ggplot2 熱圖繪製

1. 包導入

library(ggplot2)
library(reshape2)

2. 數據導入

mtcars

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

library(ggplot2)
library(reshape2)
> head(mydata)
                   mpg disp  hp drat    wt  qsec
Mazda RX4         21.0  160 110 3.90 2.620 16.46
Mazda RX4 Wag     21.0  160 110 3.90 2.875 17.02
Datsun 710        22.8  108  93 3.85 2.320 18.61
Hornet 4 Drive    21.4  258 110 3.08 3.215 19.44
Hornet Sportabout 18.7  360 175 3.15 3.440 17.02
Valiant           18.1  225 105 2.76 3.460 20.22

3. 相關性係數矩陣計算

mydata_cor<-cor(mydata)
> mydata_cor
            mpg       disp         hp        drat         wt        qsec
mpg   1.0000000 -0.8475514 -0.7761684  0.68117191 -0.8676594  0.41868403
disp -0.8475514  1.0000000  0.7909486 -0.71021393  0.8879799 -0.43369788
hp   -0.7761684  0.7909486  1.0000000 -0.44875912  0.6587479 -0.70822339
drat  0.6811719 -0.7102139 -0.4487591  1.00000000 -0.7124406  0.09120476
wt   -0.8676594  0.8879799  0.6587479 -0.71244065  1.0000000 -0.17471588
qsec  0.4186840 -0.4336979 -0.7082234  0.09120476 -0.1747159  1.00000000

4. 使用ggplot2中geom_tile畫熱力圖

#####使用melt整理數據格式
mydata_cor.m <-melt(mydata_cor)

> head(mydata_cor.m)
Var1 Var2 variable      value
1  mpg  mpg    value  1.0000000
2 disp  mpg    value -0.8475514
3   hp  mpg    value -0.7761684
4 drat  mpg    value  0.6811719
5   wt  mpg    value -0.8676594
6 qsec  mpg    value  0.4186840

繪製熱圖

p_cor <-ggplot(mydata_cor.m, aes(Var1, Var2)) + 
  geom_tile(aes(fill = value),colour = "white") 

p_cor + scale_fill_gradient(name="Value", low = "white",high = "red") +
  theme(axis.text.x = element_text(vjust = 0.5, hjust = 0.5, angle = 90))+
  coord_fixed(ratio=1)+
  theme(axis.text= element_text(size = 8,family="ARL"))+
  theme(plot.margin = unit(c(0.1,0,0,0), unit="mm"))+
  labs(x = "Var1", y = "Var2", title = "correlation")+
  theme(plot.title = element_text(size = 13,hjust = 0.5,family = "ARL" ))+
  theme(legend.key.width=unit(3,'mm'),legend.key.height=unit(3,'cm'))+
  theme(legend.title = element_text(size = 8))

5. 矩陣上三角和下三角的獲取

可以發現共線性矩陣的左上和右下是一模一樣的兩部分；畫出一部分

獲取相關性矩陣的上三角矩陣

get_lower_tri<-function(cormat){
  cormat[upper.tri(cormat)] <- NA
  return(cormat)
}
#####獲取相關性矩陣的下三角矩陣
get_upper_tri <- function(cormat){
  cormat[lower.tri(cormat)]<- NA
  return(cormat)
}

獲取相關性矩陣的上三角矩陣

upper_tri <- get_upper_tri(mydata_cor)
upper_tri
   mpg       disp         hp       drat         wt        qsec
mpg    1 -0.8475514 -0.7761684  0.6811719 -0.8676594  0.41868403
disp  NA  1.0000000  0.7909486 -0.7102139  0.8879799 -0.43369788
hp    NA         NA  1.0000000 -0.4487591  0.6587479 -0.70822339
drat  NA         NA         NA  1.0000000 -0.7124406  0.09120476
wt    NA         NA         NA         NA  1.0000000 -0.17471588
qsec  NA         NA         NA         NA         NA  1.00000000

數據格式轉換，並且刪除無效值

upper_tri.m <- melt(upper_tri, na.rm = TRUE)

熱力圖繪製

p_cor.up <-ggplot(upper_tri.m, aes(Var1, Var2)) + 
  geom_tile(aes(fill = value),colour = "white") 

p_cor.up + scale_fill_gradient(name="Value", low = "white",high = "red") +
  theme(axis.text.x = element_text(vjust = 0.5, hjust = 0.5, angle = 90))+
  coord_fixed(ratio=1)+
  theme(axis.text= element_text(size = 8,family="ARL"))+
  theme(plot.margin = unit(c(0.1,0,0,0), unit="mm"))+
  labs(x = "Var1", y = "Var2", title = "correlation")+
  theme(plot.title = element_text(size = 13,hjust = 0.5,family = "ARL" ))+
  theme(legend.key.width=unit(3,'mm'),legend.key.height=unit(3,'cm'))+
  theme(legend.title = element_text(size = 8))

6. 對數據進行層次聚類

reorder_cormat <- function(cormat){
  dd <- as.dist((1-cormat)/2)  ##使用相關性係數作爲變量間距離
  hc <- hclust(dd)
  cormat <-cormat[hc$order, hc$order]
}

利用相關性係數對變量進行聚類

mydata_cor.order <- reorder_cormat(mydata_cor)
order.upper_tri <- get_upper_tri(mydata_cor.order)

轉換數據格式

order.upper_tri.m <- melt(order.upper_tri, na.rm = TRUE)

繪製熱力圖

p_cor.order <-ggplot(order.upper_tri.m, aes(Var1, Var2)) + 
  geom_tile(aes(fill = value),colour = "white") 

p_cor.order + scale_fill_gradient(name="Value", low = "white",high = "red") +
  theme(axis.text.x = element_text(vjust = 0.5, hjust = 0.5, angle = 90))+
  coord_fixed(ratio=1)+
  theme(axis.text= element_text(size = 8,family="ARL"))+
  theme(plot.margin = unit(c(0.1,0,0,0), unit="mm"))+
  labs(x = "Var1", y = "Var2", title = "correlation")+
  theme(plot.title = element_text(size = 13,hjust = 0.5,family = "ARL" ))+
  theme(legend.key.width=unit(3,'mm'),legend.key.height=unit(3,'cm'))+
  theme(legend.title = element_text(size = 8))

7. 在熱力圖上添加相關性註釋

p_cor.order + scale_fill_gradient(name="Value", low = "white",high = "red") +
  theme(axis.text.x = element_text(vjust = 0.5, hjust = 0.5, angle = 90))+
  coord_fixed(ratio=1)+
  theme(axis.text= element_text(size = 8,family="ARL"))+
  theme(plot.margin = unit(c(0.1,0,0,0), unit="mm"))+
  labs(x = "Var1", y = "Var2", title = "correlation")+
  theme(plot.title = element_text(size = 13,hjust = 0.5,family = "ARL" ))+
  theme(legend.key.width=unit(3,'mm'),legend.key.height=unit(3,'cm'))+
  theme(legend.title = element_text(size = 8))+
  geom_text(aes(Var1, Var2, label = value), color = "black", size = 3)

參考：
ggplot2 : Quick correlation matrix heatmap