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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
library(ggplot2)
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[order(data_kesehatan$skor_kesehatan), ] |>
head(5)
##       X  id umur jenis_kelamin tinggi_badan berat_badan gula_darah
## 49   49  49   32     Perempuan     147.6860    72.92436   135.0351
## 11   11  11   68     Perempuan     162.9534    39.93666   155.9478
## 70   70  70   41     Laki-laki     170.6245    80.10678   135.5821
## 193 193 193   69     Laki-laki     176.1898    78.86749   143.8718
## 176 176 176   50     Laki-laki     165.8736    88.77864   115.3701
##     tekanan_sistolik tekanan_diastolik kolesterol skor_kesehatan
## 49          141.0703          85.51274   218.5767       45.44594
## 11          123.0276          80.65068   224.3279       48.51474
## 70          136.8850          71.69891   238.1264       53.51686
## 193         135.8586          75.48187   236.9744       54.22224
## 176         129.4345          88.19628   232.7038       57.96087
data<-read.csv("data_kesehatan.csv")
data$kelompok_umur<- cut(data$umur, breaks = c(0,29,50,120),labels = c("<30", "30-50", ">50"), right = TRUE)
rata_skor <- aggregate(skor_kesehatan~kelompok_umur,data=data,mean)
print(rata_skor)
##   kelompok_umur skor_kesehatan
## 1           <30       90.37880
## 2         30-50       89.35499
## 3           >50       89.53153
q2 <-data_kesehatan %>%
  filter(gula_darah <110) %>%
  filter(kolesterol <200) %>%
  summarise(mean(skor_kesehatan))
q2
##   mean(skor_kesehatan)
## 1             96.57272
`

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