참고: R Markdown
참고: R Tutorial

마크다운: 이탤릭
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- ```{r} ~~ ``` : R 실행구문

  1. Qualitative Data
painters$School
 [1] A A A A A A A A A A B B B B B B C C C C C C D D D D D D D D D D E E E E E E E F F F F
[44] G G G G G G G H H H H
Levels: A B C D E F G H
  1. Frequency Distribution of Qualitative Data

table : 개수를 세어서 표를 만든다

school.freq
school
 A  B  C  D  E  F  G  H 
10  6  6 10  7  4  7  4 

cbind : col/row 의 순서를 바꾼다 (결과물을 column 형태로 만든다)

cbind(school.freq)
  school.freq
A          10
B           6
C           6
D          10
E           7
F           4
G           7
H           4
  1. Relative Frequency Distribution of Qualitative Data

샘플 수 당 발생률을 얘기하는 겁니다

school.relfreq
school
         A          B          C          D          E          F          G          H 
0.18518519 0.11111111 0.11111111 0.18518519 0.12962963 0.07407407 0.12962963 0.07407407 
cbind(school.relfreq)
  school.relfreq
A           0.19
B           0.11
C           0.11
D           0.19
E           0.13
F           0.07
G           0.13
H           0.07

옵션 설정: old = options(digits=1)
옵션 되돌리기: options(old)

school.relfreq
school
         A          B          C          D          E          F          G          H 
0.18518519 0.11111111 0.11111111 0.18518519 0.12962963 0.07407407 0.12962963 0.07407407 
  1. Bar Graph

  1. Pie Chart

  1. Category Statistics
mean(c_painters$Composition)
[1] 13.16667

조건을 벡터로 변수 저장해서 그대로 꺼내 쓸 수 있습니다

tapply(painters$Composition, painters$School, mean)
       A        B        C        D        E        F        G        H 
10.40000 12.16667 13.16667  9.10000 13.57143  7.25000 13.85714 14.00000 

물론 그냥 tapply 를 쓰면 전체에 계산을 넣을 수 있습니다 (mean 계산)

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