2.19 study

.Rhistory

@@ -53,3 +53,186 @@ x1 = c(1,'love',TRUE,2+3i)
 x1
 install.packages('ggplot2')
 install.packages('dplyr')
+v1 = c(27,35,47,41)
+mode(v1)
+is.numeric(v1)
+is.na(v1)
+table(is.na(v1))
+xx1 <- c(1:100)
+v2 = c(27,35,47,NA,55)
+mode(v2)
+is.numeric(v2)
+is.na(v2)
+table(is.na(v1))
+table(is.na(v1))
+table(is.na(v1))
+table(is.na(v2))
+length(vw)
+length(v1)
+length(v2)
+names(v1)
+names(v1) = c('kim','lee','park','choi')
+names(v1)
+v1
+v1[0]
+## 기초 명령어 실습 ##
+1+1
+1:10
+dd = c(1:10)
+## c()    -> 같은 종류의 요소(numeric, int, float등)만 있을때
+## list() -> 다양한 종류의 요소가 들어올때
+dd[-10]
+## 기본유형 ##
+x1 = 3                  # numeric
+x2 = "Love is choice"   # character
+x3 = FALSE              # logical
+x4 = 3-2i               # complex
+x5 = '123'              # character
+x6 = 'false'            # character
+x7 = 'FLASE'            # character
+str(x1)
+### 문자 - 정수 - 논리
+### 결측값은 95%까지
+### 정규분포는 30번이상한것으로 해야한다
+xx1 <- c(1:100)
+xx1
+is.na(xx1)
+table(is.na(xx1))
+x1 = c(1,'love',TRUE,2+3i)
+x1
+v1 = c(27,35,47,41)
+mode(v1)
+is.numeric(v1)
+is.na(v1)
+table(is.na(v1))
+v2 = c(27,35,47,NA,55)
+mode(v2)
+is.numeric(v2)
+is.na(v2)
+table(is.na(v2))
+length(v1)
+length(v2)
+names(v1)
+names(v1) = c('kim','lee','park','choi')
+names(v1)
+## indexing ## 대괄호 == [ ] => 추출해라
+v1
+v1[1]
+v1[1:3]
+v1[-1]
+v1[:-1]
+v1[1:-1]
+v1[-1]
+v1[1:3]
+v1[-4]
+v1[1,3]
+v1[3:-1]
+v1[c(2,4)]
+v1[-4]
+v1 = 1:3
+v2 = 4:6
+v3 = v1+v2
+v3 = v1+v2
+v1 = 1:3
+v2 = 1:6
+v3 = v1+v2
+v3
+gender = c('m','f','f','m','f','f')
+gender_facotr = factor(gender)
+levels(gender_facotr)
+gender = c('m','f','f','m','f','f')
+gender_facotr = factor(gender)
+levels(gender_facotr)
+gender_factor2 = factor(gender, levels = c('m','f'), labels = c('남자','여자'))
+gender_factor2
+install.packages(c("cluster", "lattice", "viridisLite"))
+gender_factor3
+gender_factor3 = factor(gender,ordered = TRUE)
+gender_factor3
+v2 = 4:6
+m1 = rbind(v1,v2)
+m2 = m2 cbind(v1,v2)
+v1 = 1:3
+v2 = 4:6
+m1 = rbind(v1,v2)
+m2 = m2 cbind(v1,v2)
+1:3
+v2 = 4:6
+m1 = rbind(v1,v2)
+m2 =
+v1 = 1:3
+v2 = 4:6
+m1 = rbind(v1,v2)
+m2 = cbind(v1,v2)
+m2
+v1 = 1:3
+v2 = 4:6
+m1 = rbind(v1,v2)
+m2 = cbind(v1,v2)
+m1
+m2
+v1 = 1:3
+v2 = 4:6
+m1 = rbind(v1,v2)
+m2 = cbind(v1,v2)
+v1
+v2
+m1
+m2
+m1[2,1]
+m1[2,1] == 8
+m1
+[2,1] == 8
+m1
+m1
+m1[2,1] == 8
+m1[2,1] = 8
+m1
+m1[,3]
+is.na(m1)
+table(is.na(m1))
+m1[1,1] = NA
+m1
+table(is.na(m1))
+m1[2,3] = NA
+table(is.na(m1))
+str(m1)
+m3 = matrix(1:4, nrow = 2,ncol = 2)
+m3
+m4 = matrix(1:4, nrow = 2,ncol = 2,byrow = TRUE)
+m4
+m3
+m4
+m4[2,1] = NA
+a1 = array(1:10, dim=10)
+a2 = array(1:10, dim=c(2, 5))
+a3 = array(1:10, dim=c(3, 3, 4))
+a1
+a2
+a3
+a3[,,2][3,3]
+a3[,,2][3,3] = 100
+a3
+id = 1:5
+age = c(29, 32, 47, 35, 23)
+gender = c('f','m','m','f','f')
+height = c(163, 177, 172, 157, 169)
+DF1 = data.frame(id, age, gender, height)
+DF2 = data.frame(id, age, gender, height, stringsAsFactors=FALSE)
+DF1
+DF2
+View(DF1)
+str(DF1)
+DF3 = data.frame(id, age, gender, height, stringsAsFactors=TRUE)
+DF3
+View(DF2)
+View(DF3)
+str(DF2)
+str(DF3)
+DF4 = DF3[-1]
+DF4
+View(DF4)
+str(DF4)
+install.packages('psych')
+library(psych)
+describe(DF4)