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databaru <- read.csv("data_kesehatan.csv")
head(databaru)
##   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
library(readxl)
data <- read_excel("data_kesehatan.xlsx")
responden_sehat <- subset(data,
                          gula_darah < 110 &
                          kolesterol < 200 &
                          tekanan_sistolik < 130 &
                          tekanan_diastolik < 85)
responden_sehat
## # A tibble: 48 × 10
##       id  umur jenis_kelamin tinggi_badan berat_badan gula_darah
##    <dbl> <dbl> <chr>                <dbl>       <dbl>      <dbl>
##  1     1    33 Perempuan             160.        62.9       80.1
##  2     4    64 Laki-laki             174.        54.8       97.4
##  3     5    67 Perempuan             164.        62.2       49.0
##  4    15    23 Perempuan             158.        62.2       95.8
##  5    21    64 Perempuan             171.        50.2       96.7
##  6    25    52 Perempuan             156.        57.9       67.0
##  7    29    33 Perempuan             170.        50.6      108. 
##  8    33    54 Perempuan             162.        66.3       80.9
##  9    36    43 Perempuan             154.        62.3       77.3
## 10    41    25 Laki-laki             164.        61.7       79.6
## # ℹ 38 more rows
## # ℹ 4 more variables: tekanan_sistolik <dbl>, tekanan_diastolik <dbl>,
## #   kolesterol <dbl>, skor_kesehatan <dbl>
jumlah_sehat <- nrow(responden_sehat)
print(jumlah_sehat)
## [1] 48
library(readxl)
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 <- read_excel("data_kesehatan.xlsx")
terendah <- data %>%
  arrange(skor_kesehatan) %>%
  slice(1:5)
print(terendah)
## # A tibble: 5 × 10
##      id  umur jenis_kelamin tinggi_badan berat_badan gula_darah tekanan_sistolik
##   <dbl> <dbl> <chr>                <dbl>       <dbl>      <dbl>            <dbl>
## 1    49    32 Perempuan             148.        72.9       135.             141.
## 2    11    68 Perempuan             163.        39.9       156.             123.
## 3    70    41 Laki-laki             171.        80.1       136.             137.
## 4   193    69 Laki-laki             176.        78.9       144.             136.
## 5   176    50 Laki-laki             166.        88.8       115.             129.
## # ℹ 3 more variables: tekanan_diastolik <dbl>, kolesterol <dbl>,
## #   skor_kesehatan <dbl>
library(readxl)
library(dplyr)
data <- read_excel("data_kesehatan.xlsx")
terendah <- data %>%
  arrange(gula_darah) %>%
  slice(1)
print(terendah)
## # A tibble: 1 × 10
##      id  umur jenis_kelamin tinggi_badan berat_badan gula_darah tekanan_sistolik
##   <dbl> <dbl> <chr>                <dbl>       <dbl>      <dbl>            <dbl>
## 1     5    67 Perempuan             164.        62.2       49.0             129.
## # ℹ 3 more variables: tekanan_diastolik <dbl>, kolesterol <dbl>,
## #   skor_kesehatan <dbl>
data <- data %>%
  mutate(BMI = berat_badan / (tinggi_badan / 100)^2)
rata_rata <- data %>%
  filter(BMI > 30) %>%
  summarise(rata_sk = mean(skor_kesehatan)) %>%
  pull(rata_sk)
print(rata_rata)
## [1] 74.92593
rata_tinggi <- data %>%
  filter(jenis_kelamin == "Laki-laki") %>%
  summarise(rata_cm = mean(tinggi_badan)) %>%
  pull(rata_cm)
print(rata_tinggi)
## [1] 171.0215