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文章目录
  1. 高级图形进阶
    1. 1、lattice
      1. 1、条件变量
      2. 2、面板函数
      3. 3、分组变量，多个面板的数据合一
      4. 4、lattice的图形参数
      5. 5、图形摆放
    2. 2、ggplot2包

高级图形进阶

1、lattice



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library(lattice)
# Bass 男低音 
# Alto 女低音
# Soprano 女高音 
# Tenor 男高音
histogram(~height|voice.part, data = singer, main="合唱团身高和声部的分布", xlab="身高（英尺）")


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#lattice包绘图的基本形式
#graph_function(formula, data = , options)
library(lattice)
attach(mtcars)
gearPlot <- factor(mtcars$gear, levels = c(3, 4, 5), 
               labels = c("3 gears", "4 gears", "5 gears"))
plot(gearPlot)


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cylPlot <- factor(mtcars$cyl, levels = c(4, 6, 8), 
              labels = c("4 cylinders", "6 cylinders", "8 cylinders"))
plot(cylPlot)


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densityplot(~mpg, 
            main = "核密度图", xlab = "每加仑的英里数")


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densityplot(~mpg | cyl, 
            main = "各气缸的核密度图", 
            xlab = "每加仑的英里数")


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bwplot(cyl ~ mpg | gear, 
       main = "气缸和档数的箱线图", 
       xlab = "英里每加仑", ylab = "气缸数")


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xyplot(mpg ~ wt | cyl * gear, 
       main = "气缸和档数的散点图", 
       xlab = "车重", ylab = "英里每加仑")


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dotplot(cyl ~ mpg | gear, 
        main = "气缸和档数的点图", 
        xlab = "Miles Per Gallon")


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cloud(mpg ~ wt * qsec | cyl, 
      main = "各气缸的3D散点图")


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splom(mtcars[c(1, 3, 4, 5, 6)], 
      main = "散点矩阵图")
detach(mtcars)


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#修改图形
myg <-densityplot(~height | voice.part, data=singer)
#更新为红色的点，加粗的线，填充的点
update(myg, col="red", pch=16, cex=.8, lwd=2)

1、条件变量

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# 针对连续形变量先切割成瓦块可以作为条件变量进行分析
displacement <- equal.count(mtcars$disp, number = 3, 
                            overlap = 0)
xyplot(mpg ~ wt | displacement, data = mtcars, 
       main = "不同排量的汽车车重与每加仑续航的散点图", 
       xlab = "车重", ylab = "每加仑英里", 
       layout = c(3, 1), aspect = 1.5)

2、面板函数

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displacement <- equal.count(mtcars$disp, number = 3, 
                            overlap = 0)
# 自定义的面板函数，使用该函数进行图形绘制，添加轴须线，网格，回归线
mypanel <- function(x, y) {
  panel.xyplot(x, y, pch = 19)
  panel.rug(x, y)
  panel.grid(h = -1, v = -1)
  #回归线
  panel.lmline(x, y, col = "red", lwd = 1, lty = 2)
}
xyplot(mpg ~ wt | displacement, data = mtcars, 
       layout = c(3, 1), aspect = 1.5, 
       main = "不同排量的汽车车重与每加仑续航的散点图（自定义面板函数）", 
       xlab = "车重", ylab = "每加仑英里", panel = mypanel)

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# 自定义的面板函数，使用该函数进行图形绘制，添加平均线，网格，平滑曲线
library(lattice)
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 = "green")
}
xyplot(mpg ~ disp | transmission, data = mtcars, scales = list(cex = 0.8, 
                                                               col = "red"), 
       panel = panel.smoother, 
       xlab = "排量", 
       ylab = "每加仑英里", 
       main = "不同变速箱类型的排量和每加仑续航散点图", 
       sub = "虚线表示组平均值", aspect = 1)

3、分组变量，多个面板的数据合一

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library(lattice)
mtcars$transmission <- factor(mtcars$am, levels = c(0, 1), 
                              labels = c("自动档", "手动档"))
densityplot(~mpg, data = mtcars, 
            group = transmission, 
            main = "不同变速箱类型的排量和每加仑续航", 
            xlab = "每加仑续航", 
            auto.key = TRUE)

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# 自定义图例
library(lattice)
mtcars$transmission <- factor(mtcars$am, levels = c(0, 1), 
                              labels = c("Automatic", "Manual"))
colors = c("red", "blue")
lines = c(1, 2)
points = c(16, 17)
key.trans <- list(title = "传动方式", 
                  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 = 0.9)
densityplot(~mpg, data = mtcars, 
            group = transmission, 
            main = "不同变速箱类型的排量和每加仑续航", 
            xlab = "每加仑续航", 
            pch = points, lty = lines, col = colors, 
            lwd = 2, jitter = 0.005, 
            key = key.trans)

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## 复合使用-分组变量和条件变量
library(lattice)
colors <- "darkgreen"
symbols <- c(1:12)
linetype <- c(1:3)
key.species <- list(title = "植物", 
                    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 = "二氧化碳吸收率\n 在草本植物中", 
       ylab = expression(paste("吸收率 ", 
                               bgroup("(", italic(frac("umol", "m"^2)), ")"))), 
       xlab = expression(paste("浓度 ", 
                               bgroup("(", italic(frac(mL, L)), ")"))), 
       sub = "草本植物种类: 稗草", 
       key = key.species)

4、lattice的图形参数

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#通过trellis.par.get()函数来获取，并用trellis.par.set()函数来修改。
#show.settings()函数可展示当前的图形参数设置情况
show.settings()
mysettings <- trellis.par.get()
mysettings$superpose.symbol
mysettings$superpose.symbol$pch <- c(1:10)
trellis.par.set(mysettings)
#此时lattice图形将对分组变量的第一个水平使用符号1（空心圆圈），第二个使用符号2（空心三角形），
#以此类推。
show.settings()

5、图形摆放

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library(lattice)
graph1 <- histogram(~height | voice.part, data = singer, 
                    main = "Heights of Choral Singers by Voice Part")
graph2 <- densityplot(~height, data = singer, group = voice.part, 
                      plot.points = FALSE, auto.key = list(columns = 4))
#第一个plot()函数把页面分割成一列两行的矩阵，并将图形放置到第一列、第一行中（自上往下、从左至右地计数）
plot(graph1, split = c(1, 1, 1, 2))
#第二个plot()函数做同样的分割，但是把图形放置到第一列、第二行中
plot(graph2, split = c(1, 2, 1, 2), newpage = FALSE)


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library(lattice)
graph1 <- histogram(~height | voice.part, data = singer, 
                    main = "Heights of Choral Singers by Voice Part")
graph2 <- densityplot(~height, data = singer, group = voice.part, 
                      plot.points = FALSE, auto.key = list(columns = 4))
#position = c(xmin, ymin, xmax, ymax)
plot(graph1, position = c(0, 0.3, 1, 1))
plot(graph2, position = c(0, 0, 1, 0.3), newpage = FALSE)

2、ggplot2包

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##箱线散点图
library(ggplot2)
mtcars$cylinder <- as.factor(mtcars$cyl)
qplot(cylinder, mpg, data=mtcars, geom=c("boxplot", "jitter"),
      fill=cylinder,
      main="叠加数据点的箱线图",
      xlab= "气缸数",
      ylab="每加仑续航")


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#回归图，并绘制了置信区间带
library(ggplot2)
transmission <- factor(mtcars$am, levels = c(0, 1), 
                       labels = c("自动", "手动"))
qplot(wt, mpg, data = mtcars, 
      color = transmission, shape = transmission, 
      geom = c("point", "smooth"), 
      method = "lm", formula = y ~ x, 
      xlab = "车重",  ylab = "每加仑续航", 
      main = "回归示例")


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#栅栏图
library(ggplot2)
mtcars$cyl <- factor(mtcars$cyl, levels = c(4, 6, 8), 
                     labels = c("4 cylinders", "6 cylinders", "8 cylinders"))
mtcars$am <- factor(mtcars$am, levels = c(0, 1), 
                    labels = c("Automatic", "Manual"))
qplot(wt, mpg, data = mtcars, facets = am ~ cyl, size = hp)


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library(ggplot2)
data(singer, package = "lattice")
qplot(height, data = singer, geom = c("density"), 
      facets = voice.part ~ ., fill = voice.part)
#更多请关注 http://ggplot2.tidyverse.org

文章目录
  1. 高级图形进阶
    1. 1、lattice
      1. 1、条件变量
      2. 2、面板函数
      3. 3、分组变量，多个面板的数据合一
      4. 4、lattice的图形参数
      5. 5、图形摆放
    2. 2、ggplot2包