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")
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')
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
