Gallery of ggridges examples

Claus O. Wilke

2018-04-04

Evolution of movie lengths over time

Data from the IMDB, as provided in the ggplot2movies package.

library(ggplot2movies)
ggplot(movies[movies$year>1912,], aes(x = length, y = year, group = year)) +
  geom_density_ridges(scale = 10, size = 0.25, rel_min_height = 0.03) +
  theme_ridges() +
  scale_x_continuous(limits=c(1, 200), expand = c(0.01, 0)) +
  scale_y_reverse(breaks=c(2000, 1980, 1960, 1940, 1920, 1900), expand = c(0.01, 0))

Results from Catalan regional elections, 1980-2015

Modified after a figure originally created by Marc Belzunces.

library(dplyr)
library(forcats)
Catalan_elections %>%
  mutate(YearFct = fct_rev(as.factor(Year))) %>%
  ggplot(aes(y = YearFct)) +
  geom_density_ridges(aes(x = Percent, fill = paste(YearFct, Option)), 
           alpha = .8, color = "white", from = 0, to = 100) +
  labs(x = "Vote (%)",
       y = "Election Year",
       title = "Indy vs Unionist vote in Catalan elections",
       subtitle = "Analysis unit: municipalities (n = 949)",
       caption = "Marc Belzunces (@marcbeldata) | Source: Idescat") +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  scale_fill_cyclical(breaks = c("1980 Indy", "1980 Unionist"),
                      labels = c(`1980 Indy` = "Indy", `1980 Unionist` = "Unionist"),
                      values = c("#ff0000", "#0000ff", "#ff8080", "#8080ff"),
                      name = "Option", guide = "legend") +
  theme_ridges(grid = FALSE)

Temperatures in Lincoln, Nebraska

Modified from a blog post by Austin Wehrwein.

library(viridis)
ggplot(lincoln_weather, aes(x = `Mean Temperature [F]`, y = `Month`, fill = ..x..)) +
  geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01, gradient_lwd = 1.) +
  scale_x_continuous(expand = c(0.01, 0)) +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_fill_viridis(name = "Temp. [F]", option = "C") +
  labs(title = 'Temperatures in Lincoln NE',
       subtitle = 'Mean temperatures (Fahrenheit) by month for 2016\nData: Original CSV from the Weather Underground') +
  theme_ridges(font_size = 13, grid = TRUE) + theme(axis.title.y = element_blank())

Visualization of Poisson random samples with different means

Inspired by a ggridges example by Noam Ross.

# generate data
set.seed(1234)
pois_data <- data.frame(mean = rep(1:5, each = 10))
pois_data$group <- factor(pois_data$mean, levels=5:1)
pois_data$value <- rpois(nrow(pois_data), pois_data$mean)

# make plot
ggplot(pois_data, aes(x = value, y = group, group = group)) +
  geom_density_ridges2(aes(fill = group), stat = "binline", binwidth = 1, scale = 0.95) +
  geom_text(stat = "bin",
          aes(y = group + 0.95*(..count../max(..count..)),
              label = ifelse(..count..>0, ..count.., "")),
          vjust = 1.4, size = 3, color = "white", binwidth = 1) +
  scale_x_continuous(breaks = c(0:12), limits = c(-.5, 13), expand = c(0, 0),
                     name = "random value") +
  scale_y_discrete(expand = c(0.01, 0), name = "Poisson mean",
                   labels = c("5.0", "4.0", "3.0", "2.0", "1.0")) +
  scale_fill_cyclical(values = c("#0000B0", "#7070D0")) +
  labs(title = "Poisson random samples with different means",
       subtitle = "sample size n=10") +
  guides(y = "none") +
  theme_ridges(grid = FALSE) +
  theme(axis.title.x = element_text(hjust = 0.5),
        axis.title.y = element_text(hjust = 0.5))

Height of Australian athletes

library(DAAG) # for ais dataset
ais$sport <- factor(ais$sport,
                    levels = c("B_Ball", "Field", "Gym", "Netball", "Row", "Swim", "T_400m", "T_Sprnt", "Tennis", "W_Polo"),
                    labels = c("Basketball", "Field", "Gym", "Netball", "Row", "Swim", "Track 400m", "Track Sprint", "Tennis", "Water Polo"))

ggplot(ais, aes(x=ht, y=sport, color=sex, point_color=sex, fill=sex)) +
  geom_density_ridges(jittered_points=TRUE, scale = .95, rel_min_height = .01,
                      point_shape = "|", point_size = 3, size = 0.25,
                      position = position_points_jitter(height = 0)) +
  scale_y_discrete(expand = c(.01, 0)) +
  scale_x_continuous(expand = c(0, 0), name = "height [cm]") +
  scale_fill_manual(values = c("#D55E0050", "#0072B250"), labels = c("female", "male")) +
  scale_color_manual(values = c("#D55E00", "#0072B2"), guide = "none") +
  scale_discrete_manual("point_color", values = c("#D55E00", "#0072B2"), guide = "none") +
  guides(fill = guide_legend(override.aes = list(
                               fill = c("#D55E00A0", "#0072B2A0"),
                               color = NA, point_color = NA))) +
  ggtitle("Height in Australian athletes") +
  theme_ridges(center = TRUE)

A cheese plot

Inspired by this tweet by Leonard Kiefer.

set.seed(423)
n1 <- 200
n2 <- 25
n3 <- 50
cols <- c('#F2DB2F', '#F7F19E', '#FBF186')
cols_dark <- c("#D7C32F", "#DBD68C", "#DFD672")
cheese <- data.frame(cheese = c(rep("buttercheese", n1), rep("Leerdammer", n2), rep("Swiss", n3)),
                     x = c(runif(n1), runif(n2), runif(n3)),
                     size = c(rnorm(n1, mean = .1, sd = .01), rnorm(n2, mean = 9, sd = 3),
                              rnorm(n3, mean = 3, sd = 1)))
ggplot(cheese, aes(x = x, point_size = size, y = cheese, fill = cheese, color = cheese)) +
  geom_density_ridges(jittered_points = TRUE, point_color="white", scale = .8, rel_min_height = .2,
                      size = 1.5) +
  scale_y_discrete(expand = c(.01, 0)) +
  scale_x_continuous(limits = c(0, 1), expand = c(0, 0), name = "", breaks = NULL) +
  scale_point_size_continuous(range = c(0.01, 10), guide = "none") +
  scale_fill_manual(values = cols, guide = "none") +
  scale_color_manual(values = cols_dark, guide = "none") +
  theme_ridges(grid = FALSE, center = TRUE)