Plot in R

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本篇内容主要包含常用的可视化图的R实现代码及效果。

Histogram(直方图)

base:hist()

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library(DMwR)
library(car)
library(magrittr)
df <- algae
par(mfrow = c(1,3))
hist(df$mxPH,prob = T)
hist(df$mxPH,prob = T,xlab = '',main = 'Histogram of mxPH',ylim = 0:1)
density(df$mxPH, na.rm = TRUE) %>% lines()
jitter(df$mxPH) %>% rug()
qqPlot(df$mxPH, main = 'Q-Q plot of mxPH',ylab = 'mxPH')

## [1] 56 57

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par(mfrow = c(1,1))
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library(lattice)
library(gridExtra)
hisc1 <- histogram(~ mxPH | season, data = df)
# multiple factors
hisc2 <- histogram(~ mxPH | season * speed, data = df)
# stripplot ways
stri1 <- stripplot(size ~ mxPH | speed, data = df, jitter = TRUE)
grid.arrange(hisc1,hisc2,stri1,nrow = 3)

箱线图(boxplot)

lattice: bwplot()

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library(ggplot2)
library(gridExtra)
# condition boxplot with lattice
bx1 <- bwplot(size ~ a1, data = df, ylab = "River Size", xlab = "Algae a1")
bx2 <- ggplot(aes(x = size, y = a1),data = df) + geom_boxplot() + coord_flip()
grid.arrange(bx1, bx2, ncol = 2)

Hmisc:bwplot()

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library(Hmisc)
# point: mean
# vertical lines: quantiles
# 下图结论：小型河流更高频率海藻且分布更加分散
bxq1 <- bwplot(size ~ a1, data = df, 
       panel = panel.bpplot,
       probs = seq(0.01,0.49, by = 0.01),
       datadensity = TRUE, ylab = "size", xlab = "a1")
# 多个因子，因子为分类变量
bxq2 <- bwplot(season ~ a1 | size, data = df,
       panel = panel.bpplot,
       probs = seq(0.01,0.49, by = 0.01),
       datadensity = TRUE)
bxq3 <- bwplot(season ~ a1 | size, data = df)
# 多个因子，因子中包含连续变量  将其离散化后即可
## 连续变量离散化
mno2i <- equal.count(na.omit(df$mnO2), number = 4, overlap = 1/5)
## 类似散点图，从左到右从下到上
bxq4 <- stripplot(season ~ a1|mno2i, data = df[!is.na(df$mnO2),])
grid.arrange(bxq1,bxq2,bxq4,bxq3,nrow = 2,ncol = 2)

可视化参考书籍

  • Statistics for Technology, Chatfield(1983)。简单很好的统计书籍。例子简单能说明问题
  • Introductry Statistics with R, Dalgaard(2002)
  • Visualizing Data, Cleveland(1993), 物有所值
  • The Element of Graphing Data, Cleveland(1995), 更正式
  • Data Visualization, Chen(2008)
  • R Graphics, Murrell(2006), R 软件绘图
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