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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
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
aggregate(tinggi_badan ~ jenis_kelamin, data = data_kesehatan, mean)
## jenis_kelamin tinggi_badan
## 1 Laki-laki 171.0215
## 2 Perempuan 159.6470
data_kesehatan$gula_darah
## [1] 80.08403 79.20090 99.64040 97.35650 49.01314 120.81147 104.99451
## [8] 148.32415 113.70396 91.06081 155.94782 156.64452 75.62576 109.38064
## [15] 95.77506 103.74102 104.55085 74.76199 105.71179 134.98495 96.71820
## [22] 96.74147 127.97144 117.96792 67.03010 104.57114 133.07094 128.30553
## [29] 108.39903 114.42442 76.06130 106.00263 80.91102 90.83964 118.71207
## [36] 77.26214 105.33837 108.56664 101.09824 136.44378 79.55305 112.12261
## [43] 98.22139 94.78336 109.28182 79.59199 73.73098 90.11038 135.03514
## [50] 101.11530 106.62869 96.20307 109.40985 80.96641 123.15821 111.69411
## [57] 83.87094 101.09106 114.32663 111.15462 129.63868 87.74024 122.32273
## [64] 120.73096 96.75034 80.48147 78.21710 109.15574 98.57747 135.58205
## [71] 110.70276 92.56110 79.48916 88.35197 106.85777 90.98131 110.28460
## [78] 93.31324 97.88880 85.38981 138.10087 106.65243 104.61267 66.16275
## [85] 113.19584 79.52753 82.16957 118.36682 90.94599 65.03255 135.39808
## [92] 52.45186 111.45623 120.34498 87.38064 108.88574 108.78261 120.81246
## [99] 109.68199 95.10232 118.31984 116.01245 81.26862 71.98425 103.20555
## [106] 94.52075 80.28922 101.67861 73.60007 103.22453 87.50143 119.14329
## [113] 148.48978 81.68042 121.15328 116.50299 98.59612 90.92707 131.50615
## [120] 59.89084 87.13610 71.26313 127.90627 96.18593 89.50658 163.68089
## [127] 98.99925 91.12501 105.99731 68.63151 109.80605 98.07674 109.37050
## [134] 80.35259 79.54032 86.13171 84.64021 125.98100 131.58291 96.86216
## [141] 92.82127 93.41922 101.38473 101.93808 105.80069 85.06642 83.06207
## [148] 123.94155 89.02745 106.06091 98.86059 80.84301 111.82124 103.46210
## [155] 127.99567 102.34919 93.36908 105.56590 76.28817 83.28212 110.20547
## [162] 93.33758 98.68078 97.69557 86.98975 59.62623 106.97670 115.23279
## [169] 74.22568 129.64805 107.70310 126.83281 80.85659 103.33563 97.99972
## [176] 115.37015 88.48281 99.79805 64.42682 84.44757 102.50068 85.87357
## [183] 99.12861 90.64148 112.13860 123.36977 83.54997 93.85927 128.79523
## [190] 56.02153 93.60324 141.29409 143.87180 103.13191 82.72782 103.30915
## [197] 86.94451 129.05635 83.87035 107.45823