Gallery of ggridges examples

library(ggplot2) library(ggridges)

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, 0)) + scale_y_reverse( breaks = c(2000, 1980, 1960, 1940, 1920, 1900), expand = c(0, 0) ) + coord_cartesian(clip = "off")

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, 0)) + scale_x_continuous(expand = c(0, 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" ) + coord_cartesian(clip = "off") + theme_ridges(grid = FALSE)

Temperatures in Lincoln, Nebraska

Modified from a blog post by Austin Wehrwein.

ggplot(lincoln_weather, aes(x = `Mean Temperature [F]`, y = Month, fill = stat(x))) + geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01, gradient_lwd = 1.) + scale_x_continuous(expand = c(0, 0)) + scale_y_discrete(expand = expand_scale(mult = c(0.01, 0.25))) + scale_fill_viridis_c(name = "Temp. [F]", option = "C") + labs( title = 'Temperatures in Lincoln NE', subtitle = 'Mean temperatures (Fahrenheit) by month for 2016' ) + 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*stat(count/max(count)), label = ifelse(stat(count) > 0, stat(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 = expand_scale(add = c(0, 1.)), 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

ggplot(Aus_athletes, aes(x = height, 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(0, 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") + coord_cartesian(clip = "off") + 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(0, 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") + coord_cartesian(clip = "off") + theme_ridges(grid = FALSE, center = TRUE)