R강의 : Data 다루기
문건웅
2015년 11월 26일
R에서 쓰는 표현식, 연산자
r=2
circle = pi*r^2
total=100; n=10
average <- total/n
5**2[1] 25(1+2)*3[1] 9R에서 모든 데이터는 벡터다
변수에 데이타를 넣는 방법
변수에 데이타 할당 ; =, <-
combine 사용
sequence 연산자 사용 (:)
sequence 함수 사용 (seq)
repeat함수 사용(rep)
x=1
y<-2
a=c(1,2,3)
a[1] 1 2 3a[2][1] 2b=1:10
b[9][1] 9c=seq(5)
d=seq(1,3,0.25)
e=c(a,b)
f=rep(a,3)
f[1] 1 2 3 1 2 3 1 2 3기본데이터형
숫자형(numeric) 12, 4, 0.45
논리형(logical) TRUE, FALSE, T, F, 1, 0
복소수형(complex) 3+2i
문자형(character) “St.Vincent’s Hospital”,“123”,‘3.14’
데이터 구조
벡터(vector)
행렬(matrix)
배열(array)
데이타프레임(dataframe)
리스트(list)
범주형자료(categorical variable)
시계열(Time series)
왜 벡터로 되어있을까?
- 언제든지 자료를 추가할 수 있다.
a=1:5
a=c(a,101,102)
b=c(a,103)
b[1] 1 2 3 4 5 101 102 103- 자료의 연산이 아주 쉽다.
Height=c(168,173,160,145,180)
Weight=c(80,65,92,53,76)
BMI=Weight/(Height/100)^2
BMI[1] 28.34467 21.71807 35.93750 25.20809 23.45679- 연산에서 벡터는 재사용된다
a=1:10
b=c(1,-1)
a+b [1] 2 1 4 3 6 5 8 7 10 9- b^2 +/- 4ac
b=10
a=c=2
b^2+c(1,-1)*4*a*c[1] 116 84행렬이란 무엇인가 ?
- 자료를 2차원으로 배열한 것
- 수학의 행렬과 다른 점은 숫자 이외에도 가능하다는 것이다.
a=matrix(1:12,ncol=3)
a [,1] [,2] [,3]
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12b=LETTERS[1:12]
b [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L"b=matrix(b,ncol=4)
b [,1] [,2] [,3] [,4]
[1,] "A" "D" "G" "J"
[2,] "B" "E" "H" "K"
[3,] "C" "F" "I" "L" b[3,2][1] "F"b[2,4][1] "K"b[2,][1] "B" "E" "H" "K"b[,3][1] "G" "H" "I"length(b)[1] 12범주형자료
- 성별 : 남,여
sex=c("Male","Female","Female","Male","Male")
sex=factor(sex)
sex[1] Male Female Female Male Male
Levels: Female Malestr(sex) Factor w/ 2 levels "Female","Male": 2 1 1 2 2levels(sex)[1] "Female" "Male" length(sex)[1] 5- 흡연 : “none”,“ex-smoker”,“smoker”
smoking=c(1,1,2,3,1)
smoking=factor(smoking)
levels(smoking)=c("none","ex-smoker","smoker")
smoking[1] none none ex-smoker smoker none
Levels: none ex-smoker smoker배열 (array)
- 행렬과 비슷하나 다차원구조를 가질수 있다.
- 2차원 배열 = 행렬
데이타프레임
- 우리가 다루는 거의 모든 자료는 데이타프레임이다.
mydata=data.frame(height=Height,weight=Weight,sex=sex,smoking=smoking)
mydata height weight sex smoking
1 168 80 Male none
2 173 65 Female none
3 160 92 Female ex-smoker
4 145 53 Male smoker
5 180 76 Male none- 데이타프레임의 자료를 일부 선택(subset)할때는 행렬과 비슷하다.
mydata[3,] height weight sex smoking
3 160 92 Female ex-smokermydata[,1][1] 168 173 160 145 180mydata[c(1,2)] height weight
1 168 80
2 173 65
3 160 92
4 145 53
5 180 76mydata[,c(1,2)] height weight
1 168 80
2 173 65
3 160 92
4 145 53
5 180 76- 다음의 차이는?
mydata[1] height
1 168
2 173
3 160
4 145
5 180mydata[[1]][1] 168 173 160 145 180- 데이타프레임의 열에 이름으로 접근할때는 $기호를 쓴다.
mydata$height[1] 168 173 160 145 180mydata[["height"]][1] 168 173 160 145 180- 새로운 열을 추가할때
mydata$BMI=mydata$weight*10000/(mydata$height)^2
mydata height weight sex smoking BMI
1 168 80 Male none 28.34467
2 173 65 Female none 21.71807
3 160 92 Female ex-smoker 35.93750
4 145 53 Male smoker 25.20809
5 180 76 Male none 23.45679str(mydata)'data.frame': 5 obs. of 5 variables:
$ height : num 168 173 160 145 180
$ weight : num 80 65 92 53 76
$ sex : Factor w/ 2 levels "Female","Male": 2 1 1 2 2
$ smoking: Factor w/ 3 levels "none","ex-smoker",..: 1 1 2 3 1
$ BMI : num 28.3 21.7 35.9 25.2 23.5summary(mydata) height weight sex smoking BMI
Min. :145.0 Min. :53.0 Female:2 none :3 Min. :21.72
1st Qu.:160.0 1st Qu.:65.0 Male :3 ex-smoker:1 1st Qu.:23.46
Median :168.0 Median :76.0 smoker :1 Median :25.21
Mean :165.2 Mean :73.2 Mean :26.93
3rd Qu.:173.0 3rd Qu.:80.0 3rd Qu.:28.34
Max. :180.0 Max. :92.0 Max. :35.94 plot(mydata)
진짜 데이타를 가지고 실습
data(mtcars)
head(mtcars,10) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4str(mtcars)'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...summary(mtcars) mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000 mtcars$mpg [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
[15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
[29] 15.8 19.7 15.0 21.4stem(mtcars$mpg)
The decimal point is at the |
10 | 44
12 | 3
14 | 3702258
16 | 438
18 | 17227
20 | 00445
22 | 88
24 | 4
26 | 03
28 |
30 | 44
32 | 49hist(mtcars$mpg)
boxplot(mtcars$mpg)
fivenum(mtcars$mpg)[1] 10.40 15.35 19.20 22.80 33.90quantile(mtcars$mpg) 0% 25% 50% 75% 100%
10.400 15.425 19.200 22.800 33.900 데이타의 정렬(order)
order(mtcars$mpg) [1] 15 16 24 7 17 31 14 23 22 29 12 13 11 6 5 10 25 30 1 2 4 32 21
[24] 3 9 8 27 26 19 28 18 20mtcars=mtcars[order(mtcars$mpg),]
head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb
Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
Maserati Bora 15.0 8 301 335 3.54 3.570 14.60 0 1 5 8rownames(mtcars) [1] "Cadillac Fleetwood" "Lincoln Continental" "Camaro Z28"
[4] "Duster 360" "Chrysler Imperial" "Maserati Bora"
[7] "Merc 450SLC" "AMC Javelin" "Dodge Challenger"
[10] "Ford Pantera L" "Merc 450SE" "Merc 450SL"
[13] "Merc 280C" "Valiant" "Hornet Sportabout"
[16] "Merc 280" "Pontiac Firebird" "Ferrari Dino"
[19] "Mazda RX4" "Mazda RX4 Wag" "Hornet 4 Drive"
[22] "Volvo 142E" "Toyota Corona" "Datsun 710"
[25] "Merc 230" "Merc 240D" "Porsche 914-2"
[28] "Fiat X1-9" "Honda Civic" "Lotus Europa"
[31] "Fiat 128" "Toyota Corolla" order(rownames(mtcars)) [1] 8 1 3 5 24 9 4 18 31 28 10 29 21 15 2 30 6 19 20 25 26 16 13
[24] 11 12 7 17 27 32 23 14 22mtcars=mtcars[order(rownames(mtcars)),]
mtcars=mtcars[order(mtcars$mpg,mtcars$wt),]데이타의 일부 선택(subset)
# 4기통, 6기통, 8기통 중 4,6기통 만 선택
table(mtcars$cyl)
4 6 8
11 7 14 mtcars$cyl<7 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[12] FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[23] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEmtcars1=mtcars[mtcars$cyl<7,]
table(mtcars1$cyl)
4 6
11 7 # subset(data.frame, subset(행), select(열))
mtcars1=subset(mtcars,subset=cyl<7)
mtcars1 mpg cyl disp hp drat wt qsec vs am gear carb
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1mtcars2=subset(mtcars1,select=c(mpg,cyl))
mtcars2 mpg cyl
Merc 280C 17.8 6
Valiant 18.1 6
Merc 280 19.2 6
Ferrari Dino 19.7 6
Mazda RX4 21.0 6
Mazda RX4 Wag 21.0 6
Volvo 142E 21.4 4
Hornet 4 Drive 21.4 6
Toyota Corona 21.5 4
Datsun 710 22.8 4
Merc 230 22.8 4
Merc 240D 24.4 4
Porsche 914-2 26.0 4
Fiat X1-9 27.3 4
Lotus Europa 30.4 4
Honda Civic 30.4 4
Fiat 128 32.4 4
Toyota Corolla 33.9 4mtcars3=subset(mtcars1,subset=cyl==4, select=mpg)
mtcars3 mpg
Volvo 142E 21.4
Toyota Corona 21.5
Datsun 710 22.8
Merc 230 22.8
Merc 240D 24.4
Porsche 914-2 26.0
Fiat X1-9 27.3
Lotus Europa 30.4
Honda Civic 30.4
Fiat 128 32.4
Toyota Corolla 33.9mtcars4=subset(mtcars1,subset=cyl==4, select=cyl)
mtcars4 cyl
Volvo 142E 4
Toyota Corona 4
Datsun 710 4
Merc 230 4
Merc 240D 4
Porsche 914-2 4
Fiat X1-9 4
Lotus Europa 4
Honda Civic 4
Fiat 128 4
Toyota Corolla 4데이타프레임 합치기
옆으로 이어붙이기
cbind(mtcars3,mtcars4) mpg cyl
Volvo 142E 21.4 4
Toyota Corona 21.5 4
Datsun 710 22.8 4
Merc 230 22.8 4
Merc 240D 24.4 4
Porsche 914-2 26.0 4
Fiat X1-9 27.3 4
Lotus Europa 30.4 4
Honda Civic 30.4 4
Fiat 128 32.4 4
Toyota Corolla 33.9 4두 개의 데이타프레임 합치기(행으로 쌓기)
mtcars5=subset(mtcars2,mpg<21)
mtcars5 mpg cyl
Merc 280C 17.8 6
Valiant 18.1 6
Merc 280 19.2 6
Ferrari Dino 19.7 6mtcars6=mtcars2[mtcars2$mpg>21,]
mtcars6 mpg cyl
Volvo 142E 21.4 4
Hornet 4 Drive 21.4 6
Toyota Corona 21.5 4
Datsun 710 22.8 4
Merc 230 22.8 4
Merc 240D 24.4 4
Porsche 914-2 26.0 4
Fiat X1-9 27.3 4
Lotus Europa 30.4 4
Honda Civic 30.4 4
Fiat 128 32.4 4
Toyota Corolla 33.9 4rbind(mtcars5,mtcars6) mpg cyl
Merc 280C 17.8 6
Valiant 18.1 6
Merc 280 19.2 6
Ferrari Dino 19.7 6
Volvo 142E 21.4 4
Hornet 4 Drive 21.4 6
Toyota Corona 21.5 4
Datsun 710 22.8 4
Merc 230 22.8 4
Merc 240D 24.4 4
Porsche 914-2 26.0 4
Fiat X1-9 27.3 4
Lotus Europa 30.4 4
Honda Civic 30.4 4
Fiat 128 32.4 4
Toyota Corolla 33.9 4서로 다른 데이타프레임 합치기
name=c("김철수","이영희","홍길동")
국어=c(90,95,80)
수학=c(100,95,85)
성적1=data.frame(name,국어)
성적1 name 국어
1 김철수 90
2 이영희 95
3 홍길동 80성적2=data.frame(name,수학)
성적2 name 수학
1 김철수 100
2 이영희 95
3 홍길동 85성적=merge(성적1,성적2,by="name")
성적 name 국어 수학
1 김철수 90 100
2 이영희 95 95
3 홍길동 80 85merge와 cbind의 차이
name=c(name,"문건웅")
과학=c(80,85,90,100)
성적3=data.frame(name,과학)
성적3 name 과학
1 김철수 80
2 이영희 85
3 홍길동 90
4 문건웅 100cbind(성적,성적3) ##데이타의 길이가 차이가 나면 합쳐지지 않는다.Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 3, 4merge(성적,성적3,by="name") name 국어 수학 과학
1 김철수 90 100 80
2 이영희 95 95 85
3 홍길동 80 85 90merge(성적,성적3,by="name",all=TRUE) name 국어 수학 과학
1 김철수 90 100 80
2 이영희 95 95 85
3 홍길동 80 85 90
4 문건웅 NA NA 100데이타의 요약 ; 테이블만들기, 카이제곱,피셔검정
table(mtcars$cyl)
4 6 8
11 7 14 help(mtcars)
table(mtcars$cyl,mtcars$am)
0 1
4 3 8
6 4 3
8 12 2mtcars$tm=factor(mtcars$am,labels=c("automatic","manual"))
# mtcars$tm=ifelse(mtcars$am==0,"automatic","manual")
str(mtcars)'data.frame': 32 obs. of 12 variables:
$ mpg : num 10.4 10.4 13.3 14.3 14.7 15 15.2 15.2 15.5 15.8 ...
$ cyl : num 8 8 8 8 8 8 8 8 8 8 ...
$ disp: num 472 460 350 360 440 ...
$ hp : num 205 215 245 245 230 335 150 180 150 264 ...
$ drat: num 2.93 3 3.73 3.21 3.23 3.54 3.15 3.07 2.76 4.22 ...
$ wt : num 5.25 5.42 3.84 3.57 5.34 ...
$ qsec: num 18 17.8 15.4 15.8 17.4 ...
$ vs : num 0 0 0 0 0 0 0 0 0 0 ...
$ am : num 0 0 0 0 0 1 0 0 0 1 ...
$ gear: num 3 3 3 3 3 5 3 3 3 5 ...
$ carb: num 4 4 4 4 4 8 2 3 2 4 ...
$ tm : Factor w/ 2 levels "automatic","manual": 1 1 1 1 1 2 1 1 1 2 ...result=table(mtcars$cyl,mtcars$tm)
result
automatic manual
4 3 8
6 4 3
8 12 2chisq.test(result)Warning in chisq.test(result): Chi-squared approximation may be incorrect
Pearson's Chi-squared test
data: result
X-squared = 8.7407, df = 2, p-value = 0.01265plot(result)
barplot(result,legend=paste(rownames(result),"cyl"))
#xtabs(도수~가로+세로)
result1=xtabs(~cyl+tm,data=mtcars)
result1 tm
cyl automatic manual
4 3 8
6 4 3
8 12 2addmargins(result1) tm
cyl automatic manual Sum
4 3 8 11
6 4 3 7
8 12 2 14
Sum 19 13 32chisq.test(result1)Warning in chisq.test(result1): Chi-squared approximation may be incorrect
Pearson's Chi-squared test
data: result1
X-squared = 8.7407, df = 2, p-value = 0.01265#fisher.test(result1)데이타의 요약 : 평균 구하기
plot(mtcars)
# 엔진수에 따른 연비 평균
tapply(mtcars$mpg,mtcars$cyl,mean) 4 6 8
26.66364 19.74286 15.10000 aggregate(mpg~cyl,data=mtcars,mean) cyl mpg
1 4 26.66364
2 6 19.74286
3 8 15.10000aggregate(mpg~cyl+am,data=mtcars,mean) cyl am mpg
1 4 0 22.90000
2 6 0 19.12500
3 8 0 15.05000
4 4 1 28.07500
5 6 1 20.56667
6 8 1 15.40000# 엔진수에 따른 엔진출력(마력) 평균
tapply(mtcars$hp,mtcars$cyl,mean) 4 6 8
82.63636 122.28571 209.21429 plot(mpg~cyl,data=mtcars)
boxplot(mpg~cyl,data=mtcars)
out=lm(mpg~factor(cyl),data=mtcars)
anova(out)Analysis of Variance Table
Response: mpg
Df Sum Sq Mean Sq F value Pr(>F)
factor(cyl) 2 824.78 412.39 39.697 4.979e-09 ***
Residuals 29 301.26 10.39
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1결측값의 처리
Height=c(168,173,160,145,NA,180)
mean(Height)[1] NA!is.na(Height)[1] TRUE TRUE TRUE TRUE FALSE TRUEmean(Height[!is.na(Height)])[1] 165.2mean(Height,na.rm=TRUE)[1] 165.2상관분석, 회귀분석
# 마력과 연비
cor.test(mtcars$mpg,mtcars$hp)
Pearson's product-moment correlation
data: mtcars$mpg and mtcars$hp
t = -6.7424, df = 30, p-value = 1.788e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.8852686 -0.5860994
sample estimates:
cor
-0.7761684 with(mtcars,cor.test(mpg,hp))
Pearson's product-moment correlation
data: mpg and hp
t = -6.7424, df = 30, p-value = 1.788e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.8852686 -0.5860994
sample estimates:
cor
-0.7761684 plot(mpg~hp,data=mtcars)
out1=lm(mpg~hp,data=mtcars)
summary(out1)
Call:
lm(formula = mpg ~ hp, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-5.7121 -2.1122 -0.8854 1.5819 8.2360
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.09886 1.63392 18.421 < 2e-16 ***
hp -0.06823 0.01012 -6.742 1.79e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.863 on 30 degrees of freedom
Multiple R-squared: 0.6024, Adjusted R-squared: 0.5892
F-statistic: 45.46 on 1 and 30 DF, p-value: 1.788e-07abline(out1,col="red")
연속형 자료의 변형
자료를 다루다 보면 연속형 자료에서 새로운 범주형 자료를 만들어야 할때가 있다. ggplot2패키지에 있는 diamonds 자료 예를 들어보면
library(ggplot2)
data(diamonds)
str(diamonds)'data.frame': 53940 obs. of 10 variables:
$ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
$ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
$ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
$ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
$ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
$ table : num 55 61 65 58 58 57 57 55 61 61 ...
$ price : int 326 326 327 334 335 336 336 337 337 338 ...
$ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
$ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
$ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...summary(diamonds) carat cut color clarity
Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065
1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258
Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194
Mean :0.7979 Premium :13791 G:11292 VS1 : 8171
3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066
Max. :5.0100 I: 5422 VVS1 : 3655
J: 2808 (Other): 2531
depth table price x
Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.000
1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710
Median :61.80 Median :57.00 Median : 2401 Median : 5.700
Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.731
3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540
Max. :79.00 Max. :95.00 Max. :18823 Max. :10.740
y z
Min. : 0.000 Min. : 0.000
1st Qu.: 4.720 1st Qu.: 2.910
Median : 5.710 Median : 3.530
Mean : 5.735 Mean : 3.539
3rd Qu.: 6.540 3rd Qu.: 4.040
Max. :58.900 Max. :31.800
다이아몬드 가격이 제일 싼 것은 326불 제일 비싼 것은 18823불이다.
diamonds 데이타에 PriceGroup이라는 새로운 변수를 만들고 1000불 미만은 1, 1000불-5000불은 2, 5000불 이상은 3으로 바꾸려면 다음과 같이 한다.
- 첫번째 방법 :
diamonds$PriceGroup=1
diamonds$PriceGroup[diamonds$price>=1000]=2
diamonds$PriceGroup[diamonds$price>=5000]=3
table(diamonds$PriceGroup)
1 2 3
14499 24714 14727 - ifelse 함수 사용
diamonds$PriceGroup=ifelse(diamonds$price<1000,1,ifelse(diamonds$price<5000,2,3))
table(diamonds$PriceGroup)
1 2 3
14499 24714 14727 - cut, break 사용
diamonds$PriceGroup=cut(diamonds$price,breaks=c(0,999,4999,99999),labels=c(1,2,3))
table(diamonds$PriceGroup)
1 2 3
14499 24714 14727 특정 값이 아니라 price 순으로 k개의 구간으로 나누고 싶을때
예를 들어 전체 다이아몬드 가격을 1등 부터 53940등까지 순위를 매기고 이를 같은 숫자 만큼 k개의 군으로 나누고 싶다면 어떻게 할까 ? 다음과 같은 함수를 만들어 보았다. rank2group함수는 y라는 벡터를 인자로 받아들여 순위별로 k개의 군으로 나누어진 새로운 벡터를 반환한다. 사용법은 다음과 같다.
rank2group <- function (y,k=4){
count=length(y)
z=rank(y,ties.method="min")
return(floor((z-1)/(count/k))+1)
}
diamonds$PriceGroup=rank2group(diamonds$price,4)
table(diamonds$PriceGroup)
1 2 3 4
13490 13495 13470 13485 aggregate(price~PriceGroup,data=diamonds,range) PriceGroup price.1 price.2
1 1 326 950
2 2 951 2401
3 3 2402 5324
4 4 5325 18823가격이 겹치는 데이타(즉, 순위가 같은 데이타)가 있어 네군별로 n수가 다르기는 하지만 우리가 원하는대로 작동한다. 세군 , 다섯군으로 나누려면 다음과 같이 하면 된다.
diamonds$PriceGroup3=rank2group(diamonds$price,3)
table(diamonds$PriceGroup3)
1 2 3
17996 17964 17980 aggregate(price~PriceGroup3,data=diamonds,range) PriceGroup3 price.1 price.2
1 1 326 1240
2 2 1241 4287
3 3 4288 18823diamonds$PriceGroup5=rank2group(diamonds$price,5)
table(diamonds$PriceGroup5)
1 2 3 4 5
10796 10784 10789 10783 10788 aggregate(price~PriceGroup5,data=diamonds,range) PriceGroup5 price.1 price.2
1 1 326 837
2 2 838 1698
3 3 1699 3465
4 4 3466 6301
5 5 6302 18823