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
dfk<-read.csv("data_kesehatan.csv")
dfk<-dfk %>%
mutate(BMI=berat_badan/(tinggi_badan*tinggi_badan*(0.0001)))
f<-dfk %>%
filter(BMI>30) %>%
summarise(mean(skor_kesehatan))
n<-dfk %>%
filter(umur<30) %>%
summarise(mean(skor_kesehatan))
c<-dfk %>%
filter(umur>=30 & umur<=50) %>%
summarise(mean(skor_kesehatan))
d<-dfk %>%
filter(umur>50) %>%
summarise(mean(skor_kesehatan))
dfk<-dfk %>%
mutate(tekanan=tekanan_sistolik/tekanan_diastolik)
z<-dfk %>%
filter(gula_darah<110, kolesterol<200, tekanan<130/85)
z
## X id umur jenis_kelamin tinggi_badan berat_badan gula_darah
## 1 3 3 39 Perempuan 156.1915 66.84162 99.64040
## 2 4 4 64 Laki-laki 173.8024 54.83932 97.35650
## 3 14 14 48 Perempuan 159.2637 61.65035 109.38064
## 4 15 15 23 Perempuan 157.5179 62.21498 95.77506
## 5 17 17 31 Perempuan 159.4424 58.44678 104.55085
## 6 26 26 55 Perempuan 157.2980 55.26486 104.57114
## 7 31 31 68 Laki-laki 183.6871 63.21497 76.06130
## 8 33 33 54 Perempuan 161.8002 66.31568 80.91102
## 9 36 36 43 Perempuan 153.5355 62.27103 77.26214
## 10 41 41 25 Laki-laki 164.4796 61.65812 79.55305
## 11 51 51 20 Laki-laki 167.3708 50.61495 106.62869
## 12 52 52 41 Perempuan 166.2798 59.22070 96.20307
## 13 57 57 25 Laki-laki 177.5656 80.20253 83.87094
## 14 58 58 57 Laki-laki 174.4153 75.38482 101.09106
## 15 62 62 23 Perempuan 157.9092 61.13967 87.74024
## 16 65 65 60 Perempuan 153.4433 58.69372 96.75034
## 17 74 74 18 Perempuan 158.5639 67.22813 88.35197
## 18 83 83 40 Laki-laki 172.7022 62.72616 104.61267
## 19 89 89 64 Laki-laki 168.4666 66.78675 90.94599
## 20 90 90 27 Perempuan 157.7143 63.16237 65.03255
## 21 97 97 59 Perempuan 161.0911 61.92837 108.78261
## 22 99 99 42 Laki-laki 170.1469 74.20574 109.68199
## 23 103 103 43 Laki-laki 163.4302 63.06905 81.26862
## 24 106 106 64 Laki-laki 172.3183 75.89983 94.52075
## 25 107 107 66 Perempuan 154.6243 66.60738 80.28922
## 26 117 117 47 Perempuan 159.2092 71.56530 98.59612
## 27 118 118 68 Perempuan 161.8601 44.59884 90.92707
## 28 121 121 52 Laki-laki 171.6620 59.58327 87.13610
## 29 122 122 35 Perempuan 159.3503 58.30280 71.26313
## 30 127 127 26 Laki-laki 169.5742 75.63867 98.99925
## 31 129 129 25 Laki-laki 179.3486 62.67146 105.99731
## 32 130 130 54 Perempuan 156.6493 63.44879 68.63151
## 33 135 135 45 Laki-laki 172.6417 69.43944 79.54032
## 34 136 136 52 Laki-laki 155.6344 75.24914 86.13171
## 35 140 140 41 Laki-laki 169.2868 80.19157 96.86216
## 36 141 141 34 Laki-laki 174.3693 77.11602 92.82127
## 37 143 143 19 Perempuan 164.2250 46.51267 101.38473
## 38 146 146 30 Laki-laki 157.7273 47.89367 85.06642
## 39 147 147 30 Laki-laki 170.6953 96.91714 83.06207
## 40 151 151 62 Laki-laki 177.1046 85.38430 98.86059
## 41 154 154 31 Laki-laki 170.8165 72.13958 103.46210
## 42 158 158 29 Laki-laki 169.7687 67.98542 105.56590
## 43 162 162 36 Laki-laki 174.5584 76.16456 93.33758
## 44 171 171 51 Laki-laki 186.9174 53.32072 107.70310
## 45 178 178 46 Laki-laki 171.0113 67.21546 99.79805
## 46 179 179 63 Laki-laki 169.8998 74.74912 64.42682
## 47 182 182 34 Perempuan 159.3859 51.00317 85.87357
## 48 184 184 32 Perempuan 162.9919 55.86592 90.64148
## 49 190 190 65 Laki-laki 172.4227 63.46220 56.02153
## 50 196 196 42 Laki-laki 180.5273 78.18828 103.30915
## 51 200 200 48 Perempuan 171.1595 58.00647 107.45823
## tekanan_sistolik tekanan_diastolik kolesterol skor_kesehatan BMI
## 1 132.77287 87.14848 176.38007 88.52949 27.39884
## 2 108.78105 75.68339 172.18406 100.00000 18.15435
## 3 101.54354 88.06185 172.52414 100.00000 24.30534
## 4 117.95795 83.07390 174.01196 100.00000 25.07465
## 5 87.38813 85.08485 189.82547 100.00000 22.99075
## 6 132.85178 95.80432 188.86127 93.04345 22.33587
## 7 107.21211 87.84438 127.15556 100.00000 18.73539
## 8 117.53594 84.78366 151.23973 100.00000 25.33133
## 9 90.98334 83.68377 178.28878 100.00000 26.41609
## 10 109.10843 79.93154 153.14666 100.00000 22.79113
## 11 135.77552 98.50572 125.12176 99.78350 18.06839
## 12 129.34358 85.73675 184.59438 98.97290 21.41878
## 13 103.42226 88.68793 165.20358 100.00000 25.43727
## 14 128.87919 93.69352 191.03583 90.67423 24.78077
## 15 131.29943 87.29560 190.16373 97.28383 24.51932
## 16 121.10398 84.23645 170.00037 100.00000 24.92849
## 17 115.80176 78.55389 169.97974 100.00000 26.73883
## 18 118.96852 84.41383 179.83894 98.63909 21.03067
## 19 90.12693 102.14465 149.71127 100.00000 23.53220
## 20 114.68446 89.69434 194.08886 100.00000 25.39314
## 21 124.87238 88.92571 174.19194 94.20821 23.86419
## 22 119.22423 90.89112 192.61733 87.49844 25.63241
## 23 102.78344 70.97902 98.56417 100.00000 23.61302
## 24 129.72521 101.27214 156.96755 100.00000 25.56102
## 25 101.70285 83.66114 184.95677 100.00000 27.85908
## 26 109.81332 77.24110 178.61753 94.47592 28.23360
## 27 94.45553 86.82315 160.25810 100.00000 17.02331
## 28 111.24541 81.11620 197.77654 100.00000 20.21976
## 29 90.08882 77.16595 196.28898 100.00000 22.96061
## 30 138.36840 95.32424 147.44299 96.40854 26.30414
## 31 114.73206 78.00381 157.63963 100.00000 19.48380
## 32 94.09318 86.31523 193.21566 100.00000 25.85632
## 33 118.31161 84.14308 160.31403 100.00000 23.29779
## 34 92.04500 89.89058 188.41602 98.51579 31.06634
## 35 113.42155 87.29278 199.23180 89.48931 27.98224
## 36 116.72109 91.11380 188.19960 98.40598 25.36323
## 37 111.26910 79.23829 185.84155 93.74989 17.24617
## 38 107.91453 95.77852 162.56209 100.00000 19.25148
## 39 102.50723 79.38077 152.65247 100.00000 33.26270
## 40 117.80359 79.78205 168.95484 96.11934 27.22189
## 41 133.27669 89.48159 156.37965 96.80100 24.72374
## 42 97.56260 68.87455 195.76001 100.00000 23.58850
## 43 121.65014 89.93480 140.40553 100.00000 24.99607
## 44 112.62892 93.25908 167.70232 90.70285 15.26147
## 45 111.72194 82.07172 188.99285 100.00000 22.98367
## 46 102.25801 79.56653 189.46409 100.00000 25.89525
## 47 126.32827 88.51856 174.88399 100.00000 20.07693
## 48 128.35869 84.40299 162.05932 100.00000 21.02881
## 49 102.18771 86.68283 191.64378 100.00000 21.34647
## 50 101.66395 81.29966 194.55497 99.33587 23.99142
## 51 111.50836 88.38219 159.06507 100.00000 19.80043
## tekanan
## 1 1.523525
## 2 1.437317
## 3 1.153093
## 4 1.419916
## 5 1.027070
## 6 1.386699
## 7 1.220478
## 8 1.386304
## 9 1.087228
## 10 1.365023
## 11 1.378352
## 12 1.508613
## 13 1.166137
## 14 1.375540
## 15 1.504078
## 16 1.437667
## 17 1.474170
## 18 1.409349
## 19 0.882346
## 20 1.278614
## 21 1.404233
## 22 1.311726
## 23 1.448082
## 24 1.280957
## 25 1.215652
## 26 1.421695
## 27 1.087907
## 28 1.371433
## 29 1.167469
## 30 1.451555
## 31 1.470852
## 32 1.090111
## 33 1.406076
## 34 1.023967
## 35 1.299323
## 36 1.281047
## 37 1.404234
## 38 1.126709
## 39 1.291336
## 40 1.476567
## 41 1.489431
## 42 1.416526
## 43 1.352648
## 44 1.207699
## 45 1.361272
## 46 1.285189
## 47 1.427139
## 48 1.520784
## 49 1.178869
## 50 1.250484
## 51 1.261661
dfk %>%
group_by(jenis_kelamin) %>%
summarise(mean(tinggi_badan))
## # A tibble: 2 × 2
## jenis_kelamin `mean(tinggi_badan)`
## <chr> <dbl>
## 1 Laki-laki 171.
## 2 Perempuan 160.
dfk %>%
summarize(mean(umur))
## mean(umur)
## 1 44.315