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使用 Evers 博士的建议用 ggridges 对密度曲线下的区域进行阴影效果很好。但是，我发现密度曲线可能具有欺骗性，因为它们暗示数据存在，而数据不存在。因此，我想我会用普通的直方图尝试这种着色技术。

但是，当我尝试将它与直方图一起使用时，阴影有点偏离。为什么是这样？

library(tidyverse)
install.packages("ggridges", dependencies=TRUE)  # there are many
library(ggridges)
 t2 <-   structure(list(Date = c("1853-01", "1853-02", "1853-03", "1853-04", 
"1853-05", "1853-06", "1853-07", "1853-08", "1853-09", "1853-10", 
"1853-11", "1853-12", "1854-01", "1854-02", "1854-03", "1854-04", 
"1854-05", "1854-06", "1854-07", "1854-08", "1854-09", "1854-10", 
"1854-11", "1854-12"), t = c(-5.6, -5.3, -1.5, 4.9, 9.8, 17.9, 
18.5, 19.9, 14.8, 6.2, 3.1, -4.3, -5.9, -7, -1.3, 4.1, 10, 16.8, 
22, 20, 16.1, 10.1, 1.8, -5.6), year = c("1853", "1853", "1853", 
"1853", "1853", "1853", "1853", "1853", "1853", "1853", "1853", 
"1853", "1854", "1854", "1854", "1854", "1854", "1854", "1854", 
"1854", "1854", "1854", "1854", "1854")), row.names = c(NA, -24L
), class = c("tbl_df", "tbl", "data.frame"), .Names = c("Date", 
"t", "year"))


gg <- ggplot(t2, aes(x = t, y = year)) +
      geom_density_ridges(stat = "binline", bins = 10, scale = 0.8, 
                      draw_baseline = TRUE) +
      theme_ridges()

# Build ggplot and extract data
d <- ggplot_build(gg)$data[[1]]

# Add geom_ribbon for shaded area
gg +
  geom_ribbon(
    data = transform(subset(d, x >= 10), year = group),
    aes(x, ymin = ymin, ymax = ymax, group = group),
    fill = "red",
    alpha = 1.0)

在此处输入图像描述

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2 回答 2

1

如果您愿意调整大小并移动 bin 以使 bin 边界恰好位于您的分界线（此处为 10），则以下方法有效。

ggplot(t2, aes(x = t, y = year, fill = ifelse(..x..>=10, ">= 10", "< 10"))) +
  geom_density_ridges_gradient(stat = "binline", binwidth = 3,
                               center = 8.5, scale = 0.8, 
                               draw_baseline = TRUE) +
  theme_ridges() +
  scale_fill_manual(values = c("gray70", "red"), name = NULL)

在此处输入图像描述

您之所以观察到您所做的效果是因为 x 轴在第一个图和第二个图之间发生变化，并且 x 轴范围对 bin 的绘制方式有影响。有两种解决方案：您可以固定 x 轴范围或通过centerandbinwidth而不是bins. （在我看来，无论您如何处理 x 轴，第二个选项总是首选。）

首先，固定x轴范围：

gg <- ggplot(t2, aes(x = t, y = year)) +
  geom_density_ridges(stat = "binline", bins = 10, scale = 0.8, 
                      draw_baseline = TRUE) +
  theme_ridges() +
  scale_x_continuous(limits = c(-12, 28)) # this is where the change is

# Build ggplot and extract data
d <- ggplot_build(gg)$data[[1]]

# Add geom_ribbon for shaded area
gg +
  geom_ribbon(
    data = transform(subset(d, x >= 10), year = group),
    aes(x, ymin = ymin, ymax = ymax, group = group),
    fill = "red",
    alpha = 1.0)

在此处输入图像描述

二、替代bin定义：

gg <- ggplot(t2, aes(x = t, y = year)) +
  geom_density_ridges(stat = "binline",
                      binwidth = 3, center = 8.5, # this is where the change is
                      scale = 0.8, draw_baseline = TRUE) +
  theme_ridges()

# Build ggplot and extract data
d <- ggplot_build(gg)$data[[1]]

# Add geom_ribbon for shaded area
gg +
  geom_ribbon(
    data = transform(subset(d, x >= 10), year = group),
    aes(x, ymin = ymin, ymax = ymax, group = group),
    fill = "red",
    alpha = 1.0)

在此处输入图像描述

于 2018-04-24T21:39:30.027 回答
1

确实发生了一些奇怪的事情。请参阅下面的“结论”。

  1. 如果我们gg只绘制：

     gg;

    在此处输入图像描述

  2. 如果我们绘制gg一个阶梯，该阶梯应该对应于 的轨迹gg：

      gg +
      geom_step(
          data = d, 
          aes(xmax, ymax, group = group), 
          direction = "vh", col = "red",  size = 2);

在此处输入图像描述

所以添加geom_step以某种方式改变gg。我不明白这怎么可能。您可以看到geom_step（红色曲线）在单独绘制时确实对应于直方图的轨迹gg（参见第一个图）。

于 2018-04-24T05:50:42.203 回答