R语言基础之第五部分 总结数据信息

R语言基础之第五部分 总结数据信息

1、以R自带数据集airquality为例

> head(airquality)   #默认给出数据集前6行
  Ozone Solar.R Wind Temp Month Day
1    41     190  7.4   67     5   1
2    36     118  8.0   72     5   2
3    12     149 12.6   74     5   3
4    18     313 11.5   62     5   4
5    NA      NA 14.3   56     5   5
6    28      NA 14.9   66     5   6

> head(airquality,10)    #给出数据集前10行
> tail(airquality)      #默认给出数据集最后6行
> summary(airquality)
   Ozone           Solar.R           Wind             Temp           Month      
 Min.   :  1.00   Min.   :  7.0   Min.   : 1.700   Min.   :56.00   Min.   :5.000  
 1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400   1st Qu.:72.00   1st Qu.:6.000  
 Median : 31.50   Median :205.0   Median : 9.700   Median :79.00   Median :7.000  
 Mean   : 42.13   Mean   :185.9   Mean   : 9.958   Mean   :77.88   Mean   :6.993  
 3rd Qu.: 63.25   3rd Qu.:258.8   3rd Qu.:11.500   3rd Qu.:85.00   3rd Qu.:8.000  
 Max.   :168.00   Max.   :334.0   Max.   :20.700   Max.   :97.00   Max.   :9.000  
 NA's   :37       NA's   :7                                                       
      Day      
 Min.   : 1.0  
 1st Qu.: 8.0  
 Median :16.0  
 Mean   :15.8  
 3rd Qu.:23.0  
 Max.   :31.0  

> str(airquality)    #查看变量属性
'data.frame':   153 obs. of  6 variables:
 $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
 $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
 $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
 $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
 $ Month  : int  5 5 5 5 5 5 5 5 5 5 ...
 $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...

> table(airquality$Month)  #统计月份的频数

 5  6  7  8  9 
31 30 31 31 30

> table(airquality$Ozone)  #包含缺失值

  1   4   6   7   8   9  10  11  12  13  14  16  18  19  20  21  22  23  24  27  28 
  1   1   1   3   1   3   1   3   2   4   4   4   4   1   4   4   1   6   2   1   3 
 29  30  31  32  34  35  36  37  39  40  41  44  45  46  47  48  49  50  52  59  61 
  1   2   1   3   1   2   2   2   2   1   1   3   2   1   1   1   1   1   1   2   1 
 63  64  65  66  71  73  76  77  78  79  80  82  84  85  89  91  96  97 108 110 115 
  1   2   1   1   1   2   1   1   2   1   1   1   1   2   1   1   1   2   1   1   1 
118 122 135 168 
  1   1   1   1 

> table(airquality$Month,airquality$Day)
   
    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 26 27 28 29 30
  5 1 1 1 1 1 1 1 1 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  6 1 1 1 1 1 1 1 1 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  7 1 1 1 1 1 1 1 1 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  8 1 1 1 1 1 1 1 1 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  9 1 1 1 1 1 1 1 1 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
   
    31
  5  1
  6  0
  7  1
  8  1
  9  0

> any(is.na(airquality$Month))    #Month变量是否有缺失值
[1] FALSE

> sum(airquality$Month)
[1] 1070

> all(airquality$Month < 11)   #是否所有的月份都是小于11月
[1] TRUE

2、以R自带数据集Titanic为例

> Titanic
> Titanic
, , Age = Child, Survived = No

      Sex
Class  Male Female
  1st     0      0
  2nd     0      0
  3rd    35     17
  Crew    0      0

, , Age = Adult, Survived = No

      Sex
Class  Male Female
  1st   118      4
  2nd   154     13
  3rd   387     89
  Crew  670      3

, , Age = Child, Survived = Yes

      Sex
Class  Male Female
  1st     5      1
  2nd    11     13
  3rd    13     14
  Crew    0      0

, , Age = Adult, Survived = Yes

      Sex
Class  Male Female
  1st    57    140
  2nd    14     80
  3rd    75     76
  Crew  192     20

> titanic = as.data.frame(Titanic)  
> head(titanic,10) 
   Class    Sex   Age Survived Freq
1    1st   Male Child       No    0
2    2nd   Male Child       No    0
3    3rd   Male Child       No   35
4   Crew   Male Child       No    0
5    1st Female Child       No    0
6    2nd Female Child       No    0
7    3rd Female Child       No   17
8   Crew Female Child       No    0
9    1st   Male Adult       No  118
10   2nd   Male Adult       No  154

> dim(titanic)
[1] 32  5

> summary(titanic)
  Class       Sex        Age     Survived      Freq       
 1st :8   Male  :16   Child:16   No :16   Min.   :  0.00  
 2nd :8   Female:16   Adult:16   Yes:16   1st Qu.:  0.75  
 3rd :8                                   Median : 13.50  
 Crew:8                                   Mean   : 68.78  
                                          3rd Qu.: 77.00  
                                          Max.   :670.00  

> x= xtabs(Freq ~ Class + Age, data = titanic)  #交叉表
> x
      Age
Class  Child Adult
  1st      6   319
  2nd     24   261
  3rd     79   627
  Crew     0   885

> ftable(x)   #扁平化
      Age Child Adult
Class                
1st           6   319
2nd          24   261
3rd          79   627
Crew          0   885

后续请参考：
R语言基础之第一部分 : 5种数据对象类型
R语言基础之第二部分 : 操纵数据取子集
R语言基础之第三部分：重要函数apply族函数的使用
R语言基础之第四部分 : 排序