260220 study

.Rhistory

@@ -1,38 +1,3 @@
-install.packages(c("mgcv", "survival"))
-a <- 7
-cls
-## 기초 명령어 실습 ##
-## 기초 명령어 실습 ##
-1+1
-1:10
-dd = c(1:10)
-dd
-dd [2:3,]
-dd [c(2:3),]
-dd [2:3]
-dd[2:3]
-dd[1:9]
-dd[-1]
-dd[-10]
-dd[-10]
-detach("package:base", unload = TRUE)
-install.packages("foreign")
-x1 = 3
-x2 = "Love is choice"
-x3 = FALSE
-x4 = 3-2i
-str(x1)
-str(x2)
-str(x3)
-str(x4)
-str(x1,x2,x3,x4)
-4
-str(x4)
-str(x5)
-str(x5)
-x5 = '123'
-str(x5)
-str(x1)
 str(x3)
 str(x7)
 x7 = 'FLASE'
@@ -236,3 +201,312 @@ str(DF4)
 install.packages('psych')
 library(psych)
 describe(DF4)
+## 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[-4]
+v1[c(2,4)]
+v1 = 1:3
+v2 = 4:6
+v3 = v1+v2 # can add list
+v1 = 1:3
+v2 = 1:6
+v3 = v1+v2 # reusable add
+v3
+### 데이터거래사
+## Factor, ordered Factor (등급을 정할 수 있는)
+gender = c('m','f','f','m','f','f')
+gender_factor = factor(gender)
+levels(gender_factor)
+gender_factor2 = factor(gender, levels = c('m','f'), labels = c('남자','여자'))
+gender_factor2
+gender_factor3 = factor(gender,ordered = TRUE)
+gender_factor3
+## Matrix ##
+v1 = 1:3
+v2 = 4:6
+m1 = rbind(v1,v2)
+m2 = cbind(v1,v2)
+v1
+v2
+m1
+m2
+m1[2,1] == 8
+m1[2,1] = 8
+m1[,3]
+is.na(m1)
+table(is.na(m1))
+m1[1,1] = NA
+m1[2,3] = NA
+table(is.na(m1))
+str(m1)
+m3 = matrix(1:4, nrow = 2, ncol = 2)
+m4 = matrix(1:4, nrow = 2, ncol = 2, byrow = TRUE)
+m3
+m4[2,1] = NA
+## array ##
+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[,,2][3,3] = 100
+a3
+## DataFrame ##
+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)
+DF3 = data.frame(id, age, gender, height, stringsAsFactors=TRUE)
+DF1
+DF2
+DF3
+View(DF3)
+str(DF3)
+DF4 = DF3[-1]
+DF4
+View(DF4)
+str(DF4)
+## 기술통계량 ##
+library('psych')
+describe(DF4)
+rownames(DF1)
+colnames(DF1)
+rownames(DF1) = paste('R',1:5,sep='')
+rownames(DF1)
+rownames(DF1) = paste('Edge',1:5,sep='')
+rownames(DF1)
+rownames(DF1) = paste('Edge',1:5,sep='_')
+rownames(DF1)
+View(DF1)
+colnames(DF1) = paste(c('id','나이','성별','키'))
+colnames(DF1)
+colnames(DF1) = paste('id','나이','성별','키')
+colnames(DF1)
+colnames(DF1) = paste(c('id','나이','성별','키'))
+colnames(DF1) = paste(c('아이디','나이','성별','키'))
+colnames(DF1)
+str(DF1)
+str(DF4)
+dim(DF1)
+install.packages("caret")
+## 기술통계량 ##
+library(psych)
+describe(DF4)
+search()
+searchpaths()
+## read text data ##
+k200 = read.csv(file = './data/k100.csv',header = TRUE)
+k200
+## 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[-4]
+v1[c(2,4)]
+v1 = 1:3
+v2 = 4:6
+v3 = v1+v2 # can add list
+v1 = 1:3
+v2 = 1:6
+v3 = v1+v2 # reusable add
+v3
+### 데이터거래사
+## Factor, ordered Factor (등급을 정할 수 있는)
+gender = c('m','f','f','m','f','f')
+gender_factor = factor(gender)
+levels(gender_factor)
+gender_factor2 = factor(gender, levels = c('m','f'), labels = c('남자','여자'))
+gender_factor2
+gender_factor3 = factor(gender,ordered = TRUE)
+gender_factor3
+## Matrix ##
+v1 = 1:3
+v2 = 4:6
+m1 = rbind(v1,v2)
+m2 = cbind(v1,v2)
+v1
+v2
+m1
+m2
+m1[2,1] == 8
+m1[2,1] = 8
+m1[,3]
+is.na(m1)
+table(is.na(m1))
+m1[1,1] = NA
+m1[2,3] = NA
+table(is.na(m1))
+str(m1)
+m3 = matrix(1:4, nrow = 2, ncol = 2)
+m4 = matrix(1:4, nrow = 2, ncol = 2, byrow = TRUE)
+m3
+m4[2,1] = NA
+## array ##
+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[,,2][3,3] = 100
+a3
+## DataFrame ##
+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)
+DF3 = data.frame(id, age, gender, height, stringsAsFactors=TRUE)
+DF1
+DF2
+DF3
+View(DF3)
+str(DF3)
+DF4 = DF3[-1]
+DF4
+View(DF4)
+str(DF4)
+## 기술통계량 ##
+library(psych)
+describe(DF4)
+rownames(DF1) = paste('Edge',1:5,sep='_')
+rownames(DF1)
+colnames(DF1) = paste(c('아이디','나이','성별','키'))
+colnames(DF1)
+dim(DF1)
+searchpaths()
+## read text data ##
+k200 = read.csv(file = './data/k100.csv',header = TRUE)
+k200
+View(k200)
+k300 = read.csv(file = './data/ebook.csv',header = TRUE)
+View(k300)
+tsv100 = read.table(file = './data/survey_tab.txt', header = TRUE, encoding = 'CP949')
+tsv100 = read.table(file = './data/survey_tab.txt', header = TRUE, fileEncoding = 'CP949')
+View(tsv100)
+## data handling ##
+library(ggplot2)
+head(diamonds)
+head(Titanic)
+View(diamonds)
+View(Titanic)
+## data structure ##
+str(diamonds)
+str(Titanic)
+View(diamonds)
+head(diamonds)
+## indexing ##
+diamonds[ , 2]
+diamonds[ , 2, drop=FALSE]
+diamonds[ , c(2, 3, 7)]
+diamonds[ , 7:10]
+diamonds[ , seq(from=2, to=10, by=2)]
+xx=diamonds[ , c(2, 3, 7)]
+View(xx)
+xx
+ss = diamonds[,7]
+describe(ss)
+describe(diamonds)
+diamonds[ , c('cut', 'price'
+diamonds[ , 'cut']
+diamonds[ , 'cut']
+diamonds[ , c('cut', 'price')]
+diamonds[diamonds$cut == 'Fair', ]
+diamonds[diamonds$price >= 18000, ]
+a
+a = diamonds[diamonds$cut == 'Fair', ]
+a
+table(a)
+= diamonds[diamonds$price >= 18000, ]
+b
+b = diamonds[diamonds$price >= 18000, ]
+b
+'Fair') & (diamonds$price >= 18000), ]
+c = diamonds[(diamonds$cut == 'Fair') & (diamonds$price >= 18000), ]
+d = diamonds[(diamonds$cut == 'Fair') | (diamonds$price >= 18000), ]
+View(c)
+View(d)