str(x3)
str(x7)
x7 = 'FLASE'
str(x7)
x6 = 'false'            # character
str(x7)
x8 = 8.8
x8 = 8.8
str(x8)
xx1 <- c(1:100)
xx1
xx1 <- c(1:100)
xx1
is.na()
is.na(xx1)
table(is.na(xx1))
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)
## 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)