1、练习将25个点的符号绘制出来，然后用rainbow()返回25个颜色，后5个符号形状的背景颜色用蓝色填充，图的标题为"符号图"，x轴标题为符号索引，y轴标题为符号形状。

2、根据员工的销售业绩画饼状图，添加图例（右上角），并将图例的形状改为pch=c(15:17)，其中员工的销售业绩和姓名如下表：

姓名	业绩
小明	100
小红	200
小亮	300

效果图示例

3、随机生成1000个数据，均值为10 标准差为3。练习绘制直方图。

要求：（1）每个直方图用不同的颜色展示；（2）每个条柱上显示具体的数值；（3）绘制频数和频率直方图，并在频率直方图上添加曲线。

4、使用R语言内置的Titanic数据集，分析男性乘客和女性乘客的获救情况。要求：（1）绘制性别、获救情况的柱状图；（2）条柱上显示男性和女性的获救人数与遇难人数；（3）图的标题为性别与获救情况；（4）获救用绿色填充，遇难用红色填充。（5）添加图例，图例标题为获救情况。参考下图：

5、随机生成1000个数据，均值为10 标准差为5。练习绘制箱型图。要求：（1）图形主标题为箱形图；（2）使用text( )函数添加最大值、最小值、上下四分位数以及中位数；（3）打印出离群点。

6、练习使用R语言内置数据集stackloss，画出stackloss数据集各变量的直方图、箱型图、散点图，一次性画4个图。

 

1、

代码：

# 安装 ggplot2 和 viridis 包#install.packages("ggplot2")#install.packages("viridis")# 导入所需的包library(ggplot2)library(viridis)# 创建数据框data <- data.frame(index = 1:25,shape = rep(c("circle", "square", "triangle", "diamond", "star"), 5))# 绘制图形p <- ggplot(data, aes(x = factor(index), y = shape, fill = shape)) +geom_point(shape = 21, size = 5) +scale_fill_viridis(discrete = TRUE) +theme_minimal() +labs(title = "符号图", x = "符号索引", y = "符号形状")# 显示图形print(p)

截图：

2、

代码：

# 员工销售业绩和姓名sales <- c(100, 200, 300)names <- c("小明", "小红", "小亮")# 画饼状图pie(sales, labels=names)# 添加图例并更改图例形状legend("topright", names, pch=15:17, title="员工销售业绩")

截图：

3、

代码：

# 生成符合要求的随机数据mean_value <- 10std_dev <- 3data <- rnorm(1000, mean=mean_value, sd=std_dev)# 绘制频数直方图hist(data, breaks=30, col="skyblue", xlab="Value", ylab="Frequency", main="Histogram of Data")text(data, 10, labels=round(data, 1), cex=0.6, srt=90, adj=c(0.5,0.5))# 绘制频率直方图并添加曲线par(mfrow=c(1, 2))hist(data, breaks=30, freq=FALSE, col="lightgreen", xlab="Value", ylab="Frequency", main="Frequency Histogram with Density Curve")lines(density(data), col="red")

截图：

4、

代码：

# 导入所需的包library(ggplot2)# 加载Titanic数据集data("Titanic")# 转换数据集为数据框格式df <- as.data.frame(Titanic)# 重新构造数据框df <- subset(df, df$Survived == "Yes" | df$Survived == "No")df <- subset(df, df$Class == "1st" | df$Class == "2nd" | df$Class == "3rd")# 计算男性和女性的获救情况survived_counts <- aggregate(Survived ~ Sex, data = df, FUN = function(x) sum(x == "Yes"))died_counts <- aggregate(Survived ~ Sex, data = df, FUN = function(x) sum(x == "No"))# 绘制柱状图barplot_heights <- c(survived_counts$Survived, died_counts$Survived)barplot_labels <- c(paste("幸存:", survived_counts$Survived), paste("遇难:", died_counts$Survived))# 创建数据框bar_data <- data.frame(Sex = rep(c("Female", "Male"), each = 2),Survival = rep(c("Survived", "Died"), times = 2),Counts = barplot_heights,Labels = barplot_labels)# 绘制柱状图p <- ggplot(bar_data, aes(x = Sex, y = Counts, fill = Survival)) +geom_bar(stat = "identity", position = "dodge") +geom_text(aes(label = Labels), position = position_dodge(width = 0.9), vjust = -0.5, size = 3) +scale_fill_manual(values = c("green", "red"), name = "获救情况") +labs(title = "性别与获救情况", x = "性别", y = "人数") +theme_minimal()# 显示图形print(p)

截图：

5、

代码：

# 生成随机数据data <- rnorm(1000, mean=10, sd=5)# 绘制箱形图boxplot(data, main="箱形图")text(1, max(data), paste("最大值:", round(max(data), 2)))text(1, quantile(data, 0.75), paste("上四分位数:", round(quantile(data, 0.75), 2)))text(1, median(data), paste("中位数:", round(median(data), 2)))text(1, quantile(data, 0.25), paste("下四分位数:", round(quantile(data, 0.25), 2)))text(1, min(data), paste("最小值:", round(min(data), 2)))

截图：

6、

代码：

# 导入所需的包library(ggplot2)# 加载stackloss数据集data("stackloss")# 创建一个数据框来存储stackloss数据df <- data.frame(stackloss)# 绘制直方图hist_plot <- ggplot(df, aes(x = stack.loss)) +geom_histogram(fill = "lightblue", color = "black", bins = 10) +labs(title = "stack.loss的直方图", x = "stack.loss", y = "频数")# 绘制箱型图boxplot_plot <- ggplot(df, aes(x = factor(1), y = stack.loss)) +geom_boxplot(fill = "lightgreen", color = "black") +labs(title = "stack.loss的箱型图", x = "", y = "stack.loss") +theme(axis.text.x = element_blank())# 绘制散点图scatter_plot <- ggplot(df, aes(x = Air.Flow, y = stack.loss)) +geom_point(color = "darkorange") +labs(title = "Air.Flow与stack.loss的散点图", x = "Air.Flow", y = "stack.loss")# 绘制柱状图barplot_plot <- ggplot(df, aes(x = factor(1), y = stack.loss, fill = row.names(df))) +geom_bar(stat = "identity", width = 0.5) +coord_polar(theta = "y") +labs(title = "stackloss数据集中各变量的柱状图", x = "", y = "stack.loss") +theme(axis.text.x = element_blank())# 打印四个图形multiplot(hist_plot, boxplot_plot, scatter_plot, barplot_plot, cols=2)

截图：