使用R語言繪制散點圖結合邊際分佈圖教程

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主要使用ggExtra結合ggplot2兩個R包進行繪制。（勝在簡潔方便）使用cowplot與ggpubr進行繪制。（勝在靈活且美觀）

下面的繪圖我們均以iris數據集為例。

1. 使用ggExtra結合ggplot2

1）傳統散點圖

# 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 調整點的透明度；shape 調整點的形狀
  theme_bw() +
  theme(legend.position = "bottom") + # 圖例置於底部
  labs(x = "Sepal Length", y = "Sepal Width") # 添加x，y軸的名稱
p

下面我們一行代碼添加邊際分佈（分別以密度曲線與直方圖的形式來展現）：

2）密度函數

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

3）直方圖

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

4）箱線圖（寬窄的顯示會有些問題）

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

5）小提琴圖（會有重疊，不建議使用）

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

6）密度函數與直方圖同時展現

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

2. 使用cowplot與ggpubr

1）重繪另一種散點圖

# 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）有縫拼接

① 密度函數

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 未被壓縮的箱線圖

# 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）無縫拼接

# 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)

參考

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

Marginal distribution with ggplot2 and ggExtra

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