请求协助：基于示例数据创建多重饼图（Multiple pie chart）

数据预处理 #

首先需要将数据中的Number列转换为数值类型，可选择过滤掉数值为0的行（避免饼图中出现无意义的0占比区块）。

示例数据整理（R语言） #

# 加载示例数据
df <- data.frame(
  Sites = c("ANKHAB1", "ANKHAB1", "ANKHAB1", "ANKHAB1", "ANKHAB1", "ANKHAB1"),
  Transect = c("T1", "T1", "T1", "T1", "T1", "T2"),
  Genus = c("Cypraea", "Diadema", "Tridacna", "Protoreaster", "Atrina", "Diadema"),
  Number = c("1", "2", "3", "0", "0", "3")
)

# 转换Number列为数值型
df$Number <- as.numeric(df$Number)

# 过滤数值为0的行（可选）
df_filtered <- df[df$Number != 0, ]

示例数据整理（Python） #

import pandas as pd

# 加载示例数据
data = {
    "Sites": ["ANKHAB1", "ANKHAB1", "ANKHAB1", "ANKHAB1", "ANKHAB1", "ANKHAB1"],
    "Transect": ["T1", "T1", "T1", "T1", "T1", "T2"],
    "Genus": ["Cypraea", "Diadema", "Tridacna", "Protoreaster", "Atrina", "Diadema"],
    "Number": ["1", "2", "3", "0", "0", "3"]
}
df = pd.DataFrame(data)

# 转换Number列为数值型
df["Number"] = pd.to_numeric(df["Number"])

# 过滤数值为0的行（可选）
df_filtered = df[df["Number"] != 0]

R语言实现多重饼图（ggplot2） #

利用ggplot2的分面功能，为每个站点+样带组合生成独立饼图：

library(ggplot2)

ggplot(df_filtered, aes(x = "", y = Number, fill = Genus)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0) +
  facet_wrap(~ interaction(Sites, Transect)) +
  theme_void() +
  labs(fill = "属名") +
  theme(strip.text = element_text(size = 10))

若需按站点合并样带数据，先聚合再绘图：

# 按站点和属名聚合数据
df_aggregated <- aggregate(Number ~ Sites + Genus, data = df_filtered, sum)

ggplot(df_aggregated, aes(x = "", y = Number, fill = Genus)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0) +
  facet_wrap(~ Sites) +
  theme_void() +
  labs(fill = "属名")

Python语言实现多重饼图（Matplotlib） #

通过循环遍历分组，生成子图绘制饼图（适配33个站点的布局）：

import matplotlib.pyplot as plt

# 按站点+样带分组
groups = df_filtered.groupby(["Sites", "Transect"])

# 计算子图行列数（按6列排列）
n_groups = len(groups)
n_cols = 6
n_rows = (n_groups + n_cols - 1) // n_cols

# 创建画布和子图
fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, n_rows*3))
axes = axes.flatten()

# 遍历分组绘制饼图
for idx, ((site, transect), group_data) in enumerate(groups):
    ax = axes[idx]
    ax.pie(group_data["Number"], labels=group_data["Genus"], autopct='%1.1f%%')
    ax.set_title(f"{site} - {transect}")

# 隐藏多余子图
for ax in axes[n_groups:]:
    ax.axis('off')

plt.tight_layout()
plt.show()

按站点聚合数据的绘制方式：

# 按站点和属名聚合
df_aggregated = df_filtered.groupby(["Sites", "Genus"])["Number"].sum().reset_index()
groups = df_aggregated.groupby("Sites")

n_groups = len(groups)
n_cols = 6
n_rows = (n_groups + n_cols - 1) // n_cols

fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, n_rows*3))
axes = axes.flatten()

for idx, (site, group_data) in enumerate(groups):
    ax = axes[idx]
    ax.pie(group_data["Number"], labels=group_data["Genus"], autopct='%1.1f%%')
    ax.set_title(site)

for ax in axes[n_groups:]:
    ax.axis('off')

plt.tight_layout()
plt.show()

内容的提问来源于stack exchange，提问作者José Randrianandrasana