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library(caret)

## 필요한 패키지를 로딩중입니다: ggplot2

## 필요한 패키지를 로딩중입니다: lattice

library(dplyr)

## 
## 다음의 패키지를 부착합니다: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

getwd()

## [1] "C:/data"

Data1 <- read.csv("C:/data/Data1.csv")

df<-preProcess(Data1,
               method=c("BoxCox","center","scale"))


trans1<-predict(df,Data1)
glimpse(trans1)

## Rows: 1,925
## Columns: 26
## $ Q1        <dbl> 0.4377463, 0.4377463, 0.4377463, 1.8757624, 0.4377463, 0.437~
## $ Q2        <dbl> 0.7474592, 0.7474592, 0.7474592, 0.7474592, 0.7474592, 0.747~
## $ Q3        <dbl> -0.8920423, 1.0401987, 1.0401987, 1.0401987, 1.0401987, 1.04~
## $ Q4        <dbl> -0.06346727, 0.97190976, 0.97190976, 0.97190976, 0.97190976,~
## $ Q5        <dbl> 0.94156339, 0.94156339, -1.02215224, 0.94156339, 0.94156339,~
## $ Q6        <dbl> -0.6567079, 0.2594104, 1.0586709, 1.0586709, 1.0586709, 1.05~
## $ Q7        <dbl> -0.99834118, -0.99834118, 0.83982885, 0.83982885, 0.83982885~
## $ Q8        <dbl> 0.87120289, 0.87120289, 0.87120289, 0.87120289, 0.87120289, ~
## $ Q9        <dbl> 0.82060345, 0.82060345, 0.82060345, 0.82060345, -0.93652852,~
## $ Q10       <dbl> 1.1601944, 1.1601944, -0.9206375, 1.1601944, 1.1601944, 1.16~
## $ Q11       <dbl> 0.5185496, 0.5185496, 0.5185496, 0.5185496, 0.5185496, 0.518~
## $ Q12       <dbl> 0.5532587, 0.5532587, 0.5532587, 0.5532587, 0.5532587, 0.553~
## $ Q13       <dbl> 0.4036029, 0.4036029, 0.4036029, 0.4036029, 0.4036029, 0.403~
## $ Q14       <dbl> 0.2295463, 0.2295463, 0.2295463, 0.2295463, 0.2295463, 0.229~
## $ Q15       <dbl> 0.4463321, 0.4463321, -0.6616076, 0.4463321, 0.4463321, 0.44~
## $ Q16       <dbl> 0.164454, 0.164454, 0.164454, 0.164454, 0.164454, 0.164454, ~
## $ Q17       <dbl> 0.4299487, -0.6273122, 0.4299487, 0.4299487, 0.4299487, 0.42~
## $ Q18       <dbl> 0.09873339, 0.09873339, 0.09873339, 0.09873339, 0.09873339, ~
## $ Q19       <dbl> 0.6872078, -1.4629557, 0.6872078, 0.6872078, 0.6872078, 0.68~
## $ Q20       <dbl> 0.6898142, -2.2798900, -0.4197916, 0.6898142, 0.6898142, 0.6~
## $ Gender    <dbl> -0.8331755, -0.8331755, -0.8331755, -0.8331755, -0.8331755, ~
## $ EDU       <dbl> -1.8074205, -1.8074205, -0.8052983, -1.8074205, -0.8052983, ~
## $ BF        <dbl> 0.30696518, 1.11329853, 0.57574296, 1.38207632, 1.11329853, ~
## $ BM        <dbl> 0.2884518, 0.5457593, 0.8030668, 1.3176819, 0.8030668, 1.317~
## $ Happiness <dbl> 0.5786271, 0.5786271, 0.2882770, 0.5786271, 0.5786271, 0.578~
## $ Peace     <dbl> 0.64117710, -1.17299604, 0.30346180, 0.64117710, 0.64117710,~

# 시각화를 통해 확인
plot(density(Data1$BF))

plot(density(trans1$BF))

plot(density(Data1$Happiness))

plot(density(trans1$Happiness))

plot(density(Data1$Peace))

plot(density(trans1$Peace))