155 lines
2.2 KiB
Plaintext
155 lines
2.2 KiB
Plaintext
## 기초 명령어 실습 ##
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1+1
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1:10
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dd = c(1:10)
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## c() -> 같은 종류의 요소(numeric, int, float등)만 있을때
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## list() -> 다양한 종류의 요소가 들어올때
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dd[-10]
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## 기본유형 ##
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x1 = 3 # numeric
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x2 = "Love is choice" # character
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x3 = FALSE # logical
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x4 = 3-2i # complex
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x5 = '123' # character
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x6 = 'false' # character
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x7 = 'FLASE' # character
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str(x1)
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### 문자 - 정수 - 논리
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### 결측값은 95%까지
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### 정규분포는 30번이상한것으로 해야한다
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xx1 <- c(1:100)
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xx1
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is.na(xx1)
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table(is.na(xx1))
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x1 = c(1,'love',TRUE,2+3i)
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x1
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v1 = c(27,35,47,41)
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mode(v1)
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is.numeric(v1)
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is.na(v1)
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table(is.na(v1))
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v2 = c(27,35,47,NA,55)
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mode(v2)
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is.numeric(v2)
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is.na(v2)
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table(is.na(v2))
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length(v1)
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length(v2)
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names(v1)
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names(v1) = c('kim','lee','park','choi')
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names(v1)
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## Indexing ## 대괄호 == [ ] => 추출해라
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v1
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v1[1]
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v1[1:3]
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v1[-4]
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v1[c(2,4)]
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v1 = 1:3
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v2 = 4:6
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v3 = v1+v2 # can add list
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v1 = 1:3
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v2 = 1:6
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v3 = v1+v2 # reusable add
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v3
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### 데이터거래사
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## Factor, ordered Factor (등급을 정할 수 있는)
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gender = c('m','f','f','m','f','f')
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gender_factor = factor(gender)
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levels(gender_factor)
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gender_factor2 = factor(gender, levels = c('m','f'), labels = c('남자','여자'))
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gender_factor2
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gender_factor3 = factor(gender,ordered = TRUE)
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gender_factor3
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## Matrix ##
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v1 = 1:3
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v2 = 4:6
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m1 = rbind(v1,v2)
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m2 = cbind(v1,v2)
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v1
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v2
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m1
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m2
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m1[2,1] == 8
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m1[2,1] = 8
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m1[,3]
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is.na(m1)
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table(is.na(m1))
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m1[1,1] = NA
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m1[2,3] = NA
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table(is.na(m1))
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str(m1)
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m3 = matrix(1:4, nrow = 2, ncol = 2)
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m4 = matrix(1:4, nrow = 2, ncol = 2, byrow = TRUE)
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m3
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m4[2,1] = NA
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## array ##
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a1 = array(1:10, dim=10)
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a2 = array(1:10, dim=c(2, 5))
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a3 = array(1:10, dim=c(3, 3, 4))
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a1
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a2
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a3[,,2][3,3] = 100
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a3
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## DataFrame ##
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id = 1:5
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age = c(29, 32, 47, 35, 23)
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gender = c('f','m','m','f','f')
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height = c(163, 177, 172, 157, 169)
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DF1 = data.frame(id, age, gender, height)
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DF2 = data.frame(id, age, gender, height, stringsAsFactors=FALSE)
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DF3 = data.frame(id, age, gender, height, stringsAsFactors=TRUE)
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DF1
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DF2
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DF3
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View(DF3)
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str(DF3)
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DF4 = DF3[-1]
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DF4
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View(DF4)
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str(DF4)
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## 기술통계량 ##
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library('psych')
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describe(DF4)
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