data_kesehatan <- read.csv("data_kesehatan.csv")
head(data_kesehatan)## X id umur jenis_kelamin tinggi_badan berat_badan gula_darah tekanan_sistolik
## 1 1 1 33 Perempuan 159.5587 62.85027 80.08403 129.2978
## 2 2 2 59 Perempuan 152.9881 54.73592 79.20090 108.6373
## 3 3 3 39 Perempuan 156.1915 66.84162 99.64040 132.7729
## 4 4 4 64 Laki-laki 173.8024 54.83932 97.35650 108.7811
## 5 5 5 67 Perempuan 164.0242 62.21020 49.01314 129.4536
## 6 6 6 20 Perempuan 150.0967 61.15284 120.81147 136.4499
## tekanan_diastolik kolesterol skor_kesehatan
## 1 69.13882 181.3293 100.00000
## 2 73.34697 209.6954 100.00000
## 3 87.14848 176.3801 88.52949
## 4 75.68339 172.1841 100.00000
## 5 82.27615 138.6886 100.00000
## 6 92.94946 183.7165 75.39378#Berapa rata-rata umur responden dalam dataset kesehatan fisik?
aggregate(umur ~ umur, data = data_kesehatan, mean)## umur
## 1 44.315#Siapa saja yang memiliki 5 skor kesehatan terendah?
data_kesehatan[order(data_kesehatan$skor_kesehatan), c("skor_kesehatan", "id")]## skor_kesehatan id
## 49 45.44594 49
## 11 48.51474 11
## 70 53.51686 70
## 193 54.22224 193
## 176 57.96087 176
## 8 59.51184 8
## 30 60.08132 30
## 12 62.26762 12
## 123 64.11414 123
## 119 65.12971 119
## 170 65.83611 170
## 28 67.08839 28
## 20 67.80169 20
## 94 68.02514 94
## 61 68.50658 61
## 40 69.44558 40
## 68 69.50376 68
## 155 71.20314 155
## 91 71.93331 91
## 185 72.24570 185
## 112 73.44250 112
## 156 74.48061 156
## 81 74.94486 81
## 53 75.01840 53
## 168 75.20394 168
## 6 75.39378 6
## 42 75.93673 42
## 126 76.14837 126
## 9 76.40171 9
## 27 76.98552 27
## 22 77.75112 22
## 76 78.60337 76
## 63 78.62644 63
## 167 78.65485 167
## 37 78.69969 37
## 105 79.28644 105
## 198 79.65326 198
## 116 79.76589 116
## 144 80.02805 144
## 113 80.16160 113
## 7 80.21044 7
## 24 80.64646 24
## 191 81.22331 191
## 43 81.59355 43
## 115 81.77373 115
## 161 82.25335 161
## 188 83.77710 188
## 59 83.79867 59
## 38 84.01331 38
## 77 84.13958 77
## 173 84.35429 173
## 181 84.67545 181
## 189 84.71513 189
## 131 84.84621 131
## 124 85.34060 124
## 148 85.38529 148
## 172 85.38622 172
## 150 85.45402 150
## 23 85.65371 23
## 108 85.76841 108
## 16 85.92333 16
## 192 85.99595 192
## 139 86.22689 139
## 55 86.55135 55
## 165 86.60551 165
## 133 86.68373 133
## 82 86.99939 82
## 110 87.17492 110
## 29 87.27387 29
## 164 87.39012 164
## 69 87.39251 69
## 99 87.49844 99
## 100 87.53790 100
## 163 87.74158 163
## 45 87.91820 45
## 96 88.01014 96
## 111 88.14031 111
## 104 88.18862 104
## 75 88.37807 75
## 3 88.52949 3
## 186 88.64576 186
## 32 88.97758 32
## 194 89.39700 194
## 140 89.48931 140
## 67 90.30077 67
## 157 90.45487 157
## 39 90.56092 39
## 160 90.62093 160
## 58 90.67423 58
## 50 90.69250 50
## 171 90.70285 171
## 19 90.72733 19
## 183 90.91384 183
## 35 91.28614 35
## 138 91.56964 138
## 44 91.65975 44
## 71 91.99147 71
## 72 92.00174 72
## 137 92.40607 137
## 197 92.80241 197
## 26 93.04345 26
## 143 93.74989 143
## 149 93.77802 149
## 21 94.09372 21
## 97 94.20821 97
## 101 94.46348 101
## 117 94.47592 117
## 10 95.02224 10
## 145 95.43306 145
## 34 95.54148 34
## 102 95.88734 102
## 98 96.06106 98
## 151 96.11934 151
## 93 96.15151 93
## 127 96.40854 127
## 60 96.46025 60
## 134 96.66257 134
## 154 96.80100 154
## 13 96.82670 13
## 174 96.85953 174
## 54 96.87835 54
## 80 96.88081 80
## 62 97.28383 62
## 95 97.35104 95
## 78 97.46217 78
## 152 97.54007 152
## 195 97.88776 195
## 142 97.92177 142
## 141 98.40598 141
## 136 98.51579 136
## 83 98.63909 83
## 153 98.94197 153
## 52 98.97290 52
## 169 99.23040 169
## 125 99.28702 125
## 196 99.33587 196
## 51 99.78350 51
## 199 99.83833 199
## 1 100.00000 1
## 2 100.00000 2
## 4 100.00000 4
## 5 100.00000 5
## 14 100.00000 14
## 15 100.00000 15
## 17 100.00000 17
## 18 100.00000 18
## 25 100.00000 25
## 31 100.00000 31
## 33 100.00000 33
## 36 100.00000 36
## 41 100.00000 41
## 46 100.00000 46
## 47 100.00000 47
## 48 100.00000 48
## 56 100.00000 56
## 57 100.00000 57
## 64 100.00000 64
## 65 100.00000 65
## 66 100.00000 66
## 73 100.00000 73
## 74 100.00000 74
## 79 100.00000 79
## 84 100.00000 84
## 85 100.00000 85
## 86 100.00000 86
## 87 100.00000 87
## 88 100.00000 88
## 89 100.00000 89
## 90 100.00000 90
## 92 100.00000 92
## 103 100.00000 103
## 106 100.00000 106
## 107 100.00000 107
## 109 100.00000 109
## 114 100.00000 114
## 118 100.00000 118
## 120 100.00000 120
## 121 100.00000 121
## 122 100.00000 122
## 128 100.00000 128
## 129 100.00000 129
## 130 100.00000 130
## 132 100.00000 132
## 135 100.00000 135
## 146 100.00000 146
## 147 100.00000 147
## 158 100.00000 158
## 159 100.00000 159
## 162 100.00000 162
## 166 100.00000 166
## 175 100.00000 175
## 177 100.00000 177
## 178 100.00000 178
## 179 100.00000 179
## 180 100.00000 180
## 182 100.00000 182
## 184 100.00000 184
## 187 100.00000 187
## 190 100.00000 190
## 200 100.00000 200#Berapa rata-rata skor_kesehatan responden obesitas (BMI > 30)?
data_kesehatan$BMI <- data_kesehatan$berat_badan/((data_kesehatan$tinggi_badan/100)^2)
obesitas <- subset(data_kesehatan,BMI >30)
rata_rata<- mean(obesitas$skor_kesehatan)
rata_rata## [1] 74.92593