二维直方图
二维直方图用于二维数据的统计分析，X-Y 轴变量均为数值型。首先将坐标平面分割为许多大小相等的区间，并计算落在每个区间中的观察值数目，然后将观察值映射为矩形的填充色。
在 ggplot2 中，geom_bin2d 函数的区间形状是矩形，而 geom_hex 函数可以绘制六边形区间。
示例
1. geom_bin2d
d <- ggplot(diamonds, aes(x, y)) + xlim(4, 10) + ylim(4, 10)
d + geom_bin2d()
设置分箱数目
d + geom_bin2d(bins = 10)
设置分箱的宽度（水平和竖直）
d + geom_bin2d(binwidth = c(0.1, 0.1))
更改颜色
d + geom_bin2d(aes(fill = after_stat(count))) +
scale_fill_gradientn(colours = rainbow(10))
设置边缘线条大小与颜色
d + geom_bin2d(size = 1, colour = "green")
2. geom_hex
geom_hex 与 geom_bin2d 的参数一样
d <- ggplot(diamonds, aes(x, y)) +
xlim(4, 10) + ylim(4, 10)
d + geom_hex()
d + geom_hex(size = 1, colour = "#FF9900FF")
二维密度图
前面我们提到过使用 geom_density() 函数来绘制一维密度图。
对于二维核密度估计，我们使用 geom_density_2d() 函数，以线条的方式展示，使用 geom_density_2d_filled() 以填充色的方式展示
示例
绘制分布线条
m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
geom_point() +
xlim(0.5, 6) +
ylim(40, 110)
m + geom_density_2d()
填充色
m + geom_density_2d_filled(alpha = 0.5)
结合线条与填充色
m + geom_density_2d_filled(alpha = 0.5) +
geom_density_2d(size = 0.25, colour = "black")
添加分组变量
d <- sample_n(diamonds, 1000) %>%
ggplot(aes(x, y))
d + geom_density_2d(aes(colour = cut))
进行分面
d + geom_density_2d_filled() +
facet_wrap(vars(cut))
将观测值的数量映射到颜色强度
d + geom_density_2d_filled(contour_var = "count") +
facet_wrap(vars(cut))
我们也可以使用前一节提到的色块图函数，如 raster 对象
d + stat_density_2d(
geom = "raster",
aes(fill = after_stat(density)),
contour = FALSE
) + scale_fill_viridis_c()
或者其他对象，如散点图
d + stat_density_2d(geom = "point", aes(size = after_stat(density)),
n = 20, contour = FALSE)
polygon 对象
d + stat_density_2d(
geom = "polygon",
aes(fill = after_stat(level)),
bins = 30
) + scale_fill_viridis_c()
组合分布图
我们可以将二维直方图和密度图，与一维统计分布图结合起来，更加详细的展示数据的分布情况
首先，构造正态分布数据
library(cowplot)
N <- 500
df <- tibble(
x1 = rnorm(n = N, mean = 2),
x2 = rnorm(n = N, mean = 2),
y1 = rnorm(n = N, mean = 2),
y2 = rnorm(n = N, mean = 2)
)
绘制边缘即组合直方图
top_hist <- ggplot(df, aes(x1)) +
geom_histogram(bins = 35, fill = "#1f78b4", colour = "black") +
theme_void()
right_hist <- ggplot(df, aes(x2)) +
geom_histogram(bins = 35, fill = "#1f78b4", colour = "black") +
coord_flip() +
theme_void()
center <- ggplot(df, aes(x1, x2)) +
geom_hex(colour = "black") +
scale_fill_gradientn(colours = rainbow(10)) +
theme(
panel.background=element_rect(fill="white",colour="black",size=0.25),
axis.line=element_line(colour="black",size=0.25),
axis.title=element_text(size=13,face="plain",color="black"),
axis.text = element_text(size=12,face="plain",color="black"),
legend.position=c(0.10,0.80),
legend.background=element_blank()
)
p1 <- plot_grid(top_hist, center, align = "v",
nrow = 2, rel_heights = c(1, 4))
p2 <- plot_grid(NULL, right_hist, align = "v",
nrow = 2, rel_heights = c(1, 4))
plot_grid(p1, p2, ncol = 2,
rel_widths = c(4, 1))
glist <- list(top_hist, center, right_hist)
绘制二维密度图的代码类似
top_hist <- ggplot(df, aes(y1)) +
# geom_histogram(bins = 35, fill = "#1f78b4", colour = "black") +
geom_density(fill = "#1f78b4", colour = "black") +
theme_void()
right_hist <- ggplot(df, aes(y2)) +
# geom_histogram(bins = 35, fill = "#1f78b4", colour = "black") +
geom_density(fill = "#1f78b4", colour = "black") +
coord_flip() +
theme_void()
center <- ggplot(df, aes(y1, y2)) +
geom_density2d(colour = "black") +
geom_density2d_filled() +
scale_fill_brewer(palette = "Set2") +
theme(
panel.background=element_rect(fill="white",colour="black",size=0.25),
axis.line=element_line(colour="black",size=0.25),
axis.title=element_text(size=13,face="plain",color="black"),
axis.text = element_text(size=12,face="plain",color="black"),
legend.position = "none"
)
p1 <- plot_grid(top_hist, center, align = "v",
nrow = 2, rel_heights = c(1, 4))
p2 <- plot_grid(NULL, right_hist, align = "v",
nrow = 2, rel_heights = c(1, 4))
plot_grid(p1, p2, ncol = 2,
rel_widths = c(4, 1))
glist <- list(top_hist, center, right_hist)
代码：https://github.com/dxsbiocc/learn/blob/main/R/plot/hist_density_2d.R
