library(readxl)
Data_Bencana_Provinsi_Sumatera_Barat_Tahun_2018 <- read_excel("D:/Meisya/Kuliah/Komputasi Statistika/Data Bencana Provinsi Sumatera Barat Tahun 2018.xlsx")
View(Data_Bencana_Provinsi_Sumatera_Barat_Tahun_2018)

#membuat variabel baru Rumah.Rusak
Data_Bencana_Provinsi_Sumatera_Barat_Tahun_2018$Rumah.Rusak <- Data_Bencana_Provinsi_Sumatera_Barat_Tahun_2018$`Rumah Rusak Berat`+
  Data_Bencana_Provinsi_Sumatera_Barat_Tahun_2018$`Rumah Rusak Sedang`+Data_Bencana_Provinsi_Sumatera_Barat_Tahun_2018$`Rumah Rusak Ringan`

#menghapus kolom judul tiap variabel
colnames(Data_Bencana_Provinsi_Sumatera_Barat_Tahun_2018) <- ""

#membuat data.frame
data <- data.frame(Nama = c("Josephine","Peter","Adam","Lusiana","Anthoni","Gabriela","Mario","Suzan","Laura","Evelyn"),
                   Usia = c(17,20,19,22,30,21,20,21,18,19), 
                   Tinggi_cm = c(167,154,183,159,173,160,169,161,141,153),
                   Berat_kg = c(57,62,67,46,57,78,55,44,50,43))
data
##         Nama Usia Tinggi_cm Berat_kg
## 1  Josephine   17       167       57
## 2      Peter   20       154       62
## 3       Adam   19       183       67
## 4    Lusiana   22       159       46
## 5    Anthoni   30       173       57
## 6   Gabriela   21       160       78
## 7      Mario   20       169       55
## 8      Suzan   21       161       44
## 9      Laura   18       141       50
## 10    Evelyn   19       153       43
#konversi tinngi badan dari cm ke m
data$Tinggi_m <- data$Tinggi_cm/100

#menghitung BMI
data$BMI <- data$Berat_kg/(data$Tinggi_m^2)

#membuat kategori berat badan ideal berdasarkan kriteria
data$Kategori_BMI <- cut(data$BMI,breaks=c(-Inf,18.5,24.9,29.9,Inf), 
                         labels=c("Underweight","Normal","Overweight","Obesitas"))
data
##         Nama Usia Tinggi_cm Berat_kg Tinggi_m      BMI Kategori_BMI
## 1  Josephine   17       167       57     1.67 20.43817       Normal
## 2      Peter   20       154       62     1.54 26.14269   Overweight
## 3       Adam   19       183       67     1.83 20.00657       Normal
## 4    Lusiana   22       159       46     1.59 18.19548  Underweight
## 5    Anthoni   30       173       57     1.73 19.04507       Normal
## 6   Gabriela   21       160       78     1.60 30.46875     Obesitas
## 7      Mario   20       169       55     1.69 19.25703       Normal
## 8      Suzan   21       161       44     1.61 16.97465  Underweight
## 9      Laura   18       141       50     1.41 25.14964   Overweight
## 10    Evelyn   19       153       43     1.53 18.36900  Underweight

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