Eksplorasi Data Nilai Kelas
Pudja Billah Athaya
2025-05-26
nilai= rnorm(240, mean=75, sd=10)
data.frame(nilai)## nilai
## 1 76.48547
## 2 79.70389
## 3 55.02897
## 4 87.65361
## 5 85.73402
## 6 78.86601
## 7 50.21749
## 8 63.79583
## 9 79.90121
## 10 62.21173
## 11 80.55499
## 12 73.04405
## 13 91.77758
## 14 76.49389
## 15 83.31299
## 16 59.14221
## 17 88.29855
## 18 72.47366
## 19 67.27490
## 20 74.36079
## 21 69.03950
## 22 58.21296
## 23 72.36363
## 24 84.28784
## 25 67.52065
## 26 73.38703
## 27 86.36226
## 28 72.75185
## 29 83.09218
## 30 83.29095
## 31 70.30281
## 32 79.92569
## 33 90.36530
## 34 67.76040
## 35 74.06063
## 36 62.50319
## 37 79.76051
## 38 82.99425
## 39 75.50308
## 40 81.43357
## 41 71.79169
## 42 67.11775
## 43 70.50557
## 44 94.01138
## 45 61.96397
## 46 75.75672
## 47 63.98775
## 48 79.97102
## 49 70.85461
## 50 69.58422
## 51 75.34806
## 52 56.75332
## 53 61.08254
## 54 67.45049
## 55 71.74342
## 56 88.74276
## 57 100.48682
## 58 88.71008
## 59 75.59005
## 60 62.05434
## 61 54.84919
## 62 72.06515
## 63 45.96627
## 64 72.79570
## 65 84.56979
## 66 62.81397
## 67 87.26464
## 68 62.20136
## 69 64.69431
## 70 72.45503
## 71 70.86540
## 72 73.89394
## 73 76.57041
## 74 60.38963
## 75 96.85415
## 76 63.30129
## 77 92.95461
## 78 63.15441
## 79 68.55914
## 80 83.15472
## 81 79.11322
## 82 68.72469
## 83 82.89546
## 84 69.86520
## 85 77.63564
## 86 83.91041
## 87 68.80076
## 88 58.01167
## 89 76.41816
## 90 76.77539
## 91 79.67621
## 92 89.06648
## 93 46.52147
## 94 74.82020
## 95 73.39535
## 96 77.62403
## 97 58.03619
## 98 76.12061
## 99 78.02452
## 100 76.56174
## 101 83.66924
## 102 76.24315
## 103 81.16329
## 104 80.57372
## 105 68.10656
## 106 76.27756
## 107 65.10939
## 108 89.80711
## 109 64.81644
## 110 74.20170
## 111 86.13627
## 112 57.02310
## 113 60.42528
## 114 66.80062
## 115 86.13144
## 116 70.35538
## 117 65.48593
## 118 89.89893
## 119 73.22673
## 120 74.83872
## 121 64.52261
## 122 68.41611
## 123 73.66362
## 124 79.52381
## 125 79.36734
## 126 83.41571
## 127 54.58469
## 128 61.25738
## 129 82.68280
## 130 87.66856
## 131 91.07345
## 132 77.73098
## 133 44.55570
## 134 82.49303
## 135 78.10471
## 136 82.09219
## 137 60.55604
## 138 88.65134
## 139 86.10376
## 140 76.03482
## 141 66.86100
## 142 81.49412
## 143 70.78176
## 144 97.76658
## 145 70.32187
## 146 69.32046
## 147 91.23399
## 148 69.07500
## 149 92.60433
## 150 84.61519
## 151 85.47080
## 152 65.68540
## 153 68.90065
## 154 69.24682
## 155 73.09958
## 156 69.98201
## 157 82.19036
## 158 81.23659
## 159 87.82006
## 160 68.30229
## 161 68.13040
## 162 61.45293
## 163 74.24992
## 164 74.25467
## 165 92.70902
## 166 86.11873
## 167 79.18462
## 168 77.86005
## 169 72.73980
## 170 80.65235
## 171 78.86561
## 172 64.88057
## 173 84.88284
## 174 88.36553
## 175 84.41856
## 176 69.98473
## 177 56.20900
## 178 80.27073
## 179 74.56734
## 180 70.89046
## 181 79.41763
## 182 62.85761
## 183 61.44105
## 184 88.74322
## 185 86.23451
## 186 64.89226
## 187 70.50758
## 188 79.47720
## 189 78.50684
## 190 75.85676
## 191 72.15096
## 192 74.60864
## 193 76.99760
## 194 83.90969
## 195 72.32477
## 196 85.83045
## 197 78.75649
## 198 89.07154
## 199 72.44366
## 200 81.73406
## 201 79.26205
## 202 72.39343
## 203 56.79712
## 204 70.95376
## 205 77.99251
## 206 69.27762
## 207 87.14989
## 208 71.02603
## 209 84.20161
## 210 69.14617
## 211 83.25165
## 212 65.07587
## 213 68.15080
## 214 90.37854
## 215 72.58197
## 216 82.92891
## 217 83.55644
## 218 84.70588
## 219 72.25829
## 220 94.80698
## 221 68.31143
## 222 66.65696
## 223 61.31580
## 224 90.77063
## 225 82.02684
## 226 86.96812
## 227 80.08065
## 228 72.91450
## 229 82.51650
## 230 72.25396
## 231 65.66912
## 232 86.13270
## 233 74.83901
## 234 100.19175
## 235 57.48978
## 236 78.30926
## 237 64.87206
## 238 74.60686
## 239 87.64558
## 240 73.04780Data berdistribusi normal dengan mean 75 dan standar deviasi 10. Data berukuran 1x240
A=matrix(c("A"),nrow=60)
B=matrix(c("B"),nrow=60)
C=matrix(c("C"),nrow=60)
D=matrix(c("D"),nrow=60)
kelas=rbind(A,B,C,D)
data=data.frame(nilai,kelas)
data## nilai kelas
## 1 76.48547 A
## 2 79.70389 A
## 3 55.02897 A
## 4 87.65361 A
## 5 85.73402 A
## 6 78.86601 A
## 7 50.21749 A
## 8 63.79583 A
## 9 79.90121 A
## 10 62.21173 A
## 11 80.55499 A
## 12 73.04405 A
## 13 91.77758 A
## 14 76.49389 A
## 15 83.31299 A
## 16 59.14221 A
## 17 88.29855 A
## 18 72.47366 A
## 19 67.27490 A
## 20 74.36079 A
## 21 69.03950 A
## 22 58.21296 A
## 23 72.36363 A
## 24 84.28784 A
## 25 67.52065 A
## 26 73.38703 A
## 27 86.36226 A
## 28 72.75185 A
## 29 83.09218 A
## 30 83.29095 A
## 31 70.30281 A
## 32 79.92569 A
## 33 90.36530 A
## 34 67.76040 A
## 35 74.06063 A
## 36 62.50319 A
## 37 79.76051 A
## 38 82.99425 A
## 39 75.50308 A
## 40 81.43357 A
## 41 71.79169 A
## 42 67.11775 A
## 43 70.50557 A
## 44 94.01138 A
## 45 61.96397 A
## 46 75.75672 A
## 47 63.98775 A
## 48 79.97102 A
## 49 70.85461 A
## 50 69.58422 A
## 51 75.34806 A
## 52 56.75332 A
## 53 61.08254 A
## 54 67.45049 A
## 55 71.74342 A
## 56 88.74276 A
## 57 100.48682 A
## 58 88.71008 A
## 59 75.59005 A
## 60 62.05434 A
## 61 54.84919 B
## 62 72.06515 B
## 63 45.96627 B
## 64 72.79570 B
## 65 84.56979 B
## 66 62.81397 B
## 67 87.26464 B
## 68 62.20136 B
## 69 64.69431 B
## 70 72.45503 B
## 71 70.86540 B
## 72 73.89394 B
## 73 76.57041 B
## 74 60.38963 B
## 75 96.85415 B
## 76 63.30129 B
## 77 92.95461 B
## 78 63.15441 B
## 79 68.55914 B
## 80 83.15472 B
## 81 79.11322 B
## 82 68.72469 B
## 83 82.89546 B
## 84 69.86520 B
## 85 77.63564 B
## 86 83.91041 B
## 87 68.80076 B
## 88 58.01167 B
## 89 76.41816 B
## 90 76.77539 B
## 91 79.67621 B
## 92 89.06648 B
## 93 46.52147 B
## 94 74.82020 B
## 95 73.39535 B
## 96 77.62403 B
## 97 58.03619 B
## 98 76.12061 B
## 99 78.02452 B
## 100 76.56174 B
## 101 83.66924 B
## 102 76.24315 B
## 103 81.16329 B
## 104 80.57372 B
## 105 68.10656 B
## 106 76.27756 B
## 107 65.10939 B
## 108 89.80711 B
## 109 64.81644 B
## 110 74.20170 B
## 111 86.13627 B
## 112 57.02310 B
## 113 60.42528 B
## 114 66.80062 B
## 115 86.13144 B
## 116 70.35538 B
## 117 65.48593 B
## 118 89.89893 B
## 119 73.22673 B
## 120 74.83872 B
## 121 64.52261 C
## 122 68.41611 C
## 123 73.66362 C
## 124 79.52381 C
## 125 79.36734 C
## 126 83.41571 C
## 127 54.58469 C
## 128 61.25738 C
## 129 82.68280 C
## 130 87.66856 C
## 131 91.07345 C
## 132 77.73098 C
## 133 44.55570 C
## 134 82.49303 C
## 135 78.10471 C
## 136 82.09219 C
## 137 60.55604 C
## 138 88.65134 C
## 139 86.10376 C
## 140 76.03482 C
## 141 66.86100 C
## 142 81.49412 C
## 143 70.78176 C
## 144 97.76658 C
## 145 70.32187 C
## 146 69.32046 C
## 147 91.23399 C
## 148 69.07500 C
## 149 92.60433 C
## 150 84.61519 C
## 151 85.47080 C
## 152 65.68540 C
## 153 68.90065 C
## 154 69.24682 C
## 155 73.09958 C
## 156 69.98201 C
## 157 82.19036 C
## 158 81.23659 C
## 159 87.82006 C
## 160 68.30229 C
## 161 68.13040 C
## 162 61.45293 C
## 163 74.24992 C
## 164 74.25467 C
## 165 92.70902 C
## 166 86.11873 C
## 167 79.18462 C
## 168 77.86005 C
## 169 72.73980 C
## 170 80.65235 C
## 171 78.86561 C
## 172 64.88057 C
## 173 84.88284 C
## 174 88.36553 C
## 175 84.41856 C
## 176 69.98473 C
## 177 56.20900 C
## 178 80.27073 C
## 179 74.56734 C
## 180 70.89046 C
## 181 79.41763 D
## 182 62.85761 D
## 183 61.44105 D
## 184 88.74322 D
## 185 86.23451 D
## 186 64.89226 D
## 187 70.50758 D
## 188 79.47720 D
## 189 78.50684 D
## 190 75.85676 D
## 191 72.15096 D
## 192 74.60864 D
## 193 76.99760 D
## 194 83.90969 D
## 195 72.32477 D
## 196 85.83045 D
## 197 78.75649 D
## 198 89.07154 D
## 199 72.44366 D
## 200 81.73406 D
## 201 79.26205 D
## 202 72.39343 D
## 203 56.79712 D
## 204 70.95376 D
## 205 77.99251 D
## 206 69.27762 D
## 207 87.14989 D
## 208 71.02603 D
## 209 84.20161 D
## 210 69.14617 D
## 211 83.25165 D
## 212 65.07587 D
## 213 68.15080 D
## 214 90.37854 D
## 215 72.58197 D
## 216 82.92891 D
## 217 83.55644 D
## 218 84.70588 D
## 219 72.25829 D
## 220 94.80698 D
## 221 68.31143 D
## 222 66.65696 D
## 223 61.31580 D
## 224 90.77063 D
## 225 82.02684 D
## 226 86.96812 D
## 227 80.08065 D
## 228 72.91450 D
## 229 82.51650 D
## 230 72.25396 D
## 231 65.66912 D
## 232 86.13270 D
## 233 74.83901 D
## 234 100.19175 D
## 235 57.48978 D
## 236 78.30926 D
## 237 64.87206 D
## 238 74.60686 D
## 239 87.64558 D
## 240 73.04780 Dcolnames(data)<-c("nilai","kelas")Klasifikasi data menjadi 4 kelas, sehingga setiap kelas memiliki 60 data
nilaikelasA=data.frame(data$nilai[1:60])
nilaikelasB=data.frame(data$nilai[61:120])
nilaikelasC=data.frame(data$nilai[121:180])
nilaikelasD=data.frame(data$nilai[181:240])
kelasA=cbind(nilaikelasA,A)
kelasB=cbind(nilaikelasB,B)
kelasC=cbind(nilaikelasC,C)
kelasD=cbind(nilaikelasD,D)
dA=data.frame(kelasA); dA## data.nilai.1.60. A
## 1 76.48547 A
## 2 79.70389 A
## 3 55.02897 A
## 4 87.65361 A
## 5 85.73402 A
## 6 78.86601 A
## 7 50.21749 A
## 8 63.79583 A
## 9 79.90121 A
## 10 62.21173 A
## 11 80.55499 A
## 12 73.04405 A
## 13 91.77758 A
## 14 76.49389 A
## 15 83.31299 A
## 16 59.14221 A
## 17 88.29855 A
## 18 72.47366 A
## 19 67.27490 A
## 20 74.36079 A
## 21 69.03950 A
## 22 58.21296 A
## 23 72.36363 A
## 24 84.28784 A
## 25 67.52065 A
## 26 73.38703 A
## 27 86.36226 A
## 28 72.75185 A
## 29 83.09218 A
## 30 83.29095 A
## 31 70.30281 A
## 32 79.92569 A
## 33 90.36530 A
## 34 67.76040 A
## 35 74.06063 A
## 36 62.50319 A
## 37 79.76051 A
## 38 82.99425 A
## 39 75.50308 A
## 40 81.43357 A
## 41 71.79169 A
## 42 67.11775 A
## 43 70.50557 A
## 44 94.01138 A
## 45 61.96397 A
## 46 75.75672 A
## 47 63.98775 A
## 48 79.97102 A
## 49 70.85461 A
## 50 69.58422 A
## 51 75.34806 A
## 52 56.75332 A
## 53 61.08254 A
## 54 67.45049 A
## 55 71.74342 A
## 56 88.74276 A
## 57 100.48682 A
## 58 88.71008 A
## 59 75.59005 A
## 60 62.05434 AdB=data.frame(kelasB); dB## data.nilai.61.120. B
## 1 54.84919 B
## 2 72.06515 B
## 3 45.96627 B
## 4 72.79570 B
## 5 84.56979 B
## 6 62.81397 B
## 7 87.26464 B
## 8 62.20136 B
## 9 64.69431 B
## 10 72.45503 B
## 11 70.86540 B
## 12 73.89394 B
## 13 76.57041 B
## 14 60.38963 B
## 15 96.85415 B
## 16 63.30129 B
## 17 92.95461 B
## 18 63.15441 B
## 19 68.55914 B
## 20 83.15472 B
## 21 79.11322 B
## 22 68.72469 B
## 23 82.89546 B
## 24 69.86520 B
## 25 77.63564 B
## 26 83.91041 B
## 27 68.80076 B
## 28 58.01167 B
## 29 76.41816 B
## 30 76.77539 B
## 31 79.67621 B
## 32 89.06648 B
## 33 46.52147 B
## 34 74.82020 B
## 35 73.39535 B
## 36 77.62403 B
## 37 58.03619 B
## 38 76.12061 B
## 39 78.02452 B
## 40 76.56174 B
## 41 83.66924 B
## 42 76.24315 B
## 43 81.16329 B
## 44 80.57372 B
## 45 68.10656 B
## 46 76.27756 B
## 47 65.10939 B
## 48 89.80711 B
## 49 64.81644 B
## 50 74.20170 B
## 51 86.13627 B
## 52 57.02310 B
## 53 60.42528 B
## 54 66.80062 B
## 55 86.13144 B
## 56 70.35538 B
## 57 65.48593 B
## 58 89.89893 B
## 59 73.22673 B
## 60 74.83872 BdC=data.frame(kelasC); dC## data.nilai.121.180. C
## 1 64.52261 C
## 2 68.41611 C
## 3 73.66362 C
## 4 79.52381 C
## 5 79.36734 C
## 6 83.41571 C
## 7 54.58469 C
## 8 61.25738 C
## 9 82.68280 C
## 10 87.66856 C
## 11 91.07345 C
## 12 77.73098 C
## 13 44.55570 C
## 14 82.49303 C
## 15 78.10471 C
## 16 82.09219 C
## 17 60.55604 C
## 18 88.65134 C
## 19 86.10376 C
## 20 76.03482 C
## 21 66.86100 C
## 22 81.49412 C
## 23 70.78176 C
## 24 97.76658 C
## 25 70.32187 C
## 26 69.32046 C
## 27 91.23399 C
## 28 69.07500 C
## 29 92.60433 C
## 30 84.61519 C
## 31 85.47080 C
## 32 65.68540 C
## 33 68.90065 C
## 34 69.24682 C
## 35 73.09958 C
## 36 69.98201 C
## 37 82.19036 C
## 38 81.23659 C
## 39 87.82006 C
## 40 68.30229 C
## 41 68.13040 C
## 42 61.45293 C
## 43 74.24992 C
## 44 74.25467 C
## 45 92.70902 C
## 46 86.11873 C
## 47 79.18462 C
## 48 77.86005 C
## 49 72.73980 C
## 50 80.65235 C
## 51 78.86561 C
## 52 64.88057 C
## 53 84.88284 C
## 54 88.36553 C
## 55 84.41856 C
## 56 69.98473 C
## 57 56.20900 C
## 58 80.27073 C
## 59 74.56734 C
## 60 70.89046 CdD=data.frame(kelasD); dD## data.nilai.181.240. D
## 1 79.41763 D
## 2 62.85761 D
## 3 61.44105 D
## 4 88.74322 D
## 5 86.23451 D
## 6 64.89226 D
## 7 70.50758 D
## 8 79.47720 D
## 9 78.50684 D
## 10 75.85676 D
## 11 72.15096 D
## 12 74.60864 D
## 13 76.99760 D
## 14 83.90969 D
## 15 72.32477 D
## 16 85.83045 D
## 17 78.75649 D
## 18 89.07154 D
## 19 72.44366 D
## 20 81.73406 D
## 21 79.26205 D
## 22 72.39343 D
## 23 56.79712 D
## 24 70.95376 D
## 25 77.99251 D
## 26 69.27762 D
## 27 87.14989 D
## 28 71.02603 D
## 29 84.20161 D
## 30 69.14617 D
## 31 83.25165 D
## 32 65.07587 D
## 33 68.15080 D
## 34 90.37854 D
## 35 72.58197 D
## 36 82.92891 D
## 37 83.55644 D
## 38 84.70588 D
## 39 72.25829 D
## 40 94.80698 D
## 41 68.31143 D
## 42 66.65696 D
## 43 61.31580 D
## 44 90.77063 D
## 45 82.02684 D
## 46 86.96812 D
## 47 80.08065 D
## 48 72.91450 D
## 49 82.51650 D
## 50 72.25396 D
## 51 65.66912 D
## 52 86.13270 D
## 53 74.83901 D
## 54 100.19175 D
## 55 57.48978 D
## 56 78.30926 D
## 57 64.87206 D
## 58 74.60686 D
## 59 87.64558 D
## 60 73.04780 DPemisahan data kelas dan dibuat dataframe tiap kelas
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3ga=ggplot(dA,aes(data.nilai.1.60.))
ga+geom_histogram(col="white",fill="pink")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
gb=ggplot(dB,aes(data.nilai.61.120.))
gb+geom_histogram(col="white",fill="pink")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
gc=ggplot(dC,aes(data.nilai.121.180.))
gc+geom_histogram(col="white",fill="pink")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
gd=ggplot(dD,aes(data.nilai.181.240.))
gd+geom_histogram(col="white",fill="pink")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot untuk melihat sebaran data nilai setiap kelas