Data Mining-Data Preprocessing

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)

library(ggplot2)

data("airquality")
head(airquality)

##   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

datasets::airquality

##     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
## 11      7      NA  6.9   74     5  11
## 12     16     256  9.7   69     5  12
## 13     11     290  9.2   66     5  13
## 14     14     274 10.9   68     5  14
## 15     18      65 13.2   58     5  15
## 16     14     334 11.5   64     5  16
## 17     34     307 12.0   66     5  17
## 18      6      78 18.4   57     5  18
## 19     30     322 11.5   68     5  19
## 20     11      44  9.7   62     5  20
## 21      1       8  9.7   59     5  21
## 22     11     320 16.6   73     5  22
## 23      4      25  9.7   61     5  23
## 24     32      92 12.0   61     5  24
## 25     NA      66 16.6   57     5  25
## 26     NA     266 14.9   58     5  26
## 27     NA      NA  8.0   57     5  27
## 28     23      13 12.0   67     5  28
## 29     45     252 14.9   81     5  29
## 30    115     223  5.7   79     5  30
## 31     37     279  7.4   76     5  31
## 32     NA     286  8.6   78     6   1
## 33     NA     287  9.7   74     6   2
## 34     NA     242 16.1   67     6   3
## 35     NA     186  9.2   84     6   4
## 36     NA     220  8.6   85     6   5
## 37     NA     264 14.3   79     6   6
## 38     29     127  9.7   82     6   7
## 39     NA     273  6.9   87     6   8
## 40     71     291 13.8   90     6   9
## 41     39     323 11.5   87     6  10
## 42     NA     259 10.9   93     6  11
## 43     NA     250  9.2   92     6  12
## 44     23     148  8.0   82     6  13
## 45     NA     332 13.8   80     6  14
## 46     NA     322 11.5   79     6  15
## 47     21     191 14.9   77     6  16
## 48     37     284 20.7   72     6  17
## 49     20      37  9.2   65     6  18
## 50     12     120 11.5   73     6  19
## 51     13     137 10.3   76     6  20
## 52     NA     150  6.3   77     6  21
## 53     NA      59  1.7   76     6  22
## 54     NA      91  4.6   76     6  23
## 55     NA     250  6.3   76     6  24
## 56     NA     135  8.0   75     6  25
## 57     NA     127  8.0   78     6  26
## 58     NA      47 10.3   73     6  27
## 59     NA      98 11.5   80     6  28
## 60     NA      31 14.9   77     6  29
## 61     NA     138  8.0   83     6  30
## 62    135     269  4.1   84     7   1
## 63     49     248  9.2   85     7   2
## 64     32     236  9.2   81     7   3
## 65     NA     101 10.9   84     7   4
## 66     64     175  4.6   83     7   5
## 67     40     314 10.9   83     7   6
## 68     77     276  5.1   88     7   7
## 69     97     267  6.3   92     7   8
## 70     97     272  5.7   92     7   9
## 71     85     175  7.4   89     7  10
## 72     NA     139  8.6   82     7  11
## 73     10     264 14.3   73     7  12
## 74     27     175 14.9   81     7  13
## 75     NA     291 14.9   91     7  14
## 76      7      48 14.3   80     7  15
## 77     48     260  6.9   81     7  16
## 78     35     274 10.3   82     7  17
## 79     61     285  6.3   84     7  18
## 80     79     187  5.1   87     7  19
## 81     63     220 11.5   85     7  20
## 82     16       7  6.9   74     7  21
## 83     NA     258  9.7   81     7  22
## 84     NA     295 11.5   82     7  23
## 85     80     294  8.6   86     7  24
## 86    108     223  8.0   85     7  25
## 87     20      81  8.6   82     7  26
## 88     52      82 12.0   86     7  27
## 89     82     213  7.4   88     7  28
## 90     50     275  7.4   86     7  29
## 91     64     253  7.4   83     7  30
## 92     59     254  9.2   81     7  31
## 93     39      83  6.9   81     8   1
## 94      9      24 13.8   81     8   2
## 95     16      77  7.4   82     8   3
## 96     78      NA  6.9   86     8   4
## 97     35      NA  7.4   85     8   5
## 98     66      NA  4.6   87     8   6
## 99    122     255  4.0   89     8   7
## 100    89     229 10.3   90     8   8
## 101   110     207  8.0   90     8   9
## 102    NA     222  8.6   92     8  10
## 103    NA     137 11.5   86     8  11
## 104    44     192 11.5   86     8  12
## 105    28     273 11.5   82     8  13
## 106    65     157  9.7   80     8  14
## 107    NA      64 11.5   79     8  15
## 108    22      71 10.3   77     8  16
## 109    59      51  6.3   79     8  17
## 110    23     115  7.4   76     8  18
## 111    31     244 10.9   78     8  19
## 112    44     190 10.3   78     8  20
## 113    21     259 15.5   77     8  21
## 114     9      36 14.3   72     8  22
## 115    NA     255 12.6   75     8  23
## 116    45     212  9.7   79     8  24
## 117   168     238  3.4   81     8  25
## 118    73     215  8.0   86     8  26
## 119    NA     153  5.7   88     8  27
## 120    76     203  9.7   97     8  28
## 121   118     225  2.3   94     8  29
## 122    84     237  6.3   96     8  30
## 123    85     188  6.3   94     8  31
## 124    96     167  6.9   91     9   1
## 125    78     197  5.1   92     9   2
## 126    73     183  2.8   93     9   3
## 127    91     189  4.6   93     9   4
## 128    47      95  7.4   87     9   5
## 129    32      92 15.5   84     9   6
## 130    20     252 10.9   80     9   7
## 131    23     220 10.3   78     9   8
## 132    21     230 10.9   75     9   9
## 133    24     259  9.7   73     9  10
## 134    44     236 14.9   81     9  11
## 135    21     259 15.5   76     9  12
## 136    28     238  6.3   77     9  13
## 137     9      24 10.9   71     9  14
## 138    13     112 11.5   71     9  15
## 139    46     237  6.9   78     9  16
## 140    18     224 13.8   67     9  17
## 141    13      27 10.3   76     9  18
## 142    24     238 10.3   68     9  19
## 143    16     201  8.0   82     9  20
## 144    13     238 12.6   64     9  21
## 145    23      14  9.2   71     9  22
## 146    36     139 10.3   81     9  23
## 147     7      49 10.3   69     9  24
## 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

Pendeteksian Missing Values

# Cek jumlah missing values per kolom
colSums(is.na(airquality)) # Menghitung jumlah nilai NA (kosong) di setiap kolom

##   Ozone Solar.R    Wind    Temp   Month     Day 
##      37       7       0       0       0       0

is.na(airquality)

##        Ozone Solar.R  Wind  Temp Month   Day
##   [1,] FALSE   FALSE FALSE FALSE FALSE FALSE
##   [2,] FALSE   FALSE FALSE FALSE FALSE FALSE
##   [3,] FALSE   FALSE FALSE FALSE FALSE FALSE
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colSums(is.na(airquality))

##   Ozone Solar.R    Wind    Temp   Month     Day 
##      37       7       0       0       0       0

# Mengganti missing values dengan median di setiap kolom
airquality$Ozone[is.na(airquality$Ozone)] <- median(airquality$Ozone, na.rm = TRUE)
airquality$Solar.R[is.na(airquality$Solar.R)] <-median(airquality$Solar.R, na.rm = TRUE)

airquality

##     Ozone Solar.R Wind Temp Month Day
## 1    41.0     190  7.4   67     5   1
## 2    36.0     118  8.0   72     5   2
## 3    12.0     149 12.6   74     5   3
## 4    18.0     313 11.5   62     5   4
## 5    31.5     205 14.3   56     5   5
## 6    28.0     205 14.9   66     5   6
## 7    23.0     299  8.6   65     5   7
## 8    19.0      99 13.8   59     5   8
## 9     8.0      19 20.1   61     5   9
## 10   31.5     194  8.6   69     5  10
## 11    7.0     205  6.9   74     5  11
## 12   16.0     256  9.7   69     5  12
## 13   11.0     290  9.2   66     5  13
## 14   14.0     274 10.9   68     5  14
## 15   18.0      65 13.2   58     5  15
## 16   14.0     334 11.5   64     5  16
## 17   34.0     307 12.0   66     5  17
## 18    6.0      78 18.4   57     5  18
## 19   30.0     322 11.5   68     5  19
## 20   11.0      44  9.7   62     5  20
## 21    1.0       8  9.7   59     5  21
## 22   11.0     320 16.6   73     5  22
## 23    4.0      25  9.7   61     5  23
## 24   32.0      92 12.0   61     5  24
## 25   31.5      66 16.6   57     5  25
## 26   31.5     266 14.9   58     5  26
## 27   31.5     205  8.0   57     5  27
## 28   23.0      13 12.0   67     5  28
## 29   45.0     252 14.9   81     5  29
## 30  115.0     223  5.7   79     5  30
## 31   37.0     279  7.4   76     5  31
## 32   31.5     286  8.6   78     6   1
## 33   31.5     287  9.7   74     6   2
## 34   31.5     242 16.1   67     6   3
## 35   31.5     186  9.2   84     6   4
## 36   31.5     220  8.6   85     6   5
## 37   31.5     264 14.3   79     6   6
## 38   29.0     127  9.7   82     6   7
## 39   31.5     273  6.9   87     6   8
## 40   71.0     291 13.8   90     6   9
## 41   39.0     323 11.5   87     6  10
## 42   31.5     259 10.9   93     6  11
## 43   31.5     250  9.2   92     6  12
## 44   23.0     148  8.0   82     6  13
## 45   31.5     332 13.8   80     6  14
## 46   31.5     322 11.5   79     6  15
## 47   21.0     191 14.9   77     6  16
## 48   37.0     284 20.7   72     6  17
## 49   20.0      37  9.2   65     6  18
## 50   12.0     120 11.5   73     6  19
## 51   13.0     137 10.3   76     6  20
## 52   31.5     150  6.3   77     6  21
## 53   31.5      59  1.7   76     6  22
## 54   31.5      91  4.6   76     6  23
## 55   31.5     250  6.3   76     6  24
## 56   31.5     135  8.0   75     6  25
## 57   31.5     127  8.0   78     6  26
## 58   31.5      47 10.3   73     6  27
## 59   31.5      98 11.5   80     6  28
## 60   31.5      31 14.9   77     6  29
## 61   31.5     138  8.0   83     6  30
## 62  135.0     269  4.1   84     7   1
## 63   49.0     248  9.2   85     7   2
## 64   32.0     236  9.2   81     7   3
## 65   31.5     101 10.9   84     7   4
## 66   64.0     175  4.6   83     7   5
## 67   40.0     314 10.9   83     7   6
## 68   77.0     276  5.1   88     7   7
## 69   97.0     267  6.3   92     7   8
## 70   97.0     272  5.7   92     7   9
## 71   85.0     175  7.4   89     7  10
## 72   31.5     139  8.6   82     7  11
## 73   10.0     264 14.3   73     7  12
## 74   27.0     175 14.9   81     7  13
## 75   31.5     291 14.9   91     7  14
## 76    7.0      48 14.3   80     7  15
## 77   48.0     260  6.9   81     7  16
## 78   35.0     274 10.3   82     7  17
## 79   61.0     285  6.3   84     7  18
## 80   79.0     187  5.1   87     7  19
## 81   63.0     220 11.5   85     7  20
## 82   16.0       7  6.9   74     7  21
## 83   31.5     258  9.7   81     7  22
## 84   31.5     295 11.5   82     7  23
## 85   80.0     294  8.6   86     7  24
## 86  108.0     223  8.0   85     7  25
## 87   20.0      81  8.6   82     7  26
## 88   52.0      82 12.0   86     7  27
## 89   82.0     213  7.4   88     7  28
## 90   50.0     275  7.4   86     7  29
## 91   64.0     253  7.4   83     7  30
## 92   59.0     254  9.2   81     7  31
## 93   39.0      83  6.9   81     8   1
## 94    9.0      24 13.8   81     8   2
## 95   16.0      77  7.4   82     8   3
## 96   78.0     205  6.9   86     8   4
## 97   35.0     205  7.4   85     8   5
## 98   66.0     205  4.6   87     8   6
## 99  122.0     255  4.0   89     8   7
## 100  89.0     229 10.3   90     8   8
## 101 110.0     207  8.0   90     8   9
## 102  31.5     222  8.6   92     8  10
## 103  31.5     137 11.5   86     8  11
## 104  44.0     192 11.5   86     8  12
## 105  28.0     273 11.5   82     8  13
## 106  65.0     157  9.7   80     8  14
## 107  31.5      64 11.5   79     8  15
## 108  22.0      71 10.3   77     8  16
## 109  59.0      51  6.3   79     8  17
## 110  23.0     115  7.4   76     8  18
## 111  31.0     244 10.9   78     8  19
## 112  44.0     190 10.3   78     8  20
## 113  21.0     259 15.5   77     8  21
## 114   9.0      36 14.3   72     8  22
## 115  31.5     255 12.6   75     8  23
## 116  45.0     212  9.7   79     8  24
## 117 168.0     238  3.4   81     8  25
## 118  73.0     215  8.0   86     8  26
## 119  31.5     153  5.7   88     8  27
## 120  76.0     203  9.7   97     8  28
## 121 118.0     225  2.3   94     8  29
## 122  84.0     237  6.3   96     8  30
## 123  85.0     188  6.3   94     8  31
## 124  96.0     167  6.9   91     9   1
## 125  78.0     197  5.1   92     9   2
## 126  73.0     183  2.8   93     9   3
## 127  91.0     189  4.6   93     9   4
## 128  47.0      95  7.4   87     9   5
## 129  32.0      92 15.5   84     9   6
## 130  20.0     252 10.9   80     9   7
## 131  23.0     220 10.3   78     9   8
## 132  21.0     230 10.9   75     9   9
## 133  24.0     259  9.7   73     9  10
## 134  44.0     236 14.9   81     9  11
## 135  21.0     259 15.5   76     9  12
## 136  28.0     238  6.3   77     9  13
## 137   9.0      24 10.9   71     9  14
## 138  13.0     112 11.5   71     9  15
## 139  46.0     237  6.9   78     9  16
## 140  18.0     224 13.8   67     9  17
## 141  13.0      27 10.3   76     9  18
## 142  24.0     238 10.3   68     9  19
## 143  16.0     201  8.0   82     9  20
## 144  13.0     238 12.6   64     9  21
## 145  23.0      14  9.2   71     9  22
## 146  36.0     139 10.3   81     9  23
## 147   7.0      49 10.3   69     9  24
## 148  14.0      20 16.6   63     9  25
## 149  30.0     193  6.9   70     9  26
## 150  31.5     145 13.2   77     9  27
## 151  14.0     191 14.3   75     9  28
## 152  18.0     131  8.0   76     9  29
## 153  20.0     223 11.5   68     9  30

Data Mining-Data Preprocessing

Iqbal

2025-02-19

Pendeteksian Missing Values