
12 Jun        Maharash~              1777873  123144223              0.0144