2012-09-12 27 views

回答

18

鍵入get("compute_group", ggplot2::StatDensity)(或者,以前,get("calculate", ggplot2:::StatDensity))將讓你用來計算密度的算法。 (在根,這是對density()通話與kernel="gaussian"默認值)。

在劇情中使用的點是不可見的print.ggplot()返回,這樣你就可以像這樣訪問他們:

library(ggplot2) 
m <- ggplot(movies, aes(x = rating)) 
m <- m + geom_density() 
p <- print(m) 
head(p$data[[1]], 3) 
#   y  x density scaled count PANEL group ymin  ymax 
# 1 0.0073761 1.0000 0.0073761 0.025917 433.63  1  1 0 0.0073761 
# 2 0.0076527 1.0176 0.0076527 0.026888 449.88  1  1 0 0.0076527 
# 3 0.0078726 1.0352 0.0078726 0.027661 462.81  1  1 0 0.0078726 


## Just to show that those are the points you are after, 
## extract and use them to create a lattice xyplot 
library(gridExtra) 
library(lattice) 
mm <- xyplot(y ~x, data=p$data[[1]], type="l") 

enter image description here

3

正如其他答案中所建議的那樣,您可以使用print.ggplot()訪問ggplot點。但是,print() -ing代碼也會打印ggplot對象,這可能不是所期望的。

你可以提取ggplot對象數據,不打印的情節,使用ggplot_build()

library(ggplot2) 
library(ggplot2movies) 

m <- ggplot(movies, aes(x = rating)) 
m <- m + geom_density() 
p <- ggplot_build(m) # <---- INSTEAD OF `p <- print(m)` 
head(p$data[[1]], 3) 
#    y  x  density  scaled count  n PANEL group ymin 
# 1 0.007376115 1.000000 0.007376115 0.02591684 433.6271 58788  1 -1 0 
# 2 0.007652653 1.017613 0.007652653 0.02688849 449.8842 58788  1 -1 0 
# 3 0.007872571 1.035225 0.007872571 0.02766120 462.8127 58788  1 -1 0 


# Just to show that those are the points you are after, extract and use them 
# to create a lattice xyplot 
library(lattice) 
m2 <- xyplot(y ~x, data=p$data[[1]], type="l") 

library(gridExtra) 
grid.arrange(m, m2, nrow=1) 

enter image description here

+1

謝謝,威震天,那正是我在找的! – Luis

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