第23章 使用lattice进行高级绘图

注：R语言的再复习之路
 
 

1. lattice包

格式：graph_function(formula, data = , options)

• graph_function是各种绘图函数
• formula指定要展示变量和任意调节变量
• data = 指定数据框
• options是用逗号分隔的参数

在公式中，主要格式为y ~ x | A * B，竖线左侧的变量称为主要变量，右边的变量称为调节变量。对于单变量图，用~ x代替y ~ x；对于3D图，用z ~ x * y代替y ~ x。

图类型	函数	公式例子
3D等高线图	contourplot()	z ~ x * y
3D水平图	levelplot()	z ~ x * y
3D散点图	cloud()	z ~ x * y | A
3D线框图	wireframe()	z ~ y * x
条形图	barchart()	x ~ A或A ~ x
箱线图	bwplot()	x ~ A或A ~ x
点图	dotplot()	~ x | A
柱状图	histogram()	~ x
核密度图	densityplot()	~ x | A * B
平行坐标曲线图	parallelplot()	dataframe
散点图	xyplot()	y ~ x | A
散点图矩阵	splom()	dataframe
线框图	stripplot()	A ~ x或x ~ A

library(lattice)
attach(mtcars)

gear <- factor(gear, levels = c(3, 4, 5), labels = c('3 gears', '4 gears', '5 gears'))
cyl <- factor(cyl, levels = c(4, 6, 8), labels = c('4 cylinders', '6 cylinders', '8 cylinders'))

densityplot(~ mpg)
densityplot(~ mpg | cyl)
bwplot(cyl ~ mpg | gear)
xyplot(mpg ~ wt | cyl * gear)
cloud(mpg ~ wt * qsec | cyl)
dotplot(cyl ~ mpg | gear)
splom(mtcars[c(1, 3, 4, 5, 6)])
detach(mtcars)

选项	描述
aspect	指定每个面板图形的纵横比（高度/宽度）的一个数字
col, pch, lty, lwd	颜色、符号、线条类型、线条宽度
group	分组变量（因子）
index.cond	列出展示面板顺序的列表
key	支持分组变量中图例的函数
layout	指定面板设置（列数、行数）的二元数值向量
main, sub	主标题、副标题
panel	在每个面板中生成图的函数
scales	列出提供坐标轴注释信息的列标配
strip	用于自定义面板条带的函数
split, position	数值型向量，在一页上绘制多幅图形
type	指定一个或多个散点图绘图选项
xlab, ylab	横轴标签、纵轴标签
xlim, ylim	横轴范围、纵轴范围

 
 

2. 调节变量

当调节变量为连续性变量时，需要将其转换为因子

• 方法1：使用R自带的cut()函数
• 方法2：使用lattice中的equal.count()

## 格式
myshingle <- equal.count(x, number = n, overlap = proportion)

## 例子
displacement <- equal.count(mtcars$disp, number = 3, overlap = 0)
xyplot(mpg ~ wt | displacement, data = mtcars, layout = c(3, 1), aspect = 1.5)

 
 

3. 面板函数

## 例1
mypanel <- function(x, y){
  panel.xyplot(x, y, pch = 19)
  panel.rug(x, y)
  panel.grid(h = -1, v = -1)  ## 在背景图中添加网格线
  panel.lmline(x, y, col = 'red', lwd = 1, lty = 2)
}

xyplot(mpg ~ wt | displacement, 
       data = mtcars,
       layout = c(3, 1),
       aspect = 1.5,
       panel = mypanel)

## 例2
mtcars$transmission <- factor(mtcars$am, levels = c(0, 1), labels = c('Automatic', 'Manual'))
panel.smoother <- function(x, y){
  panel.grid(h = -1, v = -1)
  panel.xyplot(x, y)
  panel.loess(x, y)
  panel.abline(h = mean(y), lwd = 2, lty = 2, col = 'darkgreen')
  panel.abline(h = mtcars$mpg %>% mean(), col = 'yellow', lty = 3, lwd = 3)
}

xyplot(mpg ~ disp | transmission, data = mtcars, scales = list(cex = .8, col = 'red'), panel = panel.smoother,
       xlab = 'Displacement', ylab = 'Miles Per Gallon',
       main = 'MPG vs Displacement by Transmission Type',
       sub = 'Dotted lines are Group Means', aspect = 1)

 
 

4. 分组变量

## 例1
colors <- c('red', 'blue')
lines <- c(1, 2)
points <- c(16, 17)
key.trans <- list(title = 'Transmission', 
                  space = 'bottom', columns = 2, 
                  text = list(levels(mtcars$transmission)),
                  points = list(pch = points, col = colors),
                  lines = list(col = colors, lty = lines),
                  cex.title = 1, cex = .9
                  )

densityplot(~ mpg, data = mtcars, group = transmission, pch = points, lty = lines, col = colors, lwd = 2, jitter = .005, key = key.trans)

## 例2
colors <- 'darkgreen'
symbols <- c(1:12)
linetype <- c(1:3)
key.species <- list(title = 'Plant',
                    space = 'right',
                    text = list(levels(CO2$Plant)),
                    points = list(pch = symbols, col = colors))

xyplot(uptake ~ conc | Type * Treatment, data = CO2, group = Plant,
       type = 'o', pch = symbols, col = colors, lty = linetype,
       main = 'Carbon Dioxide Uptake\nin Grass Plants',
       ylab = expression(paste('Uptake ', bgroup('(', italic(frac('umol', 'm'^2)), ')'))),
       xlab = expression(paste('Concentration ', bgroup('(', italic(frac(mL, L)), ')'))),
       sub = 'Grass Species: Eachinochloa crus-galli',
       key = key.species)

 
 

5. 自定义图形条带

histogram(~ height | voice.part, data = singer,
          strip = strip.custom(bg = 'lightgrey',
                               par.strip.text = list(col = 'black', cex = .8, font = 3)),
          main = 'Distribution of Heights by Voice Pitch',
          xlab = 'Height (inches)'
          )

 
 

7. 页面布局

graph1 <- histogram(~ height | voice.part, data = singer, main = 'Heights of Choral Singers by Voice Part')
graph2 <- bwplot(height ~ voice.part, data = singer)
plot(graph1, split = c(1, 1, 1, 2))
plot(graph2, split = c(1, 2, 1, 2), newpage = FALSE)