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  • 4-7 总结数据信息

    > head(airquality,10)
       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
    7     23     299  8.6   65     5   7
    8     19      99 13.8   59     5   8
    9      8      19 20.1   61     5   9
    10    NA     194  8.6   69     5  10
    
    
    > tail(airquality)
        Ozone Solar.R Wind Temp Month Day
    148    14      20 16.6   63     9  25
    149    30     193  6.9   70     9  26
    150    NA     145 13.2   77     9  27
    151    14     191 14.3   75     9  28
    152    18     131  8.0   76     9  29
    153    20     223 11.5   68     9  30
    
    
    > 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)
    '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)
    
     5  6  7  8  9 
    31 30 31 31 30 
    
    
    > table(airquality$Ozone,useNA = "ifany")
    
       1    4    6    7    8    9   10   11   12   13   14   16   18   19   20   21   22 
       1    1    1    3    1    3    1    3    2    4    4    4    4    1    4    4    1 
      23   24   27   28   29   30   31   32   34   35   36   37   39   40   41   44   45 
       6    2    1    3    1    2    1    3    1    2    2    2    2    1    1    3    2 
      46   47   48   49   50   52   59   61   63   64   65   66   71   73   76   77   78 
       1    1    1    1    1    1    2    1    1    2    1    1    1    2    1    1    2 
      79   80   82   84   85   89   91   96   97  108  110  115  118  122  135  168 <NA> 
       1    1    1    1    2    1    1    1    2    1    1    1    1    1    1    1   37 
    
    
    > 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))
    [1] TRUE
    
    
    > sum(is.na(airquality))
    [1] 44
    
    
    > all(airquality$Month<12)
    [1] TRUE
    
    
    > titanic <- as.data.frame(Titanic)
    
    > head(titanic)
      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
    
    > 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)
    > x
          Age
    Class  Child Adult
      1st      6   319
      2nd     24   261
      3rd     79   627
      Crew     0   885
    
    > ftable(x)
          Age Child Adult
    Class                
    1st           6   319
    2nd          24   261
    3rd          79   627
    Crew          0   885
    
    > object.size(airquality)
    5632 bytes
    
    > print(object.size(airquality),units = "KB")
    5.5 Kb
    
    
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  • 原文地址:https://www.cnblogs.com/hankleo/p/9942357.html
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