Question

我看到这个来自五十岁的情节，不同大学的密度图略有重叠。查看this link at fivethirtyeight.com

你如何用 ggplot2 复制这个情节？

具体来说，您如何获得轻微重叠， facet_wrap 无法正常工作。

TestFrame <-  
  data.frame(
    Score =
      c(rnorm(100, 0, 1)
        ,rnorm(100, 0, 2)
        ,rnorm(100, 0, 3)
        ,rnorm(100, 0, 4)
        ,rnorm(100, 0, 5))
    ,Group =
      c(rep('Ones', 100)
        ,rep('Twos', 100)
        ,rep('Threes', 100)
        ,rep('Fours', 100)
        ,rep('Fives', 100))
  )

ggplot(TestFrame, aes(x = Score, group = Group)) +
  geom_density(alpha = .75, fill = 'black')

Answer 1

与ggplot一样，关键是以正确的格式获取数据，然后绘图非常简单。我确定会有另一种方法可以做到这一点，但我的方法是使用density()进行密度估算，然后使用geom_density()制作一种手动geom_ribbon()，需要ymin和ymax，这是将形状从x轴移开所必需的。

其余挑战在于确保打印顺序正确，因为ggplot似乎会首先打印最宽的色带。最后，需要最大代码的部分是四分位数的生成。

我还制作了一些与原始数据更加一致的数据。

library(ggplot2)
library(dplyr)
library(broom)
rawdata <- data.frame(Score = rnorm(1000, seq(1, 0, length.out = 10), sd = 1),
                  Group = rep(LETTERS[1:10], 10000))

df <- rawdata %>% 
  mutate(GroupNum = rev(as.numeric(Group))) %>% #rev() means the ordering will be from top to bottom
  group_by(Group, GroupNum) %>% 
  do(tidy(density(.$Score, bw = diff(range(.$Score))/20))) %>% #The original has quite a large bandwidth
  group_by() %>% 
  mutate(ymin = GroupNum * (max(y) / 1.5), #This constant controls how much overlap between groups there is
         ymax = y + ymin,
         ylabel = ymin + min(ymin)/2,
         xlabel = min(x) - mean(range(x))/2) #This constant controls how far to the left the labels are

#Get quartiles
labels <- rawdata %>% 
  mutate(GroupNum = rev(as.numeric(Group))) %>% 
  group_by(Group, GroupNum) %>% 
  mutate(q1 = quantile(Score)[2],
         median = quantile(Score)[3],
         q3 = quantile(Score)[4]) %>%
  filter(row_number() == 1) %>% 
  select(-Score) %>% 
  left_join(df) %>% 
  mutate(xmed = x[which.min(abs(x - median))],
         yminmed = ymin[which.min(abs(x - median))],
         ymaxmed = ymax[which.min(abs(x - median))]) %>% 
  filter(row_number() == 1)

p <- ggplot(df, aes(x, ymin = ymin, ymax = ymax)) + geom_text(data = labels, aes(xlabel, ylabel, label = Group)) +


geom_vline(xintercept = 0, size = 1.5, alpha = 0.5, colour = "#626262") + 
  geom_vline(xintercept = c(-2.5, -1.25, 1.25, 2.5), size = 0.75, alpha = 0.25, colour = "#626262") + 
  theme(panel.grid = element_blank(),
        panel.background = element_rect(fill = "#F0F0F0"),
        axis.text.y = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank())
for (i in unique(df$GroupNum)) {
  p <- p + geom_ribbon(data = df[df$GroupNum == i,], aes(group = GroupNum), colour = "#F0F0F0", fill = "black") +
    geom_segment(data = labels[labels$GroupNum == i,], aes(x = xmed, xend = xmed, y = yminmed, yend = ymaxmed), colour = "#F0F0F0", linetype = "dashed") +
    geom_segment(data = labels[labels$GroupNum == i,], x = min(df$x), xend = max(df$x), aes(y = ymin, yend = ymin), size = 1.5, lineend = "round") 
}
p <- p + geom_text(data = labels[labels$Group == "A",], aes(xmed - xlabel/50, ylabel), 
                   label = "Median", colour = "#F0F0F0", hjust = 0, fontface = "italic", size = 4)

修改我注意到原来实际上通过用水平线拉伸每个分布来做一些捏造（如果仔细观察，你可以看到一个连接......）。我在循环中添加了与第二个geom_segment()类似的内容。

Answer 2

虽然有一个伟大的＆amp;已经接受的答案 - 我在没有数据重新格式化的情况下完成了我作为替代途径的贡献。

TestFrame <-  
  data.frame(
    Score =
      c(rnorm(50, 3, 2)+rnorm(50, -1, 3)
        ,rnorm(50, 3, 2)+rnorm(50, -2, 3)
        ,rnorm(50, 3, 2)+rnorm(50, -3, 3)
        ,rnorm(50, 3, 2)+rnorm(50, -4, 3)
        ,rnorm(50, 3, 2)+rnorm(50, -5, 3))
    ,Group =
      c(rep('Ones', 50)
        ,rep('Twos', 50)
        ,rep('Threes', 50)
        ,rep('Fours', 50)
        ,rep('Fives', 50))
  )

require(ggplot2)
require(grid)

spacing=0.05

tm <- theme(legend.position="none",     axis.line=element_blank(),axis.text.x=element_blank(),
            axis.text.y=element_blank(),axis.ticks=element_blank(),
            axis.title.x=element_blank(),axis.title.y=element_blank(),
            panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
            panel.background = element_blank(), 
            plot.background = element_rect(fill = "transparent",colour = NA),
            plot.margin = unit(c(0,0,0,0),"mm"))

firstQuintile = quantile(TestFrame$Score,0.2)
secondQuintile = quantile(TestFrame$Score,0.4)
median  = quantile(TestFrame$Score,0.5)
thirdQuintile = quantile(TestFrame$Score,0.6)
fourthQuintile = quantile(TestFrame$Score,0.8)

ymax <- 1.5*max(density(TestFrame[TestFrame$Group=="Ones",]$Score)$y)
xmax <- 1.2*max(TestFrame$Score)
xmin <- 1.2*min(TestFrame$Score)

p0 <- ggplot(TestFrame[TestFrame$Group=="Ones",], aes(x = Score, group = Group)) + geom_density(fill = "transparent",colour = NA)+ylim(0-5*spacing,ymax)+xlim(xmin,xmax)+tm
p0 <- p0 + geom_vline(aes(xintercept=firstQuintile),color="gray",size=1.2)
p0 <- p0 + geom_vline(aes(xintercept=secondQuintile),color="gray",size=1.2)
p0 <- p0 + geom_vline(aes(xintercept=thirdQuintile),color="gray",size=1.2)
p0 <- p0 + geom_vline(aes(xintercept=fourthQuintile),color="gray",size=1.2)
p0 <- p0 + geom_vline(aes(xintercept=median),color="darkgray",size=2)
#previous line is a little hack for creating a working empty grid with proper sizing
p1 <- ggplot(TestFrame[TestFrame$Group=="Ones",], aes(x = Score, group = Group)) + geom_density(alpha = .85, fill = 'black', color="white",size=1)+tm+ylim(0,ymax)+xlim(xmin,xmax)+ geom_segment(aes(y=0,x=median(Score),yend=max(density(Score)$y),xend=median(Score)), color="white", linetype=2)
p2 <- ggplot(TestFrame[TestFrame$Group=="Twos",], aes(x = Score, group = Group)) + geom_density(alpha = .85, fill = 'black', color="white",size=1)+tm+ylim(0,ymax)+xlim(xmin,xmax)+ geom_segment(aes(y=0,x=median(Score),yend=max(density(Score)$y),xend=median(Score)), color="white", linetype=2)
p3 <- ggplot(TestFrame[TestFrame$Group=="Threes",], aes(x = Score, group = Group)) + geom_density(alpha = .85, fill = 'black', color="white",size=1)+tm+ylim(0,ymax)+xlim(xmin,xmax)+ geom_segment(aes(y=0,x=median(Score),yend=max(density(Score)$y),xend=median(Score)), color="white", linetype=2)
p4 <- ggplot(TestFrame[TestFrame$Group=="Fours",], aes(x = Score, group = Group)) + geom_density(alpha = .85, fill = 'black', color="white",size=1)+tm+ylim(0,ymax)+xlim(xmin,xmax)+ geom_segment(aes(y=0,x=median(Score),yend=max(density(Score)$y),xend=median(Score)), color="white", linetype=2)
p5 <- ggplot(TestFrame[TestFrame$Group=="Fives",], aes(x = Score, group = Group)) + geom_density(alpha = .85, fill = 'black', color="white",size=1)+tm+ylim(0,ymax)+xlim(xmin,xmax)+ geom_segment(aes(y=0,x=median(Score),yend=max(density(Score)$y),xend=median(Score)), color="white", linetype=2)

f <- grobTree(ggplotGrob(p1))
g <- grobTree(ggplotGrob(p2))
h <- grobTree(ggplotGrob(p3))
i <- grobTree(ggplotGrob(p4))
j <- grobTree(ggplotGrob(p5))



a1 <- annotation_custom(grob = f, xmin = xmin, xmax = xmax,ymin = -spacing, ymax = ymax)
a2 <- annotation_custom(grob = g, xmin = xmin, xmax = xmax,ymin = -spacing*2, ymax = ymax-spacing)
a3 <- annotation_custom(grob = h, xmin = xmin, xmax = xmax,ymin = -spacing*3, ymax = ymax-spacing*2)
a4 <- annotation_custom(grob = i, xmin = xmin, xmax = xmax,ymin = -spacing*4, ymax = ymax-spacing*3)
a5 <- annotation_custom(grob = j, xmin = xmin, xmax = xmax,ymin = -spacing*5, ymax = ymax-spacing*4)

pfinal <- p0 + a1 + a2 + a3 + a4 + a5
pfinal

Answer 3

使用ggjoy package中的专用 geom_joy（）：

library(ggjoy)

ggplot(TestFrame, aes(Score, Group)) +
  geom_joy()

# dummy data
set.seed(1)
TestFrame <-  
  data.frame(
    Score =
      c(rnorm(100, 0, 1)
        ,rnorm(100, 0, 2)
        ,rnorm(100, 0, 3)
        ,rnorm(100, 0, 4)
        ,rnorm(100, 0, 5))
    ,Group =
      c(rep('Ones', 100)
        ,rep('Twos', 100)
        ,rep('Threes', 100)
        ,rep('Fours', 100)
        ,rep('Fives', 100))
  )

head(TestFrame)
#        Score Group
# 1 -0.6264538  Ones
# 2  0.1836433  Ones
# 3 -0.8356286  Ones
# 4  1.5952808  Ones
# 5  0.3295078  Ones
# 6 -0.8204684  Ones

使用ggplot

3 个答案: