geom_tile（）如何處理重複的數據？

Question

如果在特定x和y位置有多個數據，那麼geom_tile()如何組合數據？

例如：

library(ggplot2) 
library(dplyr) 

mtcars %>% 
ggplot(aes(x=vs,y=am,fill=mpg)) + 
geom_tile() 

這是不是意味着：

library(ggplot2) 
library(dplyr) 

mtcars %>% 
group_by(vs,am) %>% 
summarize(mpg = mean(mpg)) %>% 
ggplot(aes(x=vs,y=am,fill=mpg)) + 
geom_tile() 

Answer 1

你可以只是看：

library(ggplot2) 
library(dplyr) 

mtcars %>% 
    ggplot(aes(x=vs, y=am, fill=mpg)) + 
    geom_tile() -> gg 

ggplot_build(gg) -> gb 

gb$data[[1]] 
##  fill x y PANEL group xmin xmax ymin ymax colour size linetype alpha 
## 1 #30648F 0 1  1 -1 -0.5 0.5 0.5 1.5  NA 0.1  1 NA 
## 2 #30648F 0 1  1 -1 -0.5 0.5 0.5 1.5  NA 0.1  1 NA 
## 3 #356E9D 1 1  1 -1 0.5 1.5 0.5 1.5  NA 0.1  1 NA 
## 4 #316692 1 0  1 -1 0.5 1.5 -0.5 0.5  NA 0.1  1 NA 
## 5 #29577E 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 6 #275379 1 0  1 -1 0.5 1.5 -0.5 0.5  NA 0.1  1 NA 
## 7 #1D3F5E 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 8 #3977A9 1 0  1 -1 0.5 1.5 -0.5 0.5  NA 0.1  1 NA 
## 9 #356E9D 1 0  1 -1 0.5 1.5 -0.5 0.5  NA 0.1  1 NA 
## 10 #2A5982 1 0  1 -1 0.5 1.5 -0.5 0.5  NA 0.1  1 NA 
## 11 #275277 1 0  1 -1 0.5 1.5 -0.5 0.5  NA 0.1  1 NA 
## 12 #234A6D 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 13 #254F73 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 14 #1F4464 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 15 #132B43 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 16 #132B43 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 17 #1E4161 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 18 #51A8EA 1 1  1 -1 0.5 1.5 0.5 1.5  NA 0.1  1 NA 
## 19 #4B9BDA 1 1  1 -1 0.5 1.5 0.5 1.5  NA 0.1  1 NA 
## 20 #56B1F7 1 1  1 -1 0.5 1.5 0.5 1.5  NA 0.1  1 NA 
## 21 #316693 1 0  1 -1 0.5 1.5 -0.5 0.5  NA 0.1  1 NA 
## 22 #204566 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 23 #1F4464 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 24 #1A3A57 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 25 #2A5982 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 26 #4288C1 1 1  1 -1 0.5 1.5 0.5 1.5  NA 0.1  1 NA 
## 27 #3E81B6 0 1  1 -1 -0.5 0.5 0.5 1.5  NA 0.1  1 NA 
## 28 #4B9BDA 1 1  1 -1 0.5 1.5 0.5 1.5  NA 0.1  1 NA 
## 29 #214769 0 1  1 -1 -0.5 0.5 0.5 1.5  NA 0.1  1 NA 
## 30 #2C5C85 0 1  1 -1 -0.5 0.5 0.5 1.5  NA 0.1  1 NA 
## 31 #1F4363 0 1  1 -1 -0.5 0.5 0.5 1.5  NA 0.1  1 NA 
## 32 #316692 1 1  1 -1 0.5 1.5 0.5 1.5  NA 0.1  1 NA 

和

mtcars %>% 
    group_by(vs,am) %>% 
    summarize(mpg = mean(mpg)) %>% 
    ggplot(aes(x=vs,y=am,fill=mpg)) + 
    geom_tile() -> gg 

ggplot_build(gg) -> gb 

gb$data[[1]] 
##  fill x y PANEL group xmin xmax ymin ymax colour size linetype alpha 
## 1 #132B43 0 0  1 -1 -0.5 0.5 -0.5 0.5  NA 0.1  1 NA 
## 2 #29577E 0 1  1 -1 -0.5 0.5 0.5 1.5  NA 0.1  1 NA 
## 3 #2E608B 1 0  1 -1 0.5 1.5 -0.5 0.5  NA 0.1  1 NA 
## 4 #56B1F7 1 1  1 -1 0.5 1.5 0.5 1.5  NA 0.1  1 NA