데이터 R지?
11차시_대면수업
jsBae
2022. 5. 11. 15:41
수업 준비
1. R_studio 에서 프로젝트 확인
2. 경로 셋팅 : ctrl +shift + h
> setwd("C:/Users/USER/Dropbox/R/Mybook")3. 기존 data 삭제
rm(list=ls())실습1. 데이터 프레임을 생성 후 .csv 로 저장 해 보자.
df <- data.frame(class=c(1:4),
korean=sample(70:90, 4),
english = c(82, 86, 83, 87),
math = c(78, 79, 81, 80))
df> df
class korean english math
1 1 76 82 78
2 2 80 86 79
3 3 86 83 81
4 4 73 87 8#save of data
write.csv(df, "class_mean.csv")실습2. .csv 파일을 불러보자.(별도의 패키지 없이 내장 함수 사용)
> read.csv("class_mean.csv")
X class korean english math
1 1 1 75 82 78
2 2 2 81 86 79
3 3 3 90 83 81
4 4 4 77 87 80
> data <- read.csv("class_mean.csv")
> str(data)
'data.frame': 4 obs. of 5 variables:
$ X : int 1 2 3 4
$ class : int 1 2 3 4
$ korean : int 75 81 90 77
$ english: int 82 86 83 87
$ math : int 78 79 81 80
> dim(data)
[1] 4 5
> nrow(data)
[1] 4
> ncol(data)
[1] 5
> names(data)
[1] "X" "class" "korean" "english" "math"
> class(data)
[1] "data.frame"
> View(data)
> head(data)
X class korean english math
1 1 1 75 82 78
2 2 2 81 86 79
3 3 3 90 83 81
4 4 4 77 87 80
> tail(data)
X class korean english math
1 1 1 75 82 78
2 2 2 81 86 79
3 3 3 90 83 81
4 4 4 77 87 80
> summary(data)
X class korean english math
Min. :1.00 Min. :1.00 Min. :75.00 Min. :82.00 Min. :78.00
1st Qu.:1.75 1st Qu.:1.75 1st Qu.:76.50 1st Qu.:82.75 1st Qu.:78.75
Median :2.50 Median :2.50 Median :79.00 Median :84.50 Median :79.50
Mean :2.50 Mean :2.50 Mean :80.75 Mean :84.50 Mean :79.50
3rd Qu.:3.25 3rd Qu.:3.25 3rd Qu.:83.25 3rd Qu.:86.25 3rd Qu.:80.25
Max. :4.00 Max. :4.00 Max. :90.00 Max. :87.00 Max. :81.00
> mean(data$korean)
[1] 80.75
> data <- data[-1]
> data
class korean english math
1 1 75 82 78
2 2 81 86 79
3 3 90 83 81
4 4 77 87 80
> summary(data)
class korean english math
Min. :1.00 Min. :75.00 Min. :82.00 Min. :78.00
1st Qu.:1.75 1st Qu.:76.50 1st Qu.:82.75 1st Qu.:78.75
Median :2.50 Median :79.00 Median :84.50 Median :79.50
Mean :2.50 Mean :80.75 Mean :84.50 Mean :79.50
3rd Qu.:3.25 3rd Qu.:83.25 3rd Qu.:86.25 3rd Qu.:80.25
Max. :4.00 Max. :90.00 Max. :87.00 Max. :81.00실습3. 내장 데이터셋(iris)을 활용한 데이터 추출
1.데이터 셋 불러오기
> data()
> data(iris)
> colnames(iris)
[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
> dim(iris)
[1] 150 52.데이터 인덱싱 & 추출
> iris[1,2]
[1] 3.5iris["Species"]=="setosa"
> data <- iris[iris["Species"]=="setosa",]
> dim(data)
[1] 50 5
> summary(data)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.300 Min. :1.000 Min. :0.100 setosa :50
1st Qu.:4.800 1st Qu.:3.200 1st Qu.:1.400 1st Qu.:0.200 versicolor: 0
Median :5.000 Median :3.400 Median :1.500 Median :0.200 virginica : 0
Mean :5.006 Mean :3.428 Mean :1.462 Mean :0.246
3rd Qu.:5.200 3rd Qu.:3.675 3rd Qu.:1.575 3rd Qu.:0.300
Max. :5.800 Max. :4.400 Max. :1.900 Max. :0.600문제 : Petal 길이가 평균값(3.76) 이하인 붓꽃 데이터 추출
> summary(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 setosa :50
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 versicolor:50
Median :5.800 Median :3.000 Median :4.350 Median :1.300 virginica :50
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500> iris$Petal.Length<=3.76
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[17] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[33] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[49] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
[65] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[81] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[97] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[113] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE> data_1 <- iris[iris$Petal.Length<=3.76,]
> dim(data_1)
[1] 57 5
> summary(data_1)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.1000 setosa :50
1st Qu.:4.800 1st Qu.:3.000 1st Qu.:1.400 1st Qu.:0.2000 versicolor: 7
Median :5.000 Median :3.400 Median :1.500 Median :0.2000 virginica : 0
Mean :5.037 Mean :3.307 Mean :1.702 Mean :0.3456
3rd Qu.:5.200 3rd Qu.:3.600 3rd Qu.:1.600 3rd Qu.:0.4000
Max. :5.800 Max. :4.400 Max. :3.700 Max. :1.3000실습4. subset() 함수를 활용한 데이터 추출
subset(데이터, 조건식, 선택열)
subset(iris, Petal.Length<=3.76)subset(iris, Petal.Length<=3.76, c("Species"))실습5. ifelse()함수를 활용한 데이터 추출
ifelse(조건식, TRUE값, FALSE값)
> x <- c(1:10)
> x
[1] 1 2 3 4 5 6 7 8 9 10
> ifelse(x%%2==1, T, F)
[1] TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE
> x[ifelse(x%%2==1, T, F)]
[1] 1 3 5 7 9문제 : iris 데이터에 꽃잎길이를 above, below로 표현
iris$"Petal.Mean" <- ifelse(iris$Petal.Length>=3.76, "above", "below")
View(iris)
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Petal.Mean
1 5.1 3.5 1.4 0.2 setosa below
2 4.9 3.0 1.4 0.2 setosa below
3 4.7 3.2 1.3 0.2 setosa below
4 4.6 3.1 1.5 0.2 setosa below
5 5.0 3.6 1.4 0.2 setosa below
6 5.4 3.9 1.7 0.4 setosa below실습6. 데이터 일괄처리
apply(x, 1(행)or2(열), 함수)
exam <- data.frame(id=c(1:10),
ex1=sample(60:100,10),
ex2=sample(50:90,10),
ex3=sample(75:99,10),
ex4=sample(70:85,10))
dim(exam)
View(exam)
str(exam)
'data.frame': 10 obs. of 5 variables:
$ id : int 1 2 3 4 5 6 7 8 9 10
$ ex1: int 72 99 84 98 82 79 88 100 81 94
$ ex2: int 77 82 70 80 66 58 72 68 75 65
$ ex3: int 85 84 81 93 76 98 75 99 89 94
$ ex4: int 82 77 79 74 81 76 84 85 73 75exam$id <- apply(exam[1],1,as.factor)
> str(exam)
'data.frame': 10 obs. of 6 variables:
$ id : Factor w/ 10 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10
$ ex1 : int 72 99 84 98 82 79 88 100 81 94
$ ex2 : int 77 82 70 80 66 58 72 68 75 65
$ ex3 : int 85 84 81 93 76 98 75 99 89 94
$ ex4 : int 82 77 79 74 81 76 84 85 73 75
$ total: int 316 342 314 345 305 311 319 352 318 328> apply(exam[2:5], 2, max)
ex1 ex2 ex3 ex4
100 82 99 85
> apply(exam[2:5], 2, mean)
ex1 ex2 ex3 ex4
87.7 71.3 87.4 78.6문제 : 전 과목 합산 열을 추가하여 1등을 추출 해 보자.
exam$"total" <- apply(exam[2:5], 1, sum)누가 1등인가?
> summary(exam)
id ex1 ex2 ex3 ex4 total
1 :1 Min. : 72.00 Min. :58.0 Min. :75.00 Min. :73.00 Min. :305.0
2 :1 1st Qu.: 81.25 1st Qu.:66.5 1st Qu.:81.75 1st Qu.:75.25 1st Qu.:314.5
3 :1 Median : 86.00 Median :71.0 Median :87.00 Median :78.00 Median :318.5
4 :1 Mean : 87.70 Mean :71.3 Mean :87.40 Mean :78.60 Mean :325.0
5 :1 3rd Qu.: 97.00 3rd Qu.:76.5 3rd Qu.:93.75 3rd Qu.:81.75 3rd Qu.:338.5
6 :1 Max. :100.00 Max. :82.0 Max. :99.00 Max. :85.00 Max. :352.0
(Other):4
> exam[exam$total==352,]
id ex1 ex2 ex3 ex4 total
8 8 100 68 99 85 352상위 6명은 명단은?
> rank <- exam[order(-exam$total),]
> head(rank)
id ex1 ex2 ex3 ex4 total
8 8 100 68 99 85 352
4 4 98 80 93 74 345
2 2 99 82 84 77 342
10 10 94 65 94 75 328
7 7 88 72 75 84 319
9 9 81 75 89 73 318728x90
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