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 <- 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
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
data_kesehatan %>%
arrange(skor_kesehatan) %>%
slice(1:5)
## X id umur jenis_kelamin tinggi_badan berat_badan gula_darah
## 1 49 49 32 Perempuan 147.6860 72.92436 135.0351
## 2 11 11 68 Perempuan 162.9534 39.93666 155.9478
## 3 70 70 41 Laki-laki 170.6245 80.10678 135.5821
## 4 193 193 69 Laki-laki 176.1898 78.86749 143.8718
## 5 176 176 50 Laki-laki 165.8736 88.77864 115.3701
## tekanan_sistolik tekanan_diastolik kolesterol skor_kesehatan
## 1 141.0703 85.51274 218.5767 45.44594
## 2 123.0276 80.65068 224.3279 48.51474
## 3 136.8850 71.69891 238.1264 53.51686
## 4 135.8586 75.48187 236.9744 54.22224
## 5 129.4345 88.19628 232.7038 57.96087
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
data_kesehatan %>%
arrange(skor_kesehatan) %>%
slice(1:5)
## X id umur jenis_kelamin tinggi_badan berat_badan gula_darah
## 1 49 49 32 Perempuan 147.6860 72.92436 135.0351
## 2 11 11 68 Perempuan 162.9534 39.93666 155.9478
## 3 70 70 41 Laki-laki 170.6245 80.10678 135.5821
## 4 193 193 69 Laki-laki 176.1898 78.86749 143.8718
## 5 176 176 50 Laki-laki 165.8736 88.77864 115.3701
## tekanan_sistolik tekanan_diastolik kolesterol skor_kesehatan
## 1 141.0703 85.51274 218.5767 45.44594
## 2 123.0276 80.65068 224.3279 48.51474
## 3 136.8850 71.69891 238.1264 53.51686
## 4 135.8586 75.48187 236.9744 54.22224
## 5 129.4345 88.19628 232.7038 57.96087
responden_sehat <- subset(data_kesehatan,
gula_darah < 100 &
kolesterol < 200 &
tekanan_sistolik < 130 &
tekanan_diastolik < 85)
responden_sehat
## X id umur jenis_kelamin tinggi_badan berat_badan gula_darah
## 1 1 1 33 Perempuan 159.5587 62.85027 80.08403
## 4 4 4 64 Laki-laki 173.8024 54.83932 97.35650
## 5 5 5 67 Perempuan 164.0242 62.21020 49.01314
## 15 15 15 23 Perempuan 157.5179 62.21498 95.77506
## 21 21 21 64 Perempuan 170.6770 50.16811 96.71820
## 25 25 25 52 Perempuan 155.6892 57.85835 67.03010
## 33 33 33 54 Perempuan 161.8002 66.31568 80.91102
## 36 36 36 43 Perempuan 153.5355 62.27103 77.26214
## 41 41 41 25 Laki-laki 164.4796 61.65812 79.55305
## 46 46 46 25 Laki-laki 183.3165 78.99354 79.59199
## 47 47 47 30 Laki-laki 169.2932 61.69188 73.73098
## 48 48 48 42 Perempuan 165.2998 68.36245 90.11038
## 65 65 65 60 Perempuan 153.4433 58.69372 96.75034
## 73 73 73 55 Laki-laki 174.7902 73.97842 79.48916
## 74 74 74 18 Perempuan 158.5639 67.22813 88.35197
## 79 79 79 36 Laki-laki 172.9972 61.46376 97.88880
## 87 87 87 69 Perempuan 165.4886 51.69495 82.16957
## 95 95 95 35 Laki-laki 184.0174 54.52223 87.38064
## 103 103 103 43 Laki-laki 163.4302 63.06905 81.26862
## 107 107 107 66 Perempuan 154.6243 66.60738 80.28922
## 117 117 117 47 Perempuan 159.2092 71.56530 98.59612
## 120 120 120 39 Laki-laki 176.3797 74.03290 59.89084
## 121 121 121 52 Laki-laki 171.6620 59.58327 87.13610
## 122 122 122 35 Perempuan 159.3503 58.30280 71.26313
## 125 125 125 37 Perempuan 166.6879 65.32128 89.50658
## 128 128 128 23 Perempuan 160.8346 61.57022 91.12501
## 132 132 132 64 Perempuan 156.9620 49.26205 98.07674
## 135 135 135 45 Laki-laki 172.6417 69.43944 79.54032
## 147 147 147 30 Laki-laki 170.6953 96.91714 83.06207
## 151 151 151 62 Laki-laki 177.1046 85.38430 98.86059
## 159 159 159 41 Laki-laki 152.7387 49.62318 76.28817
## 166 166 166 46 Laki-laki 168.5314 79.67859 59.62623
## 175 175 175 53 Laki-laki 173.5709 72.79628 97.99972
## 178 178 178 46 Laki-laki 171.0113 67.21546 99.79805
## 179 179 179 63 Laki-laki 169.8998 74.74912 64.42682
## 180 180 180 48 Perempuan 160.1566 61.17134 84.44757
## 183 183 183 55 Perempuan 152.9106 57.55624 99.12861
## 184 184 184 32 Perempuan 162.9919 55.86592 90.64148
## 197 197 197 39 Laki-laki 164.5810 73.88365 86.94451
## tekanan_sistolik tekanan_diastolik kolesterol skor_kesehatan
## 1 129.29775 69.13882 181.32930 100.00000
## 4 108.78105 75.68339 172.18406 100.00000
## 5 129.45360 82.27615 138.68861 100.00000
## 15 117.95795 83.07390 174.01196 100.00000
## 21 126.29375 78.49791 173.57117 94.09372
## 25 114.14047 74.05822 179.86958 100.00000
## 33 117.53594 84.78366 151.23973 100.00000
## 36 90.98334 83.68377 178.28878 100.00000
## 41 109.10843 79.93154 153.14666 100.00000
## 46 129.76168 68.20481 172.87288 100.00000
## 47 122.74572 62.58978 133.55706 100.00000
## 48 128.23162 60.07414 154.72905 100.00000
## 65 121.10398 84.23645 170.00037 100.00000
## 73 126.22051 70.32968 188.92180 100.00000
## 74 115.80176 78.55389 169.97974 100.00000
## 79 118.36024 61.48308 175.90637 100.00000
## 87 116.02074 74.12914 165.48475 100.00000
## 95 121.24533 69.06699 176.98302 97.35104
## 103 102.78344 70.97902 98.56417 100.00000
## 107 101.70285 83.66114 184.95677 100.00000
## 117 109.81332 77.24110 178.61753 94.47592
## 120 125.40536 76.55324 192.68540 100.00000
## 121 111.24541 81.11620 197.77654 100.00000
## 122 90.08882 77.16595 196.28898 100.00000
## 125 123.11221 79.91848 197.61179 99.28702
## 128 109.47293 66.42002 157.70810 100.00000
## 132 129.33615 84.26014 144.54028 100.00000
## 135 118.31161 84.14308 160.31403 100.00000
## 147 102.50723 79.38077 152.65247 100.00000
## 151 117.80359 79.78205 168.95484 96.11934
## 159 108.47374 69.48927 170.31271 100.00000
## 166 117.43331 68.48779 153.50958 100.00000
## 175 105.91656 64.15009 128.66569 100.00000
## 178 111.72194 82.07172 188.99285 100.00000
## 179 102.25801 79.56653 189.46409 100.00000
## 180 129.30995 74.89840 185.50631 100.00000
## 183 126.63697 65.73815 198.31034 90.91384
## 184 128.35869 84.40299 162.05932 100.00000
## 197 126.77928 76.71493 193.65011 92.80241
library(dplyr)
data_kesehatan %>%
filter(kolesterol < 200, gula_darah < 110) %>%
summarise(rata_rata = mean(skor_kesehatan, na.rm = TRUE))
## rata_rata
## 1 96.57272