Ridgeline 图(脊线图)，(有时称为Joyplot)可以同时显示几个组的数值分布情况，分布可以使用直方图或密度图来表示，它们都与相同的水平尺度对齐，并略有重叠。常常被用来可视化随时间或空间变化的多个分布/直方图变化。
安装包ggridges
# Cran安装
install.packages("ggridges")
# github上安装
library(devtools)
install_github("clauswilke/ggridges")
绘图
基础图形
主要利用geom_density_ridges函数
# library
library(ggridges)
library(ggplot2)
# Diamonds dataset is provided by R natively
#head(diamonds)
# basic example
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
geom_density_ridges(alpha = 0.5) +
theme_ridges() +
theme(legend.position = "none")
基础图
另外，有时候我们也可分面展示不同范围的脊线图
diamonds$label= ifelse(diamonds$price >= 10000,'great','bad')
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
geom_density_ridges() +
theme_ridges() +
theme(legend.position = "none")+
facet_wrap(~label)
不同范围分面展示
增加统计信息
这里利用到stat_density_ridges函数，可以在图形上增加代表统计信息的线段， 比如增加上 、下四分位数线(Q1 Q3)和中位数线 Q2 。
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
stat_density_ridges(quantile_lines = TRUE)+
theme_ridges() +
theme(legend.position = "none")
# 当然，我们也可自定义分位数线 比如2.5% 和 60%的线
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
stat_density_ridges(quantile_lines = TRUE, quantiles = c(0.025, 0.600), alpha = 0.7)+
theme_ridges() +
theme(legend.position = "none")
image
将上述代码中fill的映射值改为factor(stat(quantile))，可以将不同颜色映射在不同的分区,并自定义颜色，如：
ggplot(diamonds, aes(x = price, y = cut, fill = factor(stat(quantile)))) +
stat_density_ridges(
geom = "density_ridges_gradient", calc_ecdf = TRUE,
quantiles = 4, quantile_lines = TRUE
) +theme_ridges() +
scale_fill_manual(
name = "Probability", values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0",'gold'),
labels = c("(0, 0.025]", "(0.025, 0.050]","(0.050,0.075]", "(0.075, 1]")
)
按照范围分组
散点图显示
参考了，代码中设置jittered_points = TRUE来实现散点的绘制，无论是在stat_density_ridges还是在geom_density_ridges。
点可以有以下几种选择方式：
- position = 'sina',在基线和山脊线之间的山脊线图中随机分布点。 这是默认选项。
- position= 'jitter', 随机抖动山脊线图中的点。 点随机上下移动和/或左右移动。
- position = 'raincloud': 在山脊线图下方创建随机抖动点的云。
# 增加散点图
A <- ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(jittered_points = TRUE)
# 控制点位置
# position = "raincloud"
B <- ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(
jittered_points = TRUE, position = "raincloud",
alpha = 0.7, scale = 0.9
)
# position = "points_jitter"
C <- ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(
jittered_points = TRUE, position = "points_jitter",
alpha = 0.7, scale = 0.9
)
# 增加边际线
D <- ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(
jittered_points = TRUE,
position = position_points_jitter(width = 0.05, height = 0),
point_shape = '|', point_size = 3, point_alpha = 1, alpha = 0.7,
)
library(patchwork)
(A + B)/(C + D)+ plot_annotation(tag_levels = 'A')
image
自定义散点的样式、颜色
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +
geom_density_ridges(
aes(point_color = Species, point_fill = Species, point_shape = Species),
alpha = .2, point_alpha = 1, jittered_points = TRUE
) +
scale_point_color_hue(l = 40) +
scale_discrete_manual(aesthetics = "point_shape", values = c(21, 22, 23))
自定义
其它(渐变色)
除了上述比较单一的色彩,还可使用此包中geom_density_ridges_gradient函数添加渐变色，拿Example数据为例，可以通过?geom_density_ridges_gradient进行查看更多的控制参数，
# library
library(ggridges)
library(ggplot2)
library(viridis)
library(hrbrthemes)
# Plot
ggplot(lincoln_weather, aes(x = `Mean Temperature [F]`, y = `Month`, fill = ..x..)) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
scale_fill_viridis(name = "Temp. [F]", option = "C") +
labs(title = 'Temperatures in Lincoln NE in 2016') +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8)
)
## geom_density_ridges_gradient参数很多，可以?详细查看
geom_density_ridges_gradient(
mapping = NULL,
data = NULL,
stat = "density_ridges",
position = "points_sina",
panel_scaling = TRUE,
na.rm = TRUE,
gradient_lwd = 0.5,
show.legend = NA,
inherit.aes = TRUE,
...
)
渐变色
直方图显示
除了上述的密度图，只需添加上stat=binline参数即可。
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
geom_density_ridges(alpha = 0.5, stat="binline", bins=20) +
theme_ridges() +
theme(legend.position = "none")
直方图
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