條形圖

簡單的垂直條形圖和水平條形圖

函式barplot()

> library(vcd)
載入需要的程輯包：grid
> counts <- table(Arthritis$Improved)
> counts

  None   Some Marked 
    42     14     28 
> barplot(counts,main = "Simple Bar Plot",xlab = "Improved",ylab = "Frequency") 
> barplot(counts,main = "Horizontal Bar Plot",xlab = "Improved",ylab = "Frequency",horiz = TRUE)
 

堆砌條形圖和分組條形圖

若beside=FALSE（預設值）則每一列都將生成圖中的一個條形，各列中的值將給出堆砌的“子條”的高度。若base=TRUE則矩陣中的每一列都表示一個分組，各列中的值將並列而不是堆砌。

library(vcd)

counts <- table(Arthritis$Improved,Arthritis$Treatment)
counts

#堆砌條形圖
barplot(counts,
        main = "Stacked Bar Plot",
        xlab = "Treatment",ylab = "Frequency",
        col = c("red","yellow","green"),
        legend = rownames(counts))

#分組條形圖
barplot(counts,
        main = "Grouped Bar Plot",
        xlab = "Treatment", ylab = "Frequency",
        col = c("red","yellow","green"),
        legend = rownames(counts),
        beside = TRUE)
 

均值條形圖

條形圖不一定要基於計數資料或頻率資料。可以使用資料整合函式並將結果傳遞給barplot()函式，來建立表示均值、中位數、標準差等的條形圖。

#美國各地區平均文盲率排序的條形圖
states <- data.frame(state.region,state.x77)
means <- aggregate(states$Illiteracy,by=list(state.region),FUN=mean)

means <- means[order(means$x),]
means

barplot(means$x,names.arg = means$Group.1)
title("Mean Illiteracy Rate")
 

條形圖的微調

隨著條形圖條數的增多，條形的標籤可能會開始重疊，可以使用cex.names來減小字號，將其指定為小於1的值可以縮小標籤的大小。可選的引數names.arg允許你指定一個字元向量作為條形的標籤名，同樣可以使用圖形引數輔助調整文字間隔。

####為條形圖搭配標籤

#增加y邊界的大小
par(mar=c(5,8,4,2))
#旋轉條形的標籤
par(las=2)

counts <- table(Arthritis$Improved)

barplot(counts,
        main = "Treatment Outcome",
        horiz = TRUE,
        cex.names = 0.8,  #縮小字型大小，讓標籤更合適
        names.arg = c("No Improvement","Some Improvement",    #修改標籤文字
                      "Marked Improvement"))

棘狀圖

棘狀圖對堆砌條形圖進行了重縮放，這樣讓每個條形的高度為1，每一段的高度即表示比例。可由vcd包中的函式spine（）繪製。

library(vcd)

attach(Arthritis)
counts <- table(Treatment,Improved)
spine(counts,main = "Spinogram Example")
detach(Arthritis)

餅圖

餅圖在商業世界中應用廣泛，但是在學術領域往往被否定，這是因為相對於面積，人們對長度的判斷更精確。通過pie(x,labels）函式來建立。

par(mfrow=c(2,2))     #將四幅圖組合為一幅

slices <- c(10,12,4,16,8)
lbls <- c("US","UK","Australia","Germany","France")
pie(slices,labels = lbls,
    main = "Simple Pie Chart")

pct <- round(slices / sum(slices) * 100)    #為餅圖新增比例數值
lbls2 <- paste(lbls," ",pct,"%",sep = "")
pie(slices,labels = lbls2, col = rainbow(length(lbls2)),
    main = "Pie Chart with Percentages")

library(plotrix)
pie3D(slices,labels = lbls,explode = 0.1,
      main="3D Pie Chart")

my_table <- table(state.region)
lbls3 <- paste(names(my_table),"\n",my_table,sep = "")
pie(my_table,labels = lbls3,
    main = "Pie Chart from a Table\n (with sample sizes)")

扇形圖

通過plotrix包中的fan.plot()函式實現的。

library(plotrix)

slices <- c(10,12,4,16,8)
lbls <- c("US","UK","Australia","Germany","France")
fan.plot(slices,labels = lbls, main = "Fan Plot")

直方圖

通過函式hist()創立。其中引數freq=FALSE表示根據概率密度而不是頻數繪製圖形，引數breaks用於控制組的數量。

par(mfrow = c(2,2))

#簡單的直方圖
hist(mtcars$mpg)

#指定組數和顏色
hist(mtcars$mpg,
     breaks = 12,
     col = "red",
     xlab = "Miles Per Gallon",
     main = "Colored histogram with 12 bins")

#新增軸須圖
hist(mtcars$mpg,
     freq = FALSE,
     breaks = 12,
     col = "red",
     xlab = "Miles Per Gallon",
     main = "Histogram, rug plot, density curve")
rug(jitter(mtcars$mpg))
lines(density(mtcars$mpg), col = "blue", lwd = 2)

#新增正態密度曲線和外框
x <- mtcars$mpg
h <- hist(x,
          breaks = 12,
          col = "red",
          xlab = "Miles Per Gallon",
          main = "Histogram with normal curve and box")
xfit <- seq(min(x), max(x), length = 40)
yfit <- dnorm(xfit, mean = mean(x), sd = sd(x))
yfit <- yfit * diff(h$mids[1:2]) *length(x)
lines(xfit, yfit, col = "blue", lwd = 2)
box()