R语言-基本图形

小说:网络中介推口子作者:帝戏更新时间:2019-02-21字数:99305

那队人马立即停下来,带头的一个少校军官急忙连连挥手说的:“我们是四十七师的,奉命前来增援马当要塞的啊?你们是那一部分的?在这里干什么?”

做兼职赚钱的说说

“啊——!”那条血练从雪山子身上飞出,上化出风绝代愤怒扭曲的面孔,直接冲向了天河子,天河子来不及躲避,被风绝代扑了个中,一身精血元气瞬间被掠夺一空,只剩下一个干瘪的皮囊落了下去,被血河腐蚀掉。而后风绝代又扑向了天一子,天一子见势不好,一剑朝扑向自己的血练斩去,只见这血练被天一子一剑斩为两半,正在天一子庆幸之极,这两条血练左右夹击,冲进了自己的身体之中。
地藏缓缓道:“灵明神猿……如来下得好大一盘棋。”地藏猛地抬起头,问悟空道,“你可知道,大须弥山主叫什么名字?”

纪太虚深吸一口气,双手结印,大吼一声:“煌煌六道,主宰万物,因果业力,钟声轮回!”纪太虚手中飞出无数的手印,落在幡上,浑身穴窍大开,夺天造化丹、生生造化丹、九转金丹三枚仙丹的药力不断的冲刷着纪太虚的四肢百骸!

目的:

  1.将变量的分布进行可视化展示

  2.通过结果进行跨组比较

内容:

  1.条形图,箱线图,点图

  2.饼图和扇形图

  3.直方图与核密度图

1.条形图

         条形图通过垂直和水平的条形展示了类别型变量的分布

    1.1普通条形图

1 library(vcd)
2 counts <- table(Arthritis$Improved)
3 barplot(counts,main="Simple Bar Plot",xlab = "Inprovement",ylab = "Frequency")
4 barplot(counts,main="Simple Bar Plot",xlab = "Inprovement",ylab = "Frequency",horiz = T)

 

 

     1.2均值条形图

# 1.构建数据集
# 2.整合数据集,根据region分组来计算每个地区的平均犯罪率
# 3.对结果进行排序
# 4.画图,并给出每个bar的名称
1
states <- data.frame(state.region,state.x77) 2 means <- aggregate(states$Illiteracy,by=list(state.region),FUN=mean) 3 means <- means[order(means$x),] 4 barplot(means$x,names.arg = means$Group.1) 5 title("Mean Illiteracy Rate")

    1.3条形图微调

1 library(vcd)
2 par(mar=c(5,8,4,2))
3 par(las=2)
4 counts <- table(Arthritis$Improved)
5 # cex.name缩小字号,names.arg使用字符向量作为标签名
6 barplot(counts,main = "Treatment outcome",horiz = T,cex.names = 0.8,
7         names.arg = c("No Improvement","Some Improvement","Marked Improvement"))

   1.4 棘状图(一种特殊的堆叠条形图,对其进行重缩放,每个条形的高度都是1,每一段的高度都表示比例)

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

2. 饼图

 

library(plotrix)
# 把四幅图合并为1幅图
par(mfrow=c(2,2))
# 1,简单饼图
slices <- c(10,12,4,16,8)
lbls <- c("US","UK","Australia","Germany","France")
pie(slices,labels = lbls,main = "Simple Pie Chart")
# 2.带有比例的饼图
pct <- round(slices/sum(slices)*100)
lbls2 <- paste(lbls," ",pct,"%",sep = "" )
pie(slices,labels = lbls2,col=rainbow(length(lbls2)),main = "Pie Chart With Percentages")
# 3.3D饼图
pie3D(slices,labels = lbls,explode = 0.1,main="3D Pie Chart")
# 4.从表格中创建饼图
mytable <- table(state.region)
lbls3 <- paste(names(mytable),"
",mytable,seq="")
pie(mytable,labels = lbls3,main = "Pie Chart from a Table 
 (With sample size)")

3.扇形图(更直观的展示各个数值的比例)

fan.plot(slices,labels = lbls,main = "Fan Plot")

4.直方图

  直方图可以直观的反映连续型变量的分布

 1 par(mfrow=c(2,2))
 2 # 1.简单直方图
 3 hist(mtcars$mpg)
 4 # 2.指定直方图的组数和颜色
 5 hist(mtcars$mpg,breaks = 12,col = "red",xlab = "Mile Per Gallon",main = "Colored histogram with 12 bins")
 6 # 3.添加轴须
 7 hist(mtcars$mpg,freq = F,breaks = 12,col = "red",xlab = "Mile Per Gallon",main = "Histogram,rug plot,density curve")
 8 rug(jitter(mtcars$mpg))
 9 lines(density(mtcars$mpg),col="blue",lwd=2)
10 #添加正态分布和外边框
11 x <- mtcars$mpg
12 h <- hist(x,breaks = 12,col = "red",xlab = "Mile Per Gallon",main = "Histogram with normal curve and box")
13 xfit <- seq(min(x),max(x),length=40)
14 yfit <- dnorm(xfit,mean = mean(x),sd = sd(x))
15 yfit <- yfit * diff(h$mids[1:2])*length(x)
16 lines(xfit,yfit,col="blue",lwd=2)
17 box()

5.核密度图

  核密度图用于估计随机变量的概率分布

par(mfrow=c(2,1))
# 1.使用默认设置,创建最简单图形
d <- density(mtcars$mpg)
plot(d)
# 2/曲线修改为蓝色,使用红色填充图形
plot(d,main = "Kernel Density of Miles Per Gallon")
polygon(d,col = "red",border = "blue")
rug(mtcars$mpg,col = "brown")

6.箱线图

  箱线图通过最小值,下四分位数,中位数,上四分位数,最大值描述了连续型变量的分布

  结论:4缸车的油耗更少

1 # 描述了四缸,六缸,八缸发动机对每加仑汽油行驶的英理数的统计
2 boxplot(mpg ~ cyl,data=mtcars,main="Car Mile Data",xlab="Number of Cylinders",ylab="Mile Pre Gas")

  具有交叉因子的箱线图

  结论:说明油耗随着缸数的下降而减少,对于4,6缸数的车,标准变速箱的油耗更低.对于8缸车变速箱类型没有区别

1 # 1.创建气缸数量因子
2 mtcars$cyl.f <- factor(mtcars$cyl,levels = c(4,6,8),labels = c("4","6","8"))
3 # 2.创建变速箱类型因子
4 mtcars$am.f <- factor(mtcars$am,levels = c(0,1),labels = c("auto","standard"))
5 #生成图形
6 boxplot(mpg ~ am.f * cyl.f,data=mtcars,varwidth=T,col=c("gold","darkgreen"),main="MPG Distribution by Auto Type",
7         xlab = "Auto Type",ylab = "Mile Per Gallon")

7.点图

  点图提供了一种在简单水平刻度上的绘制大量有标签值的做法

1 dotchart(mtcars$mpg,labels = row.names(mtcars),cex = .7,
2          main = "Gas Mileage for Car Models",
3          xlab = "Miles Per Gallon")

    分组,排序,着色后的点图

 结论:从图中可以很直观的得出信息:最省油的车是丰田卡罗拉,最费油的车是林肯

# 1.根据每加仑行驶的公里数进行排序
x <- mtcars[order(mtcars$mpg),]
# 2.将气缸数转换成因子
x$cyl <- factor(x$cyl)
# 3.给不同的气缸添加颜色
x$color[x$cyl == 4] <- "red"
x$color[x$cyl == 6] <- "blue"
x$color[x$cyl == 8] <- "darkgreen"
# 4.作图,根据气缸数进行分组,根据数据点标签取数据框的行名
dotchart(x$mpg,labels = row.names(x),cex = .7,groups = x$cyl,gcolor = "black",color = x$color,pch=19,
         main = "Gas Mileage for Car Model 
 group by cylinder",xlab = "Mile per Gallon")

 

编辑:安乙秉

发布:2019-02-21 02:58:04

当前文章:http://www.leetaemin.cn/news/20180769183.html

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