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
data_kesehatan <- readxl::read_excel("data_kesehatan.xlsx")
head(data_kesehatan)
## # A tibble: 6 × 10
## id umur jenis_kelamin tinggi_badan berat_badan gula_darah tekanan_sistolik
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 33 Perempuan 160. 62.9 80.1 129.
## 2 2 59 Perempuan 153. 54.7 79.2 109.
## 3 3 39 Perempuan 156. 66.8 99.6 133.
## 4 4 64 Laki-laki 174. 54.8 97.4 109.
## 5 5 67 Perempuan 164. 62.2 49.0 129.
## 6 6 20 Perempuan 150. 61.2 121. 136.
## # ℹ 3 more variables: tekanan_diastolik <dbl>, kolesterol <dbl>,
## # skor_kesehatan <dbl>
aggregate(umur ~ jenis_kelamin, data = data_kesehatan, mean)
## jenis_kelamin umur
## 1 Laki-laki 44.02778
## 2 Perempuan 44.65217
aggregate(tinggi_badan ~ jenis_kelamin, data = data_kesehatan, mean)
## jenis_kelamin tinggi_badan
## 1 Laki-laki 171.0215
## 2 Perempuan 159.6470
aggregate(skor_kesehatan ~ umur, data = data_kesehatan, mean)
## umur skor_kesehatan
## 1 18 100.00000
## 2 19 92.51802
## 3 20 91.72576
## 4 21 80.16160
## 5 22 85.38529
## 6 23 98.66898
## 7 24 88.32077
## 8 25 91.94987
## 9 26 86.28426
## 10 27 100.00000
## 11 28 84.47480
## 12 29 87.68316
## 13 30 92.36140
## 14 31 91.38097
## 15 32 82.08423
## 16 33 93.63693
## 17 34 83.99066
## 18 35 93.42043
## 19 36 95.68925
## 20 37 94.95943
## 21 38 84.04715
## 22 39 96.41401
## 23 40 85.38979
## 24 41 90.32985
## 25 42 88.82483
## 26 43 91.26205
## 27 44 89.89671
## 28 45 89.24945
## 29 46 92.32851
## 30 47 85.30152
## 31 48 91.11692
## 32 49 77.93252
## 33 50 83.08693
## 34 51 89.45276
## 35 52 90.56225
## 36 53 87.80328
## 37 54 92.58371
## 38 55 91.43276
## 39 56 86.92382
## 40 57 89.55623
## 41 59 94.78531
## 42 60 83.70573
## 43 61 92.40607
## 44 62 92.07595
## 45 63 91.68226
## 46 64 93.37222
## 47 65 88.68294
## 48 66 100.00000
## 49 67 96.50702
## 50 68 80.90101
## 51 69 79.14937
## 52 70 80.64646
aggregate(skor_kesehatan ~ kolesterol, data = data_kesehatan, mean)
## kolesterol skor_kesehatan
## 1 98.56417 100.00000
## 2 116.12275 100.00000
## 3 125.12176 99.78350
## 4 127.15556 100.00000
## 5 127.19082 76.14837
## 6 128.66569 100.00000
## 7 131.80180 96.46025
## 8 133.55706 100.00000
## 9 137.46094 100.00000
## 10 138.68861 100.00000
## 11 140.40233 100.00000
## 12 140.40553 100.00000
## 13 141.97480 85.92333
## 14 143.62250 100.00000
## 15 144.54028 100.00000
## 16 147.44299 96.40854
## 17 149.71127 100.00000
## 18 151.23973 100.00000
## 19 152.45727 96.15151
## 20 152.65247 100.00000
## 21 153.14666 100.00000
## 22 153.50958 100.00000
## 23 154.72905 100.00000
## 24 155.97936 85.34060
## 25 155.99620 80.16160
## 26 156.37965 96.80100
## 27 156.96755 100.00000
## 28 157.27394 84.71513
## 29 157.63963 100.00000
## 30 157.70810 100.00000
## 31 157.84759 88.14031
## 32 157.88288 82.25335
## 33 157.95985 85.99595
## 34 158.95576 89.39700
## 35 159.06507 100.00000
## 36 160.25810 100.00000
## 37 160.31403 100.00000
## 38 162.05932 100.00000
## 39 162.56209 100.00000
## 40 163.05936 90.56092
## 41 165.20358 100.00000
## 42 165.36224 86.55135
## 43 165.48475 100.00000
## 44 165.73735 100.00000
## 45 167.70232 90.70285
## 46 168.95484 96.11934
## 47 169.97974 100.00000
## 48 170.00037 100.00000
## 49 170.31271 100.00000
## 50 172.18406 100.00000
## 51 172.52414 100.00000
## 52 172.87288 100.00000
## 53 173.33041 97.92177
## 54 173.57117 94.09372
## 55 174.01196 100.00000
## 56 174.19194 94.20821
## 57 174.53766 91.99147
## 58 174.71790 97.46217
## 59 174.78237 88.97758
## 60 174.88399 100.00000
## 61 175.73155 75.93673
## 62 175.90611 95.88734
## 63 175.90637 100.00000
## 64 176.38007 88.52949
## 65 176.55297 100.00000
## 66 176.98302 97.35104
## 67 177.88869 79.76589
## 68 178.28878 100.00000
## 69 178.61753 94.47592
## 70 178.74488 64.11414
## 71 179.20053 85.38529
## 72 179.83894 98.63909
## 73 179.86958 100.00000
## 74 180.65576 87.27387
## 75 181.04251 96.06106
## 76 181.28296 90.69250
## 77 181.32930 100.00000
## 78 182.33473 91.56964
## 79 183.37947 86.22689
## 80 183.68726 79.65326
## 81 183.71655 75.39378
## 82 184.22319 94.46348
## 83 184.59438 98.97290
## 84 184.95677 100.00000
## 85 185.06641 91.28614
## 86 185.50631 100.00000
## 87 185.84155 93.74989
## 88 185.99889 98.94197
## 89 188.02648 85.38622
## 90 188.19960 98.40598
## 91 188.41602 98.51579
## 92 188.69942 87.74158
## 93 188.86127 93.04345
## 94 188.92180 100.00000
## 95 188.96219 87.39012
## 96 188.99285 100.00000
## 97 189.46409 100.00000
## 98 189.67827 65.83611
## 99 189.82547 100.00000
## 100 190.16373 97.28383
## 101 190.32886 87.53790
## 102 191.03583 90.67423
## 103 191.64378 100.00000
## 104 192.61733 87.49844
## 105 192.68540 100.00000
## 106 192.85398 67.80169
## 107 193.21566 100.00000
## 108 193.65011 92.80241
## 109 194.08886 100.00000
## 110 194.23828 81.77373
## 111 194.55497 99.33587
## 112 194.93556 97.88776
## 113 195.12719 78.65485
## 114 195.76001 100.00000
## 115 196.00441 72.24570
## 116 196.28898 100.00000
## 117 197.61179 99.28702
## 118 197.69678 84.67545
## 119 197.77654 100.00000
## 120 198.31034 90.91384
## 121 198.78037 85.45402
## 122 199.23180 89.48931
## 123 200.58479 74.94486
## 124 202.83599 69.44558
## 125 203.01028 86.68373
## 126 203.03405 90.45487
## 127 203.05271 87.39251
## 128 203.45821 76.98552
## 129 203.49810 99.83833
## 130 203.83577 71.20314
## 131 204.26177 96.82670
## 132 204.48217 96.85953
## 133 205.17100 88.01014
## 134 205.27618 100.00000
## 135 206.21508 100.00000
## 136 207.05196 90.62093
## 137 207.09337 78.62644
## 138 208.51751 81.22331
## 139 208.76055 100.00000
## 140 209.30406 96.87835
## 141 209.69540 100.00000
## 142 212.10057 85.65371
## 143 212.61749 100.00000
## 144 214.15159 75.20394
## 145 214.70833 92.40607
## 146 214.88722 86.99939
## 147 215.60847 60.08132
## 148 216.21006 95.43306
## 149 216.34751 84.13958
## 150 216.46704 97.54007
## 151 216.48768 71.93331
## 152 216.64221 83.79867
## 153 216.98067 100.00000
## 154 216.98634 96.66257
## 155 217.08629 62.26762
## 156 217.44896 95.02224
## 157 217.70140 88.37807
## 158 218.57670 45.44594
## 159 219.69107 59.51184
## 160 219.94870 100.00000
## 161 220.37612 92.00174
## 162 220.69605 68.50658
## 163 220.84522 69.50376
## 164 220.95768 90.72733
## 165 221.01249 83.77710
## 166 221.31674 90.30077
## 167 221.51779 99.23040
## 168 223.20512 88.64576
## 169 223.27056 87.91820
## 170 223.40088 95.54148
## 171 223.48819 84.01331
## 172 224.32788 48.51474
## 173 227.83105 100.00000
## 174 228.29805 87.17492
## 175 229.86324 93.77802
## 176 231.07300 78.69969
## 177 231.25826 85.76841
## 178 231.33028 96.88081
## 179 231.43301 78.60337
## 180 232.30718 75.01840
## 181 232.61726 80.64646
## 182 232.70377 57.96087
## 183 233.50384 91.65975
## 184 235.72428 74.48061
## 185 236.97443 54.22224
## 186 237.95463 84.84621
## 187 238.12637 53.51686
## 188 238.49733 81.59355
## 189 238.67979 65.12971
## 190 240.26716 76.40171
## 191 240.29567 67.08839
## 192 242.93638 68.02514
## 193 243.71273 79.28644
## 194 246.05967 88.18862
## 195 246.48519 80.02805
## 196 248.19268 84.35429
## 197 260.95167 86.60551
## 198 264.36237 80.21044
## 199 267.31804 73.44250
## 200 288.71552 77.75112