[R] 221025_복습

### 2주차 복습

a <- c(1,2,3,4,5,6)

sum(a)

mean(a)

median(a)

max(a)

min(a)

var(a)

sd(a)

sort(a)

sort(a, decreasing = T)

range(a)

length(a)

a_fac <- factor(a)

a_fac

a_fac[7] <- 7

a_fac

summary(a_fac)

## 1

d <- c(100:200)

d[50]

d[seq(1,101,10)]

d[c(1:20)]

## 2

length(d)

mean(d)

sum(d<110)

sum(d[d<110])

## 3

x <- c(1:20)

y <- x[seq(3,20,3)]

y

sort(y,decreasing = T)

mean(x[-c(2:10)])

## 4

a <- c(1,6,3,8)

a <- sort(a, decreasing = F)

a

names(a) <- c('one','three','six','eight')

a

## 5

ages <- c(58,20,85)

names <- c('Tony','Ahn','Nick')

x <- list(ages,names)

names(x) <- list('numbers','names')

x

## 6

gender <- c(rep('male',5),rep('female',10))

gender_fac <- factor(gender)

gender_fac[16] <- 'male'

summary(gender_fac)

### 3주차 복습

a <- matrix(1:6, nrow = 2, ncol = 3, byrow = F)

a

b <- matrix(7:12, 2,3,F)

b

a+b

b-a

a*2

c <- cbind(a,b)

c

d <- rbind(a,b)

d

dim(d)

nrow(d)

ncol(d)

rownames(d)

colnames(d)

head(state.x77)

tail(state.x77)

str(state.x77)

unique(iris$Species) ## 종류만(factor처럼)

table(iris$Species) ## 종류별 갯수

colSums(swiss[,-4]) ## 4열 빼고 연산

colMeans(swiss[,-5]) ## 5열 빼고 연산

## 1

d <- matrix(seq(5,60,5),3,4,byrow=F)

d

## 2

d_t <- matrix(seq(5,60,5),3,4,byrow=T)

d_t

d_tt <- t(d)

d_tt

## 3

b1 <- c(4,5,7,2)

b2 <- c(19,15,12,17)

b3 <- c(20,24,28,23)

m1 <- cbind(b1,b2,b3)

m1

## 4

rich <- subset(as.data.frame(state.x77),Income>=5000)

rich

## 5

x <- c(1,2,3)

y <- c('white','blue','yellow')

z <- c(T, F, T)

df <- data.frame(x,y,z)

df

## 6

class(airquality)

air_m <- as.matrix(airquality)

class(air_m)

## 7

nrow(subset(airquality, Ozone > 120))

subset(airquality, Temp == max(airquality$Temp))

## 8

airquality['Wind']

airquality$Wind

airquality[,'Wind']

### 5주차 복습

read.csv(file = 'file.csv', header = T)

write.csv(something, 'new.csv', row.names = F, col.names = F)

library(xlsx)

read.xlsx(file = 'file.xlsx', header = F, sheetIndex = 1)

write.xlsx(something, 'new.xlsx', row.names = F, col.names = T)

read.table('file.txt', sep='\t') ## sep == 항목 구분을 탭으로 해라

read.delim('file.txt', header = T)

write.table(something, 'new.txt', row.names = T, col.names = F)

write.table(something, 'new.txt', append = T) ## 기존 파일 뒤에 추가만(기본값)

write.table(something, 'new.txt', append = F) ## 기존 내용 지우고 새로

write.table(something, 'new.txt', quoto = F) ## 파일내 따옴표 삭제(기본값 T)

write.table(something, 'new.txt', eol = '/') ## 행 내용을 /으로 구분

sink('file.txt', append = T)

cat('a','b','c')

sink() ## 세 줄 한 번에 실행

## 1

air_6_1025 <- subset(airquality, Month = 6)

air_6_1025

write.table(air_6_1025, 'air_6_1025.txt', col.names=T)

## 2

swiss_F <- subset(swiss, Fertility>=90)

swiss_F

write.csv(swiss_F, 'swiss_F_1025.csv')

## 3

sw_F <- readLines('swiss_F_1025.csv')

sw_F

## 4

swd_F <- read.csv('swiss_F_1025.csv')

swd_F

## 5

colSums(swiss['Education'])

sum(swiss$Education)

colMeans(swiss['Examination'])

mean(swiss$Examination)

sort(swiss$Agriculture)

## 6

rank <- sort(swiss$Agriculture)

rank

## 7

swd_F1 <- subset(swiss, Infant.Mortality>20)

nrow(swd_F1)

View(swd_F1)

### 6주차 복습

num <- nrow(iris)

my.Label = c()

for (i in 1:num){

  if (iris$Petal.Length[i] <= 1.6){

    my.Label[i] <- 'L'

  } else if(iris$Petal.Length[i] >= 5.1){

    my.Label[i] <- 'H'

  } else{

    my.Label[i] <- 'M'

  }

}

print(my.Label)

new_df <- data.frame(iris$Petal.Length, my.Label)

new_df

i <- 1

num <- nrow(iris)

new.Label <- c()

while (i<=num){

  if (iris$Petal.Length[i]<=1.6){

    new.Label[i] <- 'L'

  } else if (iris$Petal.Length[i]>=5.1){

    new.Label[i] <- 'H'

  } else{

    new.Label[i] <- 'M'

  }

  i <- i+1

}

print(new.Label)

new_data_frame <- data.frame(iris$Petal.Length, new.Label)

new_data_frame