9.3 分布图

9.3.1 问题

你想要绘制一组数据的分布图。

9.3.2 方案

后面的例子中会使用以下这组简单的数据：

set.seed(1234)
dat <- data.frame(cond = factor(rep(c("A", "B"), each = 200)), 
  rating = c(rnorm(200), rnorm(200, mean = 0.8)))
# 查看数据
head(dat)
#>   cond  rating
#> 1    A -1.2071
#> 2    A  0.2774
#> 3    A  1.0844
#> 4    A -2.3457
#> 5    A  0.4291
#> 6    A  0.5061
library(ggplot2)

9.3.2.1 直方图和概率密度图

qplot() 函数能够用更简单的语法绘制出与 ggplot() 相同的图像。然而，在实践过程中你会发现 ggplot() 是更好的选择，因为 qplot() 中很多参数的选项都会让人感到困惑。

# 以 rating 为横轴绘制直方图，组距设为 0.5
# 两种函数都可以绘制出相同的图:
ggplot(dat, aes(x=rating)) + geom_histogram(binwidth=.5)

# qplot(dat$rating, binwidth=.5)
# 绘制黑色边线，白色填充的图
ggplot(dat, aes(x=rating)) +
    geom_histogram(binwidth=.5, colour="black", fill="white")

# 密度曲线
ggplot(dat, aes(x=rating)) + geom_density()

# 直方图与核密度曲线重叠
ggplot(dat, aes(x=rating)) + 
    geom_histogram(aes(y=..density..),      # 这里直方图以 density (密度)为y轴
                   binwidth=.5,
                   colour="black", fill="white") +
    geom_density(alpha=.2, fill="#FF6666")  # 重合部分透明填充

添加一条均值线：

ggplot(dat, aes(x=rating)) +
    geom_histogram(binwidth=.5, colour="black", fill="white") +
    geom_vline(aes(xintercept=mean(rating, na.rm=T)),   # 忽略缺失值
               color="red", linetype="dashed", size=1)

9.3.2.2 多组数据的直方图和概率密度图

# 重叠直方图
ggplot(dat, aes(x = rating, fill = cond)) + geom_histogram(binwidth = 0.5, 
  alpha = 0.5, position = "identity")  # identity 表示将每个对象直接显示在图中，条形会彼此重叠。

# 间隔直方图
ggplot(dat, aes(x = rating, fill = cond)) + geom_histogram(binwidth = 0.5, 
  position = "dodge")  # dodge 表示将每组的条形依次并列放置。

# 密度图
ggplot(dat, aes(x = rating, colour = cond)) + geom_density()

# 半透明填充的密度图
ggplot(dat, aes(x = rating, fill = cond)) + geom_density(alpha = 0.3)

在给每组数据添加均值线前，需要将每组数据的平均值赋值到一个新的数据框。

# 求均值
library(plyr)
cdat <- ddply(dat, "cond", summarise, rating.mean = mean(rating))
cdat
#>   cond rating.mean
#> 1    A    -0.05776
#> 2    B     0.87325
# 给重叠直方图添加均值线
ggplot(dat, aes(x = rating, fill = cond)) + geom_histogram(binwidth = 0.5, 
  alpha = 0.5, position = "identity") + geom_vline(data = cdat, 
  aes(xintercept = rating.mean, colour = cond), linetype = "dashed", 
  size = 1)

# 给密度图添加均值线
ggplot(dat, aes(x = rating, colour = cond)) + geom_density() + 
  geom_vline(data = cdat, aes(xintercept = rating.mean, 
    colour = cond), linetype = "dashed", size = 1)

使用分面：

ggplot(dat, aes(x = rating)) + geom_histogram(binwidth = 0.5, 
  colour = "black", fill = "white") + facet_grid(cond ~ 
  .)

# 使用之前的 cdat 添加均值线
ggplot(dat, aes(x = rating)) + geom_histogram(binwidth = 0.5, 
  colour = "black", fill = "white") + facet_grid(cond ~ 
  .) + geom_vline(data = cdat, aes(xintercept = rating.mean), 
  linetype = "dashed", size = 1, colour = "red")

更多关于分面的细节可查看分面

9.3.2.3 箱型图

# 绘制箱型图
ggplot(dat, aes(x = cond, y = rating)) + geom_boxplot()

# 对分组填充颜色
ggplot(dat, aes(x = cond, y = rating, fill = cond)) + geom_boxplot()

# 将上图中冗余的图例删除掉：
ggplot(dat, aes(x = cond, y = rating, fill = cond)) + geom_boxplot() + 
  guides(fill = FALSE)

# 坐标轴翻转
ggplot(dat, aes(x = cond, y = rating, fill = cond)) + geom_boxplot() + 
  guides(fill = FALSE) + coord_flip()

同时可以通过 stat_summary() 来添加平均值。

# 用菱形图标指征平均值，并调整参数使该图标变更大。
ggplot(dat, aes(x = cond, y = rating)) + geom_boxplot() + 
  stat_summary(fun.y = mean, geom = "point", shape = 5, 
    size = 4)