R语言入门 Chapter05 | 因子

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发布于 2020-10-28 14:47:20

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发布于 2020-10-28 14:47:20

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Chapter05 | 因子

在R中名义型变量和有序性变量称为因子，factor。这些分类变量的可能值称为一个水平，level，例如good，better，best，都称为一个leve。由这些水平值构成的向量就称为因子。所有元素构成因子

因子类型的数据：

state.division
state.region

因子的应用：

1、计算频数
2、独立性检验
3、相关性检验
4、方差分析
5、主成分分析
6、因子分析
1、频数统计

# cyl这一列可以当作因子类型
> mtcars$cyl
 [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

> table(mtcars$cyl)
 4  6  8 
11  7 14

2、如何将向量转换为因子

使用factor()函数

> f <- factor(c("red","red","green","red","blue","green","blue","blue"))
> f
[1] red   red   green red   blue  green blue  blue 
Levels: blue green red

# 有序性变量也可以作为因子

# 不定义levels时levels自动去重
> week <- factor(c("Mon","Fri","Thu","Wed","Mon","Fri","Sun"))
> week
[1] Mon Fri Thu Wed Mon Fri Sun
Levels: Fri Mon Sun Thu Wed

# 自定义levels不允许重复
> week <- factor(c("Mon","Fri","Thu","Wed","Mon","Fri","Sun"),order = TRUE,
+                levels = c("Mon","Tue","Wed","Thu","Fri","Sat","Sun"))
> week
[1] Mon Fri Thu Wed Mon Fri Sun
Levels: Mon < Tue < Wed < Thu < Fri < Sat < Sun

# 一个向量转换成因子，直接输入到factor()内即可
> fcyl <- factor(mtcars$cyl)
> fcyl
 [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
Levels: 4 6 8

3、向量和因子图形的对比
向量

plot(mtcars$cyl)

因子

plot(fcyl)

4、cut()函数

cut函数可以将连续性变量x分割为n个水平的因子

> num <- c(1:100)
> num
  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22
 [23]  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44
 [45]  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66
 [67]  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88
 [89]  89  90  91  92  93  94  95  96  97  98  99 100

# 每隔10个进行分组

> cut (num,c(seq(0,100,10)))
  [1] (0,10]   (0,10]   (0,10]   (0,10]   (0,10]   (0,10]   (0,10]   (0,10]   (0,10]  
 [10] (0,10]   (10,20]  (10,20]  (10,20]  (10,20]  (10,20]  (10,20]  (10,20]  (10,20] 
 [19] (10,20]  (10,20]  (20,30]  (20,30]  (20,30]  (20,30]  (20,30]  (20,30]  (20,30] 
 [28] (20,30]  (20,30]  (20,30]  (30,40]  (30,40]  (30,40]  (30,40]  (30,40]  (30,40] 
 [37] (30,40]  (30,40]  (30,40]  (30,40]  (40,50]  (40,50]  (40,50]  (40,50]  (40,50] 
 [46] (40,50]  (40,50]  (40,50]  (40,50]  (40,50]  (50,60]  (50,60]  (50,60]  (50,60] 
 [55] (50,60]  (50,60]  (50,60]  (50,60]  (50,60]  (50,60]  (60,70]  (60,70]  (60,70] 
 [64] (60,70]  (60,70]  (60,70]  (60,70]  (60,70]  (60,70]  (60,70]  (70,80]  (70,80] 
 [73] (70,80]  (70,80]  (70,80]  (70,80]  (70,80]  (70,80]  (70,80]  (70,80]  (80,90] 
 [82] (80,90]  (80,90]  (80,90]  (80,90]  (80,90]  (80,90]  (80,90]  (80,90]  (80,90] 
 [91] (90,100] (90,100] (90,100] (90,100] (90,100] (90,100] (90,100] (90,100] (90,100]
[100] (90,100]
10 Levels: (0,10] (10,20] (20,30] (30,40] (40,50] (50,60] (60,70] (70,80] ... (90,100]