您可以预先计算密度并绘制极细的柱以获得渐变效果。沿着x轴添加边缘地毯图可能是显示密度所基于的点的一个很好的触摸：

library(tidyverse)

data %>%
  reframe(anomaly = density(Temperature_anomaly, n = 1000)$x,
          density = density(Temperature_anomaly, n = 1000)$y) %>%
  ggplot(aes(anomaly, density, fill = anomaly)) +
  geom_col(width = 0.05) +
  geom_line() +
  geom_vline(xintercept = 0, linetype = 2) +
  geom_rug(data = data, sides = "b", inherit.aes = FALSE,
           aes(x = Temperature_anomaly, color = Temperature_anomaly)) +
  scale_fill_gradient2(low = "blue", mid = "gray", high = "red") +
  scale_colour_gradient2(low = "blue", mid = "gray", high = "red", 
                         guide = "none") +
  labs(x = "Temperature Anomaly", y = "Probability density") +
  theme_minimal(base_size = 16)

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的数据

使用数据

set.seed(1)

data <- data.frame(Temperature_anomaly = rnorm(20, 1))

型

使用{ggridges}软件包的替代解决方案：

set.seed(123)
data <- data.frame(Temperature_anomaly = rnorm(20))

library(ggridges)
ggplot(data, aes(x = Temperature_anomaly, y = 1, fill = after_stat(x))) + 
  geom_density_ridges_gradient()+
  scale_fill_gradient2(low="blue", mid = "white", high="red")

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其给出：
x1c 0d1x的数据