ggplot2中的双框图

Question

我想用跨越x和y轴的箱形图来描述两个变量的分布.

这里链接的网站有一些很好的例子(下面),它有使用基础图的包 - boxplotdbl.

我想知道是否有可能出现类似情节ggplot2.使用下图作为示例和iris数据,我如何绘制Sepal.Length和/ Sepal.Width和颜色的框图Species？

我很惊讶地发现以下代码很接近,但是希望沿着x轴延伸胡须而不是盒子.

library(ggplot2)
ggplot(iris) + 
  geom_boxplot(aes(x = Sepal.Length, y = Sepal.Width, fill = Species), alpha = 0.3) +
  theme_bw()

Answer 1

您可以计算每个箱线图所需的相关数字，并使用不同的几何图形构建二维箱线图。

步骤1。分别绘制每个维度的箱线图：

plot.x <- ggplot(iris) + geom_boxplot(aes(Species, Sepal.Length))
plot.y <- ggplot(iris) + geom_boxplot(aes(Species, Sepal.Width))

grid.arrange(plot.x, plot.y, ncol=2) # visual verification of the boxplots

第2步。获取 1 个数据框中计算的箱线图值（包括异常值）：

plot.x <- layer_data(plot.x)[,1:6]
plot.y <- layer_data(plot.y)[,1:6]
colnames(plot.x) <- paste0("x.", gsub("y", "", colnames(plot.x)))
colnames(plot.y) <- paste0("y.", gsub("y", "", colnames(plot.y)))
df <- cbind(plot.x, plot.y); rm(plot.x, plot.y)
df$category <- sort(unique(iris$Species))

> df
  x.min x.lower x.middle x.upper x.max x.outliers y.min y.lower
1   4.3   4.800      5.0     5.2   5.8              2.9   3.200
2   4.9   5.600      5.9     6.3   7.0              2.0   2.525
3   5.6   6.225      6.5     6.9   7.9        4.9   2.5   2.800
  y.middle y.upper y.max    y.outliers   category
1      3.4   3.675   4.2      4.4, 2.3     setosa
2      2.8   3.000   3.4               versicolor
3      3.0   3.175   3.6 3.8, 2.2, 3.8  virginica

步骤 3. 为异常值创建单独的数据框：

df.outliers <- df %>%
  select(category, x.middle, x.outliers, y.middle, y.outliers) %>%
  data.table::data.table()
df.outliers <- df.outliers[, list(x.outliers = unlist(x.outliers), y.outliers = unlist(y.outliers)), 
                           by = list(category, x.middle, y.middle)]

> df.outliers
    category x.middle y.middle x.outliers y.outliers
1:    setosa      5.0      3.4         NA        4.4
2:    setosa      5.0      3.4         NA        2.3
3: virginica      6.5      3.0        4.9        3.8
4: virginica      6.5      3.0        4.9        2.2
5: virginica      6.5      3.0        4.9        3.8

步骤4。将所有内容放在一个图中：

ggplot(df, aes(fill = category, color = category)) +

  # 2D box defined by the Q1 & Q3 values in each dimension, with outline
  geom_rect(aes(xmin = x.lower, xmax = x.upper, ymin = y.lower, ymax = y.upper), alpha = 0.3) +
  geom_rect(aes(xmin = x.lower, xmax = x.upper, ymin = y.lower, ymax = y.upper), 
            color = "black", fill = NA) +

  # whiskers for x-axis dimension with ends
  geom_segment(aes(x = x.min, y = y.middle, xend = x.max, yend = y.middle)) + #whiskers
  geom_segment(aes(x = x.min, y = y.lower, xend = x.min, yend = y.upper)) + #lower end
  geom_segment(aes(x = x.max, y = y.lower, xend = x.max, yend = y.upper)) + #upper end

  # whiskers for y-axis dimension with ends
  geom_segment(aes(x = x.middle, y = y.min, xend = x.middle, yend = y.max)) + #whiskers
  geom_segment(aes(x = x.lower, y = y.min, xend = x.upper, yend = y.min)) + #lower end
  geom_segment(aes(x = x.lower, y = y.max, xend = x.upper, yend = y.max)) + #upper end

  # outliers
  geom_point(data = df.outliers, aes(x = x.outliers, y = y.middle), size = 3, shape = 1) + # x-direction
  geom_point(data = df.outliers, aes(x = x.middle, y = y.outliers), size = 3, shape = 1) + # y-direction

  xlab("Sepal.Length") + ylab("Sepal.Width") +
  coord_cartesian(xlim = c(4, 8), ylim = c(2, 4.5)) +
  theme_classic()

我们可以通过将二维箱线图与原始数据集在相同二维上的散点图进行比较来直观地验证二维箱线图是否合理：

# p refers to 2D boxplot from previous step
p + geom_point(data = iris, 
               aes(x = Sepal.Length, y = Sepal.Width, group = Species, color = Species),
               inherit.aes = F, alpha = 0.5)