使用R语言绘制散点图结合边际分布图教程

主要使用ggExtra结合ggplot2两个R包进行绘制。（胜在简洁方便）使用cowplot与ggpubr进行绘制。（胜在灵活且美观）

下面的绘图我们均以iris数据集为例。

 

1. 使用ggExtra结合ggplot2

1）传统散点图

# library
library(ggplot2)
library(ggExtra)

# classic plot
p <- ggplot(iris) +
geom_point(aes(x = Sepal.Length, y = Sepal.Width, color = Species), alpha = 0.6, shape = 16) +  # alpha 调整点的透明度；shape 调整点的形状
theme_bw() +
theme(legend.position = "bottom") + # 图例置于底部
labs(x = "Sepal Length", y = "Sepal Width") # 添加x，y轴的名称
p

下面我们一行代码添加边际分布（分别以密度曲线与直方图的形式来展现）：

2）密度函数

# marginal plot: density
ggMarginal(p, type = "density", groupColour = TRUE, groupFill = TRUE)

3）直方图

# marginal plot: histogram
ggMarginal(p, type = "histogram", groupColour = TRUE, groupFill = TRUE)

4）箱线图（宽窄的显示会有些问题）

# marginal plot: boxplot
ggMarginal(p, type = "boxplot", groupColour = TRUE, groupFill = TRUE)

5）小提琴图（会有重叠，不建议使用）

# marginal plot: violin
ggMarginal(p, type = "violin", groupColour = TRUE, groupFill = TRUE)

6）密度函数与直方图同时展现

# marginal plot: densigram
ggMarginal(p, type = "densigram", groupColour = TRUE, groupFill = TRUE)

 

2. 使用cowplot与ggpubr

1）重绘另一种散点图

# Scatter plot colored by groups ("Species")
sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width",
              color = "Species", palette = "jco",
              size = 3, alpha = 0.6) +
border() +
theme(legend.position = "bottom")
sp

2）有缝拼接

① 密度函数

library(cowplot)
# Marginal density plot of x (top panel) and y (right panel)
xplot <- ggdensity(iris, "Sepal.Length", fill = "Species",
                 palette = "jco")
yplot <- ggdensity(iris, "Sepal.Width", fill = "Species", 
                 palette = "jco") +
rotate()

# Cleaning the plots
sp <- sp + rremove("legend")
yplot <- yplot + clean_theme() + rremove("legend")
xplot <- xplot + clean_theme() + rremove("legend")
# Arranging the plot using cowplot
plot_grid(xplot, NULL, sp, yplot, ncol = 2, align = "hv", 
        rel_widths = c(2, 1), rel_heights = c(1, 2))

② 未被压缩的箱线图

# Marginal boxplot of x (top panel) and y (right panel)
xplot <- ggboxplot(iris, x = "Species", y = "Sepal.Length", 
                 color = "Species", fill = "Species", palette = "jco",
                 alpha = 0.5, ggtheme = theme_bw())+
rotate()
yplot <- ggboxplot(iris, x = "Species", y = "Sepal.Width",
                 color = "Species", fill = "Species", palette = "jco",
                 alpha = 0.5, ggtheme = theme_bw())
# Cleaning the plots
sp <- sp + rremove("legend")
yplot <- yplot + clean_theme() + rremove("legend")
xplot <- xplot + clean_theme() + rremove("legend")
# Arranging the plot using cowplot
plot_grid(xplot, NULL, sp, yplot, ncol = 2, align = "hv", 
        rel_widths = c(2, 1), rel_heights = c(1, 2))

3）无缝拼接

# Main plot
pmain <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_point() +
color_palette("jco")
# Marginal densities along x axis
xdens <- axis_canvas(pmain, axis = "x") +
geom_density(data = iris, aes(x = Sepal.Length, fill = Species),
             alpha = 0.7, size = 0.2) +
fill_palette("jco")
# Marginal densities along y axis
# Need to set coord_flip = TRUE, if you plan to use coord_flip()
ydens <- axis_canvas(pmain, axis = "y", coord_flip = TRUE) +
geom_density(data = iris, aes(x = Sepal.Width, fill = Species),
             alpha = 0.7, size = 0.2) +
coord_flip() +
fill_palette("jco")
p1 <- insert_xaxis_grob(pmain, xdens, grid::unit(.2, "null"), position = "top")
p2 <- insert_yaxis_grob(p1, ydens, grid::unit(.2, "null"), position = "right")
ggdraw(p2)

 

参考

Articles - ggpubr: Publication Ready Plots——Perfect Scatter Plots with Correlation and Marginal Histograms

Marginal distribution with ggplot2 and ggExtra

以上就是使用R语言绘制散点图结合边际分布图教程的详细内容，更多关于R语言绘制散点图结合边际分布图的资料请关注服务器之家其它相关文章！

原文链接：https://kanny.blog.csdn.net/article/details/107088937