Chapter 3 数据可视化

3.1 饼状图

饼状图是一个将圆形划分为几个扇形的统计图表，用于描述数量之间的比例关系。在饼图中，每个扇区的弧长大小为其所表示的数量比例。饼形图很少用于科技文献，但是商业领域和大众媒体中的宠儿。

#这句代码可以使R打印出中文
Sys.setlocale("LC_ALL","Chinese")

## [1] "LC_COLLATE=Chinese (Simplified)_China.936;LC_CTYPE=Chinese (Simplified)_China.936;LC_MONETARY=Chinese (Simplified)_China.936;LC_NUMERIC=C;LC_TIME=Chinese (Simplified)_China.936"

假如大学里一个班一共60位同学，他们在选修体育课， 15人选择了游泳，20人选择了武术，5个人选修了足球，8个人选修了乒乓球，12个人选修了篮球。我们怎么用饼状图来表示这组数据呢？

# 数据放在一个向量中。
pe_slices <- c(15, 20, 5, 8, 12)
#体育课的名称也放入一个向量中
lbls <- c("游泳", "武术", "足球", "乒乓球", "篮球")
#计算选修每个体育课的人数占班级总人数的百分比
pct <- round(pe_slices/sum(pe_slices)*100)

#把体育课的名称和选修该课的人数占比合成一个字符串
lbls <- paste(lbls, pct) 
#再给体育课名称和选修该课的人数占比字符串后边加一个百分号
lbls <- paste(lbls,"%",sep="") 
#使用函数pie画出饼状图
pie(pe_slices,labels = lbls, col=rainbow(length(lbls)),
   main="体育选修课")

3.2 条形图（bar chart）

colors = c("green", "orange", "brown")
months <- c("Mar", "Apr", "May", "Jun", "Jul")
regions <- c("East", "West", "North")
# Create the matrix of the values.
Values <- matrix(c(2, 9, 3, 11, 9, 4, 8, 7, 3, 12, 5, 2, 8, 10, 11),nrow = 3, ncol = 5, byrow = TRUE)

# Create the bar chart
barplot(Values, main = "Total Revenue", names.arg = months, xlab = "Month", ylab = "Revenue",col = colors, beside = TRUE)

barplot(Values, main = "Total Revenue", names.arg = months, xlab = "Month", ylab = "Revenue", col = colors)
# Add the legend to the chart
legend("topleft", regions, cex = 0.7, fill = colors)

3.3 柱状图（histogram）

# Create data for the graph.
v <- c(19, 23, 11, 5, 16, 21, 32, 14, 19, 27, 39)

# Create the histogram.
hist(v, xlab = "No.of Articles", col = "green",
     border = "black", xlim = c(0, 50),
    ylim = c(0, 5), breaks = 5)

# Creating data for the graph.
v <- c(19, 23, 11, 5, 16, 21, 32, 14, 19,
       27, 39, 120, 40, 70, 90)

# Creating the histogram.
m<-hist(v, xlab = "Weight", ylab ="Frequency",
        col = "darkmagenta", border = "pink",
        breaks = 5)
 
# Setting labels
text(m$mids, m$counts, labels = m$counts,
    adj = c(0.5, -0.5))

3.4 箱形图

input <- mtcars[, c('mpg', 'cyl')]
print(head(input))

##                    mpg cyl
## Mazda RX4         21.0   6
## Mazda RX4 Wag     21.0   6
## Datsun 710        22.8   4
## Hornet 4 Drive    21.4   6
## Hornet Sportabout 18.7   8
## Valiant           18.1   6

# Plot the chart.
boxplot(mpg ~ cyl, data = mtcars,
        xlab = "Number of Cylinders",
        ylab = "Miles Per Gallon",
        main = "Mileage Data")

3.5 线形图

# Create the data for the chart.
v <- c(17, 25, 38, 13, 41)
t <- c(22, 19, 36, 19, 23)
m <- c(25, 14, 16, 34, 29)
 
# Plot the bar chart.
plot(v, type = "o", col = "red",
    xlab = "Month", ylab = "Article Written ",
    main = "Article Written chart")
 
lines(t, type = "o", col = "blue")
lines(m, type = "o", col = "green")

3.6 散点图

# Plot the chart for cars with
# weight between 1.5 to 4 and
# mileage between 10 and 25.
plot(x = mtcars$wt, y = mtcars$mpg,
    xlab = "Weight",
    ylab = "Milage",
    xlim = c(1.5, 4),
    ylim = c(10, 25),       
    main = "Weight vs Milage"
)