Spread out density plots with ggplot

Question

I saw this great plot from fivethirty that has a slight overlap of density plots for different colleges. Check out this link at fivethirtyeight.com

How would you replicate this plot with ggplot2?

Specifically how would you get that slight overlap, facet_wrap isn't going to work.

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

Using ggridges:

library(ggplot2)
library(ggridges)

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

---

Edit: ggjoy is deprecated, please use ggridges.

Using dedicated geom_joy() from ggjoy package:

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

Answer 2

As always with ggplot, the key is getting the data in the right format, and then the plotting is pretty straightforward. I'm sure there would be another way to do this, but my approach was to do the density estimation with density() and then to make a sort of manual geom_density() with geom_ribbon(), which takes a ymin and ymax, necessary for moving the shape off the x axis.

The rest of the challenge was in getting the order of the printing correct, since it seems that ggplot will print the widest ribbon first. In the end, the part that requires the bulkiest code is the production of the quartiles.

I also produced some data that is a bit more consistent with the original figure.

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)  

Edit I noticed the original actually does a bit of fudging by stretching out each distribution with a horizontal line (you can see a join if you look closely...). I added something similar with the second geom_segment() in the loop.

Answer 3

Although there is a great & accepted answer available already - I finished my contribution as an alternative avenue without data reformatting.

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