figure显示总标题

As an AI language model, I cannot generate figures. However, I can provide you with some examples of what a figure with a title might look like:

Figure 1: Average monthly temperature in 2021

Figure 2: Top 10 popular tourist destinations in Europe

Figure 3: Comparison of GDP growth between developed and developing countries

Figure 4: Distribution of age groups in a sample population

Figure 5: Effect of different fertilizer types on crop yield

In each of these examples, the figure has a clear title that summarizes the information being presented. This helps the reader understand the purpose of the figure and what insights they can gain from it.

相关问题

请给上述两个图的总标题和分标题设置合适的英文名

为了给这两个图设置合适的英文标题，我们可以考虑它们的内容和目的。假设第一个图展示的是特征选择过程中的调整R-squared值变化，而第二个图显示了勒普斯回归分析的结果。那么，可以这样设置：

# ... (之前的代码)

# 图一：特征选择调整的R-squared值
plot(summary(leaps)$adjr2, type = "l", xlab = "Number of Features", ylab = "Adjusted R-squared",
     main="Figure 1: Adjusted R-Squared for Feature Selection")

# 图二：Leaps Regression Analysis
plot(leaps, scale="adjr2", main="Figure 2: Leaps Regression Analysis by Adjusted R-Squared")

# 总标题
mtext("Overall: Feature Selection and Model Evaluation", outer=TRUE, line=2.5, cex=1.2) # cex设置标题字号

# ... (之前的代码结束)

在这里，“Figure 1”和“Figure 2”分别对应于英文版的"图一"和"图二"，标题内容清晰地描述了图表的目的。

21.【问答题】(60分) 读取people.csv文件数据，按照以下要求绘制图表 (1)绘制显示人口变化趋势图表:添加轴标签、刻度标签、标题、图例、显示网格; (2)绘制不同年份的人口数量堆积柱形图，根据需要添加辅助元素，显示每年总人数;

要读取并分析`people.csv`文件中的数据，并按照所描述的要求绘制图表，首先你需要使用Python的数据处理库pandas来加载数据，然后利用matplotlib或seaborn等绘图库创建图形。以下是步骤概述：

1. **导入所需库**:

   import pandas as pd
   import matplotlib.pyplot as plt

2. **加载数据**:

   data = pd.read_csv('people.csv')

3. **检查数据并准备数据**:
确保数据集中有"year"和"population"列，并按"year"排序。

4. **人口变化趋势线图**:

   fig1, ax1 = plt.subplots()
   ax1.plot(data['year'], data['population'])
   
   # 添加轴标签、刻度标签、标题和图例
   ax1.set_xlabel('Year')
   ax1.set_ylabel('Population', color='blue')
   ax1.title.set_text('Population Trend Over Time')
   ax1.grid(True)
   plt.legend(['Population'], loc='upper left')

   # 可选：设置子图颜色
   ax1.tick_params(axis='y', labelcolor='blue')

5. **人口数量堆积柱状图**:

   fig2, ax2 = plt.figure(figsize=(10, 6))
   ax2.bar(data['year'], data['population'], stacked=True)

   # 添加辅助元素和总人数
   for p in ax2.patches:
       width, height = p.get_width(), p.get_height()
       total = data.loc[p.get_x() + width / 2, 'population'].sum()
       ax2.text(p.get_x() + width / 2, height + 2, f'{total:.0f}', ha="center")

   # 设置堆叠柱状图的相关属性
   ax2.set_xlabel('Year')
   ax2.set_ylabel('Population')
   ax2.title.set_text('Population Distribution by Year')
   ax2.yaxis.grid(True)

完成上述步骤后，你应该会看到符合要求的两个图表。记得运行`plt.show()`以显示图表。