2018-10-31用R绘制散点图矩阵（成对的散点图）

原文见http://www.win-vector.com/blog/2018/10/scatterplot-matrices-pair-plots-with-cdata-and-ggplot2/

散点图矩阵就是把数据集中的每个数值变量两两绘制散点图。基础的R包，绘图函数是pairs（）。这是基础包绘制的iris数据集的一个例子：

pairs(iris[1:4], 
      main = "Anderson's Iris Data -- 3 species",
      pch = 21, 
      bg = c("#1b9e77", "#d95f02", "#7570b3")[unclass(iris$Species)])

image.png

另外的绘图方式还有几种。

library(ggplot2)
library(GGally)
ggpairs(iris, columns=1:4, aes(color=Species)) + 
  ggtitle("Anderson's Iris Data -- 3 species")

Rplot1.jpeg

library(lattice)
splom(iris[1:4], 
      groups=iris$Species, 
      main="Anderson's Iris Data -- 3 species")

Rplot.jpeg

用R包中cdata绘制一下散点矩阵图

首先是加载相关的R包

library(ggplot2)
library(cdata)

然后按照需求重塑数据

meas_vars <- colnames(iris)[1:4]
# the data.frame() call strips the attributes from
# the frame returned by expand.grid()
controlTable <- data.frame(expand.grid(meas_vars, meas_vars, 
                                       stringsAsFactors = FALSE))
# rename the columns
colnames(controlTable) <- c("x", "y")

# add the key column
controlTable <- cbind(
  data.frame(pair_key = paste(controlTable[[1]], controlTable[[2]]),
             stringsAsFactors = FALSE),
  controlTable)

controlTable
#                     pair_key            x            y
## 1  Sepal.Length Sepal.Length Sepal.Length Sepal.Length
## 2   Sepal.Width Sepal.Length  Sepal.Width Sepal.Length
## 3  Petal.Length Sepal.Length Petal.Length Sepal.Length
## 4   Petal.Width Sepal.Length  Petal.Width Sepal.Length
## 5   Sepal.Length Sepal.Width Sepal.Length  Sepal.Width
## 6    Sepal.Width Sepal.Width  Sepal.Width  Sepal.Width
## 7   Petal.Length Sepal.Width Petal.Length  Sepal.Width
## 8    Petal.Width Sepal.Width  Petal.Width  Sepal.Width
## 9  Sepal.Length Petal.Length Sepal.Length Petal.Length
## 10  Sepal.Width Petal.Length  Sepal.Width Petal.Length
## 11 Petal.Length Petal.Length Petal.Length Petal.Length
## 12  Petal.Width Petal.Length  Petal.Width Petal.Length
## 13  Sepal.Length Petal.Width Sepal.Length  Petal.Width
## 14   Sepal.Width Petal.Width  Sepal.Width  Petal.Width
## 15  Petal.Length Petal.Width Petal.Length  Petal.Width
## 16   Petal.Width Petal.Width  Petal.Width  Petal.Width

iris_aug = rowrecs_to_blocks(
  iris,
  controlTable,
  columnsToCopy = "Species")

head(iris_aug)

##   Species                  pair_key   x   y
## 1  setosa Sepal.Length Sepal.Length 5.1 5.1
## 2  setosa  Sepal.Width Sepal.Length 3.5 5.1
## 3  setosa Petal.Length Sepal.Length 1.4 5.1
## 4  setosa  Petal.Width Sepal.Length 0.2 5.1
## 5  setosa  Sepal.Length Sepal.Width 5.1 3.5
## 6  setosa   Sepal.Width Sepal.Width 3.5 3.5

然后用facet_grid创建图形

# reorder the key columns to be the same order
# as the base version above
iris_aug$xv <- factor(as.character(iris_aug$xv),
                           meas_vars)
iris_aug$yv <- factor(as.character(iris_aug$yv),
                           meas_vars)


ggplot(iris_aug, aes(x=x, y=y)) +
  geom_point(aes(color=Species, shape=Species)) + 
  facet_grid(yv~xv, labeller = label_both, scale = "free") +
  ggtitle("Anderson's Iris Data -- 3 species") +
  scale_color_brewer(palette = "Dark2") +
  ylab(NULL) + 
  xlab(NULL)

image.png
用[ WVPlots的PairPlot() 函数 ]也可以绘制同样的图形，相对简单一些(https://winvector.github.io/WVPlots/reference/PairPlot.html).

library(WVPlots) 

PairPlot(iris, 
         colnames(iris)[1:4], 
         "Anderson's Iris Data -- 3 species", 
         group_var = "Species")

image.png