UTS

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

rata_rata_umur <- mean(data_kesehatan$umur, na.rm = TRUE)

print(rata_rata_umur)

## [1] 44.315

aggregate(tinggi_badan ~ jenis_kelamin, data = data_kesehatan, mean)

##   jenis_kelamin tinggi_badan
## 1     Laki-laki     171.0215
## 2     Perempuan     159.6470

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

rata_rata_skor_kesehatan <- data_kesehatan |>
  filter(
    kolesterol < 200,
    gula_darah < 110
  ) |>
  summarise(
    Rata_Rata_Skor = mean(skor_kesehatan, na.rm = TRUE)
  )

print(rata_rata_skor_kesehatan)

##   Rata_Rata_Skor
## 1       96.57272