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数据分析-R语言学习笔记（三） 因子、缺失数据

一、因子

1、概念

首先，R中数据分为定量数据和定性数据：

 因子factor专门用来存放名义型和有序型的分类变量，因子本质上是一个带有水平level属性的整数向量，其中"水平"是指事前确定可能取值的有限集合，例如good,better,best。

一般数值型数据更容易是连续型数据，而字符串型数据更容易是名义型数据。

2、因子的应用

计算频数、独立性检验、相关性检验、方差分析、主成分分析、因子分析。

3、factor相关操作

对于因子，我们只需要知道数据集中哪些可以作为因子。这里拿mtcars数据集作为案例。

> 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) #使用table()进行频数统计

 4  6  8 
11  7 14

使用factor()函数将向量转化为因子：

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

有序型变量作为因子：

> week <- factor(c("mon","tue","wed","thu","fri"))
> week
[1] mon tue wed thu fri
Levels: fri mon thu tue wed
> week <- factor(c("mon","tue","wed","thu","fri","sat","sun"))
> week
[1] mon tue wed thu fri sat sun 
Levels: fri mon sat sun thu tue wed #输出没有顺序，而且可能是不全的
> week <- factor(c("mon","tue","wed","thu","fri","sat","sun"),ordered = T,levels = c("mon","tue","wed","thu","fri","sat","sun"))
> week
[1] mon tue wed thu fri sat sun
Levels: mon < tue < wed < thu < fri < sat < sun  #此时输出有了顺序

通过plot函数绘图，比较向量和因子的图像：

> plot(mtcars$cyl) #左图
> plot(factor(mtcars$cyl)) #右图

   

cut()函数：如果是个很大的数据集，我们就可以很容易通过cut计算每个区间包含的数据有多少，方便进行频数统计。

> num<-1:100
> num
  [1]   1   2   3   4   5   6   7   8   9  10  11  12
 [13]  13  14  15  16  17  18  19  20  21  22  23  24
 [25]  25  26  27  28  29  30  31  32  33  34  35  36
 [37]  37  38  39  40  41  42  43  44  45  46  47  48
 [49]  49  50  51  52  53  54  55  56  57  58  59  60
 [61]  61  62  63  64  65  66  67  68  69  70  71  72
 [73]  73  74  75  76  77  78  79  80  81  82  83  84
 [85]  85  86  87  88  89  90  91  92  93  94  95  96
 [97]  97  98  99 100
> cut(num,c(seq(0,100,10)))
  [1] (0,10]   (0,10]   (0,10]   (0,10]   (0,10]  
  [6] (0,10]   (0,10]   (0,10]   (0,10]   (0,10]  
 [11] (10,20]  (10,20]  (10,20]  (10,20]  (10,20] 
 [16] (10,20]  (10,20]  (10,20]  (10,20]  (10,20] 
 [21] (20,30]  (20,30]  (20,30]  (20,30]  (20,30] 
 [26] (20,30]  (20,30]  (20,30]  (20,30]  (20,30] 
 [31] (30,40]  (30,40]  (30,40]  (30,40]  (30,40] 
 [36] (30,40]  (30,40]  (30,40]  (30,40]  (30,40] 
 [41] (40,50]  (40,50]  (40,50]  (40,50]  (40,50] 
 [46] (40,50]  (40,50]  (40,50]  (40,50]  (40,50] 
 [51] (50,60]  (50,60]  (50,60]  (50,60]  (50,60] 
 [56] (50,60]  (50,60]  (50,60]  (50,60]  (50,60] 
 [61] (60,70]  (60,70]  (60,70]  (60,70]  (60,70] 
 [66] (60,70]  (60,70]  (60,70]  (60,70]  (60,70] 
 [71] (70,80]  (70,80]  (70,80]  (70,80]  (70,80] 
 [76] (70,80]  (70,80]  (70,80]  (70,80]  (70,80] 
 [81] (80,90]  (80,90]  (80,90]  (80,90]  (80,90] 
 [86] (80,90]  (80,90]  (80,90]  (80,90]  (80,90] 
 [91] (90,100] (90,100] (90,100] (90,100] (90,100]
 [96] (90,100] (90,100] (90,100] (90,100] (90,100]
10 Levels: (0,10] (10,20] (20,30] (30,40] ... (90,100]

state数据集中state.division和state.region是因子类型的数据。

 

二、缺失数据

出现缺失数据的原因：

　　机器断电，设备故障导致某个测量值发生了丢失；

　　测量根本没有发生，例如在做调查问卷时，有些问题没有回答，或者有些问题是无效的回答等等。

在R中，NA(not available)，不可用，用来存储缺失信息。这里NA表示没有，但未必就一定是0，NA是不知道多少，可能是0可能不是。只要有缺失值出现的地方就返回NA值。

> 1+NA
[1] NA
> NA == 0
[1] NA

只要有缺失值出现的地方就返回NA值。这也会带来问题，只要数据集中有一个NA，整个数据集就不能用了：

> a <- c(NA,1:49)
> a
 [1] NA  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[27] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
> sum(a)
[1] NA
> mean(a)
[1] NA

解决方法是，使用 na.rm 参数调整数据集：

> sum(a,na.rm =T)
[1] 1225
> mean(a,na.rm=T)
[1] 25
> mean(1:49,na.rm=T) #这说明将NA值移除后是按照49个向量计算的，移除NA，向量个数减少
[1] 25

R中提供is.na()进行逻辑测试：

> is.na(a)
 [1]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[14] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[27] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[40] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

下面使用大一点的数据集做示例：

> library(VIM)
载入需要的程辑包：colorspace
载入需要的程辑包：grid
VIM is ready to use.

Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues

载入程辑包：‘VIM’

The following object is masked from ‘package:datasets’:

    sleep

> sleep
    BodyWgt BrainWgt NonD Dream Sleep  Span  Gest Pred Exp Danger
1  6654.000  5712.00   NA    NA   3.3  38.6 645.0    3   5      3
2     1.000     6.60  6.3   2.0   8.3   4.5  42.0    3   1      3
3     3.385    44.50   NA    NA  12.5  14.0  60.0    1   1      1
4     0.920     5.70   NA    NA  16.5    NA  25.0    5   2      3
5  2547.000  4603.00  2.1   1.8   3.9  69.0 624.0    3   5      4
6    10.550   179.50  9.1   0.7   9.8  27.0 180.0    4   4      4
7     0.023     0.30 15.8   3.9  19.7  19.0  35.0    1   1      1
8   160.000   169.00  5.2   1.0   6.2  30.4 392.0    4   5      4
9     3.300    25.60 10.9   3.6  14.5  28.0  63.0    1   2      1
10   52.160   440.00  8.3   1.4   9.7  50.0 230.0    1   1      1
11    0.425     6.40 11.0   1.5  12.5   7.0 112.0    5   4      4
12  465.000   423.00  3.2   0.7   3.9  30.0 281.0    5   5      5
13    0.550     2.40  7.6   2.7  10.3    NA    NA    2   1      2
14  187.100   419.00   NA    NA   3.1  40.0 365.0    5   5      5
15    0.075     1.20  6.3   2.1   8.4   3.5  42.0    1   1      1
16    3.000    25.00  8.6   0.0   8.6  50.0  28.0    2   2      2
17    0.785     3.50  6.6   4.1  10.7   6.0  42.0    2   2      2
18    0.200     5.00  9.5   1.2  10.7  10.4 120.0    2   2      2
19    1.410    17.50  4.8   1.3   6.1  34.0    NA    1   2      1
20   60.000    81.00 12.0   6.1  18.1   7.0    NA    1   1      1
21  529.000   680.00   NA   0.3    NA  28.0 400.0    5   5      5
22   27.660   115.00  3.3   0.5   3.8  20.0 148.0    5   5      5
23    0.120     1.00 11.0   3.4  14.4   3.9  16.0    3   1      2
24  207.000   406.00   NA    NA  12.0  39.3 252.0    1   4      1
25   85.000   325.00  4.7   1.5   6.2  41.0 310.0    1   3      1
26   36.330   119.50   NA    NA  13.0  16.2  63.0    1   1      1
27    0.101     4.00 10.4   3.4  13.8   9.0  28.0    5   1      3
28    1.040     5.50  7.4   0.8   8.2   7.6  68.0    5   3      4
29  521.000   655.00  2.1   0.8   2.9  46.0 336.0    5   5      5
30  100.000   157.00   NA    NA  10.8  22.4 100.0    1   1      1
31   35.000    56.00   NA    NA    NA  16.3  33.0    3   5      4
32    0.005     0.14  7.7   1.4   9.1   2.6  21.5    5   2      4
33    0.010     0.25 17.9   2.0  19.9  24.0  50.0    1   1      1
34   62.000  1320.00  6.1   1.9   8.0 100.0 267.0    1   1      1
35    0.122     3.00  8.2   2.4  10.6    NA  30.0    2   1      1
36    1.350     8.10  8.4   2.8  11.2    NA  45.0    3   1      3
37    0.023     0.40 11.9   1.3  13.2   3.2  19.0    4   1      3
38    0.048     0.33 10.8   2.0  12.8   2.0  30.0    4   1      3
39    1.700     6.30 13.8   5.6  19.4   5.0  12.0    2   1      1
40    3.500    10.80 14.3   3.1  17.4   6.5 120.0    2   1      1
41  250.000   490.00   NA   1.0    NA  23.6 440.0    5   5      5
42    0.480    15.50 15.2   1.8  17.0  12.0 140.0    2   2      2
43   10.000   115.00 10.0   0.9  10.9  20.2 170.0    4   4      4
44    1.620    11.40 11.9   1.8  13.7  13.0  17.0    2   1      2
45  192.000   180.00  6.5   1.9   8.4  27.0 115.0    4   4      4
46    2.500    12.10  7.5   0.9   8.4  18.0  31.0    5   5      5
47    4.288    39.20   NA    NA  12.5  13.7  63.0    2   2      2
48    0.280     1.90 10.6   2.6  13.2   4.7  21.0    3   1      3
49    4.235    50.40  7.4   2.4   9.8   9.8  52.0    1   1      1
50    6.800   179.00  8.4   1.2   9.6  29.0 164.0    2   3      2
51    0.750    12.30  5.7   0.9   6.6   7.0 225.0    2   2      2
52    3.600    21.00  4.9   0.5   5.4   6.0 225.0    3   2      3
53   14.830    98.20   NA    NA   2.6  17.0 150.0    5   5      5
54   55.500   175.00  3.2   0.6   3.8  20.0 151.0    5   5      5
55    1.400    12.50   NA    NA  11.0  12.7  90.0    2   2      2
56    0.060     1.00  8.1   2.2  10.3   3.5    NA    3   1      2
57    0.900     2.60 11.0   2.3  13.3   4.5  60.0    2   1      2
58    2.000    12.30  4.9   0.5   5.4   7.5 200.0    3   1      3
59    0.104     2.50 13.2   2.6  15.8   2.3  46.0    3   2      2
60    4.190    58.00  9.7   0.6  10.3  24.0 210.0    4   3      4
61    3.500     3.90 12.8   6.6  19.4   3.0  14.0    2   1      1
62    4.050    17.00   NA    NA    NA  13.0  38.0    3   1      1
> is.na(sleep)
      BodyWgt BrainWgt  NonD Dream Sleep  Span  Gest  Pred   Exp Danger
 [1,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
 [2,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
 [3,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
 [4,]   FALSE    FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  FALSE
 [5,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
 [6,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
 [7,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
 [8,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
 [9,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[10,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[11,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[12,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[13,]   FALSE    FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  FALSE
[14,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
[15,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[16,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[17,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[18,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[19,]   FALSE    FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  FALSE
[20,]   FALSE    FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  FALSE
[21,]   FALSE    FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  FALSE
[22,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[23,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[24,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
[25,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[26,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
[27,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[28,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[29,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[30,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
[31,]   FALSE    FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  FALSE
[32,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[33,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[34,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[35,]   FALSE    FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  FALSE
[36,]   FALSE    FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  FALSE
[37,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[38,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[39,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[40,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[41,]   FALSE    FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  FALSE
[42,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[43,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[44,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[45,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[46,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[47,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
[48,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[49,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[50,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[51,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[52,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[53,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
[54,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[55,]   FALSE    FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  FALSE
[56,]   FALSE    FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  FALSE
[57,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[58,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[59,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[60,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[61,]   FALSE    FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE
[62,]   FALSE    FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  FALSE
> colSums(sleep) # 使用colsums计算每一列缺失值数目
 BodyWgt BrainWgt     NonD    Dream    Sleep     Span     Gest     Pred      Exp   Danger 
12324.98 17554.32       NA       NA       NA       NA       NA   178.00   150.00   162.00 
> rowSums(sleep)
 [1]       NA   77.700       NA       NA 7862.800  428.650   96.723  776.800  152.900  794.560  163.825 1221.800       NA       NA   66.575  129.200   79.685  163.000
[19]       NA       NA       NA  333.260   55.820       NA  778.400       NA   77.701  110.540 1578.800       NA       NA   53.445  117.060 1768.000       NA       NA
[37]   57.023   65.978   67.800  179.600       NA  207.980  349.000   75.420  542.800   95.400       NA   61.280  139.035  405.000  264.250  274.400       NA  424.100
[55]       NA       NA   99.600  239.600   89.504  327.790   67.200       NA

na.omit()去掉数据集中的那些缺失值：

> c<-c(NA,1:20,NA,NA)
> c
 [1] NA  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 NA NA
> d<-na.omit(c)
> d  #最终结果没有NA值，还多了一些属性信息
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
attr(,"na.action")
[1]  1 22 23
attr(,"class")
[1] "omit"
> sum(d)
[1] 210
> mean(d)
[1] 10.5
> sum(c)
[1] NA

na.omit()应用于数据框，则是将包含NA的一行都删掉：

> length(rownames(sleep))
[1] 62
> length(rownames(na.omit(sleep)))
[1] 42

 

处理缺失值包：

其他缺失数据：

NaN，不可能值，不存在的；

Inf，无穷，分正负，存在的；

> -1/0
[1] -Inf
> 0/0
[1] NaN
> 1/0
[1] Inf
> is.nan(0/0)
[1] TRUE
> is.-inf(-1/0)
错误: 找不到对象'is.'
> is.infinite(-1/0)
[1] TRUE

 

posted on 2023-11-23 16:55  szdbjooo  阅读(181)  评论(0)    收藏  举报