R语言学习系列19-基本统计图形.docx

19. 基本统计图形目录：一. 条形图二. 饼图三. 直方图四. 箱线图五. 点图六. 散点图七. 相关图八. QQ图九. 等高线图十. 热图正文：要观察分类变量与定量变量的数据，最基本的方法就是用图形：1. 将变量的分布作可视化展示；2. 通过结果变量进行跨组比较。一、条形图条形图通过垂直的或水平的条形展示了类别型变量的分布（频数）。函数barplot()的最简单用法是：barplot(height)其中，height为向量或矩阵。 1. 简单条形图（height为向量）library(vcd)#使用数据集Arthritiscounts - table(Arthritis$Improved)counts None Some Marked 42 14 28 par(mfrow=c(1,2))barplot(counts, main = Simple Bar Plot, xlab = Improvement, ylab = Frequency)#竖直条形图barplot(counts, main = Horizontal Bar Plot, xlab = Frequency, ylab = Improvement, horiz = TRUE)#水平条形图2. 堆砌条形图和分组条形图（height为矩阵）若beside=FALSE（默认值），则矩阵中的每一列都将生成图中的一个条形，各列中的值将给出堆砌的“子条”的高度。若beside=TRUE，则矩阵中的每一列都表示一个分组，各列中的值将并列而不是堆砌。library(vcd) #使用数据集Arthritiscounts - table(Arthritis$Improved, Arthritis$Treatment)counts Placebo Treated None 29 13 Some 7 7 Marked 7 21par(mfrow=c(1,2))barplot(counts, main = Stacked Bar Plot, xlab = Treatment, ylab = Frequency, col = c(red, yellow, green),legend = rownames(counts)) #堆砌条形图barplot(counts, main = Grouped Bar Plot, xlab = Treatment, ylab = Frequency, col = c(red, yellow, green),legend = rownames(counts), beside = TRUE) #分组条形图注：棘状图可对堆砌条形图进行重缩放library(vcd)attach(Arthritis)counts - table(Treatment, Improved)spine(counts, main = Spinogram Example)detach(Arthritis)3. 均值条形图条形图并不一定要基于计数数据或频率数据，也可以使用数据整合函数并将结果传递给函数barplot()，来创建表示均值、中位数、标准差等的条形图。states - data.frame(state.region, state.x77)means - aggregate(states$Illiteracy, by = list(state.region), FUN = mean)means - means[order(means$x), ]#均值从小到大排序means Group.1 x3 North Central 0.7000001 Northeast 1.0000004 West 1.0230772 South 1.737500barplot(means$x, names.arg=means$Group.1)#参数2设置标签title(Mean Illiteracy Rate)4. 条形图的微调par(mar = c(5, 8, 4, 2))par(las = 2)#旋转图形标签counts - table(Arthritis$Improved)barplot(counts, main = Treatment Outcome, horiz = TRUE, s = 0.8, names.arg = c(No Improvement, Some Improvement, Marked Improvement))#s缩小字体二、饼图饼图的功能同条形图，每一个扇形的角度与相应频数成比例