factor类型的创建

1. factor( )

> credit_rating <- c("BB", "AAA", "AA", "CCC", "AA", "AAA", "B", "BB") #生成名为credit_rating的字符向量> credit_factor <- factor(credit_rating) # step 2.将credit_rating转化为因子> credit_factor[1] BB AAA AA CCC AA AAA B BB Levels: AA AAA B BB CCC> str(credit_rating) #调用str()函数，显示credit_rating结构 chr [1:8] "BB" "AAA" "AA" "CCC" "AA" "AAA" "B" "BB"> str(credit_factor) #调用str()函数，显示credit_factor结构 Factor w/ 5 levels "AA","AAA","B",..: 4 2 1 5 1 2 3 4

2. levels( )

上述代码中第二个运行后得到了levals，用于显示不同的因子（不重复）,上述代码运行一二行

>credit_rating <- c("BB", "AAA", "AA", "CCC", "AA", "AAA", "B", "BB") > credit_factor <- factor(credit_rating) # step 2.将credit_rating转化为因子> credit_factor[1] BB AAA AA CCC AA AAA B BB Levels: AA AAA B BB CCC> levels(credit_factor)[1] "AA" "AAA" "B" "BB" "CCC">levels(credit_factor) <-c("2A","3A","1B","2B","3C")> credit_factor[1] 2B 3A 2A 3C 2A 3A 1B 2BLevels: 2A 3A 1B 2B 3C

3. Factor 汇总：summary()函数

> summary(credit_rating) Length Class Mode 8 character character > summary(credit_factor) AA AAA B BB CCC 2 2 1 2 1

4. factor 可视化:plot()

# 使用plot()将credit_factor可视化plot(credit_factor)#> summary(credit_factor)# AA AAA B BB CCC # 2 2 1 2 1

5. cut( )函数 对数据进行分组

>AAA_rank <- sample(seq(1:100), 50, replace = T)> AAA_rank [1] 90 28 63 57 96 41 93 70 76 36 26 1 86 43 47 15 23 70[19] 63 1 79 100 20 59 17 23 84 96 21 33 32 19 52 58 81 37[37] 22 58 42 75 41 64 15 58 63 2 1 65 54 35> # step 1：使用cut()函数为AAA_rank创建4个组> AAA_factor <- cut(x = AAA_rank , breaks =c(0,25,50,75,100) )> > AAA_factor [1] (75,100] (25,50] (50,75] (50,75] (75,100] (25,50] (75,100] (50,75] [9] (75,100] (25,50] (25,50] (0,25] (75,100] (25,50] (25,50] (0,25] [17] (0,25] (50,75] (50,75] (0,25] (75,100] (75,100] (0,25] (50,75] [25] (0,25] (0,25] (75,100] (75,100] (0,25] (25,50] (25,50] (0,25] [33] (50,75] (50,75] (75,100] (25,50] (0,25] (50,75] (25,50] (50,75] [41] (25,50] (50,75] (0,25] (50,75] (50,75] (0,25] (0,25] (50,75] [49] (50,75] (25,50] Levels: (0,25] (25,50] (50,75] (75,100]> # step 2：使用levels()按顺序将级别重命名> levels(AAA_factor) <- c("low","medium","high","very_high")> > # step 3：输出AAA_factor> AAA_factor [1] medium medium very_high high very_high high high [8] high medium medium very_high high medium very_high[15] medium low medium low high medium low [22] medium high very_high very_high very_high medium very_high[29] low low low medium very_high low very_high[36] low very_high low low high medium medium [43] medium low low low low medium medium [50] medium Levels: low medium high very_high> > # step 4：绘制AAA_factor> plot(AAA_factor)>

6. 删除元素 ：- 表示删除

（1）-1：删除第一位的元素，-3：删除第三位的元素

（2）

> credit_factor[1] BB AAA AA CCC AA AAA B BB Levels: AA AAA B BB CCC> # 删除位于`credit_factor`第3和第7位的`A`级债券，不使用`drop=TRUE`> keep_level <- credit_factor[c(-3,-7)]> > # 绘制keep_level> plot(keep_level)> > # 使用相同的数据，删除位于`credit_factor`第3和第7位的`A`级债券，使用`drop=TRUE`> drop_level <-credit_factor[c(-3,-7),drop=TRUE]> > # 绘制drop_level> plot(drop_level)>

7. 转换Factor为String类型

>cash=data.frame(company = c("A", "A", "B"), cash_flow = c(100, 200, 300), year = c(1, 3, 2)) #创建数据框>str(cash)'data.frame': 3 obs. of 3 variables: $ company : Factor w/ 2 levels "A","B": 1 1 2 $ cash_flow: num 100 200 300 $ year : num 1 3 2

注意：创建数据框时，R的默认行为是将所有字符转换为因子

那么，如何在创建数据框时，不让r的默认行为执行呢？

采用 stringsAsFactors = FALSE

> cash=data.frame(company = c("A", "A", "B"), cash_flow = c(100, 200, 300), year = c(1, 3, 2),stringsAsFactors=FALSE) #创建数据框> str(cash)'data.frame': 3 obs. of 3 variables: $ company : chr "A" "A" "B" $ cash_flow: num 100 200 300 $ year : num 1 3 2

8. 创建有序Factor类型:ordered=TRUE

# 有序Factor类型credit_rating <- c("AAA", "AA", "A", "BBB", "AA", "BBB", "A")credit_factor_ordered <- factor(credit_rating, ordered = TRUE, levels = c("AAA", "AA", "A", "BBB"))>credit_rating <- c("BB", "AAA", "AA", "CCC", "AA", "AAA", "B", "BB") > credit_factor <- factor(credit_rating) # step 2.将credit_rating转化为因子> credit_factor #此时的credit_factor 无序>ordered(credit_factor, levels = c("AAA", "AA", "A", "BBB"))

9. 删除因子级别时，采用drop=TRUE

>credit_factor[1] AAA AA A BBB AA BBB A Levels: BBB < A < AA < AAA>credit_factor[-1][1] AA A BBB AA BBB A Levels: BBB < A < AA < AAA #可见，AAA还存在>credit_factor[-1, drop = TRUE] #完全放弃AAA级别[1] AA A BBB AA BBB A Levels: BBB < A < AA

以上为个人经验，希望能给大家一个参考，也希望大家多多支持。如有错误或未考虑完全的地方，望不吝赐教。