[R강의] 129. 분할표 만들기 (빈도, 비율)

남자 100명과 여자 100명을 임의로 선정하여 기름종이 사용여부를 조사하고 분할표로 만드는 예시입니다. 

 

 

먼저 데이터를 생성합시다. rbinom 함수를 이용하여 이항분포에서 임의로 추출했습니다. 남자는 기름종이 사용확률을 30%로, 여자는 70%로 놓았습니다. rbinom(number,size,probability) 인데 size에 1을 입력하면 0과 1 둘중 하나가 추출됩니다. 

 

#1.데이터 생성
set.seed(9999)
male=rbinom(100,1,0.3)
female=rbinom(100,1,0.7)

 

추출 결과는 아래와 같습니다. 

 

> male
  [1] 1 0 1 0 0 1 0 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0
 [28] 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0
 [55] 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0
 [82] 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0
> female
  [1] 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 1 0
 [28] 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1
 [55] 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0
 [82] 1 1 1 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 1

 

이제 데이터를 아래와 같은 형태의 데이터프레임으로 만들어줄 것입니다. 

 

 

sex 열과, use 열을 만들어줘야 합니다. 먼저 sex 열을 만들어봅시다. 

 

sex=c(  rep('male',length(male)) , rep('female',length(female))  )

 

결과는 아래와 같습니다. 

 

> sex
  [1] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
  [8] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [15] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [22] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [29] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [36] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [43] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [50] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [57] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [64] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [71] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [78] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [85] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [92] "male"   "male"   "male"   "male"   "male"   "male"   "male"  
 [99] "male"   "male"   "female" "female" "female" "female" "female"
[106] "female" "female" "female" "female" "female" "female" "female"
[113] "female" "female" "female" "female" "female" "female" "female"
[120] "female" "female" "female" "female" "female" "female" "female"
[127] "female" "female" "female" "female" "female" "female" "female"
[134] "female" "female" "female" "female" "female" "female" "female"
[141] "female" "female" "female" "female" "female" "female" "female"
[148] "female" "female" "female" "female" "female" "female" "female"
[155] "female" "female" "female" "female" "female" "female" "female"
[162] "female" "female" "female" "female" "female" "female" "female"
[169] "female" "female" "female" "female" "female" "female" "female"
[176] "female" "female" "female" "female" "female" "female" "female"
[183] "female" "female" "female" "female" "female" "female" "female"
[190] "female" "female" "female" "female" "female" "female" "female"
[197] "female" "female" "female" "female"

 

이번에는 use 열을 만들어봅시다. 범주형 변수이므로 factor 함수를 사용하여 요인으로 만들어줍니다. 

 

use=factor( c(male,female), levels=c(0,1), labels=c('no','yes') )

 

결과는 아래와 같습니다. 

 

> use
  [1] yes no  yes no  no  yes no  yes yes yes yes no  no  yes no  yes
 [17] yes no  yes no  no  yes yes no  no  no  no  no  no  no  no  no 
 [33] no  no  yes no  no  yes yes yes no  yes yes no  no  no  no  no 
 [49] no  no  no  yes no  no  yes no  no  yes no  no  no  no  no  yes
 [65] no  no  yes no  yes no  no  yes no  no  no  no  no  no  no  no 
 [81] no  no  no  yes no  no  no  yes no  yes no  no  no  no  no  yes
 [97] no  no  no  no  no  no  yes yes no  yes yes yes no  no  yes yes
[113] yes yes yes yes yes no  no  yes no  yes no  yes yes yes no  yes
[129] yes yes yes yes yes yes yes yes yes no  yes yes yes yes yes yes
[145] yes yes no  no  no  yes yes yes yes yes no  yes yes yes yes yes
[161] no  yes yes yes yes yes yes yes yes yes yes yes yes yes yes no 
[177] yes no  no  yes no  yes yes yes yes yes yes no  yes yes yes no 
[193] no  yes no  yes no  yes yes yes
Levels: no yes

 

분할표를 만들어봅시다. 먼저 빈도 분할표입니다. table 함수를 이용합니다. 

 

> table(dt)
        use
sex      no yes
  female 25  75
  male   70  30

 

확률 분할표는 prop.table 함수를 이용하고, table 함수의 결과를 넣어줘야 합니다. 

 

> prop.table(table(dt))
        use
sex         no   yes
  female 0.125 0.375
  male   0.350 0.150

 

코드 모아보기

 

#1. 데이터 생성

set.seed(9999)
male=rbinom(100,1,0.3)
female=rbinom(100,1,0.7)

#2. 데이터프레임 생성
sex=c(  rep('male',length(male)) , rep('female',length(female))  )
use=factor( c(male,female), levels=c(0,1), labels=c('no','yes') )

dt=data.frame(sex,use)

#3. 분할표 만들기 (빈도)
tab1=table(dt)

#3. 분할표 만들기 (비율)
tab2=prop.table(tab1)

 

 

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