如何在ggplot2的geom_tile中将每个瓦片划分为四个象限？

问题背景 #

我需要用ggplot2的geom_tile()创建季节性相关性图，将每个瓦片拆分为四个象限，分别对应四季的数值。已有单独季节的绘图代码和合并后的四季数据集，但无法根据Season因子列实现瓦片象限拆分，寻求解决方案。

解决方案 #

要实现每个瓦片拆分为四个季节象限，核心思路是将离散的物种变量转换为连续坐标，并为每个季节添加偏移量，让每个大瓦片内的四个小象限对应不同季节的数据。具体实现步骤如下：

1. 为季节定义坐标偏移量 #

给四个季节分配x/y方向的小偏移，确保每个大瓦片内的四个小方块位置不重叠：

• season1：x偏移+0.25，y偏移+0.25（左下象限）
• season2：x偏移+0.75，y偏移+0.25（右下象限）
• season3：x偏移+0.25，y偏移+0.75（左上象限）
• season4：x偏移+0.75，y偏移+0.75（右上象限）

2. 转换数据坐标 #

将离散的Var1/Var2转换为数值型，加上对应季节的偏移量，得到连续的x/y坐标，用于定位小瓦片。

3. 绘制带象限的热力图 #

使用转换后的连续坐标绘制小瓦片，同时保留原离散变量的轴标签，确保图表可读性。

完整代码实现 #

第一步：加载包与处理数据 #

library(dplyr)
library(ggplot2)

# 生成四季数据（复用原数据结构）
dat.season1 <- data.frame(Var1 = c("species2", "species3", "species4", "species5",
                                   "species3", "species4", "species5",
                                   "species4", "species5", "species5"),
                          Var2 = c("species1", "species1", "species1", "species1",
                                   "species2", "species2", "species2",
                                   "species3", "species3", "species4"),
                          value = runif(10, -1, 1))

dat.season2 <- data.frame(Var1 = c("species2", "species3", "species4", "species5",
                                   "species3", "species4", "species5",
                                   "species4", "species5", "species5"),
                          Var2 = c("species1", "species1", "species1", "species1",
                                   "species2", "species2", "species2",
                                   "species3", "species3", "species4"),
                          value = runif(10, -1, 1))

dat.season3 <- data.frame(Var1 = c("species2", "species3", "species4", "species5",
                                   "species3", "species4", "species5",
                                   "species4", "species5", "species5"),
                          Var2 = c("species1", "species1", "species1", "species1",
                                   "species2", "species2", "species2",
                                   "species3", "species3", "species4"),
                          value = runif(10, -1, 1))

dat.season4 <- data.frame(Var1 = c("species2", "species3", "species4", "species5",
                                   "species3", "species4", "species5",
                                   "species4", "species5", "species5"),
                          Var2 = c("species1", "species1", "species1", "species1",
                                   "species2", "species2", "species2",
                                   "species3", "species3", "species4"),
                          value = runif(10, -1, 1))

# 合并四季数据并添加季节标识
all.dat <- dplyr::bind_rows(list(season1 = dat.season1, season2 = dat.season2,
                                 season3 = dat.season3, season4 = dat.season4),
                            .id = "Season")

# 添加坐标偏移量并转换为连续坐标
all.dat <- all.dat %>%
  mutate(
    x_offset = case_when(
      Season == "season1" ~ 0.25,
      Season == "season2" ~ 0.75,
      Season == "season3" ~ 0.25,
      Season == "season4" ~ 0.75
    ),
    y_offset = case_when(
      Season == "season1" ~ 0.25,
      Season == "season2" ~ 0.25,
      Season == "season3" ~ 0.75,
      Season == "season4" ~ 0.75
    ),
    # 将离散物种转为连续坐标，减1让起始位置从0开始
    x = as.numeric(factor(Var2)) + x_offset - 1,
    y = as.numeric(factor(Var1)) + y_offset - 1,
    # 保留原因子水平用于轴标签
    Var2_factor = factor(Var2),
    Var1_factor = factor(Var1)
  )

第二步：绘制带象限的热力图 #

# 复用原配色方案
my.colors = colorRampPalette(c("#00002d", '#001a6d', '#99cce2','#ffffff', "#FED18A",'#fda416', "#ff6600"),
                             space = "rgb")
colorLevels <-9
cols_to_use= my.colors(colorLevels)

ggplot(all.dat, aes(x = x, y = y, fill = value)) +
  geom_tile(color = "gray60", size = 0.5) +  # 小瓦片边框，增强区分度
  # 设置x轴：匹配原离散变量的标签与位置
  scale_x_continuous(
    breaks = seq_along(levels(all.dat$Var2_factor)) - 0.5,
    labels = levels(all.dat$Var2_factor),
    expand = c(0, 0),
    position = "top"
  ) +
  # 设置y轴：反转顺序，匹配原单季节图的布局
  scale_y_continuous(
    breaks = seq_along(levels(all.dat$Var1_factor)) - 0.5,
    labels = rev(levels(all.dat$Var1_factor)),
    expand = c(0, 0),
    trans = "reverse"
  ) +
  scale_fill_gradientn(
    colors = cols_to_use,
    space = "Lab",
    guide = guide_colorbar(frame.colour = "gray60", frame.linewidth = 2)
  ) +
  labs(x = NULL, y = NULL) +
  theme_minimal() +
  theme(
    axis.text = element_text(size = 10),
    panel.grid = element_blank()  # 移除网格线，保持图表整洁
  )

内容的提问来源于stack exchange，提问作者C_goetsch