R语言入门 Chapter05 | 因子
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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]
如果数字较大，我们可以通过cut()函数进行频数统计，很方便
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