使用ggplot绘制风玫瑰图时遇「至少一个图层需包含所有分面变量」错误

报错原因 #

原plot.windrose函数生成的绘图数据是基于整个数据集的汇总结果，没有保留班次（或日期）这类分面变量，所以添加facet_wrap时，ggplot找不到对应的变量，导致报错。

解决方案 #

下面提供两种可行的解决方法：

方法一：分组生成单图后组合

先按班次拆分数据集，对每个班次单独生成风玫瑰图，再用patchwork包组合成多面板图。

1. 准备环境与数据

install.packages("patchwork")
library(patchwork)
library(dplyr)

# 你的示例数据
df_0 <- data.frame(UTC = c(45117.171132,45117.17371,45117.358592,45117.360963,45117.376829,45117.393799,45117.401636,45117.465761,45117.483411,45117.488394), 
                   BSP = c(7.343,8.033,6.816,8.197,11.814,8.724,6.997,10.269,10.614,10.796), 
                   TWD = c(75.5,55.5,37.6,39.9,27.7,136.1,122,33.7,46.8,45.1), 
                   TWS = c(16.41,14.45,16.5,18.68,17.33,17.79,17.93,17.97,22.36,18.43), 
                   watch_lbl_adj = c("K","K","A","A","B","B","B","C","C","C"))

df_0 <- df_0 %>% mutate(watch_asfactor = factor(watch_lbl_adj))

2. 拆分数据并生成单图

# 按班次拆分数据集
df_list <- split(df_0, df_0$watch_asfactor)

# 对每个班次生成风玫瑰图
plot_list <- lapply(df_list, function(sub_df) {
  plot.windrose(data = sub_df, spd = "BSP", dir = "TWD") +
    ggtitle(paste("班次:", unique(sub_df$watch_asfactor)))
})

3. 组合所有图形

combined_plot <- wrap_plots(plot_list, ncol = 2)
print(combined_plot)

方法二：修改plot.windrose函数支持分面

直接修改原函数，让它接受分组参数，在数据汇总时保留分组信息，直接生成分面图。

1. 修改后的函数代码

plot.windrose_facet <- function(data, spd, dir, group = NULL, spdres = 2, dirres = 30, spdmin = 0, spdmax = NA, spdseq = NA, palette = "YlGnBu", countmax = NA){
  
  # 提取风速、风向数据
  data$spd <- data[[spd]]
  data$dir <- data[[dir]]
  # 处理分组变量
  if (!is.null(group)) {
    data$group <- factor(data[[group]])
  }
  
  # 划分风速区间
  if (is.na(spdmax)) spdmax <- max(data$spd, na.rm = TRUE)
  if (is.na(spdseq)) spdseq <- seq(spdmin, spdmax, spdres)
  spd_bins <- cut(data$spd, breaks = spdseq, include.lowest = TRUE)
  spd_labels <- paste0(spdseq[-length(spdseq)], "-", spdseq[-1])
  
  # 划分风向区间
  dir_bins <- cut(data$dir, breaks = seq(0, 360, dirres), include.lowest = TRUE)
  dir_labels <- paste0(seq(0, 360-dirres, dirres), "-", seq(dirres, 360, dirres))
  
  # 生成交叉统计表（包含分组信息）
  if (!is.null(group)) {
    wind_table <- table(data$group, spd_bins, dir_bins)
    wind_data <- as.data.frame(wind_table)
    names(wind_data) <- c("group", "spd", "dir", "count")
  } else {
    wind_table <- table(spd_bins, dir_bins)
    wind_data <- as.data.frame(wind_table)
    names(wind_data) <- c("spd", "dir", "count")
  }
  
  # 整理标签与角度数据
  wind_data$spd <- factor(wind_data$spd, levels = spd_labels, labels = spd_labels)
  wind_data$dir <- factor(wind_data$dir, levels = dir_labels, labels = dir_labels)
  wind_data$dir_angle <- as.numeric(substr(wind_data$dir, 1, 3)) + dirres/2
  
  # 设置频次上限
  if (is.na(countmax)) countmax <- max(wind_data$count, na.rm = TRUE)
  
  # 绘制基础风玫瑰图
  p <- ggplot(wind_data, aes(x = dir_angle, y = count, fill = spd)) +
    geom_bar(stat = "identity", width = dirres) +
    coord_polar(start = -dirres/2*pi/180) +
    scale_x_continuous(breaks = seq(0, 360-dirres, dirres), labels = seq(0, 360-dirres, dirres)) +
    scale_fill_brewer(palette = palette) +
    theme_bw() +
    labs(x = "风向(°)", y = "频次", fill = "船速(knots)") +
    theme(axis.text.y = element_blank(), axis.ticks.y = element_blank())
  
  # 添加分面（如果有分组变量）
  if (!is.null(group)) {
    p <- p + facet_wrap(~group, ncol = 2)
  }
  
  return(p)
}

2. 直接调用生成分面图

# 生成按班次分面的风玫瑰图
facet_windrose <- plot.windrose_facet(data = df_0, spd = "BSP", dir = "TWD", group = "watch_asfactor")
print(facet_windrose)

内容的提问来源于stack exchange，提问作者inneljpn