12.9 密度图

2024-07-10 20:15| 来源: 网络整理| 查看: 265

12.9 密度图 ggplot(mpg, aes(cty)) + geom_density(aes(fill = factor(cyl)), alpha = 0.8) + labs( title = "Density plot", subtitle = "City Mileage Grouped by Number of cylinders", caption = "Source: mpg", x = "City Mileage", fill = "# Cylinders" )

图 12.37: 按汽缸数分组的城市里程

添加透明度，解决遮挡

ggplot(diamonds, aes(x = price, fill = cut)) + geom_density()

图 12.38: 密度图

ggplot(diamonds, aes(x = price, fill = cut)) + geom_density(alpha = 0.5)

图 12.39: 添加透明度的密度图

堆积密度图

ggplot(diamonds, aes(x = price, fill = cut)) + geom_density(position = "stack")

图 12.40: 堆积密度图

条件密度估计

# You can use position="fill" to produce a conditional density estimate ggplot(diamonds, aes(carat, stat(count), fill = cut)) + geom_density(position = "fill")

图 12.41: 条件密度估计图

岭线图是密度图的一种变体，可以防止密度曲线重叠在一起

ggplot(diamonds) + ggridges::geom_density_ridges(aes(x = price, y = color, fill = color))

二维的密度图又是一种延伸

ggplot(diamonds, aes(x = carat, y = price)) + geom_density_2d(aes(color = cut)) + facet_grid(~cut)

stat 函数，特别是 nlevel 参数，在密度曲线之间填充我们又可以得到热力图

ggplot(diamonds, aes(x = carat, y = price)) + stat_density_2d(aes(fill = stat(nlevel)), geom = "polygon") + facet_grid(. ~ cut)

gemo_hex 也是二维密度图的一种变体，特别适合数据量比较大的情形

ggplot(diamonds, aes(x = carat, y = price)) + geom_hex() + scale_fill_viridis_c()

heatmaps in ggplot2 二维密度图

ggplot(faithful, aes(x = eruptions, y = waiting)) + stat_density_2d(aes(fill = ..level..), geom = "polygon") + xlim(1, 6) + ylim(40, 100) ggplot(faithful, aes(x = eruptions, y = waiting)) + stat_density2d(aes(fill = stat(level)), geom = "polygon") + scale_fill_viridis_c(option = "viridis") + xlim(1, 6) + ylim(40, 100)

图 12.42: 二维密度图

MASS::kde2d() 实现二维核密度估计，ggplot2 包提供了两种等价的绘图方式

stat_density_2d() 和 .. stat_density2d() 和 stat() plotly::plot_ly( data = faithful, x = ~eruptions, y = ~waiting, type = "histogram2dcontour" ) %>% plotly::config(displayModeBar = FALSE)

图 12.43: 二维直方图/密度图/轮廓图

# plot_ly(faithful, x = ~waiting, y = ~eruptions) %>% # add_histogram2d() %>% # add_histogram2dcontour()

延伸一下，热力图

library(KernSmooth) den % plotly::add_heatmap() # 等高线图 p2 % plotly::config(displayModeBar = FALSE) %>% plotly::add_contour() htmltools::tagList(p1, p2)

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