2D密度图对研究2个数字变量之间的关系很有用，尤其是有大量的点，这篇文章将介绍几种类型的二维密度图，并且尝试用不同方法绘制。

1 geom_point()绘制二维散点图

> install.packages("tidyverse")
> library(tidyverse)

# 创建数据
> a <- data.frame( x=rnorm(30000, 12, 1.9), y=rnorm(30000, 9, 1.2) )
> b <- data.frame( x=rnorm(30000, 15.5, 1.9), y=rnorm(30000, 12.5, 1.9) )
> c <- data.frame( x=rnorm(30000, 9, 1.9), y=rnorm(30000, 15.5, 1.9) )
> data <- rbind(a,b,c)

# 绘制基础散点图
> ggplot(data, aes(x=x, y=y) ) 
+     geom_point()

OUTPUT:

2 geom_bin2d()绘制二维直方图

2.1基础绘制

> library(tidyverse)

# 创建数据
> a <- data.frame( x=rnorm(30000, 12, 1.9), y=rnorm(30000, 9, 1.2) )
> b <- data.frame( x=rnorm(30000, 15.5, 1.9), y=rnorm(30000, 12.5, 1.9) )
> c <- data.frame( x=rnorm(30000, 9, 1.9), y=rnorm(30000, 15.5, 1.9) )
> data <- rbind(a,b,c)

# 绘制基础2d直方图
> ggplot(data, aes(x=x, y=y) ) 
+     geom_bin2d() 
+     theme_bw()

OUTPUT:

2.2 参数修改

 

# 修改颜色和bin大小
> ggplot(data, aes(x=x, y=y) ) 
+     geom_bin2d(bins = 90) 
+     scale_fill_continuous(type = "viridis") 
+     theme_bw()

OUTPUT:

3 geom_hex() 绘制六边形图（Hexbin Plot）

# 绘制六边形图及参数自定义
> ggplot(data, aes(x=x, y=y) ) 
+     geom_hex(bins = 90) 
+     scale_fill_continuous(type = "viridis") 
+     theme_bw()

OUTPUT:

 

4 stat_density_2d绘制二维密度图

4.1 基础绘制二维密度图

# geom参数设置区域，color参数设置轮廓线 
> ggplot(data, aes(x=x, y=y) ) 
+     stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white")

OUTPUT:

4.2 使用raster光栅显示

> ggplot(data, aes(x=x, y=y) ) 
+     stat_density_2d(aes(fill = ..density..), geom = "raster", contour = FALSE) 
+     scale_x_continuous(expand = c(0, 0)) 
+     scale_y_continuous(expand = c(0, 0)) 

OUTPUT:

4.3 自定义调色盘等，可以用数字或名称定义 

> ggplot(data, aes(x=x, y=y) ) 
+     stat_density_2d(aes(fill = ..density..), geom = "raster", contour = FALSE) 
+     scale_fill_distiller(palette=4, direction=1) 
+     scale_x_continuous(expand = c(0, 0)) 
+     scale_y_continuous(expand = c(0, 0)) 
+     theme(
+         legend.position='none'
+     )

OUTPUT:

> ggplot(data, aes(x=x, y=y) ) 
+     stat_density_2d(aes(fill = ..density..), geom = "raster", contour = FALSE) 
+     scale_fill_distiller(palette= "Spectral", direction=1) 
+     scale_x_continuous(expand = c(0, 0)) 
+     scale_y_continuous(expand = c(0, 0)) 
+     theme(
+         legend.position='none'
+     )

OUTPUT:

 

 

标签：Plot,aes,scale,Density,density,2D,geom,OUTPUT,data
来源： https://blog.csdn.net/Luminoll/article/details/122742723